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46
ফার্নিচার থেকে আইফোন, সবই ভাড়ায় নিচ্ছে ভারতীয় তরুণরা

মিলেনিয়ালদের হাতেই চলছে সমাজের ভাঙা-গড়া। প্রযুক্তির উল্লম্ফন থেকে শুরু করে সামাজিক সংগঠন, মূল্যবোধ, অর্থনীতির স্বরূপ ও গতিপ্রকৃতি সবই পাল্টে দিচ্ছে এ প্রজন্ম। অতি আশ্চর্য (!) এ প্রজন্মের নারী-পুরুষের বয়স এখন ২৪-৩৯ বছর। এদের হাতেই গড়ে উঠছে তথাকথিত শেয়ার্ড ইকোনমি (অংশীদারিত্বের অর্থনীতি)। প্রচলিত মালিকানা নিয়ে প্রতিষ্ঠিত সামাজিক ধ্যানধারণা তাদের কাছে আবেদন হারিয়ে ফেলছে। প্রতিশ্রুতিকে তারা ভাবছে শিকলে আবদ্ধ থাকার নামান্তর। এই মিলেনিয়ানরা টাকা দিয়ে কিনছে ‘অভিজ্ঞতা’। স্থায়ী মালিকানার কোনো অর্থ নেই তাদের কাছে। এ ধরনের দৃষ্টিভঙ্গির কারণেই বিশ্বব্যাপী জনপ্রিয় হচ্ছে আসবাবপত্র থেকে শুরু করে সেলফোন ভাড়া দেয়ার বাণিজ্য।

বাংলাদেশে স্থানীয়ভাবে বিয়ের পোশাক, ইভেন্ট ম্যানেজমেন্ট প্রতিষ্ঠান, এয়ারকন্ডিশনার (এসি) ভাড়া দেয়ার ব্যবসা বেশ পুরনো। তবে ভারতে এখন রীতিমতো অ্যাপভিত্তিক বাণিজ্য শুরু হয়ে গেছে। ফারলেঙ্কো, রেন্টোমজো, গ্র্যাব অন রেন্টের মতো বেশ কয়েকটি প্রতিষ্ঠান জীবনযাপনের যাবতীয় জিনিসপত্র অনেক কম টাকায় ভাড়া দিয়ে থাকে। অনেক প্রতিষ্ঠান আবার বিনামূল্যে জিনিসপত্র স্থানান্তরের সুবিধাও দেয়।

সম্প্রতি এ নিয়ে বার্তা সংস্থা এএফপি একটি প্রতিবেদন প্রকাশ করেছে। প্রতিবেদনে মুম্বাইয়ের স্পন্দন শর্মা নামে ২৯ বছর বয়সী এক তরুণ জানান, তার নিজের ফ্ল্যাট, গাড়ি এমনকি একটি চেয়ারও নেই। ভারতের মিলেনিয়ালদের মধ্যে তার মতো লোকের সংখ্যা বাড়ছে। তারা প্রচলিত ধারণাকে ভেঙে চুরমার করে দিচ্ছে। কেনার চাইতে ফার্নিচার থেকে আইফোন পর্যন্ত ভাড়ায় ব্যবহার করছে।



স্পন্দন শর্মা প্রতি মাসে ৪ হাজার ২৪৭ রুপির বিনিময়ে তার ঘর সাজিয়েছেন। সেখানে আসবাবপত্র, ফ্রিজ, মাইক্রওয়েভ ওভেন থেকে শুরু করে সব কিছুই ভাড়ায় নেয়া।  স্পন্দনের বাবা একটি সরকারি ব্যাংকে চাকরি করার সময় একটি ফ্ল্যাট ও গাড়ি কেনার জন্য একটু একটু করে টাকা জমাতেন। কিন্তু শর্মা তার জীবনটাকে অন্যভাবে দেখতে শিখেছেন। ‘অভিজ্ঞতায় বিনিয়োগে’ বিশ্বাসী তিনি। মাত্র সাত বছরের মধ্যে তার দুটি দেশের পাঁচটি শহরে নিজের থাকার একটা জায়গা আছে। এটা তার বাবার ক্ষেত্রে অচিন্ত্যনীয় ছিল। কিন্তু শর্মার জন্য এটাই এখন বাস্তবতা।

শুধু বাসা বাড়ির জন্যই নয়, মিলেনিয়ালরা অফিসের প্রয়োজনীয় জিনিসপত্রও ভাড়ায় নিচ্ছে। এমনটাই জানিয়েছেন উদীয়মান উদ্যোক্তা বন্দিতা মোরারকা। ২০১৭ সালে নারী অধিকার বিষয়ক অলাভজনক প্রতিষ্ঠান ‘ওয়ান ফিউচার কালেক্টিভ’ প্রতিষ্ঠা করেন ২৫ বছর বয়সী বন্দিতা। তিনি তার অফিসের প্রায় সবকিছুই ভাড়ায় নিয়েছেন। সেখানে টেবিল চেয়ার থেকে শুরু করে ল্যাপটপ পর্যন্ত ভাড়া নেয়া। তিনি বলেন, এতে করে প্রচুর বিনিয়োগের টাকা বাঁচিয়ে তিনি ২৫ জন স্টাফকে ঠিকমতো বেতন দিতে পারছেন। বন্দিতা বলেন, এই সিস্টেমটি আমাকে আরো বেশি ঝুঁকি নেয়ার সুযোগ তৈরি করে দিয়েছে। আমাদের যদি কখনো অফিস পরিবর্তন করে দূরে কোথাও যেতে হয় তাহলে নতুন করে মোটা অংকের টাকা বিনিয়োগের দরকার পড়বে না। তাছাড়া জিনিসপত্র বয়ে নিয়ে যাওয়ার ঝক্কিও থাকছে না।

ব্যবসা সংশ্লিষ্টরা বলছেন, এটি এখন একটি অত্যন্ত সম্ভাবনাময় বাণিজ্য হিসেবে আবির্ভুত হয়েছে। তরুণরা এখন কোনো কিছুই কিনতে চাচ্ছে না। আর জিনিসপত্র শেয়ার করার ক্ষেত্রে তাদের প্রাচীনপন্থীদের মতো কোনো ছুৎমার্গও নেই।
বেঙ্গালুরু ভিত্তিক রেন্টোমজোর প্রতিষ্ঠাতা গীতাংশ বামনিয়া বলেন, তিনি আশা করছেন, ৩০ মাসের মধ্যে ১০ লাখ অর্ডার পাবেন। এ প্রতিষ্ঠানটি ঘর ও অফিসের আসবাবপত্র, গৃহস্থালী জিনিসপত্র, জিমের সরঞ্জাম, আইফোন এবং স্মার্ট হোম ডিভাইস যেমন, গুগল হোম এবং অ্যামাজন ইকো এসবও ভাড়া দেয়। বামনিয়া বলেন, ভাড়ায় স্মার্টফোন পাওয়ায় তরুণদের জন্য সুবিধা হয়েছে। তারা অনেক কম খরচে বাজারে আসা সর্বশেষ প্রিমিয়াম ডিভাইসটির অভিজ্ঞতা নিতে পারছে।

২০১২ সালে ফারলেঙ্কো প্রতিষ্ঠা করেন বিনিয়োগ ব্যাংকের সাবেক কর্মকর্তা অজিত করিম্পানা। কোম্পানিটির বর্তমান গ্রাহক সংখ্যা ১ লাখেরও বেশি। ২০২৩ সাল নাগাদ ফারলেঙ্কোর আয় ৩০ কোটি ডলার ছাড়িয়ে যাবে বলে আশা করছেন অজিত।

একাধিক গবেষণা প্রতিষ্ঠান বলছে, যুক্তরাষ্ট্রে রেন্ট দ্য রানওয়ে এবং নুলির মতো ওয়েবসাইটগুলো ফ্যাশন সচেতন গ্রাহকদের পোশাক কেনার পরিবর্তে ভাড়া নিতে উৎসাহিত করে। চীনা গ্রাহকদের অ্যাপের মাধ্যমে ভাড়ায় বিএমডব্লিউ গাড়ি পাওয়ারও সুযোগ করে দেয়। বিদেশে এরকম প্রতিষ্ঠানে সফলতার দৃষ্টান্ত দেখে ভারতেও এ ব্যবসা ফুলে ফেঁপে উঠছে। ফার্নিচার থেকে হোম অ্যাপ্লায়েন্স এমনকি স্বর্ণালঙ্কারও এখন অ্যাপের মাধ্যমে ভাড়ায় পাওয়া যাচ্ছে।

পরামর্শক প্রতিষ্ঠান প্রাইসওয়াটারহাউসকুপার্সের (পিডব্লিউসি) হিসাবে, ২০২৫ সাল নাগাদ অ্যাপভিত্তিক জিনিসপত্র ভাড়া দেয়ার ব্যবসার বার্ষিক রাজস্ব দাঁড়াবে ৩৩ হাজার ৫০০ কোটি ডলার। আরেক পরামর্শক প্রতিষ্ঠান রিসার্চ নেস্টারের হিসাবে, ২০২৫ সাল নাগাদ ভারতে শুধু আসবাবপত্র ভাড়ার বাজার হবে ১৮৯ কোটি ডলার।

এনডিটিভি অবলম্বনে উম্মে সালমা

Source: https://bonikbarta.net/home/news_description/216528/%E0%A6%AB%E0%A6%BE%E0%A6%B0%E0%A7%8D%E0%A6%A8%E0%A6%BF%E0%A6%9A%E0%A6%BE%E0%A6%B0-%E0%A6%A5%E0%A7%87%E0%A6%95%E0%A7%87-%E0%A6%86%E0%A6%87%E0%A6%AB%E0%A7%8B%E0%A6%A8-%E0%A6%B8%E0%A6%AC%E0%A6%87-%E0%A6%AD%E0%A6%BE%E0%A7%9C%E0%A6%BE%E0%A7%9F-%E0%A6%A8%E0%A6%BF%E0%A6%9A%E0%A7%8D%E0%A6%9B%E0%A7%87-%E0%A6%AD%E0%A6%BE%E0%A6%B0%E0%A6%A4%E0%A7%80%E0%A7%9F-%E0%A6%A4%E0%A6%B0%E0%A7%81%E0%A6%A3%E0%A6%B0%E0%A6%BE?fbclid=IwAR178MP6P3-18cM4aWHzDeM0ngGghkc19axl1HcB4eARUbvny9xfDdWsSD4

47
Manager and machine: The new leadership equation

n a 1967 McKinsey Quarterly article, “The manager and the moron,” Peter Drucker noted that “the computer makes no decisions; it only carries out orders. It’s a total moron, and therein lies its strength. It forces us to think, to set the criteria. The stupider the tool, the brighter the master has to be—and this is the dumbest tool we have ever had.”1
How things have changed. After years of promise and hype, machine learning has at last hit the vertical part of the exponential curve. Computers are replacing skilled practitioners in fields such as architecture, aviation, the law, medicine, and petroleum geology—and changing the nature of work in a broad range of other jobs and professions. Deep Knowledge Ventures, a Hong Kong venture-capital firm, has gone so far as to appoint a decision-making algorithm to its board of directors.

What would it take for algorithms to take over the C-suite? And what will be senior leaders’ most important contributions if they do? Our answers to these admittedly speculative questions rest on our work with senior leaders in a range of industries, particularly those on the vanguard of the big data and advanced-analytics revolution. We have also worked extensively alongside executives who have been experimenting most actively with opening up their companies and decision-making processes through crowdsourcing and social platforms within and across organizational boundaries.

Our argument is simple: the advances of brilliant machines will astound us, but they will transform the lives of senior executives only if managerial advances enable them to. There’s still a great deal of work to be done to create data sets worthy of the most intelligent machines and their burgeoning decision-making potential. On top of that, there’s a need for senior leaders to “let go” in ways that run counter to a century of organizational development.

If these two things happen—and they’re likely to, for the simple reason that leading-edge organizations will seize competitive advantage and be imitated—the role of the senior leader will evolve. We’d suggest that, ironically enough, executives in the era of brilliant machines will be able to make the biggest difference through the human touch. By this we mean the questions they frame, their vigor in attacking exceptional circumstances highlighted by increasingly intelligent algorithms, and their ability to do things machines can’t. That includes tolerating ambiguity and focusing on the “softer” side of management to engage the organization and build its capacity for self-renewal.

The most impressive examples of machine learning substituting for human pattern recognition—such as the IBM supercomputer Watson’s potential to predict oncological outcomes more accurately than physicians by reviewing, storing, and learning from reams of medical-journal articles—result from situations where inputs are of high quality. Contrast that with the state of affairs pervasive in many organizations that have access to big data and are taking a run at advanced analytics. The executives in these companies often find themselves beset by “polluted” or difficult-to-parse data, whose validity is subject to vigorous internal debates.

This isn’t an article about big data per se—in recent Quarterly articles we’ve written extensively on what senior executives must do to address these issues—but we want to stress that “garbage in/garbage out” applies as much to supercomputers as it did 50 years ago to the IBM System/360.2 This management problem, which transcends CIOs and the IT organization, speaks to the need for a turbocharged data-analytics strategy, a new top-team mind-set, fresh talent approaches, and a concerted effort to break down information silos. These issues also transcend number crunching; as our colleagues have explained elsewhere, “weak signals” from social media and other sources also contain powerful insights and should be part of the data-creation process.3
The incentives for getting this right are large—early movers should be able to speed the quality and pace of decision making in a wide range of tactical and strategic areas, as we already see from the promising results of early big data and analytics efforts. Furthermore, early movers will probably gain new insights from their analysis of unstructured data, such as e-mail discussions between sales representatives or discussion threads in social media. Without behavioral shifts by senior leaders, though, their organizations won’t realize the full power of the artificial intelligence at their fingertips. The challenge lies in part with the very notion that machine-learning insights are at the fingertips of senior executives.

That’s certainly an appealing prospect: customized dashboards full of metadata describing and synthesizing deeper and more detailed operational, financial, and marketing information hold enormous power for the senior team. But these dashboards don’t create themselves. Senior executives must find and set the software parameters needed to determine, for instance, which data gets prioritized and which gets flagged for escalation. It’s no overstatement to say that these parameters determine the direction of the company—and the success of executives in guiding it there; for example, a bank can shift the mix between lending and deposit taking by changing prices. Machines may be able to adjust prices in real time, but executives must determine the target. Similarly, machines can monitor risks, but only after executives have determined the level of risk they’re comfortable with.

Consider also the challenge posed by today’s real-time sales data, which can be sliced by location, product, team, and channel. Previous generations of managers would probably have given their eyeteeth for that capability. Today’s unaware executive risks drowning in minutiae, though. Some are already reacting by distancing themselves from technology—for instance, by employing layers of staffers to screen data, which gets turned into more easily digestible Power Point slides. In so doing, however, executives risk getting a “filtered” view of reality that misses the power of the data available to them.

As artificial intelligence grows in power, the odds of sinking under the weight of even quite valuable insights grow as well. The answer isn’t likely to be bureaucratizing information, but rather democratizing it: encouraging and expecting the organization to manage itself without bringing decisions upward. Business units and company-wide functions will of course continue reporting to the top team and CEO. But emboldened by sharper insights and pattern recognition from increasingly powerful computers, business units and functions will be able to make more and better decisions on their own. Reviewing the results of those decisions, and sharing the implications across the management team, will actually give managers lower down in the organization new sources of power vis-à-vis executives at the top. That will happen even as the CEO begins to morph, in part, into a “chief experimentation officer,” who draws from acute observance of early signals to bolster a company’s ability to experiment at scale, particularly in customer-facing industries.

We’ve already seen flashes of this development in companies that open up their strategy-development process to a broader range of internal and external participants. Companies such as 3M, Dutch insurer AEGON, Red Hat (the leading provider of Linux software), and defense contractor Rite-Solutions have found that the advantages include more insightful and actionable strategic plans, as well as greater buy-in from participants, since they helped to craft the plan in the first place.4
In a world where artificial intelligence supports all manner of day-to-day management decisions, the need to “let go” will be more significant and the discomfort for senior leaders higher. To some extent, we’re describing a world where top executives’ sources of comparative advantage are eroding because of technology and the manifested “brilliance of crowds.” The contrast with the command-and-control era—when holding information close was a source of power, and information moved in one direction only, up the corporate hierarchy—could not be starker. Uncomfortable as this new world may be, the costs of the status quo are large and growing: information hoarders will slow the pace of their organizations and forsake the power of artificial intelligence while competitors exploit it.

The human edge
If senior leaders successfully fuel the insights of increasingly brilliant machines and devolve decision-making authority up and down the line, what will be left for top management to do?

Asking questions
A great deal, as it turns out—starting with asking good questions. Asking the right questions of the right people at the right times is a skill set computers lack and may never acquire. To be sure, the exponential advances of deep-learning algorithms mean that executive expertise, which typically runs deep in a particular domain or set of domains, is sometimes inferior to (or can get in the way of) insights generated by deep-learning algorithms, big data, and advanced analytics. In fact, there’s a case for using an executive’s domain expertise to frame the upfront questions that need asking and then turning the machines loose to answer those questions. That’s a role for the people with an organization’s strongest judgment: the senior leaders.

The importance of questions extends beyond steering machines, to interpreting their output. Recent history demonstrates the risk of relying on technology-based algorithmic insights without fully understanding how they drive decision making, for that makes it impossible to manage business and reputational risks (among others) properly. The potential for disaster is not small. The foremost cautionary tale, of course, comes from the banks prior to the 2008 financial crisis: C-suite executives and the managers one and two levels below them at major institutions did not fully understand how decisions were made in the “quant” areas of trading and asset management.

Algorithms and artificial intelligence may broaden this kind of analytical complexity beyond the financial world, to a whole new set of decision areas—again placing a premium on the tough questions senior leaders can ask. Penetrating this new world of analytical complexity is likely to be difficult, and an increasingly important role for senior executives may be establishing a set of small, often improvisatory, experiments to get a better handle on the implications of emerging insights and decision rules, as well as their own managerial styles.

Attacking exceptions
An increasingly important element of each leader’s management tool kit is likely to be the ability to attack problematic “exceptions” vigorously. Smart machines should get better and better at telling managers when they have a problem. Early evidence of this development is coming in data-intensive areas, such as pricing or credit departments or call centers—and the same thing will probably happen in more strategic areas, ranging from competitive analysis to talent management, as information gets better and machines get smarter. Executives can therefore spend less time on day-to-day management issues, but when the exception report signals a difficulty, the ability to spring into action will help executives differentiate themselves and the health of their organizations.

Senior leaders will have to draw on a mixture of insight—examining exceptions to see if they require interventions, such as new credit limits for a big customer or an opportunity to start bundling a new service with an existing product—and inspiration, as leaders galvanize the organization to respond quickly and work in new ways. Exceptions may pave the way for innovation too, something we already see as leading-edge retailers and financial-services firms mine large sets of customer data.

Tolerating ambiguity
While algorithms and supercomputers are designed to seek answers, they are likely to be most definitive on relatively small questions. The bigger and broader the inquiry, the more likely that human synthesis will be central to problem solving, because machines, though they learn rapidly, provide many pieces without assembling the puzzle. That process of assembly and synthesis can be messy and slow, placing a fresh premium on the senior leaders’ ability to tolerate ambiguity.

A straightforward example is the comfort digitally oriented executives are beginning to feel with a wide range of A/B testing to see what does and does not appeal to users or customers online. A/B testing is a small-scale version of the kind of experimentation that will increasingly hold sway as computers gain power, with fully fledged plans of action giving way to proof-of-concept (POC) ones, which make no claim to be either comprehensive or complete. POCs are a way to feel your way in uncertain terrain. Companies take an action, look at the result, and then push on to the next phase, step by step.

This necessary process will increasingly enable companies to proceed without knowing exactly where they’re going. For executives, this will feel rather like stumbling along in the dark; reference points can be few. Many will struggle with the uncertainty this approach provokes and wrestle with the temptation to engineer an outcome before sufficient data emerge to allow an informed decision. The trick will be holding open a space for the emergence of new insights and using subtle interventions to keep the whole journey from going off the cliff. What’s required, for executives, is the ability to remain in a state of unknowing while constantly filtering and evaluating the available information and its sources, tolerating tension and ambiguity, and delaying decisive action until clarity emerges. In such situations, the temptation to act quickly may provide a false sense of security and reassurance—but may also foreclose on potentially useful outcomes that would have emerged in the longer run.

Employing ‘soft’ skills
Humans have and will continue to have a strong comparative advantage when it comes to inspiring the troops, empathizing with customers, developing talent, and the like. Sometimes, machines will provide invaluable input, as Laszlo Bock at Google has famously shown in a wide range of human-resource data-analytics efforts. But translating this insight into messages that resonate with organizations will require a human touch. No computer will ever manage by walking around. And no effective executive will try to galvanize action by saying, “we’re doing this because an algorithm told us to.” Indeed, the contextualization of small-scale machine-made decisions is likely to become an important component of tomorrow’s leadership tool kit. While this article isn’t the place for a discourse on inspirational leadership, we’re firmly convinced that simultaneous growth in the importance of softer management skills and technology savvy will boost the complexity and richness of the senior-executive role.

How different is tomorrow’s effective leader from those of the past? In Peter Drucker’s 1967 classic, The Effective Executive, he described a highly productive company president who “accomplished more in [one] monthly session than many other and equally able executives get done in a month of meetings.” Yet this executive “had to resign himself to having at least half his time taken up by things of minor importance and dubious value … specific decisions on daily problems that should not have reached him but invariably did.”5 There should be less of dubious value coming across the senior executive’s desk in the future. This will be liberating—but also raises the bar for the executive’s ability to master the human dimensions that ultimately will provide the edge in the era of brilliant machines.

About the author(s)
Martin Dewhurst and Paul Willmott are directors in McKinsey’s London office.

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48
Why digital transformation is now on the CEO’s shoulders

Big data, the Internet of Things, and artificial intelligence hold such disruptive power that they have inverted the dynamics of technology leadership.

hen science and technology meet social and economic systems, you tend to see something akin to what the late Stephen Jay Gould called “punctuated equilibrium” in his description of evolutionary biology. Something that has been stable for a long period is suddenly disrupted radically—and then settles into a new equilibrium.1 Analogues across social and economic history include the discovery of fire, the domestication of dogs, the emergence of agricultural techniques, and, in more recent times, the Gutenberg printing press, the Jacquard loom, urban electrification, the automobile, the microprocessor, and the Internet. Each of these innovations collided with a society that had been in a period of relative stasis—followed by massive disruption.

Punctuated equilibrium is useful as a framework for thinking about disruption in today’s economy. US auto technology has been relatively static since the passage of the Federal interstate-highway act, in 1956. Now the synchronous arrival of Tesla, Uber, and autonomous vehicles is creating chaos. When it’s over, a new equilibrium will emerge. Landline operators were massively disrupted by cell phones, which in turn were upended by the introduction of the iPhone, in 2007—which, in the following decade, has settled into a new stasis, with handheld computing changing the very nature of interpersonal communication.

The evidence suggests that we are seeing a mass disruption in the corporate world like Gould’s recurring episodes of mass species extinction. Since 2000, over 50 percent of Fortune 500 companies have been acquired, merged, or declared bankruptcy, with no end in sight. In their wake, we are seeing a mass “speciation” of innovative corporate entities with largely new DNA, such as Amazon, Box, Facebook, Square, Twilio, Uber, WeWork, and Zappos.

Mass-extinction events don’t just happen for no reason. In the current extinction event, the causal factor is digital transformation.

Awash in information
Digital transformation is everywhere on the agendas of corporate boards and has risen to the top of CEOs’ strategic plans. Before the ubiquity of the personal computer or the Internet, the late Harvard sociologist Daniel Bell predicted the advent of the Information Age in his seminal work The Coming of Post-Industrial Society.2 The resulting structural change in the global economy, he wrote, would be on the order of the Industrial Revolution. In the subsequent four decades, the dynamics of Moore’s law and the associated technological advances of minicomputers, relational databases, computers, the Internet, and the smartphone have created a thriving $2 trillion information-technology industry—much as Bell foretold.

In the 21st century, Bell’s dynamic is accelerating, with the introduction of new disruptive technologies, including big data, artificial intelligence (AI), elastic cloud computing (the cloud), and the Internet of Things (IoT). The smart grid is a compelling example of these forces at work. Today’s electric-power grid—composed of billions of electric meters, transformers, capacitors, phasor measurement units, and power lines—is perhaps the largest and most complex machine ever developed.3 An estimated $2 trillion is being spent this decade to “sensor” that value chain by upgrading or replacing the multitude of devices in the grid infrastructure so that all of them are remotely machine addressable.4
When a power grid is fully connected, utilities can aggregate, evaluate, and correlate the interactions and relationships of vast quantities of data from all manner of devices—plus weather, load, and generation-capacity information—in near real time. They can then apply AI machine-learning algorithms to those data to optimize the operation of the grid, reduce the cost of operation, enhance resiliency, increase reliability, harden cybersecurity, enable a bidirectional power flow, and reduce greenhouse-gas emissions. The power of IoT, cloud computing, and AI spells the digital transformation of the utility industry.

A virtuous cycle is at work here. The network effects of interconnected and sensored customers, local power production, and storage (all ever cheaper) make more data available for analysis, rendering the deep-learning algorithms of AI more accurate and making for an increasingly efficient smart grid. Meanwhile, as big data sets become staggeringly large, they change the nature of business decisions. Historically, computation was performed on data samples, statistical methods were employed to draw inferences from those samples, and the inferences were in turn used to inform business decisions. Big data means we perform calculations on all the data; there is no sampling error. This enables AI—a previously unattainable class of computation that uses machine and deep learning to develop self-learning algorithms—to perform precise predictive and prescriptive analytics.

The benefits are breathtaking. All value chains will be disrupted: defense, education, financial services, government services, healthcare, manufacturing, oil and gas, retail, telecommunications, and more. To give some flavor to this:

Healthcare. Soon all medical devices will be sensored, as will patients. Healthcare records and genome sequences will be digitized. Sensors will remotely monitor pulse, blood chemistry, hormone levels, blood pressure, temperature, and brain waves. With AI, disease onset can be accurately predicted and prevented. AI-augmented best medical practices will be more uniformly applied.
Oil and gas. Operators will use predictive maintenance to monitor production assets and predict and prevent device failures, from submersible oil pumps to offshore oil rigs. The result will be a lower cost of production and a lower environmental impact.
Manufacturing. Companies are employing IoT-enabled inventory optimization to lower inventory carrying costs, predictive maintenance to lower the cost of production and increase product reliability, and supply-network risk mitigation to assure timely product delivery and manufacturing efficiency.

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49
The promise and challenge of the age of artificial intelligence

AI promises considerable economic benefits, even as it disrupts the world of work. These three priorities will help achieve good outcomes.

The time may have finally come for artificial intelligence (AI) after periods of hype followed by several “AI winters” over the past 60 years. AI now powers so many real-world applications, ranging from facial recognition to language translators and assistants like Siri and Alexa, that we barely notice it. Along with these consumer applications, companies across sectors are increasingly harnessing AI’s power in their operations. Embracing AI promises considerable benefits for businesses and economies through its contributions to productivity growth and innovation. At the same time, AI’s impact on work is likely to be profound. Some occupations as well as demand for some skills will decline, while others grow and many change as people work alongside ever-evolving and increasingly capable machines.

This briefing pulls together various strands of research by the McKinsey Global Institute into AI technologies and their uses, limitations, and impact. It was compiled for the Tallinn Digital Summit that took place in October 2018. The briefing concludes with a set of issues that policy makers and business leaders will need to address to soften the disruptive transitions likely to accompany its adoption.

AI’s time may have finally come, but more progress is needed
The term “artificial intelligence” was popularized at a conference at Dartmouth College in the United States in 1956 that brought together researchers on a broad range of topics, from language simulation to learning machines.

Despite periods of significant scientific advances in the six decades since, AI has often failed to live up to the hype that surrounded it. Decades were spent trying to describe human intelligence precisely, and the progress made did not deliver on the earlier excitement. Since the late 1990s, however, technological progress has gathered pace, especially in the past decade. Machine-learning algorithms have progressed, especially through the development of deep learning and reinforcement-learning techniques based on neural networks.

Several other factors have contributed to the recent progress. Exponentially more computing capacity has become available to train larger and more complex models; this has come through silicon-level innovation including the use of graphics processing units and tensor processing units, with more on the way. This capacity is being aggregated in hyperscale clusters, increasingly being made accessible to users through the cloud.

Another key factor is the massive amounts of data being generated and now available to train AI algorithms. Some of the progress in AI has been the result of system-level innovations. Autonomous vehicles are a good illustration of this: they take advantage of innovations in sensors, LIDAR, machine vision, mapping and satellite technology, navigation algorithms, and robotics all brought together in integrated systems.

Despite the progress, many hard problems remain that will require more scientific breakthroughs. So far, most of the progress has been in what is often referred to as “narrow AI”—where machine-learning techniques are being developed to solve specific problems, for example, in natural language processing. The harder issues are in what is usually referred to as “artificial general intelligence,” where the challenge is to develop AI that can tackle general problems in much the same way that humans can. Many researchers consider this to be decades away from becoming reality.

Deep learning and machine-learning techniques are driving AI
Much of the recent excitement about AI has been the result of advances in the field known as deep learning, a set of techniques to implement machine learning that is based on artificial neural networks. These AI systems loosely model the way that neurons interact in the brain. Neural networks have many (“deep”) layers of simulated interconnected neurons, hence the term “deep learning.” Whereas earlier neural networks had only three to five layers and dozens of neurons, deep learning networks can have ten or more layers, with simulated neurons numbering in the millions.

There are several types of machine learning: supervised learning, unsupervised learning, and reinforcement learning, with each best suited to certain use cases. Most current practical examples of AI are applications of supervised learning. In supervised learning, often used when labeled data are available and the preferred output variables are known, training data are used to help a system learn the relationship of given inputs to a given output—for example, to recognize objects in an image or to transcribe human speech.

Unsupervised learning is a set of techniques used without labeled training data—for example, to detect clusters or patterns, such as images of buildings that have similar architectural styles, in a set of existing data.

In reinforcement learning, systems are trained by receiving virtual “rewards” or “punishments,” often through a scoring system, essentially learning by trial and error. Through ongoing work, these techniques are evolving.

Limitations remain, although new techniques show promise
AI still faces many practical challenges, though new techniques are emerging to address them. Machine learning can require large amounts of human effort to label the training data necessary for supervised learning. In-stream supervision, in which data can be labeled in the course of natural usage, and other techniques could help alleviate this issue.

Obtaining data sets that are sufficiently large and comprehensive to be used for training—for example, creating or obtaining sufficient clinical-trial data to predict healthcare treatment outcomes more accurately—is also often challenging.

The “black box” complexity of deep learning techniques creates the challenge of “explainability,” or showing which factors led to a decision or prediction, and how. This is particularly important in applications where trust matters and predictions carry societal implications, as in criminal justice applications or financial lending. Some nascent approaches, including local interpretable model-agnostic explanations (LIME), aim to increase model transparency.

Another challenge is that of building generalized learning techniques, since AI techniques continue to have difficulties in carrying their experiences from one set of circumstances to another. Transfer learning, in which an AI model is trained to accomplish a certain task and then quickly applies that learning to a similar but distinct activity, is one promising response to this challenge.

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Businesses stand to benefit from AI
While AI is increasingly pervasive in consumer applications, businesses are beginning to adopt it across their operations, at times with striking results.

AI’s potential cuts across industries and functions
AI can be used to improve business performance in areas including predictive maintenance, where deep learning’s ability to analyze large amounts of high-dimensional data from audio and images can effectively detect anomalies in factory assembly lines or aircraft engines. In logistics, AI can optimize routing of delivery traffic, improving fuel efficiency and reducing delivery times. In customer service management, AI has become a valuable tool in call centers, thanks to improved speech recognition. In sales, combining customer demographic and past transaction data with social media monitoring can help generate individualized “next product to buy” recommendations, which many retailers now use routinely.

Such practical AI use cases and applications can be found across all sectors of the economy and multiple business functions, from marketing to supply chain operations. In many of these use cases, deep learning techniques primarily add value by improving on traditional analytics techniques.

Our analysis of more than 400 use cases across 19 industries and nine business functions found that AI improved on traditional analytics techniques in 69 percent of potential use cases (Exhibit 1). In only 16 percent of AI use cases did we find a “greenfield” AI solution that was applicable where other analytics methods would not be effective. Our research estimated that deep learning techniques based on artificial neural networks could generate as much as 40 percent of the total potential value that all analytics techniques could provide by 2030. Further, we estimate that several of the deep learning techniques could enable up to $6 trillion in value annually.

Exhibit 1

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So far, adoption is uneven across companies and sectors
Although many organizations have begun to adopt AI, the pace and extent of adoption has been uneven. Nearly half of respondents in a 2018 McKinsey survey on AI adoption say their companies have embedded at least one AI capability in their business processes, and another 30 percent are piloting AI. Still, only 21 percent say their organizations have embedded AI in several parts of the business, and barely 3 percent of large firms have integrated AI across their full enterprise workflows.

Other surveys show that early AI adopters tend to think about these technologies more expansively, to grow their markets or increase market share, while companies with less experience focus more narrowly on reducing costs. Highly digitized companies tend to invest more in AI and derive greater value from its use.

At the sector level, the gap between digitized early adopters and others is widening. Sectors highly ranked in MGI’s Industry Digitization Index, such as high tech and telecommunications, and financial services are leading AI adopters and have the most ambitious AI investment plans (Exhibit 2). As these firms expand AI adoption and acquire more data and AI capabilities, laggards may find it harder to catch up.

Exhibit 2

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Several challenges to adoption persist
Many companies and sectors lag in AI adoption. Developing an AI strategy with clearly defined benefits, finding talent with the appropriate skill sets, overcoming functional silos that constrain end-to-end deployment, and lacking ownership and commitment to AI on the part of leaders are among the barriers to adoption most often cited by executives.

On the strategy side, companies will need to develop an enterprise-wide view of compelling AI opportunities, potentially transforming parts of their current business processes. Organizations will need robust data capture and governance processes as well as modern digital capabilities, and be able to build or access the requisite infrastructure. Even more challenging will be overcoming the “last mile” problem of making sure that the superior insights provided by AI are inculcated into the behavior of the people and processes of an enterprise.

On the talent front, much of the construction and optimization of deep neural networks remains an art requiring real expertise. Demand for these skills far outstrips supply; according to some estimates, fewer than 10,000 people have the skills necessary to tackle serious AI problems, and competition for them is fierce. Companies considering the option of building their own AI solutions will need to consider whether they have the capacity to attract and retain workers with these specialized skills.

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Economies also stand to benefit from AI, through increased productivity and innovation
Deployment of AI and automation technologies can do much to lift the global economy and increase global prosperity. At a time of aging and falling birth rates, productivity growth becomes critical for long-term economic growth. Even in the near term, productivity growth has been sluggish in developed economies, dropping to an average of 0.5 percent in 2010–14 from 2.4 percent a decade earlier in the United States and major European economies. Much like previous general-purpose technologies, AI has the potential to contribute to productivity growth.

AI could contribute to economic impact through a variety of channels
The largest economic impacts of AI will likely be on productivity growth through labor market effects including substitution, augmentation, and contributions to labor productivity.

Our research suggests that labor substitution could account for less than half of the total benefit. AI will augment human capabilities, freeing up workers to engage in more productive and higher-value tasks, and increase demand for jobs associated with AI technologies.

AI can also boost innovation, enabling companies to improve their top line by reaching underserved markets more effectively with existing products, and over the longer term, creating entirely new products and services. AI will also create positive externalities, facilitating more efficient cross-border commerce and enabling expanded use of valuable cross-border data flows. Such increases in economic activity and incomes can be reinvested into the economy, contributing to further growth.

The deployment of AI will also bring some negative externalities that could lower, although not eliminate, the positive economic impacts. On the economic front, these include increased competition that shifts market share from nonadopters to front-runners, the costs associated with managing labor market transitions, and potential loss of consumption for citizens during periods of unemployment, as well the transition and implementation costs of deploying AI systems.

All in all, these various channels net out to significant positive economic growth, assuming businesses and governments proactively manage the transition. One simulation we conducted using McKinsey survey data suggests that AI adoption could raise global GDP by as much as $13 trillion by 2030, about 1.2 percent additional GDP growth per year. This effect will build up only through time, however, given that most of the implementation costs of AI may be ahead of the revenue potential.

The AI readiness of countries varies considerably
The leading enablers of potential AI-driven economic growth, such as investment and research activity, digital absorption, connectedness, and labor market structure and flexibility, vary by country. Our research suggests that the ability to innovate and acquire the necessary human capital skills will be among the most important enablers—and that AI competitiveness will likely be an important factor influencing future GDP growth.

Countries leading the race to supply AI have unique strengths that set them apart. Scale effects enable more significant investment, and network effects enable these economies to attract the talent needed to make the most of AI. For now, China and the United States are responsible for most AI-related research activities and investment.

A second group of countries that includes Germany, Japan, Canada, and the United Kingdom have a history of driving innovation on a major scale and may accelerate the commercialization of AI solutions. Smaller, globally connected economies such as Belgium, Singapore, South Korea, and Sweden also score highly on their ability to foster productive environments where novel business models thrive.

Countries in a third group, including but not limited to Brazil, India, Italy, and Malaysia, are in a relatively weaker starting position, but they exhibit comparative strengths in specific areas on which they may be able to build. India, for instance, produces around 1.7 million graduates a year with STEM degrees—more than the total of STEM graduates produced by all G-7 countries. Other countries, with relatively underdeveloped digital infrastructure, innovation and investment capacity, and digital skills, risk falling behind their peers.

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AI and automation will have a profound impact on work
Even as AI and automation bring benefits to business and the economy, major disruptions to work can be expected.

About half of current work activities (not jobs) are technically automatable
Our analysis of the impact of automation and AI on work shows that certain categories of activities are technically more easily automatable than others. They include physical activities in highly predictable and structured environments, as well as data collection and data processing, which together account for roughly half of the activities that people do across all sectors in most economies.

The least susceptible categories include managing others, providing expertise, and interfacing with stakeholders. The density of highly automatable activities varies across occupations, sectors, and, to a lesser extent, countries. Our research finds that about 30 percent of the activities in 60 percent of all occupations could be automated—but that in only about 5 percent of occupations are nearly all activities automatable. In other words, more occupations will be partially automated than wholly automated.

Three simultaneous effects on work: Jobs lost, jobs gained, jobs changed
The pace at and extent to which automation will be adopted and impact actual jobs will depend on several factors besides technical feasibility. Among these are the cost of deployment and adoption, and the labor market dynamics, including labor supply quantity, quality, and associated wages. The labor factor leads to wide differences across developed and developing economies. The business benefits beyond labor substitution—often involving use of AI for beyond-human capabilities—which contribute to business cases for adoption are another factor.

Social norms, social acceptance, and various regulatory factors will also determine the timing. How all these factors play out across sectors and countries will vary, and for countries will largely be driven by labor market dynamics. For example, in advanced economies with relatively high wage levels, such as France, Japan, and the United States, jobs affected by automation could be more than double that in India, as a percentage of the total.

Given the interplay of all these factors, it is difficult to make predictions but possible to develop various scenarios. First, on jobs lost: our midpoint adoption scenario for 2016 to 2030 suggests that about 15 percent of the global workforce (400 million workers) could be displaced by automation (Exhibit 3).

Exhibit 3

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Second, jobs gained: we developed scenarios for labor demand to 2030 based on anticipated economic growth through productivity and by considering several drivers of demand for work. These included rising incomes, especially in emerging economies, as well as increased spending on healthcare for aging populations, investment in infrastructure and buildings, energy transition spending, and spending on technology development and deployment.

The number of jobs gained through these and other catalysts could range from 555 million to 890 million, or 21 to 33 percent of the global workforce. This suggests that the growth in demand for work, barring extreme scenarios, would more than offset the number of jobs lost to automation. However, it is important to note that in many emerging economies with young populations, there will already be a challenging need to provide jobs to workers entering the workforce and that, in developed economies, the approximate balance between jobs lost and those created in our scenarios is also a consequence of aging, and thus fewer people entering the workforce.

No less significant are the jobs that will change as machines increasingly complement human labor in the workplace. Jobs will change as a result of the partial automation described above, and jobs changed will affect many more occupations than jobs lost. Skills for workers complemented by machines, as well as work design, will need to adapt to keep up with rapidly evolving and increasingly capable machines.

Four workforce transitions will be significant
Even if there will be enough work for people in 2030, as most of our scenarios suggest, the transitions that will accompany automation and AI adoption will be significant.

First, millions of workers will likely need to change occupations. Some of these shifts will happen within companies and sectors, but many will occur across sectors and even geographies. While occupations requiring physical activities in highly structured environments and in data processing will decline, others that are difficult to automate will grow. These could include managers, teachers, nursing aides, and tech and other professionals, but also gardeners and plumbers, who work in unpredictable physical environments. These changes may not be smooth and could lead to temporary spikes in unemployment (Exhibit 4).

Exhibit 4

Second, workers will need different skills to thrive in the workplace of the future. Demand for social and emotional skills such as communication and empathy will grow almost as fast as demand for many advanced technological skills. Basic digital skills have been increasing in all jobs. Automation will also spur growth in the need for higher cognitive skills, particularly critical thinking, creativity, and complex information processing. Demand for physical and manual skills will decline, but these will remain the single largest category of workforce skills in 2030 in many countries. The pace of skill shifts has been accelerating, and it may lead to excess demand for some skills and excess supply for others.

Third, workplaces and workflows will change as more people work alongside machines. As self-checkout machines are introduced in stores, for example, cashiers will shift from scanning merchandise themselves to helping answer questions or troubleshoot the machines.

Finally, automation will likely put pressure on average wages in advanced economies. Many of the current middle-wage jobs in advanced economies are dominated by highly automatable activities, in fields such as manufacturing and accounting, which are likely to decline. High-wage jobs will grow significantly, especially for high-skill medical and tech or other professionals. However, a large portion of jobs expected to be created, such as teachers and nursing aides, typically have lower wage structures.

In tackling these transitions, many economies, especially in the OECD, start in a hole, given the existing skill shortages and challenged educational systems, as well as the trends toward declining expenditures on on-the-job training and worker transition support. Many economies are already experiencing income inequality and wage polarization.

AI will also bring both societal benefits and challenges
Alongside the economic benefits and challenges, AI will impact society in a positive way, as it helps tackle societal challenges ranging from health and nutrition to equality and inclusion. However, it is also creating pitfalls that will need to be addressed, including unintended consequences and misuse.

AI could help tackle some of society’s most pressing challenges
By automating routine or unsafe activities and those prone to human error, AI could allow humans to be more productive and to work and live more safely. One study looking at the United States estimates that replacing human drivers with more accurate autonomous vehicles could save thousands of lives per year by reducing accidents.

AI can also reduce the need for humans to work in unsafe environments such as offshore oil rigs and coal mines. DARPA, for example, is testing small robots that could be deployed in disaster areas to reduce the need for humans to be put in harm’s way. Several AI capabilities are especially relevant. Image classification performed on photos of skin taken via a mobile phone app could evaluate whether moles are cancerous, facilitating early-stage diagnosis for individuals with limited access to dermatologists. Object detection can help visually impaired people navigate and interact with their environment by identifying obstacles such as cars and lamp posts. Natural language processing could be used to track disease outbreaks by monitoring and analyzing text messages in local languages.


Visualizing the uses and potential impact of AI and other analytics
Explore the interactive
Our work and that of others has highlighted numerous use cases across many domains where AI could be applied for social good. For these AI-enabled interventions to be effectively applied, several barriers must be overcome. These include the usual challenges of data, computing, and talent availability faced by any organization trying to apply AI, as well as more basic challenges of access, infrastructure, and financial resources that are particularly acute in remote or economically challenged places and communities.

AI will need to address societal concerns including unintended consequences, misuse, algorithmic bias, and questions about data privacy
In economic terms, difficult questions will need to be addressed about the widening economic gaps across individuals, firms, sectors, and even countries that might emerge as an unintended consequence of deployment. Other areas of concern include the use and misuse of AI. These range from use in surveillance and military applications to use in social media and politics, and where the impact has social consequences such as in criminal justice systems. We must also consider the potential for users with malicious intent, including in areas of cybersecurity. Multiple research efforts are currently under way to identify best practices and address such issues in academic, nonprofit, and private-sector research.

Some concerns are directly related to the way algorithms and the data used to train them may introduce new biases or perpetuate and institutionalize existing social and procedural biases. For example, facial recognition models trained on a population of faces corresponding to the demographics of artificial intelligence developers may not reflect the broader population.

Data privacy and use of personal information are also critical issues to address if AI is to realize its potential. Europe has led the way in this area with the General Data Protection Regulation, which introduced more stringent consent requirements for data collection, gives users the right to be forgotten and the right to object, and strengthens supervision of organizations that gather, control, and process data, with significant fines for failures to comply. Cybersecurity and “deep fakes” that could manipulate election results or perpetrate large-scale fraud are also a concern.

Three priorities for achieving good outcomes
The potential benefits of AI to business and the economy, and the way the technology addresses some of the societal challenges, should encourage business leaders and policy makers to embrace and adopt AI. At the same time, the potential challenges to adoption, including workforce impacts, and other social concerns cannot be ignored. Key challenges to be addressed include:

The deployment challenge
We have an interest in embracing AI, given its likely contributions to business value, economic growth, and social good, at a time when many economies need to boost productivity. Businesses and countries have a strong incentive to keep up with global leaders such as the United States and China. Increased and broad deployment will require accelerating the progress being made on the technical challenges, as well making sure that all potential users have access to AI and can benefit from it. Among measures that may be needed:

Investing in and continuing to advance AI research and innovation in a manner that ensures that the benefits can be shared by all.
Expanding available data sets, especially in areas where their use would drive wider benefits for the economy and society.
Investing in AI-relevant human capital and infrastructure to broaden the talent base capable of creating and executing AI solutions to keep pace with global AI leaders.
Encouraging increased AI literacy among business leaders and policy makers to guide informed decision making.
Supporting existing digitization efforts that form the foundation for eventual AI deployment for both organizations and countries.
The future of work challenge
A starting point for addressing the potential disruptive impacts of automation will be to ensure robust economic and productivity growth, which is a prerequisite for job growth and increasing prosperity. Governments will also need to foster business dynamism, since entrepreneurship and more rapid new business formation will not only boost productivity, but also drive job creation. Addressing the issues related to skills, jobs, and wages will require more focused measures. These include:

Evolving education systems and learning for a changed workplace by focusing on STEM skills as well as creativity, critical thinking, and lifelong learning.
Stepping up private- and public-sector investments in human capital, perhaps aided by incentives and credits analogous to those available for R&D investments.
Improving labor market dynamism by supporting better credentialing and matching, as well as enabling diverse forms of work, including the gig economy.
Rethinking incomes by considering and experimenting with programs that would provide not only income for work, but also meaning and dignity.
Rethinking transition support and safety nets for workers affected, by drawing on best practices from around the world and considering new approaches.
The responsible AI challenge
AI will not live up to its promise if the public loses confidence in it as a result of privacy violations, bias, or malicious use, or if much of the world comes to blame it for exacerbating inequality. Establishing confidence in its abilities to do good, at the same time as addressing misuses, will be critical. Approaches could include:

Strengthening consumer, data, and privacy and security protections.
Establishing a generally shared framework and set of principles for the beneficial and safe use of AI.
Best practice sharing and ongoing innovation to address issues such as safety, bias, and explainability.
Striking the right balance between the business and national competitive race to lead in AI to ensure that the benefits of AI are widely available and shared.

Source: https://www.mckinsey.com/featured-insights/artificial-intelligence/the-promise-and-challenge-of-the-age-of-artificial-intelligence?cid=other-eml-cls-mip-mck&hlkid=e43707df8a79425fbab2deac048a02dc&hctky=1370656&hdpid=5de210fe-6adc-4201-9d10-1e5f9c04e8ef

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Will artificial intelligence make you a better leader?

Agile leadership and AI both depend on learning to let go.

Consider this real-life scene: Reflecting on the difficult moments of his week, the new CEO of a UK manufacturer felt angry. His attention kept going back to the tension in several executive-team meetings. He had an urge to shake the team and push several of its members, who were riven by old conflicts, to stop fighting and start collaborating to solve the company’s real problems. He also sensed, though, that a brute-force approach was unlikely to get very far, or to yield the creative insights that the company desperately needed to keep up with its fast-changing competitive environment. Instead, he calmed himself, stopped blaming his team, and asked himself whether he could break the logjam by pursuing truly new approaches to the company’s problems. It was then that his mind turned to, of all things, artificial intelligence.

Like many leaders, the CEO was struggling to cope with the stress induced by uncertainty, rising complexity, and rapid change. All of these are part and parcel of today’s business environment, which is different enough from the one many of us grew up with to challenge our well-grooved leadership approaches. In a recent article, we described five practices that can help you step back from the tried and true and become more inwardly agile (see “Leading with inner agility”). Here, we want to describe the relationship between some of those ideas and a technology that at first glance seems to add complexity but in fact can be a healing balm: artificial intelligence (AI), which we take to span the next generation of advanced data and analytics applications. Inner agility and AI may sound like strange bedfellows, but when you consider crucial facts about the latter, you can see its potential to help you lead with clarity, specificity, and creativity.

The first crucial fact about AI is that you don’t know ahead of time what the data will reveal. By its very nature, AI is a leap of faith, just as embracing your ignorance and radical reframing are. And like learning to let go, listening to AI can help you find genuinely novel, disruptive insights in surprising and unexpected places.

A second fact about AI is that it creates space and time to think by filtering the signal from the noise. You let the algorithms loose on a vast landscape of data, and they report back only what you need to know and when you need to know it.

Let’s return to the CEO above to see an example of these dynamics in action. The CEO knew that his company’s key product would have to be developed more efficiently to compete with hard-charging rivals from emerging markets. He urgently needed to take both cost and time out of the product-development process. The standard approach would have been to cut head count or invest in automation, but he wasn’t sure either was right for his company, which was exhausted from other recent cost-cutting measures.

All this was on the CEO’s mind as he mused about the problematic executive dynamics he’d been observing—which, frankly, made several of his leaders unreliable sources of information. It was the need for objective, creative insight that stoked the CEO’s interest in AI-fueled advanced data analytics. A few days later, he began asking a team of data-analytics experts a couple broad and open-ended questions: What are the causes of inefficiencies in our product design and development workflow? What and where are the opportunities to improve performance?

The AI team trained their algorithms on a vast variety of data sources covering such things as project life-cycle management, fine-grained design and manufacturing documents, financial and HR data, suppliers and subcontractors, and communications data. Hidden patterns in the communication networks led to a detailed analysis of the interactions between two key departments: design and engineering. Using aggregated data that didn’t identify individual communications, the team looked at the number of emails sent after meetings or to other departments, the use of enterprise chat groups and length of chats, texting volume, and response rates to calendar invites, the algorithms surfaced an important, alarming discovery. The two departments were barely collaborating at all. In reality, the process was static: designers created a model, engineers evaluated and commented, designers remodeled, and so on. Each cared solely about its domain. The data-analytics team handed the CEO one other critical fact: by going back five years and cross-referencing communications data and product releases, they provided clear evidence that poor collaboration slowed time to market and increased costs.

Source: https://www.mckinsey.com/business-functions/organization/our-insights/will-artificial-intelligence-make-you-a-better-leader?cid=other-eml-cls-mip-mck&hlkid=bc6a09f261c640edbfedcb256d7d95dd&hctky=1370656&hdpid=5de210fe-6adc-4201-9d10-1e5f9c04e8ef

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Economics and Business Studies / AmCham Journal (October - December 2019)
« on: January 18, 2020, 10:37:14 AM »
AmCham Journal (October - December 2019) can be seen from the below link:

https://drive.google.com/file/d/1jxr4ORGx79oEqdmuLg5JPqRBXHjf72hq/view?usp=sharing


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Quality Assurance Areas / Taxonomies of Learning
« on: January 13, 2020, 03:11:26 PM »
Taxonomies of Learning

As you design learning objectives for your courses, you’ll be thinking deeply about what type of work you want your students to do to demonstrate that they have achieved your desired outcomes. What should our students know? What skills should they have? What types of activities should they be able to do? A taxonomy of learning provides an incredibly useful tool for defining the types of work that we want our students to do.
Taxonomies of Learning
Bloom's TaxonomyIn the 1950s, Benjamin Bloom and a group of collaborating psychologists created what is known as Bloom’s Taxonomy, which is a framework for levels of understanding. Every discipline has some quibble with the specifics of these taxonomies. Our point is not to suggest that they are sacrosanct. Rather, we think that they are valuable as a heuristic—or even just as a lexicon of verbs for assignments—that can help you both when you are designing, and then when you are reflecting back on,  your lessons and assignments and the responses of your students to them.

Bloom’s taxonomy outlines six levels of cognitive gain. The lower levels of Bloom’s taxonomy focus on the knowledge that we want our students to acquire – what we want our students to remember and understand. The middle levels focus on application and analysis of information. At the top of Bloom’s taxonomy are tasks that involve creating and evaluating.

Over the years, Bloom’s Taxonomy has been revised, and alternative taxonomies have been created. In 2001, Lorin Anderson and David Krathwohl rethought Bloom’s Taxonomy, shifting the peak from evaluation to creation. Additionally, one of their important contributions was the addition of a framework of actionable verbs for each level. These verbs help you evaluate the types of assignments, activities, and questions that you develop for your students.



More recently, the shape of Bloom’s taxonomy has been represented not as a pyramid – where there is a large based composed of facts and a tiny peak of creativity (which someone might interpret to mean that we should spend the majority of our time focus purely on knowledge) – but instead as a broad wedge that better highlights the value of creating, evaluating, and analyzing.  This revised visualization of Bloom's taxonomy is shown above.

Regardless of the exact shape or the exact terms, these taxonomies function as powerful heuristics to help us analyze our learning objectives and to design our assignments. In spite of the pyramidal shape of Bloom’s taxonomy, the point is not to suggest that what's at the top is more important than what's at the bottom; or that what's at the bottom needs to be larger than what's at the top. Rather, there are two points:

The skills and actions in the higher bands require engagement, or perhaps even mastery, of the skills in the lower bands.
The assignments and assessments which we set for students—which are discussed in the next section of our online resources, on syllabus and assignment design—should be in alignment. If you want your students to perform at higher cognitive levels on an exam, then the homework and in-class activities need to prepare students for this type of work.

Source: https://bokcenter.harvard.edu/taxonomies-learning

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গবেষণা নিয়ে ৫ প্রশ্ন

আমাদের দেশের বহু তরুণ এখন দেশ-বিদেশে গবেষণা করছেন। নামী জার্নালেও প্রকাশিত হয়েছে অনেকের গবেষণাপ্রবন্ধ। গবেষণায় আগ্রহ আছে অনেকের। কিন্তু নানা প্রশ্ন ঘুরপাক খায় শিক্ষার্থীদের মনে। এমনই পাঁচটি প্রশ্নের উত্তর থাকছে এবারের মূল রচনায়।

গবেষণা নিয়ে ৫টি প্রশ্নের উত্তর দিয়েছেন ৫ জন বাংলাদেশি গবেষক।

১. রাগিব হাসান
সহযোগী অধ্যাপক, কম্পিউটারবিজ্ঞান বিভাগ, যুক্তরাষ্ট্রের ইউনিভার্সিটি অব অ্যালাবামা অ্যাট বার্মিংহাম
গবেষণা কেন গুরুত্বপূর্ণ?

বিশ্ববিদ্যালয় পর্যায়ে স্নাতকে যাঁরা গবেষণা করছেন, গবেষণাপত্র প্রকাশ করছেন, তাঁদের বিদেশে উচ্চশিক্ষার সুযোগ অনেক বেশি। বিভিন্ন জার্নালে প্রকাশিত গবেষণাপত্র, প্রবন্ধ, পোস্টার উপস্থাপনকে যুক্তরাষ্ট্রসহ বিভিন্ন দেশের নামীদামি বিশ্ববিদ্যালয়ে ভর্তির ক্ষেত্রে বিশেষভাবে গুরুত্ব দেওয়া হয়। বিশ্ববিদ্যালয় পর্যায়ে গবেষণার অভিজ্ঞতা বিদেশে স্নাতকোত্তর ও পিএইচডি ডিগ্রি অর্জনে ভর্তির ক্ষেত্রে শিক্ষার্থীদের গ্রহণযোগ্যতা বাড়িয়ে দেয়। দেশে গবেষণার অভিজ্ঞতা থাকলে বিভিন্ন বিশ্ববিদ্যালয়ে পড়ার ক্ষেত্রে অনুদান, ফেলোশিপ, অ্যাসিস্ট্যান্সি, বৃত্তির মাধ্যমে আর্থিক সহযোগিতা পাওয়ার সুযোগ থাকে।

গবেষণা শুধু উচ্চশিক্ষার সুযোগকে বিস্তৃত করে না, ভবিষ্যতের কর্মবাজারেও দারুণ কার্যকর। যে বিষয়ে গবেষণা করছেন, সে বিষয় নিয়ে কাজ করা কোনো না কোনো আন্তর্জাতিক প্রতিষ্ঠান আপনার কর্মস্থল হতে পারে। গবেষণারত অবস্থায় অনেক প্রযুক্তিগত ও বৈজ্ঞানিক গবেষণাগারে কাজের সুযোগ পান গবেষকেরা। গবেষণাকালীন সময়ে বিভিন্ন দেশে পেপার উপস্থাপন ও কনফারেন্সে অংশগ্রহণের সুযোগ থাকে।

যাঁরা ভবিষ্যতে শুধু শিক্ষক বা গবেষক হতে চান, তাঁদের জন্যই শুধু গবেষণা নয়, গবেষণা আসলে উচ্চশিক্ষার একটি অংশ। স্নাতক-স্নাতকোত্তর পর্যায়ে গবেষণায় আমাদের দেশের বিশ্ববিদ্যালয়গুলোতে বেশি জোর দেওয়া প্রয়োজন।

২. সাইফুল ইসলাম
পিএইচডি গবেষক, কুইন্সল্যান্ড বিশ্ববিদ্যালয়, অস্ট্রেলিয়া
গবেষণার কাজ কোন সময়ে শুরু করা উচিত?

আমাদের দেশে বেশির ভাগ শিক্ষার্থীই স্নাতক পর্যায়ে গবেষণার দিকে তেমন মনোযোগী নন। কিন্তু গবেষণা নিয়ে ভাবনা আসলে বিশ্ববিদ্যালয়জীবনের শুরু থেকেই করা উচিত। যে বিষয়েই পড়ুন না কেন, প্রথম ও দ্বিতীয় বর্ষ থেকেই ভবিষ্যতে কোন বিষয়ে গবেষণা করতে চান, তা খুঁজতে থাকুন।

বিশ্ববিদ্যালয়ের দ্বিতীয় ও তৃতীয় বর্ষ গবেষণার মনন বিকাশের দারুণ সময়। হুট করে তো একদিন গবেষক হয়ে ওঠা যায় না, তাই এই সময়কে গোছানোর জন্য কাজে লাগানো প্রয়োজন। স্নাতকোত্তর পর্যায়ে অনেকে তাড়াহুড়া করে গবেষণা শুরু করেন। তখন একটু বেশি চাপ হয়ে যায়। যত আগে শুরু করা যায়, যত আগে গবেষণার কৌশল সম্পর্কে শেখা যায়, আর্টিকেল লেখার চর্চা করা যায়, ততই নিজেকে সামনে এগিয়ে নেওয়ার সুযোগ থাকে। বিভিন্ন জার্নাল পেপারে আর্টিকেল জমা দেওয়ার নিয়ম জানতে হবে। বিভিন্ন সেমিনার ও সম্মেলনে অংশগ্রহণের মাধ্যমে কোন কোন ক্ষেত্রে নিজেকে গবেষক হিসেবে তৈরি করবেন, তা জানার সুযোগ আছে। যে বিষয়ে গবেষণা করতে চান, তা নিয়ে বিশেষজ্ঞ ও বিশ্ববিদ্যালয়ের শিক্ষকদের ইমেইলে যোগাযোগের চেষ্টা করতে পারেন। গবেষক হিসেবে নেটওয়ার্ক গড়ে তোলাও জরুরি।

৩. শরিফা সুলতানা
পিএইচডি ইন ইনফরমেশন সায়েন্স, কর্নেল বিশ্ববিদ্যালয়, যুক্তরাষ্ট্র
কীভাবে বেছে নেব গবেষণার বিষয়?

গবেষণার দুনিয়া অনেক বড়, উন্মুক্ত। নানা বিষয়ে গবেষণার সুযোগ আছে। যাঁরা গবেষণার অ আ ক খ মোটামুটি জানেন, তাঁদের জন্য পুরো প্রক্রিয়া বোঝা সহজ। যে বিষয়ে পড়ছেন বিশ্ববিদ্যালয়ে, তা নিয়ে যেমন গবেষণার সুযোগ আছে, তেমনি নিত্যনতুন অসংখ্য বিষয় আছে। আমি যেমন মানুষ ও কম্পিউটারে মিথস্ক্রিয়া ও ডিজিটাল সিগন্যাল প্রসেসের মতো বিষয় নিয়ে গবেষণা করছি। নিজের বিষয়ের বাইরেও আমাকে জানতে হচ্ছে, শিখতে হচ্ছে। প্রকৌশলের শিক্ষার্থী হওয়া সত্ত্বেও কবিরাজি চিকিৎসা নিয়ে আমার একটি গবেষণাপত্র ২০১৯ সালে প্রকাশিত হয়।

গবেষণার ক্ষেত্রে আসলে নিজের পছন্দের বিষয়কে যেমন গুরুত্ব দিতে হয়, তেমনি যে বিষয় নিয়ে কাজের সুযোগ আছে, তা ভাবা জরুরি। প্রকৌশল কিংবা জীববিজ্ঞানের কোনো বিষয়ে পড়েও সামাজিক বিজ্ঞানের সঙ্গে সম্পৃক্ত কোনো গবেষণাপত্র তৈরি করতে পারেন। স্নাতকে যে বিষয়ে পড়ছেন, বা যে কোর্সে আগ্রহ তৈরি হয়েছে, তা নিয়েই শুরু করুন। ধীরে ধীরে জানার দুনিয়া বড় করতে হবে, গবেষণাকে বিস্তৃত করতে হবে। গবেষণায় তাত্ত্বিক পড়াশোনার দিকে গুরুত্ব দেওয়া হয়। বিভিন্ন গবেষণা কৌশল, তথ্য বিশ্লেষণ, তথ্য সংগ্রহের মতো বিষয় সম্পর্কে জানতে হবে। ধীরে ধীরে আপনার গবেষণার বিষয় ও আগ্রহ সম্পর্কে জেনে যাবেন।

৪. আলিয়া নাহিদ
প্রধান, ইনিশিয়েটিভ ফর নন কমিউনিকেবল ডিজিজেস, আইসিডিডিআরবি ও ক্লিনিক্যাল রিসার্চ প্ল্যাটফর্ম বাংলাদেশ
একজন গবেষকের মধ্যে কী কী দক্ষতা বা গুণ থাকা উচিত?

আগ্রহ আর ধৈর্যশক্তির জোরে যেকোনো শিক্ষার্থীই গবেষক হয়ে উঠতে পারেন। বুদ্ধিমত্তা ও কৌতূহল গবেষক হওয়ার জন্য ভীষণ জরুরি। অন্যদের সঙ্গে কাজ করার আগ্রহ, নেতৃত্বদান, অন্য গবেষকের অধীনে কিংবা দলের সঙ্গে কাজ করার কৌশল আয়ত্ত করতে হবে। নিজেকে যেমন বুঝতে হবে, তেমনি নিজের যোগ্যতাকে বিকাশে সময় দিতে হবে। তথ্য সংগ্রহ ও বিশ্লেষণের সক্ষমতা থাকতে হবে।

গবেষণায় শেষ বলে কিছু নেই, তাই সব সময় পর্যবেক্ষণ মনোভাবে থাকতে হবে। অনুসন্ধিৎসু হতে হবে।

গবেষকদের আরেকটি গুণ থাকা ভীষণ জরুরি—তা হচ্ছে সততা ও নৈতিকতা। মানসিকভাবে সৎ ও নৈতিক হওয়া প্রত্যেক গবেষকের জন্য যেমন গুরুত্বপূর্ণ, তেমনি কাজের ক্ষেত্রেও নৈতিক থাকতে হবে। গবেষক হিসেবে গবেষণা নিয়ে অনেক সমালোচনা কিংবা নেতিবাচক ফল আসতে পারে, তাই বলে হাল ছেড়ে দিলে চলবে না।

শিক্ষক ও বিশেষজ্ঞ পেশাজীবীদের সঙ্গে গবেষণার সুযোগ কাজে লাগাতে হবে। বর্তমান সময়ে গবেষণার ক্ষেত্রে যেসব টুলস বা প্রযুক্তি ব্যবহৃত হয়, যেমন এসপিএসএস, ম্যাটল্যাব—এগুলোর ব্যবহার শিখতে হবে।

৫. নিগার সুলতানা
পিএইচডি, ওয়াটারলু বিশ্ববিদ্যালয়, কানাডা
গবেষণার মাঝপথে এসে থমকে গেলে কী করব?

গবেষণা অনেক সময়ের বিষয়। হুট করে শুরু করা যায় না। তবে বাস্তবতার কারণে গবেষণায় বাধা আসতেই পারে, থেমে যেতে হতে পারে। গবেষণায় হয়তো ফান্ড কমে গেল কিংবা বন্ধ হয়ে গেল। হয়তো গবেষণা করছেন, কিন্তু ফল পাচ্ছেন না। গবেষণা তত্ত্বাবধায়ক, সহগবেষকদের সঙ্গে অনেক বিষয়ে তর্ক ও বিতর্কের অবকাশ থাকে।

একজন গবেষককে সব পরিস্থিতির জন্য তৈরি থাকতে হবে। প্রয়োজনে সুপারভাইজার ও অন্যান্য গবেষকদের সহায়তা নিতে হবে। গবেষণা আসলে প্রকল্প ব্যবস্থাপনা। কাজটাকে ছোট ছোট ভাগ করে নিতে হবে। অনেকেই গবেষণা শুরুর পরে হাল ছেড়ে দেন। তরুণ গবেষকদের মধ্যে এই প্রবণতা খুব বেশি। প্রয়োজনে শিক্ষক ও মনোবিদদের পরামর্শ নিতে হবে।

গবেষক হিসেবে আপনার জীবনের চাপ অন্যরা গুরুত্ব না–ও দিতে পারে। এ ক্ষেত্রে নিজের স্বাস্থ্যের দিকে খেয়াল রাখা জরুরি। প্রয়োজনে একটু বিরতির পর আবার জেদ নিয়ে ফিরে আসুন। কাজে আগ্রহ হারিয়ে ফেলা তরুণ গবেষকদের সাধারণ সংকট বলা যায়। এ ক্ষেত্রে জীবনের অন্যান্য বিষয় আর শখকেও গুরুত্ব দিতে হবে। গবেষণাকাজ ও জীবনের মধ্যে ‘সামঞ্জস্য’ এনে নিজেকে উজ্জীবিত করতে হবে।

Source: https://www.prothomalo.com/education/article/1633959/?fbclid=IwAR1dX2EiGJELprngy2RRRGBld-9wRkRwuq-5mCUJy--VT5--3WuGIrS8AxE

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গবেষণা নিয়ে ৫ প্রশ্ন

আমাদের দেশের বহু তরুণ এখন দেশ-বিদেশে গবেষণা করছেন। নামী জার্নালেও প্রকাশিত হয়েছে অনেকের গবেষণাপ্রবন্ধ। গবেষণায় আগ্রহ আছে অনেকের। কিন্তু নানা প্রশ্ন ঘুরপাক খায় শিক্ষার্থীদের মনে। এমনই পাঁচটি প্রশ্নের উত্তর থাকছে এবারের মূল রচনায়।

গবেষণা নিয়ে ৫টি প্রশ্নের উত্তর দিয়েছেন ৫ জন বাংলাদেশি গবেষক।

১. রাগিব হাসান
সহযোগী অধ্যাপক, কম্পিউটারবিজ্ঞান বিভাগ, যুক্তরাষ্ট্রের ইউনিভার্সিটি অব অ্যালাবামা অ্যাট বার্মিংহাম
গবেষণা কেন গুরুত্বপূর্ণ?

বিশ্ববিদ্যালয় পর্যায়ে স্নাতকে যাঁরা গবেষণা করছেন, গবেষণাপত্র প্রকাশ করছেন, তাঁদের বিদেশে উচ্চশিক্ষার সুযোগ অনেক বেশি। বিভিন্ন জার্নালে প্রকাশিত গবেষণাপত্র, প্রবন্ধ, পোস্টার উপস্থাপনকে যুক্তরাষ্ট্রসহ বিভিন্ন দেশের নামীদামি বিশ্ববিদ্যালয়ে ভর্তির ক্ষেত্রে বিশেষভাবে গুরুত্ব দেওয়া হয়। বিশ্ববিদ্যালয় পর্যায়ে গবেষণার অভিজ্ঞতা বিদেশে স্নাতকোত্তর ও পিএইচডি ডিগ্রি অর্জনে ভর্তির ক্ষেত্রে শিক্ষার্থীদের গ্রহণযোগ্যতা বাড়িয়ে দেয়। দেশে গবেষণার অভিজ্ঞতা থাকলে বিভিন্ন বিশ্ববিদ্যালয়ে পড়ার ক্ষেত্রে অনুদান, ফেলোশিপ, অ্যাসিস্ট্যান্সি, বৃত্তির মাধ্যমে আর্থিক সহযোগিতা পাওয়ার সুযোগ থাকে।

গবেষণা শুধু উচ্চশিক্ষার সুযোগকে বিস্তৃত করে না, ভবিষ্যতের কর্মবাজারেও দারুণ কার্যকর। যে বিষয়ে গবেষণা করছেন, সে বিষয় নিয়ে কাজ করা কোনো না কোনো আন্তর্জাতিক প্রতিষ্ঠান আপনার কর্মস্থল হতে পারে। গবেষণারত অবস্থায় অনেক প্রযুক্তিগত ও বৈজ্ঞানিক গবেষণাগারে কাজের সুযোগ পান গবেষকেরা। গবেষণাকালীন সময়ে বিভিন্ন দেশে পেপার উপস্থাপন ও কনফারেন্সে অংশগ্রহণের সুযোগ থাকে।

যাঁরা ভবিষ্যতে শুধু শিক্ষক বা গবেষক হতে চান, তাঁদের জন্যই শুধু গবেষণা নয়, গবেষণা আসলে উচ্চশিক্ষার একটি অংশ। স্নাতক-স্নাতকোত্তর পর্যায়ে গবেষণায় আমাদের দেশের বিশ্ববিদ্যালয়গুলোতে বেশি জোর দেওয়া প্রয়োজন।

২. সাইফুল ইসলাম
পিএইচডি গবেষক, কুইন্সল্যান্ড বিশ্ববিদ্যালয়, অস্ট্রেলিয়া
গবেষণার কাজ কোন সময়ে শুরু করা উচিত?

আমাদের দেশে বেশির ভাগ শিক্ষার্থীই স্নাতক পর্যায়ে গবেষণার দিকে তেমন মনোযোগী নন। কিন্তু গবেষণা নিয়ে ভাবনা আসলে বিশ্ববিদ্যালয়জীবনের শুরু থেকেই করা উচিত। যে বিষয়েই পড়ুন না কেন, প্রথম ও দ্বিতীয় বর্ষ থেকেই ভবিষ্যতে কোন বিষয়ে গবেষণা করতে চান, তা খুঁজতে থাকুন।

বিশ্ববিদ্যালয়ের দ্বিতীয় ও তৃতীয় বর্ষ গবেষণার মনন বিকাশের দারুণ সময়। হুট করে তো একদিন গবেষক হয়ে ওঠা যায় না, তাই এই সময়কে গোছানোর জন্য কাজে লাগানো প্রয়োজন। স্নাতকোত্তর পর্যায়ে অনেকে তাড়াহুড়া করে গবেষণা শুরু করেন। তখন একটু বেশি চাপ হয়ে যায়। যত আগে শুরু করা যায়, যত আগে গবেষণার কৌশল সম্পর্কে শেখা যায়, আর্টিকেল লেখার চর্চা করা যায়, ততই নিজেকে সামনে এগিয়ে নেওয়ার সুযোগ থাকে। বিভিন্ন জার্নাল পেপারে আর্টিকেল জমা দেওয়ার নিয়ম জানতে হবে। বিভিন্ন সেমিনার ও সম্মেলনে অংশগ্রহণের মাধ্যমে কোন কোন ক্ষেত্রে নিজেকে গবেষক হিসেবে তৈরি করবেন, তা জানার সুযোগ আছে। যে বিষয়ে গবেষণা করতে চান, তা নিয়ে বিশেষজ্ঞ ও বিশ্ববিদ্যালয়ের শিক্ষকদের ইমেইলে যোগাযোগের চেষ্টা করতে পারেন। গবেষক হিসেবে নেটওয়ার্ক গড়ে তোলাও জরুরি।

৩. শরিফা সুলতানা
পিএইচডি ইন ইনফরমেশন সায়েন্স, কর্নেল বিশ্ববিদ্যালয়, যুক্তরাষ্ট্র
কীভাবে বেছে নেব গবেষণার বিষয়?

গবেষণার দুনিয়া অনেক বড়, উন্মুক্ত। নানা বিষয়ে গবেষণার সুযোগ আছে। যাঁরা গবেষণার অ আ ক খ মোটামুটি জানেন, তাঁদের জন্য পুরো প্রক্রিয়া বোঝা সহজ। যে বিষয়ে পড়ছেন বিশ্ববিদ্যালয়ে, তা নিয়ে যেমন গবেষণার সুযোগ আছে, তেমনি নিত্যনতুন অসংখ্য বিষয় আছে। আমি যেমন মানুষ ও কম্পিউটারে মিথস্ক্রিয়া ও ডিজিটাল সিগন্যাল প্রসেসের মতো বিষয় নিয়ে গবেষণা করছি। নিজের বিষয়ের বাইরেও আমাকে জানতে হচ্ছে, শিখতে হচ্ছে। প্রকৌশলের শিক্ষার্থী হওয়া সত্ত্বেও কবিরাজি চিকিৎসা নিয়ে আমার একটি গবেষণাপত্র ২০১৯ সালে প্রকাশিত হয়।

গবেষণার ক্ষেত্রে আসলে নিজের পছন্দের বিষয়কে যেমন গুরুত্ব দিতে হয়, তেমনি যে বিষয় নিয়ে কাজের সুযোগ আছে, তা ভাবা জরুরি। প্রকৌশল কিংবা জীববিজ্ঞানের কোনো বিষয়ে পড়েও সামাজিক বিজ্ঞানের সঙ্গে সম্পৃক্ত কোনো গবেষণাপত্র তৈরি করতে পারেন। স্নাতকে যে বিষয়ে পড়ছেন, বা যে কোর্সে আগ্রহ তৈরি হয়েছে, তা নিয়েই শুরু করুন। ধীরে ধীরে জানার দুনিয়া বড় করতে হবে, গবেষণাকে বিস্তৃত করতে হবে। গবেষণায় তাত্ত্বিক পড়াশোনার দিকে গুরুত্ব দেওয়া হয়। বিভিন্ন গবেষণা কৌশল, তথ্য বিশ্লেষণ, তথ্য সংগ্রহের মতো বিষয় সম্পর্কে জানতে হবে। ধীরে ধীরে আপনার গবেষণার বিষয় ও আগ্রহ সম্পর্কে জেনে যাবেন।

৪. আলিয়া নাহিদ
প্রধান, ইনিশিয়েটিভ ফর নন কমিউনিকেবল ডিজিজেস, আইসিডিডিআরবি ও ক্লিনিক্যাল রিসার্চ প্ল্যাটফর্ম বাংলাদেশ
একজন গবেষকের মধ্যে কী কী দক্ষতা বা গুণ থাকা উচিত?

আগ্রহ আর ধৈর্যশক্তির জোরে যেকোনো শিক্ষার্থীই গবেষক হয়ে উঠতে পারেন। বুদ্ধিমত্তা ও কৌতূহল গবেষক হওয়ার জন্য ভীষণ জরুরি। অন্যদের সঙ্গে কাজ করার আগ্রহ, নেতৃত্বদান, অন্য গবেষকের অধীনে কিংবা দলের সঙ্গে কাজ করার কৌশল আয়ত্ত করতে হবে। নিজেকে যেমন বুঝতে হবে, তেমনি নিজের যোগ্যতাকে বিকাশে সময় দিতে হবে। তথ্য সংগ্রহ ও বিশ্লেষণের সক্ষমতা থাকতে হবে।

গবেষণায় শেষ বলে কিছু নেই, তাই সব সময় পর্যবেক্ষণ মনোভাবে থাকতে হবে। অনুসন্ধিৎসু হতে হবে।

গবেষকদের আরেকটি গুণ থাকা ভীষণ জরুরি—তা হচ্ছে সততা ও নৈতিকতা। মানসিকভাবে সৎ ও নৈতিক হওয়া প্রত্যেক গবেষকের জন্য যেমন গুরুত্বপূর্ণ, তেমনি কাজের ক্ষেত্রেও নৈতিক থাকতে হবে। গবেষক হিসেবে গবেষণা নিয়ে অনেক সমালোচনা কিংবা নেতিবাচক ফল আসতে পারে, তাই বলে হাল ছেড়ে দিলে চলবে না।

শিক্ষক ও বিশেষজ্ঞ পেশাজীবীদের সঙ্গে গবেষণার সুযোগ কাজে লাগাতে হবে। বর্তমান সময়ে গবেষণার ক্ষেত্রে যেসব টুলস বা প্রযুক্তি ব্যবহৃত হয়, যেমন এসপিএসএস, ম্যাটল্যাব—এগুলোর ব্যবহার শিখতে হবে।

৫. নিগার সুলতানা
পিএইচডি, ওয়াটারলু বিশ্ববিদ্যালয়, কানাডা
গবেষণার মাঝপথে এসে থমকে গেলে কী করব?

গবেষণা অনেক সময়ের বিষয়। হুট করে শুরু করা যায় না। তবে বাস্তবতার কারণে গবেষণায় বাধা আসতেই পারে, থেমে যেতে হতে পারে। গবেষণায় হয়তো ফান্ড কমে গেল কিংবা বন্ধ হয়ে গেল। হয়তো গবেষণা করছেন, কিন্তু ফল পাচ্ছেন না। গবেষণা তত্ত্বাবধায়ক, সহগবেষকদের সঙ্গে অনেক বিষয়ে তর্ক ও বিতর্কের অবকাশ থাকে।

একজন গবেষককে সব পরিস্থিতির জন্য তৈরি থাকতে হবে। প্রয়োজনে সুপারভাইজার ও অন্যান্য গবেষকদের সহায়তা নিতে হবে। গবেষণা আসলে প্রকল্প ব্যবস্থাপনা। কাজটাকে ছোট ছোট ভাগ করে নিতে হবে। অনেকেই গবেষণা শুরুর পরে হাল ছেড়ে দেন। তরুণ গবেষকদের মধ্যে এই প্রবণতা খুব বেশি। প্রয়োজনে শিক্ষক ও মনোবিদদের পরামর্শ নিতে হবে।

গবেষক হিসেবে আপনার জীবনের চাপ অন্যরা গুরুত্ব না–ও দিতে পারে। এ ক্ষেত্রে নিজের স্বাস্থ্যের দিকে খেয়াল রাখা জরুরি। প্রয়োজনে একটু বিরতির পর আবার জেদ নিয়ে ফিরে আসুন। কাজে আগ্রহ হারিয়ে ফেলা তরুণ গবেষকদের সাধারণ সংকট বলা যায়। এ ক্ষেত্রে জীবনের অন্যান্য বিষয় আর শখকেও গুরুত্ব দিতে হবে। গবেষণাকাজ ও জীবনের মধ্যে ‘সামঞ্জস্য’ এনে নিজেকে উজ্জীবিত করতে হবে।

Source: https://www.prothomalo.com/education/article/1633959/?fbclid=IwAR1dX2EiGJELprngy2RRRGBld-9wRkRwuq-5mCUJy--VT5--3WuGIrS8AxE

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‘বাতাস থেকে’ উৎপাদিত ‘সোলিন’ বদলে দেবে খাদ্য উৎপাদনের ভবিষ্যৎ

‘বাতাস থেকে’ আমিষ জাতীয় খাদ্য তৈরি করেছেন ফিনল্যান্ডের একদল বিজ্ঞানী। তারা দাবি করছেন, এই খাবার পুষ্টিগুণ ও উৎপাদন খরচের দিক দিয়ে এক দশকের মধ্যে সয়াবিনের প্রতিযোগী হয়ে উঠতে পারবে। আর এই খাবার উৎপাদনে সৌরবিদ্যুৎ বা বায়ুকলের মাধ্যমে উৎপাদিত বিদ্যুৎ ব্যবহার করা গেলে গ্রিনহাউজ গ্যাস নির্গমনও প্রায় শূন্যের কোটায় নামিয়ে আনা যাবে বলে দাবি করেছেন তারা।

বলা হচ্ছে, এই বিজ্ঞানীদের পরিকল্পনা মতো বাণিজ্যিক উদ্যোগ সম্ভব হলে কৃষির কারণে পরিবেশ বা প্রকৃতিতে বর্তমানে যেসব প্রভাব পড়ছে তা অনেকখানি নিয়ন্ত্রণ করা যাবে।

বাতাস থেকে আমিষ উৎপাদনের কৌশল সম্পর্কে বিজ্ঞানীরা বলছেন, তড়িৎ সংশ্লেষণের মাধ্যমে পানি থেকে হাইড্রোজেন গ্যাস আলাদা করা হয়। তারপর সেই হাইড্রোজেন, বাতাস থেকে নেয়া কার্বন ডাই অক্সাইড ও খনিজ পদার্থ দিয়ে মাটিতে পাওয়া সাধারণ ব্যাকটেরিয়ার কালচার করা হয়। এই ব্যাকটেরিয়ায় কাঙ্ক্ষিত আমিষ তৈরি করে।

বিজ্ঞানীরা এই খাবারের নাম দিয়েছেন ‘সোলিন’। এটি একটি স্বাদহীন আটা। গবেষকরা বলছেন, তারা মূলত এমনটাই (স্বাদহীন) চাচ্ছিলেন। এই আমিষ সরাসরি খাওয়ার জন্য নয় বরং এতে প্রয়োজন মতো রঙ ও স্বাদ যুক্ত করা যাবে। এটি ব্যবহার করা যাবে পাস্তা, আইসক্রিম, বিস্কুট, নুডুলস, সসেজ বা রুটিতে। এমনকি কৃত্রিম মাংস বা মাছ তৈরির মিডিয়াম হিসেবেও ব্যবহার করা যাবে সোলিন।

ফিনল্যান্ডের হেলসিঙ্কি শহরের বাইরে সোলিন উৎপাদনের কারখানা প্রতিষ্ঠা করেছেন এই বিজ্ঞানীরা। এটির প্রধান নির্বাহী কর্মকর্তা পাসি ভাইনিক্কা। তিনি পড়াশোনা করেছেন যুক্তরাজ্যের ক্র্যানফিল্ড বিশ্ববিদ্যালয়ে; বর্তমানে ফিনল্যান্ডের ল্যাপপিনরান্টা ইউনিভার্সিটিতে অধ্যাপনা করছেন।

ভাইনিক্কা বলেন, এমন খাবার উৎপাদন প্রযুক্তির ধারণা প্রথম এসেছে ঊনিশ শতকের ষাটের দশকে। মূলত মহাকাশে এমন প্রযুক্তিতে খাবার তৈরির ধারণা নিয়ে কাজ করা হচ্ছে। তবে তিনি এটাও স্বীকার করছেন যে, তারা এখনো বেশ পিছিয়ে আছেন। তিনি আশা করছেন, আগামী দুইএক বছরের মধ্যে তাদের গবেষণা সম্পন্ন হবে। এরপর তারা বাণিজ্যিকভিত্তিতে উৎপাদনে পথে হাঁটবেন। এই প্রকল্পের জন্য এরই মধ্যে ৫৫ লাখ ইউরো বিনিয়োগ প্রতিশ্রুতি পেয়েছেন।

এই গবেষক বলছেন, সব ঠিকঠাক থাকলে ২০২৫ সাল নাগাদ বাণিজ্যিকভাবে এই খাদ্য উৎপাদন শুরু করবেন তারা। তবে সারাবিশ্বের চাহিদা মেটানোর মতো উৎপাদনে যেতে আরো অনেক বছর লেগে যাবে। আর তাদের এই প্রকল্প যদি ব্যর্থ-ও হয়; তবুও কৃত্রিম খাবার তৈরির প্রচেষ্টায় এটি একটি বড় অগ্রগতি হিসেবে বিবেচিত হবে বলেই মনে করেন তারা।

সূত্র: বিবিসি
News link: https://bonikbarta.net/home/news_description/216854/%E2%80%98%E0%A6%AC%E0%A6%BE%E0%A6%A4%E0%A6%BE%E0%A6%B8-%E0%A6%A5%E0%A7%87%E0%A6%95%E0%A7%87%E2%80%99-%E0%A6%89%E0%A7%8E%E0%A6%AA%E0%A6%BE%E0%A6%A6%E0%A6%BF%E0%A6%A4-%E2%80%98%E0%A6%B8%E0%A7%8B%E0%A6%B2%E0%A7%87%E0%A6%A8%E2%80%99-%E0%A6%AC%E0%A6%A6%E0%A6%B2%E0%A7%87-%E0%A6%AF%E0%A6%BE%E0%A6%AC%E0%A7%87-%E0%A6%95%E0%A7%83%E0%A6%A4%E0%A7%8D%E0%A6%B0%E0%A6%BF%E0%A6%AE-%E0%A6%96%E0%A6%BE%E0%A6%AC%E0%A6%BE%E0%A6%B0%E0%A7%87%E0%A6%B0-%E0%A6%AD%E0%A6%AC%E0%A6%BF%E0%A6%B7%E0%A7%8D%E0%A6%AF%E0%A6%A4?fbclid=IwAR0Jl2oLzffyghNp0aUWf_fXlYyGLHIsCXnLuOXQZHczPxAiKP_0KZOBUgc

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ফেসবুকে নতুন ৪ প্রাইভেসি ফিচার

ফেসবুক অ্যাকাউন্টের নিরাপত্তা নিয়ে অনেকেই উদ্বিগ্ন থাকেন। অনেক ব্যবহারকারী জানতে চান, তাদের অ্যাকাউন্টে কারা আনাগোনা করছে? কিংবা তাদের ব্যক্তিগত তথ্য জানতে কাদের আগ্রহ সবচেয়ে বেশি? এবার ব্যবহারকারীদের এমন বিভিন্ন জিজ্ঞাসার জবাব জানাবে ফেসবুক। সামাজিক যোগাযোগ মাধ্যমটির অ্যাকাউন্টের নিরাপত্তায় নতুন করে চারটি ফিচার যুক্ত করা হয়েছে। এসব নতুন ফিচার ব্যবহারকারীদের অ্যাকাউন্টের নিরাপত্তা আরো জোরদার করবে ও তথ্য বেহাত হওয়ার ঝুঁকি কমিয়ে আনবে বলে মনে করা হচ্ছে।

ফেসবুক সেটিংয়ের প্রাইভেসি চেকাআপের ‘হু ক্যান সি হোয়াট ইউ শেয়ার’ ফিচারের মাধ্যমে ব্যবহারকারীরা তাদের প্রোফাইলের ব্যক্তিগত তথ্য অপশনে যেসব অ্যাকাউন্ট আনাগোনা করে, সেগুলো সম্পর্কে তথ্য পাবেন। বিশেষ করে যেসব অ্যাকাউন্ট থেকে ব্যবহারকারীদের ফোন নম্বর, ই-মেইল আইডি জানার চেষ্টা করা হবে, সেসব অ্যাকাউন্টের তথ্য পাবেন ব্যবহারকারী।

‘হাউ টু কিপ ইউর অ্যাকাউন্ট সিকিউর’ ফিচারটি পাসওয়ার্ড শক্তিশালী করবে ও লগইন অ্যালার্ট দেখাবে। ‘হাউ পিপল ক্যান ফাইন্ড ইউ’ নিরাপত্তা ফিচারটি ফেসবুক ব্যবহারকারীর পোস্ট কারা দেখতে পাবেন এবং কারা ফ্রেন্ড রিকোয়েস্ট পাঠাতে পারবেন, তা নিয়ন্ত্রণে ব্যবহার করা যাবে।

অন্যদিকে ‘ইউর ডাটা সেটিংস অন ফেসবুক উইল লেট ইউ রিভিউ দি ইনফরমেশন ইউ শেয়ার উইথ অ্যাপস’ ফিচারটি বিভিন্ন অ্যাপে ব্যবহারকারীর তথ্যের সুরক্ষা নিশ্চিত করতে কাজে দেবে।

Source: https://bonikbarta.net/home/news_description/216755/%E0%A6%AB%E0%A7%87%E0%A6%B8%E0%A6%AC%E0%A7%81%E0%A6%95%E0%A7%87-%E0%A6%A8%E0%A6%A4%E0%A7%81%E0%A6%A8-%E0%A7%AA-%E0%A6%AA%E0%A7%8D%E0%A6%B0%E0%A6%BE%E0%A6%87%E0%A6%AD%E0%A7%87%E0%A6%B8%E0%A6%BF-%E0%A6%AB%E0%A6%BF%E0%A6%9A%E0%A6%BE%E0%A6%B0

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The Future of Higher Education in Bangladesh


Md Sabur Khan
Chairman, Daffodil International University


The main problem with the existing skills gap is our outdated education system. There is no analysis done by universities, government or research bodies in identifying the kind of skills needed in accordance to the market demand. Masters in Cyber Security, Social Security, Social Business, Digital Marketing, Agro-Business, Entrepreneurship, are all important for Bangladesh currently. An entrepreneurial mindset is required to seize the right opportunities. Change can be brought about if proper governance is in place when dealing with affiliated bodies. Most research analysis is done by private organisations, but it is the duty of the government to make the general public aware about issues concerning skills gap in the country. The government should also allocate the number of job recruitments for each sector.

One of our biggest battles that we fight as the representatives of universities is keeping the level of unemployment of our students as low as possible. We take full responsibility for this situation and work directly with the unemployed students to identify what needs to be done.

We award three credits to students who can launch an innovative product into the market. The students should focus on gaining knowledge and skills that will aid them in their future careers. The lack of endorsement by the government restricts students from having enough faith to try out something new. We are trying our best to endorse our subjects to the private, public and other sectors, in an attempt to reduce the number of unemployed students.

We are forming a student congress consisting of 2,500 students where the unemployed students will be given a chance to provide an explanation for their unemployment. I believe there is a lack of soft skills among the individuals. Current and past students interested in learning soft skills will be recommended by our university to other places with waivers to gain training. We have also introduced other development programmes such as EDF (Entrepreneurship Development Fund), which will facilitate a no interest loan of 10,000 to 200,000 taka for student entrepreneurs. We have also organised an innovation lab which contains a maker space for the entrepreneurs to make products and sell them at any price. The lab has 3D printers, CLC cutting machines, RMD, animation and design facilities, and test labs. We also host a monthly programme called startup market where we encourage students to sell their products.

Professor Atique Islam
Vice Chancellor, North South University


North South University is continuously working on developing employability skills for our students with a dedicated Career Placement Center (CPC). Here, our students are engaged in rigorous workshops and skill-building seminars to help equip them with the current demands of relevant industries and to meet the expected benchmark for fresh graduates. This improves the rate of placement, as we have gained the trust of our employers globally, and are consequently helping our graduates to secure a job right after graduation. CPC arranges internships and job placement opportunities, career counselling, job training, and works for professional development. An employer survey report is also generated every year, helping us further our mission to place our alumni around the world. The recently held National Career Fair, where over 130 companies from around the world participated, was a successful step toward enabling our graduates to connect with various industries, showcase their potential, and prepare themselves to embark on their professional career.

Setting the benchmark also has its fair share of challenges, such as recruitment of quality faculty members who have PhDs. To counter this obstacle, we only invite foreign degree holders from internationally accredited and reputed institutions, preferably in the related discipline.

The most recent update from UGC is that they will only approve one course per programme in a year. As a result, the current policy can limit institutions from evolving, which can work as an impediment to an institution’s adaptability to the demands of the industry. Under such bottlenecks, it is not easy to launch a new course. We would like to request UGC to take some initiatives to expedite the overall approval process. But we are also fully aware of the limitations UGC has in terms of personnel.

To foster entrepreneurship, we have launched a support platform named ‘Startup Incubator’ within the campus to enhance its co-operation to help entrepreneurs to the highest level possible. It aims to provide advisory support, high-quality mentoring, and access networks and strategic support to the portfolio companies, etc. Stories of several successful entrepreneurs among our alumni have become very well-known in the nation today.

Professor M Sekandar Khan
Vice-Chancellor, East Delta University


The gap between information need and availability to make business decisions is very high in the country. The unavailability of the right talent and skills in Bangladesh is making it challenging to bridge this gap. To fill this gap, EDU offers customised courses, professional and executive training programmes, workshops and seminars, with the help of faculty members and trainers/experts from both home and abroad to meet the needs of its students.

To build and create the best higher education environment that is required for the development of academic and institutional excellence and to increase competent individuals, to serve the needs of the nation and the world, the university is committed to serving the best learning opportunities for every student. International Graduate Leadership Program is one such platform that aims to provide an opportunity for students for doing business internationally. The programme focuses on the influence of diverse cultures, politics, institutions and local practices, and their impact on business decisions and managerial behaviour across international borders. The programme also comprises of on-campus pre-learning and off-campus seminars and company visits in a foreign country to meet business and government leaders and discuss business practices.

Under the current practices of UGC, private universities can only facilitate one new course per year. In the 21st century, universities are required to introduce new and innovative undergraduate and post-graduate courses to serve the human resource requirements in the diverse job market of the country. UGC should plan to transform private university operations by creating the tier system that can eventually provide more benefits to the private universities, which have already shifted to their permanent campuses and are serving the higher education industry with integrity and honesty. Such a tier system can distinguish good private universities from the ordinary ones. Universities can also operate more efficiently by introducing innovative courses more than once in a year.

We have established EDU Startup Foundry recently, which guides early-stage startups through a defined process. This helps teams to rapidly take their ideas and test them with customers to discard, change and build a business model. The programme welcomes the entire EDU community, to help build and enhance the entrepreneurial spirit of students, faculties, staff and alumni.

We focus on lifelong learning and leadership roles by helping students attain excellence through curriculum, research, facilities, entrepreneurship and job placements.


Professor Dr Shahid Akhtar Hossain
Vice Chancellor, Eastern University


Universities cannot take full responsibility for bridging the skills gap among students. There are many instances where students who have passed the secondary and higher secondary education are not qualified enough to attain higher education at universities. University and their faculty members work hard to shape students to help unleash their potential. Yet, despite extra endeavours, the necessary skills are lacking amongst students to use in the practical field. In light of such reality, we have designed a mandatory course titled Career Management Course to address the mismatch between academia and industry. It helps students make informed choices through aspects such as know-how of CV writing, job hunting, interview preparation, code of conduct, corporate cultures. Other necessary skills such as teamwork, leadership, and analytical skills based on the local and global labour market needs are also included in this course.

We have also made it compulsory for every student to learn Excel. We have signed an MOU with Creative IT, to train our students with the required industry courses, with emphasis on digital marketing. Besides, we have more than 18 clubs where students can acquire adequate life and employability skills. We are also changing the curriculum to meet the present workplace demands after discussing with industry experts and introducing new avenues of subjects.

The objective of introducing new courses is not only to create a good image of the university in the market but also to help students adapt to new knowledge and technology. However, obstacles such as the process of approval from UGC are tedious and time-consuming. UGC has some set rules and policies about offering certain courses as mandatory. After excluding those courses, the university has a few options to search for new courses. However, UGC is taking initiatives to enhance the quality of education by creating a Quality Assurance Cell at every university and recommending all to bring together some new courses.

The issue of approving new subjects after full operations of university’s activities at the permanent campus is not realistic. It requires substantial financial strength which a middle-class university can hardly meet. In addition, skilled and trained faculties for trendy subjects are challenging to find. For example, we have decided to start a major in data science and analytics. Unfortunately, few experts are available in the market for teaching this subject, as most qualified people in this arena live abroad. ICT ministry can take initiatives to fill this gap.

We offer courses and arrange fairs to encourage entrepreneurship among students and some of them even represent their startups in international conferences.

Professor Milan Pagon
Vice Chancellor (Acting), Independent University, Bangladesh (IUB)


The Fourth Industrial Revolution will cause many of the traditional jobs that we know now to disappear. The preparation for the revolution should start from education. The major problem is that universities are preparing students for jobs in the existing job market, or even jobs from the past. There are two important components that universities are not focusing on enough. The first is the gig economy: a job market characterised by short-term/part-time jobs and freelancing. School and college students of today will get short gigs here and there in the future instead of joining traditional jobs. They will not have their own business but will work for other businesses, for short terms. The gig economy is based on “just in time” employment, which means, if I need somebody for three months, I will employ them only for that time period, and they will have no job security.

The second component is entrepreneurship. Students have to create jobs since they may not be offered jobs in a traditional company and they might not opt for the gig economy approach. Traditional universities do not equip students with the skillsets necessary for starting their own business.

How do you prepare for jobs that do not exist yet? You must identify some universal skills which will prepare you for any kind of challenge. For example, many students question why they have to study math since they will never have to use that in their lives. But research shows that when you study math, it gives you certain cognitive skills which are transferable later in life. Skills like problem solving, critical thinking, lateral thinking are all universally applicable. For example, there is one method called Kepner Tregoe, which teaches you how to analyse the situation, the problem, the decision and the potential problems. Once you master this thinking tool, you can apply it to any problem.

Traditional universities and degrees might lose importance within the revolution. People will still come to university, but it will be more likely for the specific knowledge, skills, certification and specific courses. They might not declare a major but will want to take courses from different programmes. The focus will shift from formal degrees to acquiring knowledge and skills on a need basis. Universities need to start offering relevant modules, courses, and certifications.

If we expect our universities to adapt to the revolution, then we need to remove the administrative barriers that currently exist in Bangladesh. If we are allowed to introduce only one new programme per year, it will take us 20 years to adapt. The government should look into the infrastructure, faculty members, and staff of universities when deciding how many programmes can be introduced by a particular university.

Professor Dr Abdur Rab
Vice Chancellor, IUBAT- International University of Business Agriculture and Technology


Traditionally, the purpose of general higher education was to acquire, create and disseminate knowledge. An exception to this was professional courses such as medicine, engineering, architecture, law, accounting, business administration, etc. Courses and curricula were designed by the institutions to meet these traditional needs. The fact that education is also meant for meeting skills requirements by the employers is a more recent realisation. Even now, employers hire graduates and train them up in the skills they require. With the advancement of technology and competitive business environment, employers are expecting the universities to prepare graduates with skills so that they can use them readily.

Basic and common skills are required in the positions where graduates are employed. These include communication skills (comprehension, speaking, writing and presentation, more often in English), good technical knowledge and skills, social and interpersonal skills, critical thinking and problem-solving abilities, ability to understand the work environment and cope with its challenges and ability to pick up ‘cues’ from local, national and international environment. In our university, we try our best to provide these skills. However, universities vary in their capacity to equip their students with these skills and so, there is a gap between the needs of the employers and skills provided by universities.

Universities should prepare and provide appropriate human resources for organisations. The universities need to regularly revise courses and curricula keeping in view the changes taking place in the organisational needs.

IUBAT offers a compulsory course on entrepreneurship. Many of our students have set up businesses and many others have plans to get into business once they gain job experience and acquire the capability to mobilse initial capital. Teachers of entrepreneurship offer free consultation to students or alumni starting new ventures. At present the university is working on a project for setting up an Innovation, Incubation and Business startup consultation centre.

Dr Mahfuzul Hoque Chowdhury
Vice Chancellor, Chittagong Independent University (CIU)


The most significant existing skills gap in Bangladesh is between the theoretical and conceptual knowledge of the university graduates and the practical skills required in jobs. To overcome this, we offer a course called Live-in-Field-Experience (LFE) to the students. Here, students engage in practical work under the supervision of faculty members, to acquire skills such as leadership, people management, teamwork, adaptability to new environment. They have the opportunity to interact with the renowned professionals. CIU students also need to complete internships as part of their curriculum which enables them to get familiar with the work environment. Moreover, students are taken for industrial visits to give them a clear perception of the work going on there. 

In general, one challenge that universities in our country face is that the government and UGC have absolute control over revising the existing courses as well as introducing new ones. Universities should be given enough freedom to design and conduct courses.

We try to promote entrepreneurship among students through motivational speeches, workshops, seminars, corporate talks and inviting guests who are successful entrepreneurs in the country. However, challenges such as generating adequate funds, unavailability of required infrastructure, bureaucratic hurdles, obsolete rules and regulations, political uncertainty, social, cultural risks and legal protection, etc., still act as a barrier for entrepreneurship amongst the young people.     

Currently, we are trying to move towards more research-based activities. There is no alternative to increasing the range of creativity skills and knowledge. It is necessary to create skilled manpower in terms of craftsmanship, trade, commerce, economy, industry and state structure.

Students should also form the habit of reading books and articles and possibly write reviews on them. In this way, their written competence will increase. Educational institutions can encourage students in this regard.

Professor Dr AFM Mafizul Islam
Vice Chancellor, Southeast University


The key challenge concerning the higher education scenario in Bangladesh is delivering quality education. The government is taking steps in this regard. The government had already introduced HEQEP (Higher Education Quality Enhancement Project) earlier. Another project called HEAT (Higher Education Acceleration and Transformation) is coming up.

I strongly feel that lack of leadership in higher educational institution is the problem.

Southeast University has established the Institutional Quality Assurance Cell (IQAC) headed by an experienced professor with the rank and status of a dean. This Cell is regularly providing diverse training to our faculty members and staff so that they can achieve the goal of delivering quality education. In the meantime, several departments have gotten professional accreditation, and others are in the process. If a university complies with all conditions of accreditation, I think quality education would be automatically achieved.

We are preparing our students for the corporate world by blending practical experience and operational challenges into standard economic and business lessons. Some steps are: teaching case studies, relating theoretical content to real business challenges, hosting entrepreneurship contests, creating an entrepreneurship-in-residence programme, inviting professionals, encouraging international exchange programmes and promoting student-in-residence programmes.

Southeast University is providing quality education at affordable cost, targeting middle, lower and marginal middle class. Tuition fees of some programmes, such as Bangla, English and Economics, are very low. We run these programmes with subsidies. Another exception of this university is that the Benevolent Trustees of Southeast University Trust do not take any remuneration.

Professor Dr Chowdhury Mofizur Rahman
Vice Chancellor, United International University


Background knowledge and foundation of most of the students are very poor. They are not prepared for university level education. It is due to the weak pre-university education, i.e. education at the SSC and HSC levels. If foundation remains poor, quality education is difficult to be imparted at university level.

We are trying to improve the communication skills by offering a number of intensive English courses in the first year. We offer a course in the first semester, known as life skill for success. This is a kind of guidance to learn about the necessary attributes and skills to become a good human being and responsible citizen of the country.

There is a wide gap between academia and industry. To reduce the gap, we have included industry professionals and our alumni in the curriculum committee. In addition, final year students get hands-on practice on real-life projects by industry professionals at institutes like CDIP (Center for Development of IT Professionals), CCNA (Cisco Network Academy), IBER (Institute of Business and Economic Research), IAR (Institute of Advanced Research), CCC (Career Counseling Center), VTA (VLSI Training Academy), PETA (Power Engineering Training Academy) and CER (Center for Energy Research).

UIU has a culture that promotes and cultivates entrepreneurship among students. We frequently hold competitions among students to come up with new ideas and ways of their commercialisation. We have also set up an entrepreneurship forum. We encourage our students to participate in inter-university and national-level competitions. We have allocated one floor of our university building to accommodate startup companies at a very low cost which we have named Incubation Center of UIU. We allow the young successful entrepreneurs to showcase their achievements.

Professor Dr Vincent Chang
Vice Chancellor, BRAC University


Developed countries such as Japan, Singapore and Taiwan were as poor as Bangladesh after the World War II. Thirty years after the World War II, China was even poorer than Bangladesh, then how did they develop so fast? It was because of the unique people and how active they were. Next comes the influences of education and the virtue and philosophies people hold related to work – whether they value hard work and try to put it into their endeavours. According to my experience in Bangladesh, I am still confused if people put in the hard work or not.

The attitude here does not reflect that. People here wait to be taught, by their superiors, and make no effort to learn things themselves. They simply do not take the initiative. People are happy with their recent university rankings, but internationally, people do not care. The issue is in the mindset. We have to thrive to do better or else we will be stuck at the same place. We have to change our mindsets in order to incentivise people to want to improve. We have to understand the changing standards.

I would like to refer to the gap in the mindset instead of referring to the gap in the skillset because to me, it isn’t about the knowledge or other skills; it is about the perspectives and mindsets. These are difficult to adjust, but these are what will allow us to compete and produce appreciable outputs. We need to have a champion mindset; you face problems, you fix it. You don’t depend on others to fix it for you.

BRAC University thus tries to engage internationally so that our students have the opportunity to expand their horizons and their vision. We try to involve our teachers and students in a process where they understand international standards.

Professor Md Abu Saleh
Vice Chancellor, Bangladesh University of Business and Technology (BUBT)


Lack of need based knowledge and skills, lack of time based knowledge and skills, lack of outcome based knowledge and skills, are some of the major challenges concerning the existing skills gap in Bangladesh. Emphasis on theoretical knowledge through traditional classroom lectures is mostly prevalent in the current education system of the country, leading to a lack of focus on practical learning, which is required for the 21st century workplace.

To address these gaps, BUBT emphasises on practice-oriented knowledge and skills. We also focus on university-industry partnership, research activities, teachers’ training and development. We design the curriculum of the academic courses, highlighting both education and human resource development.

However, challenges persist such as non-availability of subject/course experts for structuring, designing and developing the curriculum of new courses. The processes for reviewing, revising and getting approval are also time-consuming. The imposition of unnecessary and irrelevant pre-conditions is also a concern. Approvals should be based on the quality and the standard of the curriculum, not on irrelevant pre-conditions. It should also be based on the need of the course(s). Cooperative attitude is direly required, instead of controlling attitude.

To encourage entrepreneurship, we offer entrepreneurship courses. In this way, students are motivated to become job providers rather than remain as job seekers only. We provide training, organise seminars and workshops on entrepreneurship. We also organise competitions, contests on business idea generation and business plans. We invite the successful entrepreneurs to share their success stories, some of whom are also the alumni of BUBT.

Source: https://www.thedailystar.net/supplements/news/the-future-higher-education-bangladesh-1841881

58
Time to formalise informal e-waste management in Bangladesh

With 4-5 percent annual growth, the global electrical and electronic waste (e-waste) reached up to 44.7 million tonnes in 2016, of which 20 percent, or 8.9 million tonnes, is documented to be collected and recycled properly. According to the UN University, 50 million tonnes of e-waste is discarded by the world inhabitants which is greater than the weight of all commercial airlines the world has seen so far.

E-waste is hazardous, complex and mostly discarded in the general waste stream, especially in developing countries. The unprecedented growth of e-waste is not only contributed by developed countries but also by developing countries like Bangladesh.

In case of Bangladesh a wide range of factors, including rapid globalisation, urbanisation, increased access to modern technology and purchasing power, substantial reduction in new product development cycle, increased frequency of offering new products, and higher use of planned obsolescence strategy by electronic products manufacturers are contributing towards the generation of a huge amount of electronic waste stream.

The growth of such amount of waste in recent years is exponential when compared to even four to five years back. The prediction that in the coming years we would consume even more types and varieties of electronic goods propelled by the increased prosperity of the country. This consumption will eventually lead to even higher growth of electronic waste. Unfortunately, we are yet to know how much we have generated recently and in the recent past, let alone the future e-waste generation data.

A 2009 estimation predicted that Bangladesh generates roughly 2.81 million tonnes of e-waste every year and the lion’s share of that waste stream is recycled by an unskilled, deregulated, unstructured and informal recycling sector. This is 2019 and naturally the e-waste volume is 4-5 folds of the amount of 2009 and still the informal sector is primarily handling the recycling process of this huge amount of e-waste. This is posing significant human and environmental health risks and leads the country to lose a significant amount of recoverable precious materials.


China, India, Ghana, the Philippines, Pakistan and Nigeria are the major countries that recycle or reuse more than 80 percent of e-waste generated by developed countries. Of them, China receives and recycles 70 percent alone.

Researches showed that e-waste is not only traded between developed and developing countries but also between developing countries. Upon treatment or recycling of the imported e-waste, China uses the recycled materials in manufacturing various types of electronic and electrical equipment. One of the major destinations of the Chinese refurbished outputs is Southeast Asian countries.

Bangladesh is also becoming an important secondary recipient of e-waste global export due to its substantial trade relationships with China, its exponential growth in internal and regional trade, illegal import by brokers and traders, use of ‘waste tourists’ and lack of e-waste specific regulations.

According to the MRC report 2017, $2.2 billion worth of consumer electronic products (HS code 84 and 85) were imported to Bangladesh in 2016, where China (69 percent) was the largest exporter. Ceiling fan and other types of fan, air conditioner, refrigerator, washing machine, battery, UPS, microwave woven, television and spare parts are included in the same code. Besides, in 2018, 40 million mobile phones were imported where 30 million imported legally and the rest through grey channels, according to an estimate of the Bangladesh Mobile Phone Importers Association.

With an annual growth rate of 15-20 percent, the laptop market of Bangladesh was worth about $175 million in 2018 and 60 percent of the demand is largely meet by the import of laptops from China, Singapore, the USA, Thailand and Malaysia.

The consumer electronics market is also growing in Bangladesh. With 15 percent yearly growth rate, the market of manufactured and imported consumer electronics was estimated at $4 billion in 2017. Many Bangladeshi companies have also started manufacturing a range of consumer electronics products in recent times to meet the demand of the lower income people. This clearly represents a potential growth trend in the consumer electronics industry. However, this also increases the worry of the exponential growth of e-waste. Bangladesh does not have any preparation whatsoever to treat or manage this huge amount of e-waste in a sustainable way.

Bangladesh currently has no specific environmental policy or act or guidelines to directly manage the e-waste problem. Though a draft regulation on ‘E-waste management rules’ was developed and amended in 2011 and 2017 respectively under the Environment Conservation Act, 1995, no progress in rules acceptance and implementation has been visible till today.

It is commonly said that the future of e-waste management in developing countries depends not only on the effectiveness of local government and the informal operators of recycling services but also on community participation and private manufacturers together with national and regional initiatives. Integrating the informal sector into the formal could result in reduced pollution and health hazards. In addition, efficient and effective resource management practice may offer the country a lot of reusable resources from the e-waste recycling process. Now-a-days, extended producer responsibility (EPR) is considered as one of the most widely-used formal waste management-related policies that help integrate the informal waste management sector.

EPR requires the producers of electronics goods to take all or partial responsibility for the disposal of their commercialised products. EPR policy requirements can be implemented in many forms.



One of them is ‘Product take-back requirements’ where at the post-consumption stage the manufacturers take the responsibility of taking back their products in whole or part. The extent to which producers are required to recycle their post-consumption products can be defined in the performance standard requirements.

The raw materials categorised as high environmental risk are often subject to material taxes. The purpose of such taxes is to encourage manufacturers to shift towards more environment-friendly parts or components. In some cases, consumers assume the responsibility of e-waste management by paying a deposit while purchasing a product and then receiving a refund -- known as deposit or refund schemes -- when returning the post-consumption product. Customer responsibility can also be extended by charging consumers advance disposal fees at the point of purchase for the cost of treating and recycling without any refund afterwards.

Landfill taxes, illegal dumping fees, tax benefits and subsidies for eco-friendly design, labeling, products and promotions are other forms of EPR implementation. Taking the various forms of EPR into account, it would be interesting to see the level of urgency from the electrical and electronic products manufacturers and/or resellers of Bangladesh regarding the adoption of EPR.

According to the proposed E-waste Management Rules, 2017, the government is planning to introduce the deposit or refund schemes. Moreover, the proposed rules also set the goal to increase the extent of producer responsibility gradually from 15 percent to 55 percent from the first year to the four year of rules implementation. Although many consumer electronics manufacturers selling home appliances, batteries and bulbs are already taking back their old, close to end of life electronic products through different types of consumer promotions, the intention is not clear – whether it is a strategic initiative motivated by EPR or something else.

There is no doubt that integrating EPR into the informal e-waste recycling sector would be highly challenging for Bangladesh. A research conducted by the authors on the informal sector of e-waste management revealed that there is a lack of coordination among collection units, insufficient data on regular supply and demand of e-waste, no quality control or check on supply, inadequate infrastructure, and dearth of education and skill on separation, dismantling and even recycling.

Factors such as manufacturing of non-branded and counterfeit products, usages of refurbished and repaired second-hand products, the likeliness of original parts being replaced by other brands or generic components are compounding the existing challenges faced by the e-waste management sector.

To mitigate these challenges and integrate with the EPR system, there is a strong need to coordinate the input and output sector of the informal e-waste system with proper institutionalisation and regulation. With adequate public awareness campaign, a basic waste separation and collection infrastructure needs to be developed and the recyclers and recycling centres must be developed with right training and education.

The monitoring process can be streamlined through recycling licensing and certificates. Interface organisations such as third-party private recyclers can be nurtured to mediate between the informal sector and the manufacturer group. The integration is likely to be possible if the informal sector serves as organised collection points for the formal waste sector and after basic sorting is able to divert as much of the e-waste as possible to treatment facilities for recycling and treatment primarily performed by manufacturers or third-party recyclers.

Paying particular attention to the role played by the informal sector of e-waste management and specifying manufacturers responsibilities for such management with the integration of right environmental policy and capacity-based regulatory enforcement can pave the pathway to develop a formalised end-of life products waste management system. This is a dying need for Bangladesh if we are to enjoy the technology-mediated development with its full potential.

The authors are professors in the Department of Marketing at the University of Dhaka.

Source: https://www.thedailystar.net/business/news/time-formalise-informal-e-waste-management-bangladesh-1841734

59
Resources / Time to formalise informal e-waste management in Bangladesh
« on: December 18, 2019, 09:54:02 AM »
Time to formalise informal e-waste management in Bangladesh

With 4-5 percent annual growth, the global electrical and electronic waste (e-waste) reached up to 44.7 million tonnes in 2016, of which 20 percent, or 8.9 million tonnes, is documented to be collected and recycled properly. According to the UN University, 50 million tonnes of e-waste is discarded by the world inhabitants which is greater than the weight of all commercial airlines the world has seen so far.

E-waste is hazardous, complex and mostly discarded in the general waste stream, especially in developing countries. The unprecedented growth of e-waste is not only contributed by developed countries but also by developing countries like Bangladesh.

In case of Bangladesh a wide range of factors, including rapid globalisation, urbanisation, increased access to modern technology and purchasing power, substantial reduction in new product development cycle, increased frequency of offering new products, and higher use of planned obsolescence strategy by electronic products manufacturers are contributing towards the generation of a huge amount of electronic waste stream.

The growth of such amount of waste in recent years is exponential when compared to even four to five years back. The prediction that in the coming years we would consume even more types and varieties of electronic goods propelled by the increased prosperity of the country. This consumption will eventually lead to even higher growth of electronic waste. Unfortunately, we are yet to know how much we have generated recently and in the recent past, let alone the future e-waste generation data.

A 2009 estimation predicted that Bangladesh generates roughly 2.81 million tonnes of e-waste every year and the lion’s share of that waste stream is recycled by an unskilled, deregulated, unstructured and informal recycling sector. This is 2019 and naturally the e-waste volume is 4-5 folds of the amount of 2009 and still the informal sector is primarily handling the recycling process of this huge amount of e-waste. This is posing significant human and environmental health risks and leads the country to lose a significant amount of recoverable precious materials.

China, India, Ghana, the Philippines, Pakistan and Nigeria are the major countries that recycle or reuse more than 80 percent of e-waste generated by developed countries. Of them, China receives and recycles 70 percent alone.

Researches showed that e-waste is not only traded between developed and developing countries but also between developing countries. Upon treatment or recycling of the imported e-waste, China uses the recycled materials in manufacturing various types of electronic and electrical equipment. One of the major destinations of the Chinese refurbished outputs is Southeast Asian countries.

Bangladesh is also becoming an important secondary recipient of e-waste global export due to its substantial trade relationships with China, its exponential growth in internal and regional trade, illegal import by brokers and traders, use of ‘waste tourists’ and lack of e-waste specific regulations.

According to the MRC report 2017, $2.2 billion worth of consumer electronic products (HS code 84 and 85) were imported to Bangladesh in 2016, where China (69 percent) was the largest exporter. Ceiling fan and other types of fan, air conditioner, refrigerator, washing machine, battery, UPS, microwave woven, television and spare parts are included in the same code. Besides, in 2018, 40 million mobile phones were imported where 30 million imported legally and the rest through grey channels, according to an estimate of the Bangladesh Mobile Phone Importers Association.



With an annual growth rate of 15-20 percent, the laptop market of Bangladesh was worth about $175 million in 2018 and 60 percent of the demand is largely meet by the import of laptops from China, Singapore, the USA, Thailand and Malaysia.

The consumer electronics market is also growing in Bangladesh. With 15 percent yearly growth rate, the market of manufactured and imported consumer electronics was estimated at $4 billion in 2017. Many Bangladeshi companies have also started manufacturing a range of consumer electronics products in recent times to meet the demand of the lower income people. This clearly represents a potential growth trend in the consumer electronics industry. However, this also increases the worry of the exponential growth of e-waste. Bangladesh does not have any preparation whatsoever to treat or manage this huge amount of e-waste in a sustainable way.

Bangladesh currently has no specific environmental policy or act or guidelines to directly manage the e-waste problem. Though a draft regulation on ‘E-waste management rules’ was developed and amended in 2011 and 2017 respectively under the Environment Conservation Act, 1995, no progress in rules acceptance and implementation has been visible till today.

It is commonly said that the future of e-waste management in developing countries depends not only on the effectiveness of local government and the informal operators of recycling services but also on community participation and private manufacturers together with national and regional initiatives. Integrating the informal sector into the formal could result in reduced pollution and health hazards. In addition, efficient and effective resource management practice may offer the country a lot of reusable resources from the e-waste recycling process. Now-a-days, extended producer responsibility (EPR) is considered as one of the most widely-used formal waste management-related policies that help integrate the informal waste management sector.

EPR requires the producers of electronics goods to take all or partial responsibility for the disposal of their commercialised products. EPR policy requirements can be implemented in many forms.

One of them is ‘Product take-back requirements’ where at the post-consumption stage the manufacturers take the responsibility of taking back their products in whole or part. The extent to which producers are required to recycle their post-consumption products can be defined in the performance standard requirements.

The raw materials categorised as high environmental risk are often subject to material taxes. The purpose of such taxes is to encourage manufacturers to shift towards more environment-friendly parts or components. In some cases, consumers assume the responsibility of e-waste management by paying a deposit while purchasing a product and then receiving a refund -- known as deposit or refund schemes -- when returning the post-consumption product. Customer responsibility can also be extended by charging consumers advance disposal fees at the point of purchase for the cost of treating and recycling without any refund afterwards.

Landfill taxes, illegal dumping fees, tax benefits and subsidies for eco-friendly design, labeling, products and promotions are other forms of EPR implementation. Taking the various forms of EPR into account, it would be interesting to see the level of urgency from the electrical and electronic products manufacturers and/or resellers of Bangladesh regarding the adoption of EPR.

According to the proposed E-waste Management Rules, 2017, the government is planning to introduce the deposit or refund schemes. Moreover, the proposed rules also set the goal to increase the extent of producer responsibility gradually from 15 percent to 55 percent from the first year to the four year of rules implementation. Although many consumer electronics manufacturers selling home appliances, batteries and bulbs are already taking back their old, close to end of life electronic products through different types of consumer promotions, the intention is not clear – whether it is a strategic initiative motivated by EPR or something else.

There is no doubt that integrating EPR into the informal e-waste recycling sector would be highly challenging for Bangladesh. A research conducted by the authors on the informal sector of e-waste management revealed that there is a lack of coordination among collection units, insufficient data on regular supply and demand of e-waste, no quality control or check on supply, inadequate infrastructure, and dearth of education and skill on separation, dismantling and even recycling.

Factors such as manufacturing of non-branded and counterfeit products, usages of refurbished and repaired second-hand products, the likeliness of original parts being replaced by other brands or generic components are compounding the existing challenges faced by the e-waste management sector.

To mitigate these challenges and integrate with the EPR system, there is a strong need to coordinate the input and output sector of the informal e-waste system with proper institutionalisation and regulation. With adequate public awareness campaign, a basic waste separation and collection infrastructure needs to be developed and the recyclers and recycling centres must be developed with right training and education.

The monitoring process can be streamlined through recycling licensing and certificates. Interface organisations such as third-party private recyclers can be nurtured to mediate between the informal sector and the manufacturer group. The integration is likely to be possible if the informal sector serves as organised collection points for the formal waste sector and after basic sorting is able to divert as much of the e-waste as possible to treatment facilities for recycling and treatment primarily performed by manufacturers or third-party recyclers.

Paying particular attention to the role played by the informal sector of e-waste management and specifying manufacturers responsibilities for such management with the integration of right environmental policy and capacity-based regulatory enforcement can pave the pathway to develop a formalised end-of life products waste management system. This is a dying need for Bangladesh if we are to enjoy the technology-mediated development with its full potential.

The authors are professors in the Department of Marketing at the University of Dhaka.

Source: https://www.thedailystar.net/business/news/time-formalise-informal-e-waste-management-bangladesh-1841734

60
চার হাজার বছর পর দ্বিঘাত সমীকরণের নতুন সমাধান

দীর্ঘ চার হাজার বছর পর বীজগণিতের দ্বিঘাত সমীকরণের একটি সহজ সমাধান পদ্ধতি খুঁজে পেয়েছেন মার্কিন গণিতবিদ, কার্নেগি মেলন বিশ্ববিদ্যালয়ের গণিতের অধ্যাপক এবং আন্তর্জাতিক গণিত অলিম্পিয়াডে যুক্তরাষ্ট্র দলের দলনেতা পো শেন লো (Po-Shen Loh)। লো ইন্টারনেটে ব্যক্তিনির্ভর গণিত শেখার জন্য কৃত্রিম বুদ্ধিমত্তা পরিচালিত ওয়েবসাইট এক্সপি ডট কমের প্রতিষ্ঠাতা।

আজ থেকে প্রায় চার হাজার বছর আগে, ব্যাবিলনে খাজনা হিসেবে শস্য প্রদানের হিসাব করতে গিয়ে দ্বিঘাত সমীকরণের সমাধানের দরকার হয়। কৃষিজীবী ব্যাবিলনীয়দের খাজনা দিতে হতো শস্যে। নির্দিষ্ট পরিমাণ খাজনা দেওয়ার জন্য ঠিক কতটুকু জমিতে আবাদ বাড়ানো দরকার, সেটিই ছিল তাদের সমস্যা। বীজগণিতের ভাষায় এটি হলো ax2+bx+c=0 সমীকরণের সমাধান।


যেকোনো বীজগাণিতিক সমীকরণে কয়টি সমাধান থাকবে, তা নির্ভর করে ওই সমীকরণের অজানা রাশির ঘাতের ওপর। যেমন x-4=0 এই সমীকরণে x এর মান 4 এবং এই একটি মানই সমীকরণটির সমাধান। কিন্তু x2-4=0 এ সমীকরণে x এর মান 2 বা -2 এর দুটি মানের জন্যই সত্য। সে হিসাবে ax2+bx+c=0 এই সমীকরণেও x এর দুটি মান থাকবে। সেই চার হাজার বছর আগেই ব্যাবিলনীয়রা এই বীজগাণিতিক সমীকরণের সমাধান বের করেছেন

কয়েক দিন আগে, ৬ ডিসেম্বর, গণিতবিদ পো শেন লো তাঁর নতুন সমাধান পদ্ধতিটি প্রকাশ করেন তাঁর ওয়েবসাইটে। একই দিন ম্যাসাচুসেটস ইনস্টিটিউট অব টেকনোলজির (এমআইটি) টেকনোলজি রিভিউতে সেটি ছাপা হয়। এর আগে গত ১৩ অক্টোবর এটি একটি গণিত সাময়িকীতে প্রকাশিত হয়। সমাধানটি এত সহজ এবং চমৎকার যে কয়েক দিন ধরে আমরা যারা ডাচ–বাংলা ব্যাংক-প্রথম আলো গণিত উৎসবের সঙ্গে জড়িত, তারা প্রায় মোহাবিষ্ট হয়ে এই সমাধান নিয়ে আলাপ করেছি। এর চেয়ে সুন্দর সমাধান আর কী হতে পারে!
আমার ভালো লাগার আরেকটি কারণ হলো পো শেন লো নিজে। পো শেন লোর সঙ্গে আমার পরিচয় আজ থেকে প্রায় ১০ বছর আগে, আন্তর্জাতিক গণিত অলিম্পিয়াডের কোনো এক আসরে। তখন লো ছিলেন মার্কিন দলের উপদলনেতা। বাংলাদেশ দলের উপদলনেতা হিসেবে আমরা সব সময় একই হোটেল বা ক্যাম্পাসে থাকতাম। দেখা হলেই লো আমাদের দলের পারফরম্যান্স নিয়ে আলাপ করতেন, আমাকে বিভিন্ন পরামর্শ দিতেন। আর একটা কাজ ছিল মার্কিন গণিত দল নির্বাচনের প্রশ্ন দিয়ে সহায়তা করা। এর মধ্যে লো হয়ে যান মার্কিন দলের দলনেতা। সেই থেকে আমার সঙ্গে খাতির কমে যায় কিন্তু বাংলাদেশের ছেলেমেয়েদের জন্য তাঁর চিন্তাটা অব্যাহত থাকে। গত বছর পো শেন লো বাংলাদেশে এসেছেন। সেই সময় বিজ্ঞানচিন্তার পক্ষ থেকে তাঁর একটি দীর্ঘ সাক্ষাৎকার নেয় ফারদীম মুনির। সেই সময় আলাপকালে পো শেন লো বলেছেন, শিক্ষার্থীদের গণিতে আগ্রহী করার একটি বড় উদ্যোগ হবে প্রচলিত কঠিন বিষয়গুলোকে সহজ পদ্ধতিতে শেখানোর বুদ্ধি বের করা। আর চার হাজার বছর ধরে চলমান একটি সমস্যার সহজ সমাধান করে লো তাঁর প্রচেষ্টা অব্যাহত রেখেছেন।

অন্যান্য গাণিতিক আবিষ্কারের মতো লোর আবিষ্কৃত সমাধানটি খুবই সহজ ও সুন্দর। লো এমন একটি সাধারণ ধারণা ব্যবহার করেছেন, যা সবাইকে চমৎকৃত করেছে। পো শেন লো তাঁর ইউটিউব চ্যানেলে প্রকাশিত একটি ভিডিও বার্তায় এ আবিষ্কারের অনুপ্রেরণার কথা জানিয়েছেন। দীর্ঘদিন ধরে তিনি মাধ্যমিক ও উচ্চমাধ্যমিকের শিক্ষার্থীদের জন্য গণিতের নানা বিষয় নিয়ে কাজ করছেন। তাঁর একটি উদ্দেশ্য ছিল কিছু কিছু কঠিন বিষয়কে সহজ করা। আর সেটার সন্ধান করতে গিয়ে তিনি এই সমাধান খুঁজে পেয়েছেন।

প্রশ্ন হচ্ছে, তাঁর এই সমাধান কত দ্রুত বিশ্বব্যাপী গণিত শিক্ষার্থীদের দ্বিঘাত সমীকরণের মূল খোঁজার কঠিন সূত্র মুখস্থ করার হাত থেকে রক্ষা করবে। আমার নিজের আশঙ্কা, আমাদের দেশে অনেক পরীক্ষক এই নিয়মে পরীক্ষার খাতায় সমাধান করলে সেটি মেনে নিতে অনেক সময় নেবেন।

Source: https://www.prothomalo.com/technology/article/1628665/%E0%A6%9A%E0%A6%BE%E0%A6%B0-%E0%A6%B9%E0%A6%BE%E0%A6%9C%E0%A6%BE%E0%A6%B0-%E0%A6%AC%E0%A6%9B%E0%A6%B0-%E0%A6%AA%E0%A6%B0-%E0%A6%A6%E0%A7%8D%E0%A6%AC%E0%A6%BF%E0%A6%98%E0%A6%BE%E0%A6%A4-%E0%A6%B8%E0%A6%AE%E0%A7%80%E0%A6%95%E0%A6%B0%E0%A6%A3%E0%A7%87%E0%A6%B0-%E0%A6%A8%E0%A6%A4%E0%A7%81%E0%A6%A8-%E0%A6%B8%E0%A6%AE%E0%A6%BE%E0%A6%A7%E0%A6%BE%E0%A6%A8?fbclid=IwAR3kbnJyRAmgpkAn4TZ1G6Im5nMuS_iq--pc7EvjpGu3dEFfXe2qaxQIdWA

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