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Concerns of an Artificial Intelligence Pioneer
n January, the British-American computer scientist Stuart Russell drafted and became the first signatory of an open letter calling for researchers to look beyond the goal of merely making artificial intelligence more powerful. “We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial,” the letter states. “Our AI systems must do what we want them to do.” Thousands of people have since signed the letter, including leading artificial intelligence researchers at Google, Facebook, Microsoft and other industry hubs along with top computer scientists, physicists and philosophers around the world. By the end of March, about 300 research groups had applied to pursue new research into “keeping artificial intelligence beneficial” with funds contributed by the letter’s 37th signatory, the inventor-entrepreneur Elon Musk.

Russell, 53, a professor of computer science and founder of the Center for Intelligent Systems at the University of California, Berkeley, has long been contemplating the power and perils of thinking machines. He is the author of more than 200 papers as well as the field’s standard textbook, Artificial Intelligence: A Modern Approach (with Peter Norvig, head of research at Google). But increasingly rapid advances in artificial intelligence have given Russell’s longstanding concerns heightened urgency.

Recently, he says, artificial intelligence has made major strides, partly on the strength of neuro-inspired learning algorithms. These are used in Facebook’s face-recognition software, smartphone personal assistants and Google’s self-driving cars. In a bombshell result reported recently in Nature, a simulated network of artificial neurons learned to play Atari video games better than humans in a matter of hours given only data representing the screen and the goal of increasing the score at the top — but no preprogrammed knowledge of aliens, bullets, left, right, up or down. “If your newborn baby did that you would think it was possessed,” Russell said.

Quanta Magazine caught up with Russell over breakfast at the American Physical Society’s 2015 March Meeting in San Antonio, Texas, where he touched down for less than 24 hours to give a standing-room-only lecture on the future of artificial intelligence. In this edited and condensed version of the interview, Russell discusses the nature of intelligence itself and the immense challenges of safely approximating it in machines.

QUANTA MAGAZINE: You think the goal of your field should be developing artificial intelligence that is “provably aligned” with human values. What does that mean?
STUART RUSSELL: It’s a deliberately provocative statement, because it’s putting together two things — “provably” and “human values” — that seem incompatible. It might be that human values will forever remain somewhat mysterious. But to the extent that our values are revealed in our behavior, you would hope to be able to prove that the machine will be able to “get” most of it. There might be some bits and pieces left in the corners that the machine doesn’t understand or that we disagree on among ourselves. But as long as the machine has got the basics right, you should be able to show that it cannot be very harmful.

How do you go about doing that?
That’s the question I’m working on right now: Where does a machine get hold of some approximation of the values that humans would like it to have? I think one answer is a technique called “inverse reinforcement learning.” Ordinary reinforcement learning is a process where you are given rewards and punishments as you behave, and your goal is to figure out the behavior that will get you the most rewards. That’s what the [Atari-playing] DQN system is doing; it is given the score of the game, and its goal is to make that score bigger. Inverse reinforcement learning is the other way around. You see the behavior, and you’re trying to figure out what score that behavior is trying to maximize. For example, your domestic robot sees you crawl out of bed in the morning and grind up some brown round things in a very noisy machine and do some complicated thing with steam and hot water and milk and so on, and then you seem to be happy. It should learn that part of the human value function in the morning is having some coffee.

There’s an enormous amount of information out there in books, movies and on the web about human actions and attitudes to the actions. So that’s an incredible resource for machines to learn what human values are — who wins medals, who goes to jail, and why.

Video: DQN, an artificial neural network developed by researchers at Google DeepMind, teaches itself to play Atari games such as Breakout. It quickly develops sophisticated strategies.
How did you get into artificial intelligence?
When I was in school, AI wasn’t thought of as an academic discipline, by and large. But I was in boarding school in London, at St. Paul’s, and I had the opportunity to avoid compulsory rugby by doing a computer science A-level [course] at a nearby college. One of my projects for A-level was a program that taught itself to play naughts and crosses, or tic-tac-toe. I became very unpopular because I used up the college’s computer for hours on end. The next year I wrote a chess program and got permission from one of the professors at Imperial College to use their giant mainframe computer. It was fascinating to try to figure out how to get it to play chess. I learned some of the stuff I would later be teaching in my book.

But still, this was just a hobby; at the time my academic interest was physics. I did physics at Oxford. And then when I was applying to grad school I applied to do theoretical physics at Oxford and Cambridge, and I applied to do computer science at MIT, Carnegie Mellon and Stanford, not realizing that I’d missed all the deadlines for applications to the U.S. Fortunately Stanford waived the deadline, so I went to Stanford.

And you’ve been on the West Coast ever since?

You’ve spent much of your career trying to understand what intelligence is as a prerequisite for understanding how machines might achieve it. What have you learned?
During my thesis research in the ’80s, I started thinking about rational decision-making and the problem that it’s actually impossible. If you were rational you would think: Here’s my current state, here are the actions I could do right now, and after that I can do those actions and then those actions and then those actions; which path is guaranteed to lead to my goal? The definition of rational behavior requires you to optimize over the entire future of the universe. It’s just completely infeasible computationally.

It didn’t make much sense that we should define what we’re trying to do in AI as something that’s impossible, so I tried to figure out: How do we really make decisions?

So, how do we do it?
One trick is to think about a short horizon and then guess what the rest of the future is going to look like. So chess programs, for example — if they were rational they would only play moves that guarantee checkmate, but they don’t do that. Instead they look ahead a dozen moves into the future and make a guess about how useful those states are, and then they choose a move that they hope leads to one of the good states.

Could you prove that your systems can’t ever, no matter how smart they are, overwrite their original goals as set by the humans?

Another thing that’s really essential is to think about the decision problem at multiple levels of abstraction, so “hierarchical decision making.” A person does roughly 20 trillion physical actions in their lifetime. Coming to this conference to give a talk works out to 1.3 billion or something. If you were rational you’d be trying to look ahead 1.3 billion steps — completely, absurdly impossible. So the way humans manage this is by having this very rich store of abstract, high-level actions. You don’t think, “First I can either move my left foot or my right foot, and then after that I can either…” You think, “I’ll go on Expedia and book a flight. When I land, I’ll take a taxi.” And that’s it. I don’t think about it anymore until I actually get off the plane at the airport and look for the sign that says “taxi” — then I get down into more detail. This is how we live our lives, basically. The future is spread out, with a lot of detail very close to us in time, but these big chunks where we’ve made commitments to very abstract actions, like, “get a Ph.D.,” “have children.”

Are computers currently capable of hierarchical decision making?
So that’s one of the missing pieces right now: Where do all these high-level actions come from? We don’t think programs like the DQN network are figuring out abstract representations of actions. There are some games where DQN just doesn’t get it, and the games that are difficult are the ones that require thinking many, many steps ahead in the primitive representations of actions — ones where a person would think, “Oh, what I need to do now is unlock the door,” and unlocking the door involves fetching the key, etcetera. If the machine doesn’t have the representation “unlock the door” then it can’t really ever make progress on that task.

But if that problem is solved — and it’s certainly not impossible — then we would see another big increase in machine capabilities. There are two or three problems like that where if all of those were solved, then it’s not clear to me that there would be any major obstacle between there and human-level AI.

What concerns you about the possibility of human-level AI?
In the first [1994] edition of my book there’s a section called, “What if we do succeed?” Because it seemed to me that people in AI weren’t really thinking about that very much. Probably it was just too far away. But it’s pretty clear that success would be an enormous thing. “The biggest event in human history” might be a good way to describe it. And if that’s true, then we need to put a lot more thought than we are doing into what the precise shape of that event might be.

The basic idea of the intelligence explosion is that once machines reach a certain level of intelligence, they’ll be able to work on AI just like we do and improve their own capabilities — redesign their own hardware and so on — and their intelligence will zoom off the charts. Over the last few years, the community has gradually refined its arguments as to why there might be a problem. The most convincing argument has to do with value alignment: You build a system that’s extremely good at optimizing some utility function, but the utility function isn’t quite right. In [Oxford philosopher] Nick Bostrom’s book [Superintelligence], he has this example of paperclips. You say, “Make some paperclips.” And it turns the entire planet into a vast junkyard of paperclips. You build a super-optimizer; what utility function do you give it? Because it’s going to do it.

What about differences in human values?
That’s an intrinsic problem. You could say machines should err on the side of doing nothing in areas where there’s a conflict of values. That might be difficult. I think we will have to build in these value functions. If you want to have a domestic robot in your house, it has to share a pretty good cross-section of human values; otherwise it’s going to do pretty stupid things, like put the cat in the oven for dinner because there’s no food in the fridge and the kids are hungry. Real life is full of these tradeoffs. If the machine makes these tradeoffs in ways that reveal that it just doesn’t get it — that it’s just missing some chunk of what’s obvious to humans — then you’re not going to want that thing in your house.

I don’t see any real way around the fact that there’s going to be, in some sense, a values industry. And I also think there’s a huge economic incentive to get it right. It only takes one or two things like a domestic robot putting the cat in the oven for dinner for people to lose confidence and not buy them.

Then there’s the question, if we get it right such that some intelligent systems behave themselves, as you make the transition to more and more intelligent systems, does that mean you have to get better and better value functions that clean up all the loose ends, or do they still continue behaving themselves? I don’t know the answer yet.

You’ve argued that we need to be able to mathematically verify the behavior of AI under all possible circumstances. How would that work?

Automating air traffic control systems may require airtight proofs about real-world possibilities.

One of the difficulties people point to is that a system can arbitrarily produce a new version of itself that has different goals. That’s one of the scenarios that science fiction writers always talk about; somehow, the machine spontaneously gets this goal of defeating the human race. So the question is: Could you prove that your systems can’t ever, no matter how smart they are, overwrite their original goals as set by the humans?

It would be relatively easy to prove that the DQN system, as it’s written, could never change its goal of optimizing that score. Now, there is a hack that people talk about called “wire-heading” where you could actually go into the console of the Atari game and physically change the thing that produces the score on the screen. At the moment that’s not feasible for DQN, because its scope of action is entirely within the game itself; it doesn’t have a robot arm. But that’s a serious problem if the machine has a scope of action in the real world. So, could you prove that your system is designed in such a way that it could never change the mechanism by which the score is presented to it, even though it’s within its scope of action? That’s a more difficult proof.

Are there any advances in this direction that you think hold promise?
There’s an area emerging called “cyber-physical systems” about systems that couple computers to the real world. With a cyber-physical system, you’ve got a bunch of bits representing an air traffic control program, and then you’ve got some real airplanes, and what you care about is that no airplanes collide. You’re trying to prove a theorem about the combination of the bits and the physical world. What you would do is write a very conservative mathematical description of the physical world — airplanes can accelerate within such-and-such envelope — and your theorems would still be true in the real world as long as the real world is somewhere inside the envelope of behaviors.

Yet you’ve pointed out that it might not be mathematically possible to formally verify AI systems.
There’s a general problem of “undecidability” in a lot of questions you can ask about computer programs. Alan Turing showed that no computer program can decide whether any other possible program will eventually terminate and output an answer or get stuck in an infinite loop. So if you start out with one program, but it could rewrite itself to be any other program, then you have a problem, because you can’t prove that all possible other programs would satisfy some property. So the question would be: Is it necessary to worry about undecidability for AI systems that rewrite themselves? They will rewrite themselves to a new program based on the existing program plus the experience they have in the world. What’s the possible scope of effect of interaction with the real world on how the next program gets designed? That’s where we don’t have much knowledge as yet.

This article was reprinted on


গুগল স্টেডিয়া: শুরু হতে যাচ্ছে ক্লাউড গেমিংয়ের যুগ

এমন একটি সময়ের কথা ভাবুন, যখন আপনার প্রিয় ওপেন ওয়ার্ল্ড গেমগুলো খেলার জন্য আপনার নিজের কম্পিউটারের ক্ষমতা নিয়ে চিন্তা করতে হবে না। অনেক দাম দিয়ে হার্ডওয়্যার কেনারও প্রয়োজন হবে না। শুধু ব্রাউজারটি খুলে একটি গেম বেছে নিয়ে প্লে বাটনে চাপ দেবেন এবং কিছু সময়ের মধ্যেই গেমটি আপনার পিসিতে চলতে শুরু করবে। যার জন্যে বিশালাকারের কোনো ফাইল ডাউনলোড কিংবা ইন্সটলেরও কোনো দরকার হবে না।

স্বপ্নের মত শোনাচ্ছে? গুগল তাদের নতুন ক্লাউড গেমিং সার্ভিসের ঘোষণা দিয়ে পৃথিবীকে এমন একটি ভবিষ্যতের কাছাকাছি নিয়ে এসেছে। তাদের দেওয়া ঘোষণা অনুযায়ী, শুধুমাত্র ক্রোম ব্রাউজার দিয়েই ফোর-কে (4K) রেজ্যুলেশনে ৬০ ফ্রেম পার সেকেন্ডে (fps) যেকোনো গেম খেলা যাবে এবং গেমগুলো আরম্ভ হতে পাঁচ সেকেন্ডের বেশি সময় নেবে না।

গত জিডিসি (গেম ডেভেলপারস কনফারেন্স) ২০১৯ সম্মেলনে গুগল তাদের ‘গুগল স্টেডিয়া’ নামে পরিচিত এই নতুন গেম স্ট্রিমিং সার্ভিসটির ঘোষণা দিয়েছে। এই প্রজেক্টটি দীর্ঘদিন থেকে ‘প্রজেক্ট স্ট্রিম’ নামে পরিচিত ছিল। স্টেডিয়ার মাধ্যমে বড় গেমগুলো চালানোর যাবতীয় জটিলতা গুগল নিজের ঘাড়ে নিয়ে নিচ্ছে। তাদের সার্ভারেই গেমগুলোর সর্বশেষ ভার্সন অনেক উচ্চ রেজ্যুলেশন আর ফ্রেম রেটে রান হবে। সেখান থেকেই গেমগুলো স্ট্রিমে এনকোড হবে এবং এরপরে ব্যবহারকারীর কাছে পাঠিয়ে দেবে। ব্যবহারকারী এরপর গেম তাদের ডেস্কটপ, ল্যাপটপ এমনকি স্মার্টফোনেও খেলতে পারবে।

সম্প্রতি ঘোষণা দেওয়া গুগল স্টেডিয়া গেমিং জগতের 'নেটফ্লিক্স' হতে চলেছে; Image Source:
কীভাবে কাজ করবে এই প্ল্যাটফর্ম? এক কথায় বলতে গেলে এটি গেমিং ইন্ডাস্ট্রির 'নেটফ্লিক্স' হতে চলেছে। এটা মূলত ক্লাউড গেমিং সার্ভিস হলেও তাতেই থেমে নেই। গেমের ডেভেলপমেন্ট থেকে অনলাইনে হোস্টিং, ব্যবহারকারীর কাছে তা পৌঁছে দেওয়া এবং একই সাথে তা প্রচারণা করা সবই এই এক প্ল্যাটফর্মে এসে একত্রিত হবে। যদিও গুগল স্টেডিয়া মুক্তি না পাওয়া পর্যন্ত বিশদভাবে কিছুই বলা যাচ্ছে না কিন্তু এই সার্ভিসটির প্রাথমিক তথ্যগুলোই এত চমকপ্রদ যে অনুমান করা হচ্ছে তা ভিডিও গেম ইন্ডাস্ট্রিতে বিশাল পরিবর্তন আনবে।

প্রাথমিক খুঁটিনাটি
স্টেডিয়ার মস্তিষ্ক হিসেবে কাজ করবে গুগল ডেটা সেন্টারের পৃথিবীব্যাপী নেটওয়ার্কটি। নেটফ্লিক্সের মতোই স্টেডিয়া তার ব্যবহারকারীদের একটা লাইব্রেরির সুবিধা দেবে যেখান থেকে তারা নিজেদের পছন্দমত গেমটি খেলা শুরু করতে পারবে। কোনো ডাউনলোড বা ইন্সটল করার প্রয়োজন হবে না। একটা সুস্থির, দ্রুতগামীর ইন্টারনেট সংযোগ এবং গুগল ক্রোম ব্রাউজারের সর্বশেষ ভার্সন থাকলেই এই সার্ভিসটি উপভোগ করতে পারবে। স্টেডিয়ার হোমপেজ অথবা ইউটিউবে নির্দিষ্ট গেমটির ভিডিও লিংক থেকেই গেমটি খেলা যাবে।


Smart City/ Green city/ SDG 11 / Debunking the smart-city myth
« on: May 28, 2019, 02:16:01 PM »
Debunking the smart-city myth

By: Adnan Zillur Morshed

I have been following the “smart city” conversation in Bangladesh for quite some time now. Last year I sat on a panel to discuss the topic during what was called the “smart-city week” in Dhaka. As Bangladesh urbanises rapidly, as mid-sized cities increasingly become its new urban frontier, the mayors of small towns across the country seem drawn to the idea of smart city. They frequently talk about how they are eager to transform their towns into smart cities. I myself spoke with a few mayors who sounded anxious to bring “smartness” to their towns.

I wondered what they actually meant. I puzzled over how they defined “smart city,” what kind of urban imageries they construed in their minds for their smart cities, what kind of life they thought people would live in their smart cities. I couldn’t help but think of the American short-story writer Raymond Carver’s anthology “What We Talk About When We Talk About Love.” What do the mayors talk about when they talk about smart cities?

I suspect that they talk about something that is not their idea, something that is not organic to their economic and cultural experiences, something that is one of the biggest hypes of our times. Do they feel pressured to jump on the smart-city bandwagon? Do they think that they would be considered backward if they didn’t talk about smart cities? I wonder whether their dream of smart city is planted in their heads by international lending agencies, multinational corporations, and real-estate organisations for marketing purposes.

I suspect most mayors see smart city as a futuristic domain of glass towers, shopping malls, apartment blocks, ICT parks, theme parks, artificial lakes, bullet trains, sleek roads, signature flyovers, a lot of neon signs, and corporate executives. Smart cities all over the world look the same, the identical technocratic glitz, the identical corporate aesthetics, the identical financial mobility. One thing is absent: the everyday life of the people.

We really can’t blame our mayors for imagining this rather faultless future of abundance and unimpeded capital flow. A swanky smart-city perception has been crystallising over the past decade or so in Bangladesh and other developing countries. The notion of smart city is often packaged with a visual language of spectacular futurism and precision. We have been told over and over again how smart city is the surefire answer to urban chaos, inefficiency, and wastefulness. And, there was a breathless impatience to accept the grand smart-city solution.   

So, what is a smart city actually? How do the pundits define it? There is no universal definition, yet its portrayal is alarmingly consistent across geographic regions. The concept of smart city is a cybernetic idea—that is, information or data can enable urban governments to establish total control over all aspects of life in the city, ranging from public transportation to electricity usage, from waste management to water supply.

This technocratic idea implies that a comprehensive system of digital infrastructures, including sensors and devices placed throughout the city, would amass a vast body of data on, among other things, people’s movement and their spatial behaviour, traffic mobility, public transportation, energy usage, utility grid, water supply, and garbage collection. The digital infrastructure would then auto-create an efficient system of energy optimisation and frictionless management. In short, smart city proposes a system of data-driven urbanisation, ensuring energy efficiency, optimal mobilisation of resources, coordinated public service delivery, and intelligent management.

IBM has been creating digital urban infrastructures that would enable city governments to consolidate all urban-service providers under a central command-and-control mechanism, eliminating all system loss. Smart-city advocates, on the other hand, hope to attract foreign investment and capital mobilisation, with a view to developing their cities as hubs of economic growth, innovation, and entrepreneurship.

These are both very inspiring and dangerous ideas. Inspiring, because everybody wants efficiency. Who wouldn’t want perfectly functioning streets with vehicles following traffic regulations and taking passengers to their destinations on time? Who wouldn’t appreciate smart street signals that auto-adjust with fluctuating traffic volume in real time? Who wouldn’t love a clean-energy utility system that lowers people’s monthly energy bill? The core idea of smart city makes sense. We should, of course, take advantage of digital infrastructures to manage urban systems and operations.

But the smart-city idea, as it is often proposed, is also dangerous. The belief that we can mitigate a city’s complex sociocultural issues with data-driven technical solutions tranquilises the very concept of the city, a place where people don’t just become a system. People also want to be free in the city. They do random things. Factory-like efficiency and big-brother digital devices in the city may stifle life and defeat the purpose of a city as a community place with its unique social characteristics and quirks. I would rather be in Kolkata than Dubai. I would rather walk on the winding medieval streets of Prague than the hyper-efficient streets of Singapore.

If the intelligence of smart city is orchestrated by software programmers, technology giants, corporate CEOs, and high priests of neoliberal capital flow, then we, the people, need to be cautious because every aspect of our lives will be programmed and monitored by these invisible power-wielders. We don’t need smart cities that only serve as neocolonial outposts, ensuring smooth capital transfer to the Wall Streets of the world, while the local glass boxes would get peanuts and false pride.

Most worryingly, the identical architecture of smart cities across the world would only ensure a new generation of corporate global domination. We must be wary of top-down mantras that reframe the city’s complex social, cultural, political, and economic issues as technical puzzles. Cities must be grounded in their unique local customs and indigenous spatial sensibilities, while also competing in the global marketplace with the strength of their future-ready aspirations and public resilience.   

This, of course, doesn’t mean that we shouldn’t use data to ensure road safety in the city; or contain dengue by pinpointing its source; or divert vehicular traffic when there is a road congestion; or create intelligent footpaths that accommodate both pedestrians and vendors. We should use digital technologies to facilitate intelligent functioning of the city.

The biggest problem with the prevailing idea of smart city is that it is woefully generic, benefitting predatory capitalism that relies on the uniformity and homogenisation of people’s lives across the world. At the heart of the smart-city hype is the misguided ideology that there is a universal technical solution to messy urban problems and unique environmental challenges. Can we get rid of urban poverty even if we have data about all aspects of the poor? It never works that way because a subject as complex as poverty can’t be quantified into a mathematical question to be answered. Instead of prematurely believing in the instant transformation of city life promised by smart cities, we should focus on an ethos of step-by-step change in the city. By centring on the public good and resilience in both urban governance and digital infrastructure discourses we can create a smart community.

Adnan Zillur Morshed is an architect, architectural historian, and urbanist. He teaches in Washington, DC, and serves as Executive Director of the Centre for Inclusive Architecture and Urbanism at BRAC University. He is an alumnus of Faujdarhat Cadet College. He can be reached at


What Are The Top 5 Major Robotics Trends To Watch In 2019?

Robots have revolutionized the manufacturing and industrial world in recent decades, and are starting to make their move into the wider world of business as well as our homes, too.

While robotic workers are now commonplace in sectors such as automobile and electronics manufacturing, 2019 should see increasingly widespread adoption across food production, retail, healthcare, and distribution operations.

So here's my rundown of some of the top predictions of where automation and robotics are set to make waves in 2019.

Robots becoming increasingly commonplace in our homes

Besides robotic vacuum cleaners, the idea of home assistance robots has been slow to take off so far. Could that change in 2019? Indeed a whole breed of startups and established companies are betting that it will. From robotic companions for the elderly to robots designed to feed, play with and care for pets while their owners are out, the apparent applications are plentiful.

The breakthrough will come when companies have gathered and analyzed real-world data on what people do, and don’t, want from robots. Robotic vacuum cleaners took off because they filled a real need, and were able to affordably carry out the duty they were designed for. Other robotic assistants – such as mobile virtual assistants – have not been so warmly received.

Promising projects which will hopefully leave the starting blocks this year include Nvidia’s collaboration with Ikea, which aims to develop the first commercially successful robotic kitchen assistant. Could 2019 be the year that tech companies finally get the recipe for domestic robots right?

Delivery robots become a reality

Robotic delivery devices are hotly tipped to solve the “last mile” problem inherent to delivery operations – the most expensive stage of the delivery process where many small, individually packaged products must be precisely targeted to reach their final destinations on time and intact.

Some of these robots are designed to work in large indoor environments – such as Segway’s Loomo robot, which carries out the delivery of internal mail in workplace settings such as office blocks and shop floors.

Others, such as Nuro, designed by a team of Google engineers, will take to the streets to deliver fresh groceries as well as hot food, thanks to its separate heated and chilled cargo bays. Nuro is already carrying out deliveries in parts of Phoenix, Arizona – a hotbed of robotic activity thanks to its grid-like streets, which are also hosting the world’s first autonomous taxis, thanks to another Google spin-off, Waymo.

Of course, there is also the delivery of parcels via airborne drone delivery – which Amazon is working on, and has already put into practice for the past year. 

More of us will work alongside robots

Collaborative robots – or cobots – are the friendly face of workplace automation – not here to steal away our jobs, but to work alongside us, providing timely advice or simply mechanical muscle at times we need it.

As robotic technology becomes more widespread and deployment costs fall, businesses will realize that they can drive efficiency by deploying robots in environments which are unsafe or inhospitable to humans. Amazon's warehouse robots are a good example – as they bring items to human workers for packaging, only comparatively small areas of their vast real estate portfolios need to be heated and made comfortable for the humans who work alongside the machines.

Collaborative robots may also provide an avenue for companies to avoid the wrathful eye of the regulators, as they look to impose punitive measures on businesses which replace humans with automation. Politicians have already proposed “robot taxes” to cover these eventualities – fostering harmonious working relationships between humans and machines could be a trend which will set people’s minds at ease in 2019.

Robots on the Edge

Robots made ideal platforms for edge computing – building sensors into the extremities of automated systems, where machines meet the real-world environments they are built to influence.

During 2019 we can expect to see advances in smart sensors – sensors with inbuilt artificial intelligence – reducing the need for information to be sent to the cloud or centralized servers for processing, before it can be acted on.

Those leading the charge include Baidu – which has just unveiled China’s first open source edge computing platform, OpenEdge, which will allow developers of robots to empower their creations with AI, reducing CPU and bandwidth overheads used by cloud infrastructure. This should enable smarter, more autonomous robots to begin to appear in homes and industrial settings throughout 2019.

The Emergence of Open Standards for Robotics

Speaking of open source – 2019 should see a consolidation of the standardization needed for AI enabled robots to achieve mass market penetration. Regulators will have a part to play here, as frameworks are put in place to govern the ways that personal data can be collected and used by autonomous machines, including self-driving cars but also autonomous home and industrial assistants.

With the legal framework offering reassurance and trust, real innovation is likely to emerge from the open source community itself – already a hotbed of robotic development and activity. Amazon recently announced the launch of its AWS Robot Maker platform, built on the open source Robotic Operating System (ROS) standards. This lowering of the entry barrier towards involvement in the development and deployment of robots should mean more organizations of all shapes and sizes stand to benefit from the robot revolution over the coming 12 months.

Thank you for reading my post. Here at LinkedIn and at Forbes I regularly write about management and technology trends. To read my future posts simply join my network here or click 'Follow'. Also feel free to connect with me via Twitter, Facebook, Instagram, Slideshare or YouTube.

About Bernard Marr

Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a strategic business & technology advisor to governments and companies. He helps organisations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things.

LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent contributor to the World Economic Forum and writes a regular column for Forbes. Every day Bernard actively engages his 1.5 million social media followers and shares content that reaches millions of readers.

অক্সফোর্ডের গবেষণা  : কিশোরদের ওপর সামাজিক যোগাযোগ মাধ্যমের প্রভাব ‘সামান্য’

কৈশোরকে বলা যায় অদম্য কৌতূহলের বয়স। স্বাভাবিক নিয়মেই তারা সবকিছু জানতে চায়, যার প্রভাব পড়ে তাদের জীবন ও আচরণে। ফলে কিশোর বয়সে একটি ছেলে অথবা মেয়ে চারপাশে কী দেখছে ও শুনছে তা ভীষণ গুরুত্বপূর্ণ। এ কারণেই কিশোর বয়সী সন্তানের সামাজিক যোগাযোগ মাধ্যম ব্যবহার নিয়ে অভিভাবকরা সবসময়ই উদ্বিগ্ন থাকেন। কিন্তু অক্সফোর্ড বিশ্ববিদ্যালয়ের গবেষক দল বলছে, সামাজিক যোগাযোগ মাধ্যম নিয়ে অভিভাবকদের এত বিচলিত না হলেও চলবে। কারণ কিশোর বয়সীদের জীবন সন্তুষ্টির ক্ষেত্রে এসব মাধ্যমের প্রভাব খুবই ‘সামান্য’। এর চেয়ে বরং সার্বিক আচার-আচরণে পরিবার, বন্ধু এবং স্কুলজীবন গভীর ভূমিকা রাখছে।

পিএনএস জার্নালে সম্প্রতি এ গবেষণা প্রতিবেদন প্রকাশিত হয়। যুক্তরাজ্যে বসবাসকারী ১০-১৫ বছর বয়সী ১২ হাজার কিশোর-কিশোরীর দেয়া মতামতের ওপর ভিত্তি করে গবেষণা প্রতিবেদনটি তৈরি করেছে অক্সফোর্ড। তুলনামূলক বেশি সময় ধরে সামাজিক যোগাযোগ মাধ্যম ব্যবহার করে এমন কিশোররা অধিক অসন্তুষ্টিতে ভুগছে কীনা, গবেষণায় এ প্রশ্নের উত্তর খোঁজার চেষ্টা হয়েছে। এছাড়া তরুণ প্রজন্মের ওপর প্রযুক্তির প্রভাব যাচাই করতে চেয়েছেন গবেষকরা। এজন্য সামাজিক যোগাযোগ মাধ্যমগুলো কীভাবে ব্যবহূত হচ্ছে, সে বিষয়ে তথ্য প্রকাশে কোম্পানিগুলোকে আহ্বান জানানো হয়েছে।

এর আগেও অক্সফোর্ড বিশ্ববিদ্যালয় স্ক্রিন, প্রযুক্তি ও শিশুদের মানসিক স্বাস্থ্যের মধ্যে যোগসূত্র নিয়ে গবেষণা করে। কিন্তু এ গবেষণা পরস্পর বিরোধী বলে বিতর্ক রয়েছে। অক্সফোর্ড দাবি করছে, তাদের এবারের গবেষণাটি তুলনামূলক অনেক গভীর ও গতিশীল।

অক্সফোর্ড বিশ্ববিদ্যালয়ের অক্সফোর্ড ইন্টারনেট ইনস্টিটিউটের অধ্যাপক এন্ড্রু প্রিবিস্কি বলেন, অনেক সময়ই সীমিত প্রমাণাদির ওপর ভিত্তি করে গবেষণা সম্পন্ন করা হয়, যে কারণে কোনো একটি বিষয়ে পুরোপুরি ধারণা পাওয়া যায় না।

সামাজিক যোগাযোগ মাধ্যমের ব্যবহার ও এর সঙ্গে জীবন সন্তুষ্টি কতটুকু প্রভাবিত হচ্ছে, তা বের করতেই সাম্প্রতিক গবেষণাটি সম্পন্ন হয়েছে। যেখানে বিষয় দুটির মধ্যে খুবই ‘সামান্য’ যোগসূত্র পাওয়া গেছে। গবেষণায় বলা হয়েছে, সামাজিক যোগাযোগ মাধ্যমের প্রভাব ‘একমুখী কোনো রাস্তা’ নয় এবং একজন কিশোরের আচার-আচরণ গঠনের ক্ষেত্রে তা ১ শতাংশেরও কম ভূমিকা রাখে।

অধ্যাপক প্রিবিস্কি বলেন, ‘একজন মানুষের ৯৯ দশমিক ৭৫ শতাংশ জীবন সন্তুষ্টির ক্ষেত্রেই সামাজিক যোগাযোগ মাধ্যম ব্যবহারের কোনো প্রভাব নেই।’

জরিপে অংশগ্রহণকারীদের কাছে জানতে চাওয়া হয় স্বাভাবিক স্কুলের দিনগুলোয় সামাজিক যোগাযোগ মাধ্যমের পেছনে তারা কত সময় ব্যয় করে। এছাড়া জীবনের ভিন্ন দিক নিয়ে তাদের সন্তুষ্টির পরিমাণও নির্ধারণ করা হয় প্রতিবেদনটিতে।

গবেষণায় দেখা গেছে, সামাজিক যোগাযোগ মাধ্যমে সময় কাটানোর প্রভাব তুলনামূলক কিশোরীদের ক্ষেত্রে বেশি। অবশ্য এ প্রভাবও খুব সামান্য। আর কিশোরদের ওপর বড় ধরনের কোনো প্রভাব পাওয়া যায়নি। এ অবস্থায় সন্তানের সামাজিক যোগাযোগ মাধ্যমে সময় কাটানো নিয়ে মা-বাবার উদ্বিগ্ন হওয়ার প্রয়োজন নেই বলে জানিয়েছেন অধ্যাপক প্রিবিস্কি।

তবে সামাজিক যোগাযোগ মাধ্যমের নির্দিষ্ট কিছু প্রভাবে ঝুঁকিগ্রস্ত তরুণদের চিহ্নিত করাই এখন সবচেয়ে গুরুত্বপূর্ণ কাজ বলে মনে করেন তারা। কোন কোন বিষয় কিশোরদের আচার-আচরণে প্রভাব ফেলছে, সেটিও খুঁজে বের করার প্রতি জোর দেন তিনি।


পুরনো স্মার্টফোনের ব্যবহার...
বিশ্বের প্রায় প্রত্যেক মানুষ পকেটে ছোট একটি সুপারকম্পিউটার বহন করছে, যা ব্যবহার করে আবহাওয়ার পূর্বাভাস দেখে নেয়া, গেম খেলা, মিডিয়া কনটেন্ট স্ট্রিম করা, ছবি ধারণ কিংবা ডাটা বিশ্লেষণের মতো কাজ অনায়াসে করা যাচ্ছে। আধুনিক প্রযুক্তির দারুণ একটি অনুষঙ্গ স্মার্টফোন। ক্রমবর্ধমান চাহিদার কারণে প্রতিনিয়ত উন্নত হচ্ছে স্মার্টফোন ডিভাইস। এতে পুরনো স্মার্টফোন ফেলে নতুন কেনার আগ্রহ দেখা যায় গ্রাহক পর্যায়ে। এক পর্যায়ে পুরনো স্মার্টফোন ফেলে রাখা হয় বাসাবাড়ির যত্রতত্র। তবে পুরনো স্মার্টফোনের ওয়াই-ফাই সংযোগ সচল থাকলে তা ফেলে না রেখে নানা কাজে লাগানো যায়। পুরনো স্মার্টফোন যেসব কাজে লাগানো যেতে পারে তা নিয়ে আয়োজনের আজ পর্ব—

ওয়্যারলেস রাউটার

বেশির ভাগ স্মার্টফোনেই বিল্টইন ওয়াই-ফাই হটস্পট ফিচার থাকে। এ ফিচার থাকলে আপনি সহজে পুরনো স্মার্টফোনটিকে পোর্টেবল রাউটার হিসেবে ব্যবহার করতে পারবেন। থ্রিজি সিম কার্ড দিয়ে এ সুবিধা নেয়া যাবে। পকেট ওয়াই-ফাই হিসেবে পুরনো স্মার্টফোন ব্যবহারের ফলে ইন্টারনেট সমর্থিত প্রত্যেকটি পণ্যে আলাদা সংযোগ নেয়ার প্রয়োজন হবে না। কাজেই পুরনো স্মার্টফোন ফেলে না রেখে নিরাপদ অ্যাকসেস পাসওয়ার্ড ব্যবহার করে ওয়াই-ফাই হটস্পট তৈরি করে নিতে পারেন নিজেই।

অ্যাপ্লিকেশন পরীক্ষা

অ্যান্ড্রয়েড ও আইওএসের পাশাপাশি উইন্ডোজ প্লাটফর্মের অ্যাপ্লিকেশন বাড়ছে। বিভিন্ন অ্যাপ্লিকেশন পরীক্ষা করে দেখার জন্য পুরনো স্মার্টফোন কাজে লাগানো যেতে পারে। নতুন স্মার্টফোনে প্রয়োজনীয় কোনো অ্যাপ্লিকেশন ইনস্টল করার আগে তা পুরনো স্মার্টফোনে পরীক্ষা করে দেখে নিলে নানা রকম ঝামেলা থেকে মুক্তি মিলতে পারে। এছাড়া অপ্রয়োজনীয় বোল্টওয়্যার বা অ্যানিমেশন ইফেক্ট সরিয়ে মোবাইলের ব্যাটারির আয়ু বাড়াতে কাস্টম রম পরীক্ষায় কাজে লাগানো যায় পুরনো স্মার্টফোন।

টিভির মিডিয়া প্লেয়ার

পুরনো স্মার্টফোনে টিভি-আউট বা এইচডিএমআই আউটপুট ফিচার থাকলে তা ফ্ল্যাশভিত্তিক মিডিয়া প্লেয়ার হিসেবে ব্যবহার করা যাবে। এজন্য ডিভাইসটিতে ৩২ কিংবা ৬৪ গিগাবাইট অভ্যন্তরীণ স্টোরেজ সুবিধার হতে হবে। ডিভাইস স্টোরেজে থাকা মুভি ও ভিডিও হাই ডেফিনেশন লিংক (এমএইচএল) বা এইচডিএমআই কেবল দিয়ে টিভির সঙ্গে সংযোগ স্থাপন করে বড় পর্দায় দেখা যাবে। ডিভাইসটি ডিএলএনএ বা মিরাকাস্ট নামের পিয়ার-টু-পিয়ার ওয়্যারলেস স্ক্রিনকাস্টিং সমর্থন করলে ওয়্যারলেস উপায়ে স্টোরেজে থাকা মাল্টিমিডিয়া অন্য ডিভাইসে সম্প্রচার করা যাবে।

ওয়্যারলেস সিকিউরিটি ক্যামেরা

পুরনো অব্যবহূত স্মার্টফোনকে ওয়্যারলেস সিকিউরিটি ক্যামেরা হিসেবে রূপান্তর করা যেতে পারে। অ্যান্ড্রয়েড কিংবা আইওএস প্লাটফর্মে এ ধরনের অসংখ্য অ্যাপ্লিকেশন পাওয়া যায়। অ্যান্ড্রয়েডের জন্য আইপি ওয়েবক্যাম, আইওএসের জন্য আইভিজিলো স্মার্টক্যাম কাজে আসতে পারে। এ অ্যাপ্লিকেশনগুলো স্মার্টফোনের ক্যামেরার সাহায্যে লাইভ ভিডিও স্ট্রিমিং করতে পারে, যা অন্য কোনো স্ট্রিমিং সমর্থিত পণ্যের যেকোনো ব্রাউজারে বা ভিডিও প্লেয়ারে দেখা যায়। স্ট্রিমিং অ্যাপ্লিকেশন ইনস্টল করা থাকলে এবং ওয়াই-ফাই নেটওয়ার্কের মধ্যে থাকলে পুরনো স্মার্টফোন ডিভাইস ওয়্যারলেস সিকিউরিটি ক্যামেরার কাজ করবে।

জিপিএস নেভিগেটর

অ্যান্ড্রয়েড ফোনে গুগল ম্যাপস ও নেভিগেশন বিনা মূল্যেই পাওয়া যায়। গাড়ি চালানো কিংবা অপরিচিত কোনো স্থানে এ নেভিগেশন ব্যবহার করে সহজে পথ চিনে নেয়া যাবে। অর্থাৎ পুরনো স্মার্টফোনটি যদি অ্যান্ড্রয়েড-চালিত হয়, তবে গাড়ির জন্য আলাদা করে জিপিএস নেভিগেটর কেনার দরকার হবে না। পুরনো স্মার্টফোন গাড়িতে নেভিগেশন করার জন্য স্থায়ীভাবে রেখে দেয়া যেতে পারে। এজন্য জেনেরিক মাইক্রো ইউএসবি ১২ ভোল্ট কার চার্জার দরকার হবে, যা মোবাইল ফোনটিকে চার্জ দিতে কাজে লাগবে। কার ড্যাশবোর্ড নামে একটি অ্যাপ্লিকেশন ইনস্টল করে নিলে এবং প্রয়োজনমতো কাস্টমাইজ করে নিলে পুরনো স্মার্টফোনকে কার নেভিগেটর হিসেবে ব্যবহার করা যাবে।

পিসির রিমোট কন্ট্রোলার

পুরনো স্মার্টফোনকে টিভি কিংবা এসির পাশাপাশি পিসির রিমোট কন্ট্রোলার হিসেবেও ব্যবহার করা যাবে। পিসির কন্ট্রোলার হিসেবে ওয়্যারলেস মাউস সবচেয়ে সুবিধার হলেও, যদি দূরে সোফা বা চেয়ারে বসে কম্পিউটার চালানোর প্রয়োজন হয়, তখন পুরনো স্মার্টফোনটিকে কন্ট্রোলার হিসেবে কাজে লাগানো যেতে পারে। কম্পিউটারের ব্রাউজিং বা কোনো ভিডিও যদি বড় স্ক্রিনে দেখতে চান, তবে পুরনো স্মার্টফোন কাজে লাগানো যাবে। বিনা মূল্যের অ্যাপ্লিকেশন ‘মোবাইল মাউস লাইট’ এক্ষেত্রে কাজে লাগানো যেতে পারে। পুরনো স্মার্টফোন পিসির রিমোট হিসেবে ব্যবহার করতে মোবাইল ও পিসি উভয় ডিভাইসই একই ওয়াই-ফাই নেটওয়ার্কে থাকতে হবে। সূত্র: পিসিম্যাগ

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চাই না মানুষ ফোনে আসক্ত হয়ে পড়ুক: টিম কুক

স্মার্টফোনে আসক্তি এখন একটি বড় সমস্যা হয়ে দেখা দিয়েছে। এর ভয়াবহতা পরিবার কিংবা সামাজিক পরিসর ছাড়িয়ে কর্মক্ষেত্রেও সমস্যা হয়ে উঠেছে। ব্যক্তিজীবনে তো বটেই, বন্ধুদের সঙ্গে আড্ডা কিংবা গুরুত্বপূর্ণ কাজের আলাপচারিতার সময়ও কারো কারো চোখ পড়ে থাকে স্মার্টফোনে! চারপাশে কী হচ্ছে, সেসবে যেন খেয়াল করেন না তারা। সম্প্রতি নিউইয়র্কে অনুষ্ঠিত ‘টাইম ১০০ সামিট’-এ নিয়ন্ত্রণ ও রাজনৈতিক অনুদানসহ বিভিন্ন বিষয়ে কথা বলেন অ্যাপলের প্রধান নির্বাহী কর্মকর্তা (সিইও) টিম কুক। এ সামিটেই তিনি বলেন, আমরা চাই না মানুষ সার্বক্ষণিক বা আসক্ত হয়ে ফোন ব্যবহার করুক। খবর ম্যাশেবল।

হাসিমুখে টিম কুকের এমন বক্তব্যের আড়ালে অনেকে তার চৌকস বুদ্ধিমত্তার বহিঃপ্রকাশ দেখছেন। অনেকে বলছেন, অবশ্যই তিনি চৌকস বুদ্ধিসম্পন্ন একজন সিইও। তবে তিনি যা-ই করুন বা বলুন না কেন, এর পেছনে ব্যবসায়িক উদ্দেশ্য আছে নিশ্চয়। তিনি সামিটে গোপনীয়তা ও ডিজিটাল ডিভাইসের স্ক্রিন টাইম বিষয়ে অ্যাপল অন্য প্রযুক্তি প্রতিষ্ঠান থেকে কীভাবে আলাদা, সে বিষয়ে কথা বলেন।

টিম কুক বলেন, বিশ্বব্যাপী স্মার্টফোনের ব্যবহার বাড়ছে। দৈনিক জীবনের অনেক গুরুত্বপূর্ণ কাজ সহজ করেছে স্মার্টফোন। এ ডিজিটাল অনুষঙ্গের নেতিবাচক দিক হলো, অনেক মানুষ আসক্ত হয়ে পড়েছে। আমরা চাই না মানুষ আসক্ত হয়ে স্মার্টফোন ব্যবহার করুক। এটা আমাদের উদ্দেশ্য না।

তিনি দাবি করেন, অ্যাপল ডিভাইসে বেশি সময় কাটানোর জন্য মানুষকে চাপ প্রয়োগ করা আমাদের উদ্দেশ্য না। টিম কুক আক্ষেপ করে বলেন, মানুষের ডিজিটাল ডিভাইসে স্ক্রিন টাইম নিয়ে নিয়মিত হাজার হাজার নোটিফিকেশন পাই। ডিজিটাল ডিভাইসে মানুষ এতটাই আসক্ত হয়ে পড়েছে যে অন্য মানুষের চেহারার দিকে তাকানোরও সময় নেই অনেকের। এটা সত্যি উদ্বেগের বিষয়।

টিম কুক বলেন, আমরা কোনোভাবেই মানুষের স্ক্রিন টাইম বাড়ানোর পক্ষে নই। ব্যবসার দিক বিবেচনায় আমরা ডিজিটাল স্ক্রিনে আরো বেশি সময় কাটানোর জন্য মানুষকে উৎসাহিত করতে পারি না। এছাড়া স্ক্রিন টাইম বাড়লেও আমাদের ব্যবসায় তা বাড়তি যোগ্যতা যোগ করবে না।

বিশ্লেষকদের মতে, ডিজিটাল স্ক্রিন টাইম নিয়ে অ্যাপল সিইওর বক্তব্য স্পষ্ট এবং বেশ উচ্চমার্গীয়। তবে অ্যাপল আইফোনের মতো জনপ্রিয় ডিভাইস এবং সংশ্লিষ্ট ইকোসিস্টেমের উদ্ভাবক। অথচ এ ধরনের ডিভাইসকেন্দ্রিক নোটিফিকেশন অপছন্দ করেন টিম কুক। সত্যিকার অর্থে এখনো আইফোন এবং আইফোনকেন্দ্রিক সংশ্লিষ্ট সেবা ইকোসিস্টেম থেকে অ্যাপলের রাজস্বের সিংহভাগ আসছে। অ্যাপ ক্রয় এবং ইন-অ্যাপ পেমেন্ট খাত থেকে বিপুল অর্থ উপার্জন করছে অ্যাপল। ২০১৮ সালে সেবা খাত থেকে অ্যাপলের রাজস্ব আয় এক বছর আগের চেয়ে ২৪ শতাংশ বেড়ে ৩ হাজার ৭২০ কোটি ডলারে পৌঁছেছে।

টিম কুক তার বক্তব্যে জানিয়েছেন, ডিজিটাল ডিভাইসে অর্থবহ এবং ক্ষমতায়নে সহায়তা করে, এমন সময় ব্যয় করুন। অর্থাৎ নিজেকে সমৃদ্ধ করে ততটুকু সময় ডিভাইস স্ক্রিনে ব্যয় করা উচিত। বাকি সময় অন্য মানুষের সঙ্গে কাটান, যা মানুষের সঙ্গে মানুষের সম্পৃক্ততা বাড়াবে।

ডিভাইস নির্মাতাদের জন্য স্মার্টফোন আসক্তি মাথাব্যথার কারণ হয়ে উঠেছে। এ নিয়ে বিভিন্ন ধরনের সমালোচনা সামাল দিতে হচ্ছে। স্ক্রিন টাইম বিষয়ে বাকি ডিভাইস নির্মাতাদের চেয়ে অ্যাপলের উদ্যোগ এরই মধ্যে প্রশংসা কুড়িয়েছে। মার্কিন প্রযুক্তি প্রতিষ্ঠানটি তাদের নতুন ডিভাইসগুলোতে স্ক্রিন টাইম কমানোর লক্ষ্যে একাধিক ফিচার এনেছে।


QA Mechanism / Creating a generation of learners and innovators
« on: May 04, 2019, 01:49:37 PM »
Creating a generation of learners and innovators

The curriculum for any undergraduate programme is highly influenced by the social, physical, economic and cultural environment. Consequently, with the change of any such setting(s), its development process will also change. The great economist and Nobel Laureate, Wassily Leontief, wrote in 1953 that “labour will become less and less important… More and more workers will be replaced by machines.”

However, an insightful way to understand the overall effect of new technology on the number of jobs in the economy is to look at it as a race between two dynamic processes. Automation tends to take jobs away while the invention of new complex tasks creates new jobs. In this context, there is an increasing demand for graduates who can speak and write effectively, have high-quality interpersonal (teamwork) and creative thinking skills, are innovative and have some understanding of world affairs, and can work effectively with individuals from different cultures and backgrounds. Universities in developed countries as well as in many developing countries are paying greater attention to the quality of education they provide to students, and to redesigning curricula. These institutions are constantly rethinking their goals and priorities, their curricula, and the way learning takes place.

The present curriculum development process is different from that of traditional curriculum development. In the traditional process, the curriculum is a collaborative effort of senior faculty members and course teachers. Course teachers are involved as they know the contents of the courses and the way courses are taught. The curriculum focuses on a specific body of knowledge to be transmitted to students and relies heavily on memorisation and drilling of facts and formulas. Education systems founded on traditional curricula often focus solely on the subject matter being taught and favour measurement of educational objectives via a great deal of testing. Statements of programme outcomes do not exist for curricula and courses. Traditionally, course improvement has been the responsibility of individual faculty, and efforts to redesign curricula have usually been assigned to departmental committees established specifically for this purpose.

Designing a quality course or curriculum is always difficult, time-consuming, and challenging. A curriculum must be developed sequentially, beginning with aligning programme educational objectives with the institutional statement of vision and ending with the assessment of each student before and after graduation. It requires thinking about programme educational objectives and programme outcomes for students, the demands of accreditation agencies, competencies and skills required at jobs, and how a teacher can facilitate the learning process.

Programme educational objectives are broad statements that describe the career and professional accomplishments of graduates. Keep in mind that although programme educational objectives are long-range and focused on performance well after graduation, it is possible within an undergraduate programme to identify the skills, attitudes, and understandings that are the underpinnings of these long-range objectives. On the other hand, programme outcomes must be achieved during the academic programme. Each programme must have documented student outcomes that prepare graduates to attain the programme educational objectives.

As the design process starts, from defining programme educational objectives to developing programme outcomes and course outcomes and then course-by-course outcomes, the statements become increasingly specific. The design of each course, the selection of instructional methods, and student assessment are based on these statements. The process of moving from a statement of objectives and outcomes to deciding on and implementing a programme and relating individual courses to the curriculum requires careful planning. If, for example, speaking skills are identified as a basic competency that every student must have by graduation, public speaking must be initially taught and then reinforced, and no student should be able to graduate without receiving appropriate instruction and practice in this skill. Courses must be analysed to identify where this skill is introduced and then reinforced, and the curriculum must be structured so that every student has the opportunity to acquire speaking skills. In the case of developing competencies in speaking, the required courses will most likely be those with smaller enrolment, or lecture courses that have discussion sessions associated with them. Developing and using interpersonal skills, problem-solving, critical thinking, basic statistics, and so on, are widely listed core objectives and can be an integral part of most courses.

In every institution, the final determinant of the quality of the academic programme is the performance of its graduates. The degree of success will depend on how well the curriculum is delivered through its courses and other learning experiences provided to students. Every student must have the opportunity to reach and demonstrate every stated basic competency. Carefully articulated learning outcomes must be the basis on which instructional methods are chosen and the criteria by which competency must be measured. The effectiveness of an institution or programme and of individual faculty members is then determined by the ability of students to meet these objectives and outcomes. At the same time, it must be recognised that not all students will reach these goals, because their attitudes, willingness to work, and ability also play an important role in determining success. It is the responsibility of an institution to do everything to facilitate the learning that is required and to give each student a fair opportunity to succeed.

Higher education generates broader economic growth as well as individual success. For example, a recent study determined that universities contributed nearly 60 billion pounds to the economy of the United Kingdom in 2007-08 (Drew Gilpin Faust, June 30, 2010). Therefore, universities in Bangladesh can also change the society and remain the centre of change and economic development. In that case, universities need to produce graduates with high-level skills, critical thinking competency and innovative quality; and such graduates can get jobs in national and global markets, and also can be successful as entrepreneurs and self-employed workforce. Such dispositions demand changes in curricula, and teaching and assessment methods to create a young generation of active learners and creators. The primary task lies with universities in Bangladesh to develop curricula for programmes following the widely accepted development process.

MM Shahidul Hassan is Vice Chancellor of East West University, Dhaka. Email:


Role of Accreditation Process and Regional Ranking System of Universities in Asia: With Special Reference to Bangladesh by Mr. Md. Sabur Khan, Chairman, BoTs, DIU

Web link:

IQAC workshop on 'Quality in Higher Education: Teaching Learning Perspective', conducted by  an US Quality Expert

In view to exchanging the global views and experiences, IQAC, DIU invited Professor Dr. Quamrul H. Mazumder, P.E., Professor, Department of Mechanical Engineering, University of Michigan-Flint, USA to conduct the workshop  on 'Quality in Higher Education: Teaching Learning Perspective'. The said workshop was held on 25 April, 2019 at Daffodil International University.
The workshop facilitator, Professor Dr. Mazumder holds various key positions like Program Evaluator, Accreditation Board of Engineering and Technology, USA, Director, American Society of Engineering Education, USA, Advisory Board Member, Center of Teaching and learning, University of Michigan-Flint. 

In the workshop, Heads of the departments, Conveners and Members of Self-Assessment Committee of each SA Departments, Additional Director, IQAC, DIU, Dr. Md. Jashim Uddin, Director (Operations),, Mr Ariful Alam and IQAC team members were present.

Director, IQAC, DIU, Professor Dr, A.K.M. Fazlul Haque, welcomed the participants and handed over the crest to the invited Key Facilitator, Professor Dr. Quamrul H. Mazumder, P.E. After the welcome session, Professor Dr. Mazumder started his key presentation. In his presentation, he has talked on several issues and global practices on quality in higher education and teaching learning practices. In sharing his experiences, he has focused on diverse areas like importance of quality in higher education, assessment and criteria of quality, overview of the globalization of quality in higher education and different models of quality in higher education in different countries. Talking on policy, he addresses different policy models of quality in higher education. Apart from these, Professor Dr. Mazumder has provided a brief overview of student motivation in higher education, evaluation process of different techniques to motivate students to become active learners, relevant motivational theories, positive learning strategies, also analyzed teaching styles and how they can be applied to improve quality of education.

The workshop was interactive and all faculty members took part into the discussion sesion and appreciated the role of IQAC for arranging such workshop.

Source: IQAC, DIU

Resources / Newsletter of IQAC, DIU Volume 1; Issue 1: 2015
« on: April 23, 2019, 02:39:26 PM »
Newsletter of IQAC, DIU
Volume 1; Issue 1: 2015


2019-2025 Global Blended Learning Market by Featuring Companies – Skillsoft, D2L, City & Guilds Group and Cegos

Analysis of Blended Learning market and its upcoming growth prospects is been mentioned with most exactness. This study includes an elaborative summary of Blended Learning market that also includes snapshots that provide depth of knowledge of various different segmentations. Through qualitative and measure of key factors that are responsible for boosting or hampering the market growth and therefore the promising opportunities in the global Blended Learning market have been providing. Primary and secondary analysis is been done in detail that helps the readers have a strong understanding of the entire market for the forecast period of 2013-2025.

The analysis report on global Blended Learning market estimates the development patterns of the business through elapsed investigation and evaluations of future prospects dependent on complete analysis. The report wisely offers the market share, development, patterns and guidelines for the amount 2013 to 2025. the global market for Blended Learning is needed to develop at a CAGR of usually XX% throughout the following ten years, can succeed XXXX million US$ in 2025, from XX million US$ in 2018. The report gives key measurements on the market status of the global Blended Learning and could be a profitable wellspring after all and direction for organizations and people intrigued by the business.

The report utilizes SWOT examination for the development appraisal of the exceptional global Blended Learning market players. It, in addition, examines the most recent enhancements while assessing the development of the leading global Blended Learning market players. It offers profitable information, as an example, product contributions, revenue division, and a business report of the instructing players within the global Blended Learning market.

Top Reported Manufacturers in Global Blended Learning Market:

Skillsoft, City & Guilds Group, Cegos, D2L, GP Strategies, NIIT

Global Blended Learning Market Key Types Segmented:


Global Blended Learning Market Key Applications Segmented:

Automotive Industry
Consumer Goods Sector
Energy Sectors

Focused Key Region in Global Blended Learning Market:

North America (the United States, Canada, and Mexico)

Europe (Germany, France, UK, Russia, and Italy)

Asia-Pacific (China, Japan, Korea, India, and Southeast Asia)

South America (Brazil, Argentina, Colombia)

The Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, and South Africa)

Table Of Content Topics Covered:

1. Global Market Overview: Scope of Statistics of Blended Learning market

2. Regional Market: Regional Production, Regional Demand, Regional Trade

3. Key Manufacturers: Company Information, Product & Services, Business Data, Recent Development

4. Major Application: Application of Blended Learning with Market Size and Forecast

5. Market by Type: Type of Blended Learning with market size and forecast

6. Price Overview: Price by Manufacturers, Price by Application, Price by Type

7. Conclusion

Report Summary:

In the initial section, the global Blended Learning report presents industry summary, definition, and scope. The second part briefs regarding global Blended Learning bifurcation by type, Application, and countries. the top industry players, market share, revenue analysis, and sales margin is explained. The raw materials analysis, production and consumption scenario is specified. The SWOT analysis by players, the growth rate for each type, application, and the region is covered. A 5-year forecast global Blended Learning perspective will lead to profitable business plans and wise moves. Towards, the end information sources, analysis methodology, and findings are offered.


Japan aims to produce 250,000 AI experts a year

TOKYO -- Japan will seek to increase the development of talent proficient in artificial intelligence to 250,000 people a year, up from just a few thousand today, according to a government plan laid out Friday.

All students enrolled in universities and technical schools will be mandated to take a beginner AI course, based on a draft proposal released at a cabinet-level innovation panel.

With about 600,000 post-secondary graduates per year, the goal is to have 250,000 of them possessing advanced AI expertise. The number would include 120,000 science and technology majors and 60,000 health science majors from four-year colleges. Another 70,000 liberal arts majors, representing 17% of the 420,000 students in that field, would round out the total.

The growing prevalence of "internet of things" technology -- where devices and systems are linked and able to share information -- and big data increasingly make AI expertise crucial in developing products and finding new business opportunities.

"The talent will extend across the sciences and the arts," said Osamu Sudoh, a professor at the University of Tokyo specializing in informatics. "If 1,000 top, world-class talent is produced out of 100,000 people, Japan will raise its competitive advantage."

Only 2,800 students currently complete master's degrees in AI research, according to a government task force. The 250,000-per-year target may seem like a high bar, but that is the level necessary to ease the labor shortage for AI experts, with the gap expected to reach 300,000 by the end of 2020.

"If college and technical students learn data science, they may apply AI to sales and projects once they start working," said Koji Kono at Mizuho Information & Research Institute.

These efforts will go hand in hand with measures being taken in academia and the private sector. Shiga University, which established Japan's first department of data science in 2017, will offer a master's degree program starting next month. Out of the 23 people due to enter, 80% will be working adults sent by businesses.

Insurance provider Sompo Holdings has offered an AI training program for people both inside and outside the company since 2017. The three-month courses are held twice a year, and have produced 100 graduates, some of whom are recruited by the company.

Not only does Persol Career match data scientists and AI technologists with employers, the staffing company also offers an educational program for talent on its roster.

A number of barriers stand in the way of freshly minting 250,000 AI experts every year, however, with one being securing enough teachers.

"The market rate [for AI educators] has soared over the past decade," said Yuki Matsumoto, chief technology officer at, an e-commerce and internet company. "If you don't provide them the remuneration at or above what they'll receive in the private sector, it will be difficult to secure AI talent for the classrooms."

Japanese data scientists make up to 12 million yen ($109,000) a year, according to British staffing agency Hays. That is less than the 1 million yuan ($148,000) earned in China, and the 180,000 Singaporean dollars ($133,000) in Singapore.

And salaries for educators are lower still. The average Japanese high school teacher makes 4.32 million yen annually, while the figure rises to 5.52 million for college instructors, government data suggests. The lopsided demand from businesses could limit the number of AI instructors available to teach college students.


QA Mechanism / Self-Assessment Mechanism
« on: April 20, 2019, 03:04:24 PM »
Self-Assessment Mechanism

Organizing for Self-Assessment: Self-assessment may be considered as the groundwork for effective decisions and work plan relating to quality assurance and further improvement. For an effective self-assessment critical review of current state of practices in respect of the set criteria and standards is very important. But the job is not a stand-alone exercise. Instead, the self-assessment should be the culmination and coordinated efforts by several group of people in the university or program offering entity. It should be done as a permanent and cyclical process. In order to be effective in organizing self-assessment major stakeholders must have a clear understanding of the self-assessment process, its scope and limitations. In many cases, self-assessment is undertaken because the leaders of an institution, or an external agency, demand it (Lemaitre et al., 2007). But if it is not considered useful and worthwhile for the academic improvement, it will not be effective. It is necessary to have significant internal motivation to go for self-assessment. Self-assessment is to be done with the spirit of team work and involvement of all the parties of the entity or institution. Strong commitment and institutional supports are also very important to maximize the benefits of self-assessment. Therefore, arrangement of workshop or discussion on the self-assessment process and its significance in quality assurance would be effective to mobilize the internal stakeholders of the entity for self-assessment. Institutional Quality Assurance Cell (IQAC) has to take the lead role in this regard.

Self-Assessment Committee (SAC): There shall be a three member Self-Assessment Committee in each program offering entity of the university. The Dean/Chairman/ Head of the program offering entity will form the Program SA Committee (PSAC) for the entity.The Head and two members of SAC shall be filled up by the interested, experienced appropriately qualified senior faculty of the entity under assessment. The SAC will be formed and function for one year. During this one year period the SAC will conduct the self-assessment and facilitate the external peer review and prepare the improvement plan for further academic development. In addition, the SAC will oversee the QA related activities within the program offering entity and will make sure that all the QA activities undertaken by the IQAC and applicable for the entity are being implemented properly. The SAC in cooperation with IQAC will work to develop the QA culture within the entity.3.2

Workshop on "Building Awareness on Orientation of SA Process Flow among 9 new SACs of IQAC of DIU"

A workshop on "Building Awareness on Orientation of SA Process Flow among 9 new SACs of IQAC of DIU" was held on March 27, 2019 at Conference Room, DT-4, Daffodil International University (DIU). The programme was organized by Institutional Quality Assurance Cell (IQAC) of DIU. The event aims to provide orientation and proper guideline on Self-Assessment Process Flow for 9 (nine) SA Departments, which are as: Department of: Architecture, Civil Engineering, Multimedia & Creative Technology, Environmental Science and Disaster Management, Tourism & Hospitality Management, Public Health, Innovation & Entrepreneurship, Development Studies, Information Science and Library Management. Conveners and members of 9 SA Committee were present.

 Professor Dr. A.K.M. Fazlul Haque, Director, IQAC, Daffodil International University has conducted the program and presented the key note, based on the theme: ‘Building Awareness on Orientation of SA Process Flow among 9 new SACs, IQAC, DIU’. At the beginning, he has introduced the incumbent Additional Director of IQAC, DIU, Dr. Md. Jashim Uddin, Associate Professor, Department of GED, DIU with the distinguished participants, attended the event. At his presentation, he has explained the importance of quality education for Higher Education Institute and depicted the necessity and importance of introducing QA system for all departments. Citing the example, he has also focused comparative analysis of national and international QA systems. While rendering his presentation, Director, IQAC showed the graphical presentation of structural framework of overall IQAC and SAC activities and the Self-Assessment Process Flow of 5 years cycle. He has talked on several vital issues of Self-Assessment and other related areas like: SA Criteria and SA Standards, Stakeholders representation, Objectives and Outcomes of SA Exercise, Strategic Approach (PDCA Approach) of IQAC, DIU, Responsibilities of SAC and IQAC. In addition to the process flow, he also shared the experiences with EPR Team. To stress on second agenda, Director, IQAC, DIU also talked on ‘Methodology to Establish a Monitoring System for Assessment of Standard of the Question Papers of All Departments of DIU’ and explained the general objectives and Terms of References (ToRs) of the committee. He has also requested Sac members to visit IQAC website where they can get all updated information regarding QA domain. Professor. Dr. Haque also shared the functions of IQAC with National Quality Framework of Bangladesh (NQFB).

After summing up of his presentation, he has invited the floor for discussion. Convenors and SA Members took participation at the discussion and they have talked on various issues on QA activities and other areas like evaluation of teacher by the student etc,
Professor Dr. A.K.M. Fazlul Haque, Director, IQAC, DIU has also requested the SA-C of 9 SA departments, to start their survey questionnaire related tasks from 01 April, 2019 and cover their activities like arranging workshops/seminars through PR section of DIU.

Director, IQAC wrapped-up the programme by extending his thanks to all of them for attending the event, in a befitting manner.

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