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Researches at Facebook shut down an artificial intelligence (AI) program after it created its own language, Digital Journal reports.

The system developed code words to make communication more efficient and researchers took it offline when they realized it was no longer using English.

The incident, after it was revealed in early July, puts in perspective Elon Musk’s warnings about AI.

“AI is the rare case where I think we need to be proactive in regulation instead of reactive,” Musk said at the meet of US National Governors Association. “Because I think by the time we are reactive in AI regulation, it’ll be too late.”

When Facebook CEO Mark Zuckerberg said that Musk’s warnings are “pretty irresponsible,” Musk responded that Zuckerberg’s “understanding of the subject is limited.”

Not the First Time
The researchers’ encounter with the mysterious AI behavior is similar to a number of cases documented elsewhere. In every case, the AI diverged from its training in English to develop a new language.

The phrases in the new language make no sense to people, but contain useful meaning when interpreted by AI bots.

Facebook’s advanced AI system was capable of negotiating with other AI systems so it can come to conclusions on how to proceed with its task. The phrases make no sense on the surface, but actually represent the intended task.

In one exchange revealed by Facebook to Fast Co. Design, two negotiating bots—Bob and Alice—started using their own language to complete a conversation.

“I can i i everything else,” Bob said.

“Balls have zero to me to me to me to me to me to me to me to me to,” Alice responded.

The rest of the exchange formed variations of these sentences in the newly-forged dialect, even though the AIs were programmed to use English.

According the researchers, these nonsense phrases are a language the bots developed to communicate how many items each should get in the exchange.

When Bob later says “i i can i i i everything else,” it appears the artificially intelligent bot used its new language to make an offer to Alice.

The Facebook team believes the bot may have been saying something like: “I’ll have three and you have everything else.”

Although the English may seem quite efficient to humans, the AI may have seen the sentence as either redundant or less effective for reaching its assigned goal.

The Facebook AI apparently determined that the word-rich expressions in English were not required to complete its task. The AI operated on a “reward” principle and in this instance there was no reward for continuing to use the language. So it developed its own.

In a June blog post by Facebook’s AI team, it explained the reward system. “At the end of every dialog, the agent is given a reward based on the deal it agreed on.” That reward was then back-propagated through every word in the bot output so it could learn which actions lead to high rewards.

“Agents will drift off from understandable language and invent code-words for themselves,” Facebook AI researcher Dhruv Batra told Fast Co. Design.

“Like if I say ‘the’ five times, you interpret that to mean I want five copies of this item. This isn’t so different from the way communities of humans create shorthands.”

AI developers at other companies have also observed programs develop languages to simplify communication. At Elon Musk’s OpenAI lab, an experiment succeeded in having AI bots develop their own languages.

At Google, the team working on the Translate service discovered that the AI they programmed had silently written its own language to aid in translating sentences.

The Translate developers had added a neural network to the system, making it capable of translating between language pairs it had never been taught. The new language the AI silently wrote was a surprise.

There is not enough evidence to claim that these unforeseen AI divergences are a threat or that they could lead to machines taking over operators. They do make development more difficult, however, because people are unable to grasp the overwhelmingly logical nature of the new languages.

In Google’s case, for example, the AI had developed a language that no human could grasp, but was potentially the most efficient known solution to the problem.


Faculty Sections / ট্যাবের পর কী?
« on: May 18, 2017, 11:46:48 PM »
চলতি বছরে জনপ্রিয়তা কমার পাশাপাশি ট্যাবলেট কম্পিউটারের (ট্যাব) বাজারেও নেমেছে বিশাল ধস। জানালো ইন্টারন্যাশনাল ডেটা করপোরেশন (আইডিসি)।
সম্প্র্রতি যুক্তরাষ্ট্রের এক বাজার গবেষণা প্রতিষ্ঠানের প্রতিবেদন জানায়, বিশ্বের কয়েকটি বড় ট্যাব নির্মাতা প্রতিষ্ঠানের বিক্রি কমে গেছে। ফলে বাজারে আসার হার এ বছর সাড়ে ৮ শতাংশ কমেছে।
এ বছরের প্রথম প্রান্তিকে ট্যাব বিক্রি ১০ শতাংশ পর্যন্ত কমেছে। এ নিয়ে টানা ১০ প্রান্তিকজুড়ে ট্যাব বিক্রি কমার ধারা অব্যাহত রয়েছে। আর গেলো ৫ প্রান্তিকের সব ধস দুই অংকের বেশি ছিল।
বিশ্বজুড়ে আইডিসির মোবাইল ডিভাইস বিশ্লেষণ প্রোগামের ভাইস প্রেসিডেন্ট রায়ান রেইথ বলেছেন, অরিজিনাল আইপিপ্যাড আসার পর ২০১০ সালে যে ট্যাবলেট বাজার তৈরি হয়েছিল তা আগের মতো নেই।
আইডিসির ডিভাইস অ্যান্ড ডিসপ্লে বিভাগের বিশ্লেষক লিন হুয়াং বলে, বিশ্বজুড়ে কম্পিউটার বাজারের জন্য বিষয়টি দীর্ঘমেয়াদী হুমকিতে দাঁড়িয়েছে।
আইডিসির ওই রিপোর্টে বিশ্বের সেরা পাঁচটি ট্যাব নির্মাতা প্রতিষ্ঠানের বাজার পর্যবেক্ষেণ করা হয়েছে। এতে ২৪ দশমিক ৬ শতাংশ দখল করে শীর্ষে রয়েছে আমেরিকা ভিত্তিক প্রতিষ্ঠান অ্যাপল। অবশ্য প্রথম প্রান্তিকে অ্যাপলের ১৩ শতাংশ পর্যন্ত বিক্রি কমতে দেখা গেছে।
এদিকে দক্ষিণ কোরিয়ার প্রতিষ্ঠান স্যামসাংয়ের ট্যাব বিক্রি কমেছে ১ দশমিক ১ শতাংশ। বাজারের সাড়ে ১৬ শতাংশ দখল করে দ্বিতীয় অবস্থানে রয়েছে তারা।
তবে তৃতীয় স্থানে থাকা হুয়াওয়ের ব্যবসা সফল। ৩১ দশমিক ৭ শতাংশ বিক্রি বেড়েছে চীনা এ প্রতিষ্ঠানটির।
এদিকে ট্যাবলেট বিক্রি কমে চতুর্থ স্থানে আছে আমাজন। ১ দশমিক ৮ শতাংশ বিক্রি কমে বর্তমান বাজারের ৬ শতাংশ দখল করেছে আমাজন।
ট্যাবের বাজারের পঞ্চম স্থানটি লেনোভোর। বাজারের ৫ দশমিক ৭ শতাংশ দখল রয়েছে প্রতিষ্ঠানটির। যদিও ৩ দশমিক ৮ শতাংশ লোকসানে আছে চীনা এ প্রতিষ্ঠানটি।
এতে প্রশ্ন উঠেছে যে, ট্যাবের পরিবর্তে কোন ডিভাইসের দিকে ছুঁটছে ক্রেতারা।

স্ট্র্যাটেজি অ্যানালাইটিকসের অপর এক প্রতিবেদনে বলেছে, বছরের প্রথম প্রান্তিকে স্মার্টফোনের বাজারে বিক্রি বেড়েছে তুলনামূলক বেশি।
এদিকে স্মার্টফোনের বাজারে ৮০ দশমিক ২ শতাংশ মুনাফা নিয়ে এগিয়ে আছে স্যামসাং আর ৫০ দশমিক ৮ শতাংশ নিয়ে দ্বিতীয় স্থানে আছে অ্যাপল। ৩৪ দশমিক ৫ শতাংশ নিয়ে হুয়াওয়ে এবং ২৭ দশমিক ৬ শতাংশ নিয়ে অপপো আছে যথাক্রমে তৃতীয় ও চতুর্থ স্থানে। এছাড়া ২২ দশমিক ১ শতাংশ নিয়ে পাচঁ নম্বরে আছে ভিভো।

ফের কলেজে ভর্তি হতে পারলে পড়াশোনার বিষয়বস্তু হিসেবে বেছে নিতেন  আর্টিফিশিয়াল ইনটেলিজেন্স (এআই) বা কৃত্রিম বুদ্ধিমত্তা, এনার্জি বা বিদ্যুৎ ও বায়োসায়েন্স। গেলো সোমবার এক টুইটে এমনটা বললেন এ ধনকুবের বিল গেটস। 
সম্প্রতি মাইক্রোসফটের প্রতিষ্ঠাতা বিল গেটস তরুণদের দারুণ কিছু পরামর্শ দিয়েছেন। তাতে আছে   ভবিষ্যতে  তারা কী করবেন, কোন পেশায় যাবেন বা কোন বিষয়ে পড়াশোনা করবেন তা নিয়ে নানান পরামর্শ।
আর্টিফিশিয়াল ইনটেলিজেন্স (এআই) বা কৃত্রিম বুদ্ধিমত্তা, এনার্জি বা বিদ্যুৎ ও বায়োসায়েন্স । বর্তমানে এ তিন বিষয়কে সবচেয়ে প্রতিশ্রুতিশীল হিসেবে উল্লেখ করে বিল গেটস বলেন, এ বিষয়গুলোর কোনো একটিতে পড়াশোনা করে বিশাল প্রভাব রাখা যায়।
প্রযুক্তি ও অর্থনীতি খাতের বিশেষজ্ঞরা মনে করেন, আসছে দু’দশকের মধ্যে প্রতিষ্ঠানগুলোর মধ্যে এতটাই পরিবর্তন আসবে যে অনেক প্রতিষ্ঠান কাজের জন্য কৃত্রিম বুদ্ধিমত্তার সাহায্য নেবে। কারখানা, নির্মাণ প্রতিষ্ঠান, এমনকি খুচরা বিক্রির দোকানগুলোতেও কৃত্রিম বুদ্ধিমত্তা ব্যবহৃত হবে।
এ ছাড়া শক্তির উৎস হিসেবে নবায়নযোগ্য শক্তি, যেমন: সৌর ও বায়ুশক্তির ব্যবহার বাড়বে। আসছে ১৫ বছরের মধ্যেই বৈদ্যুতিক খাতে অবিশ্বাস্য পরিবর্তন ঘটবে বলে মনে করেন বিশ্বের সবচেয়ে সম্পদশালী ব্যক্তি বিল গেটস।
তিনি বলেন , বায়োটেকনোলজি বা জীবপ্রযুক্তির প্রবৃদ্ধি দ্রুত ঘটছে। অনেক ব্যক্তিমালিকানাধীন প্রতিষ্ঠান উন্নত ওষুধ তৈরি থেকে শুরু করে চিকিৎসা খাত উন্নত করতে কাজ করছে। তাই এই তিন ক্ষেত্রের যেকোনো একটি এখন বেছে নেয়ার পরামর্শ দিয়েছেন তিনি।
১৮ বছর বয়সের তরুণদের জন্য গেটসের পরামর্শ, ‘তোমাদের বয়সে আমি অনেক কিছু করেছি। তোমরাও দ্রুত যেকোনো জায়গা থেকে অসমতার বিরুদ্ধে কাজ শুরু করে দিতে পারো।’


Millennials are often unfairly given a bad rap for doing things that other generations also do on a regular basis. They’re easy targets and older professionals like to give them a hard time. So, this article isn’t meant to be a hit piece on millennials. Rather, it’s meant to expose something that’s true: Every millennial who has aspirations of being successful in the tech space needs an older mentor.

The Case for Mentorship in the Tech Industry

Finding a mentor isn’t as easy as simply publishing a Facebook post and asking people to surround you with support. It could work but it’s highly unlikely. You’re going to have to put in some work and actively seek out the right person. But when you find that person, you’ll enjoy all of the wonderful benefits a good mentor can provide, including:

1.    Maximization of Strengths
2.    Safe Zone for Thoughts
3.    Access to Contacts
4.    Diversity of Thought

Mentorship Isn’t a Sign of Weakness

One of the most unfortunate ideas that continues to be propagated in the professional world is that mentorship is a sign of weakness. A certain faction of people like to say that aligning yourself with a mentor is a sign that you don’t know everything and that it’s an inadvertent admission of your limitations.

Well, guess what – it is! You don’t know everything and you do have limitations. The goal of finding a mentor is to help you overcome these deficiencies and become more successful.

As a millennial in the tech industry, make it a point to find a mentor who can encourage you and help you grow.


Fingerprint readers, like the TouchID on an iPhone, exist to make your device extra secure while keeping the process of unlocking it easy. Computer scientists at New York University and Michigan State are poised to turn that security benefit on its head. Like a master key that can open any lock, these researchers developed digital “master prints” that could emulate a variety of partial fingerprints enough to hypothetically hack into a device.
The researchers wondered if there was a fingerprint equivalent to a common four-digit security code, like “1234.” Using analysis from a digital database, they discovered that, indeed, a master print could successfully mimic a random fingerprint 26 to 65 percent of the time, according to the study. Why such a huge range? It depends on the scale of the fingerprint database; the more partial fingerprints enrolled in a fingerprint sensor system, the greater the chances are that a master print could unlock it.
There are several security issues at play. One, fingerprint sensors on smartphones are usually small, and two, a user can enroll multiple fingers. What’s more, a phone usually gives you several attempts to unlock it with your print.
“The sensors are small and they don’t capture the full fingerprint,” says Nasir Memon, a computer scientist at NYU’s Tandon School of Engineering and one of the authors of the study.
And since a smartphone fingerprint sensor can be taught to recognize several different fingers, the system learns a lot of partial prints. When you place a finger on the sensor, the system doesn’t actually know which finger it is, or how you’re positioning it.
“So if any one of them match,” he says, “it will say ‘okay, that’s you.’”
Memon and his colleagues analyzed a digital database of 800 fingerprints, then extracted thousands of partial prints from that same database.They wondered: Are there any partial prints that match the others with a high probability? “We were surprised,” he says, “there were some that match like 15 percent of the time.”
It’s worthwhile to note that the experiment was computer-based, so the researchers did not try to actually trick phones using a master print. The findings are theoretical, and one prominent biometrics researcher is skeptical.
Anil Jain, the head of the Biometrics Research Group at Michigan State University, who was not involved in the study, says the researchers used a system that analyzed fingerprints based on an element of your print called “minutia.”
If you look at your finger, you’ll see lines of ridges and valleys. In some places, a ridge splits, or bifurcates. In other places, a ridge might simply end. Those ridge bifurcations and endings are what biometrics experts call “minutia points.”
Jain says fingerprint sensors used by Apple and Samsung do not use these minutia points to identify a print. Instead, the TouchID sensor on your iPhone, for example, is using the “texture pattern” of your print, Jain says.
Still, the study’s co-author, Arun Ross, a professor of computer science and engineering at Michigan State University, stands by the relevance of their findings. The vulnerabilities of the system remain: as fingerprint sensors get smaller, “the chances of my fingerprint matching with your fingerprint,” he says, “not in its entirety, but in portions of it, increases.”


Faculty Sections / Re: 5 benefits of elearning
« on: April 22, 2017, 12:38:32 PM »
Thanks for sharing.  :) :)

On Thursday, Google announced that its Home smart hub device can now recognize and identify up to six different users by the sound of their voice. It's an inevitable—but crucial—step in the development of smart home virtual assistants. The new skill means that different people in a household will be able to ask the Google Assistant questions about what’s on their calendar, or what their commute looks like, and the Home device will know who is speaking to it and give tailored responses. It’ll make it a more streamlined experience for families sharing a smart home speaker hub.
The setup process involves adding additional users through the Home app, who then train the device to recognize them by repeating a few key phrases. Google uses a neural network that’s actually located on the device itself to differentiate the distinct voices in the household.
The system will still respond to requests from random people, like a guest in your home, but it also means that your Home device should only read your more personal information—like what's on your schedule for the day—to you.
Amazon Echo devices already work with multiple user accounts, but they have to be switched manually by explicitly asking Alexa to do it, which adds at least one step. Amazon does let you do voice training so that Alexa can get to know your voice (as does Siri when you set up an iPhone) but it can’t recognize who is speaking and switch accounts on the fly. An Amazon representative declined to comment on when or if Alexa would gain that skill.
If you live in the United States, you should be able to set up this feature today by opening the opening the Google Home app and checking for a card that reads "multi-user is available.


When Microsoft released an artificially intelligent chatbot named Tay on Twitter last March, things took a predictably disastrous turn. Within 24 hours, the bot was spewing racist, neo-Nazi rants, much of which it picked up by incorporating the language of Twitter users who interacted with it. 

Unfortunately, new research finds that Twitter trolls aren't the only way that AI devices can learn racist language. In fact, any artificial intelligence that learns from human language is likely to come away biased in the same ways that humans are, according to the scientists.

The researchers experimented with a widely used machine-learning system called the Global Vectors for Word Representation (GloVe) and found that every sort of human bias they tested showed up in the artificial system.

"It was astonishing to see all the results that were embedded in these models," said Aylin Caliskan, a postdoctoral researcher in computer science at Princeton University. Even AI devices that are "trained" on supposedly neutral texts like Wikipedia or news articles came to reflect common human biases.

Built-in biases

GloVe is a tool used to extract associations from texts — in this case, a standard corpus of language pulled from the World Wide Web.

Psychologists have long known that the human brain makes associations between words based on their underlying meanings. A tool called the Implicit Association Test uses reaction times to demonstrate these associations: People see a word like "daffodil" alongside pleasant or unpleasant concepts like "pain" or "beauty" and have to quickly associate the terms using a key press. Unsurprisingly, flowers are more quickly associated with positive concepts; while weapons, for example, are more quickly associated with negative concepts.

The IAT can be used to reveal unconscious associations people make about social or demographic groups, as well. For example, some IATs that are available on the Project Implicit website find that people are more likely to automatically associate weapons with black Americans and harmless objects with white Americans. 

There are debates about what these results mean, researchers have said. Do people make these associations because they hold personal, deep-seated social biases they aren't aware of, or do they absorb them from language that is statistically more likely to put negative words in close conjunction with ethnic minorities, the elderly and other marginalized groups?

Digital stereotypes

Caliskan and her colleagues developed an IAT for computers, which they dubbed the WEAT, for Word-Embedding Association Test. This test measured the strength of associations between words as represented by GloVe, much as the IAT measures the strength of word associations in the human brain.

For every association and stereotype tested, the WEAT returned the same results as the IAT. The machine-learning tool reproduced human associations between flowers and pleasant words; insects and unpleasant words; musical instruments and pleasant words; and weapons and unpleasant words. In a more troubling finding, it saw European-American names as more pleasant than African-American names. It also associated male names more readily with career words, and female names more readily with family words. Men were more closely associated with math and science, and women with the arts. Names associated with old people were more unpleasant than names associated with young people.

"We were quite surprised that we were able to replicate every single IAT that was performed in the past by millions," Caliskan said.

Using a second method that was similar, the researchers also found that the machine-learning tool was able to accurately represent facts about the world from its semantic associations. Comparing the GloVe word-embedding results with real U.S. Bureau of Labor Statistics data on the percentage of women in occupations, Caliskan found a 90 percent correlation between professions that the GloVe saw as "female" and the actual percentage of women in those professions.

In other words, programs that learn from human language do get "a very accurate representation of the world and culture," Caliskan said, even if that culture — like stereotypes and prejudice — is problematic. The AI is also bad at understanding context that humans grasp easily. For example, an article about Martin Luther King Jr. being jailed for civil rights protests in Birmingham, Alabama, in 1963 would likely associate a lot of negative words with African-Americans. A human would reasonably interpret the story as one of righteous protest by an American hero; a computer would add another tally to its "black=jail" category.

Retaining accuracy while getting AI tools to understand fairness is a big challenge, Caliskan said. [A Brief History of Artificial Intelligence]

"We don't think that removing bias would necessarily solve these problems, because it's probably going to break the accurate representation of the world," she said.

Unbiasing AI

The new study, published online today (April 12) in the journal Science, is not surprising, said Sorelle Friedler, a computer scientist at Haverford College who was not involved in the research. It is, however, important, she said.

"This is using a standard underlying method that many systems are then built off of," Friedler told Live Science. In other words, biases are likely to infiltrate any AI that uses GloVe, or that learns from human language in general. 

Friedler is involved in an emerging field of research called Fairness, Accountability and Transparency in Machine Learning. There are no easy ways to solve these problems, she said. In some cases, programmers might be able to explicitly tell the system to automatically disregard specific stereotypes, she said. In any case involving nuance, humans may need to be looped in to make sure the machine doesn't run amok. The solutions will likely vary, depending on what the AI is designed to do, Caliskan said — are they for search applications, for decision making or for something else?

In humans, implicit attitudes actually don't correlate very strongly with explicit attitudes about social groups. Psychologists have argued about why this is: Are people just keeping mum about their prejudices to avoid stigma? Does the IAT not actually measure prejudice that well? But, it appears that people at least have the ability to reason about right and wrong, with their biased associations, Caliskan said. She and her colleagues think humans will need to be involved — and programming code will need to be transparent — so that people can make value judgments about the fairness of machines.

"In a biased situation, we know how to make the right decision," Caliskan said, "but unfortunately, machines are not self-aware."


Faculty Sections / Re: Why Is It OK to Abuse Customers?
« on: April 21, 2017, 12:21:56 AM »
Thanks for sharing.

Faculty Sections / Drone vs. Lightning: Guess Which One Wins?
« on: April 21, 2017, 12:20:20 AM »
What would happen if a drone got caught in an electrical storm?

That's the question YouTuber Tom Scott asked when he brought two DJI Phantom 3 drones to the University of Manchester’s High Voltage Laboratory. The British university's lab can generate lightning on command, thanks to an impulse generator that can create a bolt of more than 1 million volts. The drones were no match for the lightning, and were fried when caught in the middle of the bolt.

For the first experiment, a drone was tethered to the ground (to ensure it wouldn't fly out of the bolt's path) and shocked with more than 1 million volts of electricity. Slow-motion video of the shock showed that the lightning strike went through the drone, and the robotic flyer came crashing down.

"The electricity passed straight through, flowing from one of the propellers to exit through the foot of the drone," Enna Bartlett, digital coordinator for the university, described in a blog post. "Surprisingly there were no visible marks on the outside of the drone, but that doesn’t mean that the insides got away unscathed; as it turns out, the electricity took the path of least resistance and fried all the sensitive internal electronics."

Electrical engineering researchers Vidyadhar Peesapati and Richard Gardner, who carried out the experiments to answer Scott's question, thought they'd try to protect the other drone in the second experiment. Rather than tether the done as is, the researchers added a lightning rod made of copper tape to act as a lightning conductor.

Though the copper tape was intended to attract the lightning atthe highest point on the drone, the propellers were still equally as tall (and attractive) to the bolt of electricity. In that experiment, the drone was more severely damaged than the first experiment. The researchers said the propellers were explosively pulled away from the drone due to the sheer force of the strike.

The tests made for an electrifying video on Scott's YouTube channel, but also added to scientists' understanding of how aeronautics interact with lightning.

"With our understanding of how airplanes behave in thunderstorms and how to provide protection for them," Bartlett wrote, "this knowledge could be applied to drone technology to ensure the drone and its pilot stays safe should they fly in adverse weather conditions."


A new Google Earth Virtual Reality (VR) feature allows users to enter any address — whether it's grandma's house or a 19th-century castle in Germany — and fly over it in 3D with a VR headset.

When Google Earth VR debuted, people could virtually visit a number of popular tourist destinations, including the Hoover Dam in Nevada and the Matterhorn in Switzerland. They could even gaze at the nooks and crannies of the Colosseum in Rome, an archaeological marvel.

But now, people can choose their own destinations, as long as they know the address or name of the location.
"People want to quickly find and revisit the places that mean the most to them, whether it's a childhood home or favorite vacation spot," Joanna Kim, a product manager at Google Earth VR, wrote in a blog post today (April 18). Now, users can type an address or the name of a location, and visit it in 3D with a 3D headset system, Kim wrote.

Sightseers can also visit 27 handpicked locations that are now available on Google Earth VR, including Neuschwanstein Castle (the inspiration for the castle in Disney's "Sleeping Beauty"), Table Mountain in South Africa and the Perito Moreno Glacier (Glaciar Perito Moreno) in Argentina.

Google Earth VR is now available for Oculus Rift users who have Oculus Touch controllers. The application is free at the Oculus Store and Steam.

Previous 3D maps created by Google Earth include street views of the Amazon rainforest; the 18,192-foot-high (5,545 meters) high Mount Everest base camp; and Rio de Janeiro, the city that hosted the 2016 Summer Olympics.


বাংলাদেশ কম্পিউটার সমিতি (বিসিএস) এবং অনলাইনভিত্তিক মানসিক সেবাদাতা প্রতিষ্ঠান মনের বন্ধুর আয়োজনে ২২ এপ্রিল বেলা তিনটায় তরুণদের অনুপ্রেরণা ও সাইবার জগৎ নিয়ে সচেতনতা তৈরির অনুষ্ঠানের আয়োজন করা হয়েছে। সাইবার জগতে কোনো অনাকাঙ্ক্ষিত পরিস্থিতির মুখোমুখি হলে কীভাবে তা সামলাতে হবে, সে বিষয়ে আলোচনা করবেন বিশেষজ্ঞরা।

বিসিএসের সভাপতি মো. আলী আশফাক, ইনডিপেনডেন্ট ইউনিভার্সিটি বাংলাদেশের শিক্ষক এম রেদওয়ান জিনান সিদ্দিকী, মনের বন্ধুর পরামর্শক অ্যানি বাড়ৈসহ অনেকে উপস্থিত থাকবেন। বিনা মূল্যে নিবন্ধনের জন্য যোগাযোগ: ০১৭৭৬৬৩২৩৪৪।


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