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"Jack Poulson was a senior research scientist at Google whose work on machine learning work was used to improve Google's search results; now he's quit the company over its Project Dragonfly, a once-secret plan to launch a censored Chinese search engine; Poulson called the move a "forfeiture of our values." Tech companies find it hard to qualify skilled engineers at any price, and machine learning specialists are especially prize, commanding salaries of $1MM/year or more. "

"He had discovered that human learning was communal and interactive. For a robot, the acquisition of language was abstract and formulaic. For us, it was embodied, emotive, subjective, quivering with life. The future of intelligence wouldn't be found in our machines, but in the development of our own minds."

"Second Life, Thinking Worlds, Unity3D and others were all making inroads into the realm of corporate learning and there was a buzz about it in the L&D market, which, at the time, had a reputation for churning out spectacularly boring and poorly designed compliance-based eLearning. One major mobile phone network with whom I worked back in 2008 had a vision of enlivening their learner experience by providing a 3D avatar-based portal into their learning management system, which at the time hosted solidly 2D page-turner eLearning of a very pedestrian nature."

"At Imperial College London, Murray Shanahan and colleagues are working on a way around this problem using an old, unfashionable technique called symbolic AI. "Basically this meant an engineer labelled everything for the AI," says Shanahan. His idea is to combine this with modern machine learning. Symbolic AI never took off, because manually describing everything quickly proved overwhelming. Modern AI has overcome that problem by using neural networks, which learn their own representations of the world around them. "They decide what is salient," says Marta Garnelo, also at Imperial College."

"More than just a poetic metaphor, the butterfly effect says that there are some things that even the most advanced science can never predict. Well, that list of things just got a lot shorter. Scientists from the University of Maryland have used machine learning to predict chaos."

"Computer scientists and machine learning researchers are tackling the pandemic the way they know how: compiling datasets and building algorithms to learn from them."

"ISSUES with the online learning and teaching process has caused students from the low-income group to be left behind, said the National Union of the Teaching Profession (NUTP)."

""This is the first time such machine learning tools have been used in this context," says Fluri, "and we found that the deep artificial neural network enables us to extract more information from the data than previous approaches. We believe that this usage of machine learning in cosmology will have many future applications.""

"Raji says her investigation into the data has made her gravely concerned about deep-learning-based facial recognition. "It's so much more dangerous," she says. "The data requirement forces you to collect incredibly sensitive information about, at minimum, tens of thousands of people. It forces you to violate their privacy. That in itself is a basis of harm. And then we're hoarding all this information that you can't control to build something that likely will function in ways you can't even predict. That's really the nature of where we're at.""

"The BBC spoke to several students and parents who vouched for the quality of Byju's learning content - in a country where rote learning is often the norm, Byju's has been credited for deftly using technology to create immersive, engaging lessons. It also claims to have the industry's highest net promoter score (NPS), which measures customer experience and predicts business growth. "

"Generational differences in learning techniques are apparent in how people of different ages approach technology. It has been said that we, the Net Generation, are closer to our grandparents-the Greatest Generation-in our work ethic and optimism about the future than to our parents' generation. But how we approach problems is totally different."

"Duolingo, on the other hand, is going full speed ahead with generative AI. The company announced this week that it would stop relying on human contractors for "work that AI can handle," while also committing to using AI in hiring and performance reviews. On top of that, Duolingo announced on Wednesday that it used generative AI to come up with 148 new language learning courses, doubling its total course offerings."

"Researchers have shown that given access to only an API, a way to remotely use software without having it on your computer, it's possible to reverse-engineer machine learning algorithms with up to 99% accuracy. In the real world, this would mean being able to steal AI products from companies like Microsoft and IBM, and use them for free. Small companies built around a single machine learning API could lose any competitive advantage."

"Train the Deep Learning Ahem Detector with two sets of audio files, "a negative sample with clean voice/sound" (minimum 3 minutes) and "a positive one with 'ahem' sounds concatenated" (minimum 10s) and it will detect "ahems" in any voice sample thereafter."

"DeepCoder uses a technique called program synthesis: creating new programs by piecing together lines of code taken from existing software - just like a programmer might. Given a list of inputs and outputs for each code fragment, DeepCoder learned which pieces of code were needed to achieve the desired result overall. "It could allow non-coders to simply describe an idea for a program and let the system build it""

"Upcoming cell phone chips from Qualcomm will use artificial intelligence to block malware before it infects your phone. The chip company said on Monday that the next-generation Snapdragon 820 processor used in a variety of Android smartphones will be the first chip that uses machine learning to detect threats and privacy issues thanks to an application called Snapdragon Smart Protect." Qualcomm is trying to use artificial intelligence in a chip for smart phones. The chip will learn to detect any privacy or security issues that would usually be hard to detect.

"O'Neill recounts an exercise to improve service to homeless families in New York City, in which data-analysis was used to identify risk-factors for long-term homelessness. The problem, O'Neill describes, was that many of the factors in the existing data on homelessness were entangled with things like race (and its proxies, like ZIP codes, which map extensively to race in heavily segregated cities like New York). Using data that reflects racism in the system to train a machine-learning algorithm whose conclusions can't be readily understood runs the risk of embedding that racism in a new set of policies, these ones scrubbed clean of the appearance of bias with the application of objective-seeming mathematics. "

"Hassabis was a child chess prodigy, who learned the game aged four and was able to beat his dad three weeks later - indeed, when he started playing competitively he was so small he had to bring a pillow with him to reach the board - and became a strong player. Yet in AlphaZero's case there was no human input, other than telling it the rules of each game. "In a matter of a few hours it was superhuman," Hassabis says proudly."

"One great way to tell the difference is to ask AI recruiting companies what they use artificial intelligence, machine learning and/or deep learning for. Hopefully the hiring firm can what it's using the new technology for and not just that it is. If not it's time to dig a bit deeper."

"The reason to look at humans is because there are certain things that humans do much better than deep-learning systems. That doesn't mean humans will ultimately be the right model. We want systems that have some properties of computers and some properties that have been borrowed from people. We don't want our AI systems to have bad memory just because people do. But since people are the only model of a system that can develop a deep understanding of something-literally the only model we've got-we need to take that model seriously."