There is, alas, no such thing as a free lunch. This simple and obvious truth is invariably forgotten whenever irrational exuberance teams up with digital technology in the latest quest to "change the world". A case in point was the bitcoin frenzy, where one could apparently become insanely rich by "mining" for the elusive coins. All you needed was to get a computer to solve a complicated mathematical puzzle and – lo! – you could earn one bitcoin, which at the height of the frenzy was worth $19,783.06. All you had to do was buy a mining kit (or three) from Amazon, plug it in and become part of the crypto future. The only problem was that mining became progressively more difficult the closer we got to the maximum number of bitcoins set by the scheme and so more and more computing power was required.

Those are some of the adjectives being used to describe "Death Stranding," the much-anticipated video game from famed game designer Hideo Kojima ("Metal Gear Solid") out now for Sony's PlayStation 4. Since Kojima revealed the game was under development three years ago, with a trailer showing Norman Reedus ("The Walking Dead") as the main character in a sci-fi setting, expectations have been high – and speculation has spiked – about the prospects for "Death Stranding," which is currently only available on the PS4. A PC version is in the works for summer 2020. A measurement of demand for the game comes from Nielsen, which asked 10,000 gamers, ages 7 to 54, to rank their interest in games set to be released before and during the holiday season. Nintendo Switch:'Super Smash Bros. Ultimate' is officially the best-selling game in the series While "The Legend of Zelda: Link's Awakening" (Score: 99), "Call of Duty: Modern Warfare" (98), "Luigi's Mansion 3" (96) and "Borderlands 3" (95) led the way, "Death Stranding" was close behind with a score of 94, ahead of well-known titles such as "Gears 5" (88), "FIFA 20" (87), and "Star Wars Jedi: Fallen Order" (82). But "Death Stranding" is a different game and has arrived as sort of a Rorschach test for game critics.

Would you train a neural network with random data? Moreover, are massive neural networks just lookup tables or do they truly learn something? Today's episode is about memorisation and generalisation in deep learning, with Stanislaw Jastrzębski. Stan works as post-doc at New York University. I have asked Stan a few questions I was looking answers for a long time.

As is the case with many technological developments, 'deepfakes' -- videos in which someone who did not originally appear in the clip is rendered into it using artificial intelligence (AI) -- largely started in the world of pornography. Viewers, should they so desire, can now watch videos of their favourite musicians and film stars "in action," although that celebrity was never in that video. In these cases, increasingly sophisticated tools are used to put the musicians and film stars' faces onto pre-existing pornographic videos. There can obviously be a sinister, non-celebrity side to this too. The recent Sam Bourne novel, To Kill The Truth, features a protagonist Maggie Costello who appears in such a video as part of a cruel plot to undermine her.

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Among the consequences of big data is a wealth of relevant minutiae that can be used to train machine learning and other models. That often translates into processing-intensive steps required to train models to perform a specific task. In response, technology startups and government-funded university researchers are promoting a new machine intelligence approach called "lifelong" machine learning as a way to accelerate model training. The latest entrant is Neurala, which this week took the wraps off its lifelong deep neural network technology. The Boston-based startup claims its AI approach can slash training time from upwards of 15 hours to as little as 20 seconds by utilizing "incremental learning."

There has been an exponential increase in R&D related to virtual reality, photorealistic computer animations and augmented reality. Researchers from around the globe are working in these domains and have been creating machines which have the abilities of a human -- or humanoid robots. This is an emerging research domain which is now playing a crucial role in robotics research. However, according to the uncanny valley theory, the humanoid objects which imperfectly resemble actual human beings provoke uncanny or strangely familiar feelings of revulsion in observers. The IEEE spectrum ranks robots into three categories, -- top-rated, creepiest and most wanted.

You just released a machine learning project. It can be a new product at your start-up, a proof of concept for a client demo, or a personal project that enriches your portfolio. You are not looking for a production-grade site; you want to get the job done. So that a few users can test your product. This post is a follow-up and an update over this previous post, where I introduced raplyrics.eu,

The advances in AI has enabled computers to assist doctors in detecting diseases and help keep a check on patient health remotely. Now, researchers from the University of Colorado Boulder (CU Boulder) are working to leverage ML to psychiatry using a speech-based mobile app. Peter Foltz, a research professor at the Institute of Cognitive Science says – "We are not in any way trying to replace clinicians. But we do believe we can create tools that will allow them to better monitor their patients." Notably, he is also the co-author of a new paper in Schizophrenia Bulletin that illustrates the promise and potential pitfalls of artificial intelligence in psychiatry.

In partnership with a commercial bank in the Dominican Republic, the researchers conducted two separate analyses of 20,000 low-income individuals, half of them women. In the first analysis, the researchers used the individuals' loan repayment histories and gender to train a single machine-learning model for predicting creditworthiness. In the second analysis, the researchers trained a model with only the loan repayment data from the women. They found that 93% of women got more credit in this model than in the one where men and women were mixed together.