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"These experiments in computational creativity are enabled by the dramatic advances in deep learning over the past decade. Deep learning has several key advantages for creative pursuits. For starters, it's extremely flexible, and it's relatively easy to train deep-learning systems (which we call models) to take on a wide variety of tasks."

"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."

"AI tool GNoME finds 2.2 million new crystals, including 380,000 stable materials that could power future technologies Modern technologies from computer chips and batteries to solar panels rely on inorganic crystals. To enable new technologies, crystals must be stable otherwise they can decompose, and behind each new, stable crystal can be months of painstaking experimentation. Today, in a paper published in Nature, we share the discovery of 2.2 million new crystals - equivalent to nearly 800 years' worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials."

"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."

"The first thing that came into my mind when I first heard about Deep Fakes, is what would happen if we could create DeepFakes in realtime and not just for existing videos or photos? Suppose we can go online with someone else's face, would this be funny or would this push the ethical boundaries even further? I decided to see how much effort it would be to try it out."

"In 2015, a research group fed an AI system called Deep Patient health and medical data from some 700,000 people, and tested whether it could predict diseases. It could, but Deep Patient provides no explanation for the basis of a diagnosis, and the researchers have no idea how it comes to its conclusions. A doctor either can either trust or ignore the computer, but that trust will remain blind."

"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.""

"DeepMind is trying to combine deep learning and algorithms, creating the one algorithm to rule them all: a deep learning model that can learn how to emulate any algorithm, generating an algorithm-equivalent model that can work with real-world data."

""We are seeing deep fakes being used all the time, and the technology is going to allow still images, but ultimately also video images, to be synthesised [more easily] by bad actors," he says. DALL-E has content policy rules in place that prohibit bullying, harassment, the creation of sexual or political content, or creating images of people without their consent. And while Open AI has limited the number of people who can sign up to DALL-E, its lower-grade replica, DALL-E mini, is open access, meaning people can produce anything they want."

"It is one of the first programs to combine an external memory with an approach called deep-learning, in which the program learns how to do tasks independently rather than being pre-programmed with a set of rules by a human."

"Google is using romance novels to teach its artificial intelligence (AI) system to better understand how people communicate. Researchers at Google Brain, the company's AI-focused deep learning project, presented a paper earlier this month that detailed techniques they used to teach its AI to write fiction - and the results were unexpectedly haunting."

"The two titans leading the pack are Google and Microsoft. The first battle? A new domain in artificial intelligence called "Deep Learning.""

"Going from small data analytics to Big Data analytics or to predictive and prescriptive analytics is trickier. Expanding in both dimensions is human capital intensive, requiring talented data scientists. A McKinsey report (2011) estimates that by 2018, there will be a shortage of 140,000 to 190,000 workers with "deep analytical" experience and a further 1.5 million data-literate managers in the US. Technology giants such as Google, Facebook and Amazon, and large investment banks and top hedge funds can afford such employees, however even now the competition is fierce, as is evidenced by the ongoing talent war in Silicon Valley. The data scientist is indeed a sexy job in the 21st century."

"Wired recently started an anti-ad-blocking campaign that attempts to prevent ad-blocked users from reading articles unless they pay a monthly subscription fee. On the heels of this decision comes a roundup of the major ad-blockers, some of which are pretty dodgy indeed. Adblock Plus comes off the worst of the lot. The company charges publishers fees to allow their ads through its filters, based on criteria about size and placement. Ghostery blocks trackers, but by default gathers "anonymized" data about your browsing habits (it's very hard to conclusively describe any deep data set as anonymized). "

"And that means that there's now a financial incentive for going after just about anything. While the payoff of going after businesses' networks used to depend on the long play-working deep into the network, finding and packaging data, smuggling it back out-ransomware attacks don't require that level of sophistication today. It's now much easier to convert hacks into cash."

"Affectiva is running a program that pays drivers to help train its emotion-recognition system. The company sends drivers a kit including cameras and other sensors to place within their vehicles. These record a person's facial expressions, gestures, and tone of voice on the road. That data is then labeled by trained specialists for a range of emotions, and fed into deep neural networks."

"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."

"So they trained a deep-learning neural net on tons of examples of deepfaked videos, and produced a model that's better than any previous automated technique at spotting hoaxery. (Their paper documenting the work is here.)"

"It is also self-improving. The 10-year-old grading software leverages deep learning algorithms to "compare notes" with human teachers' scores, suggestions, and comments. An engineer involved in the project compared its capabilities to those of AlphaGo, the record-breaking AI Go player developed by Google subsidiary DeepMind."

"Lexplore claims its technology is new-particularly the algorithm that separates typical from atypical readers. But the concepts it's based on aren't. Its tech draws from a deep well of previously-conducted research stretching back decades, which is generally supportive of using a combination of eye tracking and machine learning to screen for dyslexia. "Eye movements is one of the best ways to index reading ability at an incredibly in-depth level," says Julie Kirkby, a psychology professor at Bournemouth University in the United Kingdom, who has studied eye tracking and dyslexia for years."