Turns out mastering chess and Go was just for starters. On 2 December, the Google-owned artificial intelligence firm DeepMind took top honors in the 13th Critical Assessment of Structure Prediction (CASP), a biannual competition aimed at predicting the 3D structure of proteins.
Smartphone cameras that take a single picture begin to struggle at 30 lux. Phones that capture and merge several pictures (as HDR+ does) can do well down to 3 lux, but in dimmer scenes don’t perform well (more on that below), relying on using their flash.
We developed our Design for the New Normal talk for the NEXT Conference in Berlin, April 2013. This is an ongoing research piece and examines the space within which some of our design and research practice is situated. This is a gun. Made in a 3D printer.
Andrew Ng – The State of Artificial Intelligence
Professor Andrew Ng is the former chief scientist at Baidu, where he led the company’s Artificial Intelligence Group. He is an adjunct professor at Stanford University. In 2011 he led the development of Stanford University’s main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class that was offered to over 100,000 students, leading to the founding of Coursera.
Sentient artificial intelligence may take hundreds of years to develop, but AI is already beginning to transform nearly every industry, says Andrew Ng, a pioneer in the field.
Ng is the former chief scientist of Baidu, where he started a 1,300-person division that helped create the Chinese tech conglomerate’s AI-powered search engine, virtual assistant and other products. Before that, he co-founded Google Brain, the company’s deep-learning research team. His work on neural networks helped lead to the creation of an image-identification system that underpins the Android mobile operating system’s speech recognition. Ng also co-founded Coursera, an online education company. https://www.businesstelegraph.co.uk/ai-guru-andrew-ng-on-the-job-market-of-tomorrow/
Science suggests we’re hardwired to delude ourselves. Can we do anything about it? I am staring at a photograph of myself that shows me 20 years older than I am now. I have not stepped into the twilight zone.
This cautionary tale, repeated often in the academic literature on machine learning, is probably apocryphal, but it illustrates an important question about artificial intelligence: What can we know about what a machine knows? Whatever artificial intelligence might come to be, it will be fundamentally different from us, and ultimately inscrutable. Despite increasingly sophisticated systems of computation and visualization, we do not truly understand how machine learning does what it does; we can only adjudicate the results.
“For people who want to make sure the Web serves humanity, we have to concern ourselves with what people are building on top of it,” Tim Berners-Lee told me one morning in downtown Washington, D.C., about a half-mile from the White House.
Different types of maps have different uses. What they make legible is what they make possible. A map that emphasizes bike paths is useful to a cyclist, but its lack of topographic information makes it useless to a civil engineer, even though both refer to the same territory.