Artificial Intelligence Bulletin – Photonic Neural Networks
Each week on a Wednesday Nikolas Badminton, Futurist highlights the top stories from the past week relating to the incredible rise of artificial intelligence and its application in society, the workplace, in cities, and in our lives.
In we see a breakthrough is light-based neural networks, AI assisted lip-reading, cooking with IBM’s Watson, and a thought-provoking discussion on AI and Universal Basic Income.
Computing at Light Speed: The World’s First Photonic Neural Network Has Arrived
Princeton University researchers have developed the world’s first integrated silicon photonic neuromorphic chip, which contains 49 circular nodes etched into semiconductive silicon.
The chip could complete a math equation 1,960 times more quickly than a typical central processing unit, a speed that would make it ideal for use in future neural networks.
The team at Princeton believes that their development can be easily adopted by the industry to bring optical computing into the mainstream for the first time. “Silicon photonic neural networks could represent first forays into a broader class of silicon photonic systems for scalable information processing,” researcher Alexander Tait told MIT Technology Review.
Optical computing and the ultrafast processing speeds it is capable of could be the driving force behind tomorrow’s machine learning tools. Algorithms that predict trends in the stock market, wearable tech that can detect diseases or mitigate conditions like visual impairment, super-smart drones that can improve agriculture…these could just be some of the many applications for optical computing.
Read more at Futurism
AI used to support the hearing impaired
In recent months Springwise has seen some fascinating innovations that focus on improving things for those who are deaf. This webcam service offers mortgage advice for the hearing impaired and this smart t-shirt that translates music into motion for the deaf listener. Now, The University of Oxford is using AI technology to improve an existing service.
The Department of Computer Science at Oxford University has developed an app that uses AI software to read lips more accurately that human beings. The software is called LipNet and has a 92% accuracy rating. Indeed it scores 13% higher than the average lip-reading software. It does this by focussing on sentence construction, rather than deciphering one word at a time. The programme is also smart, and learns to become more accurate over time.
Read more at Springwise
Cooking with Watson
The interface for Chef Watson, I.B.M.’s artificial-intelligence cooking app, is simple and welcoming, a minimalist canvas of four empty text fields and four dove-gray circles. You type in the ingredients, or let Chef Watson choose them for you according to its own mysterious logic: tomato, garlic, onion, purple seedless grape. These four ingredients, Watson declares, have a “synergy” of a hundred per cent—they are an unimprovable combination, chemically speaking. But, as an embodied being who has tasted those ingredients, you might be skeptical about combining them—especially when you scroll down to the suggested recipes and discover, near the top of the list, something called Purple Seedless Grape Starch Dish.
The recipe also calls for “sixty-seven medium trimmed Easter-egg radishes,” black beans, cinnamon, curly parsley, marjoram, and Calvados. Cook, salt to taste, then top with Jack cheese, olive oil, and the grapes, “for squeezing over.” And there you have it: the computer-assisted future of cuisine, in the form of a pile of sweet-smelling, mud-colored radishes.
So far, artificial-intelligence researchers have mostly built machines capable of demonstrating their own prowess. At I.B.M., engineers have used natural-language processing and enormous computational power to beat the most proficient humans at our own games, like chess and “Jeopardy!” Having achieved these goals, Watson’s handlers now imagine a more intimate, domestic role for A.I. To create Chef Watson, I.B.M. exposed its algorithms to the entire recipe archive of Bon Appétit, as well as to recent research in “hedonic psychophysics”—“the psychology of what people find pleasant.” The algorithms also took note of which ingredients tended to be combined, and inferred the roles they seemed to play in a dish. The result is a browser-based Web app that allows users to generate recipes by selecting a permutation of ingredients and a style of cuisine. Watson can invent several dozen recipes that prominently feature prunes; it can satisfy a request for banana biscotti in a Creole or a Basque style; and it makes suggestions that no human would ever make, like adding milk chocolate to a clam linguine or mayonnaise to a Bloody Mary.
Read more at the New Yorker
Panel: The Future of AI and Universal Basic Income
Nikolas Badminton is a world-respected futurist speaker that researches, speaks, and writes about the future of work, how technology is affecting the workplace, how workers are adapting, the sharing economy, and how the world is evolving. He appears at conferences in Canada, USA, UK, and Europe. Email him to book him for your radio, TV show, or conference.