Before the release of Android, smartphone makers faced a similarly byzantine set of challenges. (How do you manage memory? Download content from the web? Host third-party apps?) By giving away its operating system, Android freed manufacturers from worrying about any of that stuff, resulting in an explosion of smartphone models.
And that’s just the kind of platform Rubin hopes to build with Playground—providing all the basic hardware and software components so entrepreneurs can concentrate on generating interesting devices. Those components come courtesy of the Studio, which plays a role for Playground’s startups similar to the one the Q Department plays for James Bond. If you’re building a drone and need the best available microphone array, the seasoned technologists in the Studio will simply give it to you. (And they’ll know what next year’s microphone arrays will look like, so you can be sure your design is future-proof.) “It’s modular hardware,” Rubin says. “A couple of years from now, you could roll in here with an idea, and we could just rearrange these modules.”
“Many of KPMG's audit, tax, advisory and other professional services rely heavily on judgment-driven processes. Adding cognitive technology's massive data analysis and innovative learning capabilities to these activities has the potential to advance traditional views on how talent, time, capital and other resources are deployed by professional services organizations.
KPMG's growing cognitive ecosystem will contribute significantly to the continued evolution of the firm's service offerings. Underscoring this importance is KPMG's deep commitment to working with leading technologies like IBM Watson. This includes promising work with Watson to develop select cognitive services designed to help KPMG meet its extensive audit-specific security, confidentiality and compliance requirements.”
While the Atlas robot may not boost the quarterly earnings of Google parent Alphabet for a while, it appears to be the next step for robotics, according to Max Wolff, chief economist at Manhattan Venture Partners.
"My guess is early on we're going to see defense use, law enforcement use, hazardous waste use and some surgical and medical equipment use," Wolff said on CNBC's Tech Bet.
With a tech industry one-third the size of California’s, Canada has confounded expectations by becoming a leader in the booming market for artificial intelligence. Pioneering technologies developed in Canadian labs can be found in Facebook’s facial recognition algorithms, Google’s Photos app, smartphone voice recognition and even Japanese robots.
Now Canada risks losing its AI edge to Silicon Valley.
Already members of the Canadian AI community are trying to protect what they helped build. A startup called Maluuba (in photo) which makes technology that helps computers talk, is opening a research office in Montreal; the University of Toronto has opened a startup accelerator and this fall launched a program dedicated to AI research.
Elon Musk and Sam Altman worry that artificial intelligence will take over the world. So, the two entrepreneurs are creating a billion-dollar not-for-profit company that will maximize the power of AI—and then share it with anyone who wants it.
At least, this is the message that Musk, the founder of electric car company Tesla Motors, and Altman, the president of startup incubator Y Combinator, delivered in announcing their new endeavor, an unprecedented outfit called OpenAI. In an interview with Steven Levy of Backchannel, timed to the company’s launch, Altman said they expect this decades-long project to surpass human intelligence. But they believe that any risks will be mitigated because the technology will be “usable by everyone instead of usable by, say, just Google.”
“So we’ve built an entirely new machine learning system, which we call “TensorFlow.” TensorFlow is faster, smarter, and more flexible than our old system, so it can be adapted much more easily to new products and research. It’s a highly scalable machine learning system—it can run on a single smartphone or across thousands of computers in datacenters. We use TensorFlow for everything from speech recognition in the Google app, to Smart Reply in Inbox, to search in Google Photos. It allows us to build and train neural nets up to five times faster than our first-generation system, so we can use it to improve our products much more quickly.
We've seen firsthand what TensorFlow can do, and we think it could make an even bigger impact outside Google. So today we’re also open-sourcing TensorFlow. We hope this will let the machine learning community—everyone from academic researchers, to engineers, to hobbyists—exchange ideas much more quickly, through working code rather than just research papers. And that, in turn, will accelerate research on machine learning, in the end making technology work better for everyone. Bonus: TensorFlow is for more than just machine learning. It may be useful wherever researchers are trying to make sense of very complex data—everything from protein folding to crunching astronomy data.”
He starts simply, asking for the time in Berlin and the population of Japan. Basic search-result stuff—followed by a twist: “What is the distance between them?” The app understands the context and fires back, “About 5,536 miles.”
Then Mohajer gets rolling, smiling as he rattles off a barrage of questions that keep escalating in complexity. He asks Hound to calculate the monthly mortgage payments on a million-dollar home, and the app immediately asks him for the interest rate and the term of the loan before dishing out its answer: $4,270.84.
“What is the population of the capital of the country in which the Space Needle is located?” he asks. Hound figures out that Mohajer is fishing for the population of Washington, DC, faster than I do and spits out the correct answer in its rapid-fire robotic voice.
Yann LeCun, who now serves as the director of FAIR, comes from a storied tenure of artificial intelligence research. He began his work in Bell Labs (founded by telephone father Alexander Graham Bell, and known for its experiments across myriad fields in telecommunications and technology) as a researcher starting in 1988, then moving to become a department head at AT&T Labs until developing 2003, when he began to teach at New York University. The modern convolutional neural network is a culmination of work throughout LeCun’s career. Ever wonder how an ATM can read your check? That was LeCun, whose early work included a neural network simulator called “SN” and deployed in 1996.
“When someone like Mark (Zuckerberg) comes to you and says ‘Oh, okay, you pretty much have carte blanche. You can put together a world-class research lab and I expect you to build the best research lab in AI in the world.’ I’ll say,’Hmm, interesting challenge.’”
To be precise, Dr Rubenstein’s ’bot swarm (above) has 1,024 members (210 being a conveniently binary number), known apparently without irony as kilobots. Each is a rigid-legged tripod that moves around by vibrating. Kilobots communicate with infra-red light, which can reflect off the table Dr Rubenstein uses for his experiments, and are programmed with three types of behaviour.
One is edge-following, which allows a ’bot move along the edge of a cluster. The second is gradient-formation, which lets it know how many other ’bots a signal has been relayed through, and thus gives it information about the location of these ’bots and the shape of the cluster it is in. The third is localisation, which means it can agree a system of co-ordinates with its neighbours, so that they can measure distances between themselves.