Experts from Google, T-Mobile and other tech frontiers weigh in on the future of AI

Experts from Google, T-Mobile and other tech frontiers weigh in on the future of AI

11:30pm, 25th April, 2019
SalesPal CEO Ashvin Naik, Google Cloud’s Chanchal Chatterjee, Audioburst’s Rachel Batish and T-Mobile’s Chip Reno discuss the future of artificial intelligence at the Global AI Conference in Seattle. (GeekWire Photo / Alan Boyle) Artificial intelligence can rev up recommendation engines and make self-driving cars safer. It can even . But what else will it be able to do? At today’s session of the , a panel of techies took a look at the state of AI applications — and glimpsed into their crystal balls to speculate about the future of artificial intelligence. The panelists included Chanchal Chatterjee, AI leader at ; Ashvin Naik, CEO of , which markets AI-enabled sales analysis tools; Rachel Batish, vice president of product for , an audio indexing service; and Chip Reno, senior advanced analytics manager at . The moderator was Shailesh Manjrekar, head of product and solutions marketing for , a multi-cloud data storage and management company. Here are five AI frontiers that came up in today’s conversations, plus a couple of caveats to keep in mind: Smarter grocery stores: AI-enabled grocery shopping was pioneered right here in Seattle at , but the trend is catching on. Today called the Intelligent Retail Lab in Levittown, N.Y. Britain’s takes a different tack: Users fill up a virtual shopping cart, then schedule a one-hour delivery slot. Google Cloud helped Ocado develop the , including a recommendation engine that figures out customers’ shifting preferences, an algorithm that handles and prioritizes customer service emails, and a as Ocado’s previous system. Energy-saving server farms: Chatterjee pointed to how Google used its DeepMind machine learning platform to . Before AI was put on the case, 10 years’ worth of efficiency measures could reduce energy usage by merely 12 percent, he said. Within six months, AI brought about a 40 percent reduction. “That was a huge difference that AI made in a very short amount of time that we could not do with 10 years of research,” Chatterjee said. Financial market prediction: Hedge fund managers and bankers are already , detect market manipulation and assess credit risks. But Chatterjee said the models are getting increasingly sophisticated. AI is being used to predict how margin trades could play out, or whether undervalued financial assets are ripe for the picking. AI models could even anticipate . “When the lock-in period expires … that’s a great time to short,” Chatterjee said. Deeper, wider AI conversations: Chatterjee predicted that our conversations with voice assistants are likely to get wider, deeper and more personal as AI assistants become smarter. Audioburst’s Batish said conversational AI could provide a wider opening for smaller-scale startups and for women in tech. “Women are very much prominent in conversational applications and businesses,” she said. Salespal’s Naik agreed with that view — but he worried about the dearth of compelling applications, based on his own company’s experience with voice-enabled devices like Amazon Echo and Google Home. “They’re gathering dust. … We use them just to listen to music or set up alarms. That’s it,” he said. AI for good, or evil? Chatterjee said AI could be a powerful tool to root out fraud and corruption. AI applications could be built “to see what influence relationships have on outcomes — that tells you if there are any side deals being made,” he said. But Batish worried about the rise of , virtual and . “I’m actually afraid of what that could bring into our world,” she said. “It would be interesting to see how companies are trying to be able to monitor or identify fake situations that are being built out of very complicated AI.” Watch out for job disruption: Many studies have pointed out that automation is likely to disrupt employment sectors, especially in the service, manufacturing and transportation sectors. “Anything that is repetitive, that can be extracted from multiple sources, that doesn’t have a lot of creativity amd innovation, is at risk due to AI,” Chatterjee said. “That means that more people will have to move into other sectors.” Watch out for the hype: “I’d like to see people get away from the hype a little bit,” T-Mobile’s Reno said. “I’m on the client side, so I see all the pitches involving AI and ML or deep learning. … A lot of times, AI is not applicable to certain use cases where we’re applying it. Just good old-fashioned statistics or business intelligence is fine. So I think that the future of AI relies on getting past the hype and getting more into aligning these awesome tools and algorithms to specific business cases.”
Planetary Resources’ veteran engineers launch First Mode to target wider frontiers

Planetary Resources’ veteran engineers launch First Mode to target wider frontiers

9:48am, 2nd April, 2019
A wide-angle view provides an unusual perspective of First Mode’s new lab space on Western Avenue in Seattle. Click on the image for a 360-degree view. (First Mode Photo) Planetary Resources was , but a troop of engineers who used to work for the asteroid mining company is seeking out new frontiers with a new company called . And this time, asteroids aren’t the final frontier. “First Mode is working with industries on and off the planet to do design and creative engineering work, but also to build hardware and build solutions that get deployed around the solar system as well as a lot of harsh and challenging environments here on planet Earth,” Rhae Adams, vice president of strategy and business development, told GeekWire. The company’s expertise is being applied to a wide range of technical challenges, including robotic space missions as well as clean tech, mobility, agriculture, oil and gas development, high-reliability consumer products — and yes, . “The goal for First Mode and its customers is to provide that method of looking at a problem that, at its starting point, appears to be an intractable issue … and then help the customer break that down into a set of problems that can be worked in parallel, and then brought together to form the functional whole that the marketplace needs,” said Chris Voorhees, president and chief engineer. Chris Voorhees, First Mode’s president and chief engineer. (First Mode Photo) Voorhees said First Mode has already solved what sometimes seems to be an intractable problem for startups: making money. “We’ve reached a point where the company has achieved profitability,” he said. The company has also expanded from its original core group of 11 Planetary Resources veterans to 14 employees, and Voorhees says there’s more growth ahead. That’s a big change from the final days of Planetary Resources, which made significant headway on its plan to develop asteroid-prospecting spacecraft but after a funding round fizzled. Voorhees and Adams were among those laid off. “We had a core group of engineering, scientific technical staff members that really felt like they had unfinished business coming out of Planetary, and wanted to stay together,” Voorhees recalled. The new venture started out under the name “Synchronous,” and built on the partners’ expertise and connections in the space industry. Last summer, the company said on LinkedIn that its team members were planned by NASA. Just last month, Synchronous moved into a 7,500-square-foot lab space on Western Avenue in Seattle’s Belltown neighborhood. It also . Why First Mode? Engineers know that structures have natural frequencies at which they resonate — and that the most basic frequency for that resonance is known as the “first mode.” “The founding members of First Mode realized from their previous experience working together that they too had found a natural frequency,” the company explained in its . “By working together, our talents and expertise result in technical solutions that are stronger than the contributions of team members working alone.” Voorhees said First Mode draws inspiration from NASA’s , where he began his career more than two decades ago, as well as from design and engineering companies such as and . Lockheed Martin’s and Boeing’s also serve as models, he said. Rhae Adams, First Mode’s vice president of strategy and business development. (First Mode Photo) Adams said First Mode is working with more than 10 different clients in business and government, while Voorhees said the company has taken on more than 40 different projects. Some work has even been done for folks on Capitol Hill, although Voorhees declined to go into specifics. “In general, there’s an intimate connection between the development of new space policy and technology. … We’ve had the opportunity to contribute over the past year to conversations regarding where those two things have had to intersect,” he said. Voorhees said that First Mode’s team members have “good, amicable personal connections” with their former colleagues at what used to be known as Planetary Resources and is now known as ConsenSys Space. But there are no formal business dealings. Nor are there any plans to raise money from investors, at least in the near term. “It was important to us from the get-go that we were employee-owned,” Adams said. Voorhees said he was grateful for the experience he and the other founders of First Mode gained at Planetary Resources’ headquarters in Redmond, Wash . “It would have been very difficult for us to have gone off and done this without that experience,” he said. So just how scary is it to start up a startup, especially when it’s self-funded? ” ‘Exhilarating’ is the right word, which is a simultaneous combination of excitement and terror,” Voorhees said. “That’s what I live under most every day.” Adams seconded that emotion. “I know we’ve not come across anyone else that had 11 founders who have been able to work together and build something,” he said. “It’s gone remarkably smoothly for the number of unique personalities and opinions we have. We’re always able to take that step back and approach things logically as best we can, like any technical problem. It works for founding a company too, not just for pieces of hardware.”
Xnor shrinks AI to fit on a solar-powered chip, opening up big frontiers on the edge

Xnor shrinks AI to fit on a solar-powered chip, opening up big frontiers on the edge

9:50am, 13th February, 2019
Xnor.ai machine learning engineer Hessam Bagherinezhad, hardware engineer Saman Naderiparizi and co-founder Ali Farhadi show off a chip that uses solar-powered AI. (GeekWire Photo / Alan Boyle) It was a big deal two and a half years ago when researchers the size of a candy bar — and now it’s an even bigger deal for Xnor.ai to re-engineer its artificial intelligence software to fit onto a solar-powered computer chip. “To us, this is as big as when somebody invented a light bulb,” Xnor.ai’s co-founder, Ali Farhadi, said at the company’s Seattle headquarters. Like the candy-bar-sized, Raspberry Pi-powered contraption, the camera-equipped chip flashes a signal when it sees a person standing in front of it. But the chip itself isn’t the point. The point is that Xnor.ai has figured out how to blend stand-alone, solar-powered hardware and edge-based AI to turn its vision of “artificial intelligence at your fingertips” into a reality. “This is a key technology milestone, not a product,” Farhadi explained. Shrinking the hardware and power requirements for AI software should expand the range of potential applications greatly, Farhadi said. “Our homes can be way smarter than they are today. Why? Because now we can have many of these devices deployed in our houses,” he said. “It doesn’t need to be a camera. We picked a camera because we wanted to show that the most expensive algorithms can run on this device. It might be audio. … It might be a way smarter smoke detector.” Outside the home, Farhadi can imagine putting AI chips on stoplights, to detect how busy an intersection is at a given time and direct the traffic flow accordingly. AI chips could be tethered to balloons or scattered in forests, to monitor wildlife or serve as an early warning system for wildfires. Xnor’s solar-powered AI chip is light enough to be lofted into the air on a balloon for aerial monitoring. In this image, the chip is highlighted by the lamp in the background. (Xnor. ai Photo) Sophie Lebrecht, Xnor.ai’s senior vice president of strategy and operations, said the chips might even be cheap enough, and smart enough, to drop into a wildfire or disaster zone and sense where there are people who need to be rescued. “That way, you’re only deploying resources in unsafe areas if you really have to,” she said. The key to the technology is reducing the required power so that it can be supplied by a solar cell that’s no bigger than a cocktail cracker. That required innovations in software as well as hardware. “We had to basically redo a lot of things,” machine learning engineer Hessam Bagherinezhad said. Xnor.ai’s head of hardware engineering, Saman Naderiparizi, worked with his colleagues to figure out a way to fit the software onto an FPGA chip that costs a mere $2, and he says it’s possible to drive the cost down to less than a dollar by going to ASIC chips. It only takes on the order of milliwatts of power to run the chip and its mini-camera, he told GeekWire. “With technology this low power, a device running on only a coin-cell battery could be always on, detecting things every second, running for 32 years,” Naderiparizi said in a news release. That means there’d be no need to connect AI chips to a power source, replace their batteries or recharge them. And the chips would be capable of running AI algorithms on standalone devices, rather than having to communicate constantly with giant data servers via the cloud. If the devices need to pass along bits of data, they could . That edge-computing approach is likely to reduce the strain of what could turn out to be billions of AI-enabled devices. “The carbon footprint of data centers running all of those algorithms is a key issue,” Farhadi said. “And with the way AI is progressing, it will be a disastrous issue pretty soon, if we don’t think about how we’re going to power our AI algorithms. Data centers, cloud-based solutions for edge-use cases are not actually efficient ways, but other than efficiency, it’s harming our planet in a dangerous way.” Farhadi argues that cloud-based AI can’t scale as easily as edge-based AI. “Imagine when I put a camera or sensor at every intersection of this city. There is no cloud that is going to handle all that bandwidth,” he said. “Even if there were, back-of-the-envelope calculations would show that my business will go bankrupt before it sees the light of day.” The edge approach also addresses what many might see as the biggest bugaboo about having billions of AI bugs out in the world: data privacy. “I don’t want to put a camera in my daughter’s bedroom if I know that the picture’s going to end up in the cloud,” Farhadi said. Xnor.ai was , or AI2, only a couple of years ago, and the venture is with millions of dollars of financial backing from Madrona Venture Group, AI2 and other investors. Farhadi has faith that the technology Xnor.ai is currently calling “solar-powered AI” will unlock still more commercial frontiers, but he can’t predict whether the first applications will pop up in the home, on the street or off the beaten track. “It will open up so many different things, the exact same thing when the light bulb was invented: No one knew what to do with it,” he said. “The technology’s out there, and we’ll figure out the exact products.”
Xnor shrinks AI to fit on a solar-powered chip, opening big frontiers on the edge

Xnor shrinks AI to fit on a solar-powered chip, opening big frontiers on the edge

9:20am, 13th February, 2019
Xnor.ai machine learning engineer Hessam Bagherinezhad, hardware engineer Saman Naderiparizi and co-founder Ali Farhadi show off a chip that can use solar-powered AI to detect people. (GeekWire Photo / Alan Boyle) It was a big deal two and a half years ago when researchers the size of a candy bar — and now it’s an even bigger deal for Xnor.ai to re-engineer its artificial intelligence software to fit onto a solar-powered computer chip. “To us, this is as big as when somebody invented a light bulb,” Xnor.ai’s co-founder, Ali Farhadi, said at the company’s Seattle headquarters. Like the candy-bar-sized, Raspberry Pi-powered contraption, the camera-equipped chip flashes a signal when it sees a person standing in front of it. But the chip itself isn’t the point. The point is that Xnor.ai has figured out how to blend stand-alone, solar-powered hardware and edge-based AI to turn its vision of “artificial intelligence at your fingertips” into a reality. “This is a key technology milestone, not a product,” Farhadi explained. Shrinking the hardware and power requirements for AI software should expand the range of potential applications greatly, Farhadi said. “Our homes can be way smarter than they are today. Why? Because now we can have many of these devices deployed in our houses,” he said. “It doesn’t need to be a camera. We picked a camera because we wanted to show that the most expensive algorithms can run on this device. It might be audio. … It might be a way smarter smoke detector.” Outside the home, Farhadi can imagine putting AI chips on stoplights, to detect how busy an intersection is at a given time and direct the traffic flow accordingly. AI chips could be tethered to balloons or scattered in forests, to monitor wildlife or serve as an early warning system for wildfires. Xnor’s solar-powered AI chip is light enough to be lofted into the air on a balloon for aerial monitoring. In this image, the chip is highlighted by the lamp in the background. (Xnor. ai Photo) Sophie Lebrecht, Xnor.ai’s senior vice president of strategy and operations, said the chips might even be cheap enough, and smart enough, to drop into a wildfire or disaster zone and sense where there are people who need to be rescued. “That way, you’re only deploying resources in unsafe areas if you really have to,” she said. The key to the technology is reducing the required power so that it can be supplied by a solar cell that’s no bigger than a cocktail cracker. That required innovations in software as well as hardware. “We had to basically redo a lot of things,” machine learning engineer Hessam Bagherinezhad said. Xnor.ai’s head of hardware engineering, Saman Naderiparizi, worked with his colleagues to figure out a way to fit the software onto an FPGA chip that costs a mere $2, and he says it’s possible to drive the cost down to less than a dollar by going to ASIC chips. It only takes on the order of milliwatts of power to run the chip and its mini-camera, he told GeekWire. “With technology this low power, a device running on only a coin-cell battery could be always on, detecting things every second, running for 32 years,” Naderiparizi said in a news release. That means there’d be no need to connect AI chips to a power source, replace their batteries or recharge them. And the chips would be capable of running AI algorithms on standalone devices, rather than having to communicate constantly with giant data servers via the cloud. If the devices need to pass along bits of data, they could . That edge-computing approach is likely to reduce the strain of what could turn out to be billions of AI-enabled devices. “The carbon footprint of data centers running all of those algorithms is a key issue,” Farhadi said. “And with the way AI is progressing, it will be a disastrous issue pretty soon, if we don’t think about how we’re going to power our AI algorithms. Data centers, cloud-based solutions for edge-use cases are not actually efficient ways, but other than efficiency, it’s harming our planet in a dangerous way.” Farhadi argues that cloud-based AI can’t scale as easily as edge-based AI. “Imagine when I put a camera or sensor at every intersection of this city. There is no cloud that is going to handle all that bandwidth,” he said. “Even if there were, back-of-the-envelope calculations would show that my business will go bankrupt before it sees the light of day.” The edge approach also addresses what many might see as the biggest bugaboo about having billions of AI bugs out in the world: data privacy. “I don’t want to put a camera in my daughter’s bedroom if I know that the picture’s going to end up in the cloud,” Farhadi said. Xnor.ai was , or AI2, only a couple of years ago, and the venture is with millions of dollars of financial backing from Madrona Venture Group, AI2 and other investors. Farhadi has faith that the technology Xnor.ai is currently calling “solar-powered AI” will unlock still more commercial frontiers, but he can’t predict whether the first applications will pop up in the home, on the street or off the beaten track. “It will open up so many different things, the exact same thing when the light bulb was invented: No one knew what to do with it,” he said. “The technology’s out there, and we’ll figure out the exact products.”