Tag: Artificial Intelligence

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Big Data is a bigger deal than venture capital in AI

AI panel
Optio3 CEO Sridhar Chandrashekar, far right, discusses issues surrounding artificial intelligence with moderator Melissa Hellmann of The Seattle Times, Dave Thurman of Northeastern University and Ben Wilson of Intellectual Ventures. (GeekWire Photo / Alan Boyle)

“Data is the new oil” may be a classic cliche characterizing how important raw numbers are for the computer industry, but when it comes to artificial intelligence ventures, the cliche may not go far enough.

“One of the big blocks for AI is data,” Ben Wilson, director of the Center for Intelligent Devices at Bellevue, Wash.-based Intellectual Ventures, said today at a forum about AI presented as part of the Seattle Metropolitan Chamber of Commerce’s Executive Speaker Series. “Traditionally, startup companies need capital. Now, if you’re doing AI, you need capital and you also need data. And you’re going to burn through your data before you burn through your capital.”

Wilson pointed out that the big players in the AI market are the companies that have the data, whether it’s Amazon or Microsoft, Facebook or Google.

“Before you have a good idea, start with data,” he said. “And if you’re someone who has a great idea but you have no data, that’s going to be a big roadblock for you, and you’re going to have to find some collaborators or partners who have access to the data you need.”

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AI researchers want to make it easier to be green

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High-performance computing is becoming the lifeblood of artificial intelligence research. (Intel Photo)

The development of ever more powerful models for artificial intelligence is revolutionizing the world, but it doesn’t come cheap. In a newly distributed position paper, researchers at Seattle’s Allen Institute for Artificial Intelligence argue that more weight should be given to energy efficiency when evaluating research.

The AI2 researchers call on their colleagues to report the “price tag” associated with developing, training and running their models, alongside other metrics such as speed and accuracy. Research leaderboards, including AI2’s, regularly rate AI software in terms of accuracy over time, but they don’t address what it took to get those results.

Of course, cutting-edge research can be expensive in all sorts of fields, ranging from particle physics done at multibillion-dollar colliders to genetic analysis that requires hundreds of DNA sequencers. Financial cost or energy usage isn’t usually mentioned in the resulting studies. But AI2’s CEO, Oren Etzioni, says that times are changing – especially as the carbon footprint of energy-gobbling scientific experiments becomes more of a concern.

“It is an ongoing topic for many scientific communities, the issue of reporting costs,” Etzioni, one of the position paper’s authors, told GeekWire. “I think what makes a difference here is the stunning escalation that we’ve seen” in the resources devoted to AI model development.

One study from OpenAI estimates that the computational resources required for top-level research in deep learning have increased 300,000 times between 2012 and 2018, due to the rapid development of more and more complex models. “This is much faster than Moore’s Law, doubling every three or four months,” Etzioni said.

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Allen Institute hatches new HQ for startup incubator

AI2 incubator offices
A view from Google Maps shows the building at 2101 N. 34th St. that’s due to serve as the new home for the Allen Institute for Artificial Intelligence’s startup incubator. (Google Maps Photo)

The startup incubator at Seattle’s Allen Institute for Artificial Intelligence is getting so busy that it has to move into new digs across the street.

Starting Aug. 12, the incubator will occupy a 7,250-square-foot “long-term home” at 2101 N. 34th St., near Gasworks Park and AI2’s main offices on Northlake Way, the institute said in its email newsletter for friends and families.

“We anticipate having 50+ workstations for our EIRs and CTOs [entrepreneurs in residence and chief technology officers] — complemented by numerous team pods, phone booths, conference rooms, a classroom, a lounge and our own large outdoor deck overlooking Lake Union,” AI2 said.

Jacob Colker, a managing director for AI2’s incubator, told GeekWire in a follow-up email that the new space will be nearly four times bigger than the current 1,850-square-foot office space (above a dive shop that’s next door to AI2’s headquarters).

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How long will men dominate computer science?

AI2 office
Semantic Scholar was pioneered at the Allen Institute for Artificial Intelligence. (AI2 Photo)

Today it’s mostly a man’s world in computer science — and a tally of the authors behind nearly 3 million research papers in the field suggests that could be the case for the rest of the 21st century.

The findings, reported by researchers at Seattle’s Allen Institute for Artificial Intelligence, point to how far the scientific community still has to go when it comes to gender equality in science, technology, engineering and mathematics, or STEM.

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White House AI plan pumps up partnerships

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White House technology official Michael Kratsios addresses scores of executives, experts and officials at a White House summit focusing on artificial intelligence in 2018. (White House OSTP Photo / Erik Jacobs)

The Trump administration is updating the Obama administration’s strategy for artificial intelligence to put more emphasis on public-private partnerships like the one forged this year by Amazon and the National Science Foundation.

Three years after the initial strategic plan for AI research and development was released, the update was issued online overnight. It makes tweaks in the seven policy priorities that were laid out in the waning days of the Obama White House, and adds public-private partnerships as an eighth priority.

The R&D strategy is part of a broader set of policies known as the American AI Initiative, which was the subject of an executive order signed by President Donald Trump in February.

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Xnor releases do-it-yourself AI platform

Xnor's Oliver Krengel
Xnor engineer Oliver Krengel works with the AI2GO self-serve software platform. (Xnor Photo)

Now you too can put a little AI on your device, even if you’re not up on the ins and outs of artificial intelligence.

The way to do it is with AI2GO, a newly released self-serve software platform from Xnor.ai, a Seattle AI startup. AI2GO comes with a set of ready-to-go applications and deep-learning models that can be selected and downloaded with just a few clicks.

Ali Farhadi, Xnor’s co-founder and CXO (Chief Xnor Officer), told GeekWire that the platform is designed for developers and small companies that want to take advantage of AI tools such as face recognition or object classification without having to start from scratch.

“The problem of deploying AI is getting harder and harder, and it shouldn’t be that way,” Farhadi said.

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AI experts look beyond facial recognition ban

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Cornell University information scientist Solon Barocas, at right, speaks during a panel discussion on the ethics of artificial intelligence at Seattle University, while Carnegie Mellon University’s David Danks and Google researcher Margaret Mitchell look on. (GeekWire Photo / Alan Boyle)

San Francisco’s board of supervisors took a significant step this week when it voted to ban the use of facial recognition software for law enforcement purposes, but such measures by themselves won’t resolve the ethical issues surrounding surveillance enabled by artificial intelligence.

At least those are the first impressions from a trio of experts focusing on the social implications of AI’s rapid rise.

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Tech experts weigh in on the future of AI

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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 beat humans at their own games. But what else will it do?

At today’s session of the Global Artificial Intelligence Conference, 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.

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How Amazon exec uses machine learning at home

Paul Misener
Paul Misener, Amazon’s vice president for global innovation policy and communications, talks about Amazon’s “invention machine” at the Global AI Conference. (GeekWire Photo / Alan Boyle)

Taking advantage of artificial intelligence and machine learning may be part of Paul Misener’s job as an Amazon executive, but he’s doing it for fun as well.

Misener, Amazon’s vice president for global innovation policy and communications, gave a personal endorsement for Amazon Web Services’ machine learning platform today at the Global Artificial Intelligence Conference in Seattle.

“Amazon SageMaker is a really cool service offered by Amazon Web Services,” he told the audience at the Washington State Convention Center. “This brings machine learning out to everyone, including me. I’ve done some fooling around with things on it, some hobby things.”

Like what? We had to ask.

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How Microsoft is opening up AI’s ‘black box’

Erez Barak
Erez Barak, senior director of product for Microsoft’s AI Division, speaks at the Global Artificial Intelligence Conference in Seattle. (GeekWire Photo / Alan Boyle)

Artificial intelligence can work wonders, but often it works in mysterious ways.

Machine learning is based on the principle that a software program can analyze a huge set of data and fine-tune its algorithms to detect patterns and come up with solutions that humans may miss. That’s how Google DeepMind’s Alpha Go AI agent learned to play the ancient game of Go (and other games) well enough to beat expert players.

But if programmers and users can’t figure out how AI algorithms came up with their results, that black-box behavior can be a cause for concern. It may become impossible to judge whether AI agents have picked up unjustified biases or racial profiling from their data sets.

That’s why terms such as transparency, explainability and interpretability are playing an increasing role in the AI ethics debate.

The European Commission includes transparency and traceability among its requirements for AI systems, in line with the “right to explanation” laid out in data-protection laws. The French government already has committed to publishing the code that powers the algorithms it uses. In the United States, the Federal Trade Commission’s Office of Technology Research and Investigation has been charged with providing guidance on algorithmic transparency.

Transparency figures in Microsoft CEO Satya Nadella’s “10 Laws of AI” as well — and Erez Barak, senior director of product for Microsoft’s AI Division, addressed the issue head-on today at the Global Artificial Intelligence Conference in Seattle.

“We believe that transparency is a key,” he said. “How many features did we consider? Did we consider just these five? Or did we consider 5,000 and choose these five?”

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