New Florence. New Renaissance.

HR Moneyball

More than a million job seekers have already undergone this kind of testing experience, developed by Pymetrics, a five-year-old startup cofounded by Frida Polli. An MIT-trained neuroscientist with an MBA from Harvard, Polli is pioneering new ways of assessing talent for brands such as Burger King and Unilever, based on decades of neuroscience research she says can predict behaviors common among high performers. “We realized this combination of data and machine learning would be hugely powerful, bringing recruiting from this super-antiquated, paper-and-pencil [process] into the future,” explains Polli, sitting barefoot on a couch at her spartan office near New York’s Flatiron District on a humid May morning, where about four dozen engineers, data scientists, and industrial-organizational psychologists sit behind glowing iMacs.

Pymetrics is part of a legion of buzzy startups leveraging artificial intelligence, big data, and other tech tools to disrupt the hiring space. Research firm CB Insights expects VC investments in HR-tech startups to hit $2.9 billion in 2018, up 138% year over year.

FastCompany

 

Automation and real estate appraisal

In the real estate business, Zillow Group Inc. says its algorithms are learning to capture not only the crude facts about values in the surrounding neighborhood but also more sophisticated price indicators, such as whether the living room has hardwood floors or the kitchen has granite countertops. While Zillow’s software isn’t used in appraisals—its numbers are available on its website for free—lenders have long used internal and third-party computer models to help value homes.

And now mortgage financers such as Freddie Mac are starting to get comfortable with transactions that don’t involve a human appraiser.

Bloomberg

Zillow CEO explains some of the variables that go into the algorithms that calculate its Zestimate

 

AI applications proliferate

Couple of perspectives in this McKinsey article

Gary Bradski, chief technology officer, Arraiy: AI will be used for big, high-impact applications. It is a big enabler for robotics, so autonomous vehicles is a big one. Speech recognition is really starting to work—it’s a very useful interface—that’s big. It’s going now to natural language, you see that showing up with Amazon’s Alexa and Google’s products. I call that ambient AI, where you can just ask questions and things happen.

Medicine’s a big one. You’re diagnosing problems, and you can already see these things can often do better than a doctor, in certain circumstances. They don’t get distracted, and they can ferret out very subtle patterns.

Mohak Shah, lead expert, data science, Bosch Research and Technology Center, North America: Broadly, the advancements will come into what we like to call the Internet of Things. That would mean things like developing your service efficiencies, advancing your manufacturing capabilities, reducing scraps, and looking into your operation of the fleet. And that can happen all the way from small devices to very large devices, like aviation aircraft.

Also listen to 5 super smart ladies at various parts of Alphabet/Google talk past, present and future of AI and Machine Learning

 

Remaking companies around AI

Google is not alone in this rapid re-orientation. Amazon is building a similar group cloud computing group for AI. Facebook and Twitter have created internal groups akin to Google Brain, the team responsible for infusing the search giant’s own tech with AI. And in recent weeks, Microsoft reorganized much of its operation around its existing machine learning work, creating a new AI and research group under executive vice president Harry Shum, who began his career as a computer vision researcher.

Oren Etzioni, CEO of the not-for-profit Allen Institute for Artificial Intelligence, says that these changes are partly about marketing—efforts to ride the AI hype wave. Google, for example, is focusing public attention on Fei-Fei’s new group because that’s good for the company’s cloud computing business. But Etzioni says this is also part of very real shift inside these companies, with AI poised to play an increasingly large role in our future. “This isn’t just window dressing,” he says.

Wired

Gogole’s Fei-Fei Li explaining how they use computers to understand pictures

 

 

Microsoft’s new age of intelligence

They included, but were not limited to:

• Using health insights to help the Cortana assistant notice that a user tends to binge on junk food when traveling and might miss a gym appointment while on the road
• Working with Volvo to make cars less dangerous by analyzing motorists' faces to ensure that they're alert, undistracted, and in a mood conducive to safe driving
• Bringing football/baseball legend Deion Sanders onstage to try an in-the-works NFL fantasy football bot, which Microsoft hopes to have ready for next season
• Verifying the identity of Uber drivers by having them snap a selfie and analyzing it using Microsoft facial recognition technology
• Calling on HoloLens augmented reality to preview a kitchen remodel project at a Lowe's store, with recommendations based on a shopper's Pinterest pins
• Translating "War and Peace" in a fraction of a jiffy using Azure's cloud computing service and leveraging Microsoft-designed Field Programmable Gate Array (FPGA) chips

FastCompany

The AI future

The best minds in the business—Yann LeCun of Facebook, Luke Nosek of the Founders Fund, Nick Bostrom of Oxford University and Andrew Ng of Baidu—on what life will look like in the age of the machines

Wall Street Journal

Microsoft Machine Learning use cases

All kinds of companies and industries are already using Microsoft technology. Japanese farmers are tracking cows, which walk more when ready to conceive, so they can inseminate the cow at the optimal moment. An Australian wine company is using similar algorithms to predict grape yields. A hospital about an hour from Microsoft is using Azure tools to help figure out which cardiac patients are most likely to require re-admission. Norway’s eSmart Systems uses Azure Machine Learning to forecast energy grid usage and turn down home heating when demand is high.

Bloomberg

 

Machine Learning and March Madness

“For the 2016 March Madness tournament, Microsoft Bing and NCAA are partnering up once again to bring fans their smarter bracket, powered by Bing Predicts.

The Bing Predictor tool is the foundation for the smarter bracket which has been used to pick winners for popular events such as the Oscars and Grammy awards.”

“Looking under the hood at the engine driving the Predictor tool, a user would find intelligent machine learning technology that pulls in consumer-oriented data, such as search data and social media activity, to “find signals that correlate how people will vote. This kind of information is very valuable in terms of driving signals that might not be easily available to the general public,” Sun said.””

TechCrunch

Photo Credit

The evolving machine learning landscape

From Shivon Zilis

In last year’s roundup, the focus was almost exclusively on machine intelligence in the virtual world. This time we’re seeing it in the physical world, in the many flavors of autonomous systems: self-driving cars, autopilot drones, robots that can perform dynamic tasks without every action being hard coded. It’s still very early days—most of these systems are just barely useful, though we expect that to change quickly.

These physical systems are emerging because they meld many now-maturing research avenues in machine intelligence. Computer vision, the combination of deep learning and reinforcement learning, natural language interfaces, and question-answering systems are all building blocks to make a physical system autonomous and interactive. Building these autonomous systems today is as much about integrating these methods as inventing new ones.

Canada: AI talent goldmine

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.

BusinessWeek