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The Sequence Scope: A Heroku for Machine Learning

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The Sequence Scope is a summary of the most important published research papers, released technology and startup news in the AI ecosystem in the last week. This compendium is part of TheSequence newsletter. Data scientists, scholars, and developers from Microsoft Research, Intel Corporation, Linux Foundation AI, Google, Lockheed Martin, Cardiff University, Mellon College of Science, Warsaw University of Technology, Universitat Politècnica de València and other companies and universities are already subscribed to TheSequence. Years ago, cloud startup Heroku was able to successfully challenge cloud providers such as Amazon and Microsoft by providing a super simple model for building cloud applications. For years, Heroku was able to remain competitive in the midst of AWS and Azure growth, until it was acquired by Salesforce for $212 million.


Synthetaic raises $3.5M to train AI with synthetic data – TechCrunch

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Synthetaic is a startup working to create data -- specifically images -- that can be used to train artificial intelligence. Founder and CEO Corey Jaskolski's experience includes work with both National Geographic (where he was recently named Explorer of the Year) and a 3D media startup. In fact, he told me that his time with National Geographic made him aware of the need for more data sets in conservation. Well, Jaskolski said that he was working on a project that could automatically identify poachers and endangered animals from camera footage, and one of the major obstacles was the fact that there simply aren't enough existing images of either poachers (who don't generally appreciate being photographed) or certain endangered animals in the wild to train AI to detect them. He added that other companies are trying to create synthetic AI training data through 3D worldbuilding (in other words, "building a replica of the world that you want to have an AI learn in"), but in many cases, this approach is prohibitively expensive.


Army of avatar robots readies to invade Japanese job market

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Japanese startups are getting ready to deploy a small army of remote-controlled robots in the workplace. Called avatar robots, the machines are still experimental and their initial objectives limited. But if everything goes as planned, they could soon be clerking at convenience stores, patrolling buildings as security guards, or even assisting astronauts in outer space. The technology has the potential to replace humans, helping solve labor shortages and providing relief to essential workers combating natural disasters. Convenience stores in Tokyo have already put prototypes of the robots to work stocking shelves with beverages, instant noodles and other goods.


Hockeystick launches AI-powered matchmaking platform for startups, investors

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Data platform company Hockeystick is expanding its offerings with the launch of a new "matchmaking" product that connects startups with funding opportunities. As of Monday, Hockeystick has launched the new AI platform that introduces startups to funders and other partners. The product is the second platform for Hockeystick, which launched its site that provides detailed data on startups in 2018. "This is the culmination of years of work building our database and our community." The matchmaking product leverages Hockeystick's proprietary funding database and uses AI to intelligently match companies with VCs, lenders, accelerators, grants, and other services.


Chip industry is going to need a lot more software to catch Nvidia's lead in AI

ZDNet

Anil Mankar, head of product development at AI chip startup BrainChip, presented details on the company's technology Tuesday at the prestigious Linley Fall Processor conference. The conference organizer, Linley Gwennap, presented the case that the entire industry needs more software capability to catch up with an enormous lead that Nvidia has in AI. The semiconductor industry is in the midst of a renaissance in chip design and performance improvement, but it will take a lot more software to catch up with graphics chip titan Nvidia, an industry conference Tuesday made clear. The Linley Fall Processor conference, which is taking place as a virtual event this week and next week, is one of the main meet-and-greet events every year for promising young chip companies. To kick off the show, the conference host, Linley Gwennap, who has been a semiconductor analyst for two decades, offered a keynote Tuesday morning in which he said that software remains the stumbling block for all companies that want to challenge Nvidia's lead in processing artificial intelligence.


Artificial intelligence and the antitrust case against Google

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Following the launch of investigations last year, the U.S. Department of Justice (DOJ) together with attorney generals from 11 U.S. states filed a lawsuit against Google on Tuesday alleging that the company maintains monopolies in online search and advertising, and violates laws prohibiting anticompetitive business practices. It's the first antitrust lawsuit federal prosecutors filed against a tech company since the Department of Justice brought charges against Microsoft in the 1990s. "Back then, Google claimed Microsoft's practices were anticompetitive, and yet, now, Google deploys the same playbook to sustain its own monopolies," the complaint reads. "For the sake of American consumers, advertisers, and all companies now reliant on the internet economy, the time has come to stop Google's anticompetitive conduct and restore competition." Attorneys general from no Democratic states joined the suit.


Singapore startup develops COVID-19 breath detection test

ZDNet

A Singapore startup has developed a breath test it says can detect COVID-19 in under 60 seconds. Based on clinical trials involving 180 patients, the system has clocked an accuracy rate of more than 90%. Developed by Breathonix, the breath test is a notable move away from the current screening standard involving a swab test. The latter may be uncomfortable and identifies COVID-19 through polymerase chain reaction (PCR) tests, which can take a few hours. Swift detection was key in effective contact tracing and stemming the spread of the coronavirus, and Breathonix's breath analysis technology offered a fast and convenient way to identify infections, the startup said in a statement Tuesday. A spinoff from the National University of Singapore (NUS), the company's two founders are graduates from the local university and is supported under the university's Graduate Research Innovation Programme.


How can startups make machine learning models production-ready?

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Today, every technology startup needs to embrace AI and machine learning models to stay relevant in their business. Machine learning (ML), if implemented well, can have a direct impact on a company's ability to succeed and raise the next round of funding. However, the path to implementing ML solutions comes with some specific hurdles for start-ups. Let's discuss the top considerations for getting ML models production-ready and the best approaches for a startup. An ML model is only as good as the data used to train it.



Biomanufacturers Need to Focus on Data before Intelligence

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The use of artificial intelligence in bioprocessing might still be more talk than action. "Lots of people are talking about it," says William Moss, CEO and co-founder of U.K.-based Opvia, "but most of the industry uses very little machine learning or artificial intelligence." So far, Moss sees most bioprocess R&D teams--from small startups to big pharma--struggling with the same problem: "Scientists don't have the tools to capture their data in a standardized way with all of the context." Unless a company can solve that problem, there's little they can do with the data. As Moss says, "That's the bottleneck."