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Why These Two Innovations In Artificial Intelligence Are So Important: Eye on A.I.

September 17, 2019, 2:33 PM UTC

In the past few weeks, two important developments in artificial intelligence research have gone largely unheralded. Both hint at just how earth shaking – or at least industry-shattering – A.I.’s potential really is.

The findings aren’t about using A.I. for marginal gains, like better ad targeting or for preventative maintenance. We’re talking about upending business models.

The first item was news that a Hong Kong-based biotechnology startup, InSilico Medicine, working with researchers from the University of Toronto, had used machine learning to create a potential new drug to prevent tissue scarring.

What’s eye-popping here is the timescale: just 46 days from molecular design to animal testing in mice. Considering that, on average, it takes more than a decade and costs $350 million to $2.7 billion to bring a new drug to market, depending on which study one believes, the potential impact on the pharmaceutical industry is huge.

Sure, much of that time and the vast majority of the expense comes from human clinical trials, which only happen after testing in mice. But pre-clinical research can still take years and cost millions.

What’s also interesting here is that InSilico used reinforcement learning, an A.I. technique that hasn’t yet impacted business much. Reinforcement learning is notable because it doesn’t require the vast pools of structured, historical data that other A.I. methods do.

Here researchers used reinforcement learning to rapidly design 30,000 new molecules and then narrow them down to six, which were synthesized and further tested in the lab. Look for more A.I. breakthroughs like this to start upending the balance of power between biotech startups and Big Pharma.

The second piece of underappreciated news is that researchers at DeepMind, the London A.I. shop owned by Google parent Alphabet, and Imperial College London, successfully used a deep neural network to find more precise answers to quantum mechanical problems. That’s basically the physics that underpins all of chemistry.

To date, the only element for which we can completely solve the underlying quantum equations is the simplest, hydrogen, which has just one proton and one electron. For every other element, we rely on approximations. Get better approximations, and you potentially get new chemistry – and that means new materials. Think room temperature superconductors or new kinds of batteries that will vastly extend the range of electric vehicles.

DeepMind’s A.I.-powered approximations were in some cases almost an order of magnitude better than previous methods. If you’re Dow or DuPont, or Formosa Plastics or LG Chem, that sort of advantage could be worth billions.

Jeremy Kahn


It’s a promise… Microsoft president and chief legal officer Brad Smith told Reuters that the tech giant would never sell facial-recognition technology to governments that want to use it for mass surveillance. But, the report said “Smith stopped short for calling for an outright ban on the technology, saying that Microsoft believes it has valid uses and has argued that governments should move faster to regulate it.

That’s not a lot of money. The federal government said that it plans to spend nearly $1 billion in A.I.-related research outside of the Defense Department for 2020, but executives from Intel and Nvidia said it’s not enough to keep the U.S. competitive with other countries, The Wall Street Journal reported. “A billion dollars is certainly a great thing and certainly interesting, but it’s not nearly enough,” said Anthony Robbins, Nvidia’s vice president for the North American public sector.

How A.I. startup investing is changing. Deal-tracking firm Crunchbase analyzed the venture capital market for A.I. and discovered that startups specializing in A.I.-related products are raising more money across fewer financing rounds. The report said that “Companies in the space raised $6.62 billion so far in 2019, which puts us on pace to slightly top the 2018 total of $8.67 billion.”

Baidu’s big bet. Chinese search giant Baidu said it would invest around $200 million into China tech firm Neusoft Holdings, and would collaborate on A.I. projects related to healthcare, education, and city infrastructure, reported China news site TechNode. The report said that by investing into another company specializing in different A.I. projects, Baidu “is looking for ways to make up for slowing ad revenue as advertisers tighten their belts amid a slowing economy and regulators crack down on online content.”


Adam Wilson, the CEO of data-cleaning startup Trifacta, explains that A.I. projects will likely fail if the underlying data is of low quality. Trifacta raised $100 million in funding last week. “People have woken up to the fact that if your data quality is bad, your A.I. and machine learning is going to be worthless,” Wilson said. “The last thing they want to do is to automate bad decisions faster based on bad data.”


Agribusiness company UPL hired Adrian Percy to be its chief technology officer for its crop protection unit. Percy was previously the head of research and development for Bayer’s crop science division.

Blackhawk Network, a company that specializes in prepaid gift cards and payments, picked Harel Kodesh to be its CTO. Kodesh was previously an operating partner at the investment firm Silver Lake.


An A.I. tool for cancer researchers. Researchers from Argonne National Laboratory, a U.S. Department of Energy science and engineering research center, published a paper about using A.I. techniques to create more capable neural networks—software that learns—that work with cancer data that’s not represented in images or text. The researchers used reinforcement learning—a type of A.I. that learns through repeated trials—to develop the special neural networks, and have open-sourced their A.I. tools for others to use.

Deep learning to predict consumer default. Researchers from the University of Pittsburgh published a paper about using deep learning to predict the likelihood that consumers will default on their loans. The researchers wrote that their deep-learning system “compares favorably to conventional credit scoring models in ranking individual consumers by their default risk, and is also able to capture variations in aggregate default risk."


‘Skype Mafia’ Backs A.I. Startup Automating Contract Negotiations – By Jeremy Kahn

Here Comes America’s First Privacy Law: What the CCPA Means for Business and Consumers – By Jeff John Roberts

Amazon Is Crowdsourcing Alexa’s Answers. Smart Tactic or a Questionable Move? – By David Z. Morris


All eyes on Element AI. Element AI, a high-profile startup co-founded by deep-learning pioneer Yoshua Bengio, raised $151.4 million in funding, but is still struggling to release products on time and create a sustainable business, reported The Globe and Mail. The company is one of Canada’s splashiest startups aiming to help conventional businesses adopt A.I. The newspaper reported about some of Element AI’s challenges and its huge hiring boom; the three-year-old startup now has over 500 employees. “The company generated less than US$10-million in revenue last year, primarily from consulting contracts," the report said. "By contrast, Ottawa e-commerce software provider Shopify Inc. only reached the 500-employee mark the year it hit US$100-million in revenue.”