Which A.I. planet do you live on?

A.I. researchers and A.I. practitioners live in different worlds.

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For the past three years, two London-based investors have compiled an extremely comprehensive summary of the current “State of A.I.” It’s the work of Ian Hogarth, who founded the concert discovery site Songkick and is now a prominent angel investor, and Nathan Benaich, a venture capitalist whose firm Air Street Capital focuses on startups built around applications of artificial intelligence.

This year’s report runs to 177 detailed Powerpoint slides. It’s a great way to take the pulse of the whole field.

The report touches on so many areas that I won’t be able to do it justice. But I will highlight a few things that struck me.

One trend emerging from the report that I touched on in this newsletter back in December: a growing dichotomy between the priorities of A.I. researchers and those of A.I. practitioners who work in other kinds of businesses, such as healthcare and finance.

The research community wants to push the boundaries of what A.I. can do. Benaich calls this group the “big-model world.” And for good reason. Many of the A.I. systems that are currently at the bleeding edge are truly gargantuan. Training a model that has hundreds of billions of parameters—as OpenAI’s GPT-3 language model does—takes mind-boggling amounts of computing power and costs millions of dollars.

Benaich and Hogarth question whether that is sustainable. “We are rapidly approaching outrageous computational, economic and environmental costs to gain incrementally smaller improvements in model performance,” the two write. They note that many machine learning researchers feel that progress in the field has stagnated and that fundamentally different approaches may be needed to bring us much closer to artificial general intelligence—systems that can perform many different kinds of tasks at human or super-human levels.

With the exception of the world’s tech giants, most companies can’t afford to live in “big-model world.” If more sophisticated A.I. actually depends on building larger and larger models, then “only a small number of actors will be able to compete,” Hogarth tells me. And while the likes of Google, Microsoft and IBM hope to sell their big models, pre-trained and pre-built, to customers of their cloud computing services, many businesses are reluctant to adopt giant pre-trained A.I. software because they don’t have enough insight into how it’s trained and how it’ll perform. Companies fear that by adopting it they may be inadvertently stepping into an ethical, reputational or regulatory morass.

Most businesses live on a different A.I. planet. Benaich calls this “the task-specific A.I. world.” These folks are looking to build A.I. systems that perform one highly specialized task exceptionally well. In building this task-specific software, even startups can compete. Benaich, for instance, points to a young London company called PolyAI that he’s invested in. It has built a chat bot-like conversational A.I. system that outperforms many of the larger language models, such as Google’s BERT, but is a fraction of the size of most cutting-edge NLP systems. (PolyAI’s system took in 59 million parameters compared to BERT, which even in its lightweight version uses 110 million parameters.) This allows PolyAI’s software to be trained on just a dozen GPUs—the graphics processing chips that have become the workhorses of A.I. computing—in a single day.

Benaich and Hogarth also have a nice slide deep in the report showing that 25% of the fastest-growing Github projects in the second half of 2020 were for “machine-learning operations” (MLOps), or the engineering nitty-gritty that lets companies deploy, run and maintain A.I. software over the long haul. MLOps is now trending as a Google search term for the first time. This, Benaich and Hogarth write, “signals an industry shift from technology R&D (how to build models) to operations (how to run models).”

Here are some other key takeaways from “The State of A.I.”:

• A.I. in healthcare and medicine is booming. Research on applying A.I. methods to various biology topics has grown 50% each year since 2017. The application of computer vision to medical imagery is having a massive impact in everything from ophthalmology to mammography. Advanced A.I. techniques are also making major inroads into drug discovery and drug research.

• Privacy-preserving machine learning is going to be huge. Interest in “federated learning,” which is one of the most promising techniques for allowing different parties to learn from the same data without compromising privacy, has exploded: More than 1,000 research papers on the topic were published in just the first six months of 2020, compared to just 180 in all of 2018. A major consortium of German hospitals along with Imperial College in London are testing one such system for sharing pediatric chest X-ray data.

• Demand for A.I. talent continues to far outstrip supply, despite a drop-off in job postings due to the Covid-19 pandemic. There are currently three times more job listings for A.I.-related expertise than there are people looking at such job postings, and the rate of job postings has accelerated 12 times faster than job views in recent years. This is true despite the fact that universities are now churning out many more graduates with machine-learning skills. Stanford University, for example, is now training twice as many students in NLP per year as it was between 2012 and 2014.

• The U.S. remains the best place in the world for A.I. talent—but a lot of that talent is foreign-born. American institutions and companies dominate the research output at top A.I. conferences. But the majority of top A.I. researchers working in the U.S. are not American—27% come from China, 11% from India and 11% from Europe. Less than a third—31%—are U.S.-born. The U.S. is also still doing a good job of holding onto foreign-born talent that comes to U.S. universities—85% of all international PhD students and 88% of Chinese PhD students, stay in the U.S. to work after graduation. But all of this is threatened by the Trump Administration’s restrictive visa policies.

• Regulators are finally starting to scrutinize the use of A.I. The focus right now is on facial recognition technology, with a growing number of laws coming into effect in U.S. states and around the world limiting its use. Regulatory pressure is starting to build on algorithmic decision-making in many other contexts too, including banking and insurance.

• The U.S. military is increasingly experimenting with cutting-edge A.I. techniques and incorporating A.I. into its arsenal. This has created a huge opportunity not only for established defense contractors, but also for a host of venture capital-backed startups that are selling the Pentagon everything from autonomous drones to intelligence analysis software to systems that can automatically detect and disrupt electronic communications.

Benaich and Hogarth always make a few predictions for the coming year. Last year, they got four of six predictions right. Here are three of their eight for next year:

• Nvidia will not be able to complete its acquisition of U.K. chip design company Arm.

• Someone will build a 10 trillion parameter A.I. language model.

• One of the companies using A.I. for drug discovery will either be acquired or have an IPO in a deal that values it at over $1 billion.

We’ll check in next year to see if they’re right. Meanwhile, here’s the rest of this week’s A.I. news.

Jeremy Kahn
@jeremyakahn
jeremy.kahn@fortune.com