2021 AI Predictions: What We Got Right And Wrong – Forbes

DeepMind CEO Demis Hassabis had a big 2021.

In December 2020, we published a list of 10 predictions about the world of artificial intelligence in the year 2021.

With 2021 now coming to a close, lets revisit these predictions to see how things actually played out. There is much to learn from these retrospectives about the state and trajectory of AI today.

Outcome: Wrong

As of the beginning of this year, no autonomous vehicle company had ever gone public. 2021 is the year that that all changed.

TuSimple, Embark and Aurora have all debuted on public markets this year. Argo is deep in preparations to go public. Plus.ai and Pony.ai both announced SPAC deals this year (though Pony.ai has since shelved its plans). Credible rumors are swirling about upcoming public market debuts for other autonomous players.

But Waymo and Cruise are not included on that list.

Given that Waymo and Cruise are the most well-capitalized of all AV companies, it makes sense that they would not necessarily be the first ones to need to tap public markets for more capital.

Still, while our timing proved premature, we expect both of these companies to eventually be publicly traded.

Outcome: Wrong

Deepfakes, which just a couple years ago were an oddity on the fringes of the Internet, have thrust themselves into mainstream public consciousness in 2021.

From an Anthony Bourdain documentary to viral Tom Cruise clips, from a widely condemned new pornography app to a bizarre story about a cheerleaders vindictive mom in small-town America, deepfakes are rapidly becoming a part of our societal milieu.

But no deepfake has yet fooled large numbers of viewers and caused meaningful real-world damage in the realm of U.S. politics. Lets hope it stays that way in 2022.

Outcome: Right(ish)

Research activity in federated learning has indeed surged this year.

The number of academic research papers published on federated learning grew from 254 in 2018, to 1,340 in 2019, to 3,940 in 2020, according to Google Scholar. In 2021 that number jumped to 9,110, with four weeks still left in the year.

In last years predictions we specified that this number would surpass 10,000 in 2021hence the ish. This one may come down to the wire....

Outcome: Wrong

No multi-billion-dollar acquisitions occurred in the world of AI chips in 2021.

Instead, the leading AI chip startups all raised rounds at multi-billion-dollar valuations, making clear that they aspire not to get acquired but to become large standalone public companies.

In our predictions last December, we identified three startups in particular as likely acquisition targets. Of these: SambaNova raised a $670 million Series D at a $5 billion valuation in April; Cerebras raised a $250 million Series F at a $4 billion valuation last month; and Graphcore raised $220 million at a valuation close to $3 billion amid rumors of an upcoming IPO.

Other top AI chip startups like Groq and Untether AI also raised big funding rounds in 2021.

Outcome: Wrong

In 2021, none of the leading AI drug discovery startups was acquired by a pharma incumbent. Instead, just like the AI chip startups in the previous section, these companies raised record amounts of funding to challenge the incumbents head-on.

Several AI drug discovery players completed IPOs in 2021, making them among the earliest AI-first companies in the world to trade on public markets.

Recursion went public in April; Exscientia followed it in October. Insilico is slated to IPO soon. Insitro, XtalPi and a handful of other AI drug discovery players raised massive private rounds this year. For most of these competitors, the window for an acquisition has likely passed.

Outcome: Right

Finally, a prediction that we nailed!

For years, U.S. policymakers have been relatively inattentive to the strategic importance of artificial intelligence while more forward-thinking governments like China and Canada have rolled out detailed national strategies to position themselves as global AI leaders.

This changed in a big way in 2021, with an explosion of U.S. public policy activity related to AI. At the beginning of the year, Congress passed legislation to promote and coordinate AI research. Numerous additional AI-related bills have been introduced in both chambers of Congress this year. A dedicated White House group has been established to oversee the nations overall approach to AI. The U.S. military has gone into overdrive in its AI investments. In October, the Biden administration called for an AI Bill Of Rights for the American people. The list goes on.

It would be going too far to say that the U.S. government has established a cohesive national AI strategy. But in 2021, artificial intelligence rocketed to the forefront of Washingtons policy agenda.

Outcome: Right

In January 2021, less than a month after we published our predictions, Google announced that it had trained a model with 1.6 trillion parameters, making it the largest AI model ever built.

Now the question ishow big will these models get in 2022?

Outcome: Right(ish)

The crowded MLOps landscape has begun to consolidate in 2021. In several instances this year, large AI platforms have acquired smaller startups building tools and infrastructure for machine learning.

Probably the most noteworthy example came in July with DataRobots acquisition of Algorithmia, which had raised close to $40 million in venture capital funding.

Other examples include HPEs acquisition of Determined AI and DataRobots acquisition of decision.ai.

But there was less M&A activity in MLOps this year than we expected. In last years predictions, we listed 14 MLOps startups that we saw as potential acquisition targets. Of these, only oneAlgorithmiaended up being acquired. (Several others on that listWeights & Biases, Snorkel AI, OctoMLinstead raised rounds at monster valuations.)

Outcome: Right

Regulatory momentum for antitrust action against Big Tech has been building for years given the outsize influence that companies like Alphabet, Amazon and Facebook exert over the economy. But over the past year, antitrust regulators have increasingly refined their messaging by focusing on the structural advantages that these giants enjoy in AI. The jumping-off point, almost always, is the companies unrivaled data assets and aggressive data accumulation practices.

From recent Senate antitrust hearings to presidential Executive Orders, this theme of unfair data advantages translating into unfair AI advantages is becoming an increasingly important dimension of the Big Tech antitrust movement.

Last month, for instance, Lina Khans Federal Trade Commission appointed prominent AI critic Meredith Whittaker to a special role as the FTCs senior adviser on AI. As one industry observer put it: Whittaker's hiring is just the latest evidence of the FTCs attention on algorithms and algorithmic issues.

Outcome: Right

Of the predictions on last years list, this one was the most open-ended and least verifiable. Even so, plenty of developments in 2021 point to the continued emergence of biology as the most important and high-impact of all AI application areas.

AI is transforming drug discovery, with profound implications for the pharmaceutical industry and the future of human health. AI-discovered therapeutics are now in clinic; AI drug discovery startups are now trading on public markets.

DeepMinds landmark AlphaFold work, which was published in July, is a testament to the almost magical potential for machine learning to uncover fundamental truths about how life works. We have previously argued in this column that AlphaFold is the most important achievement in the history of AI. As Alphabets big announcement about Isomorphic Labs last month underscores, AlphaFold is just the beginning.

Perhaps more so than any other area of AI, world-class talent and investment dollars are flooding into computational biology. Take, for example, Eric Schmidts $150 million donation earlier this year to establish a new center at Harvard and MIT that will catalyze a new scientific discipline at the intersection of biology and machine learning.

In the years ahead, the application of computational methods and machine learning to biology is poised to transform societyand perhaps life as we know it.

Note: The author is a Partner at Radical Ventures, which is an investor in Untether AI.

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2021 AI Predictions: What We Got Right And Wrong - Forbes