🏥 Life (and) science
Continuing on the theme of reproducible and relevant clinical AI systems, the DECIDE-AI Steering Group proposed
a new step that puts AI models through their paces before entering large-scale clinical trials. This is similar to how drugs must complete a phase ½ trial or surgical innovations a stage 2a/2b trial. Specifically, this intermediate testing phase should evaluate human decision-making as they interact with an AI model in the wild, which isn’t always how the developer intended it. Human factors might come into play, such as needing additional variables to make sense of algorithmic recommendations or perhaps tweaks to how the model integrates into the workflow. Around the same time, the FDA published an action plan
motivating the regulation of AI-based software as a medical device.
Indeed, rigorous real-world testing is even more important because there is evidence that human doctors are susceptible to taking bad advice, whether it’s from a human or from an AI. This was shown
in an experiment that gave 250+ radiologists 8 classically difficult clinical cases to diagnose with 6 correct and 2 incorrect suggestions (from an AI system or a radiologist). The over-reliance effect was more pronounced for doctors with less training, which highlights that AI in healthcare is really not as simple as deploying a model with the best ROC curves.
Another recurring topic in healthcare is data. There is a lot of discussions today about data using new privacy-preserving technologies that promise to unlock data silos. Progress appears to be slow: is this because of bad data infrastructure or privacy or inertia amongst key players? Perhaps some answers could come from the Human Genome Project
. This herculean multi-center project to sequence and assemble the first human genome was committed to doing so in the open from the start. In 1996, researchers laid out the Bermuda Principles, in which all parties agreed to publish all human genome sequences in public databases within 24 hours without delay or exception. Twenty years later, however, the situation is less rosy: “Researchers tell tales of spending months or years tracking down data sets, only to find dead ends or unusable files. And journal editors and funding agencies struggle to monitor whether scientists are sticking to their agreements.”
It’s telling that the problem of data standards, interoperability, and sharing still persists despite being open-source by design. In this project, data is stored in more than one place, researchers tend to deposit the bare minimum to meet compliance requirements, and getting data out is hard. There is no specific universal policy on the format, database, or sharing policies. Food for thought.
Next, more news in clinical trial land. Following the first trial of an AI-designed therapeutic agent thanks to Exscientia, we now have (I believe?) the second trial of an AI-selected drug, this time thanks to BenevolentAI. BEN-2293
, a novel topical multi-target drug, is designed to treat atopic dermatitis - a rather nasty and chronic inflammatory skin condition (a few of my PhD lab mates studied it). This drug’s mechanism of action means that it can treat the inflammatory symptom as well as the itch. Fingers crossed it works well!
ML is also growing in relevance for energy and climate change. In the US, the Solar Energy Technologies Office funding program announced
$7.3M for projects that focus on ML solutions that improve the affordability, reliability, and value of solar technologies on the US grid. Congrats to team Camus!
🌎 (geo)politics of AI
In last month’s newsletter, I wrote about UK’s new AI Roadmap - a call to action and set of recommendations to make UK a ( most?) compelling place to do AI. The country is building on strong foundations in two areas that matter a ton: talent and research. While Roadmap suggests doubling down on these two vectors, I was surprised to stumble across a report
from non-profit Civitas entitled Inadvertently Arming China? Chinese military complex and its potential exploitation of scientific research at UK universities
. A key finding is that “over half of 24 Russell Group universities and many or UK academic bodies have or have had productive research relationships with Chinese military-linked manufacturers and universities. Much of research at university centres and laboratories is also being sponsored by UK taxpayer through research councils, Innovate UK, and Royal Society.”
Furthermore, it turns out that almost 20%
of high-impact research in STEM published from UK is in collaboration with Chinese researchers. This exposes two problems:
First, it tells the story that many of the best UK universities are selling their AI research to the highest international bidder. The report pulls out examples of labs or individuals, and sometimes an institute or two, that have accepted financial support from the Chinese government/military complex. While financial figures aren’t quoted, I can’t imagine that we’re talking about colossal sums that couldn’t otherwise be filled by domestic UK companies or government budgets. For example, the UK Department of Defense received
a $22B budget boost last year. As a side note, selling to the highest bidder happens across the stack
in the UK, from research through to public companies.
Second, it highlights the need for a holistic AI strategy that gets all key actors on the same page. UK universities are publicly-funded institutions, so the government has a key role to play in ensuring that they are properly funded and not made reliant on dubious, controversial, or ultimately sanctioned funding sources that could represent more risks than gains to the country in long term. There is of course a lot of precedents here that Civitas report reopens. Adding more color to China’s side of this debate is a report by CNAS on myths and realities
of China’s military-civil fusion strategy. Perhaps it is these challenges that motivate China to seek research in the UK….y write: Over the past 30 years, China’s defense sector has been primarily dominated by sclerotic state-owned enterprises that remain walled off from the country’s dynamic commercial economy. At its core, MCF is intended as a remedy to this problem….Still, only a small proportion of private companies have participated in defense projects, and enterprises that are developing technologies relevant to the military have found cutting through the red tape involved in procurement to be cumbersome, not unlike frustrations of ir American counterparts.
The UK also announced a new Advanced Research and Invention Agency
with the goal of funding high-risk, high-reward scientific research to the tune of £800M. At moment, the agency is recruiting a leadership team - so its ability to deliver will highly depend on who ends up at the helm. Watch this space.
Meanwhile, Huawei is contesting
its ban in the United States, stating that FCC’s ruling is “arbitrary, capricious…and not supported by substantial evidence.” Here is a timeline
of the ban.
More news on facial recognition: Virginia state-approved limits on police use of facial recognition after a number of wrongful arrests finally pressures authorities to rethink their reliance on frail technology. Next, a new service called Exposing.AI
was launched to show consumers how facial recognition technologies had been trained using millions of personal photographs from Flickr and SmugMug. For example, MegaFace dataset was created in 2015 by University of Washington researchers without the knowledge or the consent of people whose images y used. You can check whether your own Flickr photos have been included in one of 6 image datasets here
. I gave it a test with my old Flickr account, but thankfully my photos weren’t interesting enough to make the cut.
In the last issue, we discussed the shortage of semiconductors felt by the automotive industry, in addition to big-ticket plans for investing to create a European domestic semiconductor market. News
emerged that the EU project could involve TSMC and Samsung (not sure how anything can come about in a serious way without them?). Indeed, Europe deprioritised semiconductor manufacturing in the last 20 years and now insiders feel that new initiatives are too little, too late: “If you think that you can actually replicate [a well-oiled global supply chain] within a very short time, it’s simply not possible.”
says ASML CEO. If Europe is to achieve technological sovereignty, especially in deep technology, it must absolutely pull out the stops to build advanced foundries in Europe as soon as possible. I believe the financing is available and willing, but the know-how is severely lacking.
Meanwhile, in Taiwan, TSMC is hard at work
building their latest fab facility to launch the 3-nanometer platform by H2 2022. The company is paying a 2x bonus to workers if they continue to work during the Lunar New Year because TSMC is adamant not to lose any time. Moreover, President Biden reaffirmed
the US’ “rock-solid” commitment to “assisting Taiwan in maintaining a sufficient self-defense capability” while the US continues to decouple from China.
Relatedly, NVIDIA’s Arm acquisition is attracting even more heat
. This time, Qualcomm is said to have told regulators including the US FTC, the European Commission, the UK’s CMA and China’s SAMR that they oppose the deal. Google, Microsoft and Graphcore also
protest the deal. As a reminder, Ian and I predicted in the State of AI Report 2020 that this deal would not
be consummated. Meanwhile, NVIDIA’s VP of Applied Deep Learning Research - Brain Catanzaro - said that it’s entirely possible that “in five years, a company could invest one billion dollars
in compute time to train a single language model”
Industrial robotics are finally having their moment to shine. Stats in the US show that companies ordered 64% more robots
in Q4 2020 than 12 months prior, lifting the annual total up by 3.5%. Of note, it wasn’t the auto industry that generated this demand: Robot orders from food and consumer goods, life sciences, and rubber and plastics industries rose 50% YoY.
In autonomy land, Aurora entered into a long-term partnership
with Toyota and Denso to develop and test their Aurora Driver by the end of 2021. Oxbotica completed an autonomous vehicle trial
over 180km on a bp refinery through day and night, fog, rain, sunshine, and operating around machinery on the refinery. Note there are no road signs and road marks here :-)
🏭 Big tech
Baidu became the sixth company to receive
a fully autonomous testing permit from the California DMV (after Cruise, Waymo, Nuro, Zoox, and AutoX). The company received the first license
to test fully autonomous vehicles on public roads in China last December. A historical anecdote from Cade Metz’s “Genius Makers” (an end-to-end insider’s account of 60 years of modern deep learning), Baidu’s AV project was championed by Qi Lu who joined the company as COO in 2017 after leaving Microsoft. Qi had wanted Microsoft to build an AV as a means of forcibly exploring state-of-the-art technology in domains outside of Microsoft’s comfort zone so that it could compete in the deep learning race against Google. His project wasn’t approved back then. After arriving at Baidu, he was convinced that China would get AVs onto public roads and into consumer’s hands far quicker than the US would due to the willingness of cities to retrofit their infrastructure to suit AVs.
Text predictions, which Microsoft has tested
in beta with Outlook and Word users since September last year, are slated for general release
in March this year. This is a huge step for NLP in productivity. Office 365 is the canonical business software in use by over 1M businesses with over 200M monthly active users. Microsoft Bing also announced Speller100
: a zero-shot transformer-based spelling correction service that scales to 100+ languages thanks to carefully designed large-scale pre-training tasks. They found that users clicked on spelling suggestions (15% of queries have spelling mistakes) 67% of the time.
It is now well known (in our State of AI Report) that big tech is absorbing huge numbers of talented professors and students in AI. New work
came out by Nesta and Aalborg University called “The privatization of AI Research(-ers): Causes and Potential Consequences”. It finds that the academia to industry transition of researchers is outpacing industry to academia. The former transition accounts for 25% of the transitions completed by Top 5 universities but only 10% for Top 500 universities. Google and Microsoft are by far the most popular destinations, except for Princeton, which feeds Siemens (not sure why?).
Twitter ran an analyst day
) in which their CTO, Parag, described the company’s use of machine learning. Of note, deep learning adoption has gone from 15% to 60% of all ML models over the last two years. A large fraction of the 3x mDAU growth in the last 3 years is driven by ML model improvements to content relevance. The company said that 50% of their rule enforcement against abuse or harm is done proactively including through ML-based automation. Abuse reports are down 40% thanks to changes to the home timeline using ML modeling. Twitter will up its investment in ML technology as well as research including recommender systems, NLP, and graph ML.