🏥 Life (and) science
The last newsletter issue discussed how AI models in healthcare must learn clinically-relevant tasks. To continue this thread, Xiao (who works on SPIRIT-AI and CONSORT-AI) raised an important point that is easy to forget: improved disease detection does not automatically equal better patient outcomes. In a striking review
of melanoma, it turns out that despite a six-fold increase in incidence (driven in part by better detection of the disease), patient mortality has remained totally flat. So when you consider the next paper or startup claiming improved detection of disease with AI, ask whether there is any evidence for improved outcomes
The gut is your second brain: new evidence
from a large international study found that the specific composition of gut microbe species is strongly linked to diet and health. The collaborative project involving King’s, Harvard, MGH, Trento and ZOE (an Air Street portfolio company) found a group of 15 “good” and 15 “bad” gut microbes that are linked to inflammation, blood sugar control and overall body weight. With trillions of bacteria in your gut and a precise understanding of your biology (e.g. blood fat and blood sugar control), it’s now possible to use machine learning to personalize food recommendations.
2020’s darling life science technology is undoubtedly mRNA vaccines. This technology makes use of chemical synthesis to rapidly manufacture arbitrary mRNA sequences that encode for proteins of interest. Combined with lipid nanoparticles, mRNAs can be systemically delivered to kickstart the immune system to fight infections. Personally, I see exciting ways that machine learning can be integrated into rational and predictive mRNA vaccine design. In a new Science paper
entitled Learning the language of viral evolution and escape
, MIT scientists make strides in this direction. They consider today’s topical challenge: COVID-19 is rapidly mutating to become more infectious and potentially less susceptible to vaccines developed against its parents. Today, we wait until these mutations are detected in the wild and then scramble to determine whether a) today’s vaccines are still effective and b) produce new vaccines. Instead of being reactive, why not go on the offensive by modeling viral escape using unsupervised language models? This paper does that by training models to predict whether sequence mutations to viral spike proteins would lead to structural escape. We can then empirically test these mutants and stockpile vaccine doses specific to these mutations in case they arise. AI-first mRNA vaccines 🔥
🌎 The (geo)politics of AI
The UK’s Office for AI, DCMS and BEIS published their AI Roadmap
. The document sets recommendations for a potential UK National AI strategy across R&D, skills and diversity, data, infrastructure and public trust. I’m particularly interested in solutions to increasing the magnetism of the UK as a center for AI R&D, massively funding our universities, boosting training programs, incentivizing startup creation and especially revamping our spinout playbook to make it the most permissive in the world. The stakes here are very high. Looking at recent data
from NeurIPS 2020 (Thanks, Sergei @ Criteo!), US-based organizations publish almost 500% more than UK peers. At ICML 2020, the US is again ahead
of the UK by 672%. The UK certainly has the raw potential to do far better, but it continues to lose talent to the US. Immigration in post-Brexit Britain is so far unlikely to stem the bleed unless we take a radical approach.
On a related topic, Dealroom published a report on European deep technology
, which I had the pleasure of contributing to. It’s fascinating to see funding into deep technology themes (e.g. AI, biotech, quantum, energy) grow almost 15x in the last decade to now capture one-quarter of all European venture capital funding. We clearly have a huge opportunity to be a world leader in deep technology.
In the US, the US Congress approved the National Defense Authorization Act
for 2021, which includes many provisions with consequences for AI and $741 billion for defense spending. These are summarized here
. I thought it was neat to see that the Secretary of Energy is directed to focus on integrating AI systems for energy simulations and control systems to enhance decision making. The NSF is also permitted to establish a network of research institutes focused on AI with funding for 5 years.
President Joe Biden also set up his White House Office of Science and Technology Policy. In a big win for science
, he nominated Eric Lander, a truly outstanding scientist to lead. Lander is the MIT mathematician and geneticist who played a big part in the Human Genome sequencing project and founded the powerhouse Broad Institute. In addition, Nobel laureate in Chemistry Frances Arnold, a world leader in protein engineering, was chosen to head the President’s Council of Advisors on Science and Technology.
European carmakers including Renault, Daimler, and Volkswagen are suffering from a supply shortage
of semiconductors that is making them cut vehicle production. While these chips aren’t yet used to run AI workloads in cars, the growth of electric vehicles is driving up the demand for chips in automotive. At the same time, smartphones are using more chips too, and it appears that semi fabs are prioritizing those shipments. Although the largest auto chip suppliers e.g. Infineon and NXP are both European companies with European fabs, we clearly do not have enough domestic manufacturing capacity. With landmark European auto companies suffering, this news hits where it hurts. It will add more urgency to Europe’s ambition to invest $145B into reaching technology sovereignty in the semiconductor supply chain.
International companies are dialing up the heat
on NVIDIA’s acquisition of Arm after the UK’s CMA opens up consultations for market participants to comment. Because it’s Valentine’s day, I’m featuring the best fanmail of the month. This one is a beautifully topical poem:
Roses are red
Violets are blue
If NVIDIA doesn’t acquire ARM
Your prediction will have been true
– Neal from London (winner of ‘your guide to AI fanmail’ 14 Feb, 2021)
As predicted in our recent State of AI Report, a major corporate AI lab appears to have shut its doors. Jeff Ding reported
that Alibaba’s AI lab fizzled out and its staff was absorbed into Alibaba’s Cloud Intelligence organization.
Amazon has implemented
bias detection methods developed by Brent Mittelstadt, Chris Russell and Sandra Watcher into SageMaker Clarify. As discussed in their RAAIS 2020 talk here
, their conditional demographic disparity test ensures fairness in algorithmic modeling. Bias and ethics of AI really hit the prime time in 2020 after progressing for many years without due attention in academia.