🏥 Healthcare and life science
While the software investing world appears increasingly bullish on startup opportunities in biology, the life science investing world is voicing their bearish opinion even louder. Technology investors are funding AI-first life science companies at a clip, largely in the drug discovery and synthetic biology markets. In the case of drug discovery, my view is that software can help instrument the process with new data while bringing scale and rigor to its analysis. This means discovering new biology and drug assets. I’ve written about this thesis here
. Chris Gibson from Recursion paints a great picture here
Life science investors principally value drug discovery companies on the basis of their drug asset pipeline: how many drugs are they developing, at what stages are they and how likely are they to work in the clinic. In general, these investors take the view that AI-first drug discovery companies have technology platforms that are overvalued because they have yet to produce a consistent pipeline of valuable assets. Some also think that AI approaches to biology are reductionist and do not help us discover new biology. There have been many failed attempts where pharma has burned their fingers in the past. This piece explores more
. Natural Review Drug Discovery also featured a survey of expert opinions on both sides of the aisle here
The FDA authorised
two new AI-first diagnostic imaging products. Ultromics
(an Oxford-based startup) for echocardiography to diagnose heart disease, and Hologic
(a US startup) for a 3D breast mammography diagnostic.
Babylon Health signed
a 10-year deal to provide the city of 300k residents with an integrated health app. It will provide remote consultation, diagnosis, and live monitoring of patients with chronic conditions.
So far, there have been only 5
randomised clinical trials that use AI-based diagnostic systems. Four are in gastro and one is in ophthalmology. All five were conducted in China.
A group of clinicians in London and Birmingham published a paper
to help clinicians critically appraise ML studies in healthcare. Work like this is important to establish common grounds when it comes to appraising the quality of new work. For example, the paper asks clinicians to consider how data is labeled (e.g. from physician notes or by labelers), if these labels are actually clinically meaningful to avoid spurious predictions, if datasets were split correctly, if models were evaluated in the appropriate clinical pathway setting, etc.
Benevolent.ai searched their knowledge graph of structured medical data to find approved drugs that could help treat COVID-19. They focused on drugs that might block the viral infection process in the lungs and identified baricitinib
. The drug is an inhibitor of AAK1, which promotes endocytosis into AT2 epithelial cells expressing ACE2. It is hypothesized that the COVID-19 virus binds ACE2 to endocytose into lung cells. A few weeks later, baricitinib was fast-tracked into a clinical study
of symptomatic patients infected by COVID-19 in Prato, Italy. We’ll have to wait several weeks to see the results.
🔮The politics of AI
A study between MIT and Stanford put forward
a method for mathematically expressing and regulating an undesirable behavior in an ML system such that these can be controlled during training. This is accomplished by having the creator of the ML model define what an algorithm should do in a way that allows the user to directly place probabilistic constraints on the solution that is returned to the algorithm. The paper is available here
The German government plans to clamp down
on acquisitions of 10%+ positions (down from 25%) in its domestic companies developing “critical technology” by foreign investors and buyers.
President Trump proposed to implement new export controls
on geospatial imagery software. In particular, this relates to the sale of AI technology to countries including China. It expands an export control law that came into effect in 2018. Trump was also set to halt
a large civilian 1000-strong drone programme because the robots were built in China. The White House also released a 10 point principle
to govern safe AI. Meanwhile, Washington upped pressure
on TSMC to manufacture chips that go into military projects in the US instead of in Taiwan.
The Center for Security and Emerging Technology at Georgetown University published a report on “Keeping top AI talent in the United States
”. It highlights that foreign talent is crucial to American AI research. However, international graduates who want to stay are forced with significant obstacles in the US immigration systems, which are only getting worse.
Clearview AI was exposed
for gleefully scraping billions of photos from public-facing data on Twitter, Facebook and Instagram without user consent for use in its facial recognition software. This software was then sold to more than
2,200 government agencies and law enforcement and companies in 27 countries including Walmart and Best Buy to identify people. Clearview received a class action lawsuit in Illinois, where Facebook was most recently fined $550M for privacy infringements. A guy wrote up
his experience of submitting a request for his file from Clearview, citing California’s GDPR equivalent (CCPA).
The European Commission put forward a proposal for the regulation of AI in Europe here
. The Commission will be increasing its annual investments in AI by 70% under Horizon 2020 in order to reach €1.5B spent between 2018-2020. Some of their suggestions impose significant red tape, e.g. AI systems in Europe should be trained only on “European data” and companies must run their systems through a trustworthiness check box. If not handled with care, it feels like Europe will risk regulating itself out of being globally competitive in AI.
🚗 Autonomous technology
Remember 18 months ago when Morgan Stanley came out with a forward-looking valuation note on Waymo arguing that the business could be worth $175B
? Remember in Sept 2019 when MS cut their valuation projection to $105B
? Nowadays, AV enthusiasm seems to be tempered because of just how challenging the technology and economics are for real-world self-driving. In a first for one of Alphabet’s separate businesses, Waymo raised its first external financing round of $2.25B, valuing the business at $30B
. The consummation of this deal signifies a departure from Alphabet’s founders stance of always financing their own bets. It’s also the latest deal in a string of $B+ AV financings into GM Cruise, Uber ATG, and Argo.ai that cements the AV industry as one where only $B+ balance sheet contenders stand a chance.