Est. 2018, @soundboy and I compile the most important work in AI research, industry, talent, and politics to inform conversation about the #stateofai. Our report is open-access to all.
This year, we have seen AI become increasingly pivotal to breakthroughs in everything from drug discovery (ref: @exscientiaAI@RecursionPharma - 2020 Report IPO predictions!) to mission critical infrastructure like electricity grids and logistics warehouses.
Working with @OpenClimateFix, the UK's @NationalGridESO managed to halve the error of electricity demand forecast using a transformer-based prediction system. This could lead to lower carbon emissions and costs.
In industrial facilities across 30 cities in 15 countries, @intenseye's real-time computer vision software protects employees from >35 types of health and safety incidents that would otherwise go unseen.
With computer vision use disseminating across even more and more visual tasks, ranging from KYC on new customers joining trading platforms en masse during the pandemic to the interpretation of 3D medical scans. Model-in-the-loop training for HQ data comes to the fore @V7Labs
And as the world moved online almost overnight putting our logistics infrastructure to the test, deep learning systems helped automate 98% of stock replenishment decisions for online grocers every day @OcadoTechnology
This year’s report looks particularly at the emergence of transformer models, a technique to focus machine learning algorithms on important relationships between data points to extract meaning more comprehensively for better predictions. Starting in NLP, they're now everywhere.
Powering breakthroughs in protein structure prediction @DeepMind
To multimodal self-supervision, zero-shot learning, and image generation @OpenAI
And while AI’s growing impact on society and the economy is now evident, our report highlights that research into AI safety and the impact of AI still lags behind its rapid commercial, civil, and military deployment.
Notably, <100 people work on AI Alignment in 7 leads AI orgs.
AI researchers have traditionally seen the AI arms race as a figurative one -- simulated dogfights between competing AI systems carried out in labs -- but that is changing with reports of recent use of autonomous weapons by various militaries.
With governments revving up not only the rhetoric, but matching it with real money.
Meanwhile, new governance experiments are taking shape in the AI ecosystem: @AnthropicAI as a public benefit corporation, @huggingface as an open source private company, or even EleutherAI as an open source Discord server-based community with no company attached.
🦄 In industry, there are more AI unicorns than ever before -- 182 by our latest count -- that total $1.3T of combined enterprise value🔥. This would have been unfathomable back in 2018 when we first created this report @dealroomco
Importantly, we saw a huge volume of exits in the last 12 months -- €750B across M&A, secondaries, IPOs, SPACs -- whether it's Nuance/MSFT or IPOs for @SentielOne, @Darktrace, @RecursionPharma, @exscientiaAI and more...@dealroomco
But before we take a peek into our predictions for 2021, let's review those from 2020!🚦 5/8 = YES! 🤓 2/8 = NOPE :-(
1/8= sorta, kinda...
OK, feeling confident now, let's look at 2021...
Here are @stateofaireport predictions for 2021: a mix across technical breakthroughs, politics, and industry news! What do you think?
The @stateofaireport is always a collaborative project designed as a public good and we’re incredibly grateful to @osebbouh - our star Research Assistant - as well as our Contributors and Reviewers, all of whom played a part in making the report what it is.
OK, now head over to stateof.ai to read all 188 slides at your own leisure and let us know what you think! Thank you 🙏
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The letter is entitled "Universities need investors to bridge the start-up funding gap". " Prof Gast essentially says that there is *no* problem w/how universities spin out companies, pointing to "750 active companies that raised £800M" at Imperial, i.e. £1m per spinout on avg..
Firstly, aggregate statistics don't tell the real story. Even so, £1m/spinout isn't great. Consider @ycombinator who have driven the standardisation of permissive fundraising terms. Their 3,000 companies have a combined value >$300B, or $100M/co on avg. ycombinator.com/topcompanies/
As a researcher-turned-investor, I've seen this first hand. Forming spinouts back then was discouraged, miring colleagues in bureaucracy that undermined their work. Today, startups are still not rewarded in our academic culture; worse, founders are considered problem children.
The process of spinning out a company from one's academic work is so painful and economically punitive peers ditched their entrepreneurial ambitions entirely. Some hack around official routes to become “sneak outs” while others depart for more entrepreneur-friendly universities.
- VC funds investing through the GFC were some of the best for vintages 2000-2019 by TVPI, esp 2008 vintage
- M&As likely delayed by 2 years from now
- Valuation cuts by 30% sets pricing to 2017 levels
👇
Communication is key across the board: LPs-GPs-startups
Some LPs starting to look at opportunistically at selling non-core, unfunded commitments
HNW/FOs proactively seeking liquidity
GPs: if you engage in secondary position processes with new LPs, be careful what you share to avoid folks who express interest for the sake of data hoovering.