State of AI Report 2020

Original article was published by Nathan Benaich on Deep Learning on Medium

It’s been a curious year for AI — with important breakthroughs in NLP and drug discovery tempered by AI’s to-date modest contributions in the fight against Covid-19.

Compiled with my friend Ian Hogarth during the pandemic, this year’s State of AI Report ( is a remedy to fears of a new AI winter, and we hope a much needed reminder of the extent of innovation and progress carrying on despite the present circumstances.

However, our report also demonstrates just how much of this innovation is enabled by massive computing infrastructure that is increasingly in the hands of big tech companies.

We need to think carefully about what this means for the future of AI innovation, but also acknowledge that we are seeing more and more AI-first startups implementing the core ideas that emerge from this research.

Elsewhere in the report, look out for the wealth of biotech progress. I’m particularly excited about the impact that AI is having in the life sciences as biology and medicine become large data domains ideal for AI applications. So much has changed since I finished my PhD in cancer research in 2013…we’re now on the brink of being able to decode a lot more about our health and revolutionise how we treat disease.

We write this report to compile the most interesting things we’ve seen, with the aim of provoking an informed conversation about the state of AI, and unpacking what developments in the field mean for our future. We’d love your view on the report and your take on our predictions. Please do share any observations you might have!

New to the 2020 edition are invited content contributions from two dozen well-known and up-and-coming companies and research groups. We’re incredibly grateful for their contributions that dive deeper into key themes of 2020: NLP, biology, and autonomous systems. Thank you to our reviewers for volunteering their time and valuable insights that keep us honest.

What are the key takeaways, you ask? There are many, but here are four key findings:

  1. AI R&D costs are soaring: Only a few labs that are led or funded by the likes of Google, Microsoft, and Facebook can afford the high cost of computing power needed to pursue the most exciting AI research. It most likely cost Microsoft-funded OpenAI $10 million to train GPT-3. Practitioners fear innovation could stagnate.
  2. 85% of AI research papers do not publish code: Open sourcing AI research is important for accountability, reproducibility and driving progress in AI but, in reality, the vast majority of AI research papers are behind corporate lock and key. The industry has not evolved much since 2016 when 90% of research did not publish code along with their papers.
  3. Biology experiences its “AI moment”: This year signaled a new era for AI-first drug discovery startups with mega-rounds, the first-ever AI-created drug in clinical trials and the first medical imaging product to be acknowledged by the US government (Medicare/Medicaid). In spite of pharma doubts, AI-powered medical companies started rapidly industrialising and scaling
  4. Explosion of AI-powered military: As the military applications grow, so do calls for new AI safety measures. With another wave of countries declaring national AI strategies, more governments doubled down on the military adoption of AI technology.

With thanks to: Babylon Berkshire Grey CloudNC ComplyAdvantage Disperse Faculty Graphcore Hugging Face InVivo AI LabGenius James Field Lyft Level 5 Niantic Onfido ONI OpenMined PolyAI PostEra Recursion Secondmind Signal Tessian tinyclues Tractable ZOE

And our reviewers:

Jack Clark, Jeff Ding, Chip Huyen, Rebecca KaganKagan, Andrej Karpathy, Moritz Mueller-Freitag, Torsten Reil, Charlotte Stix, and Nu (Claire) Wang.