State of AI Report 2021
For those who went to the SAFe Summit this year, you’ll be increasingly interested in the new focus on data science and AI Dean mentioned in terms of the SAFe framework.
Key themes in the 2021 Report include:
AI is stepping up in more concrete ways, including being applied to mission critical infrastructure like national electric grids and automated supermarket warehousing optimization during pandemics.
AI-first approaches have taken biology by storm with faster simulations of humans’ cellular machinery (proteins and RNA). This has the potential to transform drug discovery and healthcare.
Transformers have emerged as a general purpose architecture for machine learning, beating the state of the art in many domains including NLP, computer vision, and even protein structure prediction.
Investors have taken notice, with record funding this year into AI startups, and two first ever IPOs for AI-first drug discovery companies, as well as blockbuster IPOs for data infrastructure and cybersecurity companies that help enterprises retool for the AI-first era.
The under-resourced AI-alignment efforts from key organisations who are advancing the overall field of AI, as well as concerns about datasets used to train AI models and bias in model evaluation benchmarks, raises important questions about how best to chart the progress of AI systems with rapidly advancing capabilities.
AI is now an actual arms race rather than a figurative one. 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.
Within the US-China rivalry, China's ascension in research quality and talent training is notable, with Chinese institutions now beating the most prominent Western ones. The world’s dependence on Taiwan's semiconductor industry, which makes AI chips for global tech giants, is a central point of geopolitical tension.
As with other aspects of the so-called “splinternet”, there is an emergence and nationalisation of large language models.
You can download the report here