Thank you for keeping us company as we have tried to stay abreast of the full-throttle development of AI in 2021.

We’ve witnessed the massive hype of large language and image models, followed by doubts regarding bias, sustainability and the growing power of big tech. Then finally moves towards smaller and more efficient architectures. And we’ve watched with a mixture of hope, cynicism and trepidation as world leaders sought to take tackle the climate crisis.

At the end of the year, we send season’s greetings and a relaxing holiday so that we may return refreshed and re-energised to address the challenges that await us in 2022!

Marcel and Graham

P.S. Our next issue will be on Tuesday 4 January.

Packed inside we have

  • OpenAI augments their language model with web surfing
  • A single DeepMind AI model that plays chess, Go and poker
  • The human face of management by algorithm

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Graham Lane & Marcel Hedman

Key Recent Developments

Improving the factual accuracy
of language models through web browsing

WebGPT: Improving the factual accuracy of language models through web browsing
We’ve fine-tuned GPT-3 to more accurately answer open-ended questions using atext-based web browser. Our prototype copies how humans research answers toquestions online – it submits search queries, follows links, and scrolls up anddown web pages. It is trained to cite its sources, which makes it …

What: OpenAI has developed a new technique to more accurately answer open-ended questions based on their GPT-3 large language model. The prototype copies how humans operate on the web, searching, following links, and scrolling through web pages. The prototype cites its sources, thereby making it easier to address factual accuracy. Humans assess the truthfulness of the AI answers against reliable sources. Furthermore, the quality is evaluated by humans comparing the AI-generated answers to human-generated answers.
Key Takeaways: The enhanced system performs better than the underlying GPT-3 system. The approach is part of a trend of seeking engineering solutions to make large language models more efficient and accurate. There has been much development in this area in the past year, summarised in this VentureBeat article.

DeepMind makes AI system that can play poker, chess, Go, and more

DeepMind makes bet on AI system that can play poker, chess, Go, and more
Alphabet-backed DeepMind developed a system, called Player of Games, that can win at both imperfect and perfect information games.

What: DeepMind has launched a new single system that can play both complete and incomplete games. Complete games such as chess represent a finite space in which each move can, theoretically, be known in advance. Incomplete games, such as poker, are infinite in the sense that players might bluff or collude with one another, and the AI must account for this.
Key Takeaways: Incomplete games resemble many areas of human activity such as negotiations or volatile problems such as logistics planning. However, complex incomplete games such as NetHack (a roguelike dungeon game) remain a significant challenge for AI systems. In the recent NetHack Challenge, symbolic AI agents (incorporating rules and human experience) significantly outperformed deep neural network agents. And humans out-performed them both.
Paper: Player of Games

How a facial recognition system potentially failed to recognise a driver of colour and may have cost him his job

Pa’s story: how a facial recognition system potentially failed to recognise a driver of colour and may have cost him his job
Please note the views expressed in the video are interviewee’s own and do not necessarily reflect the views of PI.

What: Uber requires drivers to submit selfies whilst working in order to prevent fraudulent shift swapping with unlicenced drivers. A dark-skinned driver, called Pa, claims that his account was deactivated without warning. He believes it may be because the facial recognition system struggled with his dark skin in poor lighting. He has been unable to get the account reinstated. In response, Uber states that all decisions regarding deactivation are undertaken by three trained human reviewers.
Key Takeaways: The article provides a human face to the growing concern both in Europe and the UK about management by algorithm. Campaigning organisations are calling for transparency, accountability, redress, respect for privacy and an end to the exploitation of workers’ data.

AI Ethics

🚀 South Korea to test AI-powered facial recognition to track Covid cases

A controversial pilot using AI, facial recognition and thousands of CCTV cameras tracking the movement of people infected with Covid gives rise to privacy concerns.

🚀 Amsterdam introduces mandatory register for sensors

Private companies, research institutions and government agencies are now obliged to report sensors deployed in public spaces.

🚀 U.N. Secretary General urges action on "killer robots"

A campaigner warns that "the pace of technology is really beginning to outpace the rate of diplomatic talks”.

Other interesting reads

🚀 AI researchers release toolkit to promote AI that helps to achieve sustainability goals

News of a unique, high-quality labelled dataset supporting AI research by tracking progress against Sustainable Development Goals.

🚀 AI used to see proteins change in a quadrillionth of a second

Almost instantaneous structural changes in a protein excited by light have been reconstructed using machine learning and quantum mechanics.

🚀 Boffins unveil artificial intelligence that thinks just like we do

Report of a “major milestone” in AI recognising information that is not present in its training data.
An example of the problem to be solved is a military targeting system fell from 90% accuracy to 25% following a subtle tweak in the system configuration.


Data scientist - AxionRay

Axion are looking to hire a talented NLP DS lead as they enter hypergrowth. Axion is a stealth AI decision intelligence platform start-up working with electric vehicle engineering leaders to accelerate development, funded by top VCs.

Comp: $100k – $180k, meaningful equity!

If interested contact:

Cool companies found this week


Immunai - uses single-cell genomics and machine learning to discover and develop immuno-therapies. The company raised $215 million in round B funding to develop its data atlas of annotated single-cell immune data


Sweep - provides a platform that enables large companies to lower their carbon footprints through big data insights and has raised $22 million in round A funding.


Serve Robotics - recently announced a robotic delivery service for Uber Eats in San Francisco and raised $13 million in seed funding led by strategic investor Uber Technologies

And Finally ...

Stuck for Christmas present ideas - how about an electric flying car?

Is this flying car the future for commuting? | Reuters Video
Swedish company Jetson Aero has built what it says is the first commercially available personal electric aerial vehicle and hopes that in the future it will be able to replace traditional cars as a means of transport.

AI/ML must knows

Foundation Models - any model trained on broad data at scale that can be fine-tuned to a wide range of downstream tasks. Examples include BERT and GPT-3. (See also Transfer Learning)
Few shot learning - Supervised learning using only a small dataset to master the task.
Transfer Learning - Reusing parts or all of a model designed for one task on a new task with the aim of reducing training time and improving performance.
Generative adversarial network - Generative models that create new data instances that resemble your training data. They can be used to generate fake images.
Deep Learning - Deep learning is a form of machine learning based on artificial neural networks.


Marcel Hedman
Nural Research Founder

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