Welcome to Nural's newsletter where you will find a compilation of articles, news and cool companies, all focusing on how AI is being used to tackle global grand challenges.
Our aim is to make sure that you are always up to date with the most important developments in this fast-moving field.
Packed inside we have
- Meta has built a massive new language AI—and it’s giving it away for free
- Google fires another AI researcher for questioning findings. Company says otherwise
- and Data Scientists given the Superpowers they need for unstructured data machine learning
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Graham Lane & Marcel Hedman
Key Recent Developments
Meta has built a massive new language AI—and it’s giving it away for free
What: "Meta’s AI lab has created a massive new language model that shares both the remarkable abilities and the harmful flaws of OpenAI’s pioneering neural network GPT-3. And in an unprecedented move for Big Tech, it is giving it away to researchers—together with details about how it was built and trained." The model is called Open Pretrained Transformer (OPT-175B).
Key Takeaway: Meta have made a move at the forefront of open science, revealing not just the NLP-model but also the methods behind its creation. The silo of large language models by the few with the means to train them has been a growing problem as others are cut off from benefits without forking up cash. It's good to see steps by these large institutions to address this.
Google Fires Another AI Researcher for Questioning Findings. Company Says Otherwise
What: "Google fired an AI researcher who called into question a well-known paper the company published in 2020, a third high-profile termination in less than two years, although the company says his removal was for other reasons." The researcher has been removed from a division of Google known as Google Brain. Google Brain is a group housing Google's grandest AI research ambitions.
Key takeaway: Following the firing of one of the leaders of its Ethical A.I. team, Timnit Gebru, Google has had many eyes focused on how they handle dispute over research. The importance of open critiquable research is without question... but is some research more critiquable than others in reality?
AI 4 good
🚀 Imperial College London has devised a method – Machine Learning – to calculate the carbon emission of all consumer goods, incorporating their full-life cycle and enabling ranking comparable goods accordingly
Other interesting reads
Cool companies found this week
Verge ag - AI-Powered Farm Operations Planning Software designed to Fill Critical Gap in Current Autonomous Farming Solutions. They have recently raised $7.5 million Series A funding from Yamaha Motor Ventures.
Red Sea Farms- Agtech startup that recently raised $18.5 million to reduce the carbon and water footprint of our food sector by designing, developing and delivering sustainable agriculture technologies for harsh environments.
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.
Nural Research Founder
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