Welcome to Nural's newsletter where we explore how AI is being used to tackle global grand challenges.

As always in the newsletter you will find a compilation of articles, news and cool companies all focusing on using AI to tackle global grand challenges.

Packed inside we have

  • Mimicking the human mind might be the key to unlocking the next level of AI
  • Researchers have discovered a method of detecting deepfakes by comparing reflections in the eye of those in the photo
  • Recreating videos of Doctor Who using brain activity from someone watching the show
  • and more...

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Key Recent Developments

What: Researchers at MIT have created an ML technique which calculates the binding affinities between drug candidates and their target proteins in the body. The affinity is a measure of the strength of the bind and naturally a stronger bind is desired for the most effective drug action.

Key Takeaway: It should lead to faster drug discovery and protein engineering as the ability to predict the "stickiness" of a bind between the drug and the target protein is a good indicator of a drug’s potential effectiveness.

Faster drug discovery through machine learning
MIT researchers have developed DeepBAR, a machine learning technique that quickly calculates drug molecules’ binding affinity with target proteins. The advance could accelerate drug discovery and protein engineering.

What: OpenAI have published a review of some of the applications that have been launched using their GPT3 API. Where GPT3 is their large language model with billions of parameters.

Applications include:

Algolia - "Uses GPT-3 in their Algolia Answers product to offer relevant, lightning-fast semantic search for their customers."

Fable Studio - An AI Character Engine, powering story-driven Virtual Beings

They have put out a call for healthcare & climate based applications amongst other areas.

🚀Full article

What: Despite the impressive progress of AI systems, they are still no match for the human brain in terms of flexibility and adapting to changing information. Therefore, Nara Logics have launched a platform that mimics the human brain to allow for better understanding of causality and working with unlabelled data.

Key Takeaway: The ability to understand why an ML algorithm makes the recommendation that it does is essential for the increased adoption of AI especially in fields like healthcare.

Artificial intelligence that more closely mimics the mind
Nara Logics, co-founded by MIT alumnus Nathan Wilson PhD ’05, is attempting to mimic the brain with an AI platform powered by an engine it calls Nara Logics Synaptic Intelligence.

What: A nice overview of what to consider to effectively use AI to tackle climate change. This is particularly useful for those starting climate related AI projects.

Key Takeaway: Recognising the climate impact of using AI itself is sometimes missed when launching AI climate solutions. A large amount of energy is required to train models and so this must be included in the cost benefit analysis of whether to move ahead with a project.

The AI Gambit – Leveraging Artificial Intelligence to Combat Climate Change | Montreal AI Ethics Institute
🔬 Research summary by Alexandrine Royer (@AlexandrineRoy2), our Educational Program Manager. [ Original paper by Josh Cowls, Andreas Tsamados, Luciano Floridi, Mariarosaria Taddeo] Overview: 2020′

AI Ethics

🚀 Why machine learning struggles with causality

🚀Scientists developed a clever way to detect Deepfakes by analysing light reflections in the eyes

🚀Europe’s artificial intelligence blindspot: Race

Other interesting reads

🚀Why Startups Will Be At The Forefront of GPT-3 Adoption – Four Trends For 2021

🚀It is time to negotiate global treaties on artificial intelligence

🚀Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity (Translation: They looked at the brain activity of someone watching Doctor Who and used AI to recreate the images seen!)


Cool companies I have come across this week


Nara Logics - Builds a network of explicit and inferred connections between chaotic, siloed enterprise data for real-time, context relevant recommendations and gives the reasons behind them.


Open Climate Fix - Non-profit research and development lab, focused on reducing greenhouse gas emissions. Their approach is to search for ML (Machine Learning) problems where, if they solve a well-defined ML task, then there is likely to be a large climate impact.

AI/ML must knows

AutoML - The process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model.
Transfer Learning - Reusing parts or all of a model desinged for one task on a new task with the aim of reducing training time and improving performance.
Tensorflow/keras/pytorch - Widely used machine learning frameworks
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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