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.
We now have a Jobs section, currently featuring an exciting data scientist role at startup AxionRay
Reach out to advertise your own tech roles!
Packed inside we have
- An autonomous tractor controlled from a smartphone;
- machine learning used to develop both cancer treatments and new materials;
- and a video of an 80 mile, autonomous journey by a truck on public roads.
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Graham Lane & Marcel Hedman
Key Recent Developments
How John Deere created its autonomous tractor
What: U.S. tractor company John Dere demoed an autonomous tractor at the recent Consumer Electronics Show, announcing it will be available by the end of 2022. The tractor can autonomously navigate, plow, sow and avoid obstacles. It can also identify and kill unwanted weeds with a precision spray of herbicide which is claimed to reduce chemical usage by up to 80%. The tractor operates with a deep neural network and can be controlled from a smartphone.
Key Takeaways: The deployment of autonomous vehicles is progressing rapidly in farming and truck transport (reported elsewhere in this newsletter) since the operating environment is relatively simple. Some commentators are concerned that John Dere could be the “Facebook of farming” making farmers dependent on the company and exploiting data “harvested” by the tractors
Machine Learning helps repair genetic damage
What: Using ML, a team of researchers have discovered how to repair genetic damage to DNA. Chemotherapy works by causing DNA lesions, which cause cancer cells to die. Knowing how DNA lesions are repaired will shed light on cancer treatments. The researchers processed thousands of images of cells after genetic damage, introducing 300 different proteins to study what role they played and discovered nine new proteins that are involved in cell repair.
Key Takeaways: The large volume of data generated from images has been a limiting factor in studying DNA repair. The team mainly benefited from the use of ML to process high volumes of data more quickly. This included relatively simple, unsupervised K-means clustering that yielded new information.
Paper: Assessing kinetics and recruitment of DNA repair factors using high content screens
Machine learning used to predict synthesis of complex novel materials
What: A research team has applied machine learning to guide the synthesis of new nanomaterials, eliminating barriers associated with materials discovery. The team created a huge library of nano particles made of combinations of any of the 118 elements in the periodic table. This data was used to train an ML model. The model predicted entirely novel compositions of four, five and six elements that would result in a certain structural feature. It made 19 predictions, of which 18 were correct.
Key Takeaways: It is hoped this approach may drive discoveries across areas critical to the future, including plastic upcycling, solar cells, superconductors, and qubits. The team is using the approach to find green catalysts critical to fuelling processes in clean energy, automotive and chemical industries.
Paper: Machine learning–accelerated design and synthesis of polyelemental heterostructures
🚀 Online public discourse on artificial intelligence and ethics in China: context, content, and implications
The article argues that the general public in China has a sense of anxiety towards AI rather than the widely held view that Chinese people are positive towards digital technologies.
The paper argues that China produces ‘a disproportionate share’ of research about core AI-related surveillance technologies.
🚀 Data Statements for Natural Language Processing: Toward mitigating system bias and enabling better science
A discussion of the idea that "data statements" (meta data and a description of datasets) will help practitioners to use data responsibly.
Other interesting reads
🚀 TuSimple becomes first to successfully operate driver out, fully autonomous semi-truck on open public roads
A truck navigated 80 miles without a human in the vehicle, traveling on surface streets and highways, interacting naturally with other motorists, and without special traffic management.
Video of the night-time trip.
Demonstration of a 5G network of sensors and computers that guides vehicles autonomously without requiring sensors on individual vehicles. This could be used for loading and unloading at distribution centres.
The UK government has announced plans for a new AI Standard Hub with practical tools for businesses, a community platform and educational materials.
Free AI software rapidly predicted Omicron's structure from the sequence of amino acids encoded in Omicron’s genome. The result was not perfect but points the way to future development.
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: email@example.com
Cool companies found this week
And finally ... whoever thought that your home assistant would be a robotic table.
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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