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
- Archaeologists trained a neural network to sort pottery fragments for them
- The creation of fake realistic fingerprints using GANs
- OpenAI investing $100m into AI companies looking to create large disruption
- and more...
This week's newsletter was written on my birthday! Either take that as a sign of my commitment to our AI learning or poor planning :D
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Key Recent Developments
Archaeologists train a neural network to sort pottery fragments for them
What: Two researchers have trained a convolutional network (CNN) to correctly recognise and classify pottery fragments from different eras. The network was compared to four experts and managed to outperform two of the experts and tied with the other two.
Key Takeaway: The ability to correctly classify pieces of pottery to the correct time period using machine learning will save countless hours for archaeologists. It is great example of using AI in towards a new application area and bring increased efficiency while maintaining performance levels.
Google announces health tool to identify skin conditions
What: Google have built a web tool "that uses artificial intelligence to help people identify skin, hair, or nail conditions". In testing, the tool identified the correct condition in the top three suggestions 84 percent of the time. It included the correct condition as one of the possible issues 97 percent of the time.
Key Takeaway: The tool is not designed to diagnose the skin conditions on its own but could prove the be a valuable tool for remote doctor consultations. It will be interesting to see how they continue to develop as the tool received a Class I medical device mark in the European Union, designating it as a low-risk medical device.
High Fidelity Fingerprint Generation: Quality, Uniqueness, and Privacy
What: Researchers have used GANs, the networks behind deepfakes, to create 50k synthetic (fake) fingerprints which are "unique, diverse, and resemble the training dataset in terms of minutiae configuration and quality, while not revealing the underlying identities of the training data."
Key Takeaway: The generation of synthetic data based on the real source data has great implications to preserve privacy while allowing greater work on the data that has then been generated. What implications do you think we should be considering when creating unique fingerprints?
AI and Climate Change: The Promise, the Perils and Pillars for Action
Other interesting reads
Cool companies I have come across this week
Tessian - Tessian’s Human Layer Security platform automatically stops data breaches caused by employees on email.
EarthNet2021 - A machine learning challenge and dataset for Earth surface and localized impact forecasting.
AI/ML must knows
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.
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.
Nural Research Founder
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