3D printing organs in space

Welcome to the 23rd edition of the AI and Global Grand Challenges newsletter, where we explore?how AI is tackling the largest problems facing the world.

The aim: To inspire AI action by builders, regulators, leaders, researchers and those interested in the field.

If you would like to support our continued work from ￡1 then click?here!

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Packed inside

• 3D printing organs in space
• Edge ML and smoke detectors
• Diffusion ML models beating GANs performance in image generation
• and more...

Make sure to subscribe to our site as well:?www.nural.cc

Marcel

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Source:?The Sequence

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Key recent developments

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How a largely untested AI algorithm crept into hundreds of hospitals

What:?"Epic, a private electronic health record giant and a key purveyor of American health data, accelerated the deployment of a clinical prediction tool called the?Deterioration Index?[at the start of the pandemic]... A?study?of 392 COVID-19 patients admitted to Michigan Medicine shows that the index was moderately successful at discriminating between low-risk patients and those who were at high-risk of being transferred to an ICU."

Key Takeaway:?While the impact of this tool in disrupting the triage process is undeniable. What is of concern was how quickly the tool was deployed. Of course the heightened panic at the start of the pandemic is a clear reason behind this but it still raises questions... especially when it so heavily affects a Doctor's role in patient care.?

(Triage - "determining how sick a patient is at any given moment to prioritize treatment and limited resources.")

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Why astronauts are printing organs in space

What:?A Nasa astronaut and formerly a battlefield doctor has been experimenting with 3D printing organs in space which would be used in transplants. By printing in space, it helps to reduce the cellular collapse that often occurs when attempting similar techniques under the Earth's gravity.

Key Takeaway:?While this is not explicitly AI focused, it is still science for good. It's a powerful example of the new possibilities that are open to us as space travel becomes more common place and I wonder if there is a potential for ML disruption in this cell growth process...

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Using Embedded Machine Learning to Perform Smoke Detection

What:?ML on the edge is being used in smoke detectors to reduce detection latency and power consumption which are vital in building robust smoke detectors. The innovation is required as new regulation in the US requires a higher performance from smoke detectors than previously.

Key Takeaway:?Edge ML is when a device locally processes data through an ML system as opposed to requiring an internet connection and a remote based model. Edge ML has numerous use cases including agriculture and other environments where fast processing is required.

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New algorithms show accuracy, reliability in gauging unconsciousness under general anaesthesia

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AI Ethics

???Why We Should End the Data Economy

???Google says it’s committed to ethical AI research. Its ethical AI team isn’t so sure

???It’s 2021. Do You Know What Your AI Is Doing?- most companies don’t—and are deploying artificial intelligence at significant risk

???Biden has quietly deployed an app for asylum seekers. Privacy experts are worried

Other interesting reads

???OpenAI's diffusion models beat GANs at what they do best

???Microsoft’s first GPT-3 product hints at the commercial future of OpenAI

???Machine learning is booming in medicine. It’s also facing a credibility crisis

???DARPA Calling for AI Proposals to Measure How Authoritarian Regimes Control Information

Papers

???Everyone wants to do the model work, not the data work’: Data Cascades in High-Stakes AI

???Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

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Cool companies I have come across this week

Self Driving Cars

Waabi.ai?- Waabi is bringing the promise of self-driving closer to commercialization than ever before.

AI & Robotics

Sanctuary.ai?- General purpose robot designed for tasks requiring human level intelligence and manual dexterity.

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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.

Thanks for reading and I'll see you next week!

If you are enjoying this content and would like to support the work then you can get a plan?here?from ￡1/month!

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Marcel Hedman

Nural Research Founder

www.nural.cc

Marcel Hedman, is the current Choate Memorial Fellow at Harvard University focusing his efforts in data science. He also serves on the senior leadership team of the?Future Leaders Network.

This newsletter is an extension of the work done by?Nural Research, a group which explores AI use to inspire collaboration between those researching AI/ ML algorithms and those implementing them. Check out the website for more information?www.nural.cc

Feel free to send comments, feedback and most importantly things you would like to see as part of this newsletter by getting in touch?here.