AI, Space, Floods

AI, Space, Floods

Welcome to the 26th 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!

----

Packed inside

  • AI detecting floods from space for the first time
  • An open source version of AlphaFold2, DeepMind's protein folding algo
  • AI powered interview tools giving German-only speakers, high ratings in their English speaking scores (via an experiment to test the limits of the tools)
  • and more...

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

Marcel

__________________________________

No alt text provided for this image

Source

__________________________________

Key recent developments

---

Artificial Intelligence pioneered at Oxford to detect floods launches into space

What:?"A new technology, developed by Oxford researchers, in partnership with the European Space Agency’s (ESA) Φ-lab, pilots the detection of flood events from space... The work is a first step towards relaying real time information from space to disaster response teams."

Key Takeaway:?This model can significantly reduce the costs of flood detection which is of huge benefit for low income countries. It also represents a big step for ML use in space as this is the first time "a machine learning model for this type of task will be actually deployed in space."

Paper

---

These Are The Startups Applying AI To Tackle Climate Change

What:?"Climate change is the most pressing threat that the human species faces today. Artificial intelligence is the most powerful tool that humanity has at its disposal in the twenty-first century.

Can we deploy the second to combat the first? A group of promising startups has emerged to do just that."

Key Takeaway:?AI has huge potential when applied directly to solving global challenges. This article provides insight into how startups are turning the emergent technology into tangible products in the climate space. Crossing the implementation gap is vital if we want to realise the benefits of AI.

---

Analytics is at a crossroads

What:?This article outlines how data roles often require a certain niche technical background. This requirement precludes those without these explicit technical skillsets but with perhaps the same analytical rigour and with greater domain expertise. How valuable is it to assume a coder can learn the domain but a domain expert can't learn the data techniques?

Key Takeaway:?The article also touches on how these aforementioned assumptions has an impact on women entering the data field.?It is strongly worth a read!

---

Researchers match DeepMind’s AlphaFold2 protein folding power with faster, freely available model

__________________________________

AI Ethics

??How TikTok’s hate speech detection tool set off a debate about racial bias on the app

???Charting the ‘Data for Good’ Landscape

???Ethics in AI conference videos

???Cheat-maker brags of computer-vision auto-aim that works on “any game”

Other interesting reads

???Clean energy: How AI can help spot the copper we need

???We tested AI interview tools. Here’s what we found. (MIT Review)?- German-only speakers given high ratings in their English speaking scores!

???EleutherAI - the GPT3 clone. A year in review

???How to Expand an AI Services Business and Gain Traction with Enterprise Clients - with Cory Janssen of AltaML (podcast)

Papers

???Highly accurate protein structure prediction with AlphaFold

__________________________________

Cool companies I have come across this week

Health

Aidoc?- AI that flags acute radiology abnormalities as they enter the workflow.

Climate

Bamboo Energy?- The Bamboo Energy platform enables electricity flows to become bi-directional, transforming consumers into prosumers who can choose the moment to sell and buy energy. (Article)

__________________________________

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!

___________________________________

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.

Marcel Hedman

ML PhD @ Oxford | xMcKinsey | I advise and speak on AI topics

3 年
回复

要查看或添加评论,请登录

社区洞察

其他会员也浏览了