Misinformation vaccine

Welcome to the twelfth 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.

Make sure to subscribe to our site: www.nural.cc

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So let's get straight into it!

Packed inside we have

• A breakdown of the key elements of data privacy by - Emmanuel Okyere
• Visualisations of how Covid vaccine misinformation spreads
• The release of a new state of AI index with a comprehensive breakdown of all things AI, by researchers at Stanford University
• and more...

If you would like to support our continued work from ï¿¡1 then click here!

Marcel

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Nural Article - Data Privacy: what do we need to know?

To evaluate the effects of AI on data privacy, it is important to define the idea of privacy as it relates to data and present the distinction between Public and Personal Data. Public data is defined as data that is freely accessible and can be reused and redistributed both domestically and internationally without any legal restriction. Personal data can be thought of as any information that is potentially identifiable to a person, which that person can reasonably expect to be secure from public access...

Read the rest via the link below

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Key recent developments

What: How do COVID-19 sceptics use public health data and social media to advocate for reopening the economy and against mask mandates?

This group studied half a million tweets, over 41,000 visualisations, and spent six months lurking in anti-mask Facebook groups.

Key Takeaway: As well as great interactive visualisations, this site explores the interaction between key players surrounding vaccine deployment. These players include the American and British media, anti-vaccine networks and public health organisations. By examining the sources of misinformation around Covid vaccines, it provides a great central point to go about tackling the issue. The potential for a misinformation vaccine...

Link: https://vis.mit.edu/covid-story/

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What: The Stanford University based group, Human-Centred AI have released their 2021 report which shows the effects of COVID-19 on AI development from multiple perspectives.

Key Takeaway: The report found AI investment in drug design and discovery increased significantly, that AI can now generate synthetic text, audio, & images to human level, and China has overtaken the US in AI journal citations.

Link: https://aiindex.stanford.edu/report/

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What: When going after any AI/ data science problem, the biggest challenge is usually not finding the best algorithms but rather finding good data. Therefore, when I came across this dataset concerning extreme weather events I thought to share in case any of you have been inspired to go after a grand challenge from reading this newsletter!

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What: Researchers have trained a model on patients with sickle cell sickness that can make predictions on the estimated pain levels and changes in pain levels at different stages of their hospital stay.

Key Takeaway: The model was trained on a limited dataset so the applicability of this single model must be questioned, however the concept is a powerful one. Often young children and unconscious patients are unable to communicate pain levels. Treatment plans can be enhanced by having these models to augment any communication of pain from patients.

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AI Ethics

??New approach needed to address bias in machine learning and AI systems, say Oxford legal and AI ethics experts

Other interesting reads

??Biden should double down on Trump’s policy of promoting AI within government

??Top 10 AI technologies making a breakthrough in 2021

??Mapped: How climate change affects extreme weather around the world

??NHSX hosts second roundtable discussion on AI regulation in healthcare

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Cool companies I have come across this week

Health

Prediction 2020 - Providing AI-based prediction of stroke outcome.

Climate

Mattermore - Team up to solve real-world problems with data — from climate to COVID19.

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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.
• Statsmodels - Python package for ML and statistics
• 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 ï¿¡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.