Data Science Insider: July 29th, 2022

In This Week’s SuperDataScience Newsletter: DeepMind Uncovers Structure of 200 Million Protein Structures. Google’s Former Head Says AI Is as Powerful as Nuclear Weapons. Exploring Data Poverty. ML Could Fuel a Reproducibility Crisis. Analog Deep Learning Could Speed Up Neural Networks. Cheers, - The SuperDataScience Team P.S. Have friends and colleagues who could benefit from these weekly updates? Send them to [this link]( to subscribe to the Data Science Insider. --------------------------------------------------------------- [DeepMind Uncovers 200 Million Protein Structures]( brief: It has been announced that DeepMind’s AlphaFold AI model has deciphered the structure of virtually every protein known to science. The expanded database increases the number of known, catalogued proteins by over 200 times, from approximately 1 million structures to around 200 million. The expansion is vast in its scope and includes predicted structures for nearly all living things. Demis Hassabis, DeepMind’s founder and chief executive said of the breakthrough: “Essentially, you can think of it as covering the entire protein universe. It includes predictive structures for plants, bacteria, animals, and many other organisms, opening up huge new opportunities for AlphaFold to have an impact on important issues, such as sustainability, food insecurity, and neglected diseases.” The database has taken just 18 months to solve one of the greatest challenges in all biology, a breakthrough that will speed up drug development and revolutionise basic science. Why this is important: Here at SuperDataScience we have covered this story since AlphaFold was first announced to be uncovering protein folding in 2020. This latest news is of monumental significance, paving the way for the development of new medicines or technologies to tackle global challenges such as famine or pollution. [Click here to learn more!]( [Former Head Says AI Is as Powerful as Nuclear Weapons]( brief: The former chief executive of Google, Eric Schmidt, spoke at the Aspen Security Forum earlier this week and claimed that AI is as dangerous as nuclear weapons. When, as part of a panel about national security and artificial intelligence, Schmidt was asked a question about the value of morality in tech, he claimed that he had previously been naive about the power of information in the early days of Google. Schmidt’s concerns seem to partly stem from a fear of the US-China AI war and his comments on the panel involved imagining a near future where the USA and China would both have security concerns around AI which would force a deterrence treaty between the two superpowers. He said: “I’m very concerned [about relations between the US and China…W]e don’t have anyone working on that, and yet AI is that powerful.” Why this is important: Schmidt’s comparison with nuclear weapons was somewhat left field but it does raise some important questions about how the technology can be used in a moral and ethical way. [Click here to read on!]( [Exploring Data Poverty]( In brief: This article by the BBC doesn’t explore the kind of big data projects that we usually examine here at SuperDataScience but it does highlight an area that many of us may have given little thought to: Data poverty. Although many of us may take the ability to access the internet for granted, many people are increasingly having to make a choice between having access to online services and paying for essentials such as heat and food. As we fall into a cost of living crisis and inflation is predicted to soar we may find that more and more people are having to resort to using databanks - a similar setup to a foodbank but offering free mobile data, texts and calls - like those highlighted in the article. In the UK, Ofcom has estimated that 1.1 million households are "struggling to afford their home broadband service". Why this is important: Many of the discoveries and breakthroughs we read about every week in these newsletters start life with an individual experimenting at home. If people continue to suffer from financial hardships then tough decisions may be made which will ultimately have a detrimental effect on us all. [Click here to discover more!]( [ML Could Fuel a Reproducibility Crisis]( In brief: Researchers from Princeton University have claimed that the increasing use of ML as a technique to generate predictions based on patterns of data may have resulted in assertions that are likely to be exaggerated and have caused a “brewing reproducibility issue” in machine-learning-based sciences. Sayash Kapoor, co-author of the research, claims that ML is promoted as a technique that academics can pick up and utilise on their own in a matter of hours, in contrast to the many years of study and experience that it would take to be able to manage a scientific laboratory. Kapoor alleges that very few scientists are aware that the issues they face when using AI algorithms are also present in other domains. He claims that because peer reviewers do not have the time to carefully examine these models, academia, in its present form, lacks systems to identify works that are not replicable. Why this is important: This is not merely a scaremongering piece of research. Instead, the researchers have developed guidelines to try and prevent scientists from making similar mistakes. These include an explicit checklist to be submitted with each paper. [Click here to see the full picture!]( [Analog Deep Learning Could Speed Up Neural Networks]( In brief: Engineers from MIT who have been working on analog deep learning have discovered a technique which allows them to propel protons through solids at unprecedented speeds. The multidisciplinary team of researchers had previously developed a type of human-made analogue synapse which was capable of complex AI-based tasks, but they sought to improve upon its capabilities. Consequently, they developed programmable resistors which vastly increase the speed upon which the neural network is trained whilst also dramatically reducing the cost and energy at which it is performed. Lead author of the research, Murat Onen, says: “Once you have an analogue processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft.” Why this is important: The increase in speed with a reduction in energy has great potential for the future with scientists having the means to develop deep learning models much more quickly. This technology could then be used in a wide variety of applications such as; self-driving cars, fraud detection, or medical image analysis... [Click here to find out more!]( [Super Data Science podcast]( this week's [Super Data Science Podcast](, Joe Reis and Matt Housley, co-founders of Ternary Data and co-authors of the book “Fundamentals of Data Engineering” join Jon to discuss major undercurrents across the data engineering lifecycle, and their top tools and techniques." --------------------------------------------------------------- What is the Data Science Insider? This email is a briefing of the week's most disruptive, interesting, and useful resources curated by the SuperDataScience team for Data Scientists who want to take their careers to the next level. Want to take your data science skills to the next level? Check out the [SuperDataScience platform]( and sign up for membership today! Know someone who would benefit from getting The Data Science Insider? 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