Data Science Insider: December 10th, 2021

In This Week’s SuperDataScience Newsletter: Neural Network Hallucinates Novel Proteins. Marks & Spencer Launches World First Data Science Academy. Timnit Gebru Speaks on The Future of Ethical AI. DeepMind Tests the Limits of AI Language systems. AI Robot to be Tokenized for Metaverse Appearance. 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. --------------------------------------------------------------- [Neural Network Hallucinates Novel Proteins]( brief: A new study has found that by getting AI to “hallucinate” scientists are creating novel proteins with an unlimited array of properties. Proteins are key to nearly every biological process. However, the intricacy of the interactions between the amino acids comprising each protein makes it difficult to predict their structures, even if researchers know the sequence of amino acids that constitute a protein. Scientists have found that a deep network trained exclusively to model protein shapes can also dream up proteins with new structures. The researchers experimented with trRosetta, a web-based platform for protein structure prediction. They gave it completely random protein sequences and introduced mutations into them until trRosetta began making generalizations that yielded predictions about how the strings of amino acids would arrange themselves into stable 3D structures. The scientists generated 2,000 new protein sequences. All of them were originally figments of the AI's imagination. Why this is important: Scientists have long employed programs to design new proteins with potentially novel functions, to model how they might fold, and to predict if they might behave as hoped. Increasingly, deep NNs are also helping researchers predict protein structures. However, this research is the first time a network has been used to generate new folded proteins with sequences unrelated to those of the naturally occurring proteins used in training the models [Click here to sign up!]( [Marks & Spencer Launches World First DS Academy]( brief: British retail giant Marks & Spencer (M&S) says its new data science and AI academy marks a retail world-first – and will take it to the next level in harnessing the power of data. The academy will train an initial 10 members of staff on a level seven data science and AI apprenticeship, in partnership with education provider Cambridge Spark. The move is part of a wider programme to upskill its team and put data at the heart of its decision-making – and potentially help it unlock value worth millions of pounds to the business. M&S says the opportunity for data science spans the full ‘plan’, ‘buy’, ‘move’, ‘sell’ retail value chain – from the beginning of the product design process to improved buying, allocation, and then store operations and customer relationships. It uses personalisation algorithms to produce offers tailored to more than 13m members of its Sparks loyalty scheme. Why this is important: By being the first UK retailer to offer a level seven programme, M&S believes it will be able to both attract and retain digital talent while encouraging more of its 70,000 members of staff to consider a career in data via three pathways within the business. This is a move we have yet to see from a retail group and could shape the future of the high street. [Click here to read on!]( [Timnit Gebru Speaks on The Future of Ethical AI]( In brief: In these SuperDataScience newsletters, we have covered the story of Timnit Gebru, from her controversial exit from Google to her creation of Distributed Artificial Intelligence Research (DAIR). However, this self-penned article from The Guardian is the first time we’ve ever heard her in her own words. In this opinion piece, Gebru discusses the way that large tech firms handle whistleblowing, AI, and ethics and argues that AI research needs to be independent from Silicon Valley big tech firms in order to ever be truly ethical. Gebru says: “In order to truly have checks and balances, we should not have the same people setting the agendas of big tech, research, government and the non-profit sector. We need alternatives. We need governments around the world to invest in communities building technology that genuinely benefits them, rather than pursuing an agenda that is set by big tech or the military.” Why this is important: Gebru’s story made headlines around the world and she is currently in a position where she can make her voice be heard. This article allows us to listen to somebody who is likely to be setting the agenda for ethical AI reform in the coming years. [Click here to discover more!]( [DeepMind Tests the Limits of AI Language Systems]( In brief: Language generation is currently one of the most fashionable trends in AI, with a class of systems known as large language models (LLMs) being used for everything from improving Google’s search engine to creating text-based fantasy games. DeepMind, which regularly feeds its work into Google products, has probed the capabilities of this LLMs by building a language model with 280 billion parameters named Gopher. Parameters are a quick measure of a language’s models size and complexity, meaning that Gopher is larger than OpenAI’s GPT-3 (175 billion parameters) but not as big as some more experimental systems, like Microsoft and Nvidia’s Megatron model (530 billion parameters). In their latest research DeepMind evaluated a range of different-sized language models on 152 language tasks or benchmarks. They found that larger models generally delivered improved results, with Gopher itself offering state-of-the-art performance on roughly 80% of the tests selected by the scientists. Why this is important: It’s generally true in the AI world that bigger is better, with larger models usually offering higher performance. DeepMind’s research confirms this trend and suggests that scaling up LLMs does offer improved performance on the most common benchmarks, testing things like sentiment analysis and summarization. [Click here to see the full picture!]( [AI Robot to be Tokenized for Metaverse Appearance]( In brief: A virtual anime version of Sophia, the world-famous humanoid AI robot, is set to be tokenized and auctioned off as part of an up-and-coming Metaverse project dubbed “Noah’s Ark.” Sophia was developed by Hong Kong-based firm Hansen Robotics in 2016 and has addressed the United Nations and obtained Saudi citizenship. Earlier this month, former Hansen Robotics CEO and Sophia co-creator Jeanne Lim launched a virtual anime version of the robot dubbed “Sophia beingAI” at her new company, beingAI. BeingAI has partnered with Alethea AI to launch 100 iNFTs featuring Sophia: beingAI. iNFTs are the next step within the metaverses and will be smart non-fungible tokens. In other words, assets will have AI personality and immutable intelligence as part of their programming, this means that the robot Sophia will now exist in the physical and virtual worlds within the metaverse. The collection is named “The Transmedia Universe of Sophia beingAI.” Why this is important: The term iNFT refers to revolutionary NFTs that are embedded with intelligence in the form of an AI personality that adds programmability into their immutable smart contracts. These intelligent NFTs can interact autonomously with people in real-time in a gamified environment and may just become the next big thing. [Click here to find out more!]( [Super Data Science podcast]( In this week's [Super Data Science Podcast](, Dave Niewinski joins us to discuss his unique YouTube channel on his creative robots and drive to make the world unafraid of robots through everyday applications. --------------------------------------------------------------- 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. 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