Data Science Insider: October 15th, 2021

In This Week’s SuperDataScience Newsletter: Exploring Facebook’s Ego4D Project. Pentagon Official Resigned Because US AI Has Lost to China. Manchester United Appoint First-Ever Director of Data Science. Kernel Machines May Hold Key to Understanding ANNs. Beethoven’s 10th Symphony has been completed by AI. 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. --------------------------------------------------------------- [Exploring Facebook’s Ego4D Project]( brief: Facebook has announced Ego4D, a long-term project aimed at solving AI research challenges in “egocentric perception,” or first-person views. The goal is to teach AI systems to comprehend and interact with the world as humans do rather than in the third-person way that most AI currently does. It’s Facebook’s assertion that AI that understands the world from first-person could enable previously impossible augmented and virtual reality (AR/VR) experiences. Ego4D brings together a consortium of universities and labs across nine countries, which collected more than 2,200 hours of first-person video featuring over 700 participants in 73 cities going about their daily lives. Kristen Grauman, a lead research scientist at Facebook said: “For AI systems to interact with the world the way we do, the AI field needs to evolve to an entirely new paradigm of first-person perception. That means teaching AI to understand daily life activities through human eyes.” Why this is important: Computer vision models, which would form the basis of this AI, have historically learned from millions of photos and videos captured in third person. Next-generation AI systems might need to learn from a different kind of data — videos that show the world from the center of the action — to achieve truly egocentric perception. [Click here to find out!]( [Pentagon Official Resigned Because US AI Has Lost to China]( brief: A senior cybersecurity official at the Pentagon has expanded on his reasons for resigning last month, citing the US’s failure to compete with China on AI. Nicolas Chaillan joined the US Air Force as its first chief software officer in August 2018 where he worked to equip it and the Pentagon with the most secure and advanced software available. However, Chaillan soon quit his role. In his departing LinkedIn post, he cited the Pentagon's reluctance to make cybersecurity and AI a priority as the reason. This week whilst speaking to the Financial Times, in his first interview since leaving, Chaillan said China was far ahead of the US. "We have no competing fighting chance against China in 15 to 20 years. Right now, it’s already a done deal; it is already over in my opinion," he told the newspaper. "Whether it takes a war or not is kind of anecdotal." Why this is important: Here at SuperDataScience we’ve covered the AI war extensively, including last week’s revelation that the UK is seeking to compete. However, this story makes it clear that it really is a two-horse race and one that, according to Chaillan, only has one winner. [Click here to read on!]( [Man United Appoint First-Ever Director of Data Science]( In brief: Manchester United has appointed their first-ever director of data science to work across the club's football operations. Dominic Jordan, a data scientist with a background in geospatial analytics, will assist players and staff in delivering success on the pitch using data, the Old Trafford club said. Geospatial analytics adds elements such as timing and location to traditional types of data to build more detailed analysis. Jordan, a Cambridge graduate, previously led a team of 30 data scientists, engineers, and analysts at the online retailer N Brown Group and worked at a US-based firm in population movement analytics, developing algorithms to help monitor the flow of people and vehicles in transport systems. Jordan said: “There is so much potential for data science to benefit the club, from assisting with player recruitment, automatically analysing patterns of play right through to using computer vision to extract information from video feeds in real time.” Why this is important: There has been an explosion in data collection and analytics around football over recent years and Manchester United’s appointment of a director of data science reflects a movement, amongst top-tier clubs, towards our industry and its solutions. [Click here to discover more!]( [Kernel Machines May Hold Key to Understanding ANNs]( In brief: Artificial neural networks (ANNs) have astounded our industry time and time again with their increased number of parameters, often extending into the billions. However, our understanding of the networks has failed to keep up with their expansion, despite their increasing dominance in the fields of AI and ML, where they have revolutionised tasks such as classifying images, recognising speech and translating text. In order to understand how these massive networks work, researchers are turning to the older technology of kernel machines. This is due to the fact that the researchers are increasingly discovering that idealised versions of ANNs are the mathematical equivalent to the older and simpler ML models. As this fascinating article in Quanta Magazine explains, if this equivalence between kernel machines and ANNs can be extended beyond idealised neural networks, it may go some way towards explaining how practical ANNs achieve the astonishing results that we’ve repeatedly witnessed. Why this is important: The mystery surrounding how ANNs work has the potential to hold back our expansion in these areas. By using the mathematical understanding that we have around kernel machines, we may be able to expand our knowledge of neural networks and potentially their capabilities. [Click here to see the full picture!]( [Beethoven’s 10th Symphony has been completed by AI]( In brief: When he died in 1827, Ludwig van Beethoven left his 10th symphony unfinished. Only a few handwritten notes briefly detailing his plans for the piece have survived, with most just being incomplete ideas or fragments of themes or melodies. Now, a multidisciplinary team of computer scientists at Rutgers University-based start-up Playform AI has trained an AI to mimic the composer’s style and used it to write a complete symphony based on these initial sketches. The project was started in 2019 by a group made up of music historians, musicologists, composers, and computer scientists. Using AI meant they were faced with the challenge of ensuring the work remained faithful to Beethoven’s process and vision. Previous uses of AI in compositional processes include Schubert’s final symphony being completed by the AI from the Huawei Mate 20 Pro smartphone, and an AI which harmonises any melody in the style of Bach. Why this is important: Dr Ahmed Elgammal, professor at the Department of Computer Science, Rutgers University summed up the importance of the project, stating: “There are those who will say that the arts should be off limits from AI, and that AI has no business trying to replicate the human creative process. Yet when it comes to the arts, I see AI not as a replacement, but as a tool – one that opens doors for artists to express themselves in new ways.” [Click here to find out more!]( [Super Data Science podcast]( In this week's [Super Data Science Podcast](, Denis Rothman joins us to discuss his extensive work and in-depth writings on transformers, explainable AI, and much more! --------------------------------------------------------------- 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 more conversations like this? In July we held DSGO Virtual Conferences, where more than 1,000 data scientists gathered to learn, grow, and connect! If you missed them or want to repeat this fantastic experience, stay tuned to our upcoming events that will take your DS career to the next level. 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