Data Science Insider: September 17th, 2021

In This Week’s SuperDataScience Newsletter: DL Identifies Secrets of a Creative ‘Hot Streak’. UN Report Calls for Regulation of Potentially Dangerous AI. Concurrency and Parallelism in Python. Computer and Data Scientists to be Prioritised by UK Defense. Fifa 22 Harnesses the Power of ML. 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. --------------------------------------------------------------- [DL Identifies Secrets of a Creative ‘Hot Streak”]( brief: Whether an artist, scientist, or film director; trailblazers in particular fields often have a critically-acclaimed 'hot streak' where they produce a series of outstanding work in short succession. Scientists at Northwestern University claim to have pinpointed the secret formula that often triggers a pioneer's best work. Using DL, they mined data related to thousands of artists, film directors, and scientists to identify the formula for success. They found that hot streaks directly result from years of 'exploration' (studying diverse styles or topics), immediately followed by years of 'exploitation' (focusing on a narrow area to develop deep expertise). Study leader Dashun Wang used algorithms for image recognition to mine data from 800,000 visual arts images, which cover the career histories of 2,128 artists. For film directors, the team collected data sets from IMDb, which included 79,000 films by 4,337 directors. For scientists, the team analysed the career histories of 20,040 scientists. Why this is important: Wang and his team claim that with this new understanding about what triggers a hot streak, institutions can intentionally create environments that support and facilitate hot streaks in order to help their members thrive. [Click here to find out!]( [UN Calls for Regulation of Potentially Dangerous AI]( brief: The UN is calling for a moratorium on AI systems "that pose a serious risk to human rights" until research and regulation has been done. It published a report after concerns that countries and businesses are adopting AI without proper diligence. High commissioner for human rights Michelle Bachelet said that AI can be a "force for good" but stressed that it can still have a profoundly negative, "even catastrophic" effect if used without consideration. The report analyses the ways AI can affect human rights, including privacy, health, and education as well as freedom of movement, expression, and assembly. Bachelet's report says that because of its rapid growth, finding out how AI collects, stores, and uses data is "one of the most urgent human rights questions we face." The UN also calls for significantly more transparency from companies and countries that develop and use AI systems. Why this is important: The report highlighted how AI systems rely on large data sets, with information about people collected, shared, merged, and analysed in often opaque ways. The data sets themselves can be faulty, discriminatory, or out of date, and thus contribute to rights violations, it warned. These are issues that all data scientists should be aware of. [Click here to read on!]( [Concurrency and Parallelism in Python]( In brief: Concurrency and parallelism features have completely changed the landscape of software applications. If you’re about to start a big data project you will be either retrieving a lot of information or crunching big numbers on your machine or both. However, if the code is sequential or synchronous your application may start struggling, this is where concurrency and parallelism can help. This article by InfoWorld offers a clear and succinct guide about what the terms mean as well as providing instructions on how and when to use concurrency and parallelism in Python applications. This should give you a much clearer understanding of the features, which in turn should allow you to make a more informed decision about which concurrency method you should use for a given problem. In addition, you should have gathered a better understanding of some of the problems that can arise when you’re using concurrency. Why this is important: Most of us data scientists will be familiar with the basics of Python. However, this is an advanced level topic for Python developers and mastering it is a fantastic way to broaden your skillset. [Click here to discover more!]( [Computer and Data Scientists Prioritised by UK Defense]( In brief: The industry needs to focus on key areas of technology development to ensure advantage for the United Kingdom and its allies, the commander of the country’s Strategic Command has said. General Sir Patrick Sanders said future warfare needed to focus less on traditional methods and more on cyber, and called on the Armed Forces “to place equal value and afford equal status to computer scientists, data engineers, and cyber operators as we do on the traditional warrior elite.” Speaking at the DESI arms fair, Sir Patrick issued a stark warning to the military, stating that if it failed to adapt to the digital age, “we will at best become exquisite but irrelevant, and at worst we will die.” Sir Patrick added that investment in AI, such as autonomous systems, swarming, cyber defence, decision support, and intelligence processing, would be at “the heart” of the UK becoming a science and technology superpower. Why this is important: Sir Patrick acknowledged that in order to adapt, soldiers would need to be educated and trained. He said: “We will have to address the skills gap through attracting far more diverse talent, by inward investment so that coding and data literacy are seen as being as much a core skill as weapon handling, by much greater use of a larger and more diverse reserve.” This is where resources such as those available through SuperDataScience come in! [Click here to see the full picture!]( [Fifa 22 Harnesses the Power of ML]( In brief: Fifa 22 launched this week to much fanfare, powered by EA's new HyperMotion technology, a new mechanisation tool set to improve the overall feel and look of the next-gen gameplay. In previous installments of the game players off of the ball have performed a limited number of pre-set animations at the same time, creating a distraction when several players are doing the same thing simultaneously. HyperMotion is the technology that EA uses to capture players and their behaviours in-game. The days of capturing player movements in a studio were no more as EA took it to the pitch and captured individual player movement in real game scenarios, with each player wearing a state-of-the-art Xsens suit. Xsens suits are used to perform motion capture and record every tackle, touch, sprint, and duel, capturing data that powers over 4000 new animations. The software is then used to record, monitor, and review movement. Why this is important: Sam Rivera, EA Sports’ Lead Gameplay Producer has said: “So the other big part that we did with this data is train some deep neural networks to learn how players approach the ball and create animation in real-time.” This is an important step for integrating ML and NNs into mainstream gameplay. [Click here to find out more!]( [Super Data Science podcast]( In this week's [Super Data Science Podcast](, Hadelin de Ponteves joins us to discuss his latest educational work and how his skills as a data science educator helped him make his Bollywood acting debut. --------------------------------------------------------------- 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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