Data Science Insider: June 3rd, 2022

In This Week’s SuperDataScience Newsletter: Google Colab Bans Deepfakes. Python Goes Mainstream. Google Faces Internal Battle Over AI Research. ML Threatens to Make Artists Obsolete. Data and Tennis, Serving Up Lessons in Success. 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. --------------------------------------------------------------- [Google Colab Bans Deepfakes]( brief: In the last few weeks, Google changed its terms and conditions to exclude the training of Deepfakes on its platform, Colaboratory. Google made the changes by stealth but the updated terms were noticed by Unite.ai and BleepingComputer on 24th May. A screenshot from 14th May revealed no ban, meaning that the update took place somewhere in the intervening 10 days. The ban targets one of the most famous Deepfake codes, the DeepFace Lab (DFL). This was recognised when users running DFL on the Colab faced warnings and threats of bans. A Google spokesperson said: “We regularly monitor avenues for abuse in Colab that run counter to Google’s AI principles, while balancing supporting our mission to give our users access to valuable resources such as TPUs and GPUs. Deepfakes were added to our list of activities disallowed from Colab runtimes last month in response to our regular reviews of abusive patterns.” Why this is important: DFL faced Google’s wrath this time but rival Faceswap has managed to escape Google's ban for now, perhaps due to the difference in popularity. Colab is the go-to platform for demos within the AI research community and researchers often include links to Colab pages on or alongside the GitHub repositories hosting the code. [Click here to sign up!]( [Python Goes Mainstream]( brief: This article from InfoWorld explores how data science has become mainstream with a wide variety of industries embracing the way that the discipline provides users with the ability to create personalised and engaging experiences with fair pricing. As companies are increasingly expanding the use of data science into areas such as product management and marketing, they need a language that has broad appeal. The article argues that it is this need for mass appeal which ensures that ultimately it is Python rather than R which will reign supreme. It claims that R is superior to Python when it comes to statistics, but Python is a runner-up in every other category. It quotes Van Lindberg, general counsel for the Python Software Foundation: “Python is the second-best language for everything. If you want to do more than just stats, then Python’s breadth is an overwhelming win.” Why this is important: We’ve covered the rise of Python here at SuperDataScience before and this article reflects that growth. This is likely to continue following Anaconda’s release of PyScript, which makes Python more accessible to front-end developers by making it possible to write Python in HTML to build web applications. [Click here to read on!]( [Google Faces Internal Battle Over AI Research]( In brief: Google has terminated the employment of senior engineering manager, Satrajit Chatterjee, after more junior colleagues accused him of harassing behaviour. These accusations came from Google scientists Azalia Mirhoseini and Anna Goldie whose landmark research on AI software he had been trying to discredit. The dispute originates from efforts to automate chip design. In June, the scientific journal Nature published a paper by Mirhoseini and Goldie in which they claim to have discovered that AI could complete a key step in the design process for chips, known as floorplanning, faster and better than a human expert. However, in March, Chatterjee anonymously posted a paper online which found that two alternative approaches based on basic software outperform the AI. Google had refused to publish the leaked paper, which they claim used the cover of scientific debate to undermine the women personally - the latest in a long line of accusations against him. Why this is important: This is the latest in a long line of complains about Google’s handling of personnel issues and the whole debacle threatens to undermine the reputation of Google's research in the academic community. It also has the potential to threaten the millions of dollars in government grants for research into AI and chips that Google receives. [Click here to discover more!]( [ML Threatens to Make Artists Obsolete]( In brief: This fascinating article by Vanity Fair offers an in-depth look at how ML tools such as Sudowrite and Dall-E present a threat to writers and artists. A particularly interesting part comes when the article’s author asks a GPT-3-powered program called ShortlyAI to write a post about algorithms replacing journalists and artists, based on the work of AI expert Hod Lipson. It wrote: “Robots, machine learning, and other AGIs will soon be writing daily news stories. […] Artificial intelligence will make huge strides in their capability to produce high-quality written news. This will bring an end to the era of the journalist as we know it. The difference between A.G.I.s and journalists is the ability to comprehend and absorb information, not just to report it. An A.G.I. will be able to understand the “what” of a story, as well as its “why,” without breaking a sweat over details.” Why this is important: This article makes for an interesting read as it presents debates that we’ve explored before from a new angle. As a publication based on popular culture, it looks at the possibilities that ML offers but also considers the negative effects on journalism and artistry. [Click here to see the full picture!]( [Data and Tennis, Serving Up Lessons in Success]( In brief: Data analytics and sport have been familiar bedfellows since 2002 when the Oakland Athletics baseball team shunned the wisdom of coaches and scouts in favor of statistical analysis in order to choose their squad. These methods have now been applied to the game of tennis where tech is having a huge impact. In the 2015 Australian Open computers analysed the data from a match between Novak Djokovic and Andy Murray and researchers concluded that players trained in an ineffectual way. The tournament presented a turning point for the sport and coaches have embraced the conclusions that AI can draw from the vast amounts of data available. Tennis strategist and coach Craig O'Shannessy, who worked with Djokovic between 2017 to 2019, said: "This data changed our sport forever. AI can sniff out areas of significance. Humans do a very bad job at layering data, whereas AI can do it in seconds." Why this is important: Reading about the impact AI has had on this sport is incredibly illuminating. Although the article focuses on data analytics for improved performances, it also highlights how media coverage of tournaments is being shaped by AI. [Click here to find out more!]( [Super Data Science podcast]( this week's [Super Data Science Podcast](, CEO of Overjet Dr. Wardah Inam joins us to discuss the classification and quantification of dental diagnoses with computer vision, her data labeling challenges, and tips for building a successful A.I. business. --------------------------------------------------------------- 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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