Data Science Insider: October 1st, 2021

In This Week’s SuperDataScience Newsletter: AI Improves Weather Forecasting. The Intersection of Open Source and AI. Google Ads Announces ML-Based Data-Driven Attribution Models. Google's Former Executive Warns that AI Researchers are “Creating God. ”AI Analysis Says National Gallery’s Painting Isn’t a Rubens. 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. --------------------------------------------------------------- [AI Improves Weather Forecasting]( brief: AI can tell whether it is going to rain in the next two hours, research suggests. Scientists at Google-owned London AI lab DeepMind and the University of Exeter partnered with the Met Office to build the so-called nowcasting system. Traditional methods use complex equations and often forecast for only between six hours and two weeks' time. The AI system can make more accurate short-term predictions, including for critical storms and floods. The system learned how to identify common patterns of rainfall, and found, by 50 Met Office meteorologists, to be accurate in 89% of cases. The research, published in the journal Nature, found: "Meteorologists significantly preferred the [AI] approach to competing methods." DeepMind senior scientist Shakir Mohamed said: "It’s very early days but this trial shows that AI could be a powerful tool, enabling forecasters to spend less time trawling through ever-growing piles of prediction data.” Why this is important: Climate change is making it harder to anticipate adverse weather conditions, as the frequency and severity of heavy rain increases, which researchers believe will lead to both significant material damage and death. [Click here to find out!]( [The Intersection of Open Source and AI]( brief: Open source technology has been at the vanguard of a number of significant developments of the past decade. Therefore, it should come as no surprise that AI is now reaping the benefits of all that Open source has to offer. For example, it could include various technologies that are helpful for product teams, independent app developers, and enterprises. Open-source AI is different from freeware AI applications — the underlying code is exposed to the user and open for modifications and implementations in scenarios other than the ones originally intended. Open source also comes with a large active community, where developers can both contribute and ask for help. Today, open-source AI is a thriving segment, benefitting developers and enterprises alike. Research suggests that while 48% of enterprises currently use open source technology for AI/ML, this number will rise to 65% by 2023. This Venture Beat article analyses this intersection. Why this is important: Many commentators have expressed unease about how AI is being developed, but this article argues that open source has fueled AI development by streamlining developments. However, it also highlights that it is an area that has been largely ignored by policymakers. [Click here to read on!]( [Google Ads Announces ML-Based Data-Driven Attribution Models]( In brief: In an effort to provide more accurate, precise, and privacy-centric measurement to marketers, Google has announced it is updating its ad attribution model. Instead of relying on consumers' last interaction alone, Google Ads will employ ML to assess data from throughout the conversion pipeline. The move represents a shift away from what’s commonly known as “last-click” models of attribution and furthers the tech giant’s investment in consumer data privacy. While last-click attribution measures which touchpoint a consumer engaged with last before making a purchase, Google’s new framework employs ML to gauge everything from how conversions are measured to how to improve automated bidding in the media buying process. While Google's ads business already offers this data-driven attribution model, it was not previously accessible to all advertisers due to minimum data requirements. Google argues that its new attribution is a means by which to preserve consumer privacy. Why this is important: The decision to switch to data-driven attribution was informed by the changing privacy landscape. Consumers are increasingly demanding data privacy protections from big tech companies, leading the Googles and Apples of the world to introduce new policies and tools that give users greater say over how their personal information is used. [Click here to discover more!]( [AI Researchers are “Creating God”]( In brief: A former Google executive has warned about the dangers of AI, claiming that he came to the realisation that researchers are "creating God." Mo Gawdat, who joined the tech giant in 2007, served as the Chief Business Officer for Google's secretive research and development facility known then as Google X. He claims that his epiphany came after he saw AI developers collaborating with Google X on dexterous robotic arms. After what he described as slow progress, he witnessed a robotic arm reach down and pick up a ball, which it then displayed to the researchers. Speaking of the incident, he said: "And I suddenly realised, this is really scary. Like we had those things for a week. And they're doing what children will take two years to do. And then it hit me that they are children. But very, very fast children." He added: "The reality is, we're creating God." Why this is important: According to Gawdat, AI has the potential to reach technological singularity - the point at which it becomes uncontrollable and irreversible. In the case of intelligent machines, this means AI could eclipse humanity and escape our control. This may sound like something from a sci-fi film, but Gawdat’s pedigree means that his views can't be ignored out of hand. [Click here to see the full picture!]( [AI Analysis Says National Gallery’s Painting Isn’t a Rubens]( In brief: The National Gallery has always given pride of place to Peter Paul Rubens’s Samson and Delilah, listing it among the “highlights” of its collection since it purchased the picture at Christie’s in 1980 for a then-record price. It depicts the Old Testament hero in the lap of the lover who betrayed him. Critics have long suggested that the painting is not really by Rubens. And now a series of scientific tests employing groundbreaking AI technology have concluded that the 17th-century Flemish master could never have painted it. “The results are quite astonishing,” says Dr. Carina Popovici, the scientist who carried out the study. “The algorithm has returned a 91% probability for the artwork not being authentic.” In comparing Samson and Delilah with 148 uncontested Rubens paintings, the analysis produced one of the scientists’ highest-ever probabilities of the hundreds of pictures they have tested so far. Why this is important: The AI technology – “convolutional neural network” –analyses different features of an artist’s work, including brushstroke patterns. Its importance is summed up by Dr. Katarzyna Krzyżagórska-Pisarek, an art historian, who says: “The significance of this new AI method of authentication is potentially groundbreaking. Devoid of human subjectivity, emotion, and commercial interests, the software is coldly objective and scientifically accurate. Many questionable works were attributed to Rubens at the beginning of the 20th century… There is today a distinct need for more reliable methods of connoisseurship.” [Click here to find out more!]( [Super Data Science podcast]( In this week's [Super Data Science Podcast](, Parinaz Sobhani joins us to discuss the incredibly unique work Georgian does in collaborating with start-ups on implementing machine learning and accelerating growth. --------------------------------------------------------------- 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? 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