Data Science Insider: October 8th, 2021

In This Week’s SuperDataScience Newsletter: Google Owned DeepMind Turns Profit for the First Time. MEPs Back AI Mass Surveillance Ban for the EU. Google Tests AI to Time Traffic Lights in Israel. The Best Python IDEs for Data Scientists. Visa AI Blocks $350 Million in Fraudulent Transactions. 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 Owned DeepMind Turns Profit for the First Time]( brief: Google’s AI department DeepMind turned a profit for the first time ever in 2020 as turnover soared to £868m. Profits stood at £44m in 2020, up from a loss of £477m a year prior, as annual turnover jumped by more than £560m from £266m in 2019. DeepMind books most of its revenue from research carried out for other companies under the Google umbrella. The company specialises in breaking new ground in AI and ML research that can then be commercialised later. DeepMind was founded in London in 2010. It quickly developed a reputation as one of the best AI and ML research firms in the world and Google bought the business in 2014 for around $500 million. Google has heavily subsidized DeepMind since the acquisition. Sales have accelerated rapidly in recent years as the hundreds of millions spent on research have begun to yield results. Why this is important: The results, posted on Companies’ House, give the first indication that Google’s acquisition of DeepMind is paying off with parent company Alphabet forced to write off £1.1bn of debt for the AI lab in 2019. The surge in profits comes after DeepMind last year debuted the AI technology that could predict the shape of human proteins. As we’ve discussed in these SuperDataScience newsletters, the advance was seen as a major breakthrough for drug discovery by scientists and the company used an early version of the technology to map Covid-19. [Click here to find out!]( [MEPs Back AI Mass Surveillance Ban for the EU]( brief: The European Parliament has voted to support a total ban on law enforcement agencies' use of AI and facial recognition systems for mass public surveillance. MEPs passed a resolution on the use of AI systems in the police and judiciary. The European Commission presented draft legislation in April that called to ban the use of AI systems for 'indiscriminate surveillance', or for ranking people's social behaviour. The proposal suggested outlawing systems that are deployed to exploit information about groups of people and banning algorithms that judge people's trustworthiness based on their social behaviour. As we covered in these newsletters, many MEPs warned at the time that the Commission's proposals did not go far enough and lacked stronger safeguards for fundamental rights. To respect privacy and human dignity, MEPs are now calling for a permanent ban on the automated mass surveillance of people in public spaces. Why this is important: The MEPs who voted for the ban have pointed to the risk of algorithmic bias in AI applications and emphasised that human supervision is needed to prevent AI discrimination, especially in law enforcement or border control contexts. Issues that we cover regularly here at SuperDataScience and should all be aware of. [Click here to read on!]( [Google Tests AI to Time Traffic Lights in Israel]( In brief: Google said it is testing AI technology in Israel to optimize the efficiency of traffic lights. According to its early research, the solution led to a 10-20% reduction in fuel consumption and delay time at intersections. Google Chief Sustainability Officer Kate Brandt explained in a video presentation that a Google AI research group worked to calculate traffic conditions and timing at intersections in cities across the world and then began training a model to optimise those inefficient intersections. The same team conducted pilots at four locations in Israel in partnership with the municipalities of Haifa, Beersheba and the Israel National Roads Company. Brandt said: “Inefficient traffic lights are bad for the environment and bad for public health […] this is an opportunity for AI to help create breakthrough change.” The company is set to launch new pilots in Rio and other cities in the near future. Why this is important: The project was among a number of software-based initiatives trialed by Google to help combat climate change and pollution. In a blog post Google and Alphabet CEO Sundar Pichai detailed several solutions where people can use Google’s products to make sustainable choices. These include making carbon emissions data part of Google Flights, its online flight booking search service, and making eco-friendly routes available on Google Maps so drivers, cyclists and scooter riders can choose fuel-efficient routes. [Click here to discover more!]( [The Best Python IDEs for Data Scientists]( In brief: An IDE (or Integrated Development Environment) is a program dedicated to software development. As the name implies, IDEs integrate several tools specifically designed for software development. These tools usually include: An editor designed to handle code (with, for example, syntax highlighting and auto-completion), build, execution and debugging tools, and some form of source control. Most IDEs support many different programming languages and contain many more features. This list by Towards Data Science suggests that most data scientists will be using Jupyter Notebook - an open-sourced web-based application which allows you to create and share documents containing live code, equations, visualisations, and narrative text. Jupyter is great for beginners but lacks any development and debugging purposes because it’s mainly developed for testing and document sharing rather than code development. This article lists four other python IDEs, which may offer better solutions for your data science needs. 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!]( [Visa AI Blocks $350 Million in Fraudulent Transactions]( In brief: AI-powered security measures are protecting Australian businesses from card fraud, with data from Visa showing ML algorithms prevented $354 million in fraudulent transactions in the past year. Despite a boom in online retail spending of 44% in 2020, incidents of card fraud rose just 0.6%, revealing fraud prevention measures adopted by payment platforms are working, according to Visa. The AI used operates neural networks modelled on the human brain to make real-time risk assessments that determine the fraud probability of every transaction passing through its network. Account testing attacks, also known as enumeration, happen when criminals use automation to test and guess payment details, such as account numbers and expiry dates at online checkouts. According to VisaNet data, these attacks have become one of the top threats in Australia throughout the pandemic and the best way to combat them is by using AI-driven tech. Why this is important: Businesses affected by successful account testing attacks experience thousands of small transactions within a short-time frame that can easily pass under their radar. To detect these attacks, Visa’s security system analyses patterns in data to alert affected merchants and financial institutions before a fraudulent transaction is made, resulting in the AUS$350 million of fraudulent transactions being blocked. [Click here to find out more!]( [Super Data Science podcast]( In this week's [Super Data Science Podcast](, Drew Conway joins us for a live podcast interview at the NY R Conference to discuss his work in data science for private investing. --------------------------------------------------------------- What is the Data Science Insider? 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