Data Science Insider: April 1st, 2022

In This Week’s SuperDataScience Newsletter: Bridging the Gap Between Humans and ML. Data Beyond Data Scientists. AI to Detect Those Suffering ‘Personal Crises.’ Yann LeCun Discusses the Future of DL. AI Could Make Life or Death Decisions on the Battlefield. 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. --------------------------------------------------------------- [Bridging the Gap Between Humans and ML]( brief: This article from The Guardian takes the recent news that an AI called NooK has beat eight world champion bridge players as a jumping-off point to examine the relationship between ML and human intelligence. It argues that this new algorithm is succeeding where previous game-winning technology, such as IBM’s Deep Blue, Google’s AlphaGo, and Libratus, have failed. In the editorial, it is argued that the success NooK (from French startup NukkAI) represents the conquering of a new AI frontier. This is due to the fact that bridge differs from chess, Go, or poker - where AI has previously been successful - as bridge players must react to the behaviour of several other players after working with incomplete information. This is a situation that mirrors human decision-making much more closely. Based on this feature of the bridge this AI also has the advantage of being able to explain how decisions were reached. Why this is important: The ability to explain the process behind its decision makes NooK stand out from the more common “black box” AIs which train themselves by DL, essentially using brute force by playing a game billions of times until the algorithm has worked out how to win, but lacking the ability to explain their decision-making process. [Click here to sign up!]( [Data Beyond Data Scientists]( brief: Harvard Business Review offers expertise in general management and as a subsidiary of Harvard University is considered a leading voice in the world of business. This article argues that “data-driven decision-making” will just go around in circles if there isn’t sufficient buy-in from across a business or organisation, even if it’s employing the most cutting-edge AI models and data science experts. Data scientists can only do so much for a company and businesses need to start seeing regular people as part of their data strategy. Conversely, data teams must also stop seeing themselves as separate from the rest of the company and work with ordinary employees on a day-to-day basis in order to develop a feel for their problems and opportunities. By doing this data scientists will be better placed to understand the workforces’ hopes and fears surrounding data and ultimately help the business. Why this is important: Data scientists can’t just exist in a vacuum and need to understand how they can fit into an organisation as a whole, part of this is through equipping people with the tools they need to formulate and solve their own problems. [Click here to read on!]( [AI to Detect Those Suffering ‘Personal Crises’]( In brief: When times are tough and we are suffering, many of us turn to friends and family but increasingly the first port of call is to google topics relating to our woes. After becoming aware of this phenomenon, Google has announced that it is aiming to do more to direct people to the information they need. They claim that new AI techniques that have a greater ability than ever before to deconstruct the complexities of language are assisting them. Specifically, Google has announced that it is integrating its latest ML model, called MUM (Multitask Unified Model), into its search engine in order to “more accurately detect a wider range of personal crisis searches.” After detecting a greater number of sufferers based on the AI interpreting the intent behind the search, Google will display the contact information for the relevant national hotlines above the search. Why this is important: Google first unveiled MUM at its IO conference last year, where it had focused on how it may offer a better understanding of user intent, which could then be leveraged to help Google users unlock deeper insights into the topic they’re researching. The latest announcement demonstrates a desire to use these insights for good. [Click here to discover more!]( [Yann LeCun Discusses the Future of DL]( In brief: Yann LeCun is a world-renounced computer scientist who works primarily in the fields of ML, computer vision, mobile robotics, and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University, and Vice President, Chief AI Scientist at Meta. He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN) and is a founding father of convolutional nets. In 2018 he received the Turing Award (often referred to as the "Nobel Prize of Computing") in recognition of his work on DL. In this interview with ZDNet, he discusses energy-based models and argues that they could end up being as productive in AI as CNNs are. These models have apparently made him the most excited he’s been in 30 years. They borrow concepts from statistical physics and are LeCun's latest take on the energy-based neural network architecture. Why this is important: Alongside Léon Bottou and Patrick Haffner, LeCun is sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning." When he speaks on a topic, we should all listen. This interview is an approachable way to stay up-to-date with his latest research. [Click here to see the full picture!]( [AI Could Make Life-or-Death Decisions on the Battlefield]( In brief: The Defense Advanced Research Projects Agency (DARPA) has launched a new program this week aimed at introducing AI into the decision-making process for military operations. The initiative is in the early stages but aims to ultimately give AI systems the same complex, rapid decision-making capabilities as military medical staff and trauma surgeons who are in the field of battle. Called 'In the Moment', the initiative will involve utilising AI to make difficult decisions in stressful situations, using live analysis of data, such as the condition of patients in a mass-casualty event and drug availability. DARPA said "DoD missions involve making many decisions rapidly in challenging circumstances and algorithmic decision-making systems could address and lighten this load on operators … ITM seeks to develop techniques that enable building, evaluating, and fielding trusted algorithmic decision-makers for mission-critical DoD operations where there is no right answer and, consequently, ground truth does not exist." Why this is important: On the battlefield, decisions have to be made extremely quickly, but humans are liable to dither and delay. AI can weigh up the data much more quickly and reach a decision almost instantaneously. [Click here to find out more!]( [Super Data Science podcast]( this week's [Super Data Science Podcast](, it's all about APIs! Ribbon Health CTO Nate Fox joins us to reveal how he and his team build out APIs from scratch, ensure API uptime, and leverage ML models to improve the quality of healthcare delivery. --------------------------------------------------------------- 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? Send them [this link to sign up.]( # # If you wish to stop receiving our emails or change your subscription options, please [Manage Your Subscription]( SuperDataScience Pty Ltd (ABN 91 617 928 131), 15 Macleay Crescent, Pacific Paradise, QLD 4564, Australia