Data Science Insider: December 30th, 2022

In This Week’s SuperDataScience Newsletter: DL Cuts Tsunami Impact Prediction Time. Teacher Fears Over ChatGPT. AI is Revolutionising Stroke Care for NHS Patients. In-Database ML. 2022 AI Successes. 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 Cuts Tsunami Impact Prediction Time]( In brief: Japanese scientists have published a new study that claims they can use AI to cut the time for predicting how an approaching tsunami will impact the coastline to fractions of a second. Since the 2021 Fukushima earthquake caused the death of around 18,500 people, the northeast Japanese coast has continued to develop its early warning system, creating the world’s largest network of sensors with over 150 offshore stations for monitoring movements on the ocean floor. Researchers from the Riken Prediction Science Laboratory now claim that the data generated by these sensors can be converted into tsunami heights and extents along the coastline. Traditional data modeling takes around 30 minutes on a standard computer. Why this is important: By using ML scientists have been able to cut the calculation time to less than one second, meaning that lives could potentially be saved. [Click here to learn more!]( [Teacher Fears Over ChatGPT]( In brief: As we’ve covered in these newsletters, ChatGPT has exploded in popularity, with more than a million users signing up to use it within its first week. As more people are using it fears grow about its misuse, particularly by students using it to generate essays (one student in a South Carolina university has already been caught using it to produce an essay). In this Forbes, article teachers share their unease about the inaccurate information the chatbot provides. These concerns have been acknowledged by Open AI’s CEO Sam Altman, who tweeted: “ChatGPT is incredibly limited but good enough at some things to create a misleading impression of greatness. It's a mistake to be relying on it for anything important right now.” Why this is important: ChatGPT’s popularity is astonishing but it has boomed so quickly that resources aren’t yet available to mitigate its drawbacks. [Click here to read on!]( [AI is Revolutionising Stroke Care for NHS Patient]( In brief: NHS England has announced the success of a stroke diagnosis tool that, in 2020, received funding from the first round of the government’s AI in Health and Care Award. In an announcement, they claimed that the Brainomix stroke diagnosis tool has tripled the number of patients making a big enough recovery to be able to perform daily activities from 16% to 48%. This is by reducing door-in and out time from 140 to 79 minutes, where the diagnosis of a stroke is one of the most time-sensitive diagnoses in medicine. NHS England Director of Transformation Dr. Timothy Ferris said: “The NHS is harnessing the potential that AI has to support expert staff in delivering life-changing care for patients.” Why this is important: The AI in Health and Care Award is beginning to see real-life success stories. Just in England, 85,000 people are affected by strokes every year and hopefully, the use of this technology can become more widespread. [Click here to discover more!]( [In-Database ML]( brief: In October the SuperDataScience newsletter included an article from InfoWorld’s Martin Heller called ‘How to choose a cloud machine learning platform,’ he’s now back and building upon the advice that he gave to “be close to your data.” In this article Heller helpfully lists databases that support internal ML, these include Amazon Redshift, BlazingSQL, Brytlyt, Google Cloud BigQuery, IBM Db2 Warehouse, Kinetica, Microsoft SQL Server, Oracle Database, and Vertica. Heller gives a detailed overview of each of the databases’ pros and cons and concludes that each could be used to support doing ML internally, or by using MindsDB you could add to your existing database if it doesn’t already support internal ML. Why this is important: By building ML models where your data resides, you are able to keep latency low. [Click here to see the full picture!]( [2022 AI Successes]( In brief: 2022 has been a busy year for AI and data scientists and here at SuperDataScience we’ve covered all of the developments along the way. Sometimes it appears that so much has happened that it’s difficult to keep track but this list of the most interesting AI tools announced in 2022 from Learning English highlights some of the year’s biggest AI successes. It includes the development of Meta’s speech-to-speech translation, the discovery of a method to identify Parkinson’s Disease, the creation of a tool to interpret pig emotions, the development of a system to identify and predict smells, and the construction of a tool to fill in missing words in ancient writings. Why this is important: Reviewing 2022’s achievements is an enlivening way to get excited for the new year and what 2023 may have in store. [Click here to find out more!]( [Super Data Science podcast]( this week's [Super Data Science Podcast](, Jon Krohn speaks with Mariya Sha, the brains behind the YouTube channel 'Python Simplified', about putting Python into practice, the importance of productivity hacks, and her best technical tips for creating your own data science videos. --------------------------------------------------------------- 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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