Remove where-ml-research-meets-data-science-practice-learning-with-small-data
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Where ML Research Meets Data Science Practice: Learning With Small Data

Dataiku

In January, Dataiku’s Léo Dreyfus-Schmidt and Reda Affane presented our annual webinar on up-and-coming machine learning (ML) trends — this year, with a spin on grounding those research trends in reality with actual use cases from our data science team.

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What to Do When AI Fails

O'Reilly on Data

economy has ground to a halt, and social norms about our data and our privacy have been thrown out the window throughout much of the world. The most common types of AI systems are still only as good as their training data. To date, at least 1,200 reports of AI incidents have been recorded in various public and research databases.

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Humans and AI: How Should You Talk About AI? Be Positive or Give Warnings?

DataRobot

In 2019, Utah struck a deal with Banjo, a threat detection firm selling AI services to process live traffic feeds, dispatch logs, and other data. The bad news was that the risk was low because Banjo didn’t actually use techniques “that meet the industry definition of artificial intelligence.” ” AI Doomsaying.

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Our Favorite Questions

O'Reilly on Data

We do so out of genuine curiosity as well as professional necessity: Q is an ML/AI consultant, Chris is a product manager in the AI space, and Shane is an attorney. To pose useful questions requires that you first understand the present situation, know where you want to wind up, and map out stepping-stones between the two.

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Themes and Conferences per Pacoid, Episode 13

Domino Data Lab

Paco Nathan’s latest article covers data practices from the National Oceanic and Atmospheric Administration (NOAA) Environment Data Management (EDM) workshop as well as updates from the AI Conference. Data Science meets Climate Science. Data veracity, data stewardship, and heros of data science.

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated.

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The Gartner 2022 Leadership Vision for Data and Analytics Leaders Questions and Answers

Andrew White

On Thursday January 6th I hosted Gartner’s 2022 Leadership Vision for Data and Analytics webinar. I try to relate as much published research as I can in the time available to draft a response. Which trends do you see for 2022 in AI & ML technology and tools and tool capabilities? Keep a watch on Gartner.com.