The article discusses how machine learning exercises can be designed for automated assessment tools, framing them as deterministic input-output tasks. It emphasizes that this approach does not create a new grading system but enables existing platforms (e.g., VPL for Moodle, Codeforces, MOJ) to support AI education more effectively. The authors argue that integrating theory with practice through such exercises can foster dynamic, interactive AI courses.
By Artur Jordao
The article titled "5 AI Skills That Will Keep Data Scientists Relevant in 2027" outlines five specific AI competencies, explaining what each skill addresses and providing runnable code snippets that readers can directly paste into a notebook. It serves as a practical guide for data scientists aiming to stay current with emerging AI technologies.
By Sara Nobrega
Sarah Friar, CFO of OpenAI, introduces a practical AI scorecard to measure ROI through useful work, cost per successful task, dependability, and return on compute.
The article "How to Solve the Right Problem in the Age of Agentic AI" presents a practical framework aimed at reducing uncertainty before agents accelerate implementation. It offers guidance on identifying and addressing the most relevant problems in the context of increasingly autonomous AI systems.
By Mike Huls
arXiv:2602. 16763v2 Announce Type: replace Abstract: Artificial intelligence benchmarks are an important mechanism for measuring model progress and guiding deployment decisions.
By Mubashara Akhtar, Anka Reuel, Prajna Soni, Sanchit Ahuja, Pawan Sasanka Ammanamanchi, Ruchit Rawal, Vil\'em Zouhar, Srishti Yadav, Chenxi Whitehouse, Dayeon Ki, Jennifer Mickel, Leshem Choshen, Marek \v{S}uppa, Jan Batzner, Jenny Chim, Jeba Sania, Yanan Long, Hossein A. Rahmani, Christina Knight, Yiyang Nan, Jyoutir Raj, Yu Fan, Shubham Singh, Subramanyam Sahoo, Eliya Habba, Usman Gohar, Siddhesh Pawar, Robert Scholz, Arjun Subramonian, Jingwei Ni, Mykel Kochenderfer, Sanmi Koyejo, Mrinmaya Sachan, Stella Biderman, Zeerak Talat, Avijit Ghosh, Irene Solaiman
How do you set yourself up for a career that will stand the test of time when things are changing so fast?
The post Starting a Career in Data Science in the Age of AI appeared first on Towards Data Sc...
By Stephanie Kirmer
Patience, Optimism, Discipline, Projects, Teams The post Lessons Learned After 8. 5 Years of ML appeared first on Towards Data Science .
By Pascal Janetzky
The article "How to Work with AI Coding Agents" offers a practical guide aimed at improving code quality rather than merely increasing quantity. It focuses on strategies and best practices for effectively collaborating with AI coding tools to produce better code. The post was originally published on Towards Data Science.
By Sara A. Metwalli
Exploring TypeSafe's Jev and comparing it with OpenAI on intent classification
The post A New Kind of Model for AI Decision-Making? appeared first on Towards Data Science.
By Mariya Mansurova
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
By Sara Nobrega
arXiv:2607. 14044v1 Announce Type: new Abstract: By 2030, 59 of every 100 workers will need reskilling or upskilling, yet the average time to close an enterprise skills gap grew from roughly 3 days in 2014 to 36 days in 2018.
By Tam Nguyen, Hung Nguyen, Robert Ogburn