TLDRocket
Sign in
Latest Nebius looks to raise $4.5BN through bond issue — Tech.eu Also’s $3,500 e-bike is a $1 billion Trojan horse for autonomous trans... — Fortune Unitree, famous for its dancing robots, surges by 460% on its trading... — Fortune Exclusive: Replit taps OpenAI's low-cost Luna model for new 'Free Mode... — Fortune Adronite launches Codistry AI coding platform, claims half the token c... — SiliconANGLE Rundoo raises $30M to expand its AI-native operating system for small... — SiliconANGLE Temporal is in talks to raise $500M at a $12B pre-money valuation, mor... — Tech Funding News Etched raises $700M led by Jane Street, doubling to $21B and it still... — Tech Funding News

The AI intelligence platform

Every AI story that matters and the intelligence behind it.

TLDRocket reads all relevant sources, removes duplicate coverage, and publishes a short neutral summary of every story, linking back to the original. Free, no spam, unsubscribe anytime.

Add to Slack

Every story also updates live profiles event timelines weekly rankings the AI Market Index

Sunday, 7 July 2024

How to Interview and Hire ML/AI Engineers

Eugene Yan 2 years ago 43

The article discusses frameworks for interviewing and hiring machine learning and AI engineers, covering technical skills like software engineering proficiency, data literacy, understanding ML model opacity, and evaluation methodologies. Key technical assessment areas include coding exercises (30-60 minutes), data analysis skills, comfort with model uncertainty, and knowledge of evaluation practices, with specific follow-up questions provided for each. For more research-focused roles, additional assessment includes science breadth (familiarity with ML domains like recommender systems and language modeling), science depth (rigor on past projects), and science application (solving practical problems relevant to the team), alongside non-technical dimensions like ambiguity tolerance, influence, complexity, and execution.

Extrinsic Hallucinations in LLMs

Lil'Log 2 years ago 33

Researchers distinguish between in-context hallucination (output inconsistent with provided context) and extrinsic hallucination (output not grounded in the model's pre-training data or world knowledge). Extrinsic hallucination occurs when large language models fabricate information rather than admitting knowledge gaps. To reduce extrinsic hallucination, LLMs must generate factual outputs while explicitly acknowledging when they lack information about a topic.

The daily briefing

Every AI story that matters, in your inbox by 8am.

TLDRocket reads all relevant sources, removes duplicate coverage, and summarises the day in two minutes. Follow companies and topics for alerts, or get the briefing in Slack. Free, no spam, unsubscribe anytime.