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AI & Machine Learning

The most popular blog posts about ai & machine learning on Hacker News. 6,325 posts.

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2881

Why GPT-3 Matters

The sheer scale of the new GPT-3 model is hard to overstate; it’s an entire order of magnitude larger than Microsoft’s already-massive 17B parameter Turing-NLG.[1] Loading the entire model’s weights

2882

Can LLMs accurately recall the Bible?

I've often found myself uneasy when LLMs (Large Language Models) are asked to quote the Bible. While they can provide insightful discussions about faith, their tendency to hallucinate responses raises concerns when dealing with scripture, which we regard as the inspired Word of God. To explore these concerns, I

2891

Alexa+

Powered by generative AI, Alexa+ is your personal AI assistant that gets things done—she’s smarter, more conversational, more capable, and free with Prime.

2892

Evaluation of TikTok vs. Instagram Reels

Why can't Meta A/B test its way back to greatness? To move Instagram beyond short-term engagement metrics, we ran a personalized human evaluation asking 100 users to compare TikTok vs. Instagram Reels. Learn why Gen Z considers Reels the place where TikToks go to die, and what Instagram should do about it.

2894

Text Embedding Models Contain Bias

As Machine Learning practitioners, when faced with a task, we usually select or train a model primarily based on how well it performs on that task. For example, say we're building a system to classify whether a movie review is positive or negative. We take 5 different models and see how well each performs this task:

2901

Traffic Prediction with Advanced Graph Neural Networks

By partnering with Google, DeepMind is able to bring the benefits of AI to billions of people all over the world. From reuniting a speech-impaired user with his original voice, to helping users discover personalised apps, we can apply breakthrough research to immediate real-world problems at a Google scale. Today…

2907

Concrete AI Safety Problems

We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended.