News
AI hallucinations: Why humans and machines both get it wrong
3+ hour, 7+ min ago (1615+ words) If you have ever tried to understand how the mind works, you know it rarely behaves as neatly as we imagine. Thoughts do not arrive in tidy rows. Memories can drift, bend, or quietly change shape. A scent can pull…...
Beyond chatbots: How to build agentic AI systems
2+ week, 4+ day ago (189+ words) Here's what nobody tells you about getting large language models into production: it starts with user stories. I know, I know. User stories feel like something the product team is always pushing on you. But hear me out. When we…...
AI agents struggle with “why” questions: a memory-based fix
2+ week, 5+ day ago (794+ words) Large language models have a memory problem. Sure, they can process thousands of tokens at once, but ask them about something from last week's conversation, and they're lost. Even worse? Try asking them why something happened, and watch them fumble…...
The hidden risk of one-size-fits-all AI advice
1+ mon, 3+ week ago (805+ words) You've probably asked ChatGPT for advice at some point. Maybe about investing that bonus check, or how to finally tackle your credit card debt. Here's what you might not realize: the same financial advice that's perfectly safe for someone earning…...
Small AI models can see for powerful language models like GPT-4
2+ mon, 6+ day ago (771+ words) The race to build ever-larger AI models might be taking an unexpected turn. Researchers from Microsoft, USC, and UC Davis have developed a clever workaround that lets text-only language models like GPT-4 and DeepSeek-R1 tackle visual tasks without expensive retraining....
Why your ML model needs product thinking: A case study
2+ mon, 3+ week ago (1356+ words) Your model achieves 94% accuracy on the validation set, but six months after deployment, the recommendation engine isn't driving business outcomes. This is a risk that all organizations run where their technically excellent models fail to deliver business value'because they were…...
Challenges & advances of deep learning in digital pathology
3+ mon, 5+ hour ago (85+ words) Learn more Enjoyed this video? Why not check out some related reading " The impact of deep learning on CV algorithm developmentEldad Klaiman talks about how Roche is leveraging open source mentality and developer communities through its journey of digitalization.AI…...
CV algorithm development by the masses for the masses
3+ mon, 10+ hour ago (106+ words) Learn more Enjoyed this video? Why not check out some related reading " The impact of deep learning on CV algorithm developmentEldad Klaiman talks about how Roche is leveraging open source mentality and developer communities through its journey of digitalization.AI…...
The implications of AGI: What comes after the era of LLMs
3+ mon, 1+ week ago (624+ words) What happens when our machines begin to understand us as naturally as we understand each other? That's not a question for the future " it's one we're living through right now. Training today's large language models already costs upwards of $100 million....
Why RAG fails in production (And how to fix it)
4+ mon, 2+ day ago (948+ words) Let me share something that might surprise you: up to 70% of Retrieval-Augmented Generation (RAG) systems fail in production. Yes, you read that right. While RAG looks magical in demos and proof-of-concepts, the reality of production deployment tells a very different…...