Making AI Practical and Practicable
Machine learning, combinatorial optimization, computer vision, LLMs, anything that’s been understood to be AI from 1950 to now.
This post originally appeared on SEP’s Blog. Large language models are often described as general-purpose problem solvers. That’s fair; given a prompt, they can write SQL queries, summarize academic papers, troubleshoot YAML files, produce a mediocre chili recipe, and draft HR emails, all in the same breath. That breadth of…
Next week I’ll be in Kansas City for KCDC, and if you’re attending too, I hope you’ll come find me. I’ll be giving two talks, one hands-on, one high-concept, and in between I plan to soak up as many interesting sessions (and hallway conversations) as possible. Where You Can Find…
I sit at a really weird saddle point currently. Professionally, I’m obliged to engage with generative AI technologies. I’m an AI technologist. I’ve been doing this for nearly twenty years at this point, and over ten past the doctorate. I can’t just ignore a new technology because it’s built on…
If you’ve been alive in tech anytime in the past two years, you’ve been told, repeatedly, loudly, often by people who discovered prompt engineering five minutes ago, that large language models are here to revolutionize everything. Your job. Your team. Possibly civilization itself. This is not that article. Instead, I…
The AI gold rush has created a weird kind of pressure. If you’re not chasing the trend, you’re behind. If you ask hard questions, you’re a stick in the mud. If you jump in too fast? You might end up with a flashy prototype and no plan to support it.…
Let me put a flag in the sand. The documents that come out of research aren’t the value. The value is in building expertise in the researcher.
We’ve been here before. Several times. This is not to say today’s breakthroughs aren’t impressive, they are, but calm the hell down. This scale of breakthrough isn’t unprecedented, even in my lifetime.
Lately, we’ve been fielding a lot of interest in custom Retrieval Augmented Generation (RAG) systems. And honestly, it’s easy to see why. RAG systems are incredibly flexible tools for injecting up-to-date, domain-specific knowledge into AI responses for answering questions, generating content, analyzing internal data, or any other task that’s primarily completed by producing an artifact.
This post originally appeared on SEP’s Blog Artificial Intelligence is everywhere these days: headlines, investor decks, your last team meeting, and dinner conversations. Just because AI is hot doesn’t mean it’s well understood. Too often, people reach for AI like it’s duct tape: a fix-all solution for any digital dilemma.…
Last year, AI code gen tools made a huge splash into the software development world. Again. Someone had the bright idea that “Hey, source code is text. Maybe we can generate that!”. What started as one big offering has grown to dozens of tools offering to write our code for…
