One of the inventors of Siri, the original AI agent, wants you to “handle with care” when it comes to artificial intelligence. But are we becoming too cautious around AI in Europe and risking our future?
One of the inventors of Siri, the original AI agent, wants you to “handle with care” when it comes to artificial intelligence. But are we becoming too cautious around AI in Europe and risking our future?
No, it isn’t.
As per IBM https://www.ibm.com/think/topics/agentic-ai
The key part being the last sentence.
Its the idea of moving away from a monolithic (for simplicity’s sake) LLM into one where each “AI” serves a specific purpose. So imagine a case where you have one “AI” to parse your input text and two or three other “AI” to run different models based upon what use case your request falls into. The result is MUCH smaller models (that can often be colocated on the same physical GPU or even CPU) that are specialized rather than an Everything model that can search the internet, fail at doing math, and tell you you look super sexy in that minecraft hat.
And… anyone who has ever done any software development (web or otherwise) can tell you: That is just (micro)services. Especially when so many of the “agents” aren’t actually LLMs and are just bare metal code or databases or what have you. Just like how any Senior engineer worth their salt can point out that isn’t fundamentally different than calling a package/library instead of rolling your own solution for every component.
The idea of supervision remains the same. Some orgs care about it. Others don’t. Just like some orgs care about making maintainable code and others don’t. And one of the bigger buzz words these days is “human in the loop” to specifically provide supervision/training data.
But yes, it is very much a buzzword.
Hat on top of a hat technology. The underlying problems with LLMs remain unchanged, and “agentic AI” is basically a marketing term to make people think those problems are solved. I realize you probably know this, I’m just kvetching.
Not really. By breaking down the problem you can adjust the models to the task. There is a lot of work going into this stuff and there are ways to turn down the randomness to get more consistent outputs for simple tasks.
This is a tricky one… if you can define good success/failure criteria, then the randomness coupled with an accurate measure of success, is how “AI” like Alpha Go learns to win games, really really well.
In using AI to build computer programs and systems, if you have good tests for what “success” looks like, you’d rather have a fair amount of randomness in the algorithms trying to make things work because when they don’t and they fail, they end up stuck, out of ideas.
You’re both right imo. LLMs and every subsequent improvement are fundamentally ruined by marketing heads like oh so many things in the history of computing, so even if agentic AI is actually an improvement, it doesn’t matter because everyone is using it to do stupid fucking things.
Yeah like stringing 5 chatgpt’s together saying “you are scientist you are product lead engineer etc” is dumb but stringing together chatgpt into a coded tool into a vision model into a specific small time LLM is an interesting new way to build workflows for complex and dynamic tasks.