[Rinzler89]

What do you expect exactly? I'm sure every big tech has had AI in their products for a while now: Who do you think filters the spam in your Gmail, if not their AI Bots? Or the music suggestions in your Spotify?

Why do you think Microsoft would be a hellhole for doing the same? Especially considering all the productivity use cases they've shown for the Office suite.

I swear HN needs to hate everything Microsoft is doing just because.

[redserk]

I think there are two ways to go about implementing AI.

The low-key implementations that assist are the most elegant ways to implement AI functionality. If I can use a product and not realize AI is behind it, the product has successfully utilized it. Spam filters fall into this case. Automatic “radio” stations from streaming services fall into this too.

The worst forms of AI implementation are the kind that spend more screen real estate advertising AI as if the product has something to prove. This hinders my experience as a user because I do not care about AI if it isn’t seamlessly fitting in my workflow.

I’m not sure what’s happening at Microsoft, but their insistence on AI in very unusual places doesn’t give me confidence they want to embrace AI in a manner that’s helpful. It feels like someone’s resume boosting exercise. It gives me the feeling they are desperate.

[ThrowawayTestr]

>Who do you think filters the spam in your Gmail, if not their AI Bots?

I would hope it's a purpose-built ML model and not an LLM that was cajoled into doing spam filtering.

[Rinzler89]

So exactly what I just said.

Trained ML Models have been in use long before LLMs came out.

[netdevnet]

ML models are AI too. And you don't even need statistical models to call your system AI, just a set of conditional blocks behind a decision

[poisonborz]

You really think of a bunch of conditions can be labeled as AI? Do you work in marketing?

https://miro.medium.com/v2/1*gXZeYDjqLBWqbnGvlr_gyQ.png

[netdevnet]

It was an exaggeration but most of pre-ML AI is just a set of fairly rigid and general rules. They mostly differ in the sense that rules are added by different means (human vs machine) or at different times (during the "training" process or on the go while being used)

[procgen]

Is your contention that AI can't be implemented on a Turing machine?

[peteradio]

It's true I'll hate on micrsoft for about anything but they didn't say "some products" "some services" "some processes", they said "EVERY SINGLE THING!!!", see the difference?

[Rinzler89]

>they said "EVERY SINGLE THING!!!", see the difference?

Where did they say this? I read "almost every".

[peteradio]

Does it make a difference of context whether or not they've successfully shoehorned only 99%?

[Ukv]

Given the quote is referring to what they've done "for years and years and years [...] in our product groups" outside of the OpenAI arrangement, the fact that a large number their of products have come to make some use of AI models without much fanfare (search, spell-check, spam filtering, voice dictation, language translation, recommendation systems, ...) is not inherently due to the more recent LLM shoehorning. Machine learning is just the best choice for a good number of tasks.

[peteradio]

I'm not talking about LLMs in particular. I guess this is a company wide mandate to grow knowledge of how to do this stuff well, I mean that makes sense. But in the trenches (aka hells-ahole) that means a lot of bad bad stuff is being relied on and it generates calcification of business segments and kafkaesque anti-patterns for the uninitiated. This doesn't only apply to "AI" its a generic feature of shoe-hornings. The problem with the shoe-horn is that its politically costly to resist even if it makes good business sense to resist at the micro level.

[Ukv]

I'd agree that "We're going to shove a chatbot in every single one of our products", like the recent Copilot integrations, would reek of shoe-horning and a possible company wide mandate.

But remarks more along the lines of "Looking back over the past decade, we've made use of ML models in some part of almost all of our products" seems fairly reasonable to me, not necessarily indicative of much other than machine learning being the best tool for an increasing number of tasks. If they weren't using ML-based echo cancellation in Teams calls for instance, they would have a worse product than competitors that do.