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Deadboy00 t1_j91v5cp wrote

Reply to comment by IonizingKoala in Microsoft Killed Bing by Neurogence

⭐️ Refreshing to see someone who knows their shit on this sub. Where do you see this tech going for general use cases? Everything I read tells me it just isn’t ready. What is MS’s endgame for implementing all this?

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IonizingKoala t1_j927ast wrote

Classical computing / engineering advances are good at repetitive actions. A human can never put in a screw 10,000x times with 0.01mm precision or calculate 5000 graphs by hand without quitting. But it's bad at actions that require flexibility and adaptation, like what chefs, dry cleaners, or software engineers do.

LLM and AI attempt to bridge that gap, by allowing for computers to be flexible and adapt. The issue is that we don't know how much they're actually capable of adapting, and how fast. We know humans have a limit; nobody in the world fluently speaks & reads & writes in more than 10 languages (probably not even >5). Do computers have a limit? How expensive is that limit? Because materials, manufacturing, and energy are finite resources.

What do you define as general use cases? Receptionist calls? (already done, one actually fooled me into thinking it was a human) Making a cup of coffee?

Anything repetitive will be automated, if it's economical to do so. You probably still make tea by hand, because it's a waste of money to buy a $100 tea maker (and they probably dont even exist because of how easy it is to make tea). But you probably have a blender, because it's a huge waste of time and energy to chop stuff yourself.

I think humans (on this subreddit especially) tend to underestimate how much finances & logistics play into tech. We've had flying cars since the 90s, yet they'll never "transform transportation" like sci-fi said, because it's dumb to have a car-plane hybrid.

We might get an impressive AGI in the next few years, but it might be so expensive that it's just used the same way we use robots: you get the cutting-edge stuff you'll never see cause it's in some factory, the entertaining stuff like the cruise ship robo-bartenders, and the consumer-grade crap like Roombas. AGI might also kill millions of humans but I know nothing about that side of AI so I won't comment.

Btw, I'm not an expert, I'm just a software engineer that likes talking to AI engineers.

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Deadboy00 t1_j929dnb wrote

Dig it. I have a similar background and have had conversations with interns at ai firms like Palantir that have been doing the shit you described for years. I agree. It’s too expensive to train ai’s for every specific use case. That’s what I meant by “general”.

I think the most fascinating part of this current trend is seeing the general populations reaction to these tools being publicly released. And that’s what’s at the heart of my question…if the tech is unreliable, expensive, and generally not scalable …why is MS doing this?

I mean obviously they are generating data on user interactions to retrain the model but I can’t imagine that being the silver bullet.

Google implemented plenty of ai tech in their search engine but nobody raises an eyebrow, but now all this? I’m rambling at this point but it’s just not adding up in my brain ¯_(ツ)_/¯

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IonizingKoala t1_j92caso wrote

Microsoft is similar to Google; both like to experiment and make cool stuff, but Microsoft doesn't cut the fat and likes to put out products which are effectively trash under the guise of open beta. Heck, even their hardware is sometimes like that, while Google's products are typically solid, even if they have a short lifespan.

Going back to New Bing, it's genuinely innovative. It just sucks. That's not paradoxical, because a lot of new stuff does suck. We just rarely see it, because companies like Google are generally disciplined enough.

Most "deep" innovations are developed over decades. That development could be secretive (military tech), or open (SpaceX, Tesla), but it takes time nonetheless. Microsoft leans towards the latter, Google the former.

The latter is generally more efficient, if your audience is results-focused, not emotions-focused. AI is pretty emotionally charged, so maybe the former method is better.

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Deadboy00 t1_j92j3s2 wrote

That’s a good take. I think Google’s discipline is rooted in its size and prominence. There’s too much to lose. MS on the other hand wants to desperately be the king of the hill again.

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IonizingKoala t1_j92nqhq wrote

The funny thing is though, Microsoft has a market cap 58% larger than Alphabet, not just Google. We're left wondering why Microsoft continually takes these weird risks in the consumer space when they can just play it safe like most other big players. None of their (21st century) success has been due to quirky disruptions, it's usually been slow and steady progress (Surface, Office, Enterprise, Cloud, Consulting).

Yet with stuff like Edge, Windows 11, etc, it's been a mess. I'm not 12 anymore, I prefer stable products over the shinest new thing, and Windows 11 has been a collosal disappointment.

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