Nothing particular. I just don't remember major successful and speedy product launches from Google after 2003. In that year, Google launched Google Maps, shocking the IT world for how effective AJAX can be, even though the tech itself was invented by MS. Maybe GMaps was so impressive that I forgot what other major launches were after that.
GMail was pretty impressive in 2004 too. It probably sounds banal now, but not having to constantly delete emails was game changing at the time. I still have access to email going back to 2004 and I find myself looking up things I received over a decade ago.
> but not having to constantly delete emails was game changing at the time.
Funnily enough Hotmail which was the premiere email service when GMail launched didn't have any meaningful increase in storage until like 10 years later
Gmail launched on 1st of april and was thought to be a joke since it gave you a gitabyte (I think) of storage wheras email providers like hotmail gave you 50mb. Chrome launching was also a pretty big deal, I recall.
Google earth was nice too, and street view. It was hard to believe at first. But Chrome was just a re-hash of firefox, nothing exciting there.
Kind of agree. The last time I think I was this stoked to play with a mass market tech tool like this was street-view, and before that Google Earth.
Wolfram Alpha was my ChatGPT when I was in school. And many of the same issues ("how will kids learn math now?") were raised at the time. ...Yeah, this seems like a great match, no complaints from me. Those kinds of responses are exactly what LLMs need.
Wolfram Alpha was (at least initially) famously bad* at actually parsing natural language. For math, it was much easier to enter raw Mathematica expression into Wolfram Alpha than structuring the question in natural language. LLMs should give it a boost, by simplifying the kind of the natural language input into the limited forms parsable by Wolfram Alpha. *: And I say this as a paying customer of Mathematica on my c…
WolframAlpha is still bad at natural language.
I recently tried to get it to convert mpg to l/100km. I spent close to 10 minutes until I stumbled into a combination that gave the answer among a sea of useless conversions.
We're going to see more of this in the coming years: A mature technology being bolted onto another mature technology. So we should start imagining all the things being built today which can be bolted onto each other: -Speech synthesis/voice assistants -Walking robots -Deepfakes -ChatGPT/Bard -The now-many Copilots -Language recognition/transcription/translation AI bots are going to be a huge part of the workplace soo…
Walking robots is the one thing on that list which is unlikely to be solved by LLMs.
We're going to see more of this in the coming years: A mature technology being bolted onto another mature technology. So we should start imagining all the things being built today which can be bolted onto each other: -Speech synthesis/voice assistants -Walking robots -Deepfakes -ChatGPT/Bard -The now-many Copilots -Language recognition/transcription/translation AI bots are going to be a huge part of the workplace soo…
Walking robots is the one thing on that list which is unlikely to be solved by LLMs.
Yeah, but it's something likely to be bolted onto LLM's is my point.
I don’t remember Google being this interesting. Ever. It was a good service and very useful, but nothing like this. It was not astonishing. The tech was mildly interesting, but most of all very useful.
What was Google's email/chat crossover called, which had conversation threads visualized? Besides, Google's Graveyard is impressive and witness to Google's innovation abortions. https://killedbygoogle.com
Honestly so confused with all this hate for Google. Ofc they can bounce back, the only thing that’s going to be a product differentiator for a GPT product is data. OpenAI is still trained on the open internet and Microsoft’s data, and also whatever user queries happened. If we want good PERSONAL assistants, that stuff is going to come from either Google or Facebook. OpenAI end-game products will be great Productive assistants but Google will bounce back with their own GPT trained off of everything you put into anything google.
I don’t remember Google being this interesting. Ever. It was a good service and very useful, but nothing like this. It was not astonishing. The tech was mildly interesting, but most of all very useful.
What was Google's email/chat crossover called, which had conversation threads visualized? Besides, Google's Graveyard is impressive and witness to Google's innovation abortions. https://killedbygoogle.com
Wolfram himself laid out why this is unlikely to happen, https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-... >Or put another way, there’s an ultimate tradeoff between capability and trainability: the more you want a system to make “true use” of its computational capabilities, the more it’s going to show computational irreducibility, and the less it’s going to be trainable. And the more it’s fundamentally…
>and therefore has no ability to do any kind of computation with nontrivial “control flow” This isn't true if you let the model output tokens that aren't sent to the user. Basically the null api.
Exactly, giving ChatGPT a scratchpad it can use to think is trivial in this new plugin enabled ChatGPT world.
OpenAI is like Google before 2003, dazzling the world with amazing products backed by astonishing technologies that went beyond wild dreams of millions of people. And most importantly, OpenAI kept releasing new features at blazing speed with a few hundred people, just like early days of Google and Microsoft. This reminds me of stark contrast of Google's GSuite and Microsoft Office. Microsoft released new office every…
This is pretty hyperbolic. Great products that were much better than what they replaced or competed with, but "beyond wild dreams of millions of people"? You'd think they cured cancer in 2003 or something...
I don't deny that it can be hyperbolic to many people. As for the comments about people's wild dreams, I truly mean it. I used to tap into building NLP applications for enterprise customers: NERs, relation extractions, multi-round dialogs, information retrieval, and etc. We built dedicated models for each tasks, we spent millions on labeled data and annotation teams in general, we worked with customers closely to address their specific issues. It was a painful process and customers are not happy, and I overlooked the significance of OpenAI's papers on GPT and Google's paper on emerging ability out of sheer ignorance and stupidity. Then, ChatGPT came along and could have pretty intelligent multi-round conversations, could handle multiple NLP tasks easily and have better performance than my models with my canonical tests, and could be helpful to me on a daily basis. It was shocking iphone moment or a personal Sputnik moment to me. For that, I extrapolated that it was "beyond wild dreams of millions of people".