2 is just fancy talk for what high perfoming LLMs do
3 has been done many times over
4 is achievable with Imagebind if we're going for an exotic solution. Otherwise GPT-4V with AudioToText and TTS will do just fine (Open AI have something similar set up)
5 is as simple as timed prompts sent by the company unbeknownst to the user.
6 is the probably the most bespoke thing here. I'm guessing this is the "information as a quiz" thing they try to demonstrate.
7 is the same as 2
8 is High perfoming LLM with a specific prompt.
I'm not trying to discount your experience but the technology that is making any of this possible is a few years old and the new state of the art version which is far ahead of everything else is ~8 months old so unless you just worked on something like this then I'm not sure it's much indication on what is achievable.
That's exactly what I'm saying - I've just worked on something like this since January. I throttled back from universal file manager and stuff, I think they're kinda hinting at it too through the "probabilistic computing" thing. Staying focused on RAG + as much local compute as possible, user perception of citations is through the roof compared to practically anything else.
The problem I saw over and over again at Google wasn't, like, how do we do this. It's like the "draw the rest of the f*king owl" meme. You can pull some stuff off probabistically or presenting choices, but it gets real nasty real quick when you screw up (the most public version I can think of here is how immensely frustrating it is if a voice speaker mishears your query, especially twice in a row)
Failures being a series of products is something else I feel hurts, ex. getting 95% of the files right x 95% of your appointments x 95% of your sleep schedule, honestly, each individual prompts reliability is top-tier. Yet, 15% of the time it kinda has no idea what you're doing.
But I'm sort of devil's advocating here, to a point. I think you can get there, but boy it's a lot of work and they're 3-4 years away if they have 100 employees and a lot of work, non-trivial on several fronts, even just the GPT billing is a nightmare. I am 99.9% sure I am the only person on earth besides OpenAI employees who know how to bill functions correctly to the token (to be fair, getting within ~4 tokens is publicly available, just not from OpenAI, incredible reverse engineering work by a few people on a forum thread)
And then I guess there's a smidge of "if you're chasing all these things simultaneously you might not grok it" on my end. You're far better off making a slick interaction for moving files between buckets and creating buckets, than promising you can reverse-engineer from scratch, all the right buckets, with all the right files in them.
2 is just fancy talk for what high perfoming LLMs do
3 has been done many times over
4 is achievable with Imagebind if we're going for an exotic solution. Otherwise GPT-4V with AudioToText and TTS will do just fine (Open AI have something similar set up)
5 is as simple as timed prompts sent by the company unbeknownst to the user.
6 is the probably the most bespoke thing here. I'm guessing this is the "information as a quiz" thing they try to demonstrate.
7 is the same as 2
8 is High perfoming LLM with a specific prompt.
I'm not trying to discount your experience but the technology that is making any of this possible is a few years old and the new state of the art version which is far ahead of everything else is ~8 months old so unless you just worked on something like this then I'm not sure it's much indication on what is achievable.
I guess we'll see.