Google has become very good at not returning adequate results along the years. Be it in Search or Youtube, it's been a disappointement, but for an Ad-focused company, quite predictable.

I can't wait for a competitor or something else entirely ala prompt IA.

Infinite paperclips.

ChatGPT is just a language model. It can't solve any problem using actual creativity or true intelligence. We're not even close to that.

I hope they release model. without model source code useless since i don't have gpu power or dataset to train :(

I seriously implore people to pay attention to what microsoft is doing, what they're saying on conference calls, and most of all, how much cash on hand they have.

It seems whoever mentioned on the conference call a few weeks back about investing an additional 100BB into OpenAi got what they wanted because MS isn't stopping anytime soon and I don't think this is going to be 'the end' to the relationship by any means.

Also, OpenAi have a reason to be confident, we're looking at the early-stages of the next trillion dollar company.

I disabled Cortana. Hate that thing.

Not publishing the dataset is becoming less common as we start inching our way slowly to reproducible science. Public code with public data is the simplest form of reproducible research, where we can re-run your experiments with the same code and should get the same result (modulo some extremely low-level randomness or hardware differences that we may not be able to control).

That alone isn't enough to kill a paper, but it doesn't help. As another commenter said, showing your approach on public datasets and other approaches on your dataset will help, as it gives the rest of the community something that is reproducable.

It's more common in medical venues because of a few reasons:

1. Difficulties around safely releasing medical data. Proper anonymisation and informed consent.
2. It is more common in medical science to go for a higher level of reproducibility, where the same or a similar study will be done on a different population (i.e. same method, different data). This is pretty uncommon in ML, it's hard to get papers accepted in this format.

We are both right and wrong. To be pedantic, it's this paper for both https://arxiv.org/abs/2203.02155 but with different training data

Here's a sneak peek of /r/ControlProblem using the top posts of the year!

#1: I gave ChatGPT the 117 question, eight dimensional PolitiScales test | 48 comments
#2: Computers won't be intelligent for a million years – to build an AGI would require the combined and continuous efforts of mathematicians and mechanics for 1-10 million years. | 9 comments
#3: Ilya Sutskever, co-founder of OpenAI: "it may be that today's large neural networks are slightly conscious" | 43 comments

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Fine-tuning is when you take a pretrained network, change the output layer only, and run the optimizer a little more.

Transfer learning is when you take any sort of pretraining. Fine-tuning is one example of transfer learning. Using pretrained word embeddings is another example of transfer learning.

You can do deep learning without either. It's just that existing pretrained models and components are so good that it's tough to build a competitive model without either.

...Then Cortana will be about porn... * not kidding * ...

No, I'm European so I have no idea about the Zillow debacle sadly.

The outputs would probably be used as a price evaluator for the living space (my friend works as a registrar of new/ bought homes). Honestly I think the Zillow usecase might be desired ultimately.

Would you be interested? :)

ba-ba-ba-BAAAAAAA

Wow! Thank you for the concise yet complex reply!

Would you be interested in such a project? Based on your final sentence, I assume not. :(

Ah ok. On the first point I guess whoever you are looking for will need to spend a considerable amount of time building/finding a dataset to train a model.

On the second point, I might have incorrectly assumed you were familiar with the Zillow controversy around price prediction.

The TL;DR is that the ML team used a model to forecast prices using a tool made by Facebook called Prophet. The model was probably accurate enough for displaying a rough prediction on a website. Another team in Zillow started using these price predictions to flip houses and lost a whole bunch of money since the model was not designed to do this.

A lot of armchair data scientists quickly pointed the finger at Prophet for being a "bad" model. The reality is all models are bad if they are used for the wrong reason. In this case, the team flipping houses likely didn't listen to the data science team when they said the model shouldn't be used for that purpose.

This is why it's a good idea to know how the model outputs are going to be used. The obvious answer is always "as accurate as possible" but sometimes that might not be accurate enough...

Hope this helps!

Could be done but would require about 6 months for data collection and experimentation. And I think the going rate for 6 month contracts for AI engineers in the UK is 30K, US is upwards of 60k.

Good luck bro

> Why were OpenAI the first to make a model as good as ChatGPT then?

Here's a controversial take: luck

They didn't invent the wheel or faster than light travel, it was something that was going to happen sooner or later and they were just the first to do it publicly, meanwhile Google fired a guy that mass mailed people saying their own ai was sentient.

A fair question. What information would you like me to provide?

This isn't an idea guy post. But to pitch something for funding, you need a rough estimate of time and costs and some deliverable milestones.

This is an "idea guy" post, isn't it. ;)

Like, one of those "I have no idea how incredibly hard it would be to even gather the necessary data for this" ideas? A, "I need a quote to give to my guy in two days despite being unable to provide you with any more guidance than a vaguely worded pseudo-concept" proposal?

Best of luck dude.

1). Not really. Data would have to be processed, but probably it can be introduced via a well structured form:

• Nr. of rooms/ bathrooms/ etc.
• Living space (square meters)
• pictures (probably another ML to gauge how furnished and well maintained/ dilapidated the whole place is)
• etc.

2). A great question! Zillow seems like a great example. As accurate as possible I guess. But a good starting point would be a price range.

https://www.rezi.ai Something like this?

Power BI; Insights; Tenant wide sandboxed AI … etc.

OpenAI doesn't want people to use GPT directly, in the long run. They want UX to be with another layer of deep AIs on top of GPT, trained for special purposes. If they are making Cortana that deep AI over GPT, then I could believe O.P.

Lol, Amazon barely makes a profit because they reinvest a vast majority of it to pay less taxes. They don't need to make money if they use it to keep growing