I'm pretty sure it's not ML by definition. Oxford definition:

the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.

There is no data(set) involved in evolutionary algorithms, so it's not ML. Genetic algorithms are usually seen as (a part of) AI, though.

True.

Ah my bad. I think you could make it a bit more clear in your post but it's definitely on me for misunderstanding. If the information about the residence was given in the document itself then it becomes a lot more doable.

I still see quite few problems such as neighbourhood, etc. influencing the price, which means you'd need an absolutely huge dataset with very detailed features. And even then I think the accuracy will still not be optimal. Then there's still the issue with scraping competitors data from their website, which I doubt is legal.

It really depends on what this will be used for. Want to use this to recommend houses to potential buyers in a certain price range? Absolutely doable, but it seems completely overkill for an application like that. Want to use it to replace humans who's job it is to give price estimations? Probably not a good idea.

So as far as I understand the project, you want to estimate the price of real estate. There're a few ways to do this. Forget pictures for the moment, just go with listed/numeric information.

You have information like Area/Square Footage, Listed Amenities, Age, Location, etc.If you have existing data of this sort, where it lists all the above and then a price, then it is fairly straightforward to pull off – but no guarantees on the accuracy. This has been done by plenty of people, so if you just do this your investors will probably ask you about how you're going to compete with established Real Estate companies who have much bigger teams and much more data.

Now let's consider images: you have pictures of the house, and you want to use those pictures as a way to measure how broken-down/upscale the house is and use that as a parameter to base the price of. You are going to combine this with the above, of course, because it's ridiculous otherwise. I'll say this frankly – this hasn't really been done, and it's a research problem. Not a 'product problem'. You could do a whole PhD thesis on this alone. There are so many different ways to approach this.

• You can use ML to extract furniture from the picture individually, then assign a value to each item of furniture. Aggregate that value to get how well furnished the place.
  • Massive Pitfall - How do you assign a value to a furniture? A minimalistic luxury sofa and an antique cabinet could be worth equally high. Designing this NN would be a huge challenge to start with.
  • Second Pitfall - You need labelled data. You would need a whole team manually annotating the data by looking through hundreds/thousands of furniture images and assigning a value to them.
• You can use ML to determine the quality of the whole room. Forget individual objects, just rate the whole picture from "broken down" to "fancy af" on a scale from 1 to 10 or something.
  • Pitfall - Again, you need labelled data. You'd need a whole team going through images of rooms and marking them. And since you're applying the model into such a very abstracted and broad problem, your results are not really going to be reliable.
• You can use ML at a more micro level. Maybe you could detect broken or damaged furniture.
  • Massive Pitfall - There is very little data available for this, and moreover detecting such issues is still an issue for state of the art models. Some research has been done, such as detecting defects in wooden surfaces and stuff, but it's still at a fairly basic level. Making an algorithm that would detect, say, a crack on a chair, a stain on a cushion, scratches on glass, etc is possible... individually, by zooming in on that thing specifically. Doing this for a whole room on low-mid resolution images would be a nightmare.

Honestly I've given you the entire business plan you're looking for here lmao. Only reason I'm comfortable doing this is because what you're imagining is not really a feasible business plan except for at the very, very basic level.

Like, if you had a team that could pull any of these off, they would be working at AirBnB, Zillow or some other major real estate company already.

If those investors are feeling particularly generous and give you several years and an 7-figure budget, then this might be worth considering. Otherwise...

At train time, you plug decoders at many levels with the same objective, you can find out if some things can be decoded earlier, using an additional network that outputs a sort of confidence. At inference time, you run the layers one by one, and stop when the confidence is high. which allows you to skip some computations. (It's probably a simplistic description, feel free to correct me)

You'll still pay the full price at train time, right? Early decoding works by using decoders on earlier levels at train time. Conversely, if you want to spend more on some tokens, at train time, you will need to have more layers, so at some point you will hit your memory/complexity limits.

I should have been clearer with my question. What I was wondering was, if there are any extensions to the Transformer architecture that allow it to, in theory, spent indefinite amounts of compute on one token. I suppose one could train a very deep Transformer, use CALM during inference and only use all of the layers for tokens which are difficult to predict, but this would still arbitrarily limit the maximum amount of compute per token.

Circumventing bot blocking protocols is a trivial matter.

The potential lawsuit, on the other hand, is not.

For Amazon it was just a speaker and an ordering system. It has never been truly passionate about the chatbot part.

Oh okay I do apologise I thought it was just through pictures. Still a hard task . I do have some students in my phd cohort who would be willing to work on this . Lmk if you are interested and I can set up a meeting .

There's no way for us to tell for certain, but since Google has used it for creativity oriented projects/papers like Dramatron, I don't think so. I feel the researchers would have said something instead of leading the whole world intentionally astray as everyone is now following Chinchilla's scaling laws.

Chinchilla isn't just a smaller model. It's adequately trained unlike GPT3 which is severely undertrained, so simmilar, if not exceeding ( as officially claimed ), capabilities isn't unexpected.

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You do the analysis, paste your raw notebook into chatGPT and ask it to write the report for you in business language. It can be very skilled at corporate speak.

I assume they have more/better task demonstrations for the multi-task finetuning phase. But that kind of data would be very easy to generate by calling their APIs. It's also possible to use a LLM to generate this kind of data from scratch, and even to do without RLHF by using Constitutional AI.

A few hundreds is way too little. I would be comfortable with a few thousand homes' data, and more comfortable yet if I could scrape Zillow or something on top of that.

(but that has its own issues, both legally and in terms of data drift, since Zillow data would be American while you're European).

Most companies already have their mail in Microsoft Office. They already trust MS.

Well, you'd get the pics AND the info on the livable area (total livable area, nr of rooms, a sketch of the place, livable area for each room, etc.).

Copy-pasting a comment of mine since it answers your question:

As far as I can tell, a few 100s of homes would be available to be used as a dataset.

Many smaller models give good results on classification and extractive tasks. But when they need to get creative they don't sound so great. I don't know if Chinchilla is as creative as the latest from OpenAI, but my gut feeling says it isn't.

As far as I can tell, a few 100s of homes would be available to be used as a dataset.

I dunno if my English is that bad or people are in a rush when reading my post but: you get the images AND information about the residence itself (nr of rooms, total living space, space of each room, a sketch of the place, etc.).

> meanwhile Google fired a guy that mass mailed people saying their own ai was sentient.

Never imagined it would turn out so bad for Google to need Lemoine's testimony

Best answer! 😆

MS failed the search, abandoned the browser, missed the mobile, now they want to hit. It's about not fucking up again.

I don't think the GPT-3 model itself is a moat, someone will surpass it and make a free version soon enough. But the long term strategy is to become a preferred hosting provider. In a gold rush, sell shovels.

Yes, that's probably it - they will rent tons and tons of GPUs and make profit on datacenters.