Reinforcement learning, maybe?

Honestly, I'm not aware of any. That doesn't mean that they don't exist, but if they do, I don't think I've heard of them. Now, ChatGPT can write code in a bunch of different languages, and you can train models to learn different programming languages, but as far as "providing information about the world from strictly factual sources and enabling more general computation", I don't think there's any other one.

What other software language does the similar to Wolfram language to synthesizing with ChatGPT?

Thank you. Another question in another context. I am teaching thermodynamics. I was thinking about ways to use ChatGPT in my class. My students are using ChatGPT to solve their homeworks but they are have problems detecting some mistakes in the answers that AI gives to them. So I would like to assign the a homework where they get a wrong answer from the chat, but they would have to interact with it to "train" it and get the right answer. What would happen in this scenario? The whole AI is going to learn from the information my students are going to give the chat or is just their chat (account) who is going to learn from this interaction?

Thanks for the answer and for sharing your point of view. For me a chemical engineer is very enlightening.

Thank you Arben. In this scenario do you think the neural net could start to memorize the traffic information? If that happen I starting to see the problems that would trigger.

Hi! I have a question regarding the implementation of AI. In my city, Guatemala (in Central America) there is a project to use IA to control the traffic lights. I haven´t see examples of this kind of implementation in any other places. And since in todays session was mention the problems that a neurological networks can have with algorithms, do you think this implementation could be effective? I belive is not a generative IA what is going to be used, but I wonder what would be the results.

Hey Arben, I had an open session of 14.0 running on my Mac and I double clicked the Machine Learning notebook after downloading it; my Mathematica session went off to LaLa land. Wouldn't even obey the interrupt Cmd + Option+ [.] or CTRL C. Had to force quit from MacOS. I started a new session and opened the notebook from within Mathematica and it opened as I expected. But I have not "enabled dynamics"... It should have opened the same by double clicking on the file, I think, yes?

They're all a little bit different:

• "Machine learning" is generally used to describe the process of training neural nets
• "Deep learning" is a subset of machine learning referring to the training of "deep" neural networks, i.e. those with many layers
• "AI" is super general and encompasses the above and more—it's so general that it can lead to miscommunications and mystification, though
• "Generative AI" refers to "AI" (usually based on neural nets which have been trained via machine learning) which can "generate" novel text, images, audio, and so on

As for what we're doing at Wolfram, we've supported neural nets and machine learning for a long time now, and our fully symbolic approach is really well-suited to the construction and understanding of neural nets. Our new LLM (one example of generative AI) functionality takes advantage of the benefits of this new technology to do things like create helpful personas, write code, check or rephrase writing, and so on inside notebooks. Alan will address this in more detail on Friday!

I heard AI for more than 40 years and it seems like a smart design? Are there any major differences?

Hey Carl! Thanks for the kind words for our materials :). While I'm not a machine learning pro, I think that your example use case is quite doable (and depending on the image resolution, you may need fewer images than you imagine—high resolution images by definition have a lot of information stored in them, and IME even training on the order of tens of examples can be sufficient to do binary categorization with high accuracy in those cases).

As for perspective transformations, that could be a little trickier. I'm not sure what the extent of the different angles and viewpoints might be, but it could be that your training data could include an image and various transformations of it created with something like ImagePerspectiveTransformation or similar?

For spotting differences, theoretically you could train a neural network to do that by providing training data consisting of two input images with the output being a selected region where differences occur, but I'm not sure that that's the direction I'd take. If you can do something like FindGeometricTrransform to get your two "perspectives" roughly aligned, you might be able to do something like explicitly compute image differences and find the region over which they're maximized. This is kind of on the cusp of "better with traditional methods" vs "better with machine learning", to my (admittedly somewhat ignorant!) mind.

Hey Arben, I have already learned more than I expected to from Wolfram's perspective on AI, Machine Learning and ChatGPT. Looking forward to your approach to teaching.

But could I really use Wolfram Language alone, or WL combined with (?), to do industrial grade things like spot defects in images of circuit boards or components soldered down on circuit boards? Like cracks, malformed parts, missing traces, debris, etc? Or would that still require developing a specialized tool? Or look at images of objects from different angles and determine if they are images of the same thing? Like an aerial photo of a city or a farm?

Or better yet, after recognizing whether they are images of the same thing, highlight what has changed in the thing after correcting for the different look angles and lighting? Or would that be too specialized an application?

(I am certain that if I wanted to do that I would at least look at WL as my experimental laboratory for learning how to do it)

I was curious to see that the sample from the 4-gram model in today's session/notebook was the smallest to generate actual English words. Is that likely related to the fact that 4.7 letters is the average word length in English? (Source)

Oh hey, you mean Arten, ok, np!

Just kidding, my bad for the typo Arben, pls don't hurt me :D

Your code worked!

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Arden! I'm the first to plant my flag! Yay!!!!

I got the dark them working! Look, Mitch didn't register, lol :D

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