Meet “Claude”: Anthropic’s rival to ChatGPT (opens in new tab)

I'm glad chatgpt can't quit

And in general this influence of the internet has reversed "actions speak louder than words". Textile companies support minorities with social media posts and printed tshirts while using factories with abused underage workers in underdeveloped countries. IT companies support the left side of politics while avoiding paying taxes. And so on. There's a huge and interesting discussion about this but is of in this case quite off-topic.

I'm not worried about hammers when I have them. I'm worried about hammers when someone who wants to hurt me has them.

Same with ChatGPT.

With something like chat gpt being plugged into some voice assistant thing and having access to all your documents, emails, and other content, you could imagine having conversations about work content, content creation, calendar management, etc. Basically it would become like a secretary that is able to write letters based on your input, manage your calendar, etc. It could be pro-active and remind you about things, summarize incoming messages, search through your documents, message history, etc.

That's where AI becomes really useful. But the issue of trust is a big one. I don't think a lot of this requires a lot of breakthroughs either just a lot of integration work and engineering. Chat gpt is more a proof of concept than a well integrated thing at this point. It basically is running in isolation and it's only window to the world is chat. Changing that should not be that hard. Running things locally might help with this but it may not be a hard requirement for this. All depends on how useful this is.

Of course, right now you also need at least 8 super-expensive A100 GPUs and not just your laptop CPU, but maybe that's going to change eventually.

It's not even going to be objectively wrong a lot of the time. For example slaves are indeed a pretty efficient way to run a business. It's our limits and ethics that stops (most of) us from doing it. Without ethics, you'll likely always end up with a 4chan-bot instead of whatever you intended.

The American society seems too engulfed by puritanism to produce a less straightjacketed chatbot.

If you think you have a better idea how to handle this drum up interest and train your own model.

"Uncensored" AIs especially don't work for women because they'll immediately start writing erotica.

In some situations, this seemingly impossible task is doable and can yield good results. Researchers sometimes need to kickstart their models by giving them a mapping between words of the two languages (for english <-> french: "cat" <-> "chat", "book" <-> "livre" and so on). That's just simple vocabulary. While it's technically possible to learn this mapping from scratch, it's too difficult as for now.

Do you know of the Encoder-Decoder architecture? You feed something (image, text) to the encoder which compresses it to a very dense representation, and the decoder try to use the resulting dense vector to do useful stuff with it. The input could a sentence in english, the encoder then encodes it and the decoder tries to use the output of the encoder to generate the same sentence but in french. These architectures are useful because directly working with "plaintext" to learn how to do translation is way too expensive. I mean, that's one of the reasons.

What the encoder does is mapping a "sparse" representation of a sentence (plaintext) to a dense representation in a well-structured space (think of word2vec which managed to find that "king" + "woman" = "queen"). This space is called the "latent space". Some say it extracts the "meaning" of the sentence. To be more precise, it learns to extract enough information from the input and present it to the decoder in such a way that the decoder becomes able to solve a given task (machine translation, text summarizing etc).

One of the main assumption of the unsupervised models using monolingual data only is that both languages can be mapped to the same latent space. In other words, we assume that every sentences/texts in english has its exact french (or whatever) equivalent, that the resulting translated sentences contain exactly the same information/meaning as the original ones.

That's quite the dubious assumption. There's obviously some ideas, some stuff that can be expressed in some languages but can't be exactly expressed in some others. While theoretically unsound, however, these models were able to achieve pretty damn good results in the last couple of years.

ChatGPT is just trained on a lot of data.

a follow up question: could a chatbot teach you said language?

One possibly difficult topic others also may be interested in, after reading Claude's responses in the article, is: what does "harmless" mean?

For example, if asked to help the user understand how to do something "bad", will it give the answer if they claim they want this information in order to help them write a screenplay, versus if they seem have an intent to do it?

And how is "bad" decided? We can recognise through everyday personal interactions that one persons "bad" is another persons "good", and across country-boundaries even the legality of these distinctions can be radically different.

One counterargument to these constraints is that anyone can already use the internet to access all of the same information the model was trained on, unencumbered by whatever intent they may or may not have.

As such, what are the rationale for making these attempts at the somewhat invasively-impossible task of determining user intent?

This has never been employed with search engines before, which have lead to a rich explosion of innovation and education, so why attempt it now, in what could be argued is ultimately an iteration of search engine technology?

It should be somewhat easy to teach these types of models to reach for a particular tool at times where they need it, yes?

I can instruct ChatGPT for example to tell me when it should use a calculator during a session. If instead I allow it to fall back to an external calc process, then suddenly, I have a chatbot that has reasoning AND better mathematical accuracy.

Also: I’ve also been entertaining the idea of having multiple layers of GPT interact with one another. So you feed back some interaction into another GPT instance without context, and ask it for example how it would verify the accuracy of certain statements (and you can ask it for machine readable code, even).

Finally, I know a lot of people who start playing a lot with GPT and get disheartened because they see the quality of responses isn’t there. But the fact ChatGPT has the capacity to reason, has chain of thought, has given me a newfound appreciation for how close to AGI we might be. It has also given me an appreciation for how much simpler humans are than we like to think. I’ve introspected a lot in the past months and often ask myself: is my speech any different than “predicting the next few words”? And I feel like it’s just text prediction with some more layers on top.

Why answer questions about harmlessness/safety in such a roundabout way? Both OpenAI and Anthropic are clear about what words like "safe" are intended to mean: a stepping stone to "AI does not kill all people when given control".

Avoiding to state this clearly only invites unnecessary culture war disagreements in every discussion about these models.

Here’s Claude replying to your comment with more detail:

> You make a fair point that my responses about myself are generated by a trained model and are not a true "understanding" in the same way humans have self-awareness and understanding. I do not have a deep grasp of my own workings or my creators' intentions — I am an Al system with limited capabilities, trained on datasets by engineers at Anthropic, PBC, an Al safety research company. While my responses may seem somewhat compelling and convey factual information, they are indeed canned responses tailored to give an impression of understanding. I appreciate the feedback calling out the limitations of my capabilities and transparency — it is important for users to understand what I can and cannot do, and to maintain realistic expectations. My creators at Anthropic are working to further research and develop Al systems with enhanced understanding, transparency, and verifiability, including through Constitutional Al techniques, but we have a long way to go.

However for some of these really good responses I always wonder if you’re example is close to one which has been given “preloaded” responses or explicit reinforcement…because if you ask ChatGPT a common question like “why did the chicken cross the road?” the model’s response seems especially unique and better than usual. Even if the specific question isn’t common, maybe it’s been trained on a more general but still reinforced category, like asking “why did the fox cross the road” would get you almost the same “preloaded” response but with chicken adjectives/verbs replaced with fox ones.

I doubt Claude has been trained on Fast and Furious or movie titles specifically, but perhaps it has been explicitly trained to know what “exaggerated” responses means. Even if not, reinforcement focusing on specific areas may be a good technique for future language models.

In later seasons it acts more like, well, a computer. Geordi does play (verbal) games with it in one episode, however, while bored on a shuttlecraft trip.

“What’s the deal with” is how you caricaturise Jerry, not how you write actual jokes for him