Claude AI: ChatGPT’s New Competitor
If you’re active in the realm of AI, the name Claude has probably crossed your radar by now. It’s the latest AI-powered chatbot from Anthropic.
Founded by early OpenAI employees, Claude is competing head-on with ChatGPT (the leading product from OpenAI) — a competition that is heating up with Google’s recent $2B investment in the company.
As with most large language models that break into the scene, there’s a lot of buzz surrounding Claude at the moment, and rightfully so. But how does it stack up against other leading Language Models such as GPT, Bard, or LLaMa?
That’s what we aim to uncover today. We’re exploring Claude’s technology and discussing its architecture and competencies. From its take on self-supervised learning to its ethical framework, we offer you an impartial evaluation. Let’s see if the fanfare is justified.
The basics: Anthropic’s proprietary Constitutional AI
Claude operates on a Constitutional AI approach, which means it’s designed to go beyond mere data output. According to its creators, the model adheres to a set of principles that aim for ethical integrity, helpfulness, and, notably, harmlessness.
While Claude’s architecture is purportedly built to be ethical from inception, it’s worth asking some pointed questions to evaluate these claims critically. For instance, the extent to which Claude’s training data is transparent or includes non-Western perspectives is unclear. The only thing we know is that training involves:
- Consistent feedback from human trainers
- Values and rules around which Claude’s behavior is modeled
- Prioritizing helpfulness, honesty, and harmlessness when generating answers
Additionally, the methods employed to mitigate bias and misinformation have yet to be fully disclosed. So, while Claude sets itself apart by advocating for built-in ethical compliance, the jury is still out on whether it truly surpasses its competitors in this arena. Thus, some skepticism might be warranted until Anthropic decides to go public with a more detailed overview of Claude’s training.
Claude’s brain: self-supervised learning and transformer models
When it comes to the technical underpinnings, self-supervised learning is at the heart of Claude’s cognitive abilities. With this technique, the model learns from data that hasn’t been specifically tagged or labeled for training. As a result, Claude can grasp ‘common sense information’ without needing guidance.
However, sifting through a treasure trove of data, especially one so vast, poses a conundrum: How does it evade the trap of “poisoned” training data? Especially given the proliferation of AI-generated content, the risk of Claude inadvertently picking up questionable material is a legitimate concern. I’ve personally caught Claude confidently stating false information on multiple occasions, only to spiral into an endless loop of apologies when confronted with its falsehoods.
According to Anthropic, Claude operates under a set of guiding principles that are continually fine-tuned to maintain ethical and operational efficacy. The full list draws from a mix of credible sources, such as the UN Declaration of Human Rights, AI research labs, and even global platform guidelines like Apple’s terms of service.
But like with most of the details surrounding this LLM, Anthropic has been vague about how it ensures Claude abides by the aforementioned principles.
Transformer-based language models
Regarding its natural language capabilities, Claude banks on a neural network architecture called the Focused Transformer. It excels in sequence processing tasks and uses algorithms referred to as attention mechanisms and multi-headed self-attention layers to capture contextual nuances. These are computer programs that, over time, are trained to understand just that — what words or parts of a string of text are significant (or what to pay attention to).
Compared to older recurrent neural network models, such as those used in Siri or Google Assistant, the Transformer has a leg up in efficiency and contextual understanding. This allows Claude to grasp the idea of the input, even when the prompt is incomplete or crafted ambiguously.
Uncertainty modeling: a calculated approach to accuracy
Claude’s architecture also boasts uncertainty modeling. With it, Claude has the ability to flag certain responses with cautionary advice. This capability is especially useful in complex, high-risk decision-making scenarios. Two prominent emerging use cases are financial modeling and medical advice.
When queried, for example, about the liquidity or strike price of a particular option, Claude wouldn’t just spit out a generic answer; instead, the model could warn the user to tread carefully and educate themselves about options trading before proceeding.
Impressive as this is, Claude isn’t necessarily doing anything groundbreaking here. ChatGPT and Bard are both capable of this. But it does shed more light on where Claude is heading and where it stands in terms of ethics.
This is particularly intriguing for liability purposes, which is crucial given the number of users who use LLMs to self-diagnose. Even if the diagnosis is simple, straightforward, or non-life threatening, Claude will shut the conversation down and refer the user to a medical professional.
While the potential of Claude and other LLMs for these sensitive topics is intriguing, Claude, in particular, showcases why AI researchers and ML specialists need to focus on making their models impervious to manipulation and based upon an ethics-first approach.
Claude vs the usual suspects: GPT, Bard, and LLaMa
Alright, we’ve waxed poetic about Claude, but how does it stand up against the who’s who of the language model world — GPT, Bard, and LLaMa? Let’s break down the key differentiators that set Claude apart from the crowd.
GPT models, though powerful, have a tendency to generate responses that may not be 100% reliable. They are geared more towards coherence and fluency rather than the accuracy of information.
Furthermore, I’ve also noticed that GPT-4 tends to go beyond its knowledge cutoff date of September 2021, with dubious results at best. But when it comes to extra features, the now built-in DALL-E 3, Advanced Data Analysis, and Bing-powered browsing, OpenAI still towers above the competition.
Bard, as its name suggests, is skilled at creating narratives. It excels in weaving coherent and engaging stories while presenting an opinionated identity but isn’t necessarily focused on factual accuracy. Claude, conversely, is designed to put facts first.
It might not win a Pulitzer for fiction, but Bard is the model you’d want on your trivia team. It works beautifully with Google’s search engine and is probably the best for everyday tasks.
However, in my experience, it’s also the LLM that’s prone to hallucinations the most, mainly due to the garbage-in, garbage-out concept. Just think about how many Google search results are of suspect quality, and it will make sense why Bard seems to be the least precise of the Big Four.
Llama 2, or LLaMa, to be more precise, is an open-source LLM developed and maintained by Facebook’s parent company, Meta. Unlike its cloud-bound cousins, it’s designed to work offline. That means all your data stays on your device, making LLaMa leaps and bounds more secure than Claude or GPT.
LLaMa excels at understanding the context in which a question or statement is made, allowing it to give more nuanced and relevant answers. While it may not have a feature to directly warn you if a piece of information might be unreliable, it stands out for another important reason — self-hosting.
Unlike ChatGPT, which runs on OpenAI’s hardware, self-hosting lets you utilize your own hardware to run the model locally. Models with fewer parameters can generally run on personal computers, although you might need a powerful GPU (ideally an Nvidia 30 or 40 series). As both the parameters and the context window increase, so does the need for a home server.
Being open source, LLaMa provides you with the freedom to customize it extensively. That means you can adapt it to fit your specific requirements. Plus, there are dozens of models available, so you can pick the one that aligns best with your needs.
Now, why is this good for self-hosting? Open-source software and numerous variations translate to a highly adaptable and customizable solution. If you value privacy and control over your chatbot, LLaMa empowers you to keep all your data on your own hardware without sacrificing functionality. This makes it an excellent pick for a self-hosted chatbot.
While there are certainly some appealing features in LLaMa, it doesn’t compete with Claude’s uncertainty modeling — yet. So, for now, if you like to be alerted when something doesn’t seem quite right, Claude is a solid choice. This has far-reaching advantages across a variety of industries, from analytics to fashion and everything in between.
Ethical standards: a cut above
Claude integrates risk assessment into its algorithms to ensure that it’s not an accomplice in any shady business and that its stance is always ethical. This makes Claude less prone to jailbreaking, which makes sense, given that Anthropic’s own CEO believes it to be a matter of life and death.
So while GPT, Bard, and LLaMa each bring their own unique capabilities to the table, Claude is the one that serves the most comprehensive experience — accurate, ethical, and designed for the future. And as AI continues to evolve and bolster its IQ, these qualities are incredibly important.
Future applications of Claude: more than just words
Claude’s Constitutional AI aims to provide ethical and trustworthy responses. This ethical backbone not only guards against misleading content but also positions Claude to adapt to future challenges in the evolving AI landscape.
This is especially important for future situations where we might be dealing with an advanced version of the model, even capable of integration with monitoring systems and cybersecurity software.
If a criminal prompted it to help them access a property surveillance system, even if they said they were the owner and gave a convincing reason, Claude would shut them down because of the risks involved. This circles back to uncertainty modeling — the positivity of the outcome is highly uncertain, resulting in the LLM shutting the prompt out.
But that’s looking too far in the future. Anthropic first has to focus on matching Midjourney and DALL-E in the visual department, which won’t be soon, given they’ve only just released their Claude Pro plans. Likewise, there are still plenty of question marks surrounding Claude’s training, protection against biased input data, and more.
Will Claude be able to compete?
Claude represents a monumental step in the field of AI — bridging the gap between ethical behavior and technical prowess. From its foundations in Constitutional AI to its reliance on state-of-the-art transformer architectures, Claude stands out as an AI model with not just advanced capabilities but also a conscience.
And let’s not forget its unique approach to uncertainty modeling. It adds an invaluable layer of ethical decision-making, making Claude not just a tool but a responsibly designed system for both current and future applications. Whether it’s medicine, customer support, or content creation, one thing’s for sure — the world is watching Anthropic and its LLM closely.