Large language models: are they really AI?

Some of those working on the topics above were authors on the Stochastic Parrot paper. In that paper, the authors talk about ‘documentation debt’, where data sets are undocumented and become too large to retrospectively document. Interestingly, they reference work which suggests that archival principles may provide useful lessons in data collection and documentation. Frameworks like this are important because they help facilitate the construction Yakov Livshits of more complex applications. These may be reasonably straightforward (a conversational interface to documentation or PDF documents, comparison shopping app, integration with calculators or knowledge resources) or may involve more complex, iterative interactions. It underlined the challenge of smaller open source LLMs to Google, stressing that it has no competitive moat, and that, indeed, the scale of its activity may be slowing it down.

What is more likely in the Search 3.0 era is the rise of purposefully and transparently curated and deliberately trained LLMs for vertical search, which are specialized, subject-specific search engines. Meanwhile, Google is releasing its own AI tool, Bard, and Chinese tech giant Baidu is preparing to launch a ChatGPT competitor. Be prepared to revisit decisions about building or buying as the technology evolves, Lamarre warns. “The question comes down to, ‘How much can I competitively differentiate if I build versus if I buy,’ and I think that boundary is going to change over time,” he says. “We thought it would be technically difficult and costly for ordinary companies like us that haven’t made a huge investment in generative AI to build such services on our own,” he says.

GPT-3.5

We are familiar, for example, with ChatGPT telling us that it is not connected to the internet and cannot answer questions about current events. With so much time, money, and resources being invested in competing with OpenAI and all the possibilities it has opened up, let’s hope that LLMs don’t slow down progress in other worthwhile AI pursuits. Generative AI and Large Language Models have made significant strides in natural language processing, opening up new possibilities across various domains.

However, activities involving machine translation, text production, and natural language processing have all been transformed by large language models. They enable automated customer care, the creation of writing that sounds human, and intelligent chatbots. On the other hand, when talking about Generative AI vs Large Language models,  large language models are specialized AI models created to comprehend and produce text-based content. These models thoroughly comprehend language syntax, grammar, and context because they were trained on enormous volumes of text data.

Industry specific applications

The data used to train GPT, for example, was 575 gigabytes of text (more than any of us could ever read in a lifetime). It came from the web (every single word of it) – that is to say, it came from us – and it was all retrieved and downloaded for natural language processing through a technique called web scraping. It might appear that LLMs have learned to imitate the human Yakov Livshits trait of overconfidence. The plethora of amusing examples of ChatGPT giving false information with singular self-certainty suggests that. They generate natural text by predicting the next likely word, much like your smartphone does when it offers to finish your sentences for you. Yes, there are many cases of epic ChatGPT fails, but on the whole, it does its job very well.

Another problem with LLMs and their parameters is the unintended biases that can be introduced by LLM developers and self-supervised data collection from the internet. If you need to boil down an email or chat thread into a concise summary, a chatbot such as OpenAI’s ChatGPT or Google’s Bard can do that. If you need to spruce up your resume with more eloquent language and impressive bullet points, AI can help. As technology continues to advance, we’re hearing more and more about AI, Generative AI, and Large Language Models.

If your aim goes beyond merely crafting a Minimum Viable Product (MVP) and you envision a competitive, high-quality, and scalable solution, then the open-source model stands out as the better choice. With open-source, you benefit from enhanced control over latency, cost, and the overall quality of the model. Data privacy is a paramount concern in today’s digital world and plays a vital role in the decision between utilizing a pre-existing API or creating a custom AI model. This is particularly relevant when your application needs to handle sensitive or proprietary data.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

In contrast, Generative AI uses neural networks to teach itself how to create content like images or text by analyzing vast amounts of data. A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content. The term generative AI also is closely connected with LLMs, which are, in fact, a type of generative AI that has been specifically architected to help generate text-based content. Artificial intelligence called “generative AI,” is concerned with producing new and original content, such as songs, photos, and texts.

Moreover, building your own model or using an open-source one provides you with opportunities to optimize your model and infrastructure for efficiency, potentially reducing resource usage and, therefore, costs. In practice, this involves breaking down your data into discrete segments, which are then stored within a vector database. Upon receiving a user’s query, the model scans this database to identify chunks of information that bear semantic resemblance to the query. These relevant pieces are then used to provide additional context to the LLM, aiding in generating a more accurate and contextually aware response.

Tencent founder and CEO Pony Ma Huateng said in June that his company was in no rush to launch unfinished products. Last month, the Chinese social media and gaming giant launched its industry-oriented LLM service aimed at various traditional sectors, from finance to media. According to a post published to its official WeChat account, the Shenzhen-based company’s cloud arm launched its LLM as a model-as-a-service [MaaS] solution. The standardization initiative reflects how local authorities have extolled AI’s potential to help drive economic growth and become a useful daily tool while maintaining caution about its risks and asserting technology regulation. Still, internet regulator the CAC has yet to issue a license for any generative AI product in the country.

So far, more than hundreds of LLMs have been released, but which are the most capable ones? To find out, follow our list of the best large language models (proprietary and open-source) in 2023. The Stanford AI Index for 2023 notes that industry produced 32 significant machine learning models in 2022, compared to 3 from academia.

This form of AI is a machine learning model that is trained on large data sets to make more accurate decisions than if trained from a single algorithm. Machine learning is a subset of artificial intelligence (AI) that involves developing algorithms and statistical models enabling computer systems to learn and program themselves from experiences without being explicitly programmed. In other words, machine learning involves creating computer systems that can learn and improve on their own by analyzing data and identifying patterns, rather than being programmed to perform a specific task. Large Language Models (LLMs) aren’t new to AI developers and researchers, but they’re newly poised to start shaping the way we work. An LLM is a type of machine learning model that can handle a wide range of NLP use cases.

To make all this possible, a generative AI enterprise solution must provide rigorous access controls to ensure each user gets fast and accurate answers while maintaining security and data confidentiality for your entire enterprise. C3 Generative AI manages access via a comprehensive role-and-policy based framework built into the underlying C3 AI Platform. It is a level-zero requirement that any generative AI solution for the enterprise always indicates where its answers come from. It should synthesize an answer and provide a chat interface so the user can dive deeper with follow-up questions — and the dashboard needs to provide instant traceability. It’s similar to searching the web, where the user can decide whether a website that’s served up is credible.

• Prompt engineering is used to describe someone who is adept at interfacing with a generative AI model and coming up with the right prompts needed to produce the desired output.
• Large language models and generative AI have attracted a lot of attention in the field of artificial intelligence (AI) and have generated innovative innovations.
• To start on a lighter note, let’s use satire as a way to engage our audience before diving into the more serious aspects of LLM and generative AI ethics.
• There are considerations specific to use cases and decision points around cost, effort, data privacy, intellectual property and security.
• Identifying the issues that must be solved is also essential, as is comprehending historical data and ensuring accuracy.

A text which is embedded inside is collaborated together to generate predictions. By utilizing a domain-specific LLM trained on medical data, dynamic AI agents can understand complex medical queries and provide accurate information, potentially revolutionizing the way patients seek medical advice. Similarly, for the financial sector, domain-specific LLMs could generate personalized investment recommendations based on an individual’s risk profile and financial goals, creating a more effective and efficient investment experience. We will likely see more LLMs specialised by subject, by country or language group, or in other ways, alongside and overlapping with the push to open models. Alex Zhavoronkov had an interesting piece in Forbes where he argues that content generators/owners are the unexpected winners as LLMs become more widely used.