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A Large language model or LLM is a deep-learning algorithm that has been trained on massive amounts of text data, in this case, tens of millions of publicly accessible Github code repositories. Github's Copilot product is an example of a large language model (LLM) application. Copilot will make suggestions for how to finish a line of code within the coding interface, or even generate multiple lines of code from a plaintext description. Copilot is built with OpenAI's Codex's LLM, which translates natural language into a variety of popular programming languages.
You may have heard of a few other high-profile LLM programs. Google's LaMDA is an expert at generating dialogue. Google's long-term goal with LaMDA is to power a conversational interface that will allow customers to retrieve any type of information (text, images, etc.) from Google's products simply by asking – essentially, a very intelligent chatbot. In this article, we have explained the top 10 applications for large language models in 2023. Read this article to know more about applications for large language models in detail.
GPT-3 demonstrates the viability (and cost) of other copywriting generation startups, but it requires a more competitive market. Furthermore, if you decide to use a large company's API, such as OpenAI, to build your application and there are no alternatives, you are subject to their pricing power and product SLAs. More thoughts on how this dynamic could play out are included at the end of this post.
LLMs have known issues, and research is ongoing to improve their accuracy and explain their ability on a wide range of inputs. GPT-3 and Codex, for example, will occasionally output biased language and insecure or incorrect code, especially when confronted with an adversarial user. They are, however, correct enough of the time that many users find the models useful.
The most well-known model is GPT-3, but there are open-source alternatives such as BLOOM (from BigScience) and Eleuthera AI's GPT-J. Copy AI, Copysmith, Contenda, Cohere, and Jasper AI are among the startups developing applications in the space, with products to speed up writing blogs, sales, digital ads, and website copy.
Warp, a next-generation terminal, employs GPT-3 to convert natural language into executable shell commands, much like "GitHub Copilot, but for the terminal." Even experienced engineers may find shell commands confusing.
Ottertune detects and resolves database issues such as cache misses and missing indexes, which can lead to unexpected problems. We are not sure if Ottertune uses LLMs for this, but it's something we have discussed with others as a possible LLM use case.
Pygma is a tool that converts Figma designs into high-quality code. Salesforce's long-term vision for CodeGen includes allowing users to converse to design and generate a website.
The vision of Adept AI is to suggest workflow steps for any software, essentially becoming a universal copilot/assistant. There's a great demo showing early results here. Character AI and Inflection AI may also be developing in this space based on their home page descriptions, but little is known about them at the moment.
Meta has researched to translate 204 different languages, which is twice as many as had previously been attempted, at a higher quality than had previously been achieved.
Viable, Enterpret, Cohere, and Anecdote organise and summarise user feedback (e.g., support tickets, surveys, and analytics) into actionable insights for future product development.
Cogram converts plain English into database queries, allowing nontechnical users to obtain data and business insights without having to write SQL.
The most popular model is Codex (which powers Copilot), but there is an open-source alternative in Salesforce's CodeGen. Tabnine, Codiga, and Mutable AI are among the startups developing applications. The majority of the feedback on Copilot was positive, but there were some complaints, such as wanting to self-host or fine-tune their models, customise workflows, and fix some issues Codex has with frontend frameworks and test generation.
