A deep learning system known as a large language model, or LLM, was trained using enormous amounts of text data, in this case, tens of millions of publicly available Github code repositories. An example of a large language model (LLM) application is the Copilot product from GitHub. Within the coding interface, Copilot will offer advice on how to complete a line of code, or it may even produce many lines of code from a textual description. Copilot is created using Codex’s LLM from OpenAI, which converts natural language into a number of well-known programming languages.
You may be familiar with a few more well-known LLM programmes. LaMDA at Google excels at inciting conversation. With LaMDA, Google hopes to eventually power a conversational interface that will enable users to ask for any kind of information (text, photos, etc.) from any of its products and receive it from a highly intelligent chatbot. The top 10 uses for large language models in 2023 are discussed in this article. To learn more in-depth information on applications for large language models, read this article.
- Tech Risk
However, it requires a more competitive market. GPT-3 shows the potential (and expense) of various copywriting generation firms. Additionally, if there are no alternatives and you choose to use a huge company’s API, like OpenAI, to construct your application, you are subject to their pricing power and product SLAs. The post’s conclusion includes more views on the possible outcomes of this dynamic.
LLMs are known to have problems, and research is continuing to fix these problems and explain how well LLMs can work with a variety of inputs. For instance, GPT-3 and Codex occasionally produce offensive language and unsafe or erroneous code, especially when dealing with hostile users. However, they are frequently accurate enough for many users to find the models useful.
- Textwriting
Although GPT-3 is the most well-known model, there are open-source alternatives including Eleuthera AI’s GPT-J and BLOOM (from BigScience). Among the companies creating applications in this field are Copy AI, Copysmith, Contenda, Cohere, and Jasper AI. Their technologies can speed up the creation of blogs, sales copy, digital ads, and website copy. - Similar to “GitHub Copilot, but for the terminal,” Shell Command Generation Warp, a next-generation terminal, uses GPT-3 to translate spoken language into executable shell commands. Even seasoned engineers occasionally struggle to understand shell commands.
- Database Query Improvement
Ottertune finds and fixes database faults including missing indexes and cache misses that might cause unforeseen complications. Although we are unsure if Ottertune employs LLMs for this, we have talked with others about it as a potential LLM use case. - Creation of Websites
A tool called Pygma transforms Figma designs into excellent code. Users will be able to communicate while designing and creating a website, according to Salesforce’s long-term plans for CodeGen. - General Tool Assistant for Software
Adept AI’s goal is to provide workflow suggestions for any software, effectively acting as a universal copilot or assistant. Here is a wonderful demo showcasing preliminary findings. Based on the descriptions on their home pages, Character AI and Inflection AI might also be growing in this area, but nothing is currently known about them. - Translation Meta has done research to translate 204 languages—more than twice as many as had been attempted before—at a higher standard than had been accomplished before.
8. Product Insights
Users’ feedback (such as via support tickets, surveys, and analytics) is organised and summarised by Viable, Enterpret, Cohere, and Anecdote into useful insights for further product development.
- SQL Development
To access data and business insights without needing to write SQL, Cogram translates everyday English into database queries. - Creating codes
The most widely used model is Codex (which runs Copilot), however Salesforce’s CodeGen is an open-source substitute. One of the startups creating applications is Mutable AI, while others include Tabnine and Codiga. Although most comments on Copilot were favourable, there were a few criticisms, including requests to self-host or modify models, alter workflows, and address the problems Codex has with front-end frameworks and test creation.
