Overview:
Large Language Models (LLMs) have transformed how we engage with technology and access a wealth of information. LLMs are foundational machine learning models that use deep learning algorithms to process and understand natural language thus enabling them to summarize, generate and predict new content.
The number of commercial and open LLM providers has exploded in the last two years, and there are now many options to choose from for all types of language tasks.
Future of language processing:
LLM has been quietly expanding AI’s impact in healthcare, gaming, finance, robotics, and other fields and functions, including enterprise-level development of software and machine learning. The arrival of ChatGPT marked the clear coming out of a different kind of LLM as the foundation of generative AI and transformer neural networks.
One of the most significant applications of large language models is in the field of natural language processing (NLP). NLP is another subfield of artificial intelligence that focuses on the interactions between humans and computers using natural language. LLMs have made significant contributions to NLP by improving the accuracy of language understanding, sentiment analysis, and machine translation.
While LLMs have helped AI understand human language, they’re not limited to it. New developments are making it easier to train massive neural networks on biomolecular data and chemical data. The ability to understand these “languages” lets researchers develop and deploy AI that can discover new patterns and insights in biological sequences and human health conditions.
Organizations across industries are recognizing the potential of large language models to enhance customer experiences, automate processes, and drive innovation. LLMs can help enterprises codify intelligence through learned knowledge across multiple domains.
Current market landscape:
The market landscape for LLMs is still in its early stages, but it is growing rapidly. There are a number of companies that are developing and deploying LLMs, and the market is expected to continue to grow in the coming years.
Cost of designing new language models or manufacturing ML hardware is high, so it is dominated by a few large players including OpenAI, Google, Microsoft, Amazon, Nvidia, Meta AI, Huawei, and Anthropic. LLM developers have raised nearly $12bn in equity funding so far this year — 12x as much as last year — across 10 deals. Microsoft’s $10bn round to OpenAI in January drove the surge, but 4 other LLM developers have also raised mega-rounds (worth $100m+): Cohere, Mistral AI, Adept, and Anthropic.
Many startups are building applications on top of LLMs. These improved language models allows applications to creep into new areas where they didn’t previously exist. Jasper, Copy.ai, and other startups are using LLMs to automatically generate marketing material based only on topical guidance. GitHub Copilot allows developers to generate application source code from basic prompts.
Israel-based company Tabnine, has created an AI assistant for software developers that runs multiple LLMs. It has helped more than a million developers worldwide to program faster in 20 software languages and in 15 editors.
As LLMs become more powerful and accessible, we can expect to see them used in a wider range of applications. This will have a significant impact on the way we interact with computers and the way we work and learn.
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