yet powerful) size.
On the hardware front, we continued to see progress in terms of specialized chips and systems designed specifically for training and running LLMs. There’s already no shortage of options on the market, but as models continue to grow in size and complexity, we can expect this trend to continue.
Another theme that stood out to me in 2024 was the increasing focus on ethical AI and responsible deployment of LLMs. The field has come a long way in terms of understanding and addressing bias, fairness, and other ethical considerations, but there is still much work to be done. Organizations are beginning to prioritize ethical AI practices, and it will be interesting to see how this trend evolves in the coming years.
Overall, 2024 was a year of significant progress and innovation in the field of Large Language Models. With new models surpassing GPT-4, increased focus on longer input lengths, and advancements in hardware and ethical considerations, the future of LLMs looks bright. I can’t wait to see what the next year will bring.