As 2023 concludes, we find ourselves in awe of the significant transformations that have reshaped the landscape of artificial intelligence. This year has been a testament to the dynamic and ever-evolving nature of this field. Let's delve into some of the key developments that have defined AI in 2023.
The Emergence of Advanced Language Models
The year began with the groundbreaking introduction of GPT-4, an AI phenomenon that established new benchmarks in machine learning. Its advanced capabilities, rooted in Reinforcement Learning from Human Feedback (RLHF), set a new standard, spurring a race among researchers to develop scalable alternatives that reduce the need for human supervision. The release of large language models like Meta's LLaMa-1 and MosaicML’s MPT-30B demonstrated the expanding reach of this technology.
Scientific Breakthroughs Fueled by AI
In 2023, the synergy between language and diffusion models brought forth unprecedented achievements in fields like molecular biology and drug discovery. The emergence of multimodal AI opened new avenues, sparking excitement across various domains for its potential applications.
Expanding Capabilities: LLMs and Software Tools
A significant impact of LLMs on the economy was witnessed through their integration with various external tools. The most notable development was the use of web browsers within models, ensuring up-to-date information. Projects like Meta and Universitat Pompeu Fabra’s Toolformer exemplified the innovative use of AI in making efficient API calls, enhancing the decision-making capabilities of these models.
Advancements in AI Prompting Techniques
The art of prompting saw sophisticated developments with the introduction of Chain of Thought (CoT) and Tree of Thought (ToT) prompting techniques. These methods improved task performance by enabling models to output intermediate reasoning steps and represent these "thoughts" in a structured manner. The evolution into Graph of Thought (GoT) further enhanced this by turning reasoning trees into more interconnected graphs.
The Necessity of Model Drift Monitoring
The importance of monitoring model performance became evident as the same version of GPT models exhibited varying performances over time. This necessitated continuous performance monitoring and prompt updates by AI practitioners.
As we bid farewell to 2023, the AI landscape promises even more exciting developments in 2024. The journey ahead is ripe with potential for further innovation and discovery.
Keep learning
As LLM technology is there to stay and built on, we recommend watching this video to get a deep introduction to Large Language Models, the foundation of this Generative AI wave.
Sources :
Benaich, N. (2023, October 12). State of AI report 2023. Stateof.AI.
