Charting the Future of AI – A State of the Market Overview

Let’s take a closer look at the Artificial Intelligence (AI) market, along with its future developments. What follows include particularly interesting insights from the NeurIPS conference, held in New Orleans from December 10th to 16th 2023, where current trends and developments of this technology were discussed.

The conference took a specific interest in Artificial General Intelligence (AGI), a system based on human cognitive abilities that would outperform humans in nearly every economically valuable work. As the quest for AGI continues, we examine possible paths for its realisation.

A lot of conversations in the field of AI revolve around building efficient architectures that would pave the way for resource-conscious models. As AI accelerators, a general term for specific hardware that reduces computational processing time for AI, become faster, memory often leads to bottlenecks in speed. It is expected engineers will continue to develop and explore different viable architectures rooted in signal processing, mitigating this bottleneck as a result.

Developing AI with accelerated learning, enhanced reasoning and reduced computing resources is key to achieve optimised efficiency, scalability and real-world applicability. These advancements enable AI systems to adapt quickly to changing environments, making informed decisions while handling larger datasets without the usually expected proportional increase in computational – and therefore power – requirements. The resulting cost-effectiveness and improved user experience will make AI more accessible, sustainable and suitable for a wide range of applications.

Disagreement between humans can hold nuances and be insightful. Lora Aroyo, a Research Scientist at Google Research, introduced a method for holding the nuances as opposed to treating all responses as true or false. This “truth by disagreement” is a distributional approach for assessing reliability of data from different perspectives.  This approach holds ramifications for how we will teach AI algorithms in the future.

Research papers offered strategies for training in scenarios where data is limited. The paper on Direct Preference Optimization improved on the common method for training Language Models by “reparameterising” the reward model in Reinforced Learning from Human Feedback, allowing the optimal policy to be cleanly extracted and solving the complexity and instability inherent in traditional methods with a classification loss (the price paid for inaccuracy).

Algorithmic and architectural innovations and improvements will continue to enhance AI models, incorporating ever larger and diverse datasets. Transfer learning and multimodal approaches which have the ability to process and understand inputs from different models will also contribute to an increase in versatility of AI systems. Integration with quantum computing may also exploit additional computational advantages. The integration of AI into edge computing will lead to increased efficiency in applications like IoT devices.

The pursuit for AGI requires improved efficiency, emphasising resource-conscious architectures and innovative solutions to memory bottlenecks. The development of AI with accelerated learning, enhanced reasoning, and reduced computing resources marks a move towards greater adaptability and accessibility. From diffusion models to nuanced categorisation and strategies to overcome data limitations, the future for AI looks likely to hold algorithmic refinements, multimodal approaches, and efficient edge computing integration.