AI AT INFLECTION POINT ?

by Kemal A. Delic - Jeff A. Riley

We have seen AI develop over the past 70 years or so in three distinct phases: Embryonic, Embedded, and Embodied, which we view as three overlapping and evolving epochs: AI Programmed, AI Trained, and AI Actively Learning. We postulate that AI might metamorphose into the Science of Intelligence within 200 years of the dawn of research in Artificial Intelligence - 1956 (Fig. 1).

Fig. 1- The Long and Winding Road

AI research has experienced two winters already, where unreasonably high expectations paired with disappointing deliveries were met with skepticism - even derision in some quarters.  These AI winters were inflection points in the history of AI research that saw funding largely vanish, and research opportunities wither on the vine.  Each AI winter, through the efforts of dedicated researchers, was (eventually) followed by a resurgence in the field.  We see that many technologies advance in such a boost-and-burst fashion, driven by hype, fear, and greed - those typical cycles are often called ‘manias’.

CURRENT BOOST CYCLE - GAI AND LLM

The current ‘boost’ cycle for AI research is characterized by high, almost manic, interest in Large Language Models (LLMs) and Generative AI (GAI).  We recognise this as a possible point of inflection from the epoch of AI technologies being used predominantly to gather, process, and synthesize huge volumes of data, into a new epoch where AI generates, rather than just collects and processes, text, images, videos, and audio at an unprecedented scale, speed, and scope

As the generative capabilities of AI are streaming torrents of data onto the internet, concerns are being raised about the social, economic, and political impacts of those systems.  We risk unchecked and unfiltered AI-generated data becoming the predominant source material for the very AI systems that are generating more data to flood the internet.  A valid concern is that we may be embarking on a path that may allow AI systems to own our narrative and write our history.

GAI and LLMs have the potential to impact the daily lives of a vast number of people worldwide.  Indeed, we already see that the general community has both an awareness of, and trepidation about, the capabilities, and unfettered use, of LLMs.  We believe that governments are sensing the concerns of the public, and of experts in the field, and legislation, regulation, and certification of GAI in particular, and AI generally, is inevitable.  We believe strongly that AI researchers should be part of that discussion.

LIFE ON EARTH AND INTELLIGENCE

The rise of huge infrastructures, running gigantic Artificial Neural Network (ANN) models, will likely enable some new/novel developments in the field.  While the new and novel developments will no doubt have a beneficial impact on society, these installations will have the potential to impact people and the environment negatively - consuming vast amounts of energy and generating huge amounts of heat and other waste products.

As we already observed, technologies often advance in a boost-and-burst way, with each ‘burst’ usually having its own, unique, impetus. Two world wars in the previous century advanced nuclear, electronics, and computer technologies, and, by way of analogy, we expect that current and future conflicts will see the rise and importance of military drones - ground-based, airborne, marine, and submarine.  By some indications, drones and autonomous vehicles might play a key role, just as cannons and tanks did in previous conflicts.  Embodied AI - drones - might emerge as the key military/war technology of the 21st century - yet another argument that we are seeing an inflection point in AI advances.

Fig. 2. Life on Earth and Five Layers of Intelligence

We think that AI may morph into a general Science of Intelligence - as a multidisciplinary field focused not only on mimicking, or even surpassing, human behavior and capabilities, but also on the other layers and kinds of intelligence that enable life on Earth. It will take a very long time to see this field established as a science, and consequently AI research will largely continue to be about engineering systems that (try to) mimic human intelligence, while the Science of Intelligence would frame all life on Earth (Fig. 2) as the consequence of variety types of interacting intelligences - of plants, insects, animals, humans, etc. - with the new science eventually underpinning far-reaching AI research and development.

INFLECTION POINT ?

The relatively recent and rapid rise of, and hype generated by, LLMs in general, and ChatGPT in particular, has caused interest in AI to spill over from academia and specific interest groups to the wider public. Businesses are embracing AI; whole industries are changing to accommodate AI; the trajectories of economies are changing because of AI. But it is not just businesses and economies that are changing.  Advances in computer hardware and AI technology have seen the increased use of military drones in battlefield situations, thus changing the nature of conflict and affecting millions of people. The world will change more rapidly than most people imagined because of the rapid rise of GAI.

Fig. 3 - From Data Aggregation/Analytics  to Content Generation/Synthesis 

During the long history of AI research, the initial focus in the Embryonic phase was largely on a programmatic approach, which evolved into a training approach, often characterized by Artificial Neural Networks, as we moved into the Embedded AI phase. With the advent of GAI and LLMs, the focus of AI research has shifted from the creation of analytical artifacts (e.g. predictions, diagnosis, synthesis, etc.) into massive content generation (e.g. text, image, music, video, code, etc.). We see this as an inflection point in the ongoing history of AI research (Fig. 3).  

We believe the current high level of interest in Artificial Intelligence, evidenced by the implementation of national programs by governments around the world, huge investments from industry in terms of money and time, increased marketing and advertising of AI-enabled devices, and the chatter on social media from the general public, is good evidence that we are at, or approaching, an inflection point in the development, and crucially, acceptance, of AI systems.