Enterprise AI Analysis

Defining AI & Its Constituent Technologies

The quest for Artificial General Intelligence (AGI) is complicated by the lack of a universally accepted definition of artificial intelligence. For the purposes of this symposium, AI is defined as: a collection of machines and algorithms that perform tasks normally considered to require human cognition.

These AI systems are not standalone; they are composed of many existing and interdependent technologies. Understanding these components is crucial to grasping AI's capabilities and limitations:

• Search: Looking for solutions to problems and answers to queries in a large space of possible answers.
• Reasoning: Applying rules and facts to make logical deductions.
• Artificial Neural Networks (ANNs): Interconnected artificial neurons that mimic neurons and their connections in the brain.
• Natural Language Processing (NLP): Systems that analyze text and discern its meanings.
• Large Language Models (LLMs) and Generative AI (GenAI): Systems trained on huge text corpuses that respond to “prompts” with highly likely texts and images.
• Multimedia: Systems designed to make pictures, drawings, videos and sound.
• Programming: Almost all AI systems use conventional software engineering to assemble reasoning and machine learning modules into useful systems.
• Human-Computer Interaction: Design principles for interfaces that make the user experience intuitive, smooth, efficient and enjoyable.
• Communications: Networked communication including mobile phones and the internet.
• Hardware: Highly parallel chips that bring supercomputing power to the simulations, programming, and training referred to above.

Most AI systems available to the public use combinations of these technologies, and these will be further explained in the symposium.

Navigating AI's Future: Critical Questions

While AI systems perform impressive tasks, the potential for Artificial General Intelligence (AGI) raises profound questions. The symposium explores many intriguing aspects and concerns, from utopian visions to dire warnings. Key questions include:

• Will AI ever achieve a “singularity” with machines more intelligent than humans?
• Will AI replace most jobs, or can it augment work without displacing workers?
• Will GenAI based on LLMs improve, make fewer mistakes, and become trustworthy?
• Are LLMs a breakthrough or a dead end?
• Will AI revolutionize education, or will it automate cheating and frauds?
• Can we manage the penchant for abuse, conspiracies, falsehoods, fake voices and videos?
• How should AI be regulated to keep people safe, and will regulated AI stifle innovation?
• Will killer machines be able to autonomously choose human targets, e.g. swarms of drones?
• Can AI be prevented from interfering with democratic elections or exacerbating international conflicts through disinformation?
• Can AI be used to manipulate people's emotions and values, and is AI combined with social media safe for children?
• What is the ultimate value of AGI and the quest for it?

The symposium's articles will delve into these pragmatic uses and dangers, supported by relevant science, avoiding hype, and focusing on testable points-of-view within a 10 to 20-year horizon.