US President Donald Trump Proposes Rebranding Artificial Intelligence as Super Intelligence

The debate surrounding artificial intelligence has been ongoing for decades, but a recent twist has emerged in the form of a rebranding effort spearheaded by US President Donald Trump.

Trump's proposal is to replace "artificial intelligence" with "super intelligence, a term he believes better captures the capabilities and potential of modern AI systems. This move is not without precedent, as some experts have already been using the term superintelligence to describe advanced AI technology.

The origins of this rebranding effort can be traced back to last year, when Meta CEO Mark Zuckerberg declared that superintelligence was within sight. However, it's worth noting that Trump's vision for super intelligence appears to differ from this more technical definition.

Trump's executive order, signed on 29 September, emphasizes the need to redefine AI terminology in a way that reflects its growing capabilities and potential. The order also establishes a new body called the Super Intelligence Force, which will coordinate US AI policy.

The creation of the Super Intelligence Force has raised eyebrows, with some critics comparing it to a Saturday morning cartoon title. Regardless of its name, the body's purpose is clear: to promote US dominance in the field of AI and to rebrand modern AI systems as super intelligence.

The concept of superintelligence, as envisioned by Nick Bostrom, is a far cry from the current capabilities of artificial intelligence systems. To understand why this distinction matters, it's essential to delve into the history of AI research.

In 1980, philosopher John Searle introduced the terms weak AI" and "strong AI" in his paper "Minds, brains, and programs". He described weak AI as a computer simulation of human thought processes but not an actual mind. On the other hand, strong AI referred to genuine cognition emerging from software.

Searle dismissed the idea of strong AI as unrealistic, stating that no one would mistake a simulated rainstorm for the real thing. Decades later, it's hard to disagree with his assessment. The distinction between weak and strong AI has been subject to rebranding over time.

As researchers began discussing differences in AI capabilities rather than its consciousness, the terms "weak AI" and "strong AI fell out of favor. They were replaced by more practical labels that focused on specific tasks that AI systems could perform. This shift from philosophical debates to technical discussions has made it easier to measure and compare AI capabilities.

The term narrow AI emerged as a way to describe systems capable of human-level performance at a particular task. Examples of narrow AI include spam filters, chess-playing algorithms, and driverless cars. Once these tasks become achievable by narrow AI, they are essentially software solutions, a phenomenon known as the AI effect.

As researchers continue to develop more sophisticated AI systems, the concept of general AI has gained attention. General AI refers to a system capable of human-level performance at any task, rather than just one specific task. This distinction highlights the ongoing debate about the capabilities and limitations of artificial intelligence.

Achieving general AI is considered a highly desirable goal in the field of artificial intelligence because it would enable machines to transfer learning from one domain to another, much like humans do. This means that instead of developing separate systems for various tasks, a single AI system could be trained on multiple domains, allowing for greater flexibility and efficiency.

The term artificial general intelligence or AGI emerged in 2002, largely due to Shane Legg's popularization of the concept. At the time, Legg was instrumental in co-founding DeepMind, one of the leading AI companies that has since become a major player in the field.

Today, many top AI firms, including DeepMind and OpenAI, were founded with the explicit goal of achieving AGI. This focus on general intelligence has transformed from being seen as an unconventional research goal to a mainstream business model, with trillions of dollars invested in developing such systems.

The definition of AGI remains unclear, however, with various interpretations emerging across different organizations. For instance, OpenAI's charter defines AGI as highly autonomous systems that outperform humans at most economically valuable work, while another leaked agreement between the company and Microsoft suggests a system capable of generating $100 billion in profit.

Despite its lack of profitability, OpenAI has already released its Astra model, prompting its president, Greg Brockman, to declare welcome to the AGI era. This milestone highlights the growing interest in developing general AI systems that can adapt to multiple domains.

The distinction between AGI and superintelligence remains a topic of debate. Nick Bostrom defines superintelligence as any intellect that greatly exceeds human cognitive performance across virtually all domains of interest, effectively rendering humans inferior in various areas.

The concept of superintelligence is often tied to exceptional human minds like Albert Einstein and Marie Curie, but it also requires surpassing the intellects of renowned figures such as William Shakespeare, Leonardo da Vinci, Steve Jobs, and Jane Austen.

Clearly, humanity has not yet achieved this level of cognitive superiority, nor have we developed artificial intelligence systems that can match or exceed these exceptional human minds.

The emergence of a true superintelligence would likely lead to profound transformations in the world, with far-reaching consequences that are difficult to predict or comprehend.

However, one major challenge lies in defining and measuring intelligence itself, which is essential for distinguishing between different levels of cognitive ability.

A commonly accepted metric for intelligence is the intelligence quotient, or IQ, but its limitations have become increasingly apparent as it has been applied to various fields, including artificial intelligence.

As researchers and developers continue to push the boundaries of artificial intelligence, the notion of achieving superintelligence remains shrouded in uncertainty. Some experts are skeptical about the possibility of reaching this milestone, suggesting that our methods for evaluating AI advancements are constantly evolving.

The term superintelligence implies a significant leap beyond current AI capabilities, but defining what exactly constitutes superintelligence is a challenging task. The meanings and metrics used to measure AI progress are subject to change as new breakthroughs emerge.

In recent years, the landscape of AI has shifted dramatically. What was once considered cutting-edge technology can quickly become outdated. For instance, Google's AI Overviews have become a source of frustration for users, often providing confidently incorrect information at the top of search results.

The most impressive achievements in AI currently revolve around mathematical applications. However, it's possible that these advancements will soon be surpassed by new innovations. The development of AI is an iterative process, with each breakthrough building upon previous discoveries.

A recent example of this iterative progress can be seen in the release of a model capable of recreating Photoshop and other Adobe products. This open-source code has been made available on GitHub, but it's unclear what implications this may have for copyright and intellectual property laws.

The use of AI in healthcare is also expanding, with companies like Nolla Health piloting AI-powered prescriptions for acne treatment. In this pilot program, patients fill out a questionnaire and upload a photo of their face before receiving an AI-generated prescription that must be reviewed by licensed physicians before being finalized.

The ongoing development of AI technology has led to further experimentation in various fields, including healthcare. In a recent pilot program, patients are receiving AI-generated prescriptions that must be reviewed by licensed physicians before being finalized. However, this process is set to change in future trials, where the reviews will only occur after the prescription has already been sent.

Google's release of Playground, an app that generates playable games from text prompts, has sparked interest among gamers and developers alike. The app is currently available only in the US, but its capabilities have inspired some users to experiment with creating their own AI-generated games using other tools. These experiments have yielded mixed results, with some producing engaging gameplay while others fall short.

A new trend in video game development involves merging two different games by recreating one inside another using AI technology. This has resulted in unusual combinations such as Minecraft within Elden Ring, which may raise questions about the practicality and legality of such creations. While these experiments can be entertaining to watch, it remains unclear how well they function.

The advancement of chatbots like ChatGPT continues with the introduction of its Intelligent UI" feature, allowing users to interact with the bot through visualizations rather than text alone. This capability enables the model to present complex information in a more engaging and accessible format, making it easier for users to navigate and understand.