{"id":28722,"date":"2023-05-09T10:42:49","date_gmt":"2023-05-09T16:42:49","guid":{"rendered":"https:\/\/otech.uaeh.edu.mx\/noti\/?p=28722"},"modified":"2023-05-09T10:45:13","modified_gmt":"2023-05-09T16:45:13","slug":"can-machines-be-self-aware-new-research-explains-how-this-could-happen","status":"publish","type":"post","link":"https:\/\/otech.uaeh.edu.mx\/noti\/ia\/can-machines-be-self-aware-new-research-explains-how-this-could-happen\/","title":{"rendered":"Can Machines Be Self-Aware? New Research Explains How This Could Happen"},"content":{"rendered":"

To build a machine, one must know what its parts are and how they fit together. To understand the machine, one needs to know what each part does and how it contributes to its function. In other words, one should be able to explain the \u201cmechanics\u201d of how it works.<\/span><\/p>\n

According to a\u00a0philosophical approach<\/a>\u00a0called mechanism, humans are arguably a type of machine\u2014and our ability to think, speak, and understand the world is the result of a mechanical process we don\u2019t understand.<\/span><\/p>\n

To understand ourselves better, we can try to build machines that mimic our abilities. In doing so, we would have a mechanistic understanding of those machines. And the more of our behavior the machine exhibits, the closer we might be to having a mechanistic explanation of our own minds.<\/span><\/p>\n

This is what makes AI interesting from a philosophical point of view. Advanced models such as\u00a0GPT-4<\/a>\u00a0and Midjourney can now mimic human conversation, pass professional exams, and generate beautiful pictures with only a few words.<\/span><\/p>\n

Yet, for all the progress, questions remain unanswered. How can we make something self-aware, or aware that others are aware? What is identity? What is meaning?<\/span><\/p>\n

Although there are many competing philosophical descriptions of these things, they have all resisted mechanistic explanation.<\/span><\/p>\n

In a\u00a0sequence of papers<\/a>\u00a0accepted for the\u00a016th Annual Conference in Artificial General Intelligence<\/a>\u00a0in Stockholm, I pose a mechanistic explanation for these phenomena. They explain how we may build a machine that\u2019s aware of itself, of others, of itself as perceived by others, and so on.<\/span><\/p>\n

Intelligence and Intent<\/span><\/h2>\n

A lot of what we call intelligence boils down to making predictions about the world with incomplete information. The less information a machine needs to make accurate predictions, the more \u201cintelligent\u201d it is.<\/span><\/p>\n

For any given task, there\u2019s a limit to how much intelligence is actually useful. For example, most adults are smart enough to learn to drive a car, but more intelligence probably won\u2019t make them better drivers.<\/span><\/p>\n

My papers describe\u00a0the upper limit of intelligence<\/a>\u00a0for a given task, and what is required to build a machine that attains it.<\/span><\/p>\n

I named the idea Bennett\u2019s Razor, which in non-technical terms is that \u201cexplanations should be no more specific than necessary.\u201d This is distinct from the popular interpretation of Ockham\u2019s Razor (and\u00a0mathematical descriptions thereof<\/a>), which is a preference for simpler explanations.<\/span><\/p>\n

The difference is subtle, but significant. In an\u00a0experiment<\/a>\u00a0comparing how much data AI systems need to learn simple maths, the AI that preferred less specific explanations outperformed one preferring simpler explanations by as much as 500 percent.<\/span><\/p>\n

Exploring the implications of this discovery led me to a mechanistic explanation of meaning \u2014something called \u201cGricean pragmatics<\/a>.\u201d This is a concept in philosophy of language that looks at how meaning is related to intent.<\/span><\/p>\n

To survive, an animal needs to predict how its environment, including other animals, will act and react. You wouldn\u2019t hesitate to leave a car unattended near a dog, but the same can\u2019t be said of your rump steak lunch.<\/span><\/p>\n

Being intelligent in a community means being able to infer the intent of others, which stems from their feelings and preferences. If a machine was to attain the upper limit of intelligence for a task that depends on interactions with a human, then it would also have to correctly infer intent.<\/span><\/p>\n

And if a machine can ascribe intent to the events and experiences befalling it, this raises the question of identity and what it means to be aware of oneself and others.<\/span><\/p>\n

Causality and Identity<\/span><\/h2>\n

I see John wearing a raincoat when it rains. If I force John to wear a raincoat on a sunny day, will that bring rain?<\/span><\/p>\n

Of course not! To a human, this is obvious. But the subtleties of cause and effect are more difficult to teach a machine (interested readers can check out\u00a0The Book of Why<\/a>\u00a0<\/em>by Judea Pearl and Dana Mackenzie).<\/span><\/p>\n

To reason about these things, a machine needs to learn that \u201cI caused it to happen\u201d is different from \u201cI saw it happen.\u201d Typically, we\u2019d\u00a0program<\/a>\u00a0this understanding into it.<\/span><\/p>\n

However, my work explains how we can build a machine that performs at the upper limit of intelligence for a task. Such a machine must, by definition, correctly identify cause and effect\u2014and therefore also infer causal relations.\u00a0My papers<\/a>\u00a0explore exactly how.<\/span><\/p>\n

The implications of this are profound. If a machine learns \u201cI caused it to happen,\u201d then it must construct concepts of \u201cI\u201d (an identity for itself) and \u201cit.\u201d<\/span><\/p>\n

The abilities to infer intent, to learn cause and effect, and to construct abstract identities are all linked. A machine that attains the upper limit of intelligence for a task must exhibit all these abilities.<\/span><\/p>\n

This machine does not just construct an identity for itself, but for every aspect of every object that helps or hinders its ability to complete the task. It can then\u00a0use its own preferences<\/a>\u00a0as a\u00a0baseline to predict<\/a>\u00a0what others may do. This is similar to how\u00a0humans tend to ascribe<\/a>\u00a0intent to non-human animals.<\/span><\/p>\n

So What Does It Mean for AI?<\/span><\/h2>\n

Of course, the human mind is far more than the simple program used to conduct experiments in my research. My work provides a mathematical description of a possible causal pathway to creating a machine that is arguably self-aware. However, the specifics of engineering such a thing are far from solved.<\/span><\/p>\n

For example, human-like intent would require human-like experiences and feelings, which is a difficult thing to engineer. Furthermore, we can\u2019t easily test for the full richness of human consciousness.\u00a0Consciousness<\/a>\u00a0is a broad and ambiguous concept that encompasses \u2014but should be distinguished from\u2014the more narrow claims above.<\/span><\/p>\n

I have provided a mechanistic explanation of\u00a0aspects<\/em>\u00a0of consciousness\u2014but this alone does not capture the full richness of consciousness as humans experience it. This is only the beginning, and future research will need to expand on these arguments.\"The<\/span><\/p>\n

This article is republished from\u00a0<\/em>The Conversation<\/a>\u00a0under a Creative Commons license. Read the\u00a0original article<\/a>.<\/em><\/span><\/p>\n

Image Credit:\u00a0DeepMind<\/a>\u00a0on\u00a0Unsplash<\/a>\u00a0<\/em><\/span><\/p>\n

Fuente:<\/p>\n

Bennett, M. T. (2023, 4 mayo). Can Machines Be Self-Aware? New Research Explains How This Could\u00a0Happen<\/i>. Singularity Hub. https:\/\/singularityhub.com\/2023\/05\/05\/can-machines-be-self-aware-new-research-explains-how-this-could-happen\/<\/p>\n","protected":false},"excerpt":{"rendered":"

To build a machine, one must know what its parts are and how they fit together. To understand the machine, one needs to know what each part does and how it contributes to its function. In other words, one should be able to explain the \u201cmechanics\u201d of how it works.<\/p>\n","protected":false},"author":1,"featured_media":28723,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[361],"tags":[],"class_list":["post-28722","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ia"],"yoast_head":"\nCan Machines Be Self-Aware? 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