The computer scientist and CEO who popularized the term'artificial general intelligence' (AGI) believes AI is verging on an exponential'intelligence explosion.' The PhD mathematician and futurist Ben Goertzel made the prediction while closing out a summit on AGI this month: 'It seems quite plausible we could get to human-level AGI within, let's say, the next three to eight years.' 'Once you get to human-level AGI,' Goertzel, sometimes called'father of AGI,' added, 'within a few years you could get a radically superhuman AGI.' While the futurist admitted that he'could be wrong,' he went on to predict that the only impediment to a runaway, ultra-advanced AI -- far more advanced than its human makers -- would be if the bot's'own conservatism' advised caution. Mathematician and futurist Ben Goertzel made the prediction while closing out a summit on AGI las week: 'we could get to human-level AGI within, let's say, the next three to eight years' Goertzel made his predictions during his closing remarks last week at the '2024 Beneficial AI Summit and Unconference,' partially sponsored by his own firm SingularityNET where he is CEO.

Tom Newhouse, vice president of Convergence Media, discusses the potential impact of artificial intelligence on elections after an RNC AI ad garnered attention. The Future of Life Institute has issued a petition to pause the development of GPT-5 and similar Large Language Models (LLMs). Their anxieties are understandable, but I believe they are much overblown. I've heard similar fears related to the advent of Artificial General Intelligence expressed off and on since I introduced the term AGI in 2005, but I think a pause would be a badly wrong move in the current situation for several reasons. Let me first emphasize something that's been mostly forgotten in the panic: Large Language Models can't become Artificial General Intelligences.

This page features a rough depiction of different views on the question of whether artificial general intelligence (AGI) will take off in a "hard" way (fast, no time for response or competition) or a "slow" way (more gradual, more time to integrate with society, possibility of competing projects). I plot these views against a very crude estimate of how long each forecaster has worked on commercial software (not counting academic computer science). It would be interesting to slice these predictions along many other dimensions as well. It would furthermore be helpful to gather statistically valid data from surveys of AI experts. My graph here is just something I put together in a few hours based on what I already knew offhand.

On a technical chaired by Sibley Verbeck (CEO of algorithmics hugely, for instance level, the work involved using a Electric Sheep Company); and the session we can now solve Boolean satisfaction logic-based AI system to control a humanoid on neural nets was chaired by problems with hundreds of virtual agent in the Second Randal Koene (a neuroscientist from thousands of variables. We can use automated Life virtual world, which interacted Boston University).