Toggle light / dark theme

Artificial General Intelligence (AGI) is a term for Artificial Intelligence systems that meet or exceed human performance on the broad range of tasks that humans are capable of performing. There are benefits and downsides to AGI. On the upside, AGIs can do most of the labor that consume a vast amount of humanity’s time and energy. AGI can herald a utopia where no one has wants that cannot be fulfilled. AGI can also result in an unbalanced situation where one (or a few) companies dominate the economy, exacerbating the existing dichotomy between the top 1% and the rest of humankind. Beyond that, the argument goes, a super-intelligent AGI could find it beneficial to enslave humans for its own purposes, or exterminate humans so as to not compete for resources. One hypothetical scenario is that an AGI that is smarter than humans can simply design a better AGI, which can, in turn, design an even better AGI, leading to something called hard take-off and the singularity.

I do not know of any theory that claims that AGI or the singularity is impossible. However, I am generally skeptical of arguments that Large Language Models such the GPT series (GPT-2, GPT-3, GPT-4, GPT-X) are on the pathway to AGI. This article will attempt to explain why I believe that to be the case, and what I think is missing should humanity (or members of the human race) so choose to try to achieve AGI. I will also try to convey a sense for why it is easy to talk about the so-called “recipe for AGI” in the abstract but why physics itself will prevent any sudden and unexpected leap from where we are now to AGI or super-AGI.

To achieve AGI it seems likely we will need one or more of the following:

Waterborne illness is one of the leading causes of infectious disease outbreaks in refugee and internally displaced persons (IDP) settlements, but a team led by York University has developed a new technique to keep drinking water safe using machine learning, and it could be a game changer. The research is published in the journal PLOS Water.

As drinking water is not piped into homes in most settlements, residents instead collect it from public tap stands using storage containers.

“When water is stored in a container in a dwelling it is at high risk of being exposed to contaminants, so it’s imperative there is enough free residual chlorine to kill any pathogens,” says Lassonde School of Engineering Ph.D. student Michael De Santi, who is part of York’s Dahdaleh Institute for Global Health Research, and who led the research.

In today’s fast-paced technological landscape, Artificial Intelligence (AI) has emerged as a game-changer in various industries. With its ability to analyze vast amounts of data and derive meaningful insights, AI has now made its way into the realm of circuit design and hardware engineering. This article explores the transformative potential of AI in these domains, focusing on how it can accelerate component selection, enhance quality control, enable failure analysis, predict maintenance requirements, streamline supply chain management, optimize demand forecasting, and much more.

Circuit Design

Through the adoption of AI, hardware engineers are given unparalleled help in their pursuit of excellence. AI reveals secrets to sublime circuit performance through its industrious investigation of component databases and innovative simulations. Engineers can then go onto augment their own intelligence to design circuits that exceed expectations and reinvent what is possible in the realm of technology.

In the last ten years, AI systems have developed at rapid speed. From the breakthrough of besting a legendary player at the complex game Go in 2016, AI is now able to recognize images and speech better than humans, and pass tests including business school exams and Amazon coding interview questions.

Last week, during a U.S. Senate Judiciary Committee hearing about regulating AI, Senator Richard Blumenthal of Connecticut described the reaction of his constituents to recent advances in AI. “The word that has been used repeatedly is scary.”

The Subcommittee on Privacy, Technology, and the Law overseeing the meeting heard testimonies from three expert witnesses, who stressed the pace of progress in AI. One of those witnesses, Dario Amodei, CEO of prominent AI company Anthropic, said that “the single most important thing to understand about AI is how fast it is moving.”

On Tuesday, OpenAI announced fine-tuning for GPT-3.5 Turbo—the AI model that powers the free version of ChatGPT—through its API. It allows training the model with custom data, such as company documents or project documentation. OpenAI claims that a fine-tuned model can perform as well as GPT-4 with lower cost in certain scenarios.

So basically, fine-tuning teaches GPT-3.5 Turbo about custom content, such as project documentation or any other written reference. That can come in handy if you want to build an AI assistant based on GPT-3.5 that is intimately familiar with your product or service but lacks knowledge of it in its training data (which, as a reminder, was scraped off the web before September 2021).

SEATTLE — Undergirding recent budget guidance from the Biden administration to federal research and development organizations is a recognition of a steady and growing demand for microelectronics as a key enabler for advancement in nearly every technology sector, according to a senior White House technology advisor.

The White House on Aug. 17 issued its research and development priorities for the fiscal 2025 budget, offering direction to federal offices as they plan to submit their spending requests to the Office of Management and Budget in early September. The high-level focus areas include strengthening the nation’s critical infrastructure amid climate change, advancing trustworthy AI, improving healthcare and fostering industrial innovation alongside basic and applied research.

According to Steven Welby, deputy director for national security within the White House’s Office of Science and Technology Policy, most of those priorities have some sort of connection to the nation’s goals for boosting the microelectronics industrial base.

In a recent study published in the journal Frontiers in Medicine, researchers evaluated fluorescence optical imaging (FOI) as a method to accurately and rapidly diagnose rheumatic diseases of the hands.

They used machine learning algorithms to identify the minimum number of FOI features to differentiate between osteoarthritis (OA), rheumatoid arthritis (RA), and connective tissue disease (CTD). Of the 20 features identified as associated with the conditions, results indicate that reduced sets of features between five and 15 in number were sufficient to diagnose each of the diseases under study accurately.

The media frenzy surrounding ChatGPT and other large, language model, artificial intelligence systems spans a range of themes, from the prosaic – large language models could replace conventional web search – to the concerning – AI will eliminate many jobs – and the overwrought – AI poses an extinction-level threat to humanity. All of these themes have a common denominator: large language models herald artificial intelligence that will supersede humanity.

But large language models, for all their complexity, are actually really dumb. And despite the name “artificial intelligence,” they’re completely dependent on human knowledge and labor. They can’t reliably generate new knowledge, of course, but there’s more to it than that.

ChatGPT can’t learn, improve or even stay up to date without humans giving it new content and telling it how to interpret that content, not to mention programming the model and building, maintaining and powering its hardware. To understand why, you first have to understand how ChatGPT and similar models work, and the role humans play in making them work.

🍿 Watch the full interview for free at https://londonreal.tv/dr-ben-goertzel-artificial-intelligenc…e-know-it/
🤝 The Investment Club: https://londonreal.tv/club.
🔥 The Crypto & DeFi Accelerator: https://londonreal.tv/defi-ytd.
💰 The Wealth Accelerator: https://londonreal.tv/wealth.
🇺🇸 Biden bombed the Nord Stream?! https://londonreal.tv/nordstream.

Dr Ben Goertzel is a cross-disciplinary scientist, futurist, author and entrepreneur, who has spent the best part of his working life focused on creating benevolent superhuman artificial general intelligence (AGI).

🔔 SUBSCRIBE ON YOUTUBE: http://bit.ly/SubscribeToLondonReal.
▶️ FREE FULL EPISODES: https://londonreal.tv/episodes.

🎁 FREE 30 Day Audible Trial: https://londonreal.tv/audible.

#LondonReal #LondonRealTV #BrianRose.

LATEST EPISODE: https://londonreal.link/latest.

One of the most surrealist anc controversial science fiction writers.


We are living in strange times. Politics. Society. Culture wars. Things have gone weird. We’re looking for knowledge and wisdom to guide us in strange times.

I can think of no better guide to the year 2022 than Philip K Dick.

The author of Do Androids Dream of Electric Sheep, the inspiration for Blade Runner, is best known as a science fiction writer. But after his infamous spiritual awakening and the events of 2−3−74, Philip K Dick became something more…a visionary prophet for the age we’re all now living in.

00:00 Welcome to strange times.