Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 872

The show provides a glimpse into humanity’s astonishing diversity. Social scientists have a similar goal—understanding the behavior of different people, groups, and cultures—but use a variety of methods in controlled situations. For both, the stars of these pursuits are the subjects: humans.

But what if you replaced humans with AI chatbots?

The idea sounds preposterous. Yet thanks to the advent of ChatGPT and other large language models (LLMs), social scientists are flirting with the idea of using these tools to rapidly construct diverse groups of “simulated humans” and run experiments to probe their behavior and values as a proxy to their biological counterparts.

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The world’s best artificial intelligence (AI) systems can pass tough exams, write convincingly human essays and chat so fluently that many find their output indistinguishable from people’s. What can’t they do? Solve simple visual logic puzzles.

In a test consisting of a series of brightly coloured blocks arranged on a screen, most people can spot the connecting patterns. But GPT-4, the most advanced version of the AI system behind the chatbot ChatGPT and the search engine Bing, gets barely one-third of the puzzles right in one category of patterns and as little as 3% correct in another, according to a report by researchers this May1.

The team behind the logic puzzles aims to provide a better benchmark for testing the capabilities of AI systems — and to help address a conundrum about large language models (LLMs) such as GPT-4. Tested in one way, they breeze through what once were considered landmark feats of machine intelligence. Tested another way, they seem less impressive, exhibiting glaring blind spots and an inability to reason about abstract concepts.

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A high-stakes battle is unfolding between major tech giants to create dominant “everything apps” that combine digital identity, messaging, payments, and AI services. The winner of this contest could gain unrivalled data to power their AI platforms and to shape the future of society.

There is the promise of implementing a universal basic income (UBI) via these super apps as a mechanism to mitigate the downside risks of technological disruption in an era of accelerating automation and the rise of artificial general intelligence. Whether the promises will be delivered, lead to more equality, be decentralized enough to distribute power to all of humanity, or be available in time before the automation disruption will be, at the very least, interesting to monitor.

The main contenders in this race are:

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I expect this to double over at least 2 maybe 3 times between now and 2030. AND, we here need to back it at every step if we really want indefinite life extension. Time to pick a side is right now.

The arrival of artificial intelligence into healthcare means everyone could one day have a doctor in their pocket, but Google’s chief health officer has urged caution about what AI can do and what its limits should be.

“There’s going to be an opportunity for people to have even better access to services, [and] to great quality services,” Dr Karen DeSalvo told Guardian Australia in an interview last week.

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“The new research program, led by Associate Professor Adeel Razi, from the Turner Institute for Brain and Mental Health, in collaboration with Melbourne start-up Cortical Labs, involves growing around 800,000 brain cells living in a dish, which are then “taught” to perform goal-directed tasks. Last year the brain cells’ ability to perform a simple tennis-like computer game, Pong, received global attention for the team’s research.”

Monash University-led research into growing human brain cells onto silicon chips, with new continual learning capabilities to transform machine learning, has been awarded almost $600,000 AUD in the prestigious National Intelligence and Security Discovery Research Grants Program.

According to Associate Professor Razi, the research program’s work using lab-grown brain cells embedded onto silicon chips, “merges the fields of artificial intelligence and synthetic biology to create programmable biological computing platforms,” he said.

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