In order to prevent the potentially destructive impact of AI on humanity, we need open-source innovation and collective governance that is possible through blockchain protocols and Web3, rather than the monopoly defaulting structure of Web2, according to Michael Casey, CoinDesk’s chief content officer.
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Artificial intelligence (AI) is revolutionizing industries, streamlining processes, and, hopefully, on its way to improving the quality of life for people around the world — all very exciting news. That said, with the increasing influence of AI systems, it’s crucial to ensure that these technologies are developed and implemented responsibly.
Responsible AI is not just about adhering to regulations and ethical guidelines; it is the key to creating more accurate and effective AI models.
The Singularity is a technological event horizon beyond which we cannot see – a moment in future history when exponential progress makes the impossible possible. This video discusses the concept of the Singularity, related technologies including AI, synthetic biology, cybernetics and quantum computing, and their potential implications.
My previous video “AI, Robots & the Future” is here:
https://www.youtube.com/watch?v=iaGIo_Viazs.
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Do you know why humanity still doesn’t have colonies on the Moon or Mars? Because the big companies that might’ve invested their money in building the said colonies are not sure when they’ll get their investments back and start making a solid profit. Well, at least that’s one of the reasons.
There have been 4 research papers and technological advancements over the last 4 weeks that in combination drastically changed my outlook on the AGI timeline.
GPT-4 can teach itself to become better through self reflection, learn tools with minimal demonstrations, it can act as a central brain and outsource tasks to other models (HuggingGPT) and it can behave as an autonomous agent that can pursue a multi-step goal without human intervention (Auto-GPT). It is not an overstatement that there are already Sparks of AGI.
U.S. DARPA’s Robotic Autonomy in Complex Environments with Resiliency (RACER) program recently conducted its third experiment to assess the performance of off-road unmanned vehicles. These test runs, conducted March 12–27, included the first with completely uninhabited RACER Fleet Vehicles (RFVs), with a safety operator overseeing in a supporting chase vehicle. The goal of the RACER program is to demonstrate autonomous movement of combat-scale vehicles in complex, mission-relevant off-road environments that are significantly more unpredictable than on-road conditions. The multiple courses were in the challenging and unforgiving terrain of the Mojave Desert at the U.S. Army’s National Training Center (NTC) in Ft. Irwin, California. As at the previous events, teams from Carnegie Mellon University, NASA’s Jet Propulsion Laboratory, and the University of Washington participated. This completed the project’s first phase.
“We provided the performers RACER fleet vehicles with common performance, sensing, and compute. This enables us to evaluate the performance of the performer team autonomy software in similar environments and compare it to human performance,” said Young. “During this latest experiment, we continued to push vehicle limits in perceiving the environments to greater distances, enabling further increase in speeds and better adaptation to newly encountered environmental conditions that will continue into RACER’s next phase.”
Continue reading “US DARPA RACER Off-Road Autonomous Vehicles Teams Navigate Third Test” »
Despite the availability of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Deep Visual Proteomics (DVP), a recently introduced method, combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context.
Chat gpt 4 is wonderful but one thing it is lacking is sentience which could do all work for millions of years so essentially we would not need find all discoveries and all things by ourselves.
Richards said he originally wanted an AI agent to automatically email him daily AI news. But, as Motherboard, he realized in the process that existing LLMs struggle with “tasks that require long-term planning,” or are “unable to autonomously refine their approaches based on real-time feedback.” That understanding inspired him to create Auto-GPT, which, he said, “can apply GPT4’s reasoning to broader, more complex problems that require long-term planning and multiple steps.” (Richards didn’t respond for a request for an interview with Fast Company.)
“They get confused”
But that’s not true. There are concrete things regulators can do right now to prevent tech companies from releasing risky systems.
In a new report, the AI Now Institute — a research center studying the social implications of artificial intelligence — offers a roadmap that specifies exactly which steps policymakers can take. It’s refreshingly pragmatic and actionable, thanks to the government experience of authors Amba Kak and Sarah Myers West. Both former advisers to Federal Trade Commission chair Lina Khan, they focus on what regulators can realistically do today.
The big argument is that if we want to curb AI harms, we need to curb the concentration of power in Big Tech.
