Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 485

Beyond The Code: Human Factors In Robotic Surgery Risk Management

A key aspect of human factors in robotic surgery is the training and proficiency of the surgeon. Robotic systems, although designed to enhance precision, rely heavily on the expertise of the individuals operating them. Companies should invest in comprehensive training programs that extend beyond basic certification and promote a culture of continuous learning and skill development.

Simulation-based training, for example, provides a risk-free environment for practicing complex procedures, helping surgeons build confidence and proficiency. Implementing standardized certification processes ensures consistent competency levels among surgeons.

Universal Productivity Dividend: Could This Work for the AGI Era?

I’m diving deep into the concept of the Universal Productivity Dividend, a potential solution and alternative to UBI for the coming overhaul of the world’s entire socioeconomic system, when total job automation occurs.

We’ll explore what UPD is, how it works, and whether it could be the key to a more equitable future.

Since the Industrial Revolution, machines have been augmenting human labor. But the technological + AI advancements of the 2020s, and what’s coming, is a TOTALLY new shift for humanity.

With jobs up for automation, every sector is at risk – leading us to mass unemployment. Is UPD a real solution? Listen in and find out.

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AI begins its ominous split away from human thinking

AIs have a big problem with truth and correctness – and human thinking appears to be a big part of that problem. A new generation of AI is now starting to take a much more experimental approach that could catapult machine learning way past humans.

Remember Deepmind’s AlphaGo? It represented a fundamental breakthrough in AI development, because it was one of the first game-playing AIs that took no human instruction and read no rules.

Instead, it used a technique called self-play reinforcement learning to build up its own understanding of the game. Pure trial and error across millions, even billions of virtual games, starting out more or less randomly pulling whatever levers were available, and attempting to learn from the results.

CRISPR CREME: An AI Treat to Enable Virtual Genomic Experiments

Koo and his team tested CREME on another AI-powered DNN genome analysis tool called Enformer. They wanted to know how Enformer’s algorithm makes predictions about the genome. Koo says questions like that are central to his work.

“We have these big, powerful models,” Koo said. “They’re quite compelling at taking DNA sequences and predicting gene expression. But we don’t really have any good ways of trying to understand what these models are learning. Presumably, they’re making accurate predictions because they’ve learned a lot of the rules about gene regulation, but we don’t actually know what their predictions are based off of.”

With CREME, Koo’s team uncovered a series of genetic rules that Enformer learned while analyzing the genome. That insight may one day prove invaluable for drug discovery. The investigators stated, “CREME provides a powerful toolkit for translating the predictions of genomic DNNs into mechanistic insights of gene regulation … Applying CREME to Enformer, a state-of-the-art DNN, we identify cis-regulatory elements that enhance or silence gene expression and characterize their complex interactions.” Koo added, “Understanding the rules of gene regulation gives you more options for tuning gene expression levels in precise and predictable ways.”

AI start-ups generate money faster than past hyped tech companies

Artificial intelligence start-ups are making revenues more quickly than previous waves of software companies, according to new data that suggests that the transformative technology is also generating strong businesses at an unprecedented rate.

According to an analysis of payments information from fintech group Stripe, top AI groups are reaching millions of dollars in sales within a year — far faster in a start-up’s life cycle than comparable non-AI tech groups.

The findings come as investors raise questions about the economic benefits of generative AI and likely returns on Big Tech’s projected trillion-dollar investment in computing infrastructure to support the technology over the coming year.

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