The rapid advancement of AI-generated content is challenging our understanding of authenticity and creativity, raising significant ethical, regulatory, and existential questions about the future of human-AI collaboration Questions to inspire discussion AI-Generated Content Revolution 🎙️Q: How is Google’s Notebook LM.
A new biohybrid robot can be steered left, right, or straight by activating neurons and muscle cells embedded into its fins.
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.
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.
Continue reading “Universal Productivity Dividend: Could This Work for the AGI Era?” »
Altman predicts that with AI in the future, “We will be able to do things that would have seemed like magic to our grandparents.”
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.
Continue reading “AI begins its ominous split away from human thinking” »
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.”
From artificial-intelligence algorithms to zebrafish, this book take a precautionary approach to assessing how sentient such entities are.
Researchers in Abu Dhabi say they have found a faster way to search desert areas for important archaeological sites buried beneath the sand.
