Lifeboat Foundation

Superhumans are coming! Various technological advances in the field of medicine through AI and CRISPR are going to radically alter our understanding of what it means to be human. AI and Crispr technology have been making revolutionary changes to the field of medicine. Artificial intelligence is being applied in identification of harmful genes and treatment of disease.

Multiple new gene editing technologies in addition to artificial intelligence will cause major changes in healthcare.
The gene-editing tool CRISPR, short for clustered regularly interspaced short palindromic repeats, could help us to reprogram life. It gives scientists more power and precision than they have ever had to alter human DNA.

Continue reading “The Age of Superhumans — Gene Editing Through CRISPR & AI” »

Artificial Intelligence is touching almost every aspect of our lives. It’s reasonable to expect AI influence will only increase in the future. One of many fields heavily influenced by AI is the military. Particularly in the development of Supersoldiers. The notion of super-soldiers enhanced with biotechnology and cybernetics was once only possible in the realm of science fiction. But it may not be too long before these concepts become a reality.

A new worldwide arms race is pitting countries against each other to be the first to successfully create real genetically modified super soldiers by using tools such as CRISPR. Understandably many of these human enhancement technologies raise health and safety questions and it is more likely these enhancements will first gain traction in countries that do not place as much weight on ethical concerns.

Continue reading “The Rise of Supersoldiers — How AI Changes Everything” »

Outer Space, Inner Space, and the Future of Networks.
Synopsis: Does the History, Dynamics, and Structure of our Universe give any evidence that it is inherently “Good”? Does it appear to be statistically protective of adapted complexity and intelligence? Which aspects of the big history of our universe appear to be random? Which are predictable? What drives universal and societal accelerating change, and why have they both been so stable? What has developed progressively in our universe, as opposed to merely evolving randomly? Will humanity’s future be to venture to the stars (outer space) or will we increasingly escape our physical universe, into physical and virtual inner space (the transcension hypothesis)? In Earth’s big history, what can we say about what has survived and improved? Do we see any progressive improvement in humanity’s thoughts or actions? When is anthropogenic risk existential or developmental (growing pains)? In either case, how can we minimize such risk? What values do well-built networks have? What can we learn about the nature of our most adaptive complex networks, to improve our personal, team, organizational, societal, global, and universal futures? I’ll touch on each of these vital questions, which I’ve been researching and writing about since 1999, and discussing with a community of scholars at Evo-Devo Universe (join us!) since 2008.

For fun background reading, see John’s Goodness of the Universe post on Centauri Dreams, and “Evolutionary Development: A Universal Perspective”, 2019.

Continue reading “The Goodness of the Universe — John Smart” »

A free course that introduces the basics of Graph Neural Networks to give you a quick, high-level understanding.

Did they get GitHub Copilot to write it?

BenchSci AI software parses text and images from published papers to speed up selecting reagents and antibodies for biomedical research.

Called it when i saw spot w an arm on its back, its for clearing IEDs.

Cerebras Systems has developed a workaround for a major AI bottleneck.

Cerebras Systems, maker of the world’s largest processor, has broken the record for the most complex AI model trained using a single device.

Using one CS-2 system, powered by the company’s wafer-sized chip (WSE-2), Cerebras is now able to train AI models with up to 20 billion parameters thanks to new optimizations at the software level.

The firm says the breakthrough will resolve one of the most frustrating problems for AI engineers: the need to partition large-scale models across thousands of GPUs. The result is an opportunity to drastically cut the time it takes to develop and train new models.