Lifeboat Foundation

The exponential potential of longevity technologies.

Jim Mellon became a billionaire by pouncing on a wide variety of opportunities, from the dawn of business privatization in Russia to uranium mining in Africa and real estate in Germany. But all of that might eventually look small, he says, compared to the money to be made in the next decade or so from biotechnologies that will increase human longevity well past 100.

The British investor is so enthusiastic about these technologies that he co-authored a 2017 book about them, Juvenescence: Investing in the Age of Longevity, and launched a company, Juvenescence Ltd., to capitalize on them. “Juvenescence” is a real word — it’s the state of being youthful. Says Mellon, who is 61: “I’m hoping that this stuff works on me as well as on my portfolio.”

Continue reading “Longevity, the Greatest Investment Opportunity of All Time” »

At least in the developed world, cancer, heart diseases, and neurodegenerative diseases are among the greatest causes of mortality. One emerging and very promising way to prevent or cure these diseases is through bio-nanotechnology.

Nanotechnology is the design, synthesis and application of materials or devices that are on the nanometer scale (one billionth of a meter). Due to the small scale of these devices, they can have many beneficial applications, both in industry and medicine. The use of nanodevices in medicine is called nanomedicine. Here, we will look at some applications of nanomedicine in curing or preventing the diseases that are most likely to kill us.

• There has been a 14X increase in the number of active AI startups since 2000. Crunchbase, VentureSource, and Sand Hill Econometrics were also used for completing this analysis with AI startups in Crunchbase cross-referenced to venture-backed companies in the VentureSource database. Any venture-backed companies from the Crunchbase list that were identified in the VentureSource database were included.

• The share of jobs requiring AI skills has grown 4.5X since 2013., The growth of the share of US jobs requiring AI skills on the Indeed.com platform was calculated by first identifying AI-related jobs using titles and keywords in descriptions. Job growth is a calculated as a multiple of the share of jobs on the Indeed platform that required AI skills in the U.S. starting in January 2013. The study also calculated the growth of the share of jobs requiring AI skills on the Indeed.com platform, by country. Despite the rapid growth of the Canada and UK. AI job markets, Indeed.com reports they are respectively still 5% and 27% of the absolute size of the US AI job market.

• Machine Learning, Deep Learning and Natural Language Processing (NLP) are the three most in-demand skills on Monster.com. Just two years ago NLP had been predicted to be the most in-demand skill for application developers creating new AI apps. In addition to skills creating AI apps, machine learning techniques, Python, Java, C++, experience with open source development environments, Spark, MATLAB, and Hadoop are the most in-demand skills. Based on an analysis of Monster.com entries as of today, the median salary is $127,000 in the U.S. for Data Scientists, Senior Data Scientists, Artificial Intelligence Consultants and Machine Learning Managers.

Continue reading “10 Charts That Will Change Your Perspective On Artificial Intelligence’s Growth” »

The £13m RemoveDebris spacecraft was taken to the ISS in April and stored onboard ahead of Wednesday’s release.

The spacecraft was pushed out of an airlock where a robotic arm then picked it up gave it a gentle nudge down and away from the 400km-high lab.

In the process, RemoveDebris became the largest satellite to ever be deployed from the International Space Station. The time was about 12:35 BST.

Continue reading “Astronauts eject space junk demo mission” »

Project Debater is the first AI system that can debate humans on complex topics. The goal is to help people build persuasive arguments and make well-informed decisions.

Driverless vehicles could eliminate millions of jobs in the future, from cabbies to truckers to food delivery workers. But the companies that are hoping to hasten the adoption of this disruptive technology don’t want to seem callous to this brewing labor crisis, so they are joining forces to study the “human impact” of robot cars.

The Partnership for Transportation Innovation and Opportunity (PTIO) is a newly formed group comprised of most of the major companies that are building and testing on self-driving cars. This includes legacy automakers like Ford, Toyota, and Daimler; tech giants like Waymo (née Google), Uber, and Lyft; and logistics providers like FedEx and the American Trucking Association. The new organization is being formed as a 501©(6), which allows it to accept donations like a nonprofit and lobby government like a chamber of commerce.

Neither the machine or nor the professional human debaters were given prior knowledge of the subject of the on-stage debate, which took place in San Francisco on Monday.

Nature article presents an AI developed by Google’s Medical Brain team which outperforms hospitals’ own warning system in predicting the death risk among hospit…al patients.

Google’s Medical Brain team is now training its AI to predict the death risk among hospital patients — and its early results show it has slightly higher accuracy than a hospital’s own warning system.

Bloomberg describes the healthcare potential of the Medical Brain’s findings, including its ability to use previously unusable information in order to reach its predictions. The AI, once fed this data, made predictions about the likelihood of death, discharge, and readmission.

Continue reading “Google says its AI is better at predicting death than hospitals” »

Neural networks running on GPUs have achieved some amazing advances in artificial intelligence, but the two are accidental bedfellows. IBM researchers hope a new chip design tailored specifically to run neural nets could provide a faster and more efficient alternative.

It wasn’t until the turn of this decade that researchers realized GPUs (graphics processing units) designed for video games could be used as hardware accelerators to run much bigger neural networks than previously possible.

That was thanks to these chips’ ability to carry out lots of computations in parallel rather than having to work through them sequentially like a traditional CPU. That’s particularly useful for simultaneously calculating the weights of the hundreds of neurons that make up today’s deep learning networks.

Continue reading “This New Chip Design Could Make Neural Nets More Efficient and a Lot Faster” »