Lifeboat Foundation: Safeguarding Humanity
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LEAF caught up with Futurist David Wood at the recent International Longevity and Cryopreservation Summit in Madrid to talk about rejuvenation biotechnology.

We recently attended the International Longevity and Cryopreservation Summit in Madrid. LEAF director Elena Milova caught up with futurist David Wood at the summit to ask him about his work and his views on the development of rejuvenation biotechnology.

David is the Chair of the London Futurists a group who organize regular public meetings to discuss the future and how technology and science will change the world we live in. It is their aim to engage and educate the public about science and foster better understanding for what is credible in science and what is not.

Continue reading “David Wood – We Should Prepare for the Future Now” | >

Fun in fiction. Perhaps not so much in reality.

The human mind is already pretty open to manipulation—just ask anyone who works in advertising. But neural implant technology could potentially open up a direct digital link to our innermost thoughts that could be exploited by hackers.

In recent months, companies like Elon Musk’s Neuralink, Kernel, and Facebook have unveiled plans to create devices that will provide a two-way interface between human brains and machines.

While these devices could undoubtedly bring many benefits, they would be networked to computers and therefore essentially part of the Internet of Things. That should immediately set off alarm bells for anyone paying attention to cybersecurity news.

Continue reading “Neural Implant Tech Raises the Specter of Brainjacking” | >

New research suggests that the human brain is almost beyond comprehension because it doesn’t process the world in two dimensions or even three. No, the human brain understands the visual world in up to 11 different dimensions.

The astonishing discovery helps explain why even cutting-edge technologies like functional MRIs have such a hard time explaining what is going on inside our noggins. In a functional MRI, brain activity is monitored and represented as a three-dimensional image that changes over time. However, if the brain is actually working in 11 dimensions, looking at a 3D functional MRI and saying that it explains brain activity would be like looking at the shadow of a head of a pin and saying that it explains the entire universe, plus a multitude of other dimensions.

The team of scientists led by a group from Scientists at the École Polytechnique Fédérale de Lausanne in Switzerland detected the previously unknown complexities of the brain while working on the Blue Brain Project. The project’s goal is to create a biologically accurate recreation of the human brain.

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“Deep Learning” computer systems, based on artificial neural networks that mimic the way the brain learns from an accumulation of examples, have become a hot topic in computer science. In addition to enabling technologies such as face- and voice-recognition software, these systems could scour vast amounts of medical data to find patterns that could be useful diagnostically, or scan chemical formulas for possible new pharmaceuticals.

But the computations these systems must carry out are highly complex and demanding, even for the most powerful computers.

Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. Their results appear today in the journal Nature Photonics (“Deep learning with coherent nanophotonic circuits”) in a paper by MIT postdoc Yichen Shen, graduate student Nicholas Harris, professors Marin Soljacic and Dirk Englund, and eight others.

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Now you have until 6/30 to submit a proposal to the Agency’s Lifelong Learning Machines (L2M) program.

Here’s the vision: Muster all the creativity that you can with the goal of developing fundamentally new machine learning approaches that enable systems to learn continually as they operate and apply previous knowledge to novel situations. Current AI systems only compute with what they have been programmed or trained for in advance; they have no ability to learn from data input during execution time and cannot adapt online to changes they encounter in real environments. The goal of L2M is to develop substantially more capable systems that are continually improving and updating from experience.

Consult the Broad Agency Announcement for more information: https://www.fbo.gov/…/…/DARPA/CMO/HR001117S0016/listing.html

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