Lifeboat Foundation

Artificial Intelligence is the ability of machines to seemingly think and act as humans do. Humans absorb data through our various senses, process data using our cognitive abilities, and then act. Machines also, in their own narrow way, absorb whatever information is made available to them and take relevant actions when prompted. Those actions may take the form of a conversational bot or a recommender engine. Over time, our decision-making sophistication has increased. We began making decisions relying solely on our judgment. We progressed to summarizing large swaths of data and then applying our judgment to that summary. And at present, we entrust AI with taking decisions across data and recommending actions. In narrow problems, machines have a greater ability than humans to process volumes of data and accurately identify the trends within. Was AI wrong about Nadal? Not really. It said that Nadal had a 4% chance of winning; at that snapshot in time, and based on all past data of similar matches, perhaps that was a fair assessment of his chances against Medvedev. Most humans would also have predicted a Medvedev win even if they hoped for a different outcome. I am sure that as the fifth set played out, the odds of Nadal winning rose steadily in his favor. So, the earlier prediction should not be considered wildly inaccurate just because Nadal ultimately won.

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Can AI measure the heart of a champion?

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The recent advances in machine learning and artificial intelligence, coupled with increases in computational power, have led to a lot of interest and hype in longevity biotechnology 30114–2). Hundreds of data scientists and companies are taking advantage of this hype to propel research and discovery of new technologies in aging research.

One of the major new areas in aging research are biomarkers of aging that give the true biological age of humans that may be different from their chronological age. One of the most advanced biomarkers of aging are deep aging clocks that can help researchers predict biological age as well as mortality of humans. In 2013, Steven Horvath published an article called ‘DNA methylation age of human tissues and cell types,’ in which he outlined the development of a multi-tissue predictor of age that allows for the estimation of the DNA methylation age of most tissues and cell types. He also formed an aging clock that can be used to address questions in developmental biology, cancer, and aging research.

There have been several more studies on such clocks since 2013. For example, I was part of a team in 2016 and we published a study on the first deep aging clock titled ‘Deep biomarkers of human aging: Application of deep neural networks to biomarker development.’ Since our study was published, many other aging clocks that can predict age as well as mortality rapidly entered into many industries. it is clear that there is a boom in the longevity biotechnology industry and huge progress in aging research is expected to be made in the next few years. AI-based aging clocks provide a very good entry point for the insurance companies to get into the field of aging research and actually contribute while protecting their business and innovating in science and technology.

New Israeli startup aims to get product to market within two years; technology could also be used to identify early markers of cancer.

An Israeli startup is developing a non-invasive early detection method using artificial intelligence (AI) to identify genetic disorders in human embryos.

Via a simple blood test taken from the pregnant mother during the first trimester, IdentifAI Genetics can read the embryo’s entire DNA and provide in-depth analysis to detect genetic disorders.

FORT CAMPBELL, KY (AP) — A helicopter flew unmanned around Fort Campbell recently in what is the Army’s first automated flight of an empty Black Hawk, officials said.

The 14,000-pound UH-60A Black Hawk successfully navigated around the post as if it were downtown Manhattan, engineers told reporters Tuesday.

The DARPA Aircrew Labor In-Cockpit Automation System (ALIAS) program took the helicopter on 30-minute flight on Feb. 5. It was the first time the system known as ALIAS flew completely by itself. The system is being tested with 14 military aircraft.

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Waabi, a Toronto-based AI startup that came out of stealth last year, says it’s developed an advanced simulator that can train autonomous vehicles to handle nearly limitless types of driving conditions-in a virtual world-and do so faster and more thoroughly than self-driving rivals that prioritize road tests.

The Waabi World platform is more comprehensive than any used by competitors as it can more accurately mimic real-world scenarios and create the types of rare, challenging “edge cases” that occur on the road only rarely, company founder and CEO Raquel Urtasun tells *Forbes*. Learning in this elaborate virtual world is happening constantly, preparing the software to eventually drive a range of vehicles from robotaxis to semi-trucks.

It’s the “most scalable, high-fidelity, closed-loop simulator that ever existed and, we believe, the key to unlocking self-driving technology at scale,” says Urtasun, who is also professor of computer science at the University of Toronto and a past chief scientist for Uber’s autonomous vehicle team. “It’s an immersive and reactive environment that can automatically design tests for our self-driving brain, which we call the Waabi Driver, and also automatically assess the skills of the Waabi Driver. Ultimately, it can also teach the Waabi Driver to learn the skills of driving.”

Continue reading “Can Next-Generation Simulation Build A Better Self-Driving Car? This AI Upstart Thinks So” »

A non-invasive graphene-based sensor detects brain waves with high sensitivity and reliability.