Lifeboat Foundation

When most of us pick up an object, we don’t have to think about how to orient it in our hand. It’s something that comes naturally to us as we learn to navigate the world. That’s something that allows young children to be more deft with their hands than even the most advanced robots available today.

But that could quickly change. A team of scientists from MIT’s has developed a system that could one day give robots that same kind of dexterity. Using a AI algorithm, they created a simulated, anthropomorphic hand that could manipulate more than 2,000 objects. What’s more, the system didn’t need to know what it was about to pick up to find a way to move it around in its hand.

The system isn’t ready for real-world use just yet. To start, the team needs to transfer it to an actual robot. That might not be as much of a roadblock as you might think. At the start of the year, we saw researchers from Zhejiang University and the University of Edinburgh successfully transfer an AI reinforcement approach to their robot dog. The system allowed the robot to learn how to walk and recover from falls on its own.

“We are absolutely losing some science,” Jonathan McDowell, an astronomer at the Harvard-Smithsonian Center for Astrophysics, tells The Register. “How much science we lose depends on how many satellites there end up being. You occasionally lose data. At the moment it’s one in every ten images.”

Telescopes can try waiting for a fleet of satellites to pass before they snap their images, though if astronomers are trying to track moving objects, such as near-Earth asteroids or comets, for example, it can be impossible to avoid the blight.

“As we raise the number of satellites, there starts to be multiple streaks in images you take. That’s no longer irritating, you really are losing science. Ten years from now, there may be so many that we can’t deal with it,” he added.

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Continue reading “Did Time Start at the Big Bang?” »

A new method for quantum computing algorithms achieved an unprecedented efficiency that’s 2,500% more effective! And it could change everything.

According to Klaus Schwab, the founder and executive chair of the World Economic Forum (WEF), the 4-IR follows the first, second, and third Industrial Revolutions—the mechanical, electrical, and digital, respectively. The 4-IR builds on the digital revolution, but Schwab sees the 4-IR as an exponential takeoff and convergence of existing and emerging fields, including Big Data; artificial intelligence; machine learning; quantum computing; and genetics, nanotechnology, and robotics. The consequence is the merging of the physical, digital, and biological worlds. The blurring of these categories ultimately challenges the very ontologies by which we understand ourselves and the world, including “what it means to be human.”

The specific applications that make up the 4-R are too numerous and sundry to treat in full, but they include a ubiquitous internet, the internet of things, the internet of bodies, autonomous vehicles, smart cities, 3D printing, nanotechnology, biotechnology, materials science, energy storage, and more.

While Schwab and the WEF promote a particular vision for the 4-IR, the developments he announces are not his brainchildren, and there is nothing original about his formulations. Transhumanists and Singularitarians (or prophets of the technological singularity), such as Ray Kurzweil and many others, forecasted these and more revolutionary developments,. long before Schwab heralded them. The significance of Schwab and the WEF’s take on the new technological revolution is the attempt to harness it to a particular end, presumably “a fairer, greener future.”

Trust in AI. If you’re a clinician or a physician, would you trust this AI?

Clearly, sepsis treatment deserves to be focused on, which is what Epic did. But in doing so, they raised several thorny questions. Should the model be recalibrated for each discrete implementation? Are its workings transparent? Should such algorithms publish confidence along with its prediction? Are humans sufficiently in the loop to ensure that the algorithm outputs are being interpreted and implem… See more.

Earlier this year, I wrote about fatal flaws in algorithms that were developed to mitigate the COVID-19 pandemic. Researchers found two general types of flaws. The first is that model makers used small data sets that didn’t represent the universe of patients which the models were intended to represent leading to sample selection bias. The second is that modelers failed to disclose data sources, data-modeling techniques and the potential for bias in either the input data or the algorithms used to train their models leading to design related bias. As a result of these fatal flaws, such algorithms were inarguably less effective than their developers had promised.

Continue reading “Trust The AI? You Decide” »

A need exists to accurately estimate overdose risk and improve understanding of how to deliver treatments and interventions in people with opioid use…

The Microsoft 365 Defender security research team discovered a new vulnerability in macOS that allows an attacker to bypass the System integrity protection or SIP. This is a critical security feature in macOS which uses kernel permissions to limit the ability to write critical system files. Microsoft explains that they also found a similar technique […].

Thanks to this new category of algorithms that has proved its power of mimicking human skills just by learning through examples. Deep learning is a technology representing the next era of machine learning. Algorithms used in machine learning are created by programmers and they hold the responsibility for learning through data. Decisions are made based on such data.

Some of the AI experts say, t here will a shift in AI trends. For instance, the late 1990s and early 2000s saw the rise of machine learning. Neural networks gained its popularity in the early 2010s, and growth in reinforcement came into light recently.

Well, these are just a couple of caveats we’re experienced throughout the past years.

A new algorithm created by researchers at Mount Sinai Hospital, New York, has learned to identify subtle changes in electrocardiograms (ECGs) to predict whether a patient is developing heart failure.