Lifeboat Foundation

Given the potential scope and capabilities of quantum technology, it is absolutely crucial not to repeat the mistakes made with AI—where regulatory failure has given the world algorithmic bias that hypercharges human prejudices, social media that favors conspiracy theories, and attacks on the institutions of democracy fueled by AI-generated fake news and social media posts. The dangers lie in the machine’s ability to make decisions autonomously, with flaws in the computer code resulting in unanticipated, often detrimental, outcomes. In 2021, the quantum community issued a call for action to urgently address these concerns. In addition, critical public and private intellectual property on quantum-enabling technologies must be protected from theft and abuse by the United States’ adversaries.

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There are national defense issues involved as well. In security technology circles, the holy grail is what’s called a cryptanalytically relevant quantum computer —a system capable of breaking much of the public-key cryptography that digital systems around the world use, which would enable blockchain cracking, for example. That’s a very dangerous capability to have in the hands of an adversarial regime.

Continue reading “Quantum computing is an even bigger threat than artificial intelligence” »

We need the computers and sensors to better our lives, to allow everyone access to the wisdom of the ages. We can’t collect all the data ourselves and try to make sense of it without machines because our brains aren’t up to the task. Imagine if every little decision everyone has made over the past thousand years along with its outcome had been recorded on index cards and stored in a gargantuan facility somewhere. Remember that giant warehouse at the end of the first Indiana Jones movie where they ended up storing the Ark of the Covenant? That’s where index cards AA through AC are housed. Imagine five thousand more of those to store all that data. What could we do with it? Nothing useful.

Computers can do only one thing: manipulate ones and zeros in memory. But they can do that at breathtaking speeds with perfect accuracy. Our challenge is getting all that data into the digital mirror, to copy our analog lives in their digital brains. Cheap sensors and computers will do this for us, with prices that fall every year and capabilities that increase.

Coupling massive processing power with sensors will create a species-level brain and memory. Instead of being billions of separate people with siloed knowledge, we will become billions of people who share a single vast intellect. Comparisons to The Matrix are easy to make but are not really apropos. We aren’t talking about a world without human agency but with enhanced agency, information-based agency. Making decisions informed by data is immeasurably better. Even if someone ignores the suggestion of the digital mirror, they are richer for knowing it. Imagine having an AI that could not only tell you what you should do but would allow you to insert your own values into the decision process. In fact, the system would learn your values from your actions, and the suggestions it gives you would be different from those it would give everyone else, as they should be. If knowledge is power, such a system is by definition the ultimate in empowerment. Every person on the planet could effectively be smarter and wiser than anyone who has ever lived.

Scientists in the Netherlands combined a functional MRI scanner with a powerful AI algorithm to reconstruct visual stimuli.

An algorithm developed by researchers from Helmholtz Munich, the Technical University of Munich (TUM) and its University Hospital rechts der Isar, the University Hospital Bonn (UKB) and the University of Bonn is able to learn independently across different medical institutions. The key feature is that it is self-learning, meaning it does not require extensive, time-consuming findings or markings by radiologists in the MRI images.

This federated algorithm was trained on more than 1,500 MRI scans of healthy study participants from four institutions while maintaining data privacy. The algorithm then was used to analyze more than 500 patient MRI scans to detect diseases such as multiple sclerosis, vascular disease, and various forms of brain tumors that the algorithm had never seen before. This opens up new possibilities for developing efficient AI-based federated algorithms that learn autonomously while protecting privacy. The study has now been published in the journal Nature Machine Intelligence.

Health care is currently being revolutionized by artificial intelligence. With precise AI solutions, doctors can be supported in diagnosis. However, such algorithms require a considerable amount of data and the associated radiological specialist findings for training. The creation of such a large, central database, however, places special demands on data protection. Additionally, the creation of the findings and annotations, for example the marking of tumors in an MRI image, is very time-consuming.

Why we should be performing interstellar archaeology and how Avi Loeb and his team at the Galileo Project plan to recover an interstellar object at the bottom of the ocean.

“Any chemically-propelled spacecraft sent by past civilizations into interstellar space, like the five we had sent so far (Voyager 1 & 2, Pioneer 10 & 11, and New Horizons), remained gravitationally bound to the Milky Way long after these civilizations died. Their characteristic speed of tens of kilometers per second is an order of magnitude smaller than the escape speed out of the Milky Way. These rockets would populate the Milky Way disk and move around at similar speeds to the stars in it.

Continue reading “Why a Group of Scientists Plan to Search for Alien Technology with Avi Loeb” »

Managing road intersections in crowded and dynamic environments, such as urban areas, can be highly challenging. The poor management of traffic at these can lead to road accidents, wastage of fuel, and environmental pollution.

Researchers at the University of Maryland have recently developed GAMEOPT, a new algorithm that could help manage unsignalized road intersections with high traffic more efficiently. The research team with members, Nilesh Suriyarachchi, Rohan Chandra, John S. Baras and Dinesh Manocha introduced their method in a recent paper to be published in the proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2022). This method combines optimization techniques with ideas from game theory, a mathematical construct that represents situations in which different agents are competing with one another.

“Forty percent of all crashes, 50% of serious collisions, and 20% of fatalities occur at unsignalized intersections,” Chandra, a member of the research team, told TechXplore. “Our primary objective is to improve traffic flow and fuel efficiency in poorly regulated or unregulated traffic intersections. To achieve this objective, we propose an algorithm that combines ideas from optimization and game theory to understand how different traffic agents cooperate and negotiate with each other at traffic intersections.”

“I believe we can train the algorithm not only to picture accurately a face you’re looking at, but also any face you imagine vividly, such as your mother’s,” explains Dado.

“By developing this technology, it would be fascinating to decode and recreate subjective experiences, perhaps even your dreams,” Dado says. “Such technological knowledge could also be incorporated into clinical applications such as communicating with patients who are locked within deep comas.”

Continue reading “‘Mind-Reading’ Technology Translates Brainwaves into Photos” »

In recent years, deep learning algorithms have achieved remarkable results in a variety of fields, including artistic disciplines. In fact, many computer scientists worldwide have successfully developed models that can create artistic works, including poems, paintings and sketches.

Researchers at Seoul National University have recently introduced a new artistic deep learning framework, which is designed to enhance the skills of a sketching robot. Their framework, introduced in a paper presented at ICRA 2022 and pre-published on arXiv, allows a sketching robot to learn both stroke-based rendering and motor control simultaneously.

Continue reading “A deep learning framework to enhance the capabilities of a robotic sketching agent” »

Millions of children log into chat rooms every day to talk with other children. One of these “children” could well be a man pretending to be a 12-year-old girl with far more sinister intentions than having a chat about “My Little Pony” episodes.

Inventor and NTNU professor Patrick Bours at AiBA is working to prevent just this type of predatory behavior. AiBA, an AI-digital moderator that Bours helped found, can offer a tool based on behavioral biometrics and algorithms that detect sexual abusers in online chats with children.

And now, as recently reported by Dagens Næringsliv, a national financial newspaper, the company has raised capital of NOK 7.5. million, with investors including Firda and Wiski Capital, two Norwegian-based firms.