Lifeboat Foundation

Circa 2019 😀

Because they can process massive amounts of data, computers can perform analytical tasks that are beyond human capability. Google, for instance, is using its computing power to develop AI algorithms that construct two-dimensional CT images of lungs into a three-dimensional lung and look at the entire structure to determine whether cancer is present. Radiologists, in contrast, have to look at these images individually and attempt to reconstruct them in their heads. Another Google algorithm can do something radiologists cannot do at all: determine patients’ risk of cardiovascular disease by looking at a scan of their retinas, picking up on subtle changes related to blood pressure, cholesterol, smoking history and aging. “There’s potential signal there beyond what was known before,” says Google product manager Daniel Tse.

The Black Box Problem

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The strategy outlines how AI can be applied to defence and security in a protected and ethical way. As such, it sets standards of responsible use of AI technologies, in accordance with international law and NATO’s values. It also addresses the threats posed by the use of AI by adversaries and how to establish trusted cooperation with the innovation community on AI.

Artificial Intelligence is one of the seven technological areas which NATO Allies have prioritized for their relevance to defence and security. These include quantum-enabled technologies, data and computing, autonomy, biotechnology and human enhancements, hypersonic technologies, and space. Of all these dual-use technologies, Artificial Intelligence is known to be the most pervasive, especially when combined with others like big data, autonomy, or biotechnology. To address this complex challenge, NATO Defence Ministers also approved NATO’s first policy on data exploitation.

Individual strategies will be developed for all priority areas, following the same ethical approach as that adopted for Artificial Intelligence.

You have to know the past to understand the present.

Topology in optics and photonics has been a hot topic since 1,890 where singularities in electromagnetic fields have been considered. The recent award of the Nobel prize for topology developments in condensed matter physics has led to renewed surge in topology in optics with most recent developments in implementing condensed matter particle-like topological structures in photonics. Recently, topological photonics, especially the topological electromagnetic pulses, hold promise for nontrivial wave-matter interactions and provide additional degrees of freedom for information and energy transfer. However, to date the topology of ultrafast transient electromagnetic pulses had been largely unexplored.

In their paper Nat. Commun., physicists in the UK and Singapore report a new family of electromagnetic pulses, the exact solutions of Maxwell’s equation with toroidal topology, in which topological complexity can be continuously controlled, namely supertoroidal topology. The electromagnetic fields in such supertoroidal pulses have skyrmionic structures as they propagate in free space with the speed of light.

Skyrmions, sophisticated topological particles originally proposed as a unified model of the nucleon by Tony Skyrme in 1,962 behave like nanoscale magnetic vortices with spectacular textures. They have been widely studied in many condensed matter systems, including chiral magnets and liquid crystals, as nontrivial excitations showing great importance for information storing and transferring. If skyrmions can fly, open up infinite possibilities for the next generation of informatics revolution.

Deep North, a Foster City, California-based startup applying computer vision to security camera footage, today announced that it raised $16.7 million in a Series A-1 round. Led by Celesta Capital and Yobi Partners, with participation from Conviction Investment Partners, Deep North plans to use the funds to make hires and expand its services “at scale,” according to CEO Rohan Sanil.

Deep North, previously known as Vmaxx, claims its platform can help brick-and-mortar retailers “embrace digital” and protect against COVID-19 by retrofitting security systems to track purchases and ensure compliance with masking rules. But the company’s system, which relies on algorithms with potential flaws, raises concerns about both privacy and bias.

Full Story:

Oh the things we can see and accomplish when time and death can no longer hinder us.

Immortality is eternal life, being exempt from death, unending existence.
Human beings seem to be obsessed with the idea of immortality. But a study published in the journal Proceedings of the National Academy of Sciences has stated, through a mathematical equation, that it is impossible to stop ageing in multicellular organisms, which include humans, bringing the immortality debate to a possible end.
So you probably don’t want to die, most people don’t. But death takes us all no matter what we want. However, today in our scenario, humans have found a way to obtain that immortality. Watch the whole timeline video to find out how reaching immortality changes the world and the way we live.

Continue reading “Timeline: What If Humans Were Immortal” »

Smart image analysis algorithms, fed by cameras carried by drones and ground vehicles, can help power companies prevent forest fires.

Every time a human or machine learns how to get better at a task, a trail of evidence is left behind. A sequence of physical changes — to cells in a brain or to numerical values in an algorithm — underlie the improved performance. But how the system figures out exactly what changes to make is no small feat. It’s called the credit assignment problem, in which a brain or artificial intelligence system must pinpoint which pieces in its pipeline are responsible for errors and then make the necessary changes. Put more simply: It’s a blame game to find who’s at fault.

AI engineers solved the credit assignment problem for machines with a powerful algorithm called backpropagation, popularized in 1986 with the work of Geoffrey Hinton, David Rumelhart and Ronald Williams. It’s now the workhorse that powers learning in the most successful AI systems, known as deep neural networks, which have hidden layers of artificial “neurons” between their input and output layers. And now, in a paper published in Nature Neuroscience in May, scientists may finally have found an equivalent for living brains that could work in real time.

A team of researchers led by Richard Naud of the University of Ottawa and Blake Richards of McGill University and the Mila AI Institute in Quebec revealed a new model of the brain’s learning algorithm that can mimic the backpropagation process. It appears so realistic that experimental neuroscientists have taken notice and are now interested in studying real neurons to find out whether the brain is actually doing it.

Continue reading “Neuron Bursts Can Mimic Famous AI Learning Strategy” »

(2021). Nuclear Technology: Vol. 207 No. 8 pp. 1163–1181.

Focusing on nuclear engineering applications, the nation’s leading cybersecurity programs are focused on developing digital solutions to support reactor control for both on-site and remote operation. Many of the advanced reactor technologies currently under development by the nuclear industry, such as small modular reactors, microreactors, etc., require secure architectures for instrumentation, control, modeling, and simulation in order to meet their goals. ¹ Thus, there is a strong need to develop communication solutions to enable secure function of advanced control strategies and to allow for an expanded use of data for operational decision making. This is important not only to avoid malicious attack scenarios focused on inflicting physical damage but also covert attacks designed to introduce minor process manipulation for economic gain. ²

These high-level goals necessitate many important functionalities, e.g., developing measures of trustworthiness of the code and simulation results against unauthorized access; developing measures of scientific confidence in the simulation results by carefully propagating and identifying dominant sources of uncertainties and by early detection of software crashes; and developing strategies to minimize the computational resources in terms of memory usage, storage requirements, and CPU time. By introducing these functionalities, the computers are subservient to the programmers. The existing predictive modeling philosophy has generally been reliant on the ability of the programmer to detect intrusion via specific instructions to tell the computer how to detect intrusion, keep log files to track code changes, limit access via perimeter defenses to ensure no unauthorized access, etc.

Continue reading “Covert Cognizance: A Novel Predictive Modeling Paradigm” »