Lifeboat Foundation

Massive-scale particle physics produces correspondingly large amounts of data – and this is particularly true of the Large Hadron Collider (LHC), the world’s largest particle accelerator, which is housed at the European Organization for Nuclear Research (CERN) in Switzerland. In 2026, the LHC will receive a massive upgrade through the High Luminosity LHC (HL-LHC) Project. This will increase the LHC’s data output by five to seven times – billions of particle events every second – and researchers are scrambling to prepare big data computing for this deluge of particle physics data. Now, researchers at Lawrence Berkeley National Laboratory are working to tackle high volumes of particle physics data with quantum computing.

When a particle accelerator runs, particle detectors offer data points for where particles crossed certain thresholds in the accelerator. Researchers then attempt to reconstruct precisely how the particles traveled through the accelerator, typically using some form of computer-aided pattern recognition.

This project, which is led by Heather Gray, a professor at the University of California, Berkeley, and a particle physicist at Berkeley Lab, is called Quantum Pattern Recognition for High-Energy Physics (or HEP.QPR). In essence, HEP.QPR aims to use quantum computing to speed this pattern recognition process. HEP.QPR also includes Berkeley Lab scientists Wahid Bhimji, Paolo Calafiura and Wim Lavrijsen.

I’ve been reading an excellent book by David Wood, entitled, which was recommended by my pal Steele Hawes. I’ve come to an excellent segment of the book that I will quote now.

“One particular challenge that international trustable monitoring needs to address is the risk of more ever powerful weapon systems being placed under autonomous control by AI systems. New weapons systems, such as swarms of miniature drones, increasingly change their configuration at speeds faster than human reactions can follow. This will lead to increased pressures to transfer control of these systems, at critical moments, from human overseers to AI algorithms. Each individual step along the journey from total human oversight to minimal human oversight might be justified, on grounds of a balance of risk and reward. However, that series of individual decisions adds up to an overall change that is highly dangerous, given the potential for unforeseen defects or design flaws in the AI algorithms being used.”

The fifteen years from 2020 to 2035 could be the most turbulent of human history. Revolutions are gathering pace in four overlapping fields of technology: nanotech, biotech, infotech, and cognotech, or NBIC for short. In combination, these NBIC revolutions offer enormous new possibilities: enormous opportunities and enormous risks.

An AI algorithm found an antibiotic that wipes out dozens of bacterial strains, including some of the most dangerous drug-resistant bacteria in the world.

A machine learning algorithm implemented on a quantum annealer—a D-Wave machine with 1,098 superconducting qubits—is used to identify Higgs-boson decays from background standard-model processes.

Security of embedded devices is essential in today’s internet-connected world. Security is typically guaranteed mathematically using a small secret key to encrypt the private messages.

When these computationally secure encryption algorithms are implemented on a physical hardware, they leak critical side-channel information in the form of power consumption or electromagnetic radiation. Now, Purdue University innovators have developed technology to kill the problem at the source itself—tackling physical-layer vulnerabilities with physical-layer solutions.

Continue reading “Mixed-signal hardware security thwarts powerful electromagnetic attacks” »

With some reports predicting the precision agriculture market will reach $12.9 billion by 2027, there is an increasing need to develop sophisticated data-analysis solutions that can guide management decisions in real time. A new study from an interdisciplinary research group at University of Illinois offers a promising approach to efficiently and accurately process precision ag data.

Addressing problems of bias in artificial intelligence, computer scientists from Princeton and Stanford University have developed methods to obtain fairer data sets containing images of people. The researchers propose improvements to ImageNet, a database of more than 14 million images that has played a key role in advancing computer vision over the past decade.

ImageNet, which includes images of objects and landscapes as well as people, serves as a source of training data for researchers creating machine learning algorithms that classify images or recognize elements within them. ImageNet’s unprecedented scale necessitated automated image collection and crowdsourced image annotation. While the database’s person categories have rarely been used by the research community, the ImageNet team has been working to address biases and other concerns about images featuring people that are unintended consequences of ImageNet’s construction.

“Computer vision now works really well, which means it’s being deployed all over the place in all kinds of contexts,” said co-author Olga Russakovsky, an assistant professor of computer science at Princeton. “This means that now is the time for talking about what kind of impact it’s having on the world and thinking about these kinds of fairness issues.”

Yes, you can detect another person’s consciousness. Christof Koch described a method called ‘zap and zip’. Transcranial magnetic stimulation is the ‘zap’. Brain activity is detected with an EEG and analyzed with a data compression algorithm, which is the ‘zip’. Then the value of the perturbational complexity index (PCI) is calculated. If the PCI is above 0.31 then you are conscious. If the PCI is below 0.31 then you are unconscious. If this link does not work then go to the library and look at the November 2017 issue of Scientific American. It is the cover story.

Zapping the brain with magnetic pulses while measuring its electrical activity is proving to be a reliable way to detect consciousness.

Brain-computer interfaces (BCIs) are tools that can connect the human brain with an electronic device, typically using electroencephalography (EEG). In recent years, advances in machine learning (ML) have enabled the development of more advanced BCI spellers, devices that allow people to communicate with computers using their thoughts.

So far, most studies in this area have focused on developing BCI classifiers that are faster and more reliable, rather than investigating their possible security vulnerabilities. Recent research, however, suggests that machine learning algorithms can sometimes be fooled by attackers, whether they are used in computer vision, speech recognition, or other domains. This is often done using adversarial examples, which are tiny perturbations in data that are indistinguishable by humans.

Researchers at Huazhong University of Science and Technology have recently carried out a study investigating the security of EEG-based BCI spellers, and more specifically, how they are affected by adversarial perturbations. Their paper, pre-published on arXiv, suggests that BCI spellers are fooled by these perturbations and are thus highly vulnerable to adversarial attacks.