Lifeboat Foundation: Safeguarding Humanity
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Category: information science

Even in this experiment, though, the “psychology” of the algorithm in decision-making is counter-intuitive. For example, in the basketball case, the most important factor in making the decision was actually the player’s jerseys rather than the basketball.

Can You Explain What You Don’t Understand?

While it may seem trivial, the conflict here is a fundamental one in approaches to artificial intelligence. Namely, how far can you get with mere statistical associations between huge sets of data, and how much do you need to introduce abstract concepts for real intelligence to arise?

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The sophistication of autonomous systems currently being developed across various domains and industries has markedly increased in recent years, due in large part to advances in computing, modeling, sensing, and other technologies. While much of the technology that has enabled this technical revolution has moved forward expeditiously, formal safety assurances for these systems still lag behind. This is largely due to their reliance on data-driven machine learning (ML) technologies, which are inherently unpredictable and lack the necessary mathematical framework to provide guarantees on correctness. Without assurances, trust in any learning enabled cyber physical system’s (LE-CPS’s) safety and correct operation is limited, impeding their broad deployment and adoption for critical defense situations or capabilities.

To address this challenge, DARPA’s Assured Autonomy program is working to provide continual assurance of an LE-CPS’s safety and functional correctness, both at the time of its design and while operational. The program is developing mathematically verifiable approaches and tools that can be applied to different types and applications of data-driven ML algorithms in these systems to enhance their autonomy and assure they are achieving an acceptable level of safety. To help ground the research objectives, the program is prioritizing challenge problems in the defense-relevant autonomous vehicle space, specifically related to air, land, and underwater platforms.

The first phase of the Assured Autonomy program recently concluded. To assess the technologies in development, research teams integrated them into a small number of autonomous demonstration systems and evaluated each against various defense-relevant challenges. After 18 months of research and development on the assurance methods, tools, and learning enabled capabilities (LECs), the program is exhibiting early signs of progress.

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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.

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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.

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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.

Recent attacks have shown that such side-channel attacks can happen in just a few minutes from a short distance away. Recently, these attacks were used in the counterfeiting of e-cigarette batteries by stealing the secret encryption keys from authentic batteries to gain market share.

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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.

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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.”

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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.

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