Lifeboat Foundation

Scientists have developed the most accurate computing method to date to reconstruct the patchwork of genetic faults within tumors and their history during disease development, in new research funded by Cancer Research UK and published in Nature Genetics.

Their powerful approach combines artificial intelligence with the mathematical models of Charles Darwin’s theory of evolution to analyze genetic data more accurately than ever before, paving the way for a fundamental shift in how cancer’s genetic diversity is used to deliver tailored treatments to patients.

Applying these new algorithms to DNA data taken from patient samples revealed that tumors had a simpler genetic structure than previously thought. The algorithms showed that tumors had fewer distinct subpopulations of cells, called “subclones,” than previously suggested. The scientists, based at The Institute of Cancer Research, London, and Queen Mary University of London, could also tell how old each subclone was and how fast it was growing.

Three mathematicians have resolved a fundamental question about straight paths on the 12-sided Platonic solid.

Abstract: Advances in high speed imaging techniques have opened new possibilities for capturing ultrafast phenomena such as light propagation in air or through media. Capturing light-in-flight in 3-dimensional xyt-space has been reported based on various types of imaging systems, whereas reconstruction of light-in-flight information in the fourth dimension z has been a challenge. We demonstrate the first 4-dimensional light-in-flight imaging based on the observation of a superluminal motion captured by a new time-gated megapixel single-photon avalanche diode camera. A high resolution light-in-flight video is generated with no laser scanning, camera translation, interpolation, nor dark noise subtraction. A machine learning technique is applied to analyze the measured spatio-temporal data set. A theoretical formula is introduced to perform least-square regression, and extra-dimensional information is recovered without prior knowledge. The algorithm relies on the mathematical formulation equivalent to the superluminal motion in astrophysics, which is scaled by a factor of a quadrillionth. The reconstructed light-in-flight trajectory shows a good agreement with the actual geometry of the light path. Our approach could potentially provide novel functionalities to high speed imaging applications such as non-line-of-sight imaging and time-resolved optical tomography.

AI tools are shaping next-generation theorem provers, and with them the relationship between math and machine.

Google has announced a new technology, powered by an acquisition called Socratic, that will let students take photos of their math homework in order to get the solutions. Google says it wants the Lens-powered technology to help parents and caretakers who are homeschooling, likely for the first time, as a result of the global COVID-19 pandemic.

Two mathematicians say they’ve untangled the first part of Paul Erdos’s famously thorny and unproven conjecture. In a new paper they’ve uploaded to arXiv and submitted to journals, mathematicians Thomas Bloom and Olof Sisask say they’ve jumped the first hurdle in the Erdos conjecture. If this is true, the next generation of researchers could start from that point with the first part finished and in hand.

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Quantifications are produced by several disciplinary houses in a myriad of different styles. The concerns about unethical use of algorithms, unintended consequences of metrics, as well as the warning about statistical and mathematical malpractices are all part of a general malaise, symptoms of our tight addiction to quantification. What problems are shared by all these instances of quantification? After reviewing existing concerns about different domains, the present perspective article illustrates the need and the urgency for an encompassing ethics of quantification. The difficulties to discipline the existing regime of numerification are addressed; obstacles and lock-ins are identified. Finally, indications for policies for different actors are suggested.

“A neuron in the human brain can never equate the human mind, but this analogy doesn’t hold true for a digital mind, by virtue of its mathematical structure, it may – through evolutionary progression and provided there are no insurmountable evolvability constraints – transcend to the higher-order Syntellect. A mind is a web of patterns fully integrated as a coherent intelligent system; it is a self-generating, self-reflective, self-governing network of sentient components… that evolves, as a rule, by propagating through dimensionality and ascension to ever-higher hierarchical levels of emergent complexity. In this book, the Syntellect emergence is hypothesized to be the next meta-system transition, developmental stage for the human mind – becoming one global mind – that would constitute the quintessence of the looming Cybernetic Singularity.” –Alex M. Vikoulov, The Syntellect Hypothesis https://www.ecstadelic.net/e_news/gearing-for-the-2020-visio…ss-release

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