Lifeboat Foundation: Safeguarding Humanity
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IBM and the Department of Energy’s Oak Ridge National Laboratory have revealed the world’s “most powerful and smartest scientific supercomputer.” Known as Summit, IBM says that its new computer will be capable of processing 200,000 quadrillion calculations per second. To put that into perspective, if every person on Earth did a single calculation per second, it would take 305 days to do what Summit does in a single second. Assuming those numbers are accurate, that would make Summit the world’s fastest supercomputer. It would also mark the first time since 2012 that a U.S. computer held that title.

Summit has been in the works for several years now and features some truly impressive specs. According to Tech Crunch, the computer will feature 4,608 compute servers, 22 IBM Power9 chips and six Nvidia Tesla V100 GPUs each. In addition, the machine will feature more than 10 petabytes of memory. As the Nvidia GPUs attest, this machine will be primarily used for the development of artificial intelligence and machine learning. In addition to the work on A.I., Summit will also be used for research into energy and other scientific endeavors at Oak Ridge.

IBM was the Department of Energy’s general contractor for the Summit project, but it also had the help of several other partners within the tech industry. The GPUs were provided by Nvidia, which remains one of the leaders in cutting-edge GPU development. Mellanox and Redhat were also brought on to work on the development of Summit.

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The point of the experiment was to show how easy it is to bias any artificial intelligence if you train it on biased data. The team wisely didn’t speculate about whether exposure to graphic content changes the way a human thinks. They’ve done other experiments in the same vein, too, using AI to write horror stories, create terrifying images, judge moral decisions, and even induce empathy. This kind of research is important. We should be asking the same questions of artificial intelligence as we do of any other technology because it is far too easy for unintended consequences to hurt the people the system wasn’t designed to see. Naturally, this is the basis of sci-fi: imagining possible futures and showing what could lead us there. Issac Asimov gave wrote the “Three Laws of Robotics” because he wanted to imagine what might happen if they were contravened.

Even though artificial intelligence isn’t a new field, we’re a long, long way from producing something that, as Gideon Lewis-Kraus wrote in The New York Times Magazine, can “demonstrate a facility with the implicit, the interpretive.” But it still hasn’t undergone the kind of reckoning that causes a discipline to grow up. Physics, you recall, gave us the atom bomb, and every person who becomes a physicist knows they might be called on to help create something that could fundamentally alter the world. Computer scientists are beginning to realize this, too. At Google this year, 5,000 employees protested and a host of employees resigned from the company because of its involvement with Project Maven, a Pentagon initiative that uses machine learning to improve the accuracy of drone strikes.

Norman is just a thought experiment, but the questions it raises about machine learning algorithms making judgments and decisions based on biased data are urgent and necessary. Those systems, for example, are already used in credit underwriting, deciding whether or not loans are worth guaranteeing. What if an algorithm decides you shouldn’t buy a house or a car? To whom do you appeal? What if you’re not white and a piece of software predicts you’ll commit a crime because of that? There are many, many open questions. Norman’s role is to help us figure out their answers.

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Study based upon human skeletal muscle aging, mutagenesis, and the role of #satellite cells.

“A more comprehensive understanding of the interplay of stem cell–intrinsic and extrinsic factors will set the stage for improving cell therapies capable of restoring tissue homeostasis and enhancing muscle repair in the aged.”

Human aging has multiple effects on the human body. One of the effects of human aging is the reduction in skeletal muscle (SkM) function and a reduction in the number and activity of satellite cells (SCs), the resident stem cells. The whole genome of single SC clones of the leg muscle vastus lateralis from healthy individuals of different ages (21–78 years) was analyzed, to study the specific connection between SC aging and muscle impairment. In healthy adult muscle rapid increase of SCs is consistent with the accumulation rate of 13 somatic mutations per genome per year. Mutations typically do not happen in SkM-expressed genes because they are protected. However, as mutations in exons and promoters increase, genes involved in SC activity and muscle function are targeted which results in aging. Exons are coding sections of an RNA transcript, or the DNA encoding it, that are translated into protein. Proteins are the synthesis of molecules. A change in of a single base pair that caused the substitution of a different amino acid in the resulting protein (missense mutation) that was propagated to the muscle and detected in association with SC mutations affecting the whole tissue. #Somatic mutagenesis in SCs as a result is the driving force in the age related decline of SkM function.

Satellite Cells

Satellite cells (SCs) are a heterogeneous population of stem and progenitor cells. These cells play an important role in the growth and development of myofiber. The enlargement, regeneration, and remodeling in skeletal muscle (SkM) is the pivotal role of satellite cells. Satellite cells are dormant until they become activated through exercise or SkM injury. Upon injury skeletal muscle have a remarkable ability to recover from injury. Skeletal muscle goes through a sophisticated degeneration and regenerative process that takes place at the tissue, cellular, and molecular levels. This regenerative process relies upon the dynamic interplay between satellite cells and their environment (stem cell niche). SCs multiply further when committed to myogenic differentiation. As SCs proliferate further they begin to combine with existing SkM fibers and supply new nuclei to the growing and regenerating fibers. The declining of numbers of proliferative potential of SCs is one sign of aging in human SkMs.

A flawed SC compartment is foreseen as a major contributor for age-related deficiencies such as, skeletal muscle tissue having restricted mobility and voluntary functions. The results of such defects include a reduced capacity to respond to hypertrophic stimuli such as exercise and impaired recovery from muscle disuse and injury and the disruption of muscle tissue homeostasis. Moreover, the SCs of nonactive adult animals have been shown to contribute to differentiated fibers in non-injured muscles. Less important is the basal turnover of nuclei in adult fibers in the protection from sarcopenia. This hypothesis was tested and showed that lifelong reduction of satellite cells neither accelerated nor exacerbated sarcopenia and that satellite cells did not contribute to the maintenance of muscle size or fiber type composition during aging, but that their loss may contribute to age-related muscle fibrosis. The progressive loss of SkM mass and function known as sarcopenia affects up to 29% of the population aged 85 years. The accumulation of sarcopenia causes a highly disabling condition. It is essential, nonetheless, that the characterization of SCs in human pathology be further explored. SCs are a key factor in limiting the occurrence of fibrosis in the SkM of mice affected by sarcopenia.

The progressive loss of SkM mass and function known as sarcopenia affects up to 29% of the population aged 85 years. The accumulation of sarcopenia causes a highly disabling condition. It is essential, nonetheless, that the characterization of SCs in human pathology be further explored. Scs are key in limiting the occurrence of fibrosis in the SkM of mice affected by sarcopenia. Genome integrity is essential for the function of stem-cells. But there still must be some stability of the genome. Genetic mutations in the soma has diverse physiological roles and pathological consequences, such as the decline of stem-cell functions. Starting from the first division of the embryo, modifications in the genome extend from single-base changes (single-nucleotide variants (SNVs)) to insertions or deletions of a few bases (indels) to chromosomal rearrangements and occur during the whole life. Somatic variants are not propagated to the whole individual but to a subpopulation of cells in the body, which is strikingly different from germline variants. Adult human tissues become a mosaic of genetically different cells as a result. Furthermore, as a result of the buildup of errors taking place either during cell-division or because of environmental induced DNA damage, somatic mutation burden increases, causing age-related disease. Currently, somatic mutation burden in human SCs or SkM is unknown.

The purpose of the investigation of genetic alterations that occur with aging in the genome of human adult SCs is to use the results to clearly explain mutational processes and SC replication rate occurring in vivo in adult human muscles. The prediction of global consequences on muscle aging and sarcopenia was done by evaluating the functional effects of somatic mutations on SC proliferation and differentiation.

Results

  • An accumulation of 13 mutations per genome per year that results in a 2–3-fold higher mutation load in active genes and promoters in aged SCs.
  • High mutation burden correlates with defective SC function. • The accumulation of somatic mutations as an intrinsic factor contributing to impaired muscle function with aging.
  • The accumulation of somatic mutations as an intrinsic factor contributing to impaired muscle function with aging.

Resources:

“Somatic mutagenesis in satellite cells associates with human skeletal muscle aging.”

Nature Communications volume 9, Article number: 800(2018) Full Abstract Study

“Satellite Cells and the Muscle Stem Cell Niche.”

Physiological Reviews Volume 93, No.1 (2013) Physiological Reviews

“Tissue-specific mutation accumulation in human adult stem cells during life.”

Nature International Journal of Science volume 538, pages 260–264 (13 October 2016) Abstract Study

“When stem cells grow old: phenotypes and mechanisms of stem cell aging”

Development for advances in developmental biology and stem cells Development 2016 143: 314 Abstract Study “Clock-like mutational processes in human somatic cells.”

Nature Genetics volume 47, pages 1402–1407 (2015) Abstract Study

The Kennedy Space Center might be getting a major upgrade and expansion soon if Elon Musk gets his way. NASA published a plan submitted by SpaceX that dramatically reimagines the company’s presence at KSC in Cape Canaveral, Florida. The plans include everything from a control tower that resembles a flying saucer to a “rocket garden,” showcasing futuristic designs that will expand the space company’s footprint and potential influence within the US agency.

NASA published a draft environmental review for the proposed SpaceX Operations Area, as first reported by Florida Today on Friday. According to the document, SpaceX is seeking permission to build on a 67-acre patch of land about one mile north of KSC’s visitor center complex.

SpaceX wants to build a 133,000-square-foot Falcon hangar to process the used boosters and other rocket materials that it recovers. The hangar would facilitate more efficient recycling of the materials which could potentially save the company billions of dollars per launch.

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A new technique developed by MIT physicists could someday provide a way to custom-design multilayered nanoparticles with desired properties, potentially for use in displays, cloaking systems, or biomedical devices. It may also help physicists tackle a variety of thorny research problems, in ways that could in some cases be orders of magnitude faster than existing methods.

The innovation uses computational neural networks, a form of artificial intelligence, to “learn” how a nanoparticle’s structure affects its behavior, in this case the way it scatters different colors of light, based on thousands of training examples. Then, having learned the relationship, the program can essentially be run backward to design a particle with a desired set of light-scattering properties—a process called inverse design.

The findings are being reported in the journal Science Advances, in a paper by MIT senior John Peurifoy, research affiliate Yichen Shen, graduate student Li Jing, professor of physics Marin Soljacic, and five others.

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Recently we saw a new “Master algorithm” that could be used to create the first generation of super intelligent machines, and now a team of researchers from Maryland, USA, announced this week that they’ve invented a general Artificial Intelligence (AI) way for machines to identify and process 3D images that doesn’t require humans to go through the tedium of inputting specific information that accounts for each and every instance, scenario, difference, change and category that could crop up, and they claim it’s a world first, even though it follows on from a not too dissimilar breakthrough from Google DeepMind whose own platform, Alpha Zero, recently taught itself a mix of board games including chess to a grand master level, in just four hours.

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