Lifeboat Foundation

Place one clock at the top of a mountain. Place another on the beach. Eventually, you’ll see that each clock tells a different time. Why?

In his book “The Order of Time,” Italian theoretical physicist Carlo Rovelli suggests that our perception of time — our sense that time is forever flowing forward — could be a highly subjective projection. After all, when you look at reality on the smallest scale (using equations of quantum gravity, at least), time vanishes.

“If I observe the microscopic state of things,” writes Rovelli, “then the difference between past and future vanishes … in the elementary grammar of things, there is no distinction between ‘cause’ and ‘effect.’”

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Computer scientists from Rice University have displayed an artificial intelligence (AI) software that can run on commodity processors and train deep neural networks 15 times faster than platforms based on graphics processors.

According to Anshumali Shrivastava, an assistant professor of computer science at Rice’s Brown School of Engineering, the resources spent on training are the actual bottleneck in AI. Companies are spending millions of dollars a week to train and fine-tune their AI workloads.

Deep neural networks (DNN) are a very powerful type of artificial intelligence that can outperform humans at some tasks. DNN training is a series of matrix multiplication operations and an ideal workload for graphics processing units (GPUs), which costs nearly three times more than general-purpose central processing units (CPUs).

TAE Technologies, the California, USA-based fusion energy technology company, has announced that its proprietary beam-driven field-reversed configuration (FRC) plasma generator has produced stable plasma at over 50 million degrees Celsius. The milestone has helped the company raise USD280 million in additional funding.

Norman — TAE’s USD150 million National Laboratory-scale device named after company founder, the late Norman Rostoker — was unveiled in May 2017 and reached first plasma in June of that year. The device achieved the latest milestone as part of a “well-choreographed sequence of campaigns” consisting of over 25000 fully-integrated fusion reactor core experiments. These experiments were optimised with the most advanced computing processes available, including machine learning from an ongoing collaboration with Google (which produced the Optometrist Algorithm) and processing power from the US Department of Energy’s INCITE programme that leverages exascale-level computing.

Plasma must be hot enough to enable sufficiently forceful collisions to cause fusion and sustain itself long enough to harness the power at will. These are known as the ‘hot enough’ and ‘long enough’ milestone. TAE said it had proved the ‘long enough’ component in 2015, after more than 100000 experiments. A year later, the company began building Norman, its fifth-generation device, to further test plasma temperature increases in pursuit of ‘hot enough’.

Rice University computer scientists have demonstrated artificial intelligence (AI) software that runs on commodity processors and trains deep neural networks 15 times faster than platforms based on graphics processors.

“The cost of training is the actual bottleneck in AI,” said Anshumali Shrivastava, an assistant professor of computer science at Rice’s Brown School of Engineering. “Companies are spending millions of dollars a week just to train and fine-tune their AI workloads.”

Shrivastava and collaborators from Rice and Intel will present research that addresses that bottleneck April 8 at the machine learning systems conference MLSys.

But will the effort undermine A.I. ethics?

Primer, the San Francisco A.I. company, is the latest to launch a no-code software system that lets non-experts create and train A.I. algorithms.

Michio Kaku is a professor of theoretical physics at City College, New York, a proponent of string theory but also a well-known populariser of science, with multiple TV appearances and several bestselling books behind him. His latest book, The God Equation, is a clear and accessible examination of the quest to combine Einstein’s general relativity with quantum theory to create an all-encompassing “theory of everything” about the nature of the universe.

The physicist on Newton finding inspiration amid the great plague, how the multiverse can unite religions, and why a ‘theory of everything’ is within our grasp.

The researchers let the cell clusters assemble in the right proportions and then used micro-manipulation tools to move or eliminate cells — essentially poking and carving them into shapes like those recommended by the algorithm. The resulting cell clusters showed the predicted ability to move over a surface in a nonrandom way.

The team dubbed these structures xenobots. While the prefix was derived from the Latin name of the African clawed frogs (Xenopus laevis) that supplied the cells, it also seemed fitting because of its relation to xenos, the ancient Greek for “strange.” These were indeed strange living robots: tiny masterpieces of cell craft fashioned by human design. And they hinted at how cells might be persuaded to develop new collective goals and assume shapes totally unlike those that normally develop from an embryo.

But that only scratched the surface of the problem for Levin, who wanted to know what might happen if embryonic frog cells were “liberated” from the constraints of both an embryonic body and researchers’ manipulations. “If we give them the opportunity to re-envision multicellularity,” Levin said, then his question was, “What is it that they will build?”

Algorithms may be key to effectively using ReRAM devices in edge-learning systems, turning a ReRAM disadvantage to good use.

As brain activity-dependent human genes are of great importance in human neuropsychiatric disorders we also examined the expression of these genes to postmortem RNAseq databases from patients suffering from various neurological and psychiatric disorders (Table 1). Datasets were chosen based on similarities in tissue processing and RNAseq methodology to our own protocol. We performed a PCA (Principal Component Analysis) of our fresh brain compared to postmortem brain from healthy, Parkinson’s, Schizophrenia, Huntington’s, and Autism brains for the top 500 brain activity-dependent genes that showed the greatest reduction in the healthy postmortem samples. The PCA revealed a significant separation between the 4 fresh samples and the postmortem samples, independent of whether or not the fresh tissue was from epileptic (high activity, H) or non-epileptic (low activity, L) brain regions (Fig. 2 J). This further demonstrates a selective reduction of activity-dependent genes in postmortem brain independent of whether the underlying tissue is electrically active or not.

The sudden removal of brain tissue from a living person in many ways mimics a catastrophic event that occurs with a hypoxic brain injury or a traumatic death with exsanguination. The human brain has high energy needs, estimated to be 10 times that of other tissues²¹. As a means to understand how the postmortem interval selectively affects some genes and not others in human neocortex, we performed RNAseq and histological analyses in cortical brain tissue as a function of time from 0–24 h at 24 °C in order to simulate a postmortem interval. Neuropathological examination of the tissue used for this study showed a normal-appearing cortical pattern with no histopathologic abnormalities. RNAseq analysis showed a loss of brain activity-dependent genes that were 3-times more prone to be degraded than expected by chance compared to more stable housekeeping genes (Table 2). The threshold to detect activity-dependent genes was related to the probability of being affected by the PMI. The higher the relative expression of the brain activity gene, the more it was enriched in the population of genes affected by the PMI. These findings confirm that genes involved in brain activity are more prone to degradation during the PMI.

One possible explanation for the selective loss of activity-dependent genes could relate to the stability of various cell populations during the simulated PMI. As a means to implicate specific cell populations that could be responsible for the reduction of genes during the simulated PMI we used a clustering algorithm as we have previously described⁹. We found that 1427 genes (71% known brain activity-dependent genes) could be clustered across the seven time points of the simulated PMI. For these clusters, we used AllegroMcode to identify two main clusters. One cluster of 317 rapidly declining genes was predicted to be neuronal and strongly overlapped with the activity-dependent genes. A second cluster of 474 genes was predicted to be glial, including astrocytes and microglia (Fig. 3A). Remarkably, as the neuronal cell cluster rapidly fell, there was a reciprocal and dramatic increase in the expression of the glial cell cluster (Fig. 3B).