On October 22, 2020, yesterday, Dexter Johnson posted The
Lithium-Ion Battery With Built-In Fire Suppression. Within this topic, Dexter
Johnson regards a Stanford University research team and the SLAC National
Accelerator Laboratory (its former name was the Stanford Linear Accelerator
Center[1]). Johnson stated:Now [Yi] Cui and his research
team, in collaboration with SLAC National Accelerator Laboratory, have offered
some exciting new capabilities for lithium-ion batteries based around a new
polymer material they are using in the current collectors for them. The researchers
claim this new design to current collectors increases efficiency in Li-ion
batteries and reduces the risk of fires associated with these batteries.[2]Johnson was
saying this: fires are a current Li-ion battery threat that has been realized,
but a new design can secure client use-case safety, and this required this
battery redesigned. As this technology approaches marketplace entry points,
this shall confront Li-io…
In response to the Institute of Industrial Science, the University of Tokyo’s Circular Reasoning: Spiral Circuits for More Efficient AI
Circular Reasoning
On June 14, 2020, the IIS (Institute of Industrial Science) at the UTokyo (University of Tokyo) wrote Circular Reasoning: Spiraling Circuits for More Efficient AI; but a Press Release from this institute is giving a synopsis on this topic. On this press release, the IIS wrote, “Researchers from the Institute of Industrial Science at the University of Tokyo designed and built specialized computer hardware consisting of stacks of memory modules arranged in a 3D-spiral for artificial intelligence (AI) applications” (IIS, June 14, 2020). The IIS continued on, saying this research is allowing a singular way work can be done regarding the next generation energy efficient AI devices (Here is a current generation, but energy efficient, AI device, Android Pie: link) shall be implemented into production.
The Fundamentals of Machine Learning
On this press release, the IIS is explaining the fundamentals of ML (Machine Learning). The IIS wrote, “Machine learning is a type of AI that allows computers to be trained by example data to make predictions for new instances” (IIS, June 14, 2020). An example of ML the IIS is referencing is Amazon’s Assistant, Alexa (link), who can learn a reader’s speaking voice command even during the first time you want this commanding duty sent to Alexa’s services logic. Next, IIS scientists at UTokyo are developing a new design, a stacking resistive RAM (Random-Access Memory (Here is an example of an CoaC (Computer on a Chip): link) modules with oxide semiconductor (IGZO) access transistor spiraling in three-dimensions; but in laymen’s terms, this shall be like being able to make a miniature encampment with highly nutritious and compact meals delivered very rapidly. This is because the electrical signals, those regarding with the on-chip nonvolatile memory, are having places close to the processors, so the ML training process is faster and more energy effective. Also, I believe this creation shall reduce deadlock times: this is good because like the ox we ought to not muzzle (NIV (link), Deuteronomy 25:4, 1 Timothy 5:18) the machine while it is working or else heating costs are increasing overhead a great deal for opponents. Here is a link to the original press release: link.
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