Fusion power — the process that keeps stars like the Sun burning — holds the promise of nearly unlimited clean power. But scientists have struggled for decades to make it a practical energy source.
Now, laser scientists say a machine learning breakthrough has smashed the standing record for a fusion power yield. It doesn’t mean fusion power is practical yet, but the prestigious journal Naturecalled the result “remarkable” and wrote that it has “major implications” — so, at the very least, it’s another hint that the long-deferred technology is starting to come into focus.
This animation video provides a good summary, about the challenges that need to be solved in order to establish an outpost on #Mars
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Humans love to explore. Strangely enough even horrible places – like Mars. Let’s see how building a Mars base could work and how insanely nerve-wracking exactly it would be.
It is a few years since I posted here on Lifeboat Foundation blogs, but with the news breaking recently of CERN’s plans to build the FCC [1], a new high energy collider to dwarf the groundbreaking engineering triumph that is the LHC, I feel obliged to write a few words.
The goal of the FCC is to greatly push the energy and intensity frontiers of particle colliders, with the aim of reaching collision energies of 100 TeV, in the search for new physics [2]. Below linked is a technical note I wrote & distributed last year on 100 TeV collisions (at the time referencing the proposed China supercollider [3][4]), highlighting the weakness of the White Dwarf safety argument at these energy levels, and a call for a more detailed study of the Neutron star safety argument, if to be relied on as a solitary astrophysical assurance. The argument applies equally to the FCC of course:
The LSAG, and others including myself, have already written on the topic of astrophysical assurances at length before. The impact of CR on Neutron stars is the most compelling of those assurances with respect to new higher energy colliders (other analogies such as White Dwarf capture based assurances don’t hold up quite as well at higher energy levels). CERN will undoubtedly publish a new paper on such astrophysical assurances as part of the FCC development process, though would one anticipate it sooner rather than later, to lay to rest concerns of outsider-debate incubating to a larger audience?
Hope springs eternal. Hearing that folk from China’s IHEP were later in contact with the LSAG on this specific issue, one infers due diligence is in mind, albeit seemingly in retrospect again, on the premise that as CERN take up the baton, significant progress in collecting further input for the overall assessment (eg from cosmic rays, direct astrophysical observations, etc) is expected in the ~20 years timescale of development.
Meanwhile those of us keen on new science frontiers, and large scale engineering projects, have exciting times ahead yet again with a new CERN flagship.
[4] Reflecting on China’s Ambition to Build the World’s Most Powerful Supercollider, Existential Risk/Opportunity Singularity Management, 2015. http://www.global-risk-sig.org/erosmB9F.pdf
After years of promise, AI is finally becoming useful. But what usually happens to useful technologies is that they disappear. We forget about the things that just work, and we shouldn’t let that happen to AI. Any technology destined to change the world needs scrutiny, and AI, with its combination of huge imaginative presence and very real, very dangerous failings, needs that scrutiny more than most.
So, for the AI Issue at The Verge, we’re taking a closer look at some of the ways artificial intelligence and machine learning are affecting technology right now — because it’s too late to understand something after it’s changed the world.
Today, I talk not just about Exponential Technologies but also about Exponential Combinations, by combining Quantum Computing with Neural Networks we’d revolutionise both.
Facebook is working on an artificial intelligence that it hopes could one day detect people’s emotions based on their tone of their voice, aiming to alleviate the frustrations of modern voice speaker systems such as Alexa.
Engineers at the social network’s research labs are working out how to train its voice-controlled video chat device, Portal, to understand when a user is angry, an employee said during a tech conference in San Francisco.
The system could one day be used across Facebook Messenger and WhatsApp calls, but could lead to privacy fears about the scope of the company’s data collection.
New research looks at distinctive differences in brain connectivity that may underlie autism spectrum disorders (ASD) — and possibly provide much-needed biomarkers to aid in identifying the disorder.
Diagnosis for ASD is still behaviorally based. But getting a diagnosis can take longer due to several factors, including lack of resources and trained clinicians. This delays autism diagnosis, on average, until age 5 or 6.
“Within ASD, two important research questions are: How can we minimize the delay in diagnosis, and what kind of intervention can we give the child?” said Rajesh Kana, Ph.D., associate professor of psychology in the UAB College of Arts and Sciences.
