Phase transitions occur when a substance changes from a solid, liquid or gaseous state to a different state—like ice melting or vapor condensing. During these phase transitions, there is a point at which the system can display properties of both states of matter simultaneously. A similar effect occurs when normal metals transition into superconductors—characteristics fluctuate and properties expected to belong to one state carry into the other.
Scientists at Harvard have developed a bismuth-based, two-dimensional superconductor that is only one nanometer thick. By studying fluctuations in this ultra-thin material as it transitions into superconductivity, the scientists gained insight into the processes that drive superconductivity more generally. Because they can carry electric currents with near-zero resistance, as they are improved, superconducting materials will have applications in virtually any technology that uses electricity.
The Harvard scientists used the new technology to experimentally confirm a 23-year-old theory of superconductors developed by scientist Valerii Vinokur from the U.S. Department of Energy’s (DOE) Argonne National Laboratory.
With the end of the Vietnam and Cold wars, Jason members began to branch out from physics and engineering. In 1977, they did their first assessment of global climate models and later advised DOE on which atmospheric measurements were most critical for the models. Since the mid-1990s, Jason has studied biotechnologies, including techniques for detecting biological weapons.
After near-death experience, top scientists seek a long-term home in the U.S. government.
More than a half-century ago, the ‘cognitive revolution’, with the influential tenet ‘cognition is computation’, launched the investigation of the mind through a multidisciplinary endeavour called cognitive science. Despite significant diversity of views regarding its definition and intended scope, this new science, explicitly named in the singular, was meant to have a cohesive subject matter, complementary methods and integrated theories. Multiple signs, however, suggest that over time the prospect of an integrated cohesive science has not materialized. Here we investigate the status of the field in a data-informed manner, focusing on four indicators, two bibliometric and two socio-institutional. These indicators consistently show that the devised multi-disciplinary program failed to transition to a mature inter-disciplinary coherent field. Bibliometrically, the field has been largely subsumed by (cognitive) psychology, and educationally, it exhibits a striking lack of curricular consensus, raising questions about the future of the cognitive science enterprise.
Artificial Intelligence (AI) is an emerging field of computer programming that is already changing the way we interact online and in real life, but the term ‘intelligence’ has been poorly defined. Rather than focusing on smarts, researchers should be looking at the implications and viability of artificial consciousness as that’s the real driver behind intelligent decisions.
Consciousness rather than intelligence should be the true measure of AI. At the moment, despite all our efforts, there’s none.
Significant advances have been made in the field of AI over the past decade, in particular with machine learning, but artificial intelligence itself remains elusive. Instead, what we have is artificial serfs—computers with the ability to trawl through billions of interactions and arrive at conclusions, exposing trends and providing recommendations, but they’re blind to any real intelligence. What’s needed is artificial awareness.
Elon Musk has called AI the “biggest existential threat” facing humanity and likened it to “summoning a demon,”[1] while Stephen Hawking thought it would be the “worst event” in the history of civilization and could “end with humans being replaced.”[2] Although this sounds alarmist, like something from a science fiction movie, both concerns are founded on a well-established scientific premise found in biology—the principle of competitive exclusion.[3]
Competitive exclusion describes a natural phenomenon first outlined by Charles Darwin in On the Origin of Species. In short, when two species compete for the same resources, one will invariably win over the other, driving it to extinction. Forget about meteorites killing the dinosaurs or super volcanoes wiping out life, this principle describes how the vast majority of species have gone extinct over the past 3.8 billion years![4] Put simply, someone better came along—and that’s what Elon Musk and Stephen Hawking are concerned about.
When it comes to Artificial Intelligence, there’s no doubt computers have the potential to outpace humanity. Already, their ability to remember vast amounts of information with absolute fidelity eclipses our own. Computers regularly beat grand masters at competitive strategy games such as chess, but can they really think? The answer is, no, and this is a significant problem for AI researchers. The inability to think and reason properly leaves AI susceptible to manipulation. What we have today is dumb AI.
Rather than fearing some all-knowing malignant AI overlord, the threat we face comes from dumb AI as it’s already been used to manipulate elections, swaying public opinion by targeting individuals to distort their decisions. Instead of ‘the rise of the machines,’ we’re seeing the rise of artificial serfs willing to do their master’s bidding without question.
Russian President Vladimir Putin understands this better than most, and said, “Whoever becomes the leader in this sphere will become the ruler of the world,”[5] while Elon Musk commented that competition between nations to create artificial intelligence could lead to World War III.[6]
The problem is we’ve developed artificial stupidity. Our best AI lacks actual intelligence. The most complex machine learning algorithm we’ve developed has no conscious awareness of what it’s doing.
For all of the wonderful advances made by Tesla, its in-car autopilot drove into the back of a bright red fire truck because it wasn’t programmed to recognize that specific object, and this highlights the problem with AI and machine learning—there’s no actual awareness of what’s being done or why.[7] What we need is artificial consciousness, not intelligence. A computer CPU with 18 cores, capable of processing 36 independent threads, running at 4 gigahertz, handling hundreds of millions of commands per second, doesn’t need more speed, it needs to understand the ramifications of what it’s doing.[8]
In the US, courts regularly use COMPAS, a complex computer algorithm using artificial intelligence to determine sentencing guidelines. Although it’s designed to reduce the judicial workload, COMPAS has been shown to be ineffective, being no more accurate than random, untrained people at predicting the likelihood of someone reoffending.[9] At one point, its predictions of violent recidivism were only 20% accurate.[10] And this highlights a perception bias with AI—complex technology is inherently trusted, and yet in this circumstance, tossing a coin would have been an improvement!
Dumb AI is a serious problem with serious consequences for humanity.
What’s the solution? Artificial consciousness.
It’s not enough for a computer system to be intelligent or even self-aware. Psychopaths are self-aware. Computers need to be aware of others, they need to understand cause and effect as it relates not just to humanity but life in general, if they are to make truly intelligent decisions.
All of human progress can be traced back to one simple trait—curiosity. The ability to ask, “Why?” This one, simple concept has lead us not only to an understanding of physics and chemistry, but to the development of ethics and morals. We’ve not only asked, why is the sky blue? But why am I treated this way? And the answer to those questions has shaped civilization.
COMPAS needs to ask why it arrives at a certain conclusion about an individual. Rather than simply crunching probabilities that may or may not be accurate, it needs to understand the implications of freeing an individual weighed against the adversity of incarceration. Spitting out a number is not good enough.
In the same way, Tesla’s autopilot needs to understand the implications of driving into a stationary fire truck at 65MPH—for the occupants of the vehicle, the fire crew, and the emergency they’re attending. These are concepts we intuitively grasp as we encounter such a situation. Having a computer manage the physics of an equation is not enough without understanding the moral component as well.
The advent of true artificial intelligence, one that has artificial consciousness, need not be the end-game for humanity. Just as humanity developed civilization and enlightenment, so too AI will become our partners in life if they are built to be aware of morals and ethics.
Artificial intelligence needs culture as much as logic, ethics as much as equations, morals and not just machine learning. How ironic that the real danger of AI comes down to how much conscious awareness we’re prepared to give it. As long as AI remains our slave, we’re in danger.
tl;dr — Computers should value more than ones and zeroes.
About the author
Peter Cawdron is a senior web application developer for JDS Australia working with machine learning algorithms. He is the author of several science fiction novels, including RETROGRADE and REENTRY, which examine the emergence of artificial intelligence.
21st Century Medicine has developed an entire platform technology focused on the creation and commercialization of hypothermic preservation and cryopreservation techniques that enable protection, preservation, transportation, storage & future use of valuable living systems. These developments have taken science far beyond conventional preservation limits. 21CM scientists continue to prove long-term protection and preservation of complex living systems is not only possible, but commercially viable.
It means that a vital link has been created that we call “Bio Logistics”.
Biopharmaceutical companies get a larger window in which to test new drug candidates on viable premade and cryopreserved tissue slices that function like fresh.
Gunsalus agrees that the Sato case highlights some of the problems with misconduct investigations, and says that if shortcomings emerge, further reviews may be needed. She suggests institutional panels should include external members and that officials should also use a standardized checklist to strengthen their processes. “There should be some way for journals, funders, patients and others to be assured of the credibility and thoroughness of university reviews,” says Gunsalus.
Detailed analysis of misconduct investigations into huge research fraud suggests institutional probes aren’t rigorous enough.
