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      "body": "<html> <p>Inverse problem solving isn't a new technique in classical physics, but this algorithm represents one of the first successful examples of an inverse problem solving method with quantum materials. And it could make searching for interesting physics a more streamlined and deliberate process for many scientists. More physicists are working in condensed matter than any other subfield of physics -- the rich diversity of condensed matter systems and phenomena provide ample unsolved problems to explore, from superconductivity and superfluidity to magnetism and topology. Experimentalists probe the macro-and microscopic properties of materials to observe the behavior and interactions of particles in materials under a strict set of controls. 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Now, if you know the desired type of physics you would like to study, you can represent that in a wave function, and the algorithm will generate all of the Hamiltonians -- or the specific models -- for which we would get that set of properties. To be more exact, the algorithm gives us Hamiltonians with that wave function as an energy eigenstate.'</p> <p><br></p> <p><em>Clark says the algorithm gives a new way to study physical phenomena such as superconductivity.</em></p> <p>'Typically, you would guess Hamiltonians that are likely to be superconducting and then try to solve them. What this algorithm -- in theory -- will allow us to do is to write down a wave function that we know superconducts and then automatically generate all of the Hamiltonians or the specific models that give that wave function as their solution. Once you have the Hamiltonians, in some sense, that gives you all the other properties of the system -- the excitation spectrum, all the finite temperature properties.</p> <p>That requires some more steps once you have the Hamiltonian, so we didn't improve that part of the research process. But what we did, we found a way to find interesting models, interesting Hamiltonians.'</p> <p>Chertkov adds, 'There are lots of wave functions people have written down for which there are no known Hamiltonians -- maybe 50 years worth. Now we can take any of these wave functions and ask if any Hamiltonians give those as eigenstates and you may end up with one model, no models, or many. For example, we are interested in spin-liquid wave functions, highly entangled quantum states with interesting topological properties.</p> <p><br></p> <p><em>Theorists have constructed many spin-liquid wave functions, but don't know which Hamiltonians give them.</em></p> <p><br></p> <p><code>In the future, our algorithm should let us find these Hamiltonians.'</code></p> <p><br></p> <p>Clark and Chertkov tested the algorithm on wave functions related to frustrated magnetism, a topic that presents interesting physics with many open questions. Frustrated magnetism occurs in a class of materials that is insulating, so the electrons don't move around, but their spins interact. Clark explains one such wave function they tested, 'The electron spins in a frustrated magnet want to be anti-aligned, like the north and south on a magnet, but can't because they live on triangles. So we make a wave function out of a linear-superposition of all of these frustrated states and we turn the crank of this algorithm, and ask, given this wavefunction, which is an interesting quantum state on a frustrated magnet, are there</p> <p><br></p> <p><code>Hamiltonians that would give it. And we found some.'</code></p> <p><br></p> <p>Chertkov says the results of the algorithm could point experimentalists in the right direction to find interesting new physics: 'That would hopefully be one way it would be used. You pick a wave function that has some kind of physics that you care about and you see what sort of interactions can give you that sort of physics, and hopefully then the models you find through this method can be looked for in experiments. And it turns out you find many models with our method.'</p> <p>Clark sums up, 'This has inverted the part of the process where we were sort of hunting in the dark. 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      "body": "<html>https://cdn.steemitimages.com/DQmSaajiBH5dAELMteSBjkpyv24Ye3w5c7JjhjKp3ND2Eri/image.png <p><strong>A new RAND Corporation paper finds that artificial intelligence has the potential to upend the foundations of nuclear deterrence by the year 2040.</strong></p> <p><br></p> <p>While AI-controlled doomsday machines are considered unlikely, the hazards of artificial instelligence for nuclear security lie instead in its potential to encourage humans to take potentially apocalyptic risks, according to the paper.</p> <p><br></p> <p>During the Cold War, the condition of mutual assured destruction maintained an uneasy peace between the superpowers by ensuring that any attack would be met by a devastating retaliation. Mutual assured destruction thereby encouraged strategic stability by reducing the incentives for either country to take actions that might escalate into a nuclear war.</p> <p><br></p> <p>The new RAND publication says that in coming decades, artificial intelligence has the potential to erode the condition of mutual assured destruction and undermine strategic stability. Improved sensor technologies could introduce the possibility that retaliatory forces such as submarine and mobile missiles could be targeted and destroyed.</p> <p>Nations may be tempted to pursue first-strike capabilities as a means of gaining bargaining leverage over their rivals even if they have no intention of carrying out an attack, researchers say. This undermines strategic stability because even if the state possessing these capabilities has no intention of using them, the adversary cannot be sure of that.</p> <p><br></p> <blockquote>The connection between nuclear war and artificial intelligence is not new, in fact the two have an intertwined history,' said Edward Geist, co-author on the paper and associate policy researcher at the RAND Corporation, a nonprofit, nonpartisan research organization. 'Much of the early development of AI was done in support of military efforts or with military objectives in mind.</blockquote> <p><br></p> <p>He said one example of such work was the Survivable Adaptive Planning Experiment in the 1980s that sought to use AI to translate reconnaissance data into nuclear targeting plans.</p> <p>Under fortuitous circumstances, artificial intelligence also could enhance strategic stability by improving accuracy in intelligence collection and analysis, according to the paper. While AI might increase the vulnerability of second-strike forces, improved analytics for monitoring and interpreting adversary actions could reduce miscalculation or misinterpretation that could lead to unintended escalation.</p> <p>Researchers say that given future improvements, it is possible that eventually AI systems will develop capabilities that, while fallible, would be less error-prone than their human alternatives and therefore be stabilizing in the long term.</p> <p><br></p> <p><em>Some experts fear that an increased reliance on artificial intelligence can lead to new types of catastrophic mistakes said Andrew Lohn co-author on the paper and associate engineer at RAND.&nbsp;</em></p> <blockquote>There may be pressure to use AI before it is technologically mature, or it may be susceptible to adversarial subversion. Therefore, maintaining strategic stability in coming decades may prove extremely difficult and all nuclear powers must participate in the cultivation of institutions to help limit nuclear risk.</blockquote> <p><br></p> <p>RAND researchers based their perspective on information collected during a series of workshops with experts in nuclear issues, government branches, AI research, AI policy and national security.</p> </html>",
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