Genes that helped people survive during the time of the Black Death are more likely to be found in people with autoimmune diseases alive today. Does this tell scientists anything about what surviving the COVID-19 pandemic might mean for the world’s population?
Both animals and people use high-dimensional inputs (like eyesight) to accomplish various shifting survival-related objectives. A crucial aspect of this is learning via mistakes. A brute-force approach to trial and error by performing every action for every potential goal is intractable even in the smallest contexts. Memory-based methods for compositional thinking are motivated by the difficulty of this search. These processes include, for instance, the ability to: recall pertinent portions of prior experience; (ii) reassemble them into new counterfactual plans, and (iii) carry out such plans as part of a focused search strategy. Compared to equally sampling every action, such techniques for recycling prior successful behavior can considerably speed up trial-and-error. This is because the intrinsic compositional structure of real-world objectives and the similarity of the physical laws that control real-world settings allow the same behavior (i.e., sequence of actions) to remain valid for many purposes and situations. What guiding principles enable memory processes to retain and reassemble experience fragments? This debate is strongly connected to the idea of dynamic programming (DP), which using the principle of optimality significantly lowers the computing cost of trial-and-error. This idea may be expressed informally as considering new, complicated issues as a recomposition of previously solved, smaller subproblems.
This viewpoint has recently been used to create hierarchical reinforcement learning (RL) algorithms for goal-achieving tasks. These techniques develop edges between states in a planning graph using a distance regression model, compute the shortest pathways across it using DP-based graph search, and then use a learning-based local policy to follow the shortest paths. Their essay advances this field of study. The following is a summary of their contributions: They provide a strategy for long-term planning that acts directly on high-dimensional sensory data that an agent may see on its own (e.g., images from an onboard camera). Their solution blends traditional sampling-based planning algorithms with learning-based perceptual representations to recover and reassemble previously recorded state transitions in a replay buffer.
The two-step method makes this possible. To determine how many timesteps it takes for an optimum policy to move from one state to the next, they first learn a latent space where the distance between two states is the measure. They know contrastive representations using goal-conditioned Q-values acquired through offline hindsight relabeling. To establish neighborhood criteria across states, the second threshold this developed latent distance metric. They go on to design sampling-based planning algorithms that scan the replay buffer for trajectory segments—previously recorded successions of transitions—whose ends are adjacent states.
Fortunately, neuroscience can help — both to reassure you that you’re normal, and to provide support for the idea that there are specific habits and practices people can learn in order to improve memory when they need it most. Here are 8 of the most interesting I’ve found over the last couple of years:
Let’s start with this one, because it’s oh-so-easy. Michigan State University researchers studied whether Nile grass rats exhibited better memory when they were kept in an environment where the lighting resembled a corporate office (think dim fluorescent lighting), or where the lighting resembled a sunny day outside.
Sure enough, the study found that rats in dim lighting “lost about 30 percent of capacity in the hippocampus, a critical brain region for learning and memory, and performed poorly on a spatial task they had trained on previously.”
Title: Discovery and properties of the earliest galaxies with confirmed distances
Authors: * B. E. Robertson, *S. Tacchella, B. D. Johnson, K. Hainline, L. Whitler, D. J. Eisenstein, R. Endsley, M. Rieke, D. P. Stark, S. Alberts, A. Dressler, E. Egami, R. Hausen, G. Rieke, I. Shivaei, C. C. Williams, C. N. A. Willmer, S. Arribas, N. Bonaventura, A. Bunker, A. J. Cameron, S. Carniani, S. Charlot, J. Chevallard, M. Curti, E. Curtis-Lake, F. D’Eugenio, P. Jakobsen, T. J. Looser, N. Lützgendorf, R. Maiolino, M. V. Maseda, T. Rawle, H.-W. Rix, R. Smit, H. Übler, C. Willott, J. Witstok, S. Baum, R. Bhatawdekar, K. Boyett, Z. Chen, A. de Graaff, M. Florian, J. M. Helton, R. E. Hviding, Z. Ji, N. Kumari, J. Lyu, E. Nelson, L. Sandles, A. Saxena, K. A. Suess, F. Sun, M. Topping, I. E. B. Wallace (* equal contribution)
First Author’s Institution: University of California, Santa Cruz.
A concept known as “wave-particle duality” famously applies to light. But it also applies to all matter — including you.
Switch-Science has just announced a trio of quantum computing products that the company claims are the world’s first portable quantum computers. Sourced from SpinQ Technology, a Chinese quantum computing company based in Shenzen, the new quantum computing products have been designed for educational purposes. The aim is to democratize access to physical quantum computing solutions that can be deployed (and redeployed) at will. But considering the actual quantum machinery on offer, none of these (which we’re internally calling “quantops”) are likely to be a part of the future of quantum.
The new products being developed with education in mind shows in their qubit counts, which top out at three (compare that to Google’s Sycamore or IBM’s 433-qubit Osprey Quantum Processing Unit [QPU], both based on superconducting qubits). That’s not enough a number for any viable, problem-solving quantum computing to take place within these machines, but it’s enough that users can program and run quantum circuits — either the integrated, educational ones, or a single custom algorithm.
Every proton contains three quarks: two up and one down. But charm quarks, heavier than the proton itself, have been found inside. How?
Computers that can use quantum mechanics’ “spooky” properties to solve problems quicker than existing technology may seem appealing, but they must first overcome a major obstacle. Scientists from Japan may have discovered the solution by demonstrating how a superconducting material, niobium nitride, can be added as a flat, crystalline layer to a nitride-semiconductor substrate. This technique could make it simple to manufacture quantum qubits that can be used with conventional computer devices.
Conventional silicon microprocessor manufacturing techniques have grown over decades and are continually being refined and enhanced. On the other hand, the majority of quantum computing.
Performing computation using quantum-mechanical phenomena such as superposition and entanglement.
