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In 1968 Shirley Chisholm became the first Black woman elected to the United States Congress. In 1972 she launched a campaign for the Democratic presidential nomination, breaking another political barrier.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
The horizon effect, also known as the horizon problem, is a problem in artificial intelligence whereby, in many games, the number of possible states or positions is immense and computers can only feasibly search a small portion of them, typically a few plies down the game tree. Thus, for a computer searching only a fixed number of plies, there is a possibility that it will make a poor long-term move. The drawbacks of the move are not "visible" because the computer does not search to the depth at which its evaluation function reveals the true evaluation of the line. The analogy is to peering at a distance on a sphere like the earth, but a threat being beneath the horizon and hence unseen. When evaluating a large game tree using techniques such as minimax with alpha-beta pruning, search depth is limited for feasibility reasons. However, evaluating a partial tree may give a misleading result. When a significant change exists just over the horizon of the search depth, the computational device falls victim to the horizon effect. In 1973 Hans Berliner named this phenomenon, which he and other researchers had observed, the "Horizon Effect." He split the effect into two: the Negative Horizon Effect "results in creating diversions which ineffectively delay an unavoidable consequence or make an unachievable one appear achievable." For the "largely overlooked" Positive Horizon Effect, "the program grabs much too soon at a consequence that can be imposed on an opponent at leisure, frequently in a more effective form." The horizon effect can be somewhat mitigated by quiescence search. This technique extends the effort and time spent searching board states left in volatile positions and allocates less effort to easier-to-assess board states. For example, "scoring" the worth of a chess position often involves a material value count, but this count is misleading if there are hanging pieces or an imminent checkmate. A board state after the white queen has captured a protected black knight would appear to the naive material count to be advantageous to white as they are now up a knight, but is probably disastrous as the queen will be taken in the exchange one ply later. A quiescence search may tell a search algorithm to play out the captures and checks before scoring leaf nodes with volatile positions.
Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting. One of the questions that arises in a decision tree algorithm is the optimal size of the final tree. A tree that is too large risks overfitting the training data and poorly generalizing to new samples. A small tree might not capture important structural information about the sample space. However, it is hard to tell when a tree algorithm should stop because it is impossible to tell if the addition of a single extra node will dramatically decrease error. This problem is known as the horizon effect. A common strategy is to grow the tree until each node contains a small number of instances then use pruning to remove nodes that do not provide additional information. Pruning should reduce the size of a learning tree without reducing predictive accuracy as measured by a cross-validation set. There are many techniques for tree pruning that differ in the measurement that is used to optimize performance.
In physics, a Cauchy horizon is a light-like boundary of the domain of validity of a Cauchy problem (a particular boundary value problem of the theory of partial differential equations). One side of the horizon contains closed space-like geodesics and the other side contains closed time-like geodesics. The concept is named after Augustin-Louis Cauchy. The simplest example is the internal horizon of a Reissner–Nordström black hole. Under the averaged weak energy condition (AWEC), Cauchy horizons are inherently unstable and severely susceptible to time-dependent perturbations. The smallest perturbation to the horizon would cause a contraction of proper time and an increase of energy density that would grow exponentially for an observer approaching the horizon. Such an observer would see the entire future history of the universe pass by as they approached the horizon until they suddenly hit a wall of infinite energy: a curvature singularity at the Cauchy horizon. However, since the region of spacetime inside the Cauchy horizon has closed timelike curves, it is subject to periodic boundary conditions, an example of the Casimir effect. This violates the average weak energy condition. If the spacetime inside the Cauchy horizon violates AWEC, then the horizon becomes stable and frequency boosting effects causing the increase in energy density near the horizon would be canceled out by the tendency of the spacetime to act as a divergent lens. Were this conjecture shown to be empirically true, it would provide a counter-example to the strong cosmic censorship conjecture. In 2018, it was shown that the spacetime behind the Cauchy horizon of a charged, rotating black hole exists, but is not smooth, so the strong cosmic censorship conjecture is false.
Quiescence search is an algorithm typically used to extend search at unstable nodes in minimax game trees in game-playing computer programs. It is an extension of the evaluation function to defer evaluation until the position is stable enough to be evaluated statically, that is, without considering the history of the position or future moves from the position. It mitigates the effect of the horizon problem faced by AI engines for various games like chess and Go. Human players usually have enough intuition to decide whether to abandon a bad-looking move, or search a promising move to a great depth. A quiescence search attempts to emulate this behavior by instructing a computer to search "volatile" positions to a greater depth than "quiet" ones to make sure there are no hidden traps and to get a better estimate of its value. Any sensible criterion may be used to distinguish "quiet" positions from "volatile" positions. One common criterion is that moves exist in the position that can dramatically change the valuation of the position, such as captures in chess or Go. As the main motive of quiescence search is to get a stable value out of a static evaluation function, it may also make sense to detect wide fluctuations in values returned by a simple heuristic evaluator over several ply, i.e. a history criterion. The quiescence search continues as long as the position remains volatile according to the criterion. In order to get the quiescence search to terminate, plies are usually restricted to moves that deal directly with the threat, such as moves that capture and recapture (often called a 'capture search') in chess. In highly "unstable" games like Go and reversi, a rather large proportion of computer time may be spent on quiescence searching.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Madam C.J. Walker