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Movements, leaders, victories and the continuing fight for equality.
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Meet the people whose lives, choices and achievements shaped the journey.
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Moments that changed communities, movements, institutions and the nation.
Katherine Johnson’s mathematical calculations helped guide some of America’s most important early space missions while she confronted the racial and gender barriers faced by Black women in twentieth-century America.
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 concept of biological computation proposes that living organisms perform computations, and that as such, abstract ideas of information and computation may be key to understanding biology.[1][2] As a field, biological computation can include the study of computations performed by life,[3][4][5][6][7] the design of algorithms inspired by biology,[8] the design and engineering of computational devices using synthetic biology,[9][10] and computer methods for biological data[11].
According to Dominique Chu, Mikhail Prokopenko, and J. Christian J. Ray, "the most important class of natural computers can be found in biological systems that perform computation on multiple levels. From molecular and cellular information processing networks to ecologies, economies and brains, life computes. Despite ubiquitous agreement on this fact going back as far as von Neumann automata and McCulloch–Pitts neural nets, we so far lack principles to understand rigorously how computation is done in living, or active, matter".[12]
Logical circuits can be built with slime moulds.[13] Distributed systems experiments have used them to approximate motorway graphs.[14] The slime mould Physarum polycephalum is able to compute high-quality approximate solutions to the Traveling Salesman Problem, a combinatorial test with exponentially increasing complexity, in linear time.[15] Fungi such as basidiomycetes can also be used to build logical circuits. In a proposed fungal computer, information is represented by spikes of electrical activity, computation is implemented in a mycelium network, and an interface is realized via fruit bodies.[16]
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The concept of biological computation proposes that living organisms perform computations, and that as such, abstract ideas of information and computation may be key to understanding biology. As a field, biological computation can include the study of computations performed by life, the design of algorithms inspired by biology, the design and engineering of computational devices using synthetic biology, and computer methods for biological data. According to Dominique Chu, Mikhail Prokopenko, and J. Christian J. Ray, "the most important class of natural computers can be found in biological systems that perform computation on multiple levels. From molecular and cellular information processing networks to ecologies, economies and brains, life computes. Despite ubiquitous agreement on this fact going back as far as von Neumann automata and McCulloch–Pitts neural nets, we so far lack principles to understand rigorously how computation is done in living, or active, matter". Logical circuits can be built with slime moulds. Distributed systems experiments have used them to approximate motorway graphs. The slime mould Physarum polycephalum is able to compute high-quality approximate solutions to the Traveling Salesman Problem, a combinatorial test with exponentially increasing complexity, in linear time. Fungi such as basidiomycetes can also be used to build logical circuits. In a proposed fungal computer, information is represented by spikes of electrical activity, computation is implemented in a mycelium network, and an interface is realized via fruit bodies.
Models of neural computation are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof. This article aims to provide an overview of the most definitive models of neuro-biological computation as well as the tools commonly used to construct and analyze them.
The Solvay Conferences (French: Congrès Solvay) have been devoted to preeminent unsolved problems in both physics and chemistry. They began with the historic invitation-only 1911 Solvay Conference on Physics, considered a turning point in the world of physics, and are ongoing. Since the success of 1911, they have been organised by the International Solvay Institutes for Physics and Chemistry, founded by the Belgian industrialist Ernest Solvay in 1912 and 1913, and located in Brussels. The institutes coordinate conferences, workshops, seminars, and colloquia. Recent Solvay Conferences entail a three year cycle: the Solvay Conference on Physics followed by a gap year, followed by the Solvay Conference on Chemistry. The 1st Solvay Conference on Biology titled "The organisation and dynamics of biological computation" took place in April 2024.
Amorphous computing refers to computational systems that use very large numbers of identical, parallel processors each having limited computational ability and local interactions. The term amorphous computing was coined at MIT in 1996 in a paper entitled "Amorphous Computing Manifesto" by Abelson, Knight, Sussman, et al. Examples of naturally occurring amorphous computations can be found in many fields, such as developmental biology (the development of multicellular organisms from a single cell), molecular biology (the organization of sub-cellular compartments and intra-cell signaling), neural networks, and chemical engineering (non-equilibrium systems). The study of amorphous computation is hardware agnostic—it is not concerned with the physical substrate (biological, electronic, nanotech, etc.) but rather with the characterization of amorphous algorithms as abstractions with the goal of both understanding existing natural examples and engineering novel systems. Ultimately, this field extenuates to Computational Intelligence, as this computational technique is an extenuation of Artificial Intelligence (but more specifically Artificial General Intelligence) for developing Biological Computation. Amorphous computers tend to have many of the following properties: Implemented by redundant, potentially faulty, massively parallel devices. Devices having limited memory and computational abilities. Devices being asynchronous. Devices having no a priori knowledge of their location. Devices communicating only locally. Exhibit emergent or self-organizational behavior (patterns or states larger than an individual device). Fault-tolerant, especially to the occasional malformed device or state perturbation.
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 →Shirley Chisholm, elected in 1968.