Have a deep view into what people are curious about.

🔗 Psychology 🔗 Education 🔗 Homeschooling 🔗 Alternative education

Autodidacticism (also autodidactism) or self-education (also self-learning and self-teaching) is education without the guidance of masters (such as teachers and professors) or institutions (such as schools). Generally, an autodidact is an individual who chooses the subject they will study, their studying material, and the studying rhythm and time. An autodidact may or may not have formal education, and their study may be either a complement or an alternative to formal education. Many notable contributions have been made by autodidacts.

🔗 Insects

The emerald cockroach wasp or jewel wasp (Ampulex compressa) is a solitary wasp of the family Ampulicidae. It is known for its unusual reproductive behavior, which involves stinging a cockroach and using it as a host for its larvae. It thus belongs to the entomophagous parasites.

🔗 Christianity 🔗 Mythology

The gates of hell are various places on the surface of the world that have acquired a legendary reputation for being entrances to the underworld. Often they are found in regions of unusual geological activity, particularly volcanic areas, or sometimes at lakes, caves, or mountains.

🔗 Cryptography 🔗 Cryptography/Computer science

In cryptography, a random oracle is an oracle (a theoretical black box) that responds to every unique query with a (truly) random response chosen uniformly from its output domain. If a query is repeated, it responds the same way every time that query is submitted.

Stated differently, a random oracle is a mathematical function chosen uniformly at random, that is, a function mapping each possible query to a (fixed) random response from its output domain.

Random oracles as a mathematical abstraction were firstly used in rigorous cryptographic proofs in the 1993 publication by Mihir Bellare and Phillip Rogaway (1993). They are typically used when the proof cannot be carried out using weaker assumptions on the cryptographic hash function. A system that is proven secure when every hash function is replaced by a random oracle is described as being secure in the random oracle model, as opposed to secure in the standard model of cryptography.

🔗 United States/U.S. Government 🔗 United States 🔗 Mass surveillance 🔗 Technology 🔗 Private Equity 🔗 Organizations 🔗 Virginia 🔗 Cold War

In-Q-Tel (IQT), formerly Peleus and In-Q-It, is an American not-for-profit venture capital firm based in Arlington, Virginia. It invests in high-tech companies for the sole purpose of keeping the Central Intelligence Agency, and other intelligence agencies, equipped with the latest in information technology in support of United States intelligence capability. The name, "In-Q-Tel" is an intentional reference to Q, the fictional inventor who supplies technology to James Bond.

The firm is seen as a trend-setter in the information technology industry, with the average dollar invested by In-Q-Tel in 2012 attracting nine dollars of investment from other companies.

🔗 Etymology 🔗 Holidays 🔗 Scotland

Hogmanay ( HOG-mə-nay, -⁠NAY, Scots: [ˌhɔɡməˈneː]) is the Scots word for the last day of the old year and is synonymous with the celebration of the New Year in the Scottish manner. It is normally followed by further celebration on the morning of New Year's Day (1 January) or in some cases, 2 January—a Scottish bank holiday.

The origins of Hogmanay are unclear, but it may be derived from Norse and Gaelic observances of the winter solstice. Customs vary throughout Scotland, and usually include gift-giving and visiting the homes of friends and neighbours, with special attention given to the first-foot, the first guest of the new year.

The United States government's Strategic Computing Initiative funded research into advanced computer hardware and artificial intelligence from 1983 to 1993. The initiative was designed to support various projects that were required to develop machine intelligence in a prescribed ten-year time frame, from chip design and manufacture, computer architecture to artificial intelligence software. The Department of Defense spent a total of $1 billion on the project.

The inspiration for the program was Japan's fifth generation computer project, an enormous initiative that set aside billions for research into computing and artificial intelligence. As with Sputnik in 1957, the American government saw the Japanese project as a challenge to its technological dominance. The British government also funded a program of their own around the same time, known as Alvey, and a consortium of U.S. companies funded another similar project, the Microelectronics and Computer Technology Corporation.

The goal of SCI, and other contemporary projects, was nothing less than full machine intelligence. "The machine envisioned by SC", according to Alex Roland and Philip Shiman, "would run ten billion instructions per second to see, hear, speak, and think like a human. The degree of integration required would rival that achieved by the human brain, the most complex instrument known to man."

The initiative was conceived as an integrated program, similar to the Apollo moon program, where different subsystems would be created by various companies and academic projects and eventually brought together into a single integrated system. Roland and Shiman wrote that "While most research programs entail tactics or strategy, SC boasted grand strategy, a master plan for an entire campaign."

The project was funded by the Defense Advanced Research Projects Agency and directed by the Information Processing Technology Office (IPTO). By 1985 it had spent $100 million, and 92 projects were underway at 60 institutions: half in industry, half in universities and government labs. Robert Kahn, who directed IPTO in those years, provided the project with its early leadership and inspiration. Clint Kelly managed the SC Initiative for three years and developed many of the specific application programs for DARPA, such as the Autonomous Land Vehicle.

By the late 1980s, it was clear that the project would fall short of realizing the hoped-for levels of machine intelligence. Program insiders pointed to issues with integration, organization, and communication. When Jack Schwarz ascended to the leadership of IPTO in 1987, he cut funding to artificial intelligence research (the software component) "deeply and brutally", "eviscerating" the program (wrote Pamela McCorduck). Schwarz felt that DARPA should focus its funding only on those technologies which showed the most promise. In his words, DARPA should "surf", rather than "dog paddle", and he felt strongly AI was not "the next wave".

Although the program failed to meet its goal of high-level machine intelligence, it did meet some of its specific technical objectives, for example those of autonomous land navigation. The Autonomous Land Vehicle program and its sister Navlab project at Carnegie Mellon University, in particular, laid the scientific and technical foundation for many of the driverless vehicle programs that came after it, such as the Demo II and III programs (ALV being Demo I), Perceptor, and the DARPA Grand Challenge. The use of video cameras plus laser scanners and inertial navigation units pioneered by the SCI ALV program form the basis of almost all commercial driverless car developments today. It also helped to advance the state of the art of computer hardware to a considerable degree. On the software side, the initiative funded development of the Dynamic Analysis and Replanning Tool, a program that handled logistics using artificial intelligence techniques. This was a huge success, saving the Department of Defense billions during Desert Storm.

The project was superseded in the 1990s by the Accelerated Strategic Computing Initiative and then by the Advanced Simulation and Computing Program. These later programs did not include artificial general intelligence as a goal, but instead focused on supercomputing for large scale simulation, such as atomic bomb simulations. The Strategic Computing Initiative of the 1980s is distinct from the 2015 National Strategic Computing Initiative—the two are unrelated.

🔗 Biography 🔗 Biography/science and academia

David J. DeWitt is a computer scientist specializing in database management system research at the Massachusetts Institute of Technology. Prior to moving to MIT, DeWitt was the John P. Morgridge Professor (Emeritus) of Computer Sciences at the University of Wisconsin–Madison. He was also a Technical Fellow at Microsoft, leading the Microsoft Jim Gray Systems Lab at Madison, Wisconsin. Professor DeWitt received a B.A. degree from Colgate University in 1970, and a Ph.D. from the University of Michigan in 1976. He then joined the University of Wisconsin-Madison and started the Wisconsin Database Group, which he led for more than 30 years.

Professor DeWitt is known for his research in the areas of parallel databases, benchmarking, object-oriented databases, and XML databases. He is an elected member of the National Academy of Engineering (1998), and a Fellow of the Association for Computing Machinery.

He received the ACM SIGMOD Innovations Award (now renamed SIGMOD Edgar F. Codd Innovations Award) in 1995 for his contributions to the database systems field. In 2009, ACM recognized the seminal contributions of his Gamma parallel database system project with the ACM Software System Award. Also in 2009, he received the IEEE Emanuel R. Piore Award for his contributions to the database systems field.

🔗 Pharmacology 🔗 Animals 🔗 Veterinary medicine

Zoopharmacognosy is a behaviour in which non-human animals apparently self-medicate by selecting and ingesting or topically applying plants, soils, insects, and psychoactive drugs to prevent or reduce the harmful effects of pathogens and toxins. The term derives from Greek roots zoo ("animal"), pharmacon ("drug, medicine"), and gnosy ("knowing").

An example of zoopharmacognosy occurs when dogs eat grass to induce vomiting. However, the behaviour is more diverse than this. Animals ingest or apply non-foods such as clay, charcoal and even toxic plants and invertebrates, apparently to prevent parasitic infestation or poisoning.

Whether animals truly self-medicate remains a somewhat controversial subject because early evidence is mostly circumstantial or anecdotal, however, more recent examinations have adopted an experimental, hypothesis-driven approach.

The methods by which animals self-medicate vary, but can be classified according to function as prophylactic (preventative, before infection or poisoning) or therapeutic (after infection, to combat the pathogen or poisoning). The behaviour is believed to have widespread adaptive significance.