user: How does grain orientation effect the dimensional movement of wood?

assistant: Wood expands and shrinks depending on its moisture content.  The relative amount of change, expansion, or shrinkage differs for each of the three main grain directions.  Those directions are longitudinal, radial, and tangential.  Longitudinal is the direction a tree or its branches grows length-wise.  In most lumber, this is its length.  The change in this direction is very small.  That is why a wooden ruler can be used without changing its measurements when ambient moisture changes.  In almost all uses, longitudinal expansion and contraction are small enough not to be of concern.  Radial and tangential directional expansion and contraction are significant and different enough that they affect the dimensional stability of a piece of wood.  The relative amount of change in each direction is specific to the wood species.  Regardless of wood species, the change is greater in the tangential direction than in the radial direction.  The tangential direction is tangential to the growth rings in the wood, and the radial direction is perpendicular to the growth rings.  The T/R or ratio of tangential to radial change correlates to the dimensional stability of a wood species.  If you visualize a piece of lumber that is milled square and comes from a part of a log such that the rings are almost straight across in one direction, that is referred to as vertical grain or quarter sawn.  As the moisture content of the wood changes to come into equilibrium with its environment, it will move toward a diamond shape.  The closer the T/R ratio is to 1, the less the square dimension will distort. Dimensional stability and the direction of wood movement is important to plan for when cutting joinery in wood.  If two joined pieces are aligned so that they will not move in the same direction relative to the joint, the joint will be unstable when the ambient humidity changes and can cause the joint to fail.

user: What is an expert system, and when did they first emerge?

assistant: <think>
Boom 1980–1987
In the 1980s a form of AI program called "expert systems" was adopted by corporations around the world and knowledge became the focus of mainstream AI research. In those same years, the Japanese government aggressively funded AI with its fifth generation computer project. Another encouraging event in the early 1980s was the revival of connectionism in the work of John Hopfield and David Rumelhart. Once again, AI had achieved success.

The rise of expert systems
An expert system is a program that answers questions or solves problems about a specific domain of knowledge, using logical rules that are derived from the knowledge of experts. The earliest examples were developed by Edward Feigenbaum and his students. Dendral, begun in 1965, identified compounds from spectrometer readings. MYCIN, developed in 1972, diagnosed infectious blood diseases. They demonstrated the feasibility of the approach.

Expert systems restricted themselves to a small domain of specific knowledge (thus avoiding the commonsense knowledge problem) and their simple design made it relatively easy for programs to be built and then modified once they were in place. All in all, the programs proved to be useful: something that AI had not been able to achieve up to this point.

In 1980, an expert system called XCON was completed at CMU for the Digital Equipment Corporation. It was an enormous success: it was saving the company 40 million dollars annually by 1986. Corporations around the world began to develop and deploy expert systems and by 1985 they were spending over a billion dollars on AI, most of it to in-house AI departments. An industry grew up to support them, including hardware companies like Symbolics and Lisp Machines and software companies such as IntelliCorp and Aion.</think>
An expert system is a program that uses logical rules derived from expert knowledge to answer questions or solve problems in a specific domain of knowledge. The first expert system, called Dendral, was developed in 1965 by Edward Feigenbaum and his students and identified compounds from spectrometer readings.

user: Who created Doge Coin and when?

assistant: <think>
Dogecoin (/ˈdoʊ(d)ʒkɔɪn/ DOHJ-koyn or DOHZH-koyn, Abbreviation: DOGE; sign: Ð) is a cryptocurrency created by software engineers Billy Markus and Jackson Palmer, who decided to create a payment system as a "joke", making fun of the wild speculation in cryptocurrencies at the time. It is considered both the first "meme coin", and, more specifically, the first "dog coin". Despite its satirical nature, some consider it a legitimate investment prospect. Dogecoin features the face of the Shiba Inu dog from the "doge" meme as its logo and namesake. It was introduced on December 6, 2013, and quickly developed its own online community, reaching a market capitalization of over $85 billion on May 5, 2021. As of 2021, it is the sleeve sponsor of Watford Football Club.</think>
Doge coin was created by Billy Markus and Jackson Palmer and it was introduced in December 6, 2013.