user: What is the difference between stress and strain?

assistant: Stress is calculated as the pressure applied per unit area. So stress is essentially the force applied to an object divided by the cross sectional area of where the force is being applied to.

Strain is calculated as the change in  parameter value (length, height or angle) as it experiences stress. It is unitless. So strain is essentially the deformation that occurs due to the stress that is applied.

Young's Modulus is a mechanical property that is computed by dividing stress over strain. It is typically expressed in GPa (giga Pascals)

user: What are the best woods for smoking?

assistant: Oak is the quintessential go-to for smoking meat. It is a great place to start if you are a newbie to smoking.  It will lend a medium to a strong flavor that is seldom overpowering.
	- Best Meat to Smoke: Lamb, beef, brisket, and sausages
	  
Hickory is the most versatile choice as it can be used to smoke wood in many ways. Be careful, though, because too much hickory flavor will cause your meat to have a more bitter flavor. It has a sweet, savory, and hearty, a bit bacony.
	- Best Meat to Smoke: Larger cuts of ribs and pork shoulders, as well as almost all red meat and poultry.
	  
Maple is one of the most subtle smoking wood, it will impart a more subtle smoke flavor.  Use maple for a sweet, light, mild smokiness.
	- Best Meat to Smoke: Poultry, pork, game foul
	  
Mesquite is a hardy wood packing intense flavor. It is recommended for grilling, or to be used in smaller quantities. It is famous for its strong, intense, and unique.
	- Best Meat to Smoke: Red Meat and for adding additional flavor when grilling

Pecan will lend a rich, sweet, nutty flavor. In fact, the wood is so sweet that you may want to use a combination of it with another harder wood to balance out the flavor.
	- Best Meat to Smoke: Briskets, roasts, and ribs
	  
Applewood is mild and sweet, lending a mellow flavor. Apple smoke flavors take a while to permeate the meat, so anticipate several hours of smoking.
	- Best Meat to Smoke: Chicken, wild foul, pork
	  
Alder has a very light and sweet flavor profile. It lends a delicate and sweet characteristic.
	- Best Meat to Smoke: Fish, like salmon and other Pacific Northwestern types.
	  
Cherry is mild and fruity, and when mixed with other hardwood like hickory, the two flavors compliment each other for an amazing result.
	- Best Meat to Smoke: Chicken, turkey, ham

user: Please give a brief history of AI.

assistant: <think>
The Dartmouth Workshop of 1956 was organized by Marvin Minsky, John McCarthy and two senior scientists: Claude Shannon and Nathan Rochester of IBM. The proposal for the conference included this assertion: "every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it".The participants included Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Allen Newell and Herbert A. Simon, all of whom would create important programs during the first decades of AI research. At the workshop Newell and Simon debuted the "Logic Theorist" and McCarthy persuaded the attendees to accept "Artificial Intelligence" as the name of the field. The 1956 Dartmouth workshop was the moment that AI gained its name, its mission, its first success and its major players, and is widely considered the birth of AI. The term "Artificial Intelligence" was chosen by McCarthy to avoid associations with cybernetics and connections with the influential cyberneticist Norbert Wiener.

The programs developed in the years after the Dartmouth Workshop were, to most people, simply "astonishing" computers were solving algebra word problems, proving theorems in geometry and learning to speak English. Few at the time would have believed that such "intelligent" behavior by machines was possible at all. Researchers expressed an intense optimism in private and in print, predicting that a fully intelligent machine would be built in less than 20 years. Government agencies like DARPA poured money into the new field.

In the 1970s, AI was subject to critiques and financial setbacks. AI researchers had failed to appreciate the difficulty of the problems they faced. Their tremendous optimism had raised expectations impossibly high, and when the promised results failed to materialize, funding for AI disappeared. At the same time, the field of connectionism (or neural nets) was shut down almost completely for 10 years by Marvin Minsky's devastating criticism of perceptrons. Despite the difficulties with public perception of AI in the late 70s, new ideas were explored in logic programming, commonsense reasoning and many other areas.

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 business community's fascination with AI rose and fell in the 1980s in the classic pattern of an economic bubble. The collapse was due to the failure of commercial vendors to develop a wide variety of workable solutions. As dozens of companies failed, the perception was that the technology was not viable. However, the field continued to make advances despite the criticism. Numerous researchers, including robotics developers Rodney Brooks and Hans Moravec, argued for an entirely new approach to artificial intelligence.

The field of AI, now more than a half a century old, finally achieved some of its oldest goals. It began to be used successfully throughout the technology industry, although somewhat behind the scenes. Some of the success was due to increasing computer power and some was achieved by focusing on specific isolated problems and pursuing them with the highest standards of scientific accountability. Still, the reputation of AI, in the business world at least, was less than pristine. Inside the field there was little agreement on the reasons for AI's failure to fulfill the dream of human level intelligence that had captured the imagination of the world in the 1960s. Together, all these factors helped to fragment AI into competing subfields focused on particular problems or approaches, sometimes even under new names that disguised the tarnished pedigree of "artificial intelligence". AI was both more cautious and more successful than it had ever been.

In the first decades of the 21st century, access to large amounts of data (known as "big data"), cheaper and faster computers and advanced machine learning techniques were successfully applied to many problems throughout the economy. In fact, McKinsey Global Institute estimated in their famous paper "Big data: The next frontier for innovation, competition, and productivity" that "by 2009, nearly all sectors in the US economy had at least an average of 200 terabytes of stored data".

By 2016, the market for AI-related products, hardware, and software reached more than 8 billion dollars, and the New York Times reported that interest in AI had reached a "frenzy". The applications of big data began to reach into other fields as well, such as training models in ecology and for various applications in economics. Advances in deep learning (particularly deep convolutional neural networks and recurrent neural networks) drove progress and research in image and video processing, text analysis, and even speech recognition.</think>
The 1956 Dartmouth workshop was the moment that AI gained its name, its mission, its first success and its major players, and is widely considered the birth of AI. In the 1970s, AI was subject to critiques and financial setbacks. AI researchers had failed to appreciate the difficulty of the problems they faced. 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 the first decades of the 21st century, access to large amounts of data (known as "big data"), cheaper and faster computers and advanced machine learning techniques were successfully applied to many problems throughout the economy. By 2016, advances in deep learning (particularly deep convolutional neural networks and recurrent neural networks) drove progress and research in image and video processing, text analysis, and even speech recognition.