user: List four uses of white vinegar outside of cooking recipes

assistant: 1) Floor cleaning- will leave floors shining
2) Dye & brighten Easter eggs
3) Clean a Keurig coffee machine
4) Clean shower walls and doors

user: Please give me a summary of the text on the origins of skateboarding.

assistant: <think>
The first skateboards started with wooden boxes, or boards, with roller skate wheels attached to the bottom. Crate scooters preceded skateboards, having a wooden crate attached to the nose (front of the board), which formed rudimentary handlebars. The boxes turned into planks, similar to the skateboard decks of today.

Skateboarding, as it exists today, was probably born sometime in the late 1940s, or early 1950s, when surfers in California wanted something to do when the waves were flat. This was called "sidewalk surfing" – a new wave of surfing on the sidewalk as the sport of surfing became highly popular. No one knows who made the first board; it seems that several people came up with similar ideas at around the same time. The first manufactured skateboards were ordered by a Los Angeles, California surf shop, meant to be used by surfers in their downtime. The shop owner, Bill Richard, made a deal with the Chicago Roller Skate Company to produce sets of skate wheels, which they attached to square wooden boards. Accordingly, skateboarding was originally denoted "sidewalk surfing" and early skaters emulated surfing style and maneuvers, and performed barefoot.

By the 1960s a small number of surfing manufacturers in Southern California such as Jack's, Kips', Hobie, Bing's and Makaha started building skateboards that resembled small surfboards, and assembled teams to promote their products. One of the earliest Skateboard exhibitions was sponsored by Makaha's founder, Larry Stevenson, in 1963 and held at the Pier Avenue Junior High School in Hermosa Beach, California. Some of these same teams of skateboarders were also featured on a television show called Surf's Up in 1964, hosted by Stan Richards, that helped promote skateboarding as something new and fun to do.

As the popularity of skateboarding began expanding, the first skateboarding magazine, The Quarterly Skateboarder was published in 1964. John Severson, who published the magazine, wrote in his first editorial:

Today's skateboarders are founders in this sport—they're pioneers—they are the first. There is no history in Skateboarding—its being made now—by you. The sport is being molded and we believe that doing the right thing now will lead to a bright future for the sport. Already, there are storm clouds on the horizon with opponents of the sport talking about ban and restriction.

The magazine only lasted four issues, but resumed publication as Skateboarder in 1975. The first broadcast of an actual skateboarding competition was the 1965 National Skateboarding Championships, which were held in Anaheim, California and aired on ABC's Wide World of Sports. Because skateboarding was a new sport during this time, there were only two original disciplines during competitions: flatland freestyle and slalom downhill racing.

Animated cartoons of the time occasionally featured skateboard gags. Two Road Runner cartoons made in 1965, Shot and Bothered and Out and Out Rout, feature Wile E. Coyote riding a skateboard.

One of the earliest sponsored skateboarders, Patti McGee, was paid by Hobie and Vita Pak to travel around the country to do skateboarding exhibitions and to demonstrate skateboarding safety tips. McGee made the cover of Life magazine in 1965 and was featured on several popular television programs—The Mike Douglas Show, What's My Line? and The Tonight Show Starring Johnny Carson—which helped make skateboarding even more popular at the time. Some other well known surfer-style skateboarders of the time were Danny Bearer, Torger Johnson, Bruce Logan, Bill and Mark Richards, Woody Woodward, and Jim Fitzpatrick.

The growth of the sport during this period can also be seen in sales figures for Makaha, which quoted $4 million worth of board sales between 1963 and 1965. By 1966 a variety of sources began to claim that skateboarding was dangerous, resulting in shops being reluctant to sell them, and parents being reluctant to buy them. In 1966 sales had dropped significantly and Skateboarder Magazine had stopped publication. The popularity of skateboarding dropped and remained low until the early 1970s.</think>
Sidewalk surfing, known today as skateboarding, originated in the late 1940s out of the desire of California surfers hoping to still ride when waves were flat. By the 1960s, a small number of surfing manufacturers began selling skateboards hoping to bring more attention to the sport. Skateboarding began to rapidly increase in popularity in the 1970s due to improvements to the design elements and materials used on the boards.

user: What is machine learning, and when did it first emerge?

assistant: <think>
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that "learn" – that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.

Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.

Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain.

In its application across business problems, machine learning is also referred to as predictive analytics.

Overview
Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist".

Machine learning programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step.

The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used.

History and relationships to other fields
See also: Timeline of machine learning
The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used in this time period.

By the early 1960s an experimental "learning machine" with punched tape memory, called CyberTron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize patterns and equipped with a "goof" button to cause it to re-evaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".

Modern-day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform</think>
Machine learning  is a branch of AI concerned with understanding and developing methods that learn,"  that is, methods that use data to improve performance on a set of tasks.  Machine learning algorithms construct a model from sample data, referred to as training data, in order to make predictions or decisions without being explicitly programmed to do so. It first emerged in the 1960s.