42 min
Tech Talk: Language, ASL & Machine Learning Paradoxifi
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- Science
In this podcast episode, Tech Talk: Language, ASL, & Machine Learning, we interview software engineer, KJ Price, whose many work projects include the creation of a website to help people find datasets and a Machine Learning model project in conjunction with ASL Machine Learning is a sub-field of Data Science, which is a popular study field that has expanded tremendously in the last decade. Machine Learning is a form of AI or artificial intelligence
Language, as it is used by humans to communicate, involves complex and complicated sets of rules for sounds, sentences, and meaning. The use of language allows humans to collect information into broad bodies of knowledge about life and the world. Future generations draw on that collected knowledge expressed in language and do not have to start from scratch every time.
Language seems to be a uniquely human ability.
But as of yet, there are no unique finding that scientists agree upon that explain the exclusive connection of language to humans. Although chimps and apes have some capacity for language, it is primarily expressed in the use of signs. Their ability to respond to gestures and pointing varies with their environment and exposure to human interaction. One especially bright gorilla named Koko was able to understand a thousand or more signs and two thousand or so words of spoken English. (insert link)
There are many debates about how language skills are acquired by humans.
One of the more prominent language theories came from linguist Noam Chomsky in 1957. He suggested that human beings may be born with an innate understanding of how language works. What language we learn, whether it is French, Arabic, Chinese or sign language is determined by the circumstances in our lives. Chomsky believed, as did/do many linguists, that humans have some kind of genetic wiring that predisposes them to understanding communication structure.
Machines can be taught language type skills using machine learning methods.
Machine Learning works by using datasets and teaching a computer application to recognize patterns in the data so that the machine can perform tasks automatically, where these tasks used to be done manually. An simple example of this type of task would be to “teach” a machine to be able to distinguish between a cat or a dog in an image. After a few hundred examples of images that contain cats and other images that contain dogs, a Machine Learning model could be “taught” to recognize the difference. Then, none would need to manually look through each image and label them as “cat” or “dog” as a computer could label hundreds of these images in just a couple seconds.
Siri uses Machine Learning to handle requests from Apple users. Siri’s process is related to a data science field called “Natural Language Processing” Natural Language Processing, as used by Siri, is an exciting field and a bit of a holy grail to be able to have computers and humans communicate using human language.
Another example of this type of Machine Learning is to teach a machine to tell if a body of text represents a positive attitude or a negative attitude. For example, you could analyze published articles and see if they are in favor or against a political party. This is called “Sentiment Analysis,” where the machine is taught to decide the general sentiment of a body of text.
There are many other ways that Machine Learning can be used in natural language. For example, IBM’s Watson can understand the English language with very high proficiency. Another example is Google Translate. These all use Machine Learning for natural language understanding and natural language generation.
Interestingly,
In this podcast episode, Tech Talk: Language, ASL, & Machine Learning, we interview software engineer, KJ Price, whose many work projects include the creation of a website to help people find datasets and a Machine Learning model project in conjunction with ASL Machine Learning is a sub-field of Data Science, which is a popular study field that has expanded tremendously in the last decade. Machine Learning is a form of AI or artificial intelligence
Language, as it is used by humans to communicate, involves complex and complicated sets of rules for sounds, sentences, and meaning. The use of language allows humans to collect information into broad bodies of knowledge about life and the world. Future generations draw on that collected knowledge expressed in language and do not have to start from scratch every time.
Language seems to be a uniquely human ability.
But as of yet, there are no unique finding that scientists agree upon that explain the exclusive connection of language to humans. Although chimps and apes have some capacity for language, it is primarily expressed in the use of signs. Their ability to respond to gestures and pointing varies with their environment and exposure to human interaction. One especially bright gorilla named Koko was able to understand a thousand or more signs and two thousand or so words of spoken English. (insert link)
There are many debates about how language skills are acquired by humans.
One of the more prominent language theories came from linguist Noam Chomsky in 1957. He suggested that human beings may be born with an innate understanding of how language works. What language we learn, whether it is French, Arabic, Chinese or sign language is determined by the circumstances in our lives. Chomsky believed, as did/do many linguists, that humans have some kind of genetic wiring that predisposes them to understanding communication structure.
Machines can be taught language type skills using machine learning methods.
Machine Learning works by using datasets and teaching a computer application to recognize patterns in the data so that the machine can perform tasks automatically, where these tasks used to be done manually. An simple example of this type of task would be to “teach” a machine to be able to distinguish between a cat or a dog in an image. After a few hundred examples of images that contain cats and other images that contain dogs, a Machine Learning model could be “taught” to recognize the difference. Then, none would need to manually look through each image and label them as “cat” or “dog” as a computer could label hundreds of these images in just a couple seconds.
Siri uses Machine Learning to handle requests from Apple users. Siri’s process is related to a data science field called “Natural Language Processing” Natural Language Processing, as used by Siri, is an exciting field and a bit of a holy grail to be able to have computers and humans communicate using human language.
Another example of this type of Machine Learning is to teach a machine to tell if a body of text represents a positive attitude or a negative attitude. For example, you could analyze published articles and see if they are in favor or against a political party. This is called “Sentiment Analysis,” where the machine is taught to decide the general sentiment of a body of text.
There are many other ways that Machine Learning can be used in natural language. For example, IBM’s Watson can understand the English language with very high proficiency. Another example is Google Translate. These all use Machine Learning for natural language understanding and natural language generation.
Interestingly,
42 min