AI HISTORY

1950

TURING TEST

1966

1956

ELIZA

BIRTH OF AI

AIHISTORY

SIRI

DEEP BLUE

WATSON

AI FOR GOOD

ALEXA

1995

1997

2011

2002

A.L.I.C.E.

ROOMBA

GOOGLE DUPLEX

STANLEY

2005

2011

2014

2019

2018

GOOGLE BRAIN

2012

1980

EXPERT SYSTEMS

#AIBASICSFORSCHOOLS 2021

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AI WNTER1987-1993

AI WNTER1974-1980

by @abfromz

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THE TURING TEST The Turing Test, originally called the Imitation Game by Alan Turing, is a test to determine whether or not a computer can show intelligent behaviour like a human. In this test, an interrogator needs to determine which is a computer and which is a human. Turing Test Diagram by J. A.S. Margallo

BIRTH OF AI The term Artificial Intelligence was first coined by John McCarthy and his colleagues in a proposal for the Dartmouth Workshop. Professor John McCarthy, JMC Stanford

ELIZA ELIZA is one of the first natural language processing computer programs that simulated a human therapist in a conversation. It was written by Joseph Weizenbaum of MIT's AI Laboratory. Eliza was incapable of learning new words and responses through interaction with humans, but required a set of instructions (scripts) to be able to respond to the received inputs. One of the most famous scripts was DOCTOR, which you can try here.

A.L.I.C.E. A.L.I.C.E. (Artificial Linguistic Computer Entity is a natural language processing chatbot that engages in a conversation with a human by responding to the input as naturally as possible. Chat with a version of Alice here.

DEEP BLUE Deep Blue is a chess playing computer that beat the world champion Gary Kasparov in a tournament. Deep Blue was programmed to solve the complex, strategic game of chess and it enabled researchers and developers to explore and design computers to solve problems in other fields using deep knowledge to analyze possible solutions. Deep BlueImage by IBM

ROOMBA Roomba was the first successful domestic robot. Although its use of AI was very basic, this vacuum cleaner was able to change direction when it encountered an obstacle and had enough intelligence to efficiently clean a home. First generation RoombaImage by L.D. Moore

EXPERT SYSTEMS Expert systems were one of the first truly successful AI computer systems. They were less ambitious than early AI, though, as they focused on much narrower tasks. They were designed to solve problems by reasoning using a specific body of knowledge. They mimicked the decision-making process of a human expert who would feed them with responses for different types of situations or problems. The program would learn all these responses using If-Then rules and provide advice to non-experts. An early platform for expert systemsImage by M.L.Umbricht

GOOGLE BRAIN In an experiment at the Google's XLab, a team of researchers proved that it was possible to train a face detector to identify objects without having to provide it with hints and additional information. Instead of collecting thousand of pictures that have already been labeled, the software learns automatically from data. The team created an artificial neural network of 16,000 computers to see how it can interpret YouTube videos. Having seen 10 million images from YouTube videos the connected computer system began to recognize cats, without being taught what a cat looked like. A neuron in the artificial neural network

WATSON Watson is a computer system capable of answering questions posed in natural language, initially developed to answer questions on the quiz show Jeopardy. In 2011, it competed against Jeopardy champions and it won the first place. Since then, Watson has evolved to a powerful cognitive assistant to businesses that can perform complex tasks.

SIRI Siri is a voice-controlled personalized cognitive assistant that can answer questions, make recommendations, schedule events, make phone calls, read text messages etc. Siri can interpret human language, recognize its complexities, such as accents, and adapt to users' searches and preferences.

STANLEY "The impossible has been achieved" said Sebastian Thrun of the Stanford AI Laboratory after Stanley, an autonomous vehicle, crossed the finish line at the DARPA Challenge and showed that machines can be made to drive safely and speedily without any human help. Stanley used machine learning to detect and avoid obstacles and to find to best path. Stanley learned and grew smarter with lots of practice. Stanley, artificially intelligent car

ALEXA Alexa is a digital assistant capable of of voice interaction, music playback, making to-do lists, streaming podcasts, playing audiobooks, and providing real-time information. Alexa can also control several smart devices and can serve as an Internet of Things hub.

AI FOR GOOD AI For Good by Microsoft provides technology, resources and expertise to empower those working to solve humanitarian issues and create a more sustainable and accessible world.

GOOGLE DUPLEX Google Duplex is a new technology for conducting natural conversations to carry out “real world” tasks over the phone. It can schedule certain types of appointments and can make the conversational experience as natural as possible, allowing people to speak normally, like they would to another person, without having to adapt to a machine.

AI WINTER Expert systems proved to be too expensive and unable to produce significant improvements. Soon interest in AI waned, which resulted in financial setbacks and a new AI winter.

AI WINTER AI Winter refers to a period of reduced funding and loss of interest in AI research. A period of excitement and enthusiasm about the development of AI programs that could solve algebra word problems or learn to speak English was followed by disappointment and disillusionment as well as criticism. A report by the Automatic Language Processing Advisory Committee (ALPAC), published in 1966, contributed to a great extent to the end of funding of AI research, in particular machine translation, stating that machine translation was too expensive and inaccurate and could not compete with human translation. Even though computer programs could translate each and every word, they were not able understand the overall meaning of the sentence. There is a story about an early translation program that translated an English sentence into Russian and then back to English:The spirit is willing, but the flesh is weak.It was translated back into English as:The whiskey is strong, but the meat is rotten.