Evolution Of Artificial Intelligence

 Evolution Of Artificial Intelligence

1951- SNARC(Stochastic Neural Analog Reinforcement Calculator):

     

 SNARC  is a neural net machine designed by Marvin Lee Minsky in the year 1951.As an UG student at Harvard in the late 1940s and in his first year of grad school at Princeton in 1950, Marvin Minsky thought about how to build a machine that could learn. Eventhough Minsky studied mathematics,  he was curious about the human mind,what he perceived as  a mystery in science.  He wanted to better understand intelligence by prototying it.  Minsky with the help of fellow Princeton graduate student Dean Edmonds,  crafted the first artificial neural network called SNARC,which included 40 interconnected artificial neurons, each of which had short-term and long-term memory of sorts. The short-term memory came in the form of a capacitor,  that could remember for a few seconds if the neuron had recently relayed a signal. Long-term memory was handled by a potentiometer, or volume knob, that would increase a neuron’s probability of relaying a signal if it had just fired when the system was “rewarded,” either manually or through an automated electrical signal. This machine is considered one of the first pioneering attempts at the field of artificial intelligence.

 

1972 WABOT-1:

                                    

 In effort of creating anthropomorphic intelligent robot WABOT, Four laboratories in the School of Science & Engineering of  Waseda University joined to set up "The Bio-engineering group" which started the WABOT project in 1970. The WABOT-1 was the first fun-scale Anthropomorphic robot (Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. It is considered to be an innate tendency of human psychology developed in the world. It consisted of a limb-control system, a vision system and a conversation system.) The WABOT-1 was able to communicate-with a person in Japanese and to measure distances and directions to the objects using external receptors, artificial ears and eyes, and an artificial mouth. The WABOT-1 walked with his lower limbs and was able to grip and transport objects with hands that used tactile-sensors. It was estimated that the WABOT-1 has the mental faculty of a one-and-half-year-old child.

 

    1997 IBM's Deep Blue:  

                                                     

When Gary Kasparov beat IBM's chess computer in 1989 he arrogantly told the programmers to "teach it to resign earlier". Eight years later, in 1997, the world champion found himself humbled by a 1.4-ton heap of silicone in a victory for IBM's Deep Blue that marks a milestone in the progress of artificial intelligence. This gave immense attention to the world about wake of AI technology.  Deep Blue's programmers revamped the supercomputer, doubling its greatest strength: the ability to search through millions of possibilities for the strongest move.  The new machine which crushed Kasparov can scan 200 million a second, sometimes 300 million, and can analyze 74 moves ahead – compared with chess masters who typically think 10 moves ahead.

2010 IBM'S Watson:

                                               

Since Deep Blue's victory over Garry Kasparov in chess in 1997, IBM had been on the hunt for a new challenge. During 2007, the IBM team was given three to five years and a staff of 15 people to solve the problems. This led to the creation of Watson as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering. The key difference between QA technology and document search is that document search takes a keyword query and returns a list of documents, ranked in order of relevance to the query (often based on popularity and page ranking), while QA technology takes a question expressed in natural language, seeks to understand it in much greater detail, and returns a precise answer to the question. According to IBM, "The goal is to have computers start to interact in natural human terms across a range of applications and processes, understanding the questions that humans ask and providing answers that humans can understand and justify."

 

2014 FB'S Deep Face:

                                                     

 DeepFace is a deep learning facial recognition system created by a research group at Facebook in 2014. It identifies human faces in digital images. It uses a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users. DeepFace is trained on a large dataset of faces, an identity labeled dataset of four million facial images belonging to more than 4,000 identities, acquired from a population vastly different than the one used to construct the evaluation benchmarks and each identity had an average of a thousand samples. The DeepFace algorithm, first aligns a face so that the person in the picture faces forward, using a 3-D model of an “average” forward-looking face. Then it employs the deep learning to find a numerical description of the forward-looking face.. 

 

2018 AI Diagnostics:

                                                 

The number of deaths from cancers worldwide is staggering—8.8 million deaths in 2015 according to the World Health Organization. This has led to an interest in developing computational approaches such as machine learning (ML), a subfield of artificial intelligence (AI), to improve medical management by providing insights that improve patient outcomes and workflow throughout a patient's journey in 2018. A study published in Thorax found that the use of artificial intelligence (AI) may help reduce false positive rates in lung cancer screening. Comparison with existing predictors in the training and validation cohorts showed that incorporating low-dose CT scan features greatly enhances predictive accuracy and the machine  learning model  improved cancer detection over existing methods, including the Brock parsimonious model, A study recently conducted in the journal Nature suggests that Artificial intelligence is more accurate than doctors in diagnosing breast cancer from mammograms.