Artificial Intelligence was first posited in 1943 by two
mathematicians, Warren S. McCulloch and Walter Pitts .They conceived of an artificial ‘neural network’, an artificial brain that mimicked the way the human brain worked and could
perform simple thinking tasks. This idea formed the basis of AI research as we know it today.
In 1950, British computer scientist Alan Turing expanded upon this idea by considering the ways in which a computer could learn to think like a human being. His paper, ‘Computer
Machinery and Intelligence,’ was a critical turning point in the theory of Artificial Intelligence. In this paper, Turing proposed the now famous ‘Imitation Game,’ also known as ‘The Turing Test,’ an experiment whereby a computer could demonstrate ‘thinking’ that was equivalent to that of a human. Turing
After many ups and downs Deep Blue
became the first chess computer to beat a world chess champions, Garry Kasparov. On 11 May 1997 IBM’s chess computer defeated Garry Kasparov after six games with 3½–2½.
AI example, a machine like AlphaGo,
developed by Google, and taught to play the complex Chinese game Go, beat the world champion of Go in 2016.
An agent is anything that can perceive its environment
through sensors and acts upon that environment through effectors.
A human agent has sensory organs such as eyes, ears, nose, tongue and skin parallel to the sensors, and
other organs such as hands, legs, mouth, for effectors.
A robotic agent replaces cameras and infrared range finders for the sensors, and various motors and actuators for effectors.
A software agent has encoded bit strings as its programs and actions.
Generic agent
Autonomous agent
Reflex agent
Goal based agent
Utility-based agent
In artificial intelligence, an intelligent agent
is an autonomous entity which observes through sensors and acts upon an environment using
actuators and directs its activity towards achieving goals
An agent is anything that can be viewed as
perceiving its environment through sensors and acting upon that environment through effectors.
A rational agent is one that does the right thing.
Deep learning however – the ability for a computer to apply its knowledge in other areas, as humans currently do, is the
ultimate goal of AI researchers. Currently this remains elusive.
Machine learning is a field of computer
science that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly
programmed. The name machine learning was coined in 1959 by Arthur Samuel
Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to
learn a general rule that maps inputs to outputs. As special cases, the input signal can be only partially available, or restricted to special feedback:
Semi-supervised learning: the computer is given only an
incomplete training signal: a training set with some (often many) of the target outputs missing.
Active learning: the computer can only obtain training labels for a limited set of instances (based on a budget), and also has to
optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling.
Reinforcement learning: training data (in form of rewards and
punishments) is given only as feedback to the program's actions in a dynamic environment, such as driving a vehicle or playing a
game against an opponent.
Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised
learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end
Machine Learning..
A robot is a machine—especially one
programmable by a computer— capable of carrying out a complex series of actions
automatically. Robots can be guided by an external control device or the control may be embedded within. Robots may be
constructed to take on human form but most robots are machines designed to perform a task with no regard to how they look.
The branch of technology that deals with the design, construction, operation, and application of robots, as well as computer systems for their control, sensory
feedback, and information processing is robotics. These technologies deal with automated machines that can take the place of humans in dangerous environments or manufacturing processes, or resemble humans in appearance, behavior, or cognition. Many of today's robots are inspired by nature contributing to the field of bio-inspired robotics. These robots have also created a newer branch of robotics: soft robotics.
Turing (1950), where he proposes for the first time the idea of a thinking machine and the more
popular Turing test to assess whether such machine shows, in fact, any intelligence.
If we exclude the pure philosophical reasoning
path that goes from the Ancient Greek to Hobbes, Leibniz, and Pascal, AI as we know it has been
officially started in 1956 at Dartmouth College, where the most eminent experts gathered to
brainstorm on intelligence simulation.
In fact, the Automatic Language Processing
Advisory Committee (ALPAC) report in the US in 1966, followed by the “Lighthill report” (1973), assessed the feasibility of AI given the current developments and
concluded negatively about the possibility of creating a machine that could learn or be considered intelligent.
These two reports, jointly with the limited data available to feed the algorithms, as well as the scarce
computational power of the engines of that period, made the field collapsing and AI fell into disgrace for the entire decade.
In 1993 this period ended with the MIT Cog project to build a humanoid robot, and with the Dynamic Analysis and Replanning Tool (DART) — that paid back the US government of the entire funding since 1950 — and when in 1997 DeepBlue defeated Kasparov at
chess, it was clear that AI was back to the top.
2012. That Tuesday, a group of researchers
presented at the Neural Information Processing Systems (NIPS) conference detailed information about their convolutional neural networks that granted them the first place in the ImageNet Classification competition few weeks before
(Krizhevsky et al., 2012). Their work improved the classification algorithm from 72% to 85% and set the use of neural networks as fundamental for artificial intelligence.
The 3-years-old DeepMind being acquired by Google in Jan. 2014;
The open letter of the Future of Life Institute signed by more than 8,000 people and the study on reinforcement learning released by Deepmind (Mnih et al., 2015) in Feb. 2015;
The paper published in Nature on Jan. 2016 by DeepMind scientists on neural networks (Silver et al., 2016) followed by the impressive victory of AlphaGo over Lee Sedol in March 2016 (followed by a list of other impressive achievements — check out the article of Ed Newton-Rex).
AI history..
1. Unemployment. What happens after the end of jobs?
2. Inequality. How do we distribute the wealth created by machines? 3. Humanity. How do machines affect our behaviour and interaction? 4. Artificial stupidity. How can we guard against mistakes?
5. Racist robots. How do we eliminate AI bias?
6. Security. How do we keep AI safe from adversaries?
7. Evil genies. How do we protect against unintended consequences?
8. Singularity. How do we stay in control of a complex intelligent system? 9. Robot rights. How do we define the humane treatment of AI?
Etc.
https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/
