Artificial Intelligence and its Impact

Artificial intelligence (AI) has come a long way since its experimental debut as a computer program designed to beat chess grand-master. The first big achievement of IBM’S Deep blue, which beat world chess champion, Garry Kasparov. The Incident was in true sense an inflection point in AI technology.

The easiest way to think of AI is in the context of a Human since we are the most intelligent creatures we know off. The goal of this field is to develop a system that can work intelligently and independently.

First thing, memorize this, Artificial Intelligence is the Father or Mother (as you prefer) of all branches you will listen in the future such as Machine Learning (ML), Deep Learning (DL), Computer Vision, Natural Language Processing (NLP), etc. This important mainly when it comes to ML, since most times people tend to confuse both terms. AI is a broad branch of Computer Science.

History of AI

The field of AI research was born at a workshop at Dartmouth College in 1956. Attendees Allen Newell, Herbert Simon, John McCarthy, Marvin Minsky and Arthur Samuel became the founders and leaders of AI research.

They and their students produced programs that the press described as "astonishing" computers were learning checkers strategies (and by 1959 were reportedly playing better than the average human), solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world.

AI's founders were optimistic about the future, Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved”.

According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011.

Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people. In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes"

According to an article by The Economist, America and China are the superpowers in terms of Artificial Intelligence (AI). Over time America and China have collected and attracted the core information that contributed to the development of Artificial Intelligence ranging from facial recognition to driverless cars. Based on an estimate presented in The Economist Article, China is expected to hold about 30% of the world's data and America is likely to hold the same as well.

The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems.

• Reasoning
• Knowledge Representation
• Planning
• Learning
• Natural Language Processing (NLP)
• Perception
• Motion and Manipulation
• Social Intelligence
• General Intelligence

Machine Learning

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" from data, without being explicitly programmed.

Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data.

For Example-An an unsupervised learner processes 10 million videos together with related textual data such as descriptions and comments. The learner models images in the videos using statistical analysis that allows it to identify visual patterns. These patterns can then be correlated with text to develop theories about the visual traits of various things. For example, such a learner might be able to build a solid model that can identify skateboards in videos.

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances.

Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics, and genetic algorithms.

In machine learning, the environment is typically formulated as a Markov Decision Process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter does not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.

For example, let's consider a board game like Go or Chess. In order to determine the best move, the players need to think about various factors. The number of possibilities is so large that it is not possible to perform a brute-force search. If we were to build a machine to play such a game using traditional techniques, we need to specify a large number of rules to cover all these possibilities. Reinforcement learning completely bypasses this problem. We do not need to manually specify any rules. The learning agent simply learns by actually playing the game.

Machine Learning Approaches

1 Decision tree learning

2 Association rule learning

3 Artificial neural networks

     3.1  Deep learning

4 Inductive logic programming

5 Support vector machines

6 Clustering

7 Bayesian networks

8 Representation learning

9 Similarity and metric learning

10 Sparse dictionary learning

11 Genetic algorithms

12 Rule-based machine learning

Deep Learning

Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome.

It's basically has been made to compete with the human brain.

8 A practical example of Deep learning- Now that we’re in a time when machines can learn to solve complex problems without human intervention, what exactly are the problems they are tackling?

Here are just a few of the tasks that deep learning supports today and the list will just continue to grow as the algorithms continue to learn via the infusion of data.

• 1~ Virtual assistants-Whether it’s Alexa or Siri or Cortana, the virtual assistants of online service providers use deep learning to help understand your speech and the language humans use when they interact with them.
• 2~ Translations-In a similar way, deep learning algorithms can automatically translate between languages. This can be powerful for travelers, business people and those in government.
• 3~ Vision for driver-less delivery trucks, drones and autonomous cars-The way an autonomous vehicle understands the realities of the road and how to respond to them whether it’s a stop sign, a ball in the street or another vehicle is through deep learning algorithms. The more data the algorithms receive, the better they are able to act human-like in their information processing—knowing a stop sign covered with snow is still a stop sign.
• 4~ Chatbots and service bots- Chatbots and service bots that provide customer service for a lot of companies are able to respond in an intelligent and helpful way to an increasing amount of auditory and text questions thanks to deep learning.
• 5~ Image colorization-Transforming black-and-white images into color was formerly a task done meticulously by human hand. Today, deep learning algorithms are able to use the context and objects in the images to color them to basically recreate the black-and-white image in color. The results are impressive and accurate.
• 6~ Facial recognition-Deep learning is being used for facial recognition not only for security purposes but for tagging people on Facebook posts and we might be able to pay for items in a store just by using our faces in the near future. The challenges for deep-learning algorithms for facial recognition is knowing it’s the same person even when they have changed hairstyles, grown or shaved off a beard or if the image taken is poor due to bad lighting or an obstruction.
• 7~ Medicine and pharmaceuticals-From disease and tumor diagnoses to personalized medicines created specifically for an individual’s genome, deep learning in the medical field has the attention of many of the largest pharmaceutical and medical companies.
• 8~ Personalized shopping and entertainment-Ever wonder how Netflix comes up with suggestions for what you should watch next? Or where Amazon comes up with ideas for what you should buy next and those suggestions are exactly what you need but just never knew it before? Yep, it’s deep-learning algorithms at work.

Natural Language Processing

Natural Language Processing (NLP) is “ability of machines to understand and interpret human language the way it is written or spoken”. The objective of NLP is to make computer/machines as intelligent as human beings in understanding language.

Will try to Understand NLP with the below Image.

More Deeper Applications of NLP.