AI (Artificial Intelligence)-The Future

An introduction to Artificial Intelligence

“AI computers learn from experience. Computers are brilliant at computational intelligence – remembering, calculating probability and discerning patterns in datasets “

What is an AI exactly?

Artificial Intelligence (AI) emphasizes on creation of intelligent machines that work and react as humans. Computers with artificial intelligence are designed for activities such as; Speech recognition, Learning, Planning and Problem solving. ... Knowledge engineering is a core aspect of AI research.

What Is Intelligence?

All but the simplest human behaviours ascribed to intelligence, while even the most complicated insect behaviour is never taken as an indication of intelligence. What is the difference? Consider the behaviour of the digger wasp, Sphex ichneumoneus. When the female wasp returns to her burrow with food, she first deposits it on the threshold , checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasp’s instinctual behaviours revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligence—conspicuously absent in the case of Sphex —must include the ability to adapt to new circumstances.

Psychologists generally do not characterize human intelligence by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception , and using language.

A brief History of Artificial Intelligence

Artificial intelligence is a buzzword today, although this term is not new. In 1956, a group of avant-garde experts from different backgrounds decided to organize a summer research project on AI. Four bright minds led the project; John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories).

The primary purpose of the research project was to tackle "every aspect of learning or any other feature of intelligence that can in principle be so precisely described, that a machine can be made to simulate it."

The proposal of the summits included

1. Automatic Computers
2. How Can a Computer Be Programmed to Use a Language?
3. Neuron Nets
4. Self-improvement

It led to the idea that intelligent computers can be created. A new era began, full of hope -Artificial intelligence.

Type of Artificial Intelligence :

Artificial intelligence can be divided into three subfields:

• Artificial intelligence
• Machine learning
• Deep learning

Machine Learning

Deep learning

AI applications

Machine learning is the art of study of algorithms that learn from examples and experiences.

Machine learning is based on the idea that there exist some patterns in the data that were identified and used for future predictions.

The difference from hardcoding rules is that the machine learns on its own to find such rules.

Deep learning is a sub-field of machine learning. Deep learning does not mean the machine learns more in-depth knowledge; it means the machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model.

For instance, Google LeNet model for image recognition counts 22 layers.

In deep learning, the learning phase is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other.

AI vs. Machine Learning :

Most of our smartphone, daily device or even the internet uses Artificial intelligence. Very often, AI and machine learning are used interchangeably by big companies that want to announce their latest innovation. However, Machine learning and AI are different in some ways.

AI- artificial intelligence- is the science of training machines to perform human tasks. The term was invented in the 1950s when scientists began exploring how computers could solve problems on their own.

Artificial Intelligence is a computer that is given human-like properties. Take our brain; it works effortlessly and seamlessly to calculate the world around us. Artificial Intelligence is the concept that a computer can do the same. It can be said that AI is the large science that mimics human aptitudes.

Machine learning is a distinct subset of AI that trains a machine how to learn. Machine learning models look for patterns in data and try to conclude. In a nutshell, the machine does not need to be explicitly programmed by people. The programmers give some examples, and the computer is going to learn what to do from those samples.

Next generation AI

The creation of a more general purpose AI, being more versatile and closer to the way human intelligence operates, goes hand in hand with what some call the fourth industrial revolution: the coming together of AI, blockchain, internet of things and 5G. However, bringing these technologies together requires more advances in cloud storage, quantum computing and powerful computational algorithms.

There are some who argue that truly general AI will always remain a fiction. Yet AI innovation continues feverishly. Sub-disciplines of semantics, contextual programming and heuristics are developing, and human interaction is being enhanced through cyber psychology.

Artificial intelligence has made its way into a number of areas. Here are six examples.

• AI in healthcare. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best known healthcare technologies is IBM Watson. It understands natural language and is capable of responding to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema. Other AI applications include chatbots, a computer program used online to answer questions and assist customers, to help schedule follow-up appointments or aid patients through the billing process, and virtual health assistants that provide basic medical feedback.
• AI in business. Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Machine learning algorithms are being integrated into analytics and CRM platforms to uncover information on how to better serve customers. Chat bots have been incorporated into websites to provide immediate service to customers. Automation of job positions has also become a talking point among academics and IT analysts.
• AI in education. AI can automate grading, giving educators more time. AI can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. AI could change where and how students learn, perhaps even replacing some teachers.
• AI in finance. AI in personal finance applications, such as Mint or Turbo Tax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice. Other programs, such as IBM Watson, have been applied to the process of buying a home. Today, software performs much of the trading on Wall Street.
• AI in law. The discovery process, sifting through of documents, in law is often overwhelming for humans. Automating this process is a more efficient use of time. Startups are also building question-and-answer computer assistants that can sift programmed-to-answer questions by examining the taxonomy and ontology associated with a database.
• AI in manufacturing. This is an area that has been at the forefront of incorporating robots into the workflow . Industrial robots used to perform single tasks and were separated from human workers, but as the technology advanced that changed.
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Next Post : Machine Learning :)