Artificial Intelligence

What is Artificial intelligence

Artificial intelligence is a field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze.?

AI is a broad field that encompasses many different disciplines, including computer science, data analytics and statistics, hardware and software engineering, linguistics, neuroscience, and even philosophy and psychology.?

On an operational level for business use, AI is a set of technologies that are based primarily on machine learning and deep learning, used for data analytics, predictions and forecasting, object categorization, natural language processing, recommendations, intelligent data retrieval, and more.

Artificial intelligence can be organized in several ways:

1. Reactive machines: Limited AI that only reacts based on preprogrammed rules. Does not use memory. IBM’s Deep Blue that beat chess champion Garry Kasparov in 1997 was an example of a reactive machine.
2. Limited memory: Most modern AI is considered to be limited memory. It can use memory to improve over time by being trained with new data, typically through an artificial neural network or other training model.

Artificial intelligence training models

Limited-memory artificial intelligence is AI that improves over time by being trained with new data. Machine learning is a subset of artificial intelligence that uses algorithms to train data to obtain results.

Three kinds of learnings models are often used in machine learning:

Supervised learning is a machine learning model that maps a specific input to an output using labeled training data (structured data). In simple terms, to train the algorithm to recognize pictures of cats, feed it pictures labeled as cats.

Unsupervised learning is a machine learning model that learns patterns based on unlabeled data (unstructured data). Unlike supervised learning, the end result is not known ahead of time. Rather, the algorithm learns from the data, categorizing it into groups based on attributes. For instance, unsupervised learning is good at pattern matching and descriptive modeling.?

Reinforcement learning is a machine learning model that can be broadly described as “learn by doing.” An “agent” learns to perform a defined task by trial and error (a feedback loop) until its performance is within a desirable range. The agent receives positive reinforcement when it performs the task well and negative reinforcement when it performs poorly. An example of reinforcement learning would be teaching a robotic hand to pick up a ball.?

Common types of artificial neural networks

A common type of training model in AI is an artificial neural network, a model loosely based on the human brain.?A neural network is a system of artificial neurons that are computational nodes used to classify and analyze data. The data is fed into the first layer of a neural network, with each perceptron making a decision, then passing that information onto multiple nodes in the next layer. Training models with more than three layers are referred to as “deep neural networks” or “deep learning.” The output of the final perceptions accomplish the task set to the neural network, such as classify an object or find patterns in data.?

Benefits of AI

1. Automation - AI can automate workflows and processes or work independently and autonomously from a human team. For example, AI can help automate aspects of cybersecurity by continuously monitoring and analyzing network traffic. A smart factory may have AI in use, such as robots using computer vision to navigate the factory floor or to inspect products for defects, or use real-time analytics to measure efficiency and output.
2. Reduce human error - AI can eliminate manual errors in data processing, analytics, assembly in manufacturing, and other tasks through automation and algorithms that follow the same processes every single time.
3. Eliminate repetitive tasks - AI can be used to perform repetitive tasks, freeing human capital to work on higher impact problems. AI can be used to automate processes, like verifying documents, transcribing phone calls. Robots are often used to perform “dirty, or dangerous” tasks.
4. Fast and accurate - AI can process information more quickly than a human, finding patterns or discovering relationships in data that a human may miss.
5. Infinite availability - AI is not limited by time of day, the need for breaks, or other human encumbrances. When running in the cloud, AI and machine learning can be “always on,” continuously working on its assigned tasks.?
6. Accelerated research and development - The ability to analyze vast amounts of data quickly can lead to accelerated breakthroughs in research and development. For instance, AI has been used in predictive modeling of potential new pharmaceutical treatments, or to quantify the human genome.?

Next Generation

1. Theory of mind: can emulate the human mind and has decision-making capabilities equal to that of a human, including recognizing and remembering emotions and reacting in social situations.?Theory of mind AI does not currently exist.
2. Self aware: describes a mythical machine that is aware of its own existence and has the intellectual and emotional capabilities of a human. Theory of mind AI does not currently exist, and it above of theory of mind.