What Is Artificial Intelligence?

The concept of artificial intelligence is a powerful concept. it has made us see things from a whole different perspective. It is for this reason we have decided to talk about artificial intelligence and its type. Read through this guide keenly.

What Is Artificial Intelligence?

Artificial intelligence (AI) is currently one of the most common catchwords in tech and with good reason.

Artificial Intelligence is defined as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. AI is also defined as,

• An Intelligent Entity Created by humans
• Capable of Performing Tasks intelligently without being explicitly instructed.
• Capable of thinking and acting rationally and humanely.

A layman with a fleeting understanding of technology would link it to robots. They’d say Artificial Intelligence is a terminator like-figure that can act and think on its own.

The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning (ML), which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enable this automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video.

How does AI work?

As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R, and Java, are popular.

In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.

AI programming focuses on three cognitive skills: learning, reasoning, and self-correction.

What Are the 4 Types of AI?

Artificial intelligence can be categorized into one of four types.

• Reactive AI uses algorithms to optimize outputs based on a set of inputs. Chess-playing AIs, for example, are reactive systems that optimize the best strategy to win the game. Reactive AI tends to be fairly static, unable to learn or adapt to novel situations. Thus, it will produce the same output given identical inputs.
• Limited memory AI can adapt to past experience or update itself based on new observations or data. Often, the amount of updating is limited (hence the name), and the length of memory is relatively short. Autonomous vehicles, for example, can “read the road” and adapt to novel situations, even “learning” from past experience.
• Theory-of-mind AI is fully-adaptive and has an extensive ability to learn and retain past experiences. These types of AI include advanced chat-bots that could pass the Turing Test, fooling a person into believing the AI was a human being. While advanced and impressive, these AI are not self-aware.
• Self-aware AI, as the name suggests, become sentient and aware of their own existence. Still, in the realm of science fiction, some experts believe that an AI will never become conscious or “alive”.

Applications of Artificial Intelligence

The applications for artificial intelligence are infinite. The technology can be applied to many different sectors and industries. AI is being tested and used in the healthcare industry for dosing drugs and doling out different treatments tailored to specific patients, and for aiding in surgical procedures in the operating room.

Other examples of machines with artificial intelligence include computers that play chess and self-driving cars. Each of these machines must weigh the consequences of any action they take, as each action will impact the end result. In chess, the end result is winning the game. For self-driving cars, the computer system must account for all external data and compute it to act in a way that prevents a collision.

Artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity in banking and finance such as unusual debit card usage and large account deposits—all of which help a bank’s fraud department. Applications for AI are also being used to help streamline and make trading easier. This is done by making the supply, demand, and pricing of securities easier to estimate.

AI Programming Cognitive Skills: Learning, Reasoning, and Self-Correction

Artificial Intelligence emphasizes three cognitive skills of learning, reasoning, and self-correction, skills that the human brain possesses to one degree or another. We define these in the context of AI as:

• Learning: The acquisition of information and the rules needed to use that information.
• Reasoning: Using the information rules to reach definite or approximate conclusions.
• Self-Correction: The process of continually fine-tuning AI algorithms and ensuring that they offer the most accurate results they can.

However, researchers and programmers have extended and elaborated the goals of AI to the following:

• Logical Reasoning

AI programs enable computers to perform sophisticated tasks. On February 10, 1996, IBM’s Deep Blue computer won a game of chess against a former world champion, Garry Kasparov.

• Knowledge Representation

Smalltalk is an object-oriented, dynamically typed, reflective programming language that was created to underpin the “new world” of computing exemplified by “human-computer symbiosis.”

• Planning and Navigation

The process of enabling a computer to get from point A to point B. A prime example of this is Google’s self-driving Toyota Prius.

• Natural Language Processing

Set up computers that can understand and process language.

• Perception

Use computers to interact with the world through sight, hearing, touch, and smell.

• Emergent Intelligence

Intelligence that is not explicitly programmed, but emerges from the rest of the specific AI features. The vision for this goal is to have machines exhibit emotional intelligence and moral reasoning.

Some of the tasks performed by AI-enabled devices include:

• Speech recognition
• Object detection
• Solve problems and learn from the given data
• Plan an approach for future tests to be done

What are the advantages and disadvantages of artificial intelligence?

Artificial neural networks and deep learning artificial intelligence technologies are quickly evolving, primarily because AI processes large amounts of data much faster and makes predictions more accurately than humanly possible.

While the huge volume of data being created on a daily basis would bury a human researcher, AI applications that use machine learning can take that data and quickly turn it into actionable information. As of this writing, the primary disadvantage of using AI is that it is expensive to process the large amounts of data that AI programming requires.

Advantages

• Good at detail-oriented jobs;
• Reduced time for data-heavy tasks;
• Delivers consistent results; and
• AI-powered virtual agents are always available.

Disadvantages

• Expensive;
• Requires deep technical expertise;
• A limited supply of qualified workers to build AI tools;
• Only knows what it’s been shown; and
• Lack of ability to generalize from one task to another.

What are examples of AI technology and how is it used today?

AI is incorporated into a variety of different types of technology. Here are six examples:

1. When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA’s tactical bots to pass along intelligence from AI and respond to process changes.
2. Machine learning. This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:

• Supervised learning. Data sets are labeled so that patterns can be detected and used to label new data sets.
• Unsupervised learning. Data sets aren’t labeled and are sorted according to similarities or differences.
• Reinforcement learning. Data sets aren’t labeled but, after performing an action or several actions, the AI system is given feedback.

3. Machine vision. This technology gives a machine the ability to see. Machine vision captures and analyzes visual information using a camera, analog-to-digital conversion, and digital signal processing. It is often compared to human eyesight, but machine vision isn’t bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.
4. Natural language processing (NLP). This is the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and decides if it’s junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis, and speech recognition.
5. This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. For example, robots are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings.
6. Self-driving cars. Autonomous vehicles use a combination of computer vision, image recognition, and deep learning to build automated skills for piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.

Conclusion

In conclusion, Artificial Intelligence is emerging as the next big thing in technology. Organizations are adopting AI and budgeting for certified professionals in the field, thus the growing demand for trained and certified professionals.