The Ultimate Guide to Understanding
Artificial Intelligence (AI)

Introduction to Artificial Intelligence (AI)

Artificial Intelligence is “a core, transformative way by which we’re rethinking how we’re doing everything” – Google CEO Sundar Pinchai

The development and daily applications of artificial intelligence have become a mainstay in our everyday lives. More than a thing of science fiction, it’s become a field of real-world dreamers making dreams come true.

The surge of innovation pulsing through the tech community has followed the growth of systems that perform complex activities that we can only compare with “thinking.” They are thinking systems. These activities include problem solving, speech recognition, sensory perception, learning and reasoning, among other things that we previously only associated with mankind.

Generally speaking, the capabilities considered to fall under “artificial intelligence” today include any number of experimental and even consumer technologies like speech recognition programs, autonomous cars, and intelligent routing systems in military technology.

In other words, computers displaying a high level of intelligence can perceive their environment and take action accordingly.

Artificial intelligence is everywhere, making a bigger difference in our everyday lives than you might realize. We use smart phones, we drive smart cars, we wear smart watches, we consume ads online, we have conversations with Alexa—you feed the machine with data, and it justifies that data into improved function and action over time.

Artificial Intelligence (AI) Terminology

What is Terminology of Artificial Intelligence (AI)?

In order to appreciate the impact of AI and the scope of its developments, it’s important to familiarize ourselves with the common terms describing types of artificial intelligence and the technology around it.

Artificial Intelligence (a standard definition): The term “artificial intelligence” refers to a field of study and creation in computer science of an atmosphere capable of replicating thought.

AGI (Artificial General Intelligence): This field of AI is newly emerging and refers specifically to intelligence that can be compared with human intelligence. It is also called “human-level AI.”

ANI (Artificial Narrow Intelligence): This field of AI refers to intelligence with a specific purpose, such as a program that can win every game of chess, recognize faces, or translate foreign languages.

ASI (Artificial Super Intelligence): This field of AI is the new contrast with AGI (human-level AI), in that it refers to machines that are smarter and more capable of perceiving and “thinking” than any man.

Chatbot: This is a computer program that conducts conversations with human users using human-like behavior, including interpreting and learning from all user input. These programs imitate humans in their language.

Deep learning: This is a subset of machine learning (which is a subset of AI) where the neural networks are layered to support huge computing power to process data more efficiently, including discoveries in unstructured data.

Generic algorithm: Used all over AI, generic algorithms are equations and sets of if/then programming that are written to imitate the process of reasoning through data.

Machine Learning: This is a subset of AI where programs are created that actually “learn” how to complete tasks with increasing efficiency over time. This is where machines take the very data they output to find the more effective way to go about each process.

NLG (Natural Language Generation): This is where an algorithm tries to imitate the most understandable and human-sounding language. With today’s technology you might still realize that you’re talking to a machine, but the goal in the future is to make NLG indiscernible from human-generated language.

NLP (Natural Language Processing): This is how computers understand and deduce meaning from human language, including not only written input but speech recognition.

Supervised Learning: This is machine learning where human supervision (and regular data input) are leveraged to promote the machine’s improved processing cognition.

Strong AI: This is the area of AI study and development that theorizes that both human-level (AGI) and non-human level (ASI) are possible.

Weak AI: This is the area of AI study and development that theorizes that AI cannot achieve human-level intelligence.

Artificial Intelligence (AI) News

Latest Developments in Artificial Intelligence (AI) News

The field of artificial intelligence (AI) is continually growing with new technology advancements, software improvements, and products. Staying up to date with the latest artificial intelligence news is important to stay on top of this rapidly growing industry. We cover the latest in artificial intelligence news, machine learning news, and natural language processing news.

Artificial Intelligence (AI) Explained

What is Artificial Intelligence (AI)?

The concept of “artificial intelligence” for many of us implies a humanoid villain rebelling against mankind. But even where this story has left lasting impressions, most of us also view artificial intelligence as a staple of the bright future where programs can enrich our quality of life. Not only can they take care of difficult or monotonous tasks for us, but programs are also serving purposes and needs that we didn’t have before these programs were created.

Artificial Intelligence Simple Explanation

It is often easier to understand complex concepts, when you view them in analogous manners. This holds true for the complex concept of artificial intelligence.

When we talk of artificial, we mean something that’s not natural. Let’s consider “‘artificial light.” This is a man-made light source, but from a physicist’s perspective, light is light.

The term intelligence is also open to debate. But for most developers around AI, intelligence encompasses the power to perceive, to reason, and to learn. The difference with AI, however, is that both artificial and man’s intelligence are set for success in whatever their purpose, but the process behind the machine’s intelligence is different.

When we bring the two together, artificial + intelligence, according to Science Daily, we get the “study and design of intelligent agents.” In other words, AI can be described as the automation of intelligent behavior.

Artificial Intelligence Technical Explanation

Providing a technical explanation of artificial intelligence involves taking a further step into the technological world of this new technology.

Artificial intelligence is the area of computer science that emphasizes human-like intelligence demonstrated by intelligent machines. It aims to emulate the way in which the human brain works, and then apply such knowledge to a technological application.

Artificial intelligence covers a broad field of study which includes other technologies such as Chatbots, Cognitive Computing, Computer Vision, Deep Learning, Natural Language Processing (NLP), Speech Recognition, and others.

The goal of artificial intelligence is to provide software that can reason on input and explain on output. It will provide human-like interactions with software, but is intended as a supplement for human cognition; not a replacement.

Artificial Intelligence (AI) History

What is the History of Artificial Intelligence (AI)?

To better understand artificial intelligence, hindsight gives us a 20/20 view. Artificial intelligence is a term the scientific community has been using since the 1950s, and always with ambitious theories behind it. Come the 60s, AI enjoyed what many considered the “golden era” of research alongside more aggressive space travel projects. In short, the race was on, and the most potent powers of the world were eager to see what artificial intelligence could do.

By the mid-seventies (1970’s), many of those ambitious theories came back to bite the scientific community. When artificial intelligence did not develop what many scientists had promised, funding was slowed. In the 1980s, however, where computers became more popular, AI fell into good grace again as the notion of using it for consumer goods took shape.

Through the 80s, AI grew rapidly but with a glass ceiling looming over the scientific community. Machines were getting more powerful in their intelligence, or perception and discovery power, but they were only learning effectively when the computer was large enough to process the volumes of data being fed in.

A lasting solution came in 1989 in the form of the internet. Not only did the internet solve the capacity problem, but it started the long trail of development that lead to cloud computing (or access to limitless processors at the touch of a button).

The internet also meant the rapid aggregation of more data than ever before—which, in turn, has given artificial intelligence more data to perceive, discover and learn from.

Artificial Intelligence (AI) Categories

What are the Categories are Artificial Intelligence (AI)?

Today, the AI community identifies four categories of artificial intelligence that help us divvy up the study a into easier-to-understand degrees of thinking capacity. These are considered the levels of machine cognition.

1. Reactive Machines – AI Category

This is the most elemental category of AI systems. It has no capacity to form memories nor to use past experiences to improve its own performance.

IBM’s chess-playing computer Deep Blue, which gained notoriety in the 1990s, is a perfect example. Deep Blue calculated the best move every time, including the likelihood of which move its opponent would select, but it always lived in the moment. There was no memory or “thought” around what had happened even one move before.

2. Limited Memory – AI Category

The second type of AI systems does have a limited capacity to build from past experiences. The machine’s small reservoir of remembered “perceptions” allows it to take action based on its core algorithms in combination with a small store of recent data.

An example would be self-driving cars. Each autonomous car is programmed to adjust its speed according to the speed of other cars around it. But this cannot be done “in the moment,” with data from a single instant—instead, it has to be done with a short sequence of data gathered by monitoring nearby objects over a period of time.

3. Theory of mind – AI Category

This is where artificial intelligence starts to really evolve, and it’s the next frontier of development we’re about to crack. A theory of mind AI system will be able to react intelligently to other agents of the world around it. This includes comprehension of feelings, motives, intentions, and expectations; including socially interacting.

The concept of “theory of mind” is actually a term from psychology that speaks to the forming of societies. Without understanding the motives and behaviors of beings around us, we’re unable to work together. The AI theory of mind recognizes that. When machines walk among us, they would have to have a similar understanding of man and how we tick.

4. Self-awareness – AI Category

This is the final step in the evolution of artificial intelligence: building a system that can form representations of consciousness around itself. Think of this as the difference between “I want to go shopping” versus “I know I want to go shopping.”

Not only will these systems be able to work with other agents by understanding them, but they will have a sense of self. This self-awareness will allow programs to understand the emotional behavior of their human counterparts because the because they, too, will have the same set of behavioral reactions.

It’s important to note that the first two categories of artificial intelligence are those we’ve already created, and the second two are those we will build in the future. It’s what some scientists playfully refer to as the “boring AI” versus the “future’s AI.” It’s only a matter of time until there are examples in daily life of each of these classifications.

How Does Artificial Intelligence (AI) Work?

Explain How Artificial Intelligence (AI) Works?

Artificial intelligence works when machines mimic cognitive functions (like perceiving, learning, problem solving and reasoning).

This technology works with overlapping fields of study like big data, machine learning and the internet. The process revolves around developing a channel through which data can be taken to make a decision about something else.

Large computer processors enable AI to optimize a constant onslaught of data, picking out patterns to draw conclusions and to later execute on whatever the system’s duty is. This continual data crunch enables artificial intelligence to do everything from detect bank fraud to recognize faces to chat like a customer service rep on the other side of your favorite online shop.

The internet plays an enormous role in artificial intelligence today with cloud computing offering access to the processors that no longer have to be stored on-site with each learning machine. Picture the theory of the expansion of the universe. With cloud computing, the growing capacity for AI to discover and learn isn’t far off.

Artificial Intelligence (AI) Companies

Discover Innovative Artificial Intelligence (AI) Startups and Companies

It takes bold visionaries and risk-takers to build future technologies into realities. In the field of artificial intelligence (AI), there are many companies across the globe working on this mission. Our mega list of machine learning, natural language processing, and artificial intelligence companies, covers the top companies and startups who are innovating in this space.

Artificial Intelligence (AI) Key Components

Components of AI Technology

We’ve defined AI, and we’ve looked at how it works. But how is it recognized? How is intelligence broken down into the qualities that machines and programs should display to be “intelligent?” What are the elements that have to be present and work together in AI?

Discovery

This is where a program can learn from data without a human agent telling it what to look at. This means the system can learn without is programming giving it a defined target. Discovery is the keystone for future artificial intelligence, where humans will have data so complex that we don’t even know what questions to ask.

Prediction

After discovery, once data has been reviewed by the intelligent system, the program can predict what will happen in the future. This entails classifying and ranking its own data output. This is normally achieved with carefully crafted algorithms.

Justification

In order to work with their human counterparts, AI programs need to be able to justify the predictions they come up with. This plays into the theory of mind, where artificial intelligence understands our need for believable and recognizable outcomes. AI systems craft their output accordingly.

Action

All the artificial intelligence in the world is useless without the user interface where man can interact with it. This is the “acting” component of AI which must be present for it to “push data to the edge” where it can be consumed.

Learning

Intelligence also requires detecting the evolution of data and reacting appropriately to it. AI systems should always be learning and continually improving—because, in today’s world, a program that is not constantly getting smarter is, in practice, getting dumber.

Artificial Intelligence (AI) Applications

What are Applications of Artificial Intelligence (AI)?

Artificial intelligence is being applied to medicine, to law enforcement, to space exploration and to weapons development.

But the applications of artificial intelligence are no longer just a thing of research behind closed doors. Innovations are quickly getting packaged, marketed and delivered to your front door, too. Whether your new Alexa installation, your self-driving car or your facial-recognition dating app, the applications of AI today are expansive and celebrating a great deal of worldwide attention.

Here, we’ll break down the general fields of AI’s application today:

• Artificial intelligence is being applied to natural language processing, or understanding and interacting with natural language spoken by man.
• Expert AI systems are providing insights and advice on specific, narrow duties or functions, like watching the stock market and recommending bids.
• Visual systems are intelligent systems that understand and interpret visual data, including satellite pictures, facial recognition, and any other application you can dream up.
• AI has also taken a hold in gaming, namely to generate those computer-based opponents who can strategize against us in chess, backgammon or the like.
• Intelligent robots are able to perform physical tasks using AI. They are also able to detect and interpret physical data from the world around them, such as light and temperature.

Artificial Intelligence (AI) Tools

What are Artificial Intelligence (AI) Tools?

The specific tools—and sometimes toys—using artificial intelligence compose a long and growing list. To date, no one media source has captured and catalogued them all.

Data giants like Google and Facebook are investing billions toward research in this field, not only for consumer products but for the development of the software their programmers use to continue crafting even more AI solutions.

Every day, we’re finding new ways to work smarter, live more comfortably, or meet the new needs and expectations—many of which we’re creating as quickly as we decide we want them. For example:

• Abi helps connect patients to the right doctors depending on input to the app
• Deepgram transcribes insights, not just who said what, from phone calls and video
• Cue helps you quit smoking
• Einstein is the modern business solution to a smart CRM
• ai is a dating app with face search
• Luminoso captures, interprets and acts on customer feedback on a company’s behalf
• Clarifai helps organize media libraries
• Amélie is a chatbot that offers mental health services
• HireVue uses facial recognition to help employers identify the ideal job candidates
• Ems helps you find where to live

Today’s tools leveraging artificial intelligence number in the thousands. Consumer solutions, business solutions, and even AI solutions that help further develop AI are multiplying every day. Artificial intelligence will continue to impact everyday life. And it will only become more beneficial—even required—to understand how it works and what’s on the horizon.