How does AI work?

How does AI work?

If AI is a complex but necessary technology, how does it work? To put it simply, AI works by combining large data sets with intuitive processing algorithms. AI can manipulate these algorithms by learning behavior patterns within the data set. It's important to understand that AI is not just one algorithm.

Artificial intelligence (AI) is a field of computer science that deals with the creation of intelligent agents, which are systems that can reason, learn, and act autonomously. AI research has been highly successful in developing effective techniques for solving a wide range of problems, from game playing to medical diagnosis.

 

There are many different ways to define AI, but one common definition is that AI is "the ability of a machine to mimic the cognitive functions that humans associate with other human minds, such as learning and problem-solving."

 

AI systems work by combining large data sets with intuitive processing algorithms. AI can manipulate these algorithms by learning behavior patterns within the data set. It's important to understand that AI is not just one algorithm. Instead, it is an entire machine learning system that can solve problems and suggest outcomes.

 

Here are some of the most common techniques used in AI:

 

Machine learning: Machine learning is a type of AI that allows computers to learn without being explicitly programmed. Machine learning algorithms are trained on large data sets, and they use this data to learn how to perform tasks. For example, a machine learning algorithm could be trained to recognize objects in images by being shown a large set of images with labeled objects.

Natural language processing: Natural language processing (NLP) is a field of AI that deals with the interaction between computers and human (natural) languages. NLP algorithms are used to process and understand human language, and they are used in a wide variety of applications, such as machine translation, text summarization, and question-answering.

Computer vision: Computer vision is a field of AI that deals with the extraction of meaningful information from digital images or videos. Computer vision algorithms are used in a wide variety of applications, such as facial recognition, object detection, and scene understanding.

AI is a rapidly evolving field, and new techniques are being developed all the time. As AI technology continues to improve, we can expect to see even more amazing applications of AI in the years to come.

Input

The first step of AI is input. In this step, an engineer must collect the data needed for AI to perform properly.

 

Data does not necessarily have to be a text input; it can also be images or speech. However, it’s important to ensure the algorithms can read inputted data.

 

It’s also necessary to clearly define the context of the data and the desired outcomes in this step.

 

Processing

The processing step is when AI takes the data and decides what to do with it. While processing, AI interprets the pre-programmed data and uses the behaviors it has learned to recognize the same or similar behavior patterns in real-time data, depending upon the particular AI technology.

 

Data Outcomes

After the AI technology has processed the data, it predicts the outcomes. This step determines if the data and its given predictions are a failure or a success.

 

Adjustments

If the data set produces a failure, AI technology can learn from the mistake and repeat the process differently. The algorithms’ rules may need to be adjusted or changed to fit the data set.

 

Outcomes may also shift during the adjustment phase to reflect a more desired or appropriate outcome.

 

Assessments

Once AI has finished its assigned task, the last step is assessment. The assessment phase allows the technology to analyze the data and make inferences and predictions. It can also provide necessary, helpful feedback before running the algorithms again.

 

AI is extremely beneficial in business. However, choosing the right AI technology for your business needs is important.

 

The Four Concepts of AI

As previously mentioned, not every type of AI will be appropriate for your business, your processes, or your data set. In fact, there are four main concepts of AI that you should consider.

 

1. Reactive Machine

Reactive machines live up to their concept name. This type of AI can respond or react to real-time data. However, this AI is limited and can’t store information or build a memory bank.

 

Because it can’t store memories, the AI can’t use past experience to analyze data based on new data behavior.

 

Reactive machine technologies are best used for repetitive tasks designed for simple outcomes. Consider using reactive machines to organize new client information or filter spam from your inbox.

 

2. Limited Memory

Unlike reactive machines, limited memory technologies can store and use information to learn new tasks. A limited memory machine will need pre-programmed data to be set in motion.

 

Once it has processed that information, it can analyze real-time data to make predictions and observations.

 

Limited memory technology is the most common AI technology used in business. In fact, this is the technology that makes self-driving cars work.

 

A chatbot is an example of limited memory technology. Chatbots use pre-programmed data to interact with customers and predict their needs based on their actions and inquiries.

 

3. Theory of Mind

Theory of mind technology is more advanced than limited memory. Like limited memory, theory of mind technology can store information and make observations based on the real-time data it observes.

 

This technology is more advanced, though, meaning it can respond to human emotions.

 

Theory of mind technology must be designed to understand that humans are complex, with individual thought patterns and past experiences that affect how they respond to certain stimuli. Because of this, the theory of mind technologies is not yet fully developed.

 

As it stands now, AI cannot fully respond to people in a human-like manner.

 

4. Self-Aware

Self-aware technology takes the theory of mind technology one step further. It can process information, store it, use it to inform decision-making processes, understand human emotions and feelings, and is also self-aware on a human level.

 

In other words, self-aware machines operate like human consciousness and can have their own thoughts and feelings.

 

Self-aware technology is still a very long way off from being fully developed. But, scientists and researchers are making small strides in understanding how to implement human emotions into AI technology.