What Is Artificial Intelligence AI? And How does it work

What is Artificial Intelligence AI? Although there are several definitions of artificial intelligence (AI) that have appeared in recent decades. John McCarthy provides the following description in this 2004 study (PDF, 106 KB) (link is located external to IBM), “Making clever machines, brilliant computer programmes, requires knowledge of both science and engineering. Using computers to understand human intellect is related, but AI need not be limited to biologically observable processes.”

However, years before this term came into being, Alan Turing’s groundbreaking book “Computing Machinery and Intelligence” (PDF, 89.8 KB) (link lives outside of IBM) marked the beginning of the artificial intelligence debate. The “father of computer science,” Turing, poses the following query in this essay: “Can machines think?” From there, he proposes a test now infamously known as the “Turing Test,” in which a human interrogator would attempt to differentiate between a computer-generated and a human-written text response. Although this test has been under intense criticism since it was published, it nonetheless contributes significantly to the history of AI. Moreover, it continues to be a topic of discussion in philosophy because it uses language theories.

The next step was the publication of Stuart Russell and Peter Norvig’s Artificial Intelligence: A Modern Approach, which became one of the field’s most influential texts. They explore four potential objectives or definitions of AI, dividing computer systems according to their rationality and thinking vs acting:

Human approach:

• Systems that think like humans
• Systems that act like humans

Ideal approach:

• Systems that think rationally
• Systems that act rationally

Artificial intelligence is a topic that, in its most basic form, combines computer science and substantial datasets to facilitate problem-solving. Additionally, it includes the branches of artificial intelligence known as deep learning and machine learning, which commonly address together. These fields use AI algorithms to build expert systems that make predictions or categorise information based on incoming data.

Artificial intelligence: weak vs strong

Weak AI–  also known as Narrow AI or Artificial Narrow Intelligence (ANI)- is AI honed and targeted to carry out particular tasks. Unfortunately, most of the AI we encounter today is weak. However, this form of AI is anything but soft; it supports some incredibly sophisticated applications, including Apple’s Siri, Amazon’s Alexa, IBM Watson, and autonomous vehicles. “Narrow” could be a more apt word for it.

Strong AI- Artificial General Intelligence (AGI) and Artificial Super Intelligence are strong AI (ASI) components. A computer with an intellect comparable to humans, a self-aware awareness, and the capacity to learn, reason, and make plans for the future would be said to have artificial general intelligence or general AI. Artificial Super Intelligence (ASI), also referred to as super intelligence, would be more intelligent and capable than the human brain. Even though there are now no real-world applications for strong AI, and it is only theoretical, experts in the field of artificial intelligence are still looking into its potential. The best instances of ASI in the interim might come from science fiction, like HAL, the superhuman, rogue computer aide in 2001: A Space Odyssey.

Machine learning vs deep learning

Since deep learning and machine learning frequently uses synonymously, it is essential to understand their differences. In addition to being a subfield of artificial intelligence, deep learning is also a subfield of machine learning, as was already mentioned.

Neural networks are the actual building blocks of deep learning. A neural network with more than three layers, including the inputs and output, is referred to as a “deep learning algorithm” and is defined as having more than three layers. In general, as shown in the diagram below:

The learning processes used by each algorithm are where deep learning and machine learning diverge. It can use more extensive data sets since deep learning automates much of the feature extraction portion of the process, reducing the need for manual human participation. According to Lex Fridman in the same MIT lecture cited above, deep learning consider “scalable machine learning.” Traditional, or “non-deep,” machine learning relies more on human input. Usually requiring more structured data to learn, human specialists decide the hierarchy of characteristics to grasp the distinctions between data inputs.

Although “deep” machine learning can use labelled datasets, commonly referred to as supervised learning, to guide its algorithm, not necessary, it can automatically discover. The hierarchy of features separates distinct data types from one another and can ingest unstructured material in its raw form (such as text and photos). We can scale machine learning in more exciting ways since it doesn’t need human assistance to handle data, unlike machine learning.

uses for artificial intelligence

Today, AI systems have a wide range of practical uses. Some of the more typical examples are list below:

Speech synthesis:

It is a capability that turns spoken utterances into written words using natural language processing (NLP). It sometimes refers to speech-to-text, computer voice recognition, and automatic speech recognition (ASR). Mobile devices frequently have speech recognition capabilities that enable voice search (like Siri) or enhanced texting accessibility.

Customer service:

Online virtual agents are taking the place of human agents throughout the customer journey. They respond to commonly asked questions (FAQs) regarding subjects like shipping or offer individualised advice, cross-selling products or making sizing suggestions for users, transforming how we think about customer involvement on websites and social media platforms. Examples include duties often carried out by virtual assistants and voice assistants, messaging apps like Slack and Facebook Messenger, and messaging bots on e-commerce sites with virtual agents.

Computer vision:

This artificial intelligence (AI) technology enables computers and systems to extract useful information from digital photos, videos, and other visual inputs, and based on those inputs, it can take action. It differs from picture recognition tasks since it may make recommendations. Computer vision uses nervous networks for self-driving cars in the automotive industry, radiological imaging in health care, and photo tagging on social media.

Recommendation engines:

By analysing data on prior consumer behaviour, AI algorithms can assist in identifying data trends that apply to create more successful cross-selling tactics. For example, when a consumer is checking out with an online retailer, recommend appropriate add-ons to them.

Automate stock trading:

High-frequency trading platforms driven by AI carry out hundreds of thousands or millions of deals daily. Without human intervention to maximise stock portfolios.

Conclusion:

The powers of the human mind model and even improved upon by machines thanks to artificial intelligence. AI is becoming increasingly prevalent daily, from the emergence of self-driving cars to the explosion of intelligent assistants like Siri and Alexa. As a result, numerous IT firms from various sectors are investing in artificial intelligence technologies.

Also read: Components of Al What are there uses