avril 20, 2021
The term artificial intelligence might take you back to those sleepless nights when the T-800 from The Terminator haunted you like nothing before.
But in reality, we’re far away from creating such intelligent and superior machines.
As a matter of fact, some AI experts feel we might never create machines that can outsmart us – at least not for a century or two.
If you’re curious to know what is artificial intelligence and how it works to help us, keep reading to have your eyes opened to this incredible technology.
Artificial intelligence (AI) is the branch of computer science that deals with the simulation of human intelligence in machines. Another way of describing AI is as a quest toward building machines capable of performing specific tasks that require human intelligence.
AI can free us from monotonous tasks, make fast decisions with accuracy, act as a catalyst for boosting inventions and discoveries, and even complete dangerous operations in extreme environments.
There’s no magic here. It’s a collection of intelligent algorithms trying to mimic human intelligence. AI uses techniques such as machine learning and deep learning to learn from data and use the acquired knowledge to improve periodically.
And AI isn’t just a branch of computer science. Instead, it draws on aspects of statistics, mathematics, information engineering, neuroscience, cybernetics, psychology, linguistics, philosophy, economics, and much more.
The notion that reasoning could be artificially implemented on machines dates back to the 14th century when Catalan poet, Ramon Llull, published Ars generalis ultima (The Ultimate General Art). In his book, Llull discussed combining concepts to create new knowledge with the help of paper-based mechanical means.
For centuries, many mathematicians and philosophers, through a number of varying concepts, shaped the idea of artificially intelligent machines. But the field gained prominence when Alan Turing, an English mathematician, published his paper Computing Machinery and Intelligence in 1950 with a simple proposition: can machines think?
In 1956, John McCarthy coined the term “artificial intelligence” at the Dartmouth Summer Research Project on Artificial Intelligence – a conference McCarthy hosted along with Marvin Minsky. Although the conference fell short of McCarthy’s expectations, the idea carried on, and AI research and development has been progressing at an incredible rate ever since.
As a term, artificial intelligence might be easy to understand and discuss. But when considered as a concept, AI can be quite overwhelming, especially if you’ve just started exploring. To better understand how AI works, let’s take a closer look at the five core components that make the technology a reality.
Machine learning (ML) is an application of artificial intelligence that offers computers the ability to learn and improve from experience automatically without being explicitly programmed to do so.
Machine learning algorithms are capable of analyzing data, identifying patterns, and making predictions. These algorithms are designed to continually improve by learning and adapting to newer datasets exposed to them. An excellent example of the application of ML is the spam filtering algorithm in your email account.
Deep learning (DL) is a subset of machine learning. It utilizes artificial neural networks to enable machines to learn by processing data. Deep learning helps machines to solve complex problems even if the dataset provided is unstructured and intensely diverse.
Here, the learning process takes place by adjusting system actions based on a continuous feedback loop. The system is rewarded for every right action and punished for the wrong ones. The system tries to modify the actions in order to maximize the reward.
An artificial neural network (ANN) is a component of artificial intelligence, designed to simulate the manner in which the human brain analyzes and processes information. ANN offers AI with self-learning capabilities and can also be considered as the foundation of the same technology.
Artificial neural networks are built to mimic the biological neural networks of human brains. The artificial counterparts of neurons – the fundamental units of the brain – are perceptrons. A massive number of perceptrons are stacked together to form ANNs.
Natural language processing (NLP) is a branch of AI that offers machines the ability to read, understand, and produce human language. The majority of voice assistants use NLP.
As you probably know, computers use low-level language or machine language to communicate. Such a language is composed of ones and zeros, and humans will have a hard time decoding it.
Similarly, computers will have a tough time understanding human languages – if not for NLP. NLP uses intelligent algorithms to convert unstructured language data into a form the computers can understand.
Computer vision (CV) is a field of computer science that aims at replicating the human vision system to enable machines to “see” and understand the content of images and videos.
With advancements in DL, the field of CV has been successful in breaking free from its previous barriers. Computer vision grants image recognition capabilities to machines to detect and label objects. CV is a critical component that makes self-driving cars possible. With CV, such vehicles can see lane markings, signs, other automobiles, and drive safely without hitting any obstacles.
Another excellent application of computer vision is the auto-tagging feature in Google Photos. It can sort pictures based on their content and place them in albums. For instance, if you take a lot of pictures of your cat, the app will automatically group all those cat photos into a single album.
Artificial intelligence works the same way the human brain works. It isn’t coincidental at all as AI is all about mimicking human intelligence. Although all the components discussed in the previous section significantly contribute to AI’s effectiveness, machine learning takes it a step further. ML helps AI to analyze and understand information and adapt based on experience.
To better understand how artificial intelligence works, consider a standard software application that identifies the rainfall intensity based on the precipitation rate. If the precipitation rate is under 2.5 mm per hour, the rain intensity will be “light”. Similarly, if it’s fewer than 7.5 mm per hour but greater than 2.5 mm per hour, then the rain intensity will be “moderate” – you get the gist.
Since it’s a standard application, a developer will have to hardcode the range of each category for the classification to be precise. If the developer makes a mistake while setting the range, the application will work, but with the wrong range and will have no means to correct itself.
But if a developer decides to create an application powered by AI, they would just have to provide a dataset that contains precipitation rate and their classification. The AI would train using this dataset and will be able to determine the rainfall intensity without requiring any range.
Artificial intelligence can be classified into three categories based on its capabilities to mimic human intelligence. The easiest way to categorize them is as weak, strong, and super. To know more about how artificial intelligence works and why you don’t have to be concerned about the same technology outsmarting us, let’s look at its three classification types.
Artificial narrow intelligence (ANI) or weak AI is the most basic and limited type of AI.
But don’t be misled by the term “weak“. Even though this type of machine intelligence is labeled as narrow and weak, it’s pretty adept when it comes to performing the specific task it’s programmed to do.
Virtual personal assistants like Siri, Alexa, and Google Assistant are examples of weak AI. But they aren’t the best examples as weak AI can do more than that. IBM Watson, Facebook’s newsfeed, Amazon’s product recommendations, and self-driving cars are all powered by ANI.
Narrow AI is very good at performing monotonous tasks. Speech recognition, object detection, and facial recognition are all a child’s play for this kind of AI. However, this type of AI works under certain limitations and constraints – hence, it is weak.
Weak AI can also identify patterns and correlations in real-time on large amounts of data, also known as big data. Also, ANI is the only type of AI that humanity currently has access to, meaning, any form of artificial intelligence you come across will be a weak AI.
An AI agent said to possess artificial general intelligence (AGI) would be able to learn, perceive, comprehend, and function just like a human being. AGI is also known as strong AI or deep AI, and in theory, it can do anything a human can do.
Unlike ANI, strong AI is not restricted to any form of narrow sets of limitations or constraints. It can learn, improve, and perform a variety of tasks. Achieving AGI also means that we’ll be able to create computer systems capable of exhibiting multi-functional capabilities like us.
The fear of AI enslaving the human race starts with AGI. The self-aware killer robots like the T-800 of The Terminator – if they ever exist – would possess this level of artificial intelligence.
And yes, we’re years away from creating strong AI. Since this type of artificial intelligence can think, understand, and act like humans, it also means it will have the full set of cognitive abilities the humans take for granted.
Scientists are trying to figure out how to make machines conscious and instill the cognitive abilities that make us intelligent. If scientists succeed, we would be surrounded by machines, not just capable of improving their efficiency in performing specific tasks, but also with the ability to apply the knowledge acquired through experience.
This also means that deep AI will be able to recognize emotions, beliefs, needs, and also the thought process of other intelligent systems. If you’re wondering how the intelligence levels of AI systems are measured, it is with the help of tests like the Turing test, which determines whether an AI system can think and communicate like a human.
Artificial super intelligence, or ASI for short, is a hypothetical AI. ASI is also referred to as super AI, and only after achieving AGI can we even think of ASI. Super AI is where machines surpass the capacity of human intelligence and cognitive abilities.
Once we unlock ASI, machines will have a heightened level of predictive capabilities and will be able to think in a manner that is simply impossible for humans to comprehend. Machines powered by ASI will beat us at everything. Our decision-making and problem-solving capabilities will look inferior in front of a super AI.
Many experts in the industry are still skeptical about the feasibility of creating ASI. The chances are high that none of us will live to see this type of AI – unless, of course, if we unlock immortality before.
Even if we somehow manage to attain super AI and lay down rigid rules to control it, there are almost zero reasons why a machine with superior intelligence must listen to us. Even if we try to pull the plug, it would’ve already initiated countermeasures to nullify our actions as its predictive abilities would be tremendous.
If you were ever terrified thinking AI might outsmart and enslave humans, here’s a reality check – it’s not going to happen anytime soon – if ever. Although scientists have invested decades in this field, we are only making baby steps. But our pace is something the forefathers of artificial intelligence technology would have always envied to achieve.
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