Today, we hear a lot about AI or Artificial intelligence. It is a new jargon most people do not understand. Most of us think of AI as some kind of Jarvis or Skynet type of machine that can take over the world. Well, that is how it has always been shown to us in movies and stories. But is it really like that? Let's explore the meaning of True AI and whatever AI we have available with us.
True artificial intelligence, also known as strong artificial intelligence or general artificial intelligence, refers to the hypothetical development of a machine or system that is able to perform tasks and solve problems at a level of intelligence that is equal to or exceeds that of a human being. So, it will be able to perform a wide range of tasks and solve problems in a flexible and adaptable way, much like a human being.
True AI is still in the realm of science fiction, and it is not yet clear when or if it will be possible to develop a machine or system that is able to achieve this level of intelligence. However, many researchers and scientists are working on developing AI systems that are able to perform tasks and solve problems at increasingly advanced levels, and it is possible that we may see significant progress in the development of true AI in the future.
So, if true AI is still fiction, what is this AI we keep reading about? AI (Artificial Intelligence) is the simulation of human intelligence in machines that are programmed to think and act like humans. These machines are designed to learn, reason, and self-correct based on their experiences, allowing them to improve their performance on a specific task. Some common applications of AI include language translation, image and speech recognition, and decision-making. Confusing? Don’t worry, in simple language, current AI is nothing but an advanced level of automated tasks performed by machines. The difference between simple old age automation and so-called AI is that AI depends on complex algorithms and dataset/parameters that are constantly updated by the machine itself. This keeps refining the level of results.
A vending machine is an example of automation that has become more and more complex over the years, but it is still static automation, without the AI and self learning. The earlier vending machines had only one product, and one task, accept pre-defined coins, and spill out the stuff if the required number of coins (based on denominations) is inserted. There are modern versions of similar vending machines, that have multiple products, and can accept currency notes, let you choose the product, and along with the product, it will refund the remaining balance amount as well. Then there are more sophisticated versions that open the door based on some barcode scanned from your mobile app, and deduct the amount from your wallet for only the items you picked from the self .
Now, even with so much efficiency, there is a limitation of these machines. These have limited functionality as the instructions fed into these are not updated, and the functions remain static. So, it will continue to perform the task it is assigned with complex instructions, but it will not improvise based on change in scenario. That’s where AI along with ML or Machine Learning steps in to improve this scenario.
Netflix or any other OTT platform will show you dynamically generated recommendations based on the type of movies you have watched. When you log into any online shopping portal like Amazon, it will show you recommendations based on your shopping behavior. These are taking automated decisions to recommend you something that is not defined in their set of instructions. So, these platforms are making decisions based on your behavior. With time, these decisions (recommendations) also keep changing based on how you use the platform. This is possible with the use of complex algorithms and machine learning. In order to understand this in simple terms, here is the basis behind these recommendations. Each movie or show will have multiple parameters associated with weightage of those parameters. Like a movie can be classified as 100% for action, 35% for drama, 50% for comedy, 65% for women empowerment, 10% for bank robbery and so on. Now, whenever you consume any show on their platform, the system will record the parameters of the show you watched in your profile preference. And then search for the closest match in the system to provide recommendations to you. As these will be based on similar parameters, there is a high chance you will pick one of these recommended shows or maybe you search for something new. Now your profile is updated again based on your second choice. This way, the data keeps refreshing and your recommendations keep getting better and closer to your liking. To make it more complex, along with your preferences parameters, it will also check if there is any live event going on that can interest you, or if there is any festival coming. For example, based on your preferences, you are an action movie lover, but it is the time of Christmas, so it will choose Die Hard and recommend it to you. Basically, it matched your preference parameters, and then added the ongoing festival parameter, to choose movies that have action as well as christmas in it. This is the simplest example of AI that is in action today.
With complex algorithms, and tons of data to train the machine, you can actually create a good AI that can perform a lot of complex decision making or diagnostic related tasks like humans. Here are a few examples of how AI is being used in different fields:
In healthcare, AI is being used to analyze medical images, such as x-rays and CT scans, to identify patterns and make diagnoses.
In finance, AI is being used to detect fraud, make investment decisions, and automate trading.
In transportation, AI is being used to develop self-driving cars, which use sensors and algorithms to navigate roads and make decisions about when to turn, accelerate, or brake.
In customer service, AI is being used to develop chatbots, which are software programs that can simulate conversation with human users and provide answers to their questions.
In education, AI is being used to develop personalized learning systems, which can adapt to the individual needs and abilities of each student.
In manufacturing, AI is being used to automate production processes, such as quality control and supply chain management.
In agriculture, AI is being used to monitor crops, predict yields, and optimize irrigation and fertilization.
Basically AI systems are designed to mimic human intelligence, allowing them to perform tasks that would require human-like intelligence, such as recognizing patterns, making decisions, and learning from experience. There are many different ways to build AI systems, but most of them involve the use of algorithms and statistical models that enable the system to learn from data and improve its performance over time.
One common approach to building AI systems is to use machine learning, which involves training a model on a large dataset and then using that trained model to make predictions or decisions. For example, an AI system that is designed to recognize objects in an image might be trained on a dataset of labeled images, where the system is able to learn the patterns that are associated with different objects. Once the model is trained, it can then be used to make predictions on new images that it has not seen before.
Another approach to building AI systems is to use rule-based systems, which are designed to mimic the decision-making processes of human experts. In a rule-based AI system, a set of rules is defined that the system can use to make decisions or take actions. For example, a rule-based AI system that is designed to diagnose medical conditions might be given a set of rules that define the symptoms and characteristics of different diseases, allowing the system to make a diagnosis based on those rules.
Overall, the specific way that an AI system works will depend on the specific problem that it is trying to solve and the approach that was used to build the system.
AI based functions have a direct impact on the economy and jobs. On one hand, it has improved the efficiency and experience of any service, but on the other hand it also removed or reduced the dependency of humans in those fields. This is nothing new, as every automation or machine introduction in history has done this. Unlike these AIs, we humans are more creative and should be able to adapt to this new reality, learn new skills and find new jobs. This is what has been happening since the industrial revolution, and will continue in this new era of AI revolution as well. The good news is, these AIs are not smart, but still same old dumb machines feeding on data and algorithms, so we don’t have to fear for Skynet taking over this world, at least not yet.
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