Artificial Intelligence (AI)
Machines in this modern era are not only performing complex tasks for which they were designed, but they are also outclassing humans in terms of intelligence. Virtual digital assistants have changed the way we go about our daily lives. Alexa and Siri have grown into real people with whom we have daily interactions for both small and large tasks. They are progressing so quickly and becoming more intelligent and faster in their interactions because of their natural language abilities and ability to learn without human intervention.
We usually aren’t aware of how much AI is used in our daily lives. Siri, Alexa, Google Maps, and other assistants rely on AI to function. AI’s goal is to help us complete complex tasks faster and more efficiently, thus reducing our workload and saving time.
“Are machines capable of reasoning?” A question posed by Alan Turing, a mathematician, in 1950, we now know that it is not only possible but that several machines have been built to perform tasks similar to those performed by humans. Different tests have been performed to determine AI’s human-likeness, such as the Turing Test, which concludes that AI should be able to converse with humans in a way that humans do not understand if they are conversing with machines. Among the other tests are:
Cognitive Modelling Approach: In this test, AI models are built on the basis of human cognition to observe our thoughts, behavior, and brain imaging using MRI to see how our brain works in different scenarios.
Law of Thought Approach: It’s a long list of logical statements that govern how our minds work, which can be coded and used in AI algorithms, but its application would be quite different.
Rational Agent Approach: According to its law, the AI system must behave in accordance with logical statements, but if the logical action is not the best one to perform in a given situation then it must take the best possible decision and act accordingly.
Where can we find simple, everyday examples? Have you ever tried to type something into Google only to find it already written after typing just one word? Or you might be wondering to see recommendations for movies you were planning to watch? Many organizations use predictive search algorithms (AI) to help you have a better experience with them. Uber, the largest company is also using machine learning algorithms to connect riders to taxis which was not possible before AI.
To reduce human errors and increase efficiency, AI systems are assisting with routine tasks. NLP (Natural Language Processing) is used to transcribe medical notes and to help structure patient information so that doctors can read it more easily. AI can now read MRI scans to check for tumors and other malignant growths at an exponentially faster rate than radiologists can, with a much lower margin of error, thanks to computer vision and convolutional neural networks. With NLP, AI can go through millions of CVs in seconds to see whether they’re a suitable match. This is advantageous since there would be no human errors or prejudices, and hiring cycles would be far shorter. AI is being used to analyze employee data and position them in the appropriate teams, collect feedback about the workplace, assign tasks based on their abilities, and even determine if they are ready to leave.
Narrow AI and Artificial General Intelligence (AGI) are two vast categories of AI. Weak AI, a term which is sometimes used for Narrow AI performs simple tasks, it basically focuses on doing a specific task efficiently such as a Google search engine, Siri, and other assistants. AGI sometimes referred to as Strong AI is closer to human capabilities like robots we expect to build in the future. There is also an Artificial Super Intelligence (ASI), which is only a step ahead of AGI but it would be like science fiction because it includes AI systems making decisions, building emotional relations, and having proper control but we have no idea how far in the future would it be possible.
In 2016, Arend Hintze, an assistant professor at Michigan State University categorized and explained the types of AI:
Type 1: These AI systems are called Reactive machines which work on specific tasks and have no memory like Deep blue (IBM Chess program), which can identify pieces on the chessboard and can do predictions but because it has no memory, it can’t inform future ones by using previous experiences.
Type 2: This kind of AI system can be referred to as the theory of mind and would be able to understand emotions and could predict human intentions and behavior.
Type 3: These types of systems can use past experiences for future predictions means they have limited memory, like self-driving cars.
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Type 4: It does not exist yet, these types of AI systems will be conscious of their self-existence.
In 1943, the first paper was published by Warren McCullough and Walter Pitts regarding the building of neural networks, and then in 1949, Donald Hebb proposed a Neuropsychological theory which is still important in AI, in which he claimed that neural pathways are created from experiences and the neural connection becomes stronger when they’re more frequently used.
We can’t deny the fact that AI has helped humans in several ways by suppressing human error, improving security, efficient communication, faster decision making, and much more. In 2020, during the Covid-19 pandemic, LinearFold AI Algorithms helped in predicting the RNA sequence of the virus (only in 27seconds), which was 120 times faster than other methods. Artificial Intelligence is very beneficial when used correctly but if not then the results could be so destructive and devastating. The killer robots, also known as slaughterbots, use AI and are designed to automatically detect human presence, select and kill. Things could become out of hand and it is very dangerous because it could result in mass casualties. Despite that, AI is way too expensive.
Artificial intelligence has a wide variety of applications like machine learning algorithms, which are being incorporated into analytics and customer relationship management (CRM) platforms, in order to uncover data on how to better serve customers. Chatbots have been integrated into websites to provide customers with immediate service. Academics and IT analysts are debating whether or not job positions should be automated. Numerous AI entities are being used by banks to inform customers about services and offerings as well as process transactions. Banks are also using artificial intelligence to improve loan decision-making, set credit limits, and identify investment opportunities. AI is also used in transportation to, estimate flight delays, manage traffic, and make ocean shipping more efficient and safe. To improve security, different organizations are using AI which can identify and indicate suspicious activities or attacks more efficiently and quickly than humans. It also saves us from upcoming attacks by analyzing data and using logic, hence it plays an important role in fighting back the cyber-attacks.
To completely comprehend how Artificial Intelligence works, one must first gain a thorough understanding of the numerous sub-domains of AI and how they are applied to various industrial industries. Automation tools, when combined with AI technologies, can enhance the volume and variety of tasks performed. Robotic process automation (RPA), for example, is a type of software that automates rules-based, repetitive data processing tasks that were previously performed by humans. When RPA is integrated with machine learning and new AI tools, it may automate a bigger amount of corporate jobs, allowing RPA’s tactical bots to pass along AI intelligence and react to process changes.
A subset of AI, machine learning has the ability to take actions without programming and by using experience. Machine learning algorithms automatically learn and improve. To produce the desired result, they do not require specific guidelines. They learn by watching and comparing their accessible data sets to finished product examples. They try to reverse-engineer the facets to produce output by looking for patterns in the final output.
Deep learning is a subfield of machine learning that deals with artificial neural networks, which are algorithms inspired by the structure and function of the brain. Deep Learning concepts are used to teach machines what we humans know instinctively. A computer model can be instructed to run classification acts using deep learning and an image, text, or sound as input. Deep learning is gaining popularity because the models are capable to achieve accuracy. Large labeled data sets, as well as neural network architectures, are used to train these models. In short, deep learning employs brain simulations in the hopes of learning algorithms more efficiently.
Artificial Intelligence plays an important role in the field of robotics engineering that focuses on the design and manufacture of robots that are frequently used to perform tasks that are difficult or inconsistent for humans to complete. Robots, for example, are used in car assembly lines and by NASA to move large objects in space. Machine learning is also being used by researchers to create robots that can interact in social situations.
Machine learning and deep learning are the building blocks of Artificial Intelligence, and they demand a massive amount of power to perform algorithms, as well as a huge number of cores and GPU for efficient operation. They require the same computing power as supercomputers and are quite expensive. Although Cloud Computing and parallel processing systems enable developers to work more efficiently on AI systems, they come at a price that not everyone can afford.
Some of the largest companies like Google, Facebook, and Apple are facing charges for unethical use of user data. Labeled data is used to train machines to learn and make predictions, which is an important aspect of AI. Some businesses are attempting to develop new methodologies and are concentrating on developing AI models that can provide accurate results despite a lack of data. The entire system could be tainted if the information is skewed. The output prediction by deep learning models and their uncertain nature raise major concerns for AI.
Many people around the world are unaware of Artificial Intelligence’s use or existence even if it is being used in almost every device around us. Data privacy is one of the most important concerns that the modern world is facing because it is collected by millions of users around the world and there is a risk of misusing this data. Let’s say a medical service provider serves 1 million people in a city, and due to a cyber-attack, all of the one million users’ personal information ends up in the hands of everyone on the dark web. This information includes information on diseases, health issues, medical history, and more.
Some businesses have already begun to develop innovative solutions to overcome these obstacles. It uses smart devices to train the data, so it isn’t sent back to the servers; only the trained model is returned to the organization.
Artificial intelligence and technology are two areas of life that never stop to excite and astonish us with new ideas, concepts, discoveries, and products, among other things. Although AI has yet to be implemented as described in movies (i.e. intelligent robots), there have been numerous significant attempts to reach that level and compete in the market, such as the robots that appear on television. Despite this, there are hidden projects and developments in industrial firms.
AI has made life easier for humans. One of the most significant benefits is that it speeds up the production of many items while also lowering costs. AI saves a lot of time and money in everyday life. AI machines are capable of doing effective work and can be used to replace humans in dangerous situations.
However, there are some drawbacks to AI machines on the other side of the equation. First and foremost, they can be used to kill people in wars. They also replace workers in the public sector, potentially raising unemployment rates. They can make people lazy to use their minds in general because they rely on machines to do everything for them.
Well, this is just the beginning and the future of AI is very vast, there is much more to come in the future. We have no idea what AI would be able to do for us in the future.
