Benefits And Risks Of AI Technologies

In recent years, members of the information systems research community have been paying more attention to artificial intelligence (AI). Artificial intelligence (AI) is a broad discipline that encompasses a variety of subfields and aims to automate activities that traditionally require the intelligence of humans. The term “artificial intelligence” (AI) refers to a technology that, despite the general public not being familiar with it, is revolutionizing every aspect of existence. The goal of this article is to educate non-experts about artificial intelligence (AI) and encourage them to use AI as a tool in various fields to rethink the way they integrate data, analyze it, and make decisions. Collecting enough data, along with its processing and analysis, to gain important insights, has evolved as a central pillar of decision making in almost all modern enterprises. However, the volume and diversity of data produced by humans and sensors cannot be managed at scale by humans alone. According to data scientists, the process of human thought can be described as the mechanical manipulation of symbols, which eventually led to the development of AI. These data have been the seeds of the present artificial intelligence. The benefits and drawbacks of Artificial Intelligence (AI) were also discussed along with this topic.

Introduction

Artificial intelligence (AI), sometimes referred to as AI, is a solution that was developed in response to the issues of data and numbers. This important discovery has resulted in many significant technological advancements in practically all fields of study and practice, including engineering, architecture, education, accounting, business, medicine, and many others. Both machine learning and deep learning, which are major subcategories of artificial intelligence, have emerged as powerful and useful techniques for understanding and analyzing data in various business settings. These include retail, healthcare, and financial services. There are various benefits and drawbacks associated with artificial intelligence that need to be weighed in the context of a more comprehensive analysis. In this essay, we will learn about the fundamentals of AI, including its concepts, development stages, advantages and disadvantages, application cases, and its future outlook. To advance in data science and build a solid foundation that will help us better understand AI and its applications, we need to know more about the data science courses offered in India.

The pharmaceutical industry has also not been left behind as AI has revolutionized the healthcare industry, making it more effective and efficient. In the pharmaceutical industry, artificial intelligence is collaborating with researchers to help expand decision-making processes for existing pharmaceuticals and treatments for additional diseases. Additionally, AI aims to accelerate the process of clinical trials by selecting suitable patients from various data sources. As defined by the “father of artificial intelligence” John McCarthy, “The science and engineering of creating intelligent machines is artificial intelligence.” AI is the ability of computer-enabled robotics systems to process information and produce results in a manner comparable to human learning, decision-making, and problem-solving thinking processes. This ability is called “artificial intelligence.”

A world of the future where,

• AI is capable of developing new pharmaceuticals;

• It can find new drug combinations;

• It can provide clinical trials within minutes;

• Pharmaceuticals are not tested on real humans or animals before release; rather, they are tested on virtual models that are engineered to simulate the physiology of organs.

• Robots are becoming increasingly important, both in the production and distribution of medicines.

• Your neighborhood druggist can 3D print personalized medicines in any form and at the dose you want.

Classification Of AI

There are two categories that can be applied to artificial intelligence.

• According to the Caliber publication,

• According to the evidence that existed

The following is a classification of AI systems determined by their caliber:

Artificial narrow intelligence (ANI), also known as weak intelligence: This system has been developed and is being trained to perform a specific task, such as recognizing faces, operating a vehicle, competing in chess, or managing traffic signals. Examples include Apple’s virtual personal assistant Siri and the tagging feature found in social media.

Strong artificial intelligence, often referred to as artificial general intelligence (AGI): This is sometimes referred to as human level AI. It has the ability to simplify the intellectual abilities of humans. Because of this, when faced with an unusual challenge, it is still able to figure out how to accomplish it. AGI is capable of doing anything a human can do.

ASI stands for “artificial superintelligence.” It is a form of brain power that is more active than smart humans in sketching, mathematics, space, and other fields; it excels in every discipline, from art to science. It can range from computers being less intelligent than humans to being trillions of times more intelligent than humans. AI researchers classified AI technology based on whether it was currently present or not yet available. Some of them are as follows: The first category of AI system is known as reactive machines, and it falls under this category. For example, the chess program Deep Blue developed by IBM defeated the current world champion Garry Kasparov in the 1990s. It is able to recognize the pieces moving on the chessboard and it can make predictions, but it does not have a memory to base its past experiences on. Its use is limited, and it is not applicable to any other situation due to the design of the product.

The second type of artificial intelligence system is known as a limited memory system. This technology is able to leverage past experiences to solve both current and upcoming problems. Only this method can be used to develop certain decision-making functions in autonomous vehicles, such as cruise control and lane departure warning systems. The recorded observations are used to keep track of future actions, such as changing lanes in an automobile. Memories of observations do not remain permanently in the brain. “Theory of mind” is the name given to the third type of artificial intelligence system. It indicates that every human being has his or her own unique thoughts, motives, and desires, which have an impact on the choices they make. This is an artificial intelligence that does not exist without a limit.

Tools of AI

To achieve the goals mentioned above, research in AI uses a wide range of different technologies.

Processes of search and optimization

The intelligent search of a wide variety of possible solutions is one of the many ways AI can solve issues. In the field of artificial intelligence, there are two very different types of search: state space search and local search.

State space search

The purpose of state space search is to navigate through a hierarchy of possible states and arrive at a desired state. For example, to perform a process known as means-end analysis, planning algorithms traverse hierarchical goal and sub-goal trees in an attempt to figure out a path that leads to the final objective. Exhaustive simple searches are inadequate to solve most problems that arise in the real world because the search space, or number of places to look, soon expands to enormous levels. The end result is a search that either takes too long or never ends. The use of “heuristics” or “rules of thumb” can be helpful in prioritizing decisions that will be more likely to achieve the goal. In computer programs that play games such as chess or Go, adversarial search algorithms are used. To find a winning position, it works its way through a tree that contains all possible moves and counter-moves.

Local search

A swarm of particles looking for a local minimum Mathematical optimization is used in local search to come up with a numerical answer to a problem. It starts with some sort of educated guess and then gradually improves upon that guess until no further improvement can be made. These algorithms can be thought of as a form of blind hill climbing: we begin the search at a random location on the landscape, and then, by jumping or taking steps, we continue moving our guess upward until we reach the top of the hill. Stochastic gradient descent is the term that describes this process. Variation is used in the search for optimal solutions by evolutionary computation.

For example, they might start with a population of organisms (the guesses), then let the organisms mutate and recombine, while allowing only the healthiest individuals to pass on their genes from generation to generation (refining the guesses). Swarm intelligence techniques can allow distributed search processes to collaborate effectively. Particle swarm optimization and ant colony optimization are two common swarm algorithms employed in search. Both of these algorithms draw inspiration from flocks of birds and ant trails, respectively. Local search is also used by neural networks and statistical classifiers, both of which will be covered in more detail below. In this type of search, the “landscape” that is to be searched is constructed through learning.

Logic

Reasoning and knowledge representation are two applications of formal logic. The most common types of formal logic are propositional logic (which relies on statements to determine whether they are true or false and uses logical connectives such as “and”, “or”, “not”, and “implies”) and deductive logic . and predicate logic, which uses quantifiers such as “every X is a Y” and “there are some Xs that are Y” and also operates on objects, predicates, and relations.

The method of proving a new statement (conclusion) through logical inference (also known as deduction) is the process of demonstrating a new statement (premises) from existing statements that are already known to be true. In addition to inference, questions and claims can be handled by a logical knowledge base as subsets of the latter. An inference rule specifies the criteria for what constitutes a valid step in a proof. Resolution is the most common form of inference rule. The process of deriving a conclusion from given premises and evidence can be summarized as searching for a path that connects the two, with each step involving the execution of a specific inference rule. When done in this manner, inference is impossible, except for short proofs in restricted domains. There is currently no known approach that is simultaneously effective, powerful, and general. The “degree of truth” specified in fuzzy logic ranges from 0 to 1, and is used to manage uncertain and probabilistic scenarios. The ability to handle default reasoning is the primary focus of non-monotonic logic. Many other specialized forms of logic have been devised that are used to describe a variety of complex fields.

Probabilistic methods for uncertain reasoning

The clustering of data from Old Faithful’s eruptions begins with random guesswork, but eventually it manages to converge on the correct clustering of two physically distinct modes of eruption. To solve many problems in artificial intelligence (including thinking, planning, learning, vision, and robotics), the agent must be able to work with ambiguous or partial knowledge. Researchers working in the field of artificial intelligence have developed a variety of tools to handle these issues by applying strategies drawn from the fields of probability theory and economics.

Bayesian networks are a fairly general tool that can be used for a wide variety of problems, including reasoning (using Bayesian inference algorithms), learning (using expectation-maximization algorithms), planning (using decision networks), and perception (using dynamic Bayesian networks). Bayesian networks can be used for all of these difficulties. Probabilistic algorithms can also be used to filter, predict, smooth, and find explanations for streams of data. This helps perception systems analyze processes that occur over time.

Using decision theory, decision analysis, and information value theory, precise mathematical tools have been created that analyze how an agent can make decisions and plans. These tools are extremely accurate. These tools include models such as Markov decision processes, dynamic decision networks, game theory, and mechanism design.

Classifiers and Statistical Methods of Learning

The most basic uses of artificial intelligence can be divided into two categories: classifiers on the one hand, and controllers (for example, “pick a diamond if it is there”) on the other. Classifiers are functions that determine the closest possible match by comparing a pattern to the pattern being observed. Using supervised learning, it is possible to fine-tune them based on the specific examples selected. Each pattern, sometimes called an “observation”, is assigned a certain class that has been predefined. A data set is a collection of those observations, along with the labels they have been given for their categories.

When a new observation is received, that observation is classified based on the experience gained in the past. There are many types of classifiers that can be used. Decision trees are symbolic machine learning algorithms that are the easiest to understand and the most frequently used algorithm. Kernel approaches such as support vector machines (SVMs) replaced k-nearest neighbor algorithms in the 1990s. By the mid-1990s, k-nearest neighbor algorithms were the most widely used form of analogous artificial intelligence. According to reports, the Naive Bayes classifier is Google’s “most widely used learner”. This is apparently partly due to the scalability of the model. Classification is another application for neural networks.

Artificial Neural Networks

A neural network is a group of nodes that are connected to each other. This is analogous to the extensive network of neurons seen in the human brain. The architecture of the human brain served as the inspiration for the development of artificial neural networks. A simple “neuron” N accepts inputs from other neurons, and when these other neurons activate (also called “fire”), they individually cast a crucial “vote” for or against whether or not neuron N should activate itself. In reality, “neurons” are represented by a list of numbers, “weights” are matrices, and learning is done by performing linear algebra operations on vectors and matrices. When trained, neural networks are able to carry out a specific type of mathematical optimization known as stochastic gradient descent on a multi-dimensional topology. This topology is built up as a result of training the neural network.

Neural networks are able to discover patterns in data as a result of their training and learn to model complex interactions between inputs and outputs. In theory, a neural network should be able to master any task. The back propagation algorithm is the training method used most often. Hebbian learning, sometimes known as “fire together, wire together”, was one of the earliest methods of learning used by neural networks.

In a feed forward neural network the signal is only allowed to travel in one direction. The output signal is fed back into the input of the recurrent neural network, which enables the network to have short-term memories of events that have been input in the past. Deep learning uses multiple layers of neurons, unlike perceptrons which use only one layer. This is particularly important in image processing, as a local collection of neurons must recognize an “edge” before the network can identify an item. Convolutional neural networks increase connections between neurons that are “close” to each other.

Deep learning

Deep learning involves representing images at multiple levels of abstraction simultaneously. Deep learning involves using different levels of abstraction to represent pictures. When performing deep learning, multiple layers of neurons are interconnected between the input and output of the network. Raw inputs can be processed by different layers, which can then gradually extract higher-level information. For example, when it comes to image processing, lower layers may be able to recognize edges, while higher layers may be able to recognize concepts that are important to humans, such as digits, letters, or faces.

It has been shown that deep learning significantly increases the performance of programs in various essential aspects of artificial intelligence, such as computer vision, speech recognition, image classification, and many others.

Hardware and software specifically designed for the task.

Programming languages for artificial intelligence and hardware specifically designed for artificial intelligence. In the late 2010s, graphics processing units (GPUs), increasingly designed with AI-specific enhancements and used with specialized TensorFlow software, largely replaced previously used central processing units (CPUs) as the dominant means of large-scale (commercial and academic) training of machine learning models. Throughout history, specialized languages such as Lisp, Prolog, and a few others were used.

Advantages of AI

1. Reduction of human error

Because people make mistakes from time to time, the concept of “human error” was first expressed. Computers, on the other hand, are incapable of making such mistakes, provided they are programmed appropriately. The decisions made by artificial intelligence are influenced by the data and algorithms collected in the past. As a direct result of this, the number of errors made is reduced, and the chances of achieving greater accuracy and precision are increased.

2. Instead of humans, it willingly takes risks

This is undoubtedly one of the most important advantages that come from the use of artificial intelligence. If we create an AI robot that is capable of performing dangerous activities on our behalf then we will be able to overcome many risky limitations of humanity. It is possible to use it effectively in every form of natural or man-made disaster, whether the goal is to fly to Mars, defuse a bomb, explore the deepest parts of the ocean, or mine coal and oil. It applies to all these endeavors.

3. Availability at all times

Without any rest, a human’s typical working day lasts between four and six hours. Humans are designed to be able to take breaks to refresh themselves and get ready for a new day’s work. Also, humans have a weekly day off so that they can keep their personal and professional lives separate. However, unlike humans, we can use AI so that robots can work continuously, seven days a week, twenty-four hours a day, without taking any breaks, and they won’t get bored.

4. Assistance provided through technology

Some of the most cutting-edge businesses have begun connecting with customers through the use of digital assistants, resulting in a reduction in the need for human workers. There are now many websites that use digital assistants to provide customers with the things they want. We are able to talk to them about the things we are looking for. When using some chatbots it can be challenging to determine whether we are interacting with a person or a machine because they are programmed to look human.

Risks and Disadvantages

• Theoretical physicist and professor Stephen Hawking has said that attempts by humans to create machines that are able to think like humans pose a significant threat to the survival of the human race, and that the current competition to produce fully human artificial intelligence may one day result in the destruction of the human species.

• Very high cost because artificial intelligence programs are incredibly complex machines that require a lot of work to develop.

• Unemployment – AI has the potential to cause unemployment because things will be automated in this system, which means that there will be less demand for human labor.

• No match for the intelligence of the human brain.

• • No improvement with experience.
• No fundamental innovation.
• No fundamental invention.

Artificial Intelligence and its Applications

AI and machine learning technology is used in most of the essential applications of 2020, including the following: search engines (such as Google Search), targeting online ads, recommendation systems (offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising (AdSense, Facebook), virtual assistants (such as Siri or Alexa), autonomous vehicles (including drones, ADAS and self-driving cars), and automatic language translation (Microsoft Translator).

There are also thousands of more effective AI apps that are used to solve specific challenges for certain businesses or institutions. These applications can be found all over the world. In a survey conducted in 2017, one in every five businesses said they used “AI” in some aspect of their products or processes. Some examples include energy storage, diagnosis of medical conditions, military logistics, applications predicting the outcome of judicial decisions, foreign policy, or supply chain management, etc.

Since the 1950s, the most cutting-edge methods of artificial intelligence have been demonstrated and tested using game-playing programs. On May 11, 1997, Deep Blue made history by becoming the first computer chess-playing system to defeat the reigning world chess champion. That champion was Garry Kasparov. IBM’s Watson, a question answering system, competed against two of the greatest Jeopardy! champions, Brad Rutter and Ken Jennings, in an exhibition match for the Jeopardy! quiz program in 2011. Watson came out on top and won by a substantial margin. In March of 2016, AlphaGo defeated reigning world Go champion Lee Sedol by a score of four games to one, becoming the first computer Go-playing system to defeat a professional Go player without using an advantage or disadvantage. Other computer programs tackle games with faulty knowledge, such as Pluribus and Cepheus, which play poker beyond human level. In the 2010s, DeepMind created “generalized artificial intelligence” that was able to learn a variety of Atari games on its own.

In the early 2020s, generative AI began to gain widespread attention. 14% of individuals in the United States have tried ChatGPT, which is based on GPT-3 in addition to other major language models. The increasing realism of AI-based text-to-image generators. Rumors of an attack on the Pentagon, a fictional arrest of Donald Trump, and a fake shot of Pope Francis wearing a white puffer coat have gained widespread notice, not to mention their use in professional creative arts. AlphaFold 2 (2020) demonstrated the ability to predict the three-dimensional structure of proteins in a matter of hours rather than months.

Conclusion

A huge amount of potential exists in artificial intelligence to make the world a better place to live. The primary concern should be to limit the amount of reliance on artificial intelligence. The impact of artificial intelligence on businesses around the world cannot be ignored, despite the fact that it has both positive and negative aspects. One can study the different types of tasks that can be done with artificial intelligence (AI) and get promotions according to his level of knowledge if he enrolls in an AI course. Training, learning, and development opportunities are provided in both management and technology through the use of the various courses available. In a word, everything is going to move very quickly, resulting in significant changes and progress. Therefore, it is possible to acquire the skill sets needed to effectively collaborate with AI in business settings.

Read Also: