AI is a set of concepts, tools, and techniques that represents huge disruptive and transformative potential. Definition-wise, we can think of AI simply as intelligence exhibited by machines that can be used in a beneficial way (e.g., carrying out tasks, making decisions, assisting humans, saving lives). More specifically, AI describes when a machine is able to learn from information (data), generate some degree of understanding, and then use the knowledge learned to do something. AI includes machine learning and specific techniques such as deep learning as subsets.
Figure 1. The relationship between AI, machine learning, neural networks, and deep learning.
The field of AI is related to and draws from aspects of neuroscience, psychology, philosophy, mathematics, statistics, computer science, computer programming, and more. AI is also sometimes referred to as machine intelligence or cognitive computing given its foundational basis and relationship to cognition; that is, the mental processes associated with developing knowledge and comprehension.
So what powers intelligence? In the case of humans and animals, new information is constantly collected from experience and the surrounding environment through the five senses. For AI applications, the answer is information in the form of data.
Experience factors heavily into AI, as well. AI is made possible by a training and optimization process that uses relevant data for a given task. AI applications can be updated and improved over time as new data becomes available, and this is the learning-from-experience aspect of AI.
Ultimately, data and the models trained from it can become stale, a phenomena referred to as model drift. It is therefore critical that any applications of AI are refreshed and continue to gain experience and knowledge through continued learning from new data.
AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
Learning processes. This aspect of AI programming focuses on acquiring data and creating rules for how to turn the data into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
Reasoning processes. This aspect of AI programming focuses on choosing the right algorithm to reach a desired outcome.
Self-correction processes. This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.
Four types of artificial intelligence
Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, explained in a 2016 article that AI can be categorized into four types, beginning with the task-specific intelligent systems in wide use today and progressing to sentient systems, which do not yet exist. The categories are as follows:
Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
Cognitive computing and AI
The terms AI and cognitive computing are sometimes used interchangeably, but, generally speaking, the label AI is used in reference to machines that replace human intelligence by simulating how we sense, learn, process and react to information in the environment.
The label cognitive computing is used in reference to products and services that mimic and augment human thought processes
Components of AI
As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular.
AI is not just one technology.
AI as a service (AIaaS)
Because hardware, software and staffing costs for AI can be expensive, many vendors are including AI components in their standard offerings or providing access to artificial intelligence as a service (AIaaS) platforms. AIaaS allows individuals and companies to experiment with AI for various business purposes and sample multiple platforms before making a commitment.
Popular AI cloud offerings include the following:
Amazon AI-Amazon AI is a set of artificial intelligence (AI) services that offer machine learning and deep learning technologies for Amazon Web Services (AWS) customers. ... It also has services that can understand human languages, turn text into speech and analyze images to identify places, faces and objects
IBM Watson Assistant-to build your own branded live chatbot into any device, application, or channel. Your chatbot, which is also known as an assistant, connects to the customer engagement resources you already use to deliver an engaging, unified problem-solving experience to your customers
Microsoft Cognitive Services-Cognitive Services are a set of machine learning algorithms that Microsoft has developed to solve problems in the field of Artificial Intelligence (AI). ... The Cognitive Services APIs are grouped into five categories… Vision—analyze images and videos for content and other useful information.
Google AI-Google’s mission is to organize the world’s information and make it universally accessible and useful. AI is helping us do that in exciting new ways, solving problems for our users, our customers, and the world.
Examples of AI technology
AI is incorporated into a variety of different types of technology. Here are six examples:
Automation. When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA's tactical bots to pass along intelligence from AI and respond to process changes.
Machine learning. This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:
Supervised learning. Data sets are labeled so that patterns can be detected and used to label new data sets.
Unsupervised learning. Data sets aren't labeled and are sorted according to similarities or differences.
Reinforcement learning. Data sets aren't labeled but, after performing an action or several actions, the AI system is given feedback.
Machine vision. This technology gives a machine the ability to see. Machine vision captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.
Natural language processing. This is the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and decides if it's junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition.
Robotics. This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. For example, robots are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings.
Self-driving cars. Autonomous vehicles use a combination of computer vision, image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.
ADVANTAGES
The advantages of Artificial intelligence applications are enormous and can revolutionize any professional sector. Let’s see some of them
1) Reduction in Human Error:
The phrase “human error” was born because humans make mistakes from time to time. Computers, however, do not make these mistakes if they are programmed properly. With Artificial intelligence, the decisions are taken from the previously gathered information applying a certain set of algorithms. So errors are reduced and the chance of reaching accuracy with a greater degree of precision is a possibility.
Example: In Weather Forecasting using AI they have reduced the majority of human error.
2) Takes risks instead of Humans:
This is one of the biggest advantages of Artificial intelligence. We can overcome many risky limitations of humans by developing an AI Robot which in turn can do the risky things for us. Let it be going to mars, defuse a bomb, explore the deepest parts of oceans, mining for coal and oil, it can be used effectively in any kind of natural or man-made disasters.
Example: Have you heard about the Chernobyl nuclear power plant explosion in Ukraine? At that time there were no AI-powered robots that can help us to minimize the effect of radiation by controlling the fire in early stages, as any human went close to the core was dead in a matter of minutes. They eventually poured sand and boron from helicopters from a mere distance.
AI Robots can be used in such situations where intervention can be hazardous.
3) Available 24x7:
An Average human will work for 4–6 hours a day excluding the breaks. Humans are built in such a way to get some time out for refreshing themselves and get ready for a new day of work and they even have weekly offed to stay intact with their work-life and personal life. But using AI we can make machines work 24x7 without any breaks and they don’t even get bored, unlike humans.
Example: Educational Institutes and Helpline centers are getting many queries and issues which can be handled effectively using AI.
4) Helping in Repetitive Jobs:
In our day-to-day work, we will be performing many repetitive works like sending a thanking mail, verifying certain documents for errors and many more things. Using artificial intelligence we can productively automate these mundane tasks and can even remove “boring” tasks for humans and free them up to be increasingly creative.
Example: In banks, we often see many verifications of documents to get a loan which is a repetitive task for the owner of the bank. Using AI Cognitive Automation the owner can speed up the process of verifying the documents by which both the customers and the owner will be benefited.
5) Digital Assistance:
Some of the highly advanced organizations use digital assistants to interact with users which saves the need for human resources. The digital assistants also used in many websites to provide things that users want. We can chat with them about what we are looking for. Some chatbots are designed in such a way that it’s become hard to determine that we’re chatting with a chatbot or a human being.
Example: We all know that organizations have a customer support team that needs to clarify the doubts and queries of the customers. Using AI the organizations can set up a Voice bot or Chatbot which can help customers with all their queries. We can see many organizations already started using them on their websites and mobile applications.
6) Faster Decisions:
Using AI alongside other technologies we can make machines take decisions faster than a human and carry out actions quicker. While taking a decision human will analyze many factors both emotionally and practically but AI-powered machine works on what it is programmed and delivers the results in a faster way.
Example: We all have played Chess games in Windows. It is nearly impossible to beat CPU in the hard mode because of the AI behind that game. It will take the best possible step in a very short time according to the algorithms used behind it.
7) Daily Applications:
Daily applications such as Apple’s Siri, Window’s Cortana, Google’s OK Google are frequently used in our daily routine whether it is for searching a location, taking a selfie, making a phone call, replying to a mail and many more.
Example: Around 20 years ago, when we are planning to go somewhere we used to ask a person who already went there for the directions. But now all we have to do is say “OK Google where is Visakhapatnam”. It will show you Visakhapatnam’s location on google map and the best path between you and Visakhapatnam.
8) New Inventions:
AI is powering many inventions in almost every domain which will help humans solve the majority of complex problems.
Example: Recently doctors can predict breast cancer in the woman at earlier stages using advanced AI-based technologies.
DISADVANTAGES
As every bright side has a darker version in it. Artificial Intelligence also has some disadvantages. Let’s see some of them
1) High Costs of Creation:
As AI is updating every day the hardware and software need to get updated with time to meet the latest requirements. Machines need repairing and maintenance which need plenty of costs. It’ s creation requires huge costs as they are very complex machines.
2) Making Humans Lazy:
AI is making humans lazy with its applications automating the majority of the work. Humans tend to get addicted to these inventions which can cause a problem to future generations.
3) Unemployment:
As AI is replacing the majority of the repetitive tasks and other works with robots, human interference is becoming less which will cause a major problem in the employment standards. Every organization is looking to replace the minimum qualified individuals with AI robots which can do similar work with more efficiency.
4) No Emotions:
There is no doubt that machines are much better when it comes to working efficiently but they cannot replace the human connection that makes the team. Machines cannot develop a bond with humans which is an essential attribute when comes to Team Management.
5) Lacking Out of Box Thinking:
Machines can perform only those tasks which they are designed or programmed to do, anything out of that they tend to crash or give irrelevant outputs which could be a major backdrop.
Applications of AI
Artificial intelligence has made its way into a wide variety of markets. Here are eight examples.
AI in healthcare. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best-known healthcare technologies is IBM Watson. It understands natural language and can respond to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema. Other AI applications include using online virtual health assistants and chatbots to help patients and healthcare customers find medical information, schedule appointments, understand the billing process and complete other administrative processes. An array of AI technologies is also being used to predict, fight and understand pandemics such as COVID-19.
AI in business. Machine learning algorithms are being integrated into analytics and customer relationship management (CRM) platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers. Automation of job positions has also become a talking point among academics and IT analysts.
AI in education. AI can automate grading, giving educators more time. It can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. And it could change where and how students learn, perhaps even replacing some teachers.
AI in finance. AI in personal finance applications, such as Intuit Mint or TurboTax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice. Other programs, such as IBM Watson, have been applied to the process of buying a home. Today, artificial intelligence software performs much of the trading on Wall Street.
AI in law. The discovery process -- sifting through documents -- in law is often overwhelming for humans. Using AI to help automate the legal industry's labor-intensive processes is saving time and improving client service. Law firms are using machine learning to describe data and predict outcomes, computer vision to classify and extract information from documents and natural language processing to interpret requests for information.
AI in manufacturing. Manufacturing has been at the forefront of incorporating robots into the workflow. For example, the industrial robots that were at one time programmed to perform single tasks and separated from human workers, increasingly function as cobots: Smaller, multitasking robots that collaborate with humans and take on responsibility for more parts of the job in warehouses, factory floors and other workspaces.
AI in banking. Banks are successfully employing chatbots to make their customers aware of services and offerings and to handle transactions that don't require human intervention. AI virtual assistants are being used to improve and cut the costs of compliance with banking regulations. Banking organizations are also using AI to improve their decision-making for loans, and to set credit limits and identify investment opportunities.
AI in transportation. In addition to AI's fundamental role in operating autonomous vehicles, AI technologies are used in transportation to manage traffic, predict flight delays, and make ocean shipping safer and more efficient.
THE TECH PLATFORM