History And Applications Of A I Artificial Intelligence

The emergence of artificial intelligence (AI) is shaping an increasing range of sectors. For instance, AI is expected to affect global productivity, equality and inclusion, environmental outcomes, and several other areas, both in the short and long term. Today we will find down the history and the applications of A I artificial intelligence.

1. What Is Artificial Intelligence (AI)?

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Using artificial intelligence in business has lots of capabilities to boost revenue

While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004 paper (PDF, 106 KB) (link resides outside IBM), ” It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”

However, decades before this definition, the birth of the  Aartificial intelligence conversation was denoted by Alan Turing’s seminal work, “Computing Machinery and Intelligence” (PDF, 89.8 KB)(link resides outside of IBM), which was published in 1950. In this paper, Turing, often referred to as the “father of computer science”, asks the following question, “Can machines think?”  From there, he offers a test, now famously known as the “Turing Test”, where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its publication, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.

Stuart Russell and Peter Norvig then proceeded to publish, A I Artificial Intelligence A Modern Approach (link resides outside IBM), becoming one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting:

Human approach:

  • Systems that think like humans
  • Systems that act like humans

Ideal approach:

  • Systems that think rationally
  • Systems that act rationally

Alan Turing’s definition would have fallen under the category of “systems that act like humans.”

At its simplest form, A I artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms that seek to create expert systems that make predictions or classifications based on input data.

Today, a lot of hype still surrounds AI development, which is expected of any new emerging technology in the market. As noted in Gartner’s hype cycle (link resides outside IBM), product innovations like self-driving cars and personal assistants, follow “a typical progression of innovation, from overenthusiasm through a period of disillusionment to an eventual understanding of the innovation’s relevance and role in a market or domain.”

As conversations emerge around the ethics of AI, we can begin to see the initial glimpses of the trough of disillusionment.

2. How Does AI Work?

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AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms

Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using its computational prowess to surpass what we are capable of.

To understand How Artificial Intelligence actually works, one needs to deep dive into the various sub-domains of A I Artificial Intelligence and understand how those domains could be applied to the various fields of the industry. You can also take up an artificial intelligence course that will help you gain a comprehensive understanding.

  • Machine Learning: ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns, analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data saves human time for businesses and helps them make better decisions.
  • Deep Learning: Deep Learning is an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome.
  • Neural Networks: Neural Networks work on similar principles as Human Neural cells. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain does.
  • Natural Language Processing: NLP is a science of reading, understanding, interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly.
  • Computer Vision: Computer vision algorithms try to understand an image by breaking down an image and studying different parts of the objects. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations.
  • Cognitive Computing: Cognitive computing algorithms try to mimic a human brain by analyzing text/speech/images/objects in a manner that a human does and tries to give the desired output.

3. Why Is Artificial Intelligence Important?

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A I artificial intelligence has helped fuel an explosion of inefficiency and opened the door to entirely new business opportunities for some larger enterprises

AI is important because it can give enterprises insights into their operations that they may not have been aware of previously and because, in some cases, AI can perform tasks better than humans. Particularly when it comes to repetitive, detail-oriented tasks like analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and with relatively few errors.

This has helped fuel an explosion of inefficiency and opened the door to entirely new business opportunities for some larger enterprises. Prior to the current wave of AI, it would have been hard to imagine using computer software to connect riders to taxis, but today Uber has become one of the largest companies in the world by doing just that. It utilizes sophisticated machine learning algorithms to predict when people are likely to need rides in certain areas, which helps proactively get drivers on the road before they’re needed. As another example, Google has become one of the largest players for a range of online services by using machine learning to understand how people use their services and then improving them. In 2017, the company’s CEO, Sundar Pichai, pronounced that Google would operate as an “AI-first” company.

Today’s largest and most successful enterprises have used AI to improve their operations and gain an advantage over their competitors.

4. What Are The Pros and Cons Of Artificial Intelligence?

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Is there a balance between the pros and cons of artificial intelligence

4.1 Advantages Or Benefits Of Artificial Intelligence

Zero Human Error

This is why the adoption of artificial intelligence in various domains has shot up. When you can nullify human errors completely, you get accurate results. The catch is, to program properly.

Machines take accurate decisions based on the previous information that they gather over time while applying certain algorithm sets. Thus, there is a reduction in error and a spike in inaccuracy.

For instance, Google recently shared on its AI blog about a machine learning method that will work around predicting the weather. Google is going to call it ‘Nowcasting‘ which will predict whether zero tp six hours ahead of time. Google believes by using less data and simple methodology, they can predict weather more accurately, especially in events like thunderstorms or ones related to precipitation.

Zero Risks

Putting machines into tasks that can be a danger to humans can pay off well. For instance, enabling machines to deal with natural calamities can result in faster recovery and lesser pressure on human teams.

This idea comes riding high on Google and Harvard’s initiative of developing an AI system that can predict the aftershock locations of an earthquake. After studying over 131,000 earthquakes and their aftershocks, scientists have tested this neural network on 30,000 events. It showed more accuracy in pinpointing aftershock locations when pitted against traditional methods.

Round-The-Clock Availability

It is obvious that machines don’t get tired. Machines can work endlessly without breaks and don’t even get bored doing the same thing repeatedly, unlike a human.

In November last year, Google announced Contact Center AI for businesses to improve customer experience. This is a classic example of an AI-enabled helpline system for businesses to relentlessly address customer queries and issues, and resolve them on priority for improved customer experience.

Similarly, Amazon Lex, the chatbot designed for call centers by Amazon is capable of having intelligent conversations against human queries. It incorporates the same technology as Amazon Alexa – i.e. to recognize the caller’s intent, ask related follow-up questions, and provide answers. These chatbots are available round-the-clock catering to customers across the globe and varied timezones seamlessly.

AI Machines Have No Emotions

Machines have no emotions [well, unless you are Chitti, the robot, that, quite frankly, had me confused with its obsession over the female protagonist Sana]. This single attribute about Ai-enabled machines can help you deal with customer grievances more steadily.

Imagine a feature in your software not functioning suddenly causing inconvenience to your users. They will surely raise tickets, ping on your chat support, and write emails. Often people attempt for ‘live chat’ rather than waiting on email or tickets to get resolved.

Now imagine having a human at the receiving end, who fully understands the issues and is trying hard to resolve queries in hundreds of numbers. At one point, that person will break. Chances are chats that may go hay-way in terms of sensitivity of language.

However, with an AI-enabled chat system, no such breaking point arises. Customers can throw any amount of weird queries, your chatbot has pre-fed answers that it will keep on showing as per its assessment of query.

It is definitely one of the safest ways to handle such scenarios because machines don’t feel anything. They will only analyze the words used in the query, matching them against the pre-fed information, and give out relevant answers.

AI-Machines Can Take Decisions Fast

Using A I artificial intelligence and other technologies can help make machines that can make data-driven decisions much faster than humans.

Why trust a machine’s decisions?

Simple – it is devoid of any emotions and biased views. When a human takes a decision, a lot of it it is driven by emotions. A machine, on the other hand, is highly practical and rational in its approach. This ensures more accurate and result-oriented decision-making.

For instance, IBM’s Deep Blue supercomputer takes decisions based on all the probabilities possible from the opponent’s end. A human cannot fathom so many probabilities in one go like a machine.

4.2 Disadvantages Of Artificial Intelligence

AI-Enabled Machines Incur Heavy Costs

Looking at the complexity and AI-enabled machine handles, it makes sense that AI-driven initiatives can be heavy on pockets. Creating a machine that can mimic human logic and reasoning requires plenty of resources and time, making it quite costly.

Machines Lack Creativity

The issue with machines is that it functions as programmed. While A I artificial intelligence has made machines capable of learning over time, they cannot learn to think outside the box. A machine will always analyze a situation in terms of pre-fed data and past experiences. It is difficult for a machine to be creative in its approach.

One of the classic examples is the Forbes earning reports. Forbes uses Quil, a bot, to write these reports. These reports are facts and data that are pre-fed to the bot. Forrester predicts that such bot-driven content may replace 16 percent of jobs in the US by 2025 but it is a debatable topic.

The problem with this bot-written article is that it lacks the human touch, unlike other Forbes articles. The creative touch to explaining events and use cases while writing an article is not present when a machine does it.

This analogy leads us to the next pointer.

AI-Enabled Machines May Kill Job Employments

AI is replacing the majority of repetitive tasks with bots. The need for human interference is going down as businesses look towards more error-free and risk-free work. Add to this; machines bring speed with them. This has resulted in the killing of many job opportunities that were once prevalent. Job roles like simple data entry or talking to customers at the first touchpoint i.e. chat supports are now handled by bots that can do it more effectively and round-the-clock.

A two-year-long study by McKinsey has suggested that by 2030 robots will replace at least 30 percent of human labor, i.e., almost 400 to 800 million jobs that will require 375 million people to switch jobs.

No Emotions Can Be Daunting At Times

While this is one of the key benefits of A I artificial intelligence, it is also a con of artificial intelligence. Machines cannot bond with humans, because they do not have emotions or sympathy. While machine learning and NLP have helped brands set up initial customer support through bot-enabled chat systems, they still require a human of blood and flesh to intervene at one point to resolve an ongoing issue.

If the whole of it is left to bots, customer experience across the globe will go downhill. Bots can do the initial touch basing. If customers query is resolved through pre-fed guide documents, great. If not, a ticket is automatically raised by the bot for a human to manually follow up.

Sometimes, a bot cannot understand your pain point because you do not emotionally drive it. You will always need a human ear to get things done.

AI-Enabled Machines Do Not Understand Ethics

Another human feature that is hard to incorporate inside a machine – ethics. Morality is absent in a machine and it is also hard to design and convey through technology. A I Artificial intelligence can help businesses cut down the time taken to complete a monotonous task but expecting a machine to follow ethical values is as vague as drawing sketches on water.

5. What Are The 4 Types Of Artificial Intelligence?

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  • 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 psychological 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.

6. History Of Artificial Intelligence: Key Dates And Names

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Brief History of Artificial Intelligence

Maturation Of Artificial Intelligence (1943-1952)

  • 1943: The first work which is now recognized as AI was done by Warren McCulloch and Walter pits in 1943. They proposed a model of artificial neurons.
  • 1949: Donald Hebb demonstrated an updating rule for modifying the connection strength between neurons. His rule is now called Hebbian learning.
  • 1950: The Alan Turing was an English mathematician and pioneered Machine learning in 1950: Alan Turing publishes “Computing Machinery and Intelligence” in which he proposed a test. The test can check the machine’s ability to exhibit intelligent

The Birth Of Artificial Intelligence (1952-1956)

  • 1955: An Allen Newell and Herbert A. Simon created the “first A I artificial intelligence program”Which was named as “Logic Theorist”. This program had proved 38 of 52 Mathematics theorems, and find new and more elegant proofs for some theorems.
  • 1956: The word “Artificial Intelligence” was first adopted by American Computer scientist John McCarthy at the Dartmouth Conference. For the first time, AI was coined as an academic field.

The Golden Years-Early Enthusiasm (1956-1974)

  • 1966: The researchers emphasized developing algorithms that can solve mathematical problems. Joseph Weizenbaum created the first chatbot in 1966, which was named ELIZA.
  • 1972: The first intelligent humanoid robot was built in Japan which was named WABOT-1.

The First AI Winter (1974-1980)

  • The duration between the years 1974 to 1980 was the first AI winter duration. AI winter refers to the time period where computer scientists dealt with a severe shortage of funding from the government for AI researches.
  • During AI winters, an interest in publicity on A I artificial intelligence was decreased.

A Boom Of AI (1980-1987)

  • 1980: After AI winter duration, AI came back with “Expert System”. Expert systems were programmed that emulate the decision-making ability of a human expert.
  • 1980: The first national conference of the American Association of A I Artificial Intelligence was held at Stanford University.

The Emergence Of Intelligent Agents (1993-2011)

  • 1997: In the year 1997, IBM Deep Blue beats world chess champion, Gary Kasparov, and became the first computer to beat a world chess champion.
  • 2002: for the first time, AI entered the home in the form of Roomba, a vacuum cleaner.
  • 2006: AI came into the Business world till the year 2006. Companies like Facebook, Twitter, and Netflix also started using AI.

Deep Learning, Big Data, And Artificial General Intelligence (2011-Present)

  • 2011: In the year 2011, IBM’s Watson won jeopardy, a quiz show, where it had to solve complex questions as well as riddles. Watson had proved that it could understand natural language and can solve tricky questions quickly.
  • 2012: Google has launched an Android app feature “Google now”, which was able to provide information to the user as a prediction.
  • 2014: In the year 2014, Chatbot “Eugene Goostman” won a competition in the infamous “Turing test.”
  • 2018: The “Project Debater” from IBM debated on complex topics with two master debaters and also performed extremely well.

7. Applications Of Artificial Intelligence

Artificial intelligence has made its way into a wide variety of markets. Here are nine examples.

AI In Healthcare

artificial intelligence 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

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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, A I 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

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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 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.

Security

AI and machine learning are at the top of the buzzword list security vendors use today to differentiate their offerings. Those terms also represent truly viable technologies. Organizations use machine learning in security information and event management (SIEM) software and related areas to detect anomalies and identify suspicious activities that indicate threats. By analyzing data and using logic to identify similarities to known malicious code, AI can provide alerts to new and emerging attacks much sooner than human employees and previous technology iterations. The maturing technology is playing a big role in helping organizations fight off cyber attacks.

8. What Are Examples Of Artificial Intelligence Technology And How Is It Used Today?

Siri

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 Siri – a i artificial intelligence, she’s the friendly voice-activated computer that we interact with on a daily basis.

Everyone is familiar with Apple’s personal assistant, Siri. She’s the friendly voice-activated computer that we interact with on a daily basis. She helps us find information, gives us directions, adds events to our calendars, helps us send messages, and so on. Siri is a pseudo-intelligent digital personal assistant. She uses machine-learning technology to get smarter and better able to predict and understand our natural-language questions and requests.

Amazon

Amazon’s transactional A.I. is something that’s been in existence for quite some time, allowing it to make astronomical amounts of money online. With its algorithms refined more and more with each passing year, the company has gotten acutely smart at predicting just what we’re interested in purchasing based on our online behavior. While Amazon plans to ship products to us before we even know we need them, it hasn’t quite gotten there yet. But it’s most certainly on its horizons.

Netflix

Netflix provides highly accurate predictive technology based on customer’s reactions to films. It analyzes billions of records to suggest films that you might like based on your previous reactions and choices of films. This tech is getting smarter and smarter by the year as the dataset grows. However, the tech’s only drawback is that most small-labeled movies go unnoticed while big-named movies grow and balloon on the platform.

Tesla

If you don’t own a Tesla, you have no idea what you’re missing. This is quite possibly one of the best cars ever made. Not only for the fact that it’s received so many accolades but because of its predictive capabilities, self-driving features, and sheer technological “coolness.” Anyone that’s into technology and cars needs to own a Tesla, and these vehicles are only getting smarter and smarter thanks to their over-the-air updates.

In Conclusion

Artificial intelligence is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. For more information on artificial intelligence, check out some of the best content from our library: OutsourcingVN Blog

Related: Everything you need to know about the Internet of Things (IoT)

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