Artificial Intelligence Class 9 | Edunique

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition, and computer vision.

How does AI work?

As the hype around AI has picked up, vendors have struggled to promote the way their products and services use AI. Often times what they call AI is just a component of AI, like machine learning. AI requires a base of specialized hardware and software to write and train machine learning algorithms. No programming language is synonymous with AI, but some, including Python, R, and Java, are popular.

In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and models, and using those models to make predictions about future states. This way, a chatbot that receives text chat examples can learn how to produce realistic exchanges with people, or an image recognition tool can learn to identify and describe objects in pictures by browsing millions of images. Examples.

AI programming focuses on three cognitive skills: learning, reasoning, and self-correction.

 

Learning process. This aspect of AI programming focuses on acquiring data and creating rules on how to turn data into actionable insights. Rules, called algorithms, provide computing devices with step-by-step instructions on how to accomplish a specific task.

 

Reasoning process. This aspect of AI programming focuses on choosing the right algorithm to achieve the desired result.

 

Self-correction process. This aspect of AI programming is designed to continually tune algorithms and ensure that they deliver the most accurate results possible.

 

Why is artificial intelligence important?

Artificial intelligence is important because it can provide companies with information about their operations that they may not have known before, and because in some cases artificial intelligence can perform tasks better than humans. . Especially when it comes to repetitive and detailed tasks, such as analyzing a large number of legal documents to ensure that the relevant fields are filled in correctly, artificial intelligence tools often finish the work quickly. and with relatively few errors.

 

This has contributed to an explosion in efficiency and opened the door to whole new business opportunities for some large companies. Before the current wave of AI, it would have been hard to imagine using computer software to connect passengers to taxis, but today Uber has grown into one of the biggest companies in the world doing just that. It uses sophisticated machine learning algorithms to predict when people are likely to need trips in certain areas, helping drivers proactively start before they need it. As another example, Google has become a major player in a variety of online services by using machine learning to understand how people use its services and then improve them. In 2017, the company’s CEO, Sundar Pichai, said Google would operate as an “AI first” company.

 

Today’s largest and most successful companies have used artificial intelligence Class 9 to improve their operations and gain advantages over their competitors.

 

What are the advantages and disadvantages of artificial intelligence?

Artificial neural networks and deep learning artificial intelligence technologies are evolving rapidly, mainly because AI processes large amounts of data much faster and makes predictions with greater accuracy than is humanly possible. .

 

While the sheer volume of data created daily would bury a human researcher, artificial intelligence applications using machine learning can take that data and quickly turn it into actionable information. At the time of this writing, the main downside to using AI is that it is expensive to process the large amounts of data required by AI programming.

 

Advantage

 

1. Good at detail-oriented work;

2. Reduced time for data-intensive tasks;

3. Offers consistent results; and

4. Virtual agents with artificial intelligence technology are always available.

 

Disadvantages

 

1. This requires a great deal of technical experience

2. Limited supply of skilled workers to create AI tools

3. You only know what has been shown

4. Lack of ability to generalize from one task to another.

 

Strong AI vs. weak AI

AI can be classified as weak or strong.

 

1. Weak AI, also known as narrow AI, is an AI system designed and trained to accomplish a specific task. Industrial robots and virtual personal assistants, like Apple’s Siri, use weak artificial intelligence.

 

2. Strong AI, also known as General Artificial Intelligence (AGI), describes programming that can mimic the cognitive abilities of the human brain. When faced with an unknown task, a robust artificial intelligence system can use fuzzy logic to apply knowledge from one domain to another and find a solution on its own. In theory, a robust artificial intelligence Class 9 program should be able to pass both a Turing test and the Chinese indoor test.

 

What are the 4 types of artificial intelligence?

Arend Hintze, assistant professor of integrative biology and computer science and engineering at Michigan State University, explained in a 2016 article that AI can be classified into four types, starting with task-specific intelligent systems that are widely used today and progressing to sensitive 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 defeated Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but since it has no memory it can cannot use past experiences to inform future ones.

Type 2: limited memory. These artificial intelligence systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in autonomous vehicles are designed this way.

Type 3: Theory of the mind. Theory of mind is a term of psychology. Applied to AI, this 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 skill necessary for AI systems to become full members of human teams.

Type 4: Self-awareness. In this category, AI systems have a sense of themselves, which gives them awareness. Self-conscious machines understand their own present state. This type of AI does not yet exist.

 

What are the examples of AI technology and how is it used today?

AI is embedded in various types of technologies. Here are six examples:

 

Automating. When combined with artificial intelligence technologies, automation tools can increase the volume and types of tasks performed. One example is robotic process automation (RPA), a type of software that automates repetitive rule-based data processing tasks traditionally performed by humans. When paired with machine learning and emerging AI tools, RPA can automate larger parts of business work, enabling RPA tactical bots to spread AI intelligence and respond to process changes.

Machine learning. It’s the science of running a computer without programming. Deep learning is a subset of machine learning which, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:

Supervised teaching. Data sets are labeled so that patterns can be detected and used to label new data sets.

Unsupervised learning. Data sets are not labeled and are ranked based on similarities or differences.

Reinforced learning. The datasets are not labeled, but after performing one or more actions, the AI ​​system receives feedback.

Industrial 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 sight, but computer vision is not limited by biology and can be programmed to see through walls, for example. It is used in a variety of applications, from signature identification to medical image analysis. Computer vision, which focuses on computer image processing, is often associated with machine vision.

Natural language processing (NLP). It is the processing of human language by a computer program. One of the oldest and most well-known examples of NLP is spam detection, which examines the subject line and text of an email and decides whether it is spam. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis, and speech recognition.

Robotics This area of ​​engineering focuses on the design and manufacture of robots. Robots are often used to perform tasks that are difficult for humans to perform or consistently perform. For example, robots are used on assembly lines for automobile production or at NASA to move large objects in space. Researchers are also using machine learning to build robots capable of interacting in social environments.

Autonomous cars. Autonomous vehicles use a combination of computer vision, image recognition, and deep learning to develop automated skills to steer a vehicle while staying in a certain lane and avoiding unexpected obstacles, such as pedestrians.

 

What are the applications of AI?

Artificial intelligence has found its place in a wide variety of markets. Here are nine examples.

 

AI in health. The biggest bets are on improving patient outcomes and reducing costs. Businesses apply machine learning to perform better and faster diagnostics than humans. One of the most well-known health technologies is IBM Watson. Understands natural language and can answer questions asked. The system extracts patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring scheme. Other AI applications include the use of virtual health assistants and online chatbots to help patients and health care clients find medical information, schedule appointments, understand the process. billing and other administrative processes. Various artificial intelligence technologies are also used to predict, combat and understand pandemics like COVID-19.

 

AI in business. Machine learning algorithms are built into customer relationship management (CRM) and analytics platforms to discover how to better serve customers. Chatbots have been integrated into websites to provide immediate service to customers. Task automation has also become a topic of conversation among academics and IT analysts.

 

AI in education. AI can automate grading, giving teachers more time. You can assess students and adapt to their needs, helping them to work at their own pace. AI tutors can provide additional support to students, making sure 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 apps like Intuit Mint or TurboTax is disrupting financial institutions. Apps like these collect personal data and provide financial advice. Other programs, like IBM Watson, have been applied to the home buying process. Today, artificial intelligence software conducts a large portion of transactions on Wall Street.

 

AI in law. The discovery process (document review) in law is often overwhelming for humans. Using artificial intelligence Class 9 to help automate labor-intensive processes in the legal industry saves time and improves customer service. Law firms use 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 integrating robots into the workflow. For example, industrial robots that were once programmed to perform individual tasks distinct from human workers increasingly function as cobots: smaller, multitasking robots that collaborate with humans and take responsibility for more of the work. warehouse work. , factories. and other workspaces.

 

AI in banking. Banks successfully use chatbots to keep their customers up to date with services and offers and to manage transactions that do not require human intervention. AI virtual assistants are used to improve and reduce the costs of banking regulatory compliance. Banking organizations are also using artificial intelligence to improve their lending decision-making, set credit limits, and identify investment opportunities.

 

AI in transport. In addition to AI’s essential role in the functioning of autonomous vehicles, AI technologies are used in transportation to manage traffic, predict flight delays, and make shipping safer and more efficient.

Security. Artificial intelligence and machine learning are at the top of the list of buzzwords that security providers use today to differentiate their offerings. These terms also represent truly viable technologies. Organizations use machine learning in security information and event management (SIEM) software and related fields to detect anomalies and identify suspicious activity that indicates threats. By analyzing data and using logic to identify similarities to known malicious code, AI can provide alerts on new and emerging attacks long before human employees and previous iterations of technology. Mature technology plays an important role in helping organizations fight cyberattacks.

Augmented intelligence versus artificial intelligence

Some industry experts believe the term artificial intelligence is tied too closely to popular culture, which has led the general public to have unlikely expectations of how AI will change the workplace and life in general. .

 

Intelligence increased. Some researchers and marketers are hoping that the augmented intelligence label, which has a more neutral connotation, will help people understand that most AI implementations will be weak and simply improve products and services. Examples include automatically displaying important information in business intelligence reports or highlighting important information in legal presentations.

Artificial intelligence. True AI, or general artificial intelligence, is closely associated with the concept of technological singularity: a future ruled by an artificial superintelligence that far exceeds the ability of the human brain to understand it or the way it shapes our reality. This remains in the realm of science fiction, although some developers are working on the issue. Many believe that technologies like quantum computing could play an important role in realizing AGI and that we should reserve the use of the term AI for this type of general intelligence.

 

Ethical use of artificial intelligence

While artificial intelligence tools present an array of new features for businesses, the use of artificial intelligence also raises ethical questions because, for better or worse, an artificial intelligence system will enhance what it is. he has already learned.

 

This can be problematic because the machine learning algorithms, which underpin many of the more advanced artificial intelligence Class 9 tools, are only as smart as the data provided during training. Since a human selects the data used to train an AI program, the potential for machine learning bias is inherent and should be closely monitored.

 

Anyone looking to use machine learning in real production systems should consider ethics in their AI training processes and strive to avoid bias. This is especially true when using AI algorithms that are inherently inexplicable in deep learning applications and contradictory generative networks (GANs).

The explanation is a potential barrier to the use of AI in industries that operate under strict regulatory compliance requirements. For example, financial institutions in the United States operate under regulations that require them to explain their credit issuance decisions. However, when the decision to deny credit is made using AI programming, it can be difficult to explain how the decision was made because the AI ​​tools used to make such decisions work by revealing subtle correlations between thousands of variables. When the decision-making process cannot be explained, the program can be called AI black box.

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