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Artificial Intelligence and the Internet of Things

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

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

•  learning processes: this aspect of programming artificial intelligence to obtain data and create rules focuses on how to convert the data into actionable information, rules that are called algorithms for computing devices provides step -by- step instructions on how to complete a particular task.

•  thinking processes: this aspect of programming artificial intelligence on choosing the correct algorithm focuses to reach the desired result.

•  Self-correcting processes: This aspect of AI programming is designed to continually fine-tune algorithms and ensure that they provide the most accurate results possible.

Advantages and disadvantages of artificial intelligence:

Artificial neural networks and AI technologies for deep learning are developing rapidly, mainly because AI processes large amounts of data faster and makes predictions more accurate than humans.

While the sheer volume of data generated on a daily basis would bury a human researcher, AI applications that use machine learning can quickly take that data and turn it into actionable information, but the primary drawback of using AI is that it is costly to process. The large amounts of data required by artificial intelligence programming.

Strong AI vs. Weak AI:

Weak AI is an artificial intelligence system that is designed and trained to complete a specific task. Industrial robots and virtual personal assistants, such as Apple's Siri, use weak AI.

Strong artificial intelligence, also known as artificial general intelligence (AGI), describes programming that can rival the cognitive capabilities of the human brain.

When presenting an unfamiliar task to a powerful AI system, it can use logic to apply knowledge from one domain to another and independently find a solution.

In theory, a strong AI program should be able to pass the Turing Test and the Chinese Chamber Exam.

Augmented Intelligence vs. Artificial Intelligence:

Some industry experts believe that the term AI is closely associated with popular culture, and this has caused the general public to have unlikely expectations about how AI will change the workplace and life in general.

Some researchers and marketers hope that augmented intelligence, which has a more neutral connotation, will help people understand that most applications of AI will be weak and simply improve products and services.

The concept of a technological singularity is a future governed by artificial superintelligence that far exceeds the human brain’s ability to understand or how it shapes our reality but remains within the realm of science fiction (Stone, 2019).

Ethical use of artificial intelligence:

While AI tools offer a range of new functions to companies, the use of AI also raises ethical questions, as it can be used for better or for worse. The AI ​​system will reinforce what it has already learned.

This can be a problem because machine learning algorithms, which underpin many of the most advanced AI tools, only have the intelligence of the data presented in training. 

Since a human chooses the data used to train an AI program, the potential for machine learning bias is ingrained and must be closely monitored.

Anyone looking to use machine learning as part of real-world systems in production needs to be ethical in their AI training and strive to avoid bias. 

This is especially true when AI algorithms that are inherently inexplicable are used in deep learning and Generative Adversarial Network (GAN) applications.

Interpretation and analysis is a potential stumbling block to the use of AI in industries that operate under strict regulatory compliance requirements, for example US financial institutions operating under regulations that require them to explain their credit issuance decisions (Wilkins, 2019).

When a decision to refuse credit is made by means of AI programming, it can be difficult to explain how the decision was arrived at because the AI ​​tools used to make such decisions work by provoking precise correlations between thousands of variables, when the decision-making process cannot be explained. The program is called AI.

Artificial Intelligence Components:

With the hype around AI accelerating, vendors have been scrambling to promote how their products and services use AI.

AI primarily requires specialized hardware and software to write and train machine learning algorithms, and there is no single programming language synonymous with AI, but a few including Python, R, and Java are commonly used (Lawless, Mittu, Sofge, Moskowitz, & Russell, 2019).

Types of artificial intelligence:

Arend Hintze, assistant professor of integrative biology, computer science and engineering at Michigan State University, explained in a 2016 article that AI can be classified into four types (Hintze, 2016), ranging from task-specific intelligent systems widely used today and progressing to Conscious systems that do not yet exist, and the types are as follows:

• The  first type This reaction machines: artificial intelligence systems have no memory of a specific task. 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 since it has no memory, it cannot use past experiences to inform future experiences.

•  Type II: limited memory: Enjoy these artificial intelligence systems memory, so they can use past experiences to inform future decisions, some functions of the design of decision - making in a self - driving cars in this way.

•  Type III Theory of Mind: Mind the theory of the term psychology when applied to artificial intelligence, it means that the system will have a social human intelligence to understand the feelings, this will be a kind of artificial intelligence is able to infer human intentions and predict behavior, which is a necessary skill for systems of artificial intelligence to become Integral members of human teams.

•  Type IV self - awareness: in this type of artificial intelligence systems a sense of self, giving them awareness, instruments of self - awareness has to understand the current situation, this kind of artificial intelligence does not exist yet.

The development of artificial intelligence:

  Cognitive computing and artificial intelligence:

The terms artificial intelligence and cognitive computing are sometimes used interchangeably, but in general, the term AI is used to refer to machines that replace human intelligence by simulating how we feel, learn, process, and interact with information in the environment.

The label cognitive computing is used to refer to products and services that simulate and augment human thought processes.

  Examples of Artificial Intelligence Technology:

Artificial intelligence has been integrated into a variety of different types of technology, and here are six examples:

Automation: When paired with AI technologies, automation tools can expand the size and types of tasks being performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rule-based data processing tasks that humans typically perform. Traditionally, when combined with machine learning and emerging AI tools, RPA can automate larger parts of enterprise functions, enabling tactical bots in RPA to respond to process changes.

Machine Learning: This is the science of making a computer work without programming. Deep learning is a subset of machine learning that can be considered the automation of predictive analytics. There are three types of machine learning algorithms:

Supervised Learning: Data sets are categorized so that patterns can be discovered and used to name new data sets.

Unsupervised education: Data sets are not categorized and are sorted according to similarities or differences.

Reinforcement Learning: The data sets are not categorized but after one or several actions are performed, feedback is given to the AI ​​system.

Machine vision: This technology gives a machine the ability to see. Machine vision captures visual information and analyzes it using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human sight, but machine vision is not biologically bound and can be programmed to see through walls, for example. Example Used in a range of applications from signature identification to medical image analysis, computer vision focused on machine-based image processing is often confused with machine vision itself.

Natural language processing: the processing of human language by a computer program, spam detection is one of the oldest and most famous examples of natural language processing, which looks at the subject line and body of an email message and decides if it is junk. Current methods in NLP rely on learning Automated, NLP tasks include text translation, sentiment analysis, and speech recognition.

Robotics: This engineering field focuses on the design and manufacture of robots. Robots are often used to perform tasks that are difficult for humans to consistently perform, for example robots are used in assembly lines to produce cars or by NASA to transport large objects in space, researchers also use machine learning To build robots that can interact in social environments.

Autonomous driving: Autonomous vehicles use a combination of computer vision, image recognition, and deep learning to build automated skill in driving a vehicle while staying in a lane and avoiding unexpected obstacles, such as pedestrians.

Artificial intelligence applications:

AI in healthcare improves patient outcomes and lowers costs, companies apply machine learning to make better and faster diagnoses than humans, and IBM Watson is one of the most popular healthcare technologies, as it understands natural language and can answer questions put to it, the system extracts patient data and sources Other data available to form a hypothesis, which it then provides with a confidence scoring scheme.

Other AI applications include the use of online virtual health assistants and chatbots to help patients and healthcare clients find medical information, schedule appointments, understand the billing process, and complete other administrative processes. A range of AI technologies are also used to predict, control, and understand epidemics such as COVID-19.

AI in Business: Machine learning algorithms are integrated into analytics and customer relationship management (CRM) platforms to reveal information about how to best serve customers.

Chat bots are integrated into websites to provide instant service to customers, job placement automation has also become a talking point among academics and IT analysts.

AI in Education: AI can automate the grading process, giving teachers more time, it can assess students, adapt to their needs, and help them work at their own pace, AI teachers can provide additional support to students, ensuring that they stay on the right track, and it can change And how students learn, and may replace some teachers.

Artificial intelligence in finance: AI is working in personal finance applications, such as Intuit Mint or TurboTax in financial institutions, where applications such as these collect personal data and provide financial advice, and other programs such as IBM Watson, have been applied in the process of buying a home today, which leads Artificial intelligence software a lot of trading on Wall Street.

AI in Law: The process of discovery by sifting documents in law is often difficult for humans Using AI to help automate legal processes saves time and improves customer service Law firms use machine learning to describe data and predict outcomes, and computer vision to categorize and extract information of documents and natural language processing to interpret requests for information.

AI in Manufacturing: Manufacturing has been at the forefront of integrating robots into workflows, for example industrial robots that were simultaneously programmed to perform single tasks and separate from human workers, increasingly operating as multitasking robots collaborating with humans and taking responsibility for more parts of the work in Warehouses, factory floors and other workspaces.

AI in Banks: Banks are successfully employing chatbots to educate their customers about services and offers and to handle transactions that do not require human intervention, AI virtual assistants are used to improve and reduce the costs of compliance with banking regulations, and banking institutions are also using AI to improve their loan decisions, and set limits Credit and investment opportunities.

AI in Transportation: In addition to the essential role of AI in operating autonomous vehicles, AI technologies in transportation are used to manage traffic, predict flight delays, and make sea freight safer and more efficient.

AI in Security: AI and machine learning in cybersecurity products add real value to security teams looking for ways to identify attacks, malware, and other threats.

Organizations use AI in their Information and Event Management (SIEM) software and related domains to detect anomalies and identify suspicious activities that indicate threats. By analyzing data and using logic to identify similarities with known malicious code, AI can provide alerts to new and emerging attacks in Much sooner than human personnel and iterations of previous technology.

As a result, AI security technology dramatically reduces the number of false positives and gives organizations more time to confront real threats before the damage is done, as modern technology plays a huge role in helping organizations fight cyberattacks.

Regulating artificial intelligence technology:

Despite the potential risks, there are currently only a few regulations governing the use of AI tools, and where there are laws they usually relate to AI indirectly.

For example: US fair lending regulations require financial institutions to explain credit decisions to potential customers. This limits the extent to which lenders can use artificial intelligence algorithms that are inherently opaque and lack interpretability.

The European Union's General Data Protection Regulation (GDPR) places strict limits on how organizations use consumer data, hampering training and functionality for many consumer-facing AI applications.

In October 2016, the National Science and Technology Council released a report examining the potential role government regulations might play in the development of AI, but did not recommend that specific legislation be considered.

Creating laws to regulate AI will not be easy, partly because AI encompasses a variety of technologies that companies use for different ends, and partly because regulations can come at the expense of AI advancement and development.

The rapid development of artificial intelligence technologies is another obstacle to the formation of a meaningful organization of artificial intelligence. Technological breakthroughs and new applications can instantly render existing laws obsolete.

For example: Existing laws regulating the privacy of conversations and recorded conversations do not cover the challenge posed by voice assistants like Amazon's Alexa and Apple's Siri who collect but don't distribute conversations except for technology teams in companies that use them to improve device learning algorithms, and of course the laws that governments have been able to craft To regulate artificial intelligence does not prevent criminals from using technology with malicious intent.

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