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AI (artificial intelligence), Apuntes de Ingeniería

Universidad del Zulia (LUZ)Ingeniería

I would luv to work in Ai in the near future, think about it Too

Tipo: Apuntes

2018/2019

Subido el 13/09/2019

anasnej
anasnej 🇪🇸

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AI (artificial intelligence)
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Artificial intelligence (AI) is the simulation of human intelligence processes by
machines, especially computer systems. These processes include learning
(the acquisition of information and rules for using the information), reasoning
(using rules to reach approximate or definite conclusions) and self-correction.
Particular applications of AI include expert systems, speech recognition and
machine vision.
AI can be categorized as either weak or strong. Weak AI, also known as narrow
AI, is an AI system that is designed and trained for a particular task. Virtual
personal assistants, such as Apple's Siri, are a form of weak AI. Strong AI, also
known as artificial general intelligence, is an AI system with generalized
human cognitive abilities. When presented with an unfamiliar task, a strong AI
system is able to find a solution without human intervention.
Because hardware, software and staffing costs for AI can be expensive, many
vendors are including AI components in their standard offerings, as well as
access to Artificial Intelligence as a Service (AIaaS) platforms. AI as a Service
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 Amazon AI services, IBM Watson Assistant, Microsoft
Cognitive Services and Google AI services.
While AI tools present a range of new functionality for businesses ,the use of
artificial intelligence raises ethical questions. This is because deep learning
algorithms, which underpin many of the most advanced AI tools, are only as
smart as the data they are given in training. Because a human selects what
data should be used for training an AI program, the potential for human bias is
inherent and must be monitored closely.
Some industry experts believe that the term artificial intelligence is too closely
linked to popular culture, causing the general public to have unrealistic fears
about artificial intelligence and improbable expectations about how it will
change the workplace and life in general. Researchers and marketers hope
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AI (artificial intelligence)

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems , speech recognition and machine vision.

AI can be categorized as either weak or strong. Weak AI, also known as narrow AI, is an AI system that is designed and trained for a particular task. Virtual personal assistants, such as Apple's Siri, are a form of weak AI. Strong AI, also known as artificial general intelligence, is an AI system with generalized human cognitive abilities. When presented with an unfamiliar task, a strong AI system is able to find a solution without human intervention.

Because hardware, software and staffing costs for AI can be expensive, many vendors are including AI components in their standard offerings, as well as access to Artificial Intelligence as a Service ( AIaaS ) platforms. AI as a Service 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 Amazon AI services, IBM Watson Assistant, Microsoft Cognitive Services and Google AI services.

While AI tools present a range of new functionality for businesses ,the use of artificial intelligence raises ethical questions. This is because deep learning algorithms, which underpin many of the most advanced AI tools, are only as smart as the data they are given in training. Because a human selects what data should be used for training an AI program, the potential for human bias is inherent and must be monitored closely.

Some industry experts believe that the term artificial intelligence is too closely linked to popular culture, causing the general public to have unrealistic fears about artificial intelligence and improbable expectations about how it will change the workplace and life in general. Researchers and marketers hope

the label augmented intelligence, which has a more neutral connotation, will help people understand that AI will simply improve products and services, not replace the humans that use them.

Types of artificial intelligence

Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, categorizes AI into four types, from the kind of AI systems that exist today to sentient systems, which do not yet exist. His categories are as follows:

  • Type 1: Reactive machines. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chess board and make predictions, but it has no memory and cannot use past experiences to inform future ones. It analyzes possible moves -- its own and its opponent -- and chooses the most strategic move. Deep Blue and Google's AlphaGO were designed for narrow purposes and cannot easily be applied to another situation.
  • Type 2: Limited memory. These AI systems can use past experiences to inform future decisions. Some of the decision-making functions in self- driving cars are designed this way. Observations inform actions
  • Automation : What makes a system or process function automatically. For example, robotic process automation (RPA) can be programmed to perform high-volume, repeatable tasks that humans normally performed. RPA is different from IT automation in that it can adapt to changing circumstances.
  • Machine learning: 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: The science of allowing computers to see. This technology 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 (NLP): The processing of human -- and not computer -- 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 the 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: A field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. They 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: These 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.

AI applications

Artificial intelligence has made its way into a number of areas. Here are six 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 is capable of responding 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 chatbots , a computer program used online to answer questions and assist customers, to help schedule follow-up appointments or aid patients through the billing process, and virtual health assistants that provide basic medical feedback.
  • AI in business. Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Machine learning algorithms are being integrated into analytics and 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. AI 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. AI could change where and how students learn, perhaps even replacing some teachers.
  • AI in finance. AI in personal finance applications, such as Mint or Turbo Tax, 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, software performs much of the trading on Wall Street.

How data bias impacts AI outputs

Regulation of AI technology

Despite these potential risks, there are few regulations governing the use AI tools, and where laws do exist, the typically pertain to AI only indirectly. For example, federal Fair Lending regulations require financial institutions to explain credit decisions to potential customers, which limit the extent to which lenders can use deep learning algorithms, which by their nature are typically opaque. Europe's GDPR puts strict limits on how enterprises can use consumer data, which impedes the training and functionality of many consumer-facing AI applications.

Margaret Rouse asks:

How is your company adapting AI to

the enterprise?

Join the Discussion

In 2016, the National Science and Technology Council issued a report examining the potential role governmental regulation might play in AI development, but it did not recommend specific legislation be considered. Since that time the issue has received little attention from lawmakers.

This was last updated in August 2018