Chapter 2

Linguistic and verbal Logical spatial body/movement musical interpersonal/intrapersonal naturalist

Types of intelligence

chatbot

a conversational robot that is used for chatting with people. can be in the form of intelligent agents that retreive information or personal assistants tha provide advice.

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a major issue with robots is their trend to take human jobs.

effective computing - technologies detect the emotional conditions of people and suggest how to deal with discovered problems biometric analysis - technologies verify an identity based on unique biological traits that are compared to stored ones (facial recognition)

emerging AI technologies

assisted intelligence

equivalent mostly to weak AI, which works only in narrow domains. It requires clearly defined inputs and outputs. (monitoring systems - in cars or healthcare, low-level virtual personal assistants) identifies problems or symptoms and suggests predetermined known solutions.

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At this time, AI systems do not have hte same learning capabilities as humans. They have simplistic (but improving) machine learning (modeled after human learning methods). The machine-learning scientists try to teach computers to identify patterns and make connections by showing the machines a large volume of examples and related data. Machine learning also allows computer systems to monitor and sense thier environmental activiteis so they can adjust their behavior to deal with changes in the environment.

to create intelligent machines that are capable of executing a variety of tasks currently done by people. Ideally, they should be able to reason, think abstractly, plan, solve problems, and learn. to perceive and properly react to changes in the environment that influence specific business processes and operations to introduce creaivity in business processes and decision making

Goals of AI

Deep Blue

System that beat the world famous chess player, Garry Kasparov.

Foundations Technologies & Applications PG. 77

The landscape of AI consists of two groups

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There is an increasing trend to make computers smarter.

China

country expecting to be the world leader in AI

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executives need to know what AI can do economically and how companies can use it to benefit their business executives need to know what AI cannot economically do

intelligence

measured by an IQ test composed of reasoning, learning, logic, problem-solving ability, perception, and linguistic ability.

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AI applications are based on how simple input is converted to simple output as a response Applications in these areas (retailing, Web search, banking, logistics, entertainment) are normally fully automated. Automated tasks are usually repetitive and done by people with short periods of training. Ai machines dpend on data that may be difficult to get or data that is innacurate. A second barrier is the need for AI experts - which can be hard to find or expensive to hire.

1. the study of human thought processes (to understand what intelligence is) 2. the representation and duplication of those thought processes in machines (computers, robots). The machines are expected to have human-like thought processes.

AI is concerned with 2 basic ideas:

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Not all AI systems deliver all benefits. SPecific systems may deliver only some of them.

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the major benefti of machine vision and computer vision is lowering the costs of performing tasks, espscially those that are repetitive and make the human eyes tired. The 2 technologies are also combined with image processing that facilitates complex applications, such as visual quality control.

speech (voice) understanding

the recognition and understanding of spoken languages by a computer.

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two areas in which large benefits have already been reaped because of AI are customer experience and enjoyment.

Assisted intelligence Autonomous AI augmented intelligence

3 levels of the capabilities of AI systems

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AI machines have demonstrated superiority over humans in playing complex games such as chess, Jeaprody, and Go (chinese game) - using a computer program known as Google's DeepMind

-lack human touch and feel -lack attention to nontask surroundings -can lead people to rely on machines too much -can be programmed to create distruction -can cause many people to lose their jobs -can start to think by themselves, causing significant damage

Limitations of AI machines

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Machine learning can be used to predict performance, to reconfigure programs based on changing conditions, and more. Machine learning is a scientific discipline concerned with the design and development of algorithms that allow computers to learn based on data coming from sensors, databases, and other sources. This learning is then used for making predictions, recognizing patterns, and suporting decision makers.

1. natural language understanding - investigates methods of enabling computers to comprehend instructions or queries provided in ordinary english or other human languages 2. natural language generation - strives to have computers product ordinary spoken language so that people can understand computers more easily.

Natural language processing includes 2 subfields

artificial brain

a people-made machine that is desired to be as intelligent, creative, and self-aware as humans. To date, nobody has been able to create such a machine. the human brain contains 100 billion neurons. the system tries to imitate a biological brain and be energy efficient.

natural language processing

a technology that gives users the ability to communicate with a computer in their native language. the communication can be in written text and/or in voice (speech). allows for a conversational type of interface in contrast with using a programming language that consists of computer jargon, syntax, and commands.

-learning or understanding from experience -making sense out of ambiguous, incomplete, or even contradictory messages and information -understanding and inferring in a rational way, solving problems, and directing conduct effectively. -responding quickly and successfully to a new situation (using the most correct responses) -applying knowledge to manipulate environments and situations -recognizing and judging the relative importance of different elements in a situation

abilities that are considered signs of human intelligence

Artificial intelligence

aim is to make machines exhibit intelligence as close as possible to what people exhibit, hopefully for the benefit of humans. the theoretical background of it is based on logic.

scene recognition

an applied area of machine vision, which is performed by computer vision. It enables recognition and interpretation of objects, scenery, and photos.

intelligent agent (IA)

an autonomous, relatively small computer software program that observes and acts upon changes in its environment by running specific tasks autonomously. An IA directs an agents activities to achieve goals related to the changes in the surrounding environment. They are effective tools for overcoming the most critical burden of the Internet information overload and making computers more viable decision support tools. They may have the ability to learn by using and expanding the knowledge embedded in them. They were initially used to support routine activities (searching for products, getting recommendations, determining product pricing). Their major benefits were increasing speed, reducing costs and errors, and improving customer service. Their applications are much more sophisticated today. (virus detection program)

robot

an electromechanical device that is guided by a computer program to perform manual and/or mental tasks. a programmable multifunctional manipulator designed to move materials, parts, toosl, or specialized devices through variable programmed motions for the performance of a variety of tasks. An intelligent bot has some type of sensory apparatus, such as a camera, that collects information about the surroundings and its operations. they can be fully autonomous or remotely controlled by a human. (androids resemble humans)

computer vision

an interdisciplinary filed that deals with how computers can be made for gaining high-level understanding from digital images and videos. From the perspective of engineering, it seeks to automate tasks that hte human visual system can do. It acquires or processes, analyzes, and interprets digital images and produces meaningful information for making decisions. Scene and item recognition are important elements.

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companies use automated decision making for both their external operations (sales) and internal operations (resource allocation, inventory management).

machine learning

computer programs that learn as they face new situations. Such programs collect data and analyze them and then "train" themselves to arrive at conclusions. "learning from data"

augmented intelligence (intelligent augmentation)

existing AI applications between assisted and autonomous. Their technology can augment computer tasks to extend human cognitive abilities, resulting in high performance. can offer new solutions combining existing and discovered information, can provide insights and recommendations and explanations, these machines also facilitate creativity.

reasoning from knowledge

feature processes user's requests and provides answers (solutions, recommendations) to the user.

1. the autonomous advisor = data-driven management model that uses AI algorithms to generate best strategies and instructions on what to do and makes specific recommendations. (only humans can approve the recommendations) 2. the autonomous outsource = the traditional business process outsourcing model is changed to a business process algorithm. Need to create clear rules and instructions. 3. people-machine collaboration = must train people to work with AI machines. 4. comple machine autonomy = organizations fully automate entire processes.Management must fully trust AI models.

four models for AI to make autonomous business decisions

autonomous AI

in the realm of strong AI, but in a very narrow domain. Eventually, a computer will take over many tasks, automating them completely. (replace people) Machines act as experts and have absolute decision-making power. (pure robo-advisors) (autonomous vehicles, robots that can fix themselves)

-the nature of the decision (routine decisions more likely to be fully automated) -the method of support, what technology(ies) is(are) used. -cost/benefit and risk analyses - necessary for making large scale decisions, but computing these values may not be simple with AI models due to difficulties in measuring costs, risks, and benefits. -using business rules -the quality of automated decisions depends on the quality of rules -AI algorithms - the quality of decisions depends on input of algorithms, which may be affected by changes in the environment -speed - some decisions cannot be automated because it takes too much time to get all the relevant input data.

issues that determine the justification of using AI and its chance of success:

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over time, the cost of humans work increases and the cost of AI/robotics decreases

knowledge acquisition

process of acquiring knowledge for intelligent systems to work has several methods: observing, interviewing, scenario building, and discussing) Experts may be used to verify it. The acquired knowledge needs to be organized in an activity referred to as knowledge representation

starts with knowledge acquisition and creation of a knowlege repository. users submit questions to the system brain, which generates a resonse and submits it to the users. solutions are evaluated so the knowledge repository and the reasoning from it can be improved.

process of automated decision making

shopbots

robots that help with online shopping by collecting shopping information, mathing buyers and products, and conducting price and capability comparisons.

deep learning

subset of machine learning that treis to mimic how the human brain works. Uses artificial neural technology and plays a major role in dealing with complex applications that regular machine learning adn other AI technologies cannot handle. DL delivers systems that not only think but also keep learning, enabling self-direction based on fresh data that flow in. Most useful in real-time interactive applications in the areas of machine vision, scene recognition, robotics, and speech and voice processing. (autonomous vehicles) The KEY is continuous learning. as long as new data arrive, learning occurs.

turing test (Alan Turing)

test to determine whether a computer exhibits intelligent behavior. A computer can be considered smart only when a human interviewer asking the same question to both an unseen human and an unseen computer cannot determine which is which. To pass the test, a computer needs to be able to understand a human language (NLP), to posses human intelligence (have a knowledge base), to reason using its stored knowledge, and be able to learn from its experiences (machine learning).

cognitive computing

the application of knowledge derived from cognitive science (study of the human brain) and computer scinece theories, in order to stimulate human thought processes (an AI objective) so that computers can exhibit and/or support decision-making and problem-solving capabilities. to do so, computers must be able to use self-learining algorithms, pattern recognition, NLP, machine vision, and other AI technologies.

artificial intelligence

the capabilities of a machine to imitate intelligent human behavior. Considered a subfield of computer science.

augmented reality (AR)

the integration of digital information with the user environment in real time (mostly vision and sound) provides people real world interactive experience with the environment. These systems use data caputred by sensors (vision, sound, temperature) to augment and supplement real-world environments.

machine vision

the technology and methods used to provide imaging-based automated inspection and analysis for applications such as robot guidance, process control, autonomous vehicles, and inspection. A major part is the industrial camera, which captures, stores, and archives visual information. treated more as an engineering subfield, while computer vision belongs to the computer science area