16.7 Reading

Artificial Intelligence's description is as follows:

"Software that automates and mimics or improves upon tasks that would otherwise require human intelligence."

Machine Learning is defined as follows:

"Subset of AI. Results improved without explicit programming."

Deep Learning is lastly described as:

"Type of ML (Machine Learning). Includes several layers of analysis between input data and output result." Below these three descriptions at the top of the diagram, there are six examples of AI with images attached to them as follows: Pattern Recognition (shown as a magnifying glass), Medical diagnosis (shown as a medical kit), Computer vision (shown as a graphic of a human eye), Speech Recognition (shown as a graphic of human lips), Self-driving automobiles (shown as a graphic of a car), and Natural Language Processing (shown as a graphic of a book).

AI starts with what are sometimes referred to as

"naked algorithms" that need to be trained. These are starting point algorithms, and many are in the public domain or accessible from cloud provider services (e.g., TensorFlow, PyTorch) or through vendor APIs and inside software development kits (e.g., Apple Core ML). However, training implies access to a large quantity of consistent, reliable historical data.

Expert systems

A set of technologies used in the development of AI systems that use a set of programmed decision rules or example outcomes to perform a task in a way that mimics applied human expertise. are AI systems that leverage a set of programmed decision rules or example outcomes to perform a task in a way that mimics applied human expertise. Expert systems are used in tasks ranging from medical diagnoses to product configuration. They may be programmed with explicit rules (think a big "if this, then do that" decision tree), or rules may be automatically built by analyzing specific cases against outcomes (e.g., make less product if the weather is below forty degrees and rainy, since there will be less foot traffic). Unlike many modern machine learning techniques, expert systems typically don't require massive amounts of data to set up, however they do require the ability to extract rules or expertise, which often involves the time and expense of working with domain experts, building systems, and thorough testing and iteration to ensure outcomes are what is expected

Machine learning

A type of artificial intelligence that leverages massive amounts of data so that computers can improve the accuracy of actions and predictions on their own without additional programming. is a type of AI often broadly defined as software with the ability to learn or improve without being explicitly programmed. Many of the data mining techniques described in the prior section use machine learning.

Deep learning

A type of machine learning that uses multiple layers of interconnections among data to identify patterns and improve predicted results. Deep learning most often uses a set of techniques known as neural networks and is popularly applied in tasks like speech recognition, image recognition, and computer vision. is a subcategory of machine learning. The "deep" in deep learning typically refers to the layers of interconnections and analysis that are examined to arrive at results. Some technologists refer to "deep" as having more than one "hidden" layer between input and output.

supervised learning

A type of machine learning where algorithms are trained by providing explicit examples of results sought, like defective versus error-free, or stock price.

Semi-supervised learning

A type of machine learning where the data used to build models contains data with explicit classifications but is also free to develop its own additional classifications that may further enhance result accuracy. is machine learning where data used to build models that determine an end result may contain data that has explicitly labeled outputs but is also free to develop and use its own additional classifications that may further enhance result accuracy (e.g., "hey software, take a look at my categorizations and see if they are valid or you can come up with better or missing ones").

Genetic algorithms

AI technologies that seek an optimal model by transforming or "mutating" an algorithm (versus neural networks, which add weights and mappings to a combination of inputs)—iteratively testing the result and choosing the best outcome. of evolutionary computing are model-building techniques where computers examine many potential solutions to a problem, iteratively modifying (mutating) various mathematical models, and comparing the mutated models to search for a best alternative function. Many computer scientists would say that neural networks approximate functions, while genetic algorithms refine existing functions to optimize solutions. For most managers it's useful just to know the term as a type of automated model development that's another arrow in the AI quiver. Genetic algorithms have been used for a wide variety of applications, including designing superior financial models, improving satellite deployment for global coverage, designing earthquake-resistant transport systems, suggesting the most efficient layout for solar arrays, and solving traffic congestion problems.

CAPTCHAs

An acronym standing for completely automated public Turing test to tell computers and humans apart. The Turing test is, rather redundantly, an idea (rather than an official test) that one can create a test to tell computers apart from humans

key takeaways

Artificial intelligence (AI) encompasses software that imitates or enhances human-like intelligence. Machine learning, a subset of AI, enables software to learn and improve without explicit programming. Deep learning, a subfield of machine learning, involves complex, multi-layered analysis. Neural networks, a part of AI, detect patterns through multilayered relationships. Expert systems use rules or examples to mimic human expertise. Genetic algorithms are model-building techniques that iteratively modify potential solutions to find the best one. However, implementing AI presents challenges such as the need for technical skills, clean datasets, and considerations for legal issues like data privacy

artificial intelligence

Computer software that can mimic or improve upon functions that would otherwise require human intelligence.

Turing test

Conceived by Alan Turing, a Turing test of software's ability to exhibit behavior equivalent to, or indistinguishable from, a human being

Since most data mining is a subset of AI, many of the challenges mentioned in the prior section also apply to machine learning. Issues that managers, as well as concerned citizens, might want to be aware of include:

Data quality and inconsistency: Ensuring high-quality, consistent data is crucial for effective machine learning. Data integration: Combining data from various sources into a single dataset suitable for machine learning can be challenging. Insufficient data: Having an inadequate amount of data can hinder the development of accurate machine learning models. Technical skills: Developing and maintaining machine learning systems requires specialized skills that may be scarce. Change management: Implementing AI systems often involves significant organizational change, requiring effective change management practices. Legal restrictions: Some machine learning approaches may be restricted due to legal concerns, such as data privacy and discrimination. Unintended consequences: Misuse of data can lead to negative consequences and regulatory responses that limit current practices. Competitive advantage: Early adopters of AI and data-driven strategies may gain a significant competitive edge, potentially leading to market dominance and innovation challenges.

Figure 16.8 Artificial Intelligence, Machine Learning, and Deep Learning

Deep learning is a subset of machine learning, which is a subset of artificial intelligence. These techniques involve some form of pattern recognition and are used in many applications, including those depicted above. ML automates discovery, which can improve as more data is added, while deep learning provides several layers of analysis and comparison to uncover results.

OCR

Optical Character Recognition. Software that can scan images and identify text within them

change management

Refers to techniques to facilitate organization change, including preparing individuals for change and offering training and support during and after implementation. Change management is especially important in IS use, as many information systems implementations involve radical change to the way a firm conducts business or the way individuals and teams operate within the organization.

self-supervised learning

Sometimes called unsupervised learning, where systems build pattern-recognizing algorithms using data that has not been pre-classified. is machine learning where data is not explicitly labeled and doesn't have a predetermined result. Researchers at Google used self-supervised learning in a robot that "taught" itself to walk.

Neural networks

Statistical techniques used in AI and particularly in machine learning. Neural networks hunt down and expose patterns, building multilayered relationships that humans can't detect on their own which are statistical techniques used to hunt down and expose patterns. Neural networks identify patterns by testing multilayered relationships that humans can't detect on their own. Many refer to the multilayered interconnections among data as mimicking the neurons of the brain (hence the name). If a set of interrelationships is strong, they go into the pattern-matching scheme. If a better set of relationships is found, old ones are tweaked or discarded. Neural networks are often referred to as a "black box," meaning that the weights and relationships of data that identify patterns approximate a mathematical function, but are difficult to break out as you would in a traditional mathematical formula. You'll often see neural networks used as the technology that enables specific categories of AI use, such as image recognition or natural language processing.

The goal of AI is to

create computer programs that are able to mimic or improve upon functions that would otherwise require human intelligence

Do understand that AI is not a single technology

—terms and categorizations may overlap or have debated definitions—various forms of AI can show up as part of analytics products, CRM tools, transaction processing systems, and other information systems.