This is the CTOvision guide on the megatrend of Artificial Intelligence. This report gives a high level overview of the most important factors of the trend, gives updated insights into the activities of the major AI companies, and succinct descriptions of AI tech. It also points to key CTOvision reporting to help readers dive deeper into the topic.

We start with a definition of AI:

Artificial Intelligence (AI) is the application of thinking machines to real world problems.

This definition is unique. We like it because it focused on practitioners. Once you take a practitioners view you see AI is really about far more than algorithms. There are a wide range of technical and non-technical factors that must come together to deliver results.

Examples of tech components and non-tech components of real world AI solutions follow:

Tech Components:

• Analytic Algorithms (including Machine Learning, Deep Learning)
• Natural Language Processing
• Robotics
• Computer Vision
• Data Management
• Sensors
• Hardware architectures
• Technical security measures

Non Tech Components:

• New business strategies
• Cybersecurity policies
• Business risk policies
• Ethics
• Legal and regulatory regimes
• Training and testing
• Operation and maintenance
• Hiring, promotion, career management

Business Impact of AI:

Here are the key points we recommend any enterprise tech professional consider regarding AI:

• With business models returning profit now, all indications are AI will continue to improve.
• The evolution of AI has been accelerating due to its coupling with incredibly low cost cloud computing.
• Creators use a “generate and test” approach to creating functionality, with no accepted protocol for security or testing in AI. This is a huge negative.
• There are four major problems with AI today: 1) Some of the most capable AI is not scrutable (you can’t see how it works), 2) AI can be easy to deceive, trick or hack, 3) AI can be unfair, unethical and unwanted, and 4) AI can be leveraged by competitors and even criminals to your detriment.
• There are ways to balance the risks and opportunities around AI.
• AI, especially Machine Learning, is playing a huge role in modernizing the cybersecurity industry.
• AI is also being used by cyber criminals, with many in the security community predicting AI enabled malware coming soon.
• AI can be easier to deceive than current computer software (see Generative Adversary Networks: A very exciting development in Artificial Intelligence).
• There are many lessons that can be learned from others on ways to improve your corporate governance over AI including ethics around AI.
• More on optimizing AI for business can be found at OODAloop.com 

Open AI questions decision-makers should track:

• Will job displacement caused by AI be a crisis? Will government put regulations on companies because of this?
• Will companies use AI in ways their customers regard as ethical?
• Will there ever be a widely-accepted security framework for AI?
• Can behavioral analytics enhance security?
• How can machine learning improve cybersecurity?

The field is growing dramatically with the proliferation of high powered computers into homes and businesses and especially with the growing power of smartphones and other mobile devices. AI requires lots of data to be effective and with the proliferation of mobile devices there is more data now than ever.

The Technologies of Artificial Intelligence

There are many key technologies used in fielding AI. These are the components of AI technologies we recommend tracking:

Machine Learning: Machine Learning is a subset of AI that focused on giving computers the ability to learn without being explicitly programmed. Machine Learning involves the automated training and fitting models to data. ML is the most widely used AI related technology and is frequently the front end of more complex solutions. This is a broad technique with many methods. Methods commonly taught and applied in ML solutions all have different strengths and weaknesses and part of the art of ML is knowing which applies to the need at hand.

Neural Networks: Considered a more complex form of Machine Learning, this approach uses data flow mappings similar to artificial “neurons” to weigh inputs and related them to outputs. This approach views problems in terms of inputs, outputs and variables that associated inputs with outputs.

Deep Learning: Highly evolved neural networks with many layers of variables and features. Important to most modern image and voice recognitions and for extracting meaning from text. Deep learning models use a technique called “back propagation“ to optimize the models that predict or classify outputs, which adds to complexity of the end model. The end model may have so many 1000’s of variables that no human can really understand how the model functions or how a conclusion was arrived at.

Natural Language processing: This class of technology analyzes and understands human speech and text. Used in modern applications of speech recognition including chatbots and intelligent agents. NLP also requires training data, in this case the output is knowledge about how language relates, often referred to as a “knowledge graph” for a particular domain.

Rule-based expert systems: This is an older approach to AI solutions. It involves establishing sets of logical rules derived from the way people actually work. Used in many processes where sets can be clearly defined. This was the dominant form of AI in the past and is still around today, but is really just complex programming. Imagine a large number of “if-then” statements in a program, but in this case the rules were built by domain experts.

Robots and Robotics: This is the automation of physical tasks. Primarily used in factory and warehouse tasks but growing use in heath care, small businesses and homes. Training data for robots is critically important, but in this case the training data may include location for movement or a wide variety of expected changes in the environment.

Robotic Process Automation: This is the automation of structured digital tasks in the enterprise or factory. This is a highly evolved form of scripting actions. It is a combination of software and workflows built to help automate business processes. RPA is at its best when it provides users with the benefits of other AI capabilities like Machine Learning.

Other Related AI Terms and Concepts:

Supervised Learning: The most common type of training for AI models. Data is labeled by humans so the algorithm can be taught based on what was established by humans. This is very similar to older techniques of statistics like regression analysis. Once a model has been developed using supervised learning, it can be used with new data to provide predictions. This is called “scoring”. Training models on labeled data generally takes large quantities of data that have known outcomes, and in many use cases the outcome that is being sought is actually a rare occurrence (this is called a “class imbalance”).

Unsupervised Learning:This is the development of AI models in ways that detect patterns in data that are not labeled and results are not known.

Training Data:The data used for the development of the model. This is often validated using another subset of data for which the outcome to be predicted is known.

The methods and concepts above are almost always combined in any real world AI solution.

The AI Vendor Community

Most AI capabilities today have their roots in academia, but real implementations are being driven by the corporate world. There are many reasons for this. One is the profit motive. Another is the large collections of data available to the big firms. We provide more focused reporting on the firms driving AI forward in our Disruptive IT Directory in the categories of Tech Titans and Artificial Intelligence Companies. It is especially important to track the AI developments of Google, Microsoft, IBM, Amazon, Apple,

You can see our reference to Truly Useful AI You Can Use Right Today.

For Further Study

Artificial intelligence software is assisting people in most every discipline. The many functions of AI are considered to be threatening jobs across multiple industries, but others consider it a great producer of jobs since it will help create entirely new industries and free more humans to innovate and create. This impact on jobs is best considered in conjunction with the megatrend of Robotics, since together those two trends are going to impact some of the largest sectors of jobs in the U.S. (consider, for example, the impact on retail and shipping).

For deeper considerations of the impact of Artificial Intelligence on enterprise IT it is important to track all seven MegaTrends and consider them together. Dive deeper into all the trends and examine their impact on your organization via a CTOvision Pro membership, available for enterprises and individuals.

For alerts on future posts on this topic see CTOvision Newsletters.

Other reports in this series:

• CTOvision Assessment On The Megatrend of Cloud Computing
• CTOvision Assessment On The Megatrend of Mobility
• CTOvision Assessment On The Megatrend of Bigdata
• CTOvision Assessment On The Megatrend of Robotics
• CTOvision Assessment On The Megatrend of the Internet of Things
• CTOvision Assessment On The Megatrend of Cybersecurity

For more on these topics see:

• Amazon opens its internal machine learning courses to all for free
• Is Artificial Intelligence Dangerous? Probably not, but here are a few categories to noodle on
• Crafting an AI-Relevant, Data-First, Agile Methodology for AI & ML Projects
• Eric Schmidt Provides Insights Into The Future of Artificial Intelligence and Machine Learning at RSAC2017
• From Machine Learning to Machine Reasoning

There are seven key megatrends driving the future of enterprise IT. You can remember them all with the mnemonic acronym CAMBRIC, which stands for Cloud Computing, Artificial Intelligence, Mobility, Big Data, Robotics, Internet of Things, CyberSecurity.