Understanding AI and Machine Learning concepts to build your AI Leadership Brain Trust

This post was originally published by Cindy Gordon at Forbes (Innovation)

Building AI Leadership Brain Trust

Understanding AI and Machine Learning concepts to build your ai leadership brain trust

This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEO’s to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results.

My last two blogs focused on the importance of AI professionals having some foundation in science discipline as a cornerstone for designing and developing AI models and production processes, and explored value of computing science, the richness of complexity sciences and the value of physics to appreciate the importance of integrating diverse disciplines into complex AI programs – key for successful returns on investments (ROI).

This blog discusses key AI and machine learning (ML) terms that every board director and CEO must know to stay relevant and advance their duty of care. If you want a good starter on the responsibility and duty of care, I recommend you read my earlier blog here.

In the Brain Trust Series, I have identified over 50 skills required to help evolve talent in organizations committed to advancing AI literacy. The last few blogs have been discussing the technical skills relevancy. To see the full AI Brain Trust Framework introduced in the first blog, reference here.

We are currently focused on the technical skills in the AI Brain Trust Framework advancing the key AI and machine learning terms.

Technical Skills:

1.    Research Methods Literacy

2.   Agile Methods Literacy

3.  User Centered Design Literacy

4.   Data Analytics Literacy

5.   Digital Literacy (Cloud, SaaS, Computers, etc.)

6.   Mathematics Literacy

7.   Statistics Literacy

8.  Sciences (Computing Science, Complexity Science, Physics) Literacy

9.   Artificial Intelligence (AI) and Machine Learning (ML) Literacy

10.Sustainability Literacy

Understanding Key AI Terms

The AI field is a deep and rich field that includes many fields, including statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics.

Hence I am only going to dip into three key basic concepts to answer: what is AI?, what is an algorithm?, and what is an AI Model? I will continue in the next two blogs to define other key AI concepts and definitions that I believe every CEO or Board Director must master at the basic AI proficiency levels. After all, how can you lead if you don’t know your basics in one of the most significant disruptors of our lifetime.

I always say to executives, it is never too late to learn and to stay relevant you have a business imperative to be sharper about digital transformation and AI is a cornerstone for not only countries to compete against, but for corporations to rethink their business models.

The first order of business is to ensure that you can define what is Artificial Intelligence? In the most simplistic terms, AI is the computer simulation of human intelligence in machines that is programmed to think like humans and mimic human actions. A typical AI analyzes its environment and takes actions that maximize its chance of success.

AI was first defined, by John McCarthy in 1956, when he held the first academic conference on the subject. Then five years later, Alan Turing wrote a paper on the notion of machines being able to simulate human beings and the ability to do intelligent things.

AI is not new – it’s just that the time is right now for AI everywhere, due to the proliferation of volumes of data, both structured and unstructured data, and more importantly the ability of computing processing power to crunch the data and produce the insights that were near to impossible to generate prior.

More information on AI history: Refer to: Gil Press, a senior Forbes contributor’s excellent summary of AI, so if you are history buff, recommend you read his blog here. You will find many definitions of AI, but distilling AI to is basic roots, recommend you read the additional more detailed definition here.

The second most important concept reg: AI is to understand is what is an algorithm?

An algorithm is a process or a set of rules to be followed in calculations or other problem-solving operations, especially by a computer. The Wikipedia defines an algorithm as “a step-by-step procedure for calculations. Algorithms are used for calculation, data processing, and automated reasoning.” Whether you are aware of it or not, algorithms are increasingly ubiquitous – everywhere in our lives.

The goal of an algorithm is to solve a specific challenge or problem which is usually defined as a sequence of rules or steps. An algorithm tells a computer what to do next with an “and,” “or,” or “not” statements.

Algorithms provide the instructions for AI systems and without a set of algorithms AI cannot perform a function (outcome).

In terms of AI, we use the term machine learning to describe that an algorithm or series of algorithms perform a software function that enables software to update and automatically learn from without the need for a programmer. ML algo’s are fed into a data set to perform a specific task and solve a problem, without being programmed. There are literally hundreds of AI Algorithms, and this blog defines a number of the most popular types of clustering algorithms and is worth a read.

AI requires lots of data so it can find patterns and then builds predictions based on the data being analyzed.

The third key concept of AI to ensure you understand is What is an AI Model? AI/ML models are the mathematical algorithms that are “trained” using data and human expert input to replicate a decision an expert would make when provided that same information. Artificial intelligence is generally divided into two types of AI – narrow (or weak) AI and general AI, also known as AGI or strong AI.

Forbes contributor Tom Taulli wrote an excellent post on defining how to build an AI model which offers practical steps perspectives to give more depth to this point. See his writings here.

See this blog reference for additional information on basic AI terms, and some simple learning visualizations to make your AI learning easier and more fun.

Board Directors and CEOs ask to evaluate their depth of talent in artificial intelligence?

1.) How many resources do you have that have an undergraduate degree in Artificial Intelligence, or a masters or a Ph.D.?

2.) How many projects underway in your company are using internal AI resources vs external resources?

3.) Is the balance of your resourcing aligned to your strategic vision of modernizing your talent base?

4.) How many of the Board Directors or C-Suite have expertise in AI or Machine Learning disciplines?

Conclusion

I believe that board directors and CEOs need to appreciate AI fundamentals, and also ensure that they understand their talent depth in AI and machine learning disciplines, but as discussed through this series, there are many other skills and competencies required to thrive in using AI efficiently and effectively. Stay tuned for more helpful AI concepts simplified to increase your AI knowledge and vocabulary.

More Information:

To see the full AI Brain Trust Framework introduced in the first blog, reference here. 

To learn more about Artificial Intelligence, and the challenges, both positive and negative, refer to my new book, The AI Dilemma, to guide leaders foreword.

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This post was originally published by Cindy Gordon at Forbes (Innovation)

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