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.
In this blog series, I have identified forty skill domains in an AI Leadership Brain Trust Framework to guide board directors and CEOs to ensure they can develop and accelerate their investments in successful AI initiatives. You can see the full roster of the forty leadership Brain Trust skills in my first blog.
In the last two blogs, the focus has been on Mathematics Literacy, which is one of the ten technical skills to develop in building a strong foundation of AI Literacy in board directors and in CEO’s to lead and govern AI effectively and efficiently.
The premise I have is that AI will underpin all business processes and practices and will be more than the new electricity in our organizations, rather it will be like the new oxygen, in every business process, every software system, every infrastructure, and eventually hard wired into every human. It is simply a matter of time – so boards that want to think beyond the next five years or the next 25 years must accelerate AI literacy and ensure their duty of care on corporate oversight starts to ask far more precise questions of their CEO’s on how AI is positioned in their company operations, products and services to simply stay – relevant.
1. Research Methods Literacy
2. Agile Methods Literacy
3. User Centered Design Literacy
4. Data Analytics Literacy
5. Digital Literacy
6. Mathematics Literacy
7. Statistics Literacy
8. Sciences (Computing Science, Complexity Science, Physics) Literacy
9. Artificial Intelligence (AI) and Machine Learning (ML) Literacy
This is the last blog in the three part Mathematics Literacy blog series which blog one: defined mathematic’s literacy, and explored linear algebra concepts, one of the most important skills in advancing AI methods, blog two explained graph theory, a subset of Algebra, basic statistical and probability concepts which underlie diverse AI methods, in particular predictive analytics. This last blog in the series will discuss basic calculus concepts.
In the context of AI, the two most important concepts from calculus are gradient and gradient descent. On the other hand, −∇f(x) points in the direction of steepest descent from x. To become skilled in AI, linear algebra is key to understanding most AI or machine learning methods (See prior blogs on linear algebra).
The two most important terms relevant to AI in calculus are understanding what a gradient is mathematically and appreciating that the machine learning algorithm, gradient descent, is one of the most well used machine learning methods.
1.) Gradient – gradient is a fancy word for derivative, or the rate of change of a function. It’s a vector (a direction to move) that points in the direction of greatest increase of a function (intuition on why). Gradient groups are all partial derivatives, the gradient is just the vector containing all the partial derivatives. In summary, a gradient is a vector-valued function that represents the slope of the tangent of the graph of the function, pointing the direction of the greatest rate of increase of the function. It is a derivative that indicates the incline or the slope of the cost function. In essence, it generalizes derivatives to scalar functions of several variables. Well, just like the first derivative of a function with one variable equals to zero in stationary points, the same goes for gradient for the functions with multiple variables (More definition insights on Gradient and a simple video defining gradient with mathematical context and as it is so important, here is another short video).
2.) Gradient Descent – is an optimization AI algorithm that’s used when training a machine learning model. It’s based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum. Notes: In mathematics, a real-valued function defined on an n-dimensional interval is called a convex if the line segment between any two points on the graph of the function lies above the graph between the two points. Equivalently, a function is convex if its epigraph is a convex set (Source: WikiEncyclopedia). This algorithm is one of the most used algorithms for solving machine learning optimization problems and is often used in deep learning methods, regression methods and if you want to understand one type of AI algorithm, this is the one term to understand. Two good videos that further explain gradient descent is here and here.
This three part Mathematics skill literacy blog series was written not to be a full representation of all the relevant concepts to appreciate more the language of AI, but more to illustrate that everyone can improve and learn mathematics at any time in one’s life. Understanding graph theory, vectors, probabilities, are all germaine to the field of artificial intelligence.
As discussed in the second blog on mathematics literacy, artificial intelligence’s primary goal is to create an accurate AI model that gains confidence for human understanding and decision making. The AI models are prepared with the strategies and methods from various branches of mathematics. Mathematics is a discipline that explains data models and guides leaders to validate their business knowledge. Mathematics is a core skill to deepen the rigour of AI on understanding key concepts: like probability, correlation, causation to predict future outcomes, often much better and more accurately than humans. Behind all of the innovations and advances, mathematics is at the core.
What questions can board directors and CEOs ask to ensure their companies have a mathematic’s literacy capability focus? (this list adds another five additional mathematic literacy questions concluding this three part blog series to accelerate board director and CEO’s investing time to learn more rapidly to advance their AI literacy to help their companies modernize).
1.) Does your company have a mathematics literacy strategy integrated with your digital literacy and artificial intelligence programs?
2.) Is your company testing for digital and mathematical literacy for all employees to evaluate your overall risk in advancing into deeper analytics capabilities? (See my last week’s blog on digital literacy )
3.) Is your company testing for statistical literacy in any of your operational roles?
4.) Does your company know what your competitors are doing in advancing their mathematical and digital literacy strategies and how does your company compare?
5.) Is AI skill competency development integrated into your digital literacy strategy, where mathematics literacy is a foundational skill in digital enablements?
6.) Do you have the ability to scan skills across your talent base to know the depth of math skills, statistical skills, AI skills and mobilize the right talent to solve the right use case in real time?
7.) How skilled are your board directors in mathematics and AI to guide your companies foreward into AI enablements?
8.) Is your company discussing the importance of Mathematics Literacy in your corporate boardrooms?
9.) Are you testing your senior executives on their basic mathematic’s proficiency levels
10.) Are you investing in your overall workforce training on relevant mathematical concepts to ensure all talent can communicate with your AI and Data science professionals?
11.) Has your company identified coaching communities to develop stronger skills in mathematics and statistics?
12.) Has your company developed a knowledge or learning competency center so your employees can easily can find relevant content on mathematics and artificial intelligence concepts that can help them build relevant skills?
13.) Is mathematics literacy a core leadership skill and is part of your talent life-cycle (On-boarding and development practices)?
14.) Do your employees have the ability to search for internal experts on different mathematical or artificial concepts to access expertise, in house easily or externally with online coaching?
15.) Do you celebrate and recognize talent for building formalized skills in mathematics literacy, artificial intelligence and general digital literacy?
In closing this three part series, if you cannot communicate, lead and inspect in using the language of AI, how can you trust and be confident the AI experts directions and recommendations are accurate to risk your future investments. Board directors have a duty of care responsibility, so starting to appreciate the 40 skills domains required to advance a fully literature company with deeper AI skills and enabling competencies is key to modernize businesses. Core to AI is understanding mathematics, and with the declining skills in North America on mathematics competency, board directors and CEO’s and educators must drive greater leadership and accountability or the longer term implications to our economic health will decline.
In closing, Albert Einstein said: “Do not worry too much about your difficulties in mathematics, I can assure you that mine are still greater. .. Pure mathematics is, in its way, the poetry of logical ideas. .. Not everything that counts can be counted.”
Historical Perspective: Archimedes is known as the Father Of Mathematics. He lived between 287 BC – 212 BC. Syracuse, the Greek island of Sicily was his birthplace. “Give me but a firm spot on which to stand, and I shall move the earth ,” is one of his most famous quotes – and in the context of AI – it is giving humans the power to not just move earth, but all worlds, and things – which is perhaps why the late and visionary Dr. Steven Hawking warned leaders of the inherent longer term risks of AI.
To see the full AI Brain Trust Framework introduced in the first blog, reference here.
If you have any ideas, please do advise as I welcome your thoughts and perspectives.
Original post: https://www.forbes.com/sites/cindygordon/2021/03/22/building-ai-leadership-brain-trust-blog-series-mathematics-literacy-is-foundational-to-artificial-intelligence-and-executive-leadership-governing-ai/