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 prior blogs did a deep dive into data literacy, digital literacy, mathematics literacy, and statistics literacy, as all key skills to mature AI skills in an enterprise. This blog focuses on sciences literacy, and has three blogs to explore the relevance of these three disciplines to AI of 1.) computer science, 2.) complexity science and 3.) physics. It is not to say that there are not other sciences that have relevance to AI, such as health sciences, biological sciences, etc. , rather these are just the three I have chosen to focus on.
In this 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.
1. Research Methods Literacy
2. Agile Methods Literacy
3. User Centered Design Literacy
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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
What is the relevance of computer science to AI as a discipline?
Sometimes academics debate if AI is a discipline separate from Computer Science or a sub-set. I fall into the subset camp, as there is still computational logic being applied.
Computer science is the study of algorithmic processes, computational machines and computation itself. As a discipline, computer science spans a range of topics from theoretical studies of algorithms, computation and information to the practical issues of implementing computational systems in hardware and software. Source: (Wiki Encyclopedia). If you are studying computer science, you usually cover topics like: artificial intelligence, algorithmic design, computer systems, database, human centered design and interaction, networks, machine learning, programming languages, security, software engineering methods.
As discussed in earlier blogs, artificial intelligence is a branch of computer science that focuses on creating intelligent machines which try to act like humans. AI is able to perform human like actions if AI has been fed with quality data representations of the world.
What key questions can Board Directors and CEOs ask to evaluate their depth of computing science skills linkages to artificial intelligence relevance?
1.) How many resources do you have that have an undergraduate degree in computing science versus a masters degree or a doctoral degree?
2.) Of these total resources trained in computer science disciplines, how many have a specialization in Artificial Intelligence?
3.) Of these total resources, how many are trained in data sciences disciplines versus computer science disciplines, hence are stronger in data modelling, statistical analytics and data visualizations, pattern interpretations?
4.) What percentage of the resource groups are male or female to evaluate your diversity health?
5.) Of each of these degree types, how many of these resources are trained in AI?
6.) Of the total number of resources, what percentage are only working full time in AI methods (ie: programming in Python, R, applying AI algorithms).
These are some starting questions above to help guide leaders to understand their talent mix in pure computing science disciplines, versus specializations in artificial intelligence or data sciences.
One of the key mistakes I often see is leaders assuming that pure computing science programmers that are software developers and engineers have the skills to design and build AI models. This is not true unless the computing scientists have learned how to program in AI related programming languages for data discovery such as programming in R, or Python, to name a few AI model building languages or skilled in how to apply AI algorithms to solve specific use cases. They will have the aptitude to learn new programming languages but will require new training and coaching given the breadth of methods in the AI discipline.
The time has come for every computing science professional to learn AI methods and gain experience in this area, given the explosive growth and demand of AI skills. Life long learning needs to be a core value of companies striving to remain relevant and computing science resources also need to recognize the learning imperative to keep employed over the long-term.
Perhaps the most important question that board directors and CEOs need to ask is what is the depth of your Chief Information Officer (CIO) skills in AI and Data Sciences? To date, many of the CIO’s have not upgraded their skills in these areas, yet are responsible for guiding business and technology practices foreward. Gartner estimates that by 2020, AI will be a priority for more than 30% of CIOs.
Are CIOs ready?
Being prepared for the new world order where AI is everywhere, requires tremendous vision and leadership of board directors and CEO’s. Boards have a critical leadership role to play to ensure that their companies advance in modernization needs to serve the best interests of diverse stakeholders and shareholders.
There is an accelerated requirement to augment board director governance and learn about AI. AI has such strategic and transformative implications and new risks in cyber increasingly require board directors to advance their knowledge in AI, as well as to ensure their C suite leadership teams are building new AI skills and also digging into their talent base skills to evaluate strengths or gaps.
Augmenting technical innovation board committees to include experts in AI and Ethical AI skills is a business imperative. Timing is of the essence, and although some progressive companies like Proctor and Gamble, TD Bank to name a few publicly traded companies that are elevating AI into board room discussions and recruiting new leaders , I don’t see the speed of changes needed in North America as I do in China. The USA National Security Report recently was issued, the warning of the USA being unprepared for AI leadership was stark.
As a bipartisan commission of fifteen technologists, national security professionals, business executives, and academic leaders, the National Security Commission on Artificial Intelligence (NSCAI) delivered an uncomfortable message: “America is not prepared to defend or compete in the AI era. This is the tough reality we must face. And it is this reality that demands comprehensive, whole-of-nation action. Our final report presents a strategy to defend against AI threats, responsibly employ AI for national security, and win the broader technology competition for the sake of our prosperity, security, and welfare. The U.S. government cannot do this alone. It needs committed partners in industry, academia, and civil society. And America needs to enlist its oldest allies and new partners to build a safer and freer world for the AI era.”
As a Canadian citizen, this naked exposure left me thinking more clearly than Canada is spending far too much time in discovery versus leading in viable AI application innovations and building killer AI companies that drive accelerated GDP Growth.
It takes a great deal of courage for the USA to lay out so clearly in this comprehensive report what its country must do to modernize and maintain its strength against China, in particular.
Board directors and CEOs need to understand their talent depth in computing science and start to retrain them to be more skilled in AI and data science disciplines. According to the 2019 National Association of Corporate Directors (NACD) Blue Ribbon Commission report, “Fit for the Future: An Urgent Imperative for Board Leadership,” 86% of board members “fully expect to deepen their engagement with management on new drivers of growth and risk in the next five years.”
AI is one of the top areas recommended as an urgent imperative for board governance leadership. Change always is rooted in talent enablement and organizational design changes. I can already see that the majority of future CEOs will have a strong technology and AI foundation in addition to traditional business disciplines. There has never been a time when digital literacy has never been so pronounced. The echo from the National Security Commission Report on AI, only reinforces the time has come to accelerate change and engage all community stakeholders.
Computing science and AI skills are critical science literacy foundations to help get us to a more capable North American economy.
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 The AI Dilemma, to guide leaders foreward.
If you have any ideas, please do advise as I welcome your thoughts and perspectives.