Mark Minevich is a highly regarded and trusted Digital Cognitive AI Strategist, Artificial Intelligence expert, Global Social Innovation and Technology Executive, UN Advisor, Leading Author and Columnist, Private Investor/Venture Capitalist, and the principal founder and President of Going Global Ventures. He is an award-winning technology executive and has published two books and over 40 articles on AI, Industry 4.0, IoT. Mark is newly appointed Chairman of the Executive committee of AI for Good Foundation. Mark is a Chief Digital Strategist at the International Research Centre for AI, under the auspices of UNESCO. Mark is Sr. Advisor to Boston Consulting Group Boston Consulting Group. Currently, he serves as the strategic advisor and Global ambassador to the CEO and Chairman of New York based Amelia/ IPsoft Inc. Mark collaborates and advises large global enterprises both in the US and Japan (Hitachi). Mark is Advisory Partner to Canadian Growth Investments and Business Advisor to Infosec Global.   What is the definition of leadership for you? As a leader, should one create more followers or leaders? I believe that a leader should create more leaders, not followers. A leader is someone who inspires and motivates others to achieve their goals. A leader also has […]

As we say goodbye to summer and fall enters, our newsletter highlights the accomplishments of this year's Summer Fellows; announces the launch of the next phase of our work in Ukraine; and shares the innovative work of members on the AI for Good Council for Good. Our team has been hard at work preparing to launch the DE&I Scorecard, and our Visiting Scholar Dr. Randon Taylor and Fellow Bessie O'Dell have prepared an excerpt from their soon-to-be-published paper. Join us as we reflect and move forward. The AI for Good Foundation is excited to announce the launch of LifeForce in Ukraine. LifeForce provides real-time access to aid and basic needs from over 17,000 locations across the country, coordinates grass-roots humanitarian efforts, and renders war-time supply chains and logistics resilient to attack.

These are 200 flashcards I made to review everything from my years of ML research, classes, and independent study. Creating them helped me prepare for machine learning engineer interviews and was able to get offers from several companies in 2022 (including Google, Tesla, Samsung, Motional, UiPath, and TikTok). Hopefully other people can benefit from them as well! The PDFs in this repo are mostly for convenience. Questions are loosely based off Chip Huyen's ML Interviews Book questions. However, some slides might be overly technical or academic, and not relevant to engineers.

Lanfrica is an online resource centre that catalogues, archives and links African language resources. These resources include research papers, datasets, projects, software and models that have to do with one or more African languages. The team behind Lanfrica is Chris Emezue, Handel Emezue, and Bonaventure Dossou, with contribution from Daria Yasafova. We caught up with them to find out more about the project, what inspired them to begin, and the potential that Lanfrica offers the AI community and beyond. Chris: The inspiration came while Bona and I were working as undergraduate students.

Familiarity with basic statistics and mathematical notation is helpful. An Introduction to Statistical Learning is one of the best introductory textbooks on classical machine learning techniques such as linear regression. It was the first machine learning book I've bought and has given me a great foundation. The explanations are held on a high level, so you don't need advanced math skills. Every chapter comes with code examples and labs in R. It is a great book to work through cover-to-cover. Get "An Introduction to Statistical Learning" on Amazon

In the past decade, the Machine Learning community has achieved breakthrough improvements on a variety of inference, prediction, and control tasks. Primarily, these improvements have been facilitated by an explosion in computational power and large clusters of machines working efficiently together. The cost of a 10% reduction in model error rate can often translate into a 1,000 fold increase in model size, and several orders of magnitude more energy being expended in training and running these eventual models. As data become ever more plentiful, and data scientists rely more and more on large state-of-the-art modelling algorithms, the question of the efficiency of learning per Watt of expended energy–and how we compare the ultimate utility of relative improvements in model accuracy–becomes ever more salient. The question of the sustainability of running large-scale computations and ML applications has also gained traction since conservative estimates of the AlphaGo winning model against Lee Sedol placed it at around 1MW of power consumption just during the match.

A large portion of feeling more fulfilled in your career is from the feeling of progress. For a while, that feeling has been void for me. Yes, I've still been landing freelance machine learning contracts but not exactly the ones I want -- with all due respect to my current clients. While I'm grateful for the opportunities I've been receiving, I know that I must improve if I want to reach the goals I've set for my career. I didn't become a Freelancer to do work I don't want to do.

Artificial Intelligence (AI) is often discussed in terms of the threat it poses, whether real (automation disrupting numerous industries) or imagined (AI taking over the human race). The AI for Good Foundation, however, is committed to helping the world through AI, and has partnered with Syngenta, an agrochemical company, to launch the Syngenta AI Challenge. Participants are required'to develop a model that could be used to help scientists analyse large amounts of seed data more efficiently and effectively'. They will be tasked to find out'which soybean varieties will perform better in farmers' fields in 2015 & 2016?'. To do this, entrants will be provided with a large range of data over a four-month period, before officially submitting their efforts by June 1 of this year.

Syngenta and the AI for Good Foundation have partnered to launch the Syngenta AI Challenge, a new international competition focused on leveraging Artificial Intelligence (AI) tools for use in seed breeding. The competition is accepting submissions from applicants who are ready to put their programming skills to the test for the chance to win $7,500. "This new competition will give entrants the chance to use their talents to take on the extraordinary complexity of seed genetic data," said Joseph Byrum, Ph.D., MBA, PMP and senior R&D strategic marketing executive with Syngenta. "In the face of a rising global population, we need to grow plants that can adapt and thrive in changing conditions – especially as vital resources like water and land are finite. The Syngenta AI Challenge is about creating models that can help solve this puzzle and ensure world food security."