Machine learning wouldn’t be possible without all the great minds. If it wasn’t for them, AI wouldn’t be so advanced and our lives would look completely different.
Following the great minds of machine learning can help you discover new things and deepen your knowledge. It’s fascinating to learn from the best scientists. Among them, you will find influencers, teachers, business leaders, and many more. Undeniably their expertise can help change the world and make it a better place.
On this list, you will find not only influencers but also renowned personalities from the world of Data Science. Take a look at all the names you should know as a machine learning researcher. Learn and get inspired to discover new things!
1. Vladimir Vapnik
Vladimir Naumovich Vapnik is one of the main developers of the Vapnik–Chervonenkis theory of statistical learning, and the co-inventor of the support-vector machine method, and support-vector clustering algorithm.
Professor Vapnik gained his Masters Degree in Mathematics in 1958 at Uzbek State University, Samarkand, USSR. From 1961 to 1990 he worked at the Institute of Control Sciences, Moscow, where he became Head of the Computer Science Research Department. He then joined AT&T Bell Laboratories, Holmdel, NJ, having been appointed Professor of Computer Science and Statistics at Royal Holloway in 1995. Vladimir Vapnik is a renowned scientist in the field of machine learning.
⇒ Make sure to listen to Lex Fridman interview with Vladimir Vapnik on AI Podcast
⇒ Also, read about Alexey Chervonenkis
2. Andrej Karpathy
The Sr. Director of AI at Tesla, where he leads the team responsible for all neural networks on the Autopilot. Previously, he was a Research Scientist at OpenAI working on Deep Learning in Computer Vision, Generative Modeling and Reinforcement Learning. Andrej Karpathy received his PhD from Stanford, where he worked with Fei-Fei Li on Convolutional/Recurrent Neural Network architectures and their applications in Computer Vision, Natural Language Processing and their intersection.
3. Gregory Piatetsky-Shapiro
Gregory Piatetsky-Shapiro, Ph.D., is the president of KDnuggets. He is a well-known expert in Business Analytics, Data Mining, and Data Science and a top influencer in the field. He was no. 1 on LinkedIn Top Voices in 2018 on Data Science and Analytics. Gregory is a co-founder of KDD (Knowledge Discovery and Data mining conferences) and co-founder and past chair of SIGKDD, a professional organization for Knowledge Discovery and Data Mining. Gregory has over 60 publications and edited several books and collections on data mining and knowledge discovery.
4. Allie K. Miller
Allie Miller is the US Head of AI Business Development for Startups and Venture Capital at Amazon, advancing the greatest AI companies in the world.
Previously, Allie was the youngest-ever woman to build an artificial intelligence product at IBM—spearheading large-scale product development across computer vision, conversation, data, and regulation.
Outside of work, Allie is changing the game of AI. Allie has spoken about AI and field diversity around the world, addressed the European Commission, drafted foreign AI strategies, and created eight guidebooks to educate businesses on how to build successful AI projects.
5. Yann LeCun
Yann LeCun is VP & Chief AI Scientist at Facebook and Silver Professor at NYU affiliated with the Courant Institute of Mathematical Sciences & the Center for Data Science.
He was the founding Director of Facebook AI Research and of the NYU Center for Data Science. He received an Engineering Diploma from ESIEE (Paris) and a PhD from Sorbonne Université. After a postdoc in Toronto he joined AT&T Bell Labs in 1988, and AT&T Labs in 1996 as Head of Image Processing Research. He joined NYU as a professor in 2003 and Facebook in 2013.
His interests include AI machine learning, computer perception, robotics and computational neuroscience.
He is the recipient of the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio) for “conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing”, a member of the National Academy of Engineering and a Chevalier de la Légion d’Honneur.
⇒ To learn about Yann LeCun interesting work, listen to Lex Fridman interview with Yann LeCun on AI Podcast
6. Fei-Fei Li
A computer scientist, non-profit executive, and writer. She is a professor at Stanford University and the co-director of Stanford’s Human-Centered AI Institute and the Stanford Vision and Learning Lab.
She served as the director of the Stanford Artificial Intelligence Laboratory (SAIL) from 2013 to 2018. In 2017, she co-founded AI4ALL, a nonprofit organization working to increase diversity and inclusion in the field of artificial intelligence.
Her research expertise includes artificial intelligence (AI), machine learning, deep learning, computer vision, and cognitive neuroscience.
She was the leading scientist and principal investigator of ImageNet.
She has been described as an “AI pioneer” and a “researcher bringing humanity to AI”.
Fei-Fei Li has been elected to the National Academy of Engineering which is among the highest professional distinctions for engineers.
7. Jürgen Schmidhuber
A computer scientist most noted for his work in the field of artificial intelligence, deep learning and artificial neural networks. He is a co-director of the Dalle Molle Institute for Artificial Intelligence Research in Manno, in the district of Lugano, in Ticino in southern Switzerland.
He is sometimes called the “father of (modern) AI” or, one time, the “father of deep learning.”
Schmidhuber did his undergraduate studies at the Technische Universität München in Munich, Germany. He taught there from 2004 until 2009 when he became a professor of artificial intelligence at the Università della Svizzera Italiana in Lugano, Switzerland.
8. Nick Bostrom
Nick Bostrom is a Swedish philosopher at the University of Oxford known for his work on existential risk, the anthropic principle, human enhancement ethics, superintelligence risks, and the reversal test.
In 2011, he founded the Oxford Martin Programme on the Impacts of Future Technology, and is the founding director of the Future of Humanity Institute at Oxford University. In 2009 and 2015, he was included in Foreign Policy’s Top 100 Global Thinkers list.
Bostrom is the author of over 200 publications, and has written two books and co-edited two others. The two books he has authored are Anthropic Bias: Observation Selection Effects in Science and Philosophy (2002) and Superintelligence: Paths, Dangers, Strategies (2014). Superintelligence was a New York Times bestseller, was recommended by Elon Musk and Bill Gates among others, and helped to popularize the term “superintelligence“.
9. Angelica Lim
Dr. Angelica Lim received her Ph.D. and M.Sc. in Intelligence Science from Kyoto University, and B.Sc. in Computing Science with Minor in French from Simon Fraser University, Canada. A key member on the Pepper humanoid robot project with Softbank and Aldebaran Robotics, she has interned as a software engineer and researcher at Google Santa Monica, Honda Research Institute Japan, and I3S-CNRS, France.
She has worked on robots and artificial intelligence for over 10 years, and is currently interested in signal processing, machine learning and developmental robotics for intelligent systems, particularly in the field of emotions. She is one of four journalists for the IEEE Spectrum Automaton Robotics Blog, and was a speaker at TEDx Kyoto 2012 (“On Designing User-Friendly Robots”) and TEDx KualaLumpur 2014 (“Robots, Emotions and Empathy”).
She was a Guest Editor for the International Journal of Synthetic Emotions, and has received various awards including CITEC Award for Excellence in Doctoral HRI Research (2014), NTF Award for Entertainment Robots and Systems IROS (2010), and the Google Canada Anita Borg Scholarship (2008). She has been featured on the BBC, given talks at SXSW and TEDx, hosted a TV documentary on robotics, and was recently featured in Forbes 20 Leading Women in AI.
10. Fabio Moioli
Fabio Moioli is Head Consulting & Services at Microsoft. Faculty at Harvard, SingularityU, MIP – Artificial & Human Intelligences – AI TEDx. Has 250.000+ followers on Linkedin & Twitter, where he mainly addresses opportunities and challenges raised by Artificial Intelligence and exponential technologies, including societal and ethical perspectives.
Major areas of expertise include Artificial Intelligence, Digital Platforms, Transformation programs, Lean Operations, Product & Services Innovation, and more.
11. Andrew Ng
A businessman, computer scientist, investor, and writer. He is focusing on machine learning and AI. As a businessman and investor, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company’s Artificial Intelligence Group into a team of several thousand people.
Ng is an adjunct professor at Stanford University (formerly associate professor and Director of its AI Lab). Also a pioneer in online education, Ng co-founded Coursera and deeplearning.ai. With his online courses, he has successfully spearheaded many efforts to “democratize deep learning” teaching over 2.5 million students through his online courses.
He is one of the world’s most famous and influential computer scientists being named one of Time magazine’s 100 Most Influential People in 2012, and Fast Company’s Most Creative People in 2014. Since 2018 he launched and currently heads AI Fund, initially a $175-million investment fund for backing artificial intelligence startups. He has founded Landing AI, which provides AI-powered SaaS products and Transformation Program to empower enterprises into cutting-edge AI companies.
⇒ Listen to Lex Fridman interview with Andrew Ng
12. Oriol Vinyals
Oriol Vinyals is a Principal Scientist at Google DeepMind, working in Deep Learning and Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the Google Brain team.
He holds a Phd. in EECS from the University of California, Berkeley and is a recipient of the 2016 MIT TR35 innovator award.
Some of his contributions are used in Google Translate, Text-To-Speech, and Speech recognition, serving billions of queries every day, and he was the lead researcher of the AlphaStar project, creating an agent that defeated a top professional at the game of StarCraft, achieving Grandmaster level.
At DeepMind he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, deep learning and reinforcement learning.
⇒ Check out Lex Fridman interview with Oriol Vinyals
13. Reza Zadeh
Reza Zadeh is founder and CEO at Matroid and an Adjunct Professor at Stanford. His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics.
He’s served on the Technical Advisory Boards of Microsoft and Databricks, and has been working on Machine Learning since 2005 when he worked in Google’s AI research team. His awards include a KDD Best Paper Award and the Gene Golub Outstanding Thesis Award at Stanford.
14. Ben Goertzel
Ben Goertzel is an artificial intelligence researcher. Goertzel is the chief scientist of chairman of AI software company Novamente LLC; chairman of the OpenCog Foundation; and advisor to Singularity University. He was Director of Research of the Machine Intelligence Research Institute.
His research work encompasses artificial general intelligence, natural language processing, cognitive science, data mining, machine learning, computational finance, bioinformatics, virtual worlds and gaming and other areas. He has published a dozen scientific books, 100+ technical papers, and numerous journalistic articles. Before entering the software industry he served as a university faculty in several departments of mathematics, computer science and cognitive science, in the US, Australia and New Zealand.
15. Adam Coates
Director at Apple. He received his PhD from Stanford University in 2012 and was the director of the Silicon Valley AI Lab at Baidu Research until September 2017, then an Operating Partner at Khosla Ventures until 2018.
During his graduate career, he co-developed an autonomous aerobatic helicopter, worked on perception systems for household robots, and early large-scale deep learning methods. He developed deep learning software for high-performance computing systems with a team at Stanford, used for unsupervised learning, object detection and self-driving cars.
Previous projects: Baidu Deep Voice, Deep Speech. DL on COTS HPC, Stanford AI Robot, Stanford Autonomous Helicopter.
16. Kirk Borne
Worldwide top influencer since 2013. Data Scientist. Global Speaker. Consultant. Astrophysicist. Space Scientist.
Big Data & Data Science advisor, TedX speaker, researcher, blogger, Data Literacy advocate. Currently Principal Data Scientist and Executive Advisor at Booz Allen Hamilton, Annapolis Junction, MD.
17. Ronald von Loon
A recognized expert and thought leader in Data Science, works with data-driven companies to generate business value so that they may meet and exceed goal after goal.
Ronald van Loon has been recognized for his work in the field of digital transformation by such publications and organizations as Onalytica, Dataconomy, and Klout. In addition to these recognitions, he is also an author for a number of leading big data websites, including The Guardian, The Datafloq, and Data Science Central, and he regularly speaks at renowned events and conferences.
18. Noam Chomsky
Noam Chomsky is an American linguist, philosopher, cognitive scientist, historian, social critic, and political activist. Sometimes called “the father of modern linguistics“, Chomsky is also a major figure in analytic philosophy and one of the founders of the field of cognitive science.
He holds a joint appointment as Institute Professor Emeritus at the Massachusetts Institute of Technology (MIT) and Laureate Professor at the University of Arizona, and is the author of more than 100 books on topics such as linguistics, war, politics, and mass media.
If you are interested in Natural Language Processing and cognitive science, you should follow Noam Chomsky.
19. Lex Fridman
Lex Fridman fields of expertise include research in human-centered AI, deep learning, autonomous vehicles & robotics at MIT and beyond. Also, he teaches courses on deep learning.
He is known for his Artificial Intelligence Podcast where he talks about all Data Science related topics with the most renowned scientists from the field.
20. Kai-Fu Lee
Dr. Kai-Fu Lee is one of the world’s leading AI experts and has been in AI research, development, and investment for over 30 years. Dr. Lee is the Chairman and CEO of Sinovation Ventures, and the President of Sinovation’s Artificial Intelligence Institute, and former President of Google China.
21. Elon Musk
Elon Musk co-founded and leads Tesla, SpaceX, Neuralink, and The Boring Company.
Previously, Musk co-founded and sold PayPal, the world’s leading Internet payment system, and Zip2, one of the first internet maps and directions services.
Although known for his controversial opinions, he’s one of the leading AI influencers in the world.
22. Bernard Marr
He’s a world-renowned futurist, influencer, and thought leader in the field of business and technology. He is the author of 18 best-selling books, writes a regular column for Forbes, and advises and coaches many of the world’s best-known organizations. He has 2 million social media followers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.
23. Rachel Thomas
Rachel Thomas is the co-founder of fast.ai, which created the Practical Deep Learning for Coders course taken by over 200,000 students and which has been featured in The Economist, MIT Tech Review, and Forbes. She was selected by Forbes as one of 20 Incredible Women in AI, earned her math PhD at Duke, and was an early engineer at Uber. Rachel is a popular writer and keynote speaker on topics of data ethics, AI accessibility, and bias in machine learning.
24. Moustapha Cisse
Moustapha Cisse is a research scientist at Google and head of the Google AI center in Accra, Ghana, where he leads research efforts in foundational machine learning and its applications to solving complex societal challenges.
Moustapha is also a professor of machine learning at the African Institute of Mathematical Sciences, where he is the founder and director of the African Masters of Machine Intelligence program. He was previously a research scientist at Facebook AI Research. Before that, he completed his PhD at University Pierre and Marie Curie in France.
25. Kate Crawford
Kate Crawford is a leading researcher and professor in the fields of social implications of data systems, machine learning and artificial intelligence. She is a Senior Principal Researcher at MSR-NYC, the inaugural Visiting Chair for AI and Justice at the École Normale Supérieure in Paris, and the Miegunyah Distinguished Visiting Fellow at the University of Melbourne.
Kate is the co-founder of the AI Now Institute at New York University, the world’s first university institute dedicated to researching the social implications of artificial intelligence and related technologies.
26. Sam Altman
Sam Altman is an entrepreneur, investor, programmer, and blogger. He is the CEO of OpenAI and the Chairman of Y Combinator, a leading silicon valley startup accelerator that has helped launch companies such as Reddit, Dropbox, and Airbnb. He is an investor in many companies, and a chairman of the board for Helion and Oklo, two nuclear energy companies.
27. Martin Ford
His book Rise of the Robots: Technology and the Threat of a Jobless Future, was a New York Times bestseller and won the £30,000 Financial Times and McKinsey Business Book of the Year Award.
Martin Ford is also the consulting artificial intelligence expert for the new Robotics and AI ETF from Lyxor/Societe Generale (Ticker ROAI), which is focused specifically on investing in companies that will be significant participants in the AI and robotics revolution. He holds a computer engineering degree from the University of Michigan, Ann Arbor and a graduate business degree from the University of California, Los Angeles.
28. Alexis Conneau
Alexis Conneau is a resident Ph.D. student at Facebook AI Research in Paris.
He focuses is in the area of deep learning for natural language processing (NLP). Specifically, he is working on transferable text representations using neural networks.
Conneau’s research interests include natural language understanding, sequence to sequence learning, and neural machine translation.
29. Andreas Maier
Andreas Maier is an ML researcher and Professor at the Pattern Recognition Lab at the University of Erlangen-Nuremberg. He developed PEAKS, the first online tool to assess speech intelligibility. Since 2016, he is a member of the steering committee of the European Time Machine Consortium.
His current research interests focus on medical imaging, image and audio processing, digital humanities, and interpretable machine learning, and the use of known operators.
30. François Chollet
François Chollet is a software engineer and AI researcher currently working as a Staff Software Engineer at Google. He’s the creator of Keras, a leading deep learning framework for Python, and the author of Deep Learning with Python.
His primary interests involve general intelligence, making AI technology easy to understand, helping people use the full potential of AI, and understanding and simulating the early stages of human cognitive development.
31. Geoffrey Hinton
Geoffrey Hinton is an emeritus professor at the Department of Computer Science at
the University of Toronto. He is also a VP Engineering fellow at Google and Chief Scientific Adviser at the Vector Institute. He was one of the researchers who introduced the backpropagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning and deep learning. His research group in Toronto made major breakthroughs in deep learning that revolutionized speech recognition and object classification.
Geoffrey Hinton is a fellow of the UK Royal Society and a foreign member of the US National Academy of Engineering and the American Academy of Arts and Sciences.
Hinton received the 2018 Turing Award, together with Yoshua Bengio and Yann LeCun, for their work on deep learning. They are sometimes referred to as the “Godfathers of AI” and “Godfathers of Deep Learning”.
32. Demis Hassabis
Demis Hassabis is an artificial intelligence researcher and neuroscientist. He is the CEO and co-founder of DeepMind and a UK Government AI Advisor since 2018. He’s also a five times winner of the Pentamind board games championship. Hassabis is recognized worldwide as one of the smartest thinkers in his field.
33. Ian J. Goodfellow
Ian Goodfellow is a machine learning researcher. He’s the Director of Machine Learning at Apple’s Special Projects Group. He was previously employed as a research scientist at Google Brain. He’s the author of the Deep Learning textbook. Goodfellow has made several contributions to the field of deep learning.
34. Jerome Pesenti
Jerome Pesenti is the AI team leader at Facebook pursuing fundamental and applied research in AI and making Facebook products safer and more valuable to people through the use of AI. Prior to joining Facebook, Jerome joined IBM to lead the development of its Watson platform after the startup he co-founded, Vivisimo, was acquired by the company in 2012. He went on to later become the CEO of BenevolentTech.
35. Yoshua Bengio
Yoshua Bengio is recognized as one of the world’s leading experts in artificial intelligence and a pioneer in deep learning. Since 1993, he has been a professor in the Department of Computer Science and Operational Research at the Université de Montréal. CIFAR’s Learning in Machines & Brains Program Co-Director, he is also the founder and scientific director of Mila, the Quebec Artificial Intelligence Institute, the world’s largest university-based research group in deep learning.
In 2019, he received the ACM A.M. Turing Award, “the Nobel Prize of Computing”, jointly with Geoffrey Hinton and Yann LeCun for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
Make sure to follow these great influencers to stay on top of the latest machine learning news, get inspired, and learn new, wonderful things.
What other machine learning influencers do you follow?
Where Can You Learn About MLOps? What Are the Best Books, Articles, or Podcasts to Learn MLOps?ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It
4 mins read | Paweł Kijko | Updated May 31st, 2021
MLOps is not a piece of cake. Especially in today’s changing environment. There are many challenges—construction, integrating, testing, releasing, deployment, and infrastructure management. You need to follow good practices and know how to adjust to the challenges.
And if you don’t learn and develop your knowledge, you’ll fall out of the loop. The right resources can help you follow the best practices, discover helpful tips, and learn about the latest trends.
You don’t have to look far, we’ve got you covered! Here’s your list of the best go-to resources about MLOps—books, articles, podcasts, and more. Let’s dive in!
1. Introducing MLOps from O’Reilly
Introducing MLOps: How to Scale Machine Learning in the Enterprise is a book written by Mark Treveil and the Dataiku Team (collective authors). It introduces the key concepts of MLOps, shows how to maintain and improve ML models over time, and tackles the challenges of MLOps.
The book was written specifically for analytics and IT operations team managers—the people directly facing the task of scaling machine learning (ML) in production. It’s a guide for creating a successful MLOps environment, from the organizational to the technical challenges involved.
The book is divided into three parts:
- An introduction to the topic of MLOps, how and why it has developed as a discipline, who needs to be involved to execute MLOps successfully, and what components are required.
- The second part follows the machine learning model life cycle, with chapters on developing models, preparing for production, deploying to production, monitoring, and governance.
- Provides tangible examples of how MLOps looks in companies today, so readers can understand the setup and implications in practice.