As of 2023, the field of Artificial Intelligence (AI) and Machine Learning (ML) has witnessed rapid growth, innovation, and adoption across various industries. Many individuals have played pivotal roles in shaping and advancing this dynamic field. These influencers have made significant contributions through their groundbreaking research, influential publications, thought leadership, and active participation in the AI/ML community. In this article, we will highlight 124 AI and ML technology influencers who have had a profound impact on the industry.

Please note that this list is not exhaustive, and there are many other notable AI/ML influencers who are contributing to the field. The individuals listed here are in alphabetical order, and their contributions span various areas within AI and ML.

1. Adam Gibson – Adam Gibson is one of the original creators of Deeplearning4j, an open-source deep learning library for the Java Virtual Machine (JVM), designed to support scalable and distributed machine learning workflows.
2. Adrien Treuille and Amanda Kelly – Streamlit was created by Adrien Treuille and Amanda Kelly at Streamlit Inc. Streamlit is an open-source Python library that allows data scientists and developers to create web applications for data exploration and visualization with minimal effort.
3. Alan Edelman – MLJ (Machine Learning in Julia) is a machine learning framework for the Julia programming language, designed to provide flexible and efficient tools for data science and model building.
4. Alex Johnson – Plotly is an open-source graphing library that provides interactive and customizable visualizations for data analysis and presentation in Python, R, and other programming languages.
5. Alex Krizhevsky (@akrizhevsky) – The co-author of the seminal AlexNet paper, Alex Krizhevsky’s work has played a pivotal role in the resurgence of neural networks and the deep learning revolution.
6. Alexandre Gramfort – Another co-creator of Scikit-learn, Alexandre Gramfort’s expertise in machine learning algorithms and optimization has been vital in developing the library’s core functionalities.
7. Anca Dragan (@ancadianadragan) – A leading researcher in human-robot interaction and robotics, Anca Dragan’s work has focused on making AI systems more understandable and interactive with humans.
8. Andreas Mueller – An active contributor to Scikit-learn, Andreas Mueller’s work has centered on the development of the library’s features, including ensemble methods and clustering algorithms.
9. Andrej Karpathy (@karpathy) – As the Director of AI at Tesla and a former research scientist at OpenAI, Andrej Karpathy has been a key figure in applying AI to real-world systems.
10. Andrew Ng (@AndrewYNg) – As a leading AI researcher and co-founder of Coursera and Google Brain, Andrew Ng has been instrumental in popularizing machine learning education with his online courses and contributing to cutting-edge research.
11. Anima Anandkumar (@AnimaAnandkumar) – A leading researcher in machine learning theory and optimization, Anima Anandkumar’s work has advanced the understanding of AI models’ capabilities and limitations.
12. Armin Ronacher – Flask is a lightweight and extensible Python web framework that provides a simple and flexible way to build web applications.
13. Brian Granger – Brian Granger is a leading AI and ML open source thought leader, known for his impactful contributions to projects like Jupyter, and for inspiring collaboration and innovation within the community.
14. Bryan Van de Ven – Bryan Van de Ven is one of the creators of Bokeh, a powerful Python library for interactive visualization that enables users to build dynamic, interactive, and web-ready visualizations with ease.
15. Chris Lattner – Chris Lattner is an AI and ML tools thought leader known for his contributions to the development of LLVM, MLIR, the Swift programming language. He recently started Modular.AI building a language for AI called Mojo.
16. Clive Cox – Clive Cox is one of the co-founders of Seldon, an open-source platform for deploying and managing machine learning models at scale, helping businesses implement machine learning into their applications and services.
17. Cynthia Dwork (@CynthiaDwork) – A prominent computer scientist and cryptographer, Cynthia Dwork’s research on fairness and privacy in machine learning has been pivotal for AI ethics.
18. Daniel C. Dennett – Co-founder of OpenCog, an open-source AI framework that focuses on artificial general intelligence (AGI).
19. Daphne Koller (@DaphneKoller) – A prominent AI researcher and co-founder of Coursera, Daphne Koller’s work has contributed to probabilistic modeling and Bayesian networks.
20. David Heinemeier Hansson (DHH) – Creator of Ruby on Rails, the open-source web application framework that has enabled rapid development and scalable applications with Ruby.
21. Demis Hassabis (@demishassabis) – As the co-founder of DeepMind, Demis Hassabis has led the development of AI systems that have achieved groundbreaking achievements in games and real-world applications.
22. Dmitry Petrov – DVC (Data Version Control) was created by Dmitry Petrov to provide a version control system specifically designed for machine learning projects, enabling data scientists to track and manage large-scale datasets and models efficiently.
23. DMLC team (led by Tianqi Chen) – The MXNet deep learning framework was developed by the DMLC team, with Tianqi Chen playing a key role in its creation, making it a flexible and efficient library for training and deploying deep neural networks.
24. Doug Cutting – Doug Cutting is one of the original creators of Apache Hadoop. He started the Hadoop project in 2005 along with Mike Cafarella while working at Yahoo!. Doug is a renowned software engineer and technologist with extensive experience in open-source projects.
25. Elad Hazan (@EladH0) – As a prominent researcher in machine learning theory, Elad Hazan’s work has addressed fundamental challenges in optimization and generalization.
26. Elon Musk (@elonmusk) – Although primarily known as the CEO of Tesla and SpaceX, Elon Musk’s advocacy for responsible AI and concerns about its potential dangers have brought AI ethics into the public discourse.
27. Emma Brunskill (@BrunskillEmma) – A leading researcher in AI education and reinforcement learning, Emma Brunskill’s work has implications for personalized learning and adaptive tutoring systems.
28. Fabian Pedregosa – One of the original creators of Scikit-learn, Fabian Pedregosa has been instrumental in shaping the library’s design and functionality. His contributions have helped make machine learning accessible to a wide audience of Python developers.
29. Fangjin Yang – Fangjin Yang is one of the original creators of Apache Druid, an open-source distributed data store designed for real-time analytics on large-scale data sets, allowing users to perform fast, interactive queries and gain insights from streaming and batch data sources.
30. Fei-Fei Li (@drfeifei) – A prominent AI researcher and former Chief Scientist of AI/ML at Google Cloud, Fei-Fei Li’s work on image recognition and natural language processing has been transformative.
31. Felix Meschberger – A core contributor to Apache Sling, Felix Meschberger’s expertise has been instrumental in creating an open-source framework for building RESTful web applications.
32. Fernando Pérez – Fernando Pérez is the creator of the Jupyter Notebook, and he played a pivotal role in its development. He is a data scientist, researcher, and educator known for his contributions to the scientific Python ecosystem.
33. François Chollet (@fchollet) – François Chollet is the original creator of Keras. He developed the library with the vision of providing a user-friendly, modular, and intuitive interface for building deep learning models. His expertise and dedication have been instrumental in shaping Keras into one of the most widely used deep learning libraries.
34. Frederick Reiss – Frederick Reiss is one of the creators of Apache SystemML, an open-source machine learning platform designed to provide scalable and distributed execution of machine learning algorithms, especially for big data.
35. Gael Varoquaux – A data science researcher and one of the original developers of Scikit-learn, Gael Varoquaux’s contributions have revolved around statistical methods and machine learning algorithms.
36. Gary Bradski – Gary Bradski is the founder and creator of OpenCV, a widely used computer vision library that has revolutionized the field of computer vision and image processing.
37. Gary Marcus (@GaryMarcus) – A prominent AI researcher and advocate for hybrid AI approaches, Gary Marcus has been vocal about the limitations of deep learning and the need for more explainable AI.
38. Geoffrey Hinton – Often referred to as the “Godfather of Deep Learning,” Geoffrey Hinton’s work on backpropagation and neural networks laid the foundation for the deep learning revolution.
39. Gilles Louppe – An active contributor to Scikit-learn, Gilles Louppe’s work has centered on improving the library’s efficiency, particularly for large-scale machine learning tasks.
40. Greg Brockman (@gdb) – As the co-founder and Chairman of OpenAI, Greg Brockman has been at the forefront of pushing the boundaries of AI research and safety.
41. Guido van Rossum – The creator of Python, one of the most popular programming languages for AI and machine learning development.
42. Guolin Ke – LightGBM is a gradient boosting framework that uses tree-based learning algorithms and is designed to be efficient and scalable for large-scale machine learning tasks.
43. Han Xiao – Han Xiao is the creator of Jina, an open-source deep learning-powered search framework that simplifies building scalable and efficient search systems with neural networks.
44. Ian Goodfellow (@goodfellow_ian) – The author of the influential book “Deep Learning,” Ian Goodfellow’s work on generative adversarial networks (GANs) has opened new possibilities for AI-generated content.
45. Ilya Sutskever (@ilyasut) – As the co-founder and Chief Scientist of OpenAI, Ilya Sutskever’s contributions to natural language processing and reinforcement learning have been transformative.
46. Jacob Devlin – BERT (Bidirectional Encoder Representations from Transformers) was introduced in a research paper titled “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” by Jacob Devlin and his team at Google AI Language in 2018.
47. Jay Kreps, Neha Narkhede, and Jun Rao – Apache Kafka was originally created by Jay Kreps, Neha Narkhede, and Jun Rao at LinkedIn in 2011. Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming applications.
48. Jeff Dean (@JeffDean) – As a Senior Fellow at Google and co-founder of the Google Brain team, Jeff Dean has played a crucial role in advancing AI research and infrastructure.
49. Jeremy Achin – Jeremy Achin co-founded DataRobot, an automated machine learning platform that empowers organizations to build, deploy, and manage machine learning models efficiently and effectively without requiring extensive data science expertise.
50. Jeremy Howard – Co-founder of fastai, a popular open-source deep learning library built on top of PyTorch.
51. Jerome Pesenti (@jp) – As the VP of AI at Facebook, Jerome Pesenti has been instrumental in driving AI innovation within the company and its various products.
52. Joe Witt – Apache NiFi is an open-source data integration and data flow automation tool that enables the automation of data movement and processing across different systems.
53. John D. Hunter – John D. Hunter was one of the co-creators of Matplotlib, a widely-used data visualization library for Python. His vision and contributions to Matplotlib have had a profound impact on the data science and visualization community.
54. John Schulman – John Schulman is one of the creators of OpenAI Gym, an open-source platform for developing and comparing reinforcement learning algorithms. It provides a collection of environments and benchmark tasks to help researchers and developers work on various reinforcement learning problems.
55. Judea Pearl (@yudapearl) – A pioneer in the field of causal reasoning, Judea Pearl’s work on Bayesian networks and causal inference has been influential in AI reasoning and decision-making.
56. Jürgen Schmidhuber – Co-creator of the Long Short-Term Memory (LSTM) neural network architecture, a fundamental building block in modern AI systems.
57. Kai-Fu Lee (@kaifulee) – A prominent AI investor and former executive at Google and Microsoft, Kai-Fu Lee has been an influential figure in the AI industry in both research and business.
58. Karen Hao (@_KarenHao) – As an AI and technology journalist at MIT Technology Review, Karen Hao’s insightful reporting has provided valuable perspectives on AI advancements and ethics.
59. Karthik Ram – Co-founder of rOpenSci, an organization that promotes open and reproducible research in R, Karthik Ram has been a driving force behind the open-source R ecosystem.
60. Kate Crawford (@katecrawford) – A leading AI ethics researcher, Kate Crawford’s work has focused on AI’s social and ethical implications and its impact on society.
61. Lars Buitinck – A core contributor to Scikit-learn, Lars Buitinck’s expertise in machine learning algorithms and data structures has been valuable in optimizing the library’s performance.
62. Lex Fridman (@lexfridman) – As a researcher and host of the “Artificial Intelligence Podcast,” Lex Fridman has been instrumental in sharing AI knowledge and insights from leading experts.
63. Marc Benioff (@Benioff) – As the co-founder and CEO of Salesforce, Marc Benioff has championed the use of AI and ML in enterprise applications and customer relationship management.
64. Marc Raibert (@BostonDynamics) – As the founder of Boston Dynamics, Marc Raibert’s work on advanced robotics and dynamic walking has pushed the boundaries of AI-driven locomotion.
65. Matei Zaharia – Matei Zaharia is the original creator of Apache Spark. He developed Spark as part of his Ph.D. research at UC Berkeley. His vision was to design a distributed computing framework that could outperform Hadoop MapReduce by efficiently utilizing distributed memory.
66. Matt DeBergalis – Co-founder of Apollo GraphQL and a driving force behind the GraphQL movement, Matt DeBergalis has played a significant role in popularizing GraphQL as an open-source data query language.
67. Matthew Honnibal – spaCy is an open-source natural language processing library designed for efficient and production-ready text processing, including tokenization, named entity recognition, part-of-speech tagging, and more.
68. Matthew Mayo – ML.NET Model Explainability provides tools and techniques for understanding and interpreting the decisions made by machine learning models built using ML.NET framework.
69. Matthew Peters – Matthew Peters is one of the creators of AllenNLP, an open-source natural language processing (NLP) library built on top of PyTorch, designed to facilitate research and production use of NLP models.
70. Matthew Rocklin – Dask is a parallel computing library that enables scalable and flexible data processing in Python, making it easier to work with large datasets across multiple computing resources.
71. Matthieu Brucher – A contributor to Scikit-learn, Matthieu Brucher’s focus has been on the development of audio processing and machine learning modules in the library.
72. Max Welling (@mhwelling) – A leading researcher in probabilistic modeling and Bayesian deep learning, Max Welling’s work has contributed to making AI systems more robust and interpretable.
73. Maxime Beauchemin – Maxime Beauchemin is the creator of Apache Airflow, an open-source platform to programmatically author, schedule, and monitor workflows, allowing users to define, manage, and execute complex data workflows with ease.
74. Michael I. Jordan – As a prominent AI researcher and professor at UC Berkeley, Michael I. Jordan’s work has spanned machine learning, statistics, and AI foundations.
75. Mikhail Semeniuk – MLeap is a machine learning serialization and serving framework designed to deploy Apache Spark pipelines for real-time scoring and inference.
76. Moez Ali – PyCaret is an open-source Python library that automates the machine learning workflow, making it easier for data scientists to experiment with various algorithms and build predictive models efficiently.
77. Moon Soo Lee – Moon Soo Lee is the original creator of Apache Zeppelin, an open-source web-based notebook for data exploration, visualization, and collaboration, supporting various programming languages and data sources.
78. Mu Li – GluonCV is a deep learning toolkit built on top of Apache MXNet that offers a wide range of pre-trained models, algorithms, and tools for computer vision tasks.
79. Nelle Varoquaux – As a contributor to Scikit-learn, Nelle Varoquaux’s work has involved improving the library’s documentation, making it more accessible to users and developers.
80. Olivier Grisel – A key contributor to Scikit-learn, Olivier Grisel’s work has focused on enhancing the library’s usability, documentation, and support for various machine learning models.
81. ONDŘEJ ČERTÍK – ONDŘEJ ČERTÍK is the principal compiler engineer at GSI Technology. Former scientist at Los Alamos National Laboratory. Ondřej is the original author of SymPy, SymEngine and LFortran.
82. Oren Etzioni (@etzioni) – As the CEO of the Allen Institute for AI, Oren Etzioni has been a strong advocate for responsible AI development and ethical AI research.
83. Peter Norvig – As the Director of Research at Google, Peter Norvig’s work has focused on natural language processing, search algorithms, and AI applications.
84. Peter Wang – As the co-founder and CEO of Anaconda and an organizer of PyData, Peter Wang has been a force behind the development of data science platforms and open-source tools. Anaconda is the leading tool to help AI and ML practitioners to rely on in production all the open-source libraries for AI, ML, and scientific computing.
85. Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, and Ion Stoica – Ray was created by Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, and Ion Stoica at the RISELab, UC Berkeley. Ray is an open-source distributed computing framework for parallel and distributed Python applications, providing easy-to-use APIs for scalable and efficient computation.
86. Piero Molino – The creator of Ludwig is Piero Molino, a software engineer and researcher in the field of artificial intelligence and machine learning. Ludwig is an open-source, flexible, and extensible deep learning framework that allows users to build and train machine learning models without writing code.
87. Pieter Abbeel (@pabbeel) – A leading AI researcher in robotics and reinforcement learning, Pieter Abbeel’s work has advanced the development of autonomous systems.
88. Piotr Plonsk – The MLJAR project was created by Piotr Plonski, and it is an open-source AutoML library that enables users to build and deploy machine learning models with ease and efficiency.
89. Rachel Thomas (@math_rachel) – A leading advocate for diversity and inclusion in AI, Rachel Thomas co-founded fast.ai, a platform that offers accessible AI education to diverse learners worldwide.
90. Radim Řehůřek – Gensim is a Python library for topic modeling and document similarity analysis using various unsupervised learning algorithms like Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and Word2Vec.
91. Rana el Kaliouby (@Kaliouby) – As the co-founder and CEO of Affectiva, Rana el Kaliouby’s work on emotion AI and affective computing has potential applications in human-computer interaction and mental health.
92. Rashid Khan – Kibana is an open-source data visualization and exploration tool that works in conjunction with ElasticSearch, allowing users to interact with and visualize their data through various charts, graphs, and dashboards.
93. Ray Smith – Ray Smith is one of the original creators of Tesseract OCR, an open-source optical character recognition engine used for text recognition from images and scanned documents.
94. Regina Barzilay – As a prominent researcher in natural language processing and machine learning, Regina Barzilay’s work has focused on applying AI to healthcare and cancer research.
95. Rich Sutton – A pioneer in reinforcement learning, Rich Sutton’s contributions to the field have been instrumental in advancing autonomous agents and decision-making systems.
96. Richard Liaw – Richard Liaw is one of the creators of Ray Tune, a scalable hyperparameter tuning library built on top of Ray, designed to automate the process of finding optimal hyperparameters for machine learning models.
97. Richy Vink – Richy Vink is an AI and ML open source thought leader, recognized for his influential work in the Polars project, fostering advancements in data manipulation and analytics, and contributing to the growth of the open source AI and ML ecosystem.
98. Rodney Brooks (@rodneyabrooks) – One of the early pioneers of AI and robotics, Rodney Brooks’ research on autonomous systems has laid the groundwork for modern robotics.
99. Satya Mallick – Satya Mallick is one of the creators of OpenCV AI Kit (OAK), a compact hardware module powered by the Myriad X vision processing unit that enables fast and efficient AI inferencing on the edge.
100. Sean J. Taylor – Prophet is an open-source forecasting library developed by Facebook that provides time series forecasting using additive models, designed for high-quality, accurate predictions with ease of use.
101. Sebastian Thrun (@SebastianThrun) – As the founder of Google’s self-driving car project and co-founder of Udacity, Sebastian Thrun has been a driving force in the development of autonomous vehicles and AI education.
102. Seiya Tokui – Seiya Tokui is the creator of Chainer, a flexible and user-friendly deep learning framework that emphasizes dynamic computation graphs and allows for easy customization and extension.
103. Shay Banon – ElasticSearch is an open-source distributed search and analytics engine built on top of Apache Lucene, designed to provide fast and scalable full-text search capabilities.
104. Siu Kwan Lam – Numba was created by Travis Olliphant and Siu Kwan Lam. It is an open-source just-in-time compiler that translates Python functions to optimized machine code, allowing for accelerated numerical computations.
105. Southmith Chintala – Soumith Chintala is a highly influential AI and ML open source thought leader, renowned for his pivotal role in advancing PyTorch, empowering countless individuals to explore the world of AI and ML, and bridging the gap between academia and industry.
106. SriSatish Ambati – H2O.ai is a leading open-source AI platform that offers scalable machine learning and deep learning solutions for businesses and data scientists.
107. SriSatish Ambati, Cliff Click, and Arno Candel – H2O was created by SriSatish Ambati, Cliff Click, and Arno Candel. It is an open-source platform for AI and machine learning that provides a scalable, fast, and user-friendly environment for data analysis and model building.
108. Stéfan van der Walt – Stéfan van der Walt is one of the key developers of the scikit-image library, which is an open-source image processing library built on top of SciPy that provides various algorithms for image analysis and computer vision tasks.
109. Steven Bird – Steven Bird is one of the co-creators of the Natural Language Toolkit (NLTK), a leading platform for building Python programs to work with human language data.
110. Takuya Akiba – Optuna was created by Takuya Akiba and his team at Preferred Networks, and it is an open-source hyperparameter optimization framework designed to automate the tuning process for machine learning models efficiently.
111. Thomas Wolf – Hugging Face Transformers is a popular open-source library that provides pre-trained models and utilities for Natural Language Processing (NLP) tasks, making it easier for developers to work with state-of-the-art language models.
112. Tiangolo (Sebastián Ramírez) – FastAPI is a modern, fast, and web-based Python framework for building APIs with automatic validation, serialization, and interactive documentation.
113. Tianqi Chen – Tianqi Chen is the creator of XGBoost, an open-source gradient boosting library that has become one of the most popular and effective machine learning algorithms for structured/tabular data.
114. Timnit Gebru (@timnitGebru) – A prominent AI ethics researcher, Timnit Gebru’s work has shed light on the societal implications of AI technologies and biases in data.
115. Tom Mitchell – A renowned AI researcher and professor at CMU, Tom Mitchell’s work on machine learning and neural representations has advanced the field.
116. Tom White – Author of the book “Hadoop: The Definitive Guide” and a key contributor to the Apache Hadoop project, Tom White’s expertise has been crucial in making Hadoop a leading open-source big data processing platform.
117. Travis Oliphant – As a founder of Anaconda, NumFOCUS, OpenTeams, and Quansight and the creator of SciPy, NumPy, Numba, and PyData, Travis’s work has been foundational in advancing scientific computing and data analysis with Python. His contributions have made it possible for researchers and data scientists to leverage the power of Python for AI and ML applications.
118. Trevor Darrell (@trevor_darrell) – A leading researcher in computer vision and AI ethics, Trevor Darrell’s work has focused on making AI technologies more inclusive and accountable.
119. Uwe L. Korn – Uwe L. Korn is an ML, data-science, and open source thought leader, recognized for his contributions to the Arrow, Parquet, and Conda-Forge
120. Wes McKinney – Wes McKinney is the creator of Pandas, a widely used open-source data manipulation library for Python. His work on Pandas has revolutionized data analysis and made it easier for data scientists and analysts to work with structured data.
121. William Dally (@William_Dally) – A prominent AI researcher and Chief Scientist at NVIDIA, William Dally’s work has contributed to advances in GPU computing and deep learning.
122. Yangqing Jia – Yangqing Jia is the creator of Caffe, a deep learning framework that has been widely used for its modularity, speed, and scalability in training deep neural networks.
123. Yann LeCun (@ylecun) – Known as one of the pioneers of deep learning, Yann LeCun’s contributions to convolutional neural networks (CNNs) have revolutionized computer vision and image recognition.
124. Yoshua Bengio (@yoshuabengio) – Yoshua Bengio is one of the creators of Theano, a popular numerical computation library widely used for building and training deep learning models.

AI and ML technology influencers listed above have made significant contributions to the advancement of artificial intelligence and machine learning. Their research, leadership, and advocacy have not only shaped the field but also influenced its ethical, social, and industrial applications. As AI and ML continue to evolve, these influencers will likely play key roles in guiding its responsible development and fostering innovation across various domains. Their work will continue to inspire and impact the future of AI, ensuring that it remains a transformative force for good.

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