From researchers to students, industry experts, and machine learning (ML) enthusiasts — keeping up with the best and the latest machine learning research is a matter of finding reliable sources of scientific work. While blogs usually update in a more informal and conversational style, we have found that the sources in this list are accurate, resourceful, and reliable sources of machine learning research. Fit for all of those interested in learning more about the scientific field of ML.
Please know that the blogs listed below are by no means ranked or in a particular order. They are all incredible sources of machine learning research. …
Last updated on October 8, 2020
Anyone curious who wants a straightforward and accurate overview of what machine learning is, about how it works, and its importance. We go through each of the pertinent questions raised above by slicing technical definitions from machine learning pioneers and industry leaders to present you with a basic, simplistic introduction to the fantastic, scientific field of machine learning.
A glossary of terms can be found at the bottom of the article, along with a small set of resources for further learning, references, and disclosures.
The scientific field of machine learning (ML) is a branch of artificial intelligence, as defined by Computer Scientist and machine learning pioneer  Tom M. Mitchell: “Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience .” …
This past October, I had the opportunity to be part of a fantastic discussion panel organized by the Bank of New York Mellon and ALPFA. Next to Christopher Mager, global head of innovation at the Bank of New York Mellon, Neysha Arcelay, founder and CEO of Precixa, and Natalia Castillejo, principal product manager with Duolingo.
While my responses were not necessarily the same during the discussion, these are some (close) transcripts of such interaction during the panel.
Using a creative approach for innovation is critical. Usually, people tend to think that the best time to innovate at a company is when a company’s revenue decreasing. The best time to innovate is by continuously applying A/B testing to current and new strategies to keep strategies that work and dismiss those that do not. …
Recently, I had the pleasure of visiting one of the most innovative AI companies in Pittsburgh. Gather AI, an autonomous inventory management AI company, revolutionizing the warehousing industry as the title mentions “one drone at a time.”
Gather AI’s founding team comprises three graduates from Carnegie Mellon University’s Robotics Institute, including co-founder and chief robotics officer Sankalp Arora, co-founder and chief technology officer Daniel Maturana, and co-founder and chief security officer Geetesh Dubey.
Gather AI uses state-of-the-art robotics, classic AI methods, and a deep learning engine to enable autonomous inventory monitoring and management, which uses a drone fleet. Each drone is fully autonomous and paired to a tablet device, which provides inventory data. …
Machine learning careers are being sought out by many, from researchers, industry experts, to machine learning enthusiasts. Everyone is trying to get their feet wet working with machine learning to contribute to such a rapidly moving field.
With massive open online courses (MOOCs) offering machine learning paths from Coursera, Udacity, edX, and others. To leader institutions in academic research such as Carnegie Mellon, Berkeley, MIT, Georgia Tech, and others.
How do you know what’s the right path to follow with so many options?
It depends. It is best if you weight what is essential for you to pursue in terms of your career in machine learning. Below, please find some of the main differences between pursuing machine learning coursework with an MOOC or with a university. …
Below please find a searchable glossary of terms that are relevant to the scientific field of machine learning — on ongoing growth.
This glossary is by no means complete. If a term it’s missing, please let me know in the comments, and I’ll add its definition to this growing glossary. Also, if you have feedback regarding a definition of any of the term(s), please let me know as well.
An AI agent or intelligent agent is a bot used in AI-related tasks.
An algorithm is a process that follows a set of rules, a problem solver — mainly used by computers. …
The Machine Learning Department at Carnegie Mellon University was founded in the spring of 2006 as the world’s first machine learning academic department. It evolved from an earlier organization called the Center for Automated Learning and Discovery (CALD), created in 1997. CALD was designed to bring together an interdisciplinary group of researchers with a shared interest in statistics and machine learning.
The first collection of CALD faculty participants were primarily from the Statistics Department and departments within the School of Computer Science, but also included faculty from philosophy, engineering, the business school, and biological science.
Statistics Professor Stephen Fienberg and Computer Science Professor Tom Mitchell were the primary faculty involved in creating CALD. In 1999 CALD began its first educational program, a Master’s degree in “Knowledge Discovery and Data Mining.” …
We will present the following papers at the 36th International Conference on Machine Learning (ICML) in Long Beach, California. This achievement is a demonstration of the cutting-edge machine learning research being done at Carnegie Mellon University.
If you are attending ICML 2019, please stop by to say hello and hear more about what we are doing!
Anson Kahng (Carnegie Mellon University) · Min Kyung Lee (CMU) · Ritesh Noothigattu (Carnegie Mellon University) · Ariel Procaccia (Carnegie Mellon University) · Christos-Alexandros Psomas (Carnegie Mellon University)
Abhishek Das (Georgia Tech) · Theophile Gervet (Carnegie Mellon University) · Joshua Romoff (McGill University) · Dhruv Batra (Georgia Institute of Technology / Facebook AI Research) · Devi Parikh (Georgia Tech & Facebook AI Research) · Michael Rabbat (Facebook) · Joelle Pineau…
Last updated November 15, 2020
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March 13, 2019 by Roberto Iriondo — Updated: April 12, 2019
There is a record amount of exciting artificial intelligence (AI) conferences worldwide and keeping track of them poses a challenge. The list below provides an overview of the top upcoming artificial intelligence (AI) conferences worldwide. Check them out and register to join the growing AI community and learn all the latest developments in machine learning, deep learning, natural language processing, neural networks, computer vision, big data and more.
Please feel free to comment below if you think of a conference that should be added to this list.
NVIDIA’s GPU Technology Conference (GTC) is the head AI and deep learning event, furnishing you with training, bits of knowledge, and direct access to specialists from NVIDIA and other driving associations. See the most recent leaps forward in self-driving cars, healthcare, data science, elite performance computing and processing, augmented reality, and more. …