Typing what is machine learning? into a Google search opens up a pandoras box of forums, academic research, and here-say and the purpose of this article is to simplify the definition and understanding of machine learning thanks to the direct help from our panel of machine learning researchers.
In addition to an informed, working definition of machine learning (ML), we aim toprovide a succinct overview of the fundamentals of machine learning, the challenges and limitations of getting machine to think, some of the issues being tackled today in deep learning (the frontier of machine learning), and key takeaways for developingmachine learningapplications.
This article will be broken up into the following sections:
We put together this resource to help with whatever your area of curiosity about machine learning so scroll along to your section of interest, or feel free to read the article in order, starting with our machine learning definition below:
* Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.
The above definition encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field. The purpose of this article is to provide a business-minded reader with expert perspective on how machine learning is defined, and how it works.Machine learning and artificial intelligence share the same definition in the minds of many however, there are some distinct differences readers should recognize as well. References and related researcher interviews are included at the end of this article for further digging.
(Our aggregate machine learning definition can be found at the beginning of this article)
As with any concept, machine learning may have a slightly different definition, depending on whom you ask. We combed the Internet to find five practicaldefinitions from reputable sources:
We sent these definitions to experts whom weve interviewed and/or included in one of our past research consensuses, and asked them to respond with their favorite definition or to provide their own. Our introductory definition is meant to reflect the varied responses. Below are someof their responses:
Dr. Yoshua Bengio,Universit de Montral:
ML should not be defined by negatives (thus ruling 2 and 3). Here is my definition:
Machine learning research is part of research on artificial intelligence, seeking to provide knowledge to computers through data, observations and interacting with the world. That acquired knowledge allows computers to correctly generalize to new settings.
Dr. Danko Nikolic, CSC and Max-Planck Institute:
(edit of number 2 above): Machine learning is the science of getting computers to act without being explicitly programmed, but instead letting them learn a few tricks on their own.
Dr. Roman Yampolskiy, University ofLouisville:
Machine Learning is the science of getting computers to learn as well as humans do or better.
Dr. Emily Fox, University of Washington:
My favorite definition is #5.
There are many different types of machine learning algorithms, with hundreds published each day, and theyretypically grouped by either learning style (i.e. supervised learning, unsupervised learning, semi-supervised learning) or by similarity in form or function (i.e. classification, regression, decision tree, clustering, deep learning, etc.). Regardless of learning style or function, all combinations of machine learning algorithms consist of the following:
Image credit: Dr. Pedro Domingo, University of Washington
The fundamental goal of machine learning algorithms is togeneralize beyond the training samples i.e. successfully interpret data that it has never seen before.
Concepts and bullet points can only take one so far in understanding.When people ask What is machine learning?, they often want to see what it is and what it does. Below are some visual representations of machine learning models, with accompanying links for further information. Even more resources can be found at the bottom of this article.
Decision tree model
Gaussian mixture model
Dropout neural network
Merging chrominance and luminance using Convolutional Neural Networks
There are different approaches to getting machines to learn, from using basic decision trees to clustering to layers of artificial neural networks (the latter of which has given way to deep learning), depending on what task youre trying to accomplish and the type and amount of data that you have available. This dynamic sees itself played out in applications as varyingas medical diagnostics or self-driving cars.
While emphasis is often placed on choosing the best learning algorithm, researchers have found that some of the most interesting questions arise out of none of the available machine learning algorithms performing to par. Most of the time this is a problem with training data, but this also occurs when working with machine learning in new domains.
Research done when working on real applications often drives progress in the field, and reasons are twofold: 1. Tendency to discover boundaries and limitations of existing methods 2. Researchers and developers working with domain experts andleveraging time and expertise to improve system performance.
Sometimes this also occurs by accident. We might consider model ensembles, or combinations of many learning algorithms to improve accuracy, to be one example. Teams competing for the 2009 Netflix Price found that they got their best results when combining their learners with other teams learners, resulting in an improved recommendation algorithm (read Netflixs blog for more on why theydidnt end up using this ensemble).
One important point (based on interviews and conversations with experts in the field), in terms of application within business and elsewhere, is that machine learning is not just, or even about, automation, an often misunderstood concept. If you think this way, youre bound to miss the valuable insights that machines can provide and the resulting opportunities (rethinking an entire business model, for example, as has been in industries like manufacturing and agriculture).
Machines that learn are useful to humans because, with all of their processing power, theyre able to more quickly highlight or find patterns in big (or other) data that would have otherwise been missed by human beings. Machine learning is a tool that can be used to enhance humans abilities to solve problems and make informed inferences on a wide range of problems, from helping diagnose diseases to coming up with solutions for global climate change.
Machine learning cant get something from nothingwhat it does is get more from less. Dr. Pedro Domingo, University of Washington
The two biggest, historical (and ongoing) problems in machine learning have involved overfitting (in which the model exhibits bias towards the training data and does not generalize to new data, and/or variance i.e. learns random things when trained on new data) and dimensionality (algorithms with more features work in higher/multiple dimensions, making understanding the data more difficult). Having access to a large enough data set has in some cases also been a primary problem.
One of the most common mistakes among machine learning beginners is testing training data successfully and having the illusion of success; Domingo (and others) emphasize the importance of keeping some of the data set separate when testing models, and only using that reserved data to test a chosen model, followed by learning on the whole data set.
When a learning algorithm (i.e. learner) is not working, often the quicker path to success is to feed the machine more data, the availability of which is by now well-known as a primary driver of progress in machine and deep learning algorithms in recent years; however, this can lead to issues with scalability, in which we have more data but time to learn that data remains an issue.
In terms of purpose, machine learning is not an end or a solution in and of itself. Furthermore, attempting to use it as a blanket solution i.e. BLANKis not a useful exercise; instead, coming to the table with a problem or objective is often best driven bya more specific question BLANK.
Deep learning involves the study and design of machine algorithms for learning good representation of data at multiple levels of abstraction (ways of arranging computer systems). Recent publicity of deep learning through DeepMind, Facebook, and other institutionshas highlighted it as the next frontier of machine learning.
The International Conference on Machine Learning (ICML) is widely regarded as one of the most important in the world. This years took place in June in New York City, and it brought together researchers from all over the world who are working on addressing the current challenges in deep learning:
Deep-learning systems have made great gains over the past decade in domains like bject detection and recognition, text-to-speech, information retrieval and others. Research is now focused on developingdata-efficient machine learning i.e. deep learning systems that can learn more efficiently, with the same performance in less time and with less data, in cutting-edge domains like personalized healthcare, robot reinforcement learning, sentiment analysis, and others.
Below is a selection of best-practices and concepts of applying machine learning that weve collated from our interviews for out podcast series, and from select sources cited at the end of this article. We hope that some of these principles will clarify how ML is used, and how to avoid some of the common pitfalls that companies and researchers might be vulnerable to in starting off on an ML-related project.
One of the best ways to learn about artificial intelligence concepts is to learn from the research and applications of the smartest minds in the field. Below is a brief list of some of our interviews with machine learning researchers, many of which may be of interest for readers who want to explore these topics further:
Read more here:
- Machine Learning - November 3rd, 2019
- Machine Learning | Udacity - November 3rd, 2019
- Machine Learning Artificial Intelligence | McAfee - November 3rd, 2019
- Artificial Intelligence vs. Machine Learning vs. Deep ... - November 3rd, 2019
- Machine Learning in R for beginners (article) - DataCamp - November 3rd, 2019
- definition - What is machine learning? - Stack Overflow - November 3rd, 2019
- How to Learn Machine Learning, The Self-Starter Way - October 17th, 2019
- Machine Learning | Stanford Online - October 2nd, 2019
- What is Machine Learning? A definition - Expert System - October 2nd, 2019
- Machine Learning Basics | What Is Machine Learning? | Introduction To Machine Learning | Simplilearn - October 1st, 2019
- Microsoft Azure Machine Learning Studio - October 1st, 2019
- Start Here with Machine Learning - September 22nd, 2019
- What Is Machine Learning? | How It Works, Techniques ... - September 5th, 2019