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:
- New Research Claims to Have Found a Solution to Machine Learning Attacks - Analytics Insight - May 18th, 2020
- Patent Analytics Market to Reach USD 1,668.4 Million by 2027; Integration of Machine Learning and Artificial Intelligence to Spur Business... - May 18th, 2020
- AI, machine learning, and blockchain are key for healthcare innovation - Health Europa - May 18th, 2020
- Our Behaviour in This Pandemic Has Seriously Confused AI Machine Learning Systems - ScienceAlert - May 18th, 2020
- The impact of the coronavirus on the Machine Learning in Healthcare Cybersecurity Market Report 2020 - News Distinct - May 18th, 2020
- Associations with No Place to Meet Are Turning to JUNO, A Live and On-Demand Digital Platform - AiThority - May 18th, 2020
- Reality Of Metrics: Is Machine Learning Success Overhyped? - Analytics India Magazine - May 18th, 2020
- Cloud Storage Market to Reach USD 297.54 Billion by 2027; Higher Adoption of Machine Learning to Boost Growth, Says Fortune Business Insights -... - May 18th, 2020
- Q&A on the Book Hands-On Genetic Algorithms with Python - InfoQ.com - May 18th, 2020
- Bitglass Integrates CrowdStrike's Machine-Learning Technology to Provide Zero-Day Advanced Threat Protection in the Cloud - Business Wire - May 18th, 2020
- Parasoft Unleashes Artificial Intelligence and Machine Learning to Accelerate Time to Market for the Safety-Critical Industry - PRNewswire - May 12th, 2020
- How to overcome AI and machine learning adoption barriers - Gigabit Magazine - Technology News, Magazine and Website - May 12th, 2020
- Canaan's Kendryte K210 and the Future of Machine Learning - CapitalWatch - May 12th, 2020
- Machine Learning Software Market Growth by Top Companies, Trends by Types and Application, Forecast to 2026 - Cole of Duty - May 12th, 2020
- Eta Compute Partners with Edge Impulse to Accelerate the Development and Deployment of Machine Learning at the Edge - Yahoo Finance - May 12th, 2020
- Quantzig Launches New Article Series on COVID-19's Impact - 'Understanding Why Online Food Delivery Companies Are Betting Big on AI and Machine... - May 12th, 2020
- Five Strategies for Putting AI at the Center of Digital Transformation - Knowledge@Wharton - May 12th, 2020
- Twitter adds former Google VP and A.I. guru Fei-Fei Li to board as it seeks to play catch up with Google and Facebook - CNBC - May 12th, 2020
- Millions of historic newspaper images get the machine learning treatment at the Library of Congress - TechCrunch - May 9th, 2020
- Could quantum machine learning hold the key to treating COVID-19? - Tech Wire Asia - May 9th, 2020
- Machine Learning Engineer: Challenges and Changes Facing the Profession - Dice Insights - May 9th, 2020
- How Machine Learning Is Redefining The Healthcare Industry - Small Business Trends - May 9th, 2020
- Udacity partners with AWS to offer scholarships on machine learning for working professionals - Business Insider India - May 9th, 2020
- Tackling climate change with machine learning: Covid-19 and the energy transition - pv magazine International - May 9th, 2020
- Machine Learning Engineers Will Not Exist In 10 Years - Machine Learning Times - machine learning & data science news - The Predictive Analytics... - May 9th, 2020
- The Struggle is Real 3 Considerations to Make Machine Learning More Effective - Martechcube - May 9th, 2020
- Determined AI makes its machine learning infrastructure free and open source - TechCrunch - May 1st, 2020
- Microsoft: This is how to protect your machine-learning applications - TechRepublic - May 1st, 2020
- Tecton.ai Launches with New Data Platform to Make Machine Learning Accessible to Every Company - insideBIGDATA - May 1st, 2020
- How To Verify The Memory Loss Of A Machine Learning Model - Analytics India Magazine - May 1st, 2020
- AI, machine learning and automation in cybersecurity: The time is now - GCN.com - May 1st, 2020
- Could Machine Learning Replace the Entire Weather Forecast System? - HPCwire - May 1st, 2020
- Global Machine Learning As A Service (Mlaas) Market : Industry Analysis And Forecast (2020-2027) - MR Invasion - May 1st, 2020
- Dascena Announces Publication of Prospective Study Evaluating Effect of its Machine Learning Algorithm on Severe Sepsis Prediction - Business Wire - May 1st, 2020
- Machine Learning in Medicine Market 2020-2024 Review and Outlook - Latest Herald - May 1st, 2020
- Rise in the demand for Machine Learning & AI skills in the post-COVID world - Times of India - May 1st, 2020
- A.I. can't solve this: The coronavirus could be highlighting just how overhyped the industry is - CNBC - May 1st, 2020
- Is Machine Learning Model Management The Next Big Thing In 2020? - Analytics India Magazine - May 1st, 2020
- Apple is on a hiring freeze ... except for its Hardware, Machine Learning and AI teams - Thinknum Media - May 1st, 2020
- Global Machine Learning as a Service Market Industry Raesearch Report, Growth Trends and Competitive Analysis 2020-2026 - Cole of Duty - May 1st, 2020
- Infragistics Adds Predictive Analytics, Machine Learning and More - Patch.com - April 15th, 2020
- Machine Learning as a Service Market Coronavirus (COVID-19) Impact Analysis with Global Innovations, Competitive Analysis, New Business Developments... - April 15th, 2020
- Nothing to hide? Then add these to your ML repo, Papers with Code says DEVCLASS - DevClass - April 15th, 2020
- Global Machine Learning Software Market 2020 by Manufacturers, Countries, Type and Application, Forecast to 2025 - Bandera County Courier - April 15th, 2020
- Why the information security of your company depends on machine learning - SC Magazine - April 13th, 2020
- Automated Machine Learning is the Future of Data Science - Analytics Insight - April 13th, 2020
- Artificial Intelligence: From Machine Learning to NLP, these are the best 8 reasonable topics for Research ... - Gizmo Posts 24 - April 13th, 2020
- The impact of machine learning on the legal industry - ITProPortal - April 13th, 2020
- WekaIO Recognized as One of CRN's Top 100 Storage Vendors for 2020 - AiThority - April 13th, 2020
- Global Machine Learning Market expected to grow USD XX.X million by 2025 , at a CAGR of XX% during forecast period: Microsoft, IBM, SAP, SAS, Google,... - April 13th, 2020
- Artificial Intelligence Is Going to Revolutionize the Executive Search World - BRINK - April 13th, 2020
- What Is The Difference Between Artificial Intelligence And ... - April 10th, 2020
- Machine Learning: Making Sense of Unstructured Data and Automation in Alt Investments - Traders Magazine - April 10th, 2020
- Machine Learning Improves Weather and Climate Models - Eos - April 10th, 2020
- What Will Be the Future Prospects Of the Machine Learning Software Market? Trends, Factors, Opportunities and Restraints - Science In Me - April 10th, 2020
- How Microsoft Teams will use AI to filter out typing, barking, and other noise from video calls - VentureBeat - April 10th, 2020
- Infragistics Adds Predictive Analytics, Machine Learning and More to Reveal Embedded Business Intelligence Tool - GlobeNewswire - April 3rd, 2020
- Google is using machine learning to improve the quality of Duo calls - The Verge - April 3rd, 2020
- Data Science and Machine-Learning Platformss Market Share Opportunities Trends, And Forecasts To 2020-2027 with Key Players: SAS, Alteryx, IBM,... - April 3rd, 2020
- Google TensorFlow Cert Suggests AI, ML Certifications on the Rise - Dice Insights - April 3rd, 2020
- AI cant predict how a childs life will turn out even with a ton of data - MIT Technology Review - April 3rd, 2020
- Machine Learning in Life Sciences Market Report History and Forecast 2020 Breakdown Data by Manufacturers, by Key Regions, Types and Applications -... - April 3rd, 2020
- The Global Machine Learning Market is expected to grow by USD 11.16 bn during 2020-2024, progressing at a CAGR of 39% during the forecast period -... - April 3rd, 2020
- Well-Completion System Supported by Machine Learning Maximizes Asset Value - Journal of Petroleum Technology - April 3rd, 2020
- Weekend Roundup: Anything-Other-Than-COVID-19 Edition (Seriously!) - Dice Insights - April 3rd, 2020
- Intel + Cornell Pioneering Work in the Science of Smell - insideBIGDATA - March 27th, 2020
- Data to the Rescue! Predicting and Preventing Accidents at Sea - JAXenter - March 27th, 2020
- Return On Artificial Intelligence: The Challenge And The Opportunity - Forbes - March 27th, 2020
- Noble.AI Contributes to TensorFlow, Google's Open-Source AI Library and the Most Popular Deep Learning - AiThority - March 27th, 2020
- PSD2: How machine learning reduces friction and satisfies SCA - The Paypers - March 27th, 2020
- Machine learning teams with antibody science on COVID-19 treatment discovery - AI in Healthcare - March 27th, 2020
- Neural networks facilitate optimization in the search for new materials - MIT News - March 27th, 2020
- Natural Language Processing is an Untapped AI Tool for Innovation - Yahoo Finance - March 27th, 2020
- Coronavirus lockdown: 10 free online computer science courses from Harvard, Princeton & other top universities to study - Gadgets Now - March 27th, 2020
- How AI Can Realize The Promise Of Adaptive Education - Forbes - March 27th, 2020
- Udacity offers free tech training to laid-off workers due to the coronavirus pandemic - CNBC - March 27th, 2020
- Will COVID-19 Create a Big Moment for AI and Machine Learning? - Dice Insights - March 25th, 2020
- How our publisher harnessed machine learning to overhaul Techday websites - CFOtech New Zealand - March 25th, 2020
- dotData Receives APN Machine Learning Competency Partner of the Year Award - WFMZ Allentown - March 25th, 2020
- Machine Learning Engineer Interview Questions: What You Need to Know - Dice Insights - March 25th, 2020