Tag: Machine Learning

When Is Machine Learning Useful?

In a past blog post, I defined and described what machine learning is. I briefly highlighted four instances where machine learning algorithms are useful. This is what I wrote:

  1. Autonomy: To teach computers to do a task without the direct aid/intervention of humans (e.g. autonomous vehicles)
  2. Fluctuation: Help machines adjust when the requirements and data change over time
  3. Intuitive Processing: Conduct or assist in tasks humans do but are unable to explain how computationally/algorithmically (e.g. image recognition)
  4. Big Data: Breaking down data that is too large to handle otherwise

The goal of this blog post is to explain each in more detail.

Case #1: Autonomy

Car, Automobile, 3D, Self-Driving

The first major use of machine learning centers around teaching computers to do a task or tasks without the direct aid or intervention of humans. Self-driving vehicles are a high-profile example of this: teaching a vehicle to drive (scanning the road and determining how to respond to what is around it) without the aid of or with minimal direct oversight from a human driver.

There are two types basic types of tasks that machine learning systems might perform autonomously:

  1. Tasks humans frequently perform
  2. Tasks humans are unable to perform.

Self-driving cars exemplify the former: humans drive cars, but self-driving cars would perform all or part of the driving process. Another example would be chatbots and virtual assistants like Alexa, Cortana, and Ok Google, which seek to converse with users independently. Such tasks might completely or partially complete the human activity: for example, some customer service chatbots are designed to determine the customer’s issue but then to transfer to a human when the issue has a certain complexity.

Humans have also sought to build autonomous machine learning algorithms to perform tasks that humans are unable to perform. Unlike self-driving cars, which conduct an activity many people do, people might also design a self-driving rover or submarine to drive and operate in a world that humans have so far been unable to inhabit, like other planets in our Solar System or the deep ocean. Search engines are another example: Google uses machine learning to help refine search results, which involves analyzing a massive amount of web data beyond what a human could normally do.

Case #2: Fluctuating Data

Business, Success, Curve, Hand, Draw, Present, Trend

Machine learning is also powerful tool for making sense of and incorporating fluctuating data. Unlike other types of models with fixed processes for how it predicts its values, machine learning models can learn from current patterns and adjust both if the patterns fluctuate overtime or if new use cases arise. This can be especially helpful when trying to forecast the future, allowing the model to decipher new trends if and when they emerge. For example, when predicting stock prices, machine learning algorithms can learn from new data and pick up changing trends to make the model better at predicting the future.

Of course, humans are notorious for changing overtime, so fluctuation is often helpful in models that seek to understand human preferences and behavior. For example, user recommendations – like Netflix’s, Hulu’s, or YouTube’s video recommendation systems – adjust based on the usage overtime, enabling them to respond to individual and/or collective changes in interests.

Case #3: Intuitive Processing

Flat, Recognition, Facial, Face, Woman, System

Data scientist frequently develop machine learning algorithms to teach computers how to do processes that humans do naturally but for which we are unable to fully explain how computationally. For example, popular applications of machine learning center around replicating some aspect of sensory perception: image recognition, sound or speech recognition, etc. These replicate the process of inputting sensory information (e.g. sight and sound) and processing, classifying, and otherwise making sense of that information. Language processing, like chatbots, form another example of this. In these contexts, machine learning algorithms learn a process that humans can do intuitively (see or hear stimuli and understand language) but are unable to fully explain how or why.

Many early forms of machine learning arose out of neurological models of how human brains work. The initial intention of neural nets, for instance, were to model our neurological decision-making process or processes. Now, much contemporary neurological scholarship since has disproven the accuracy of neural nets in representing how our brains and minds work.[i] But, whether they represent how human minds work at all, neural networks have provided a powerful technique for computers to use to process and classify information and make decisions. Likewise, many machine learning algorithms replicate some activity humans do naturally, even if the way they conduct that human task has little to do with how humans would.

Case #4: Big Data

Technology, 5G, Aerial, Abstract Background

Machine learning is a powerful tool when analyzing data that is too large to break down through conventional computational techniques. Recent computer technologies have increased the possibility of data collection, storage, and processing, a major driver in big data. Machine learning has arisen as a major, if not the major, means of analyzing this big data.

Machine learning algorithms can manage a dizzying array of variables and use them to find insightful patterns (like lasso regression for linear modeling). Many big data cases involve hundreds, thousands, and maybe even tens or hundreds of thousands of input variables, and many machine learning techniques (like best subsets selection, stepwise selection, and lasso regression) process the myriads of variables in big data and determine the best ones to use. 

Recent developments computing provides the incredible processing power necessary to do such work (and debatably, machine learning is currently helping to push computational power and provide a demand for greater computational abilities). Hand-calculations and computers several decades ago were often unable to handle the calculations necessary to analyze large information: demonstrated, for example, by the fact that computer scientists invented the now popular neural networks many decades ago, but they did not gain popularity as a method until recent computer processing made them easy and worthwhile to run.

Tractors and other large-scale agricultural techniques coincided historically with the enlargement of farm property sizes, where the such machinery not only allowed farmers to manage large tracks of land but also incentivized larger farms economically. Likewise, machine learning algorithms provide the main technological means to analyze big data, both enabling and in turn incentivized by rise of big data in the professional world.

Conclusion

Here I have described four major uses of machine learning algorithms. Machine learning has become popular in many industries because of at least one of these functionalities, but of course, they are not the only potential current uses. In addition, as we develop machine learning tools, we are constantly inventing more. Given machine learning’s newness compared to many other century-old technologies, time will tell all the ways humans utilize it.

Photo credit #1: Mike MacKenzie at https://www.flickr.com/photos/mikemacmarketing/30212411048/

Photo credit #2: julientromeur at https://pixabay.com/illustrations/car-automobile-3d-self-driving-4343635/

Photo credit #3: geralt at https://pixabay.com/illustrations/business-success-curve-hand-draw-1989130/

Photo credit #4: geralt at https://pixabay.com/illustrations/flat-recognition-facial-face-woman-3252983/

Photo credit #5: mohamed_hassan at https://pixabay.com/illustrations/technology-5g-aerial-4816658/

[i] See Richard, Nagyfi. The differences between Artificial and Biological Neural Networks. 4 September 2018. https://towardsdatascience.com/the-differences-between-artificial-and-biological-neural-networks-a8b46db828b7; and Tcheang, Lili. Are Artificial Neural Networks like the Human Brain? And does it matter? 7 November 2018. https://medium.com/digital-catapult/are-artificial-neural-networks-like-the-human-brain-and-does-it-matter-3add0f029273.

The Stages of Learning a New Data or Programming Skill

Many people have admirably sought to learn data science, data analytics, a programming language, or some other data or programming skill in order to develop themselves professionally and/or seek a new career path. Excitingly, learning such skills has become significantly easier to do online. But this online learning can also foster unrealistic understandings of what learning one of these skills entails, since it can remove prospective learners from the physical community of experts who help introduce prospective learners to the expectations of that field.

The goal of this article is to help rectify that by explaining the basic steps typically needed to develop a mastery of a new data or programming skill. This will hopefully help inform high-level expectations for learning the skill would entail but also help you choose the right courses or set of courses to ensure you develop all three stages.

By data skill, I mean any data field like data science, data analytics, or data engineering, or any specific skill or practice within a data field that someone might seek to learn, and by programming skill I mean the skills necessary to learn and code in a programming language.

These are the three basic learning stages to master any of these topics:

Stage 1: Grasp the basic concepts of the topic
Stage 2: Complete a guided project
Stage 3: Complete a self-directed project

Stage 1: Grasping Basic Concepts

Grasping basic concepts entails learning the relevant vocabulary, syntax, and key approaches. Often programs teach each concept distinctly, one at a time. For example, when learning a new programming language, you might learn the major commands and syntax rules, and for data science, you might learn about each of the most prominent machine learning models one at a time.

This is different from applying the concepts widely, and at this stage, you may not be able to handle mixing all the concepts together in a complex problem yet (that’s Stage 2). Programs often teach the material at this point sequentially (even though that can be difficult for nonlinear learners).

For example, W3Schools provides grounded Stage 1 teaching for most programming languages and data science skills. They provide sequential exercises working through the basic syntax components of a new language, ever so slightly increasing in complexity along the way.

Now, only performing the first stage does not entail a full mastery of topic. After practicing each piece one at a time, you must also transition into Stage 2 where you start to learn how to combine them when completing a more complex problem.

Stage 2: Guided Project

Here you practice putting all the pieces together through a guided project(s). This guided project is a model for how each of the components fit together in an actual project. I liken these to building a Lego kit: following step-by-step instructions to build a cool model (instead of building your own object from scratch, which is Stage 3). They hold your hand through its completion to illustrate what putting all of the isolated skills and concepts together during a complicated project would entail.

Stage 3: Independent Project

In the third stage, you bring everything have learned together to complete a project on your own. Unlike in Stage 2, when they held your hand, you now have the freedom to struggle, which is necessary to learn. You are developing the skills involved in forming and carrying out a project on your own.

At the same time, you are learning what it looks like to implement those skills “in the wild” of a real-life project. In the previous stages, instructors often coddle their students: providing cleaned and perfectly ready-to-do example problems that you might find in a textbook, necessary to learn the basic concepts. Like a Lego kit, the components of the project have been groomed to make what you are producing. In Stage 3, you often start to experience the types of messiness common in real-world projects, when you have to find the pieces you need and/or figure out how to make do with the ones what you have.

For example, among data science learners, this stage is when students first learn to deal with the complexities of finding the right data for their problem; determining the best questions for a given dataset; and/or cleaning inconsistent data. Beforehand, most examples probably had already cleaned data that matched the specific task they were built for.

A certain amount of trial by fire is often needed to learn how to develop your own project. Your instructor(s) might take a little more of a backseat role during this process, looking over what you have done, answering any questions you might have, and nudging you when necessary. In my experience, exploring strategies yourself is the best way to learn Stage 3. Hopefully, at the end of it all, you will produce a nifty project that you can show prospective employers or whoever else you might wish to impress.

Conclusion

These are the three most common stages to develop initial mastery of a new data or programming skill or field. Now, they are the skill levels generally necessary to learn the new skill, but there are plenty of further levels of learning after you complete these. For example, grasping basic data science concepts, completing a guided project, and learning how to conduct your own self-directed data science project would be enough to make you a new inductee into the data science community, but you would still be a newbie data scientist. It is only the tip of the iceberg for what you can learn and how you would grow as a data scientist.

Now, despite calling them stages, not everyone learns them in sequential order, especially given the variety of extenuating circumstances and learning styles. For example, some might complete all three stages for a specific subset of skills in the field they are learning, and then go back to Stage 1 for another subset. Most education programs will include all three stages, more or less in order.

Some education programs, however, might completely lack or provide insufficient resources for one or two stages. Assessing whether a program adequately includes all three can be an effective way to determine how good they are at teaching and whether they are worth your money and/or time. When choosing to learn a new skill, I would recommend a program or combination of programs that includes all three. If a program you want to do or are currently completing lacks one or two of these stages, you can try to find another (hopefully free) way to complete that stage yourself online. For example, online courses and tutorials very frequently fail to provide Stage 3 (and in some cases, Stage 2), so after you complete one, I would recommend finding a project to work on.

Finally, when you are encountering a difficulty learning, it might be because you need to go backwards to a previous stage. For example, when many learners move to Stage 2, they must periodically swing back into Stage 1 to review a few core concepts when they see those concepts applied in a new way. Similarly, when completing a project in Stage 3, there is nothing wrong with reviewing Stage 2 or even Stage 1 materials.  

Now, be careful because you can falsely attribute this. Learning anything can be frustrating. Sometimes the difficulties you are having are not rooted in the need to review or relearn past material, but you simply need to push through with the new material until you start to get it. In those cases, some students revert backwards into a set of material in which they can feel safe and confident instead of challenging themselves. Even in those cases, however, like rocking a car by going into reverse and then drive to get over a bump, quickly going backwards can help launch you forward over the hurdle. In such cases, what is most important is to know yourself – your learning tendencies and how you typically respond – and check in as much as you can with instructors and/or experts in the field who have been there and done that to help you determine the best ways to overcome whatever challenge you are having.  

Photo credit #1: Jukan Tateisi at https://unsplash.com/photos/bJhT_8nbUA0

Photo credit #2: qimono at https://pixabay.com/illustrations/cog-wheels-gear-wheel-machine-2125178/

Photo credit #3: Bonneval Sebastien at https://unsplash.com/photos/lG-6_ox_UXE

Photo credit #4: Holly Mandarich at https://unsplash.com/photos/UVyOfX3v0Ls

Photo credit #5: George Bakos at https://unsplash.com/photos/VDAzcZyjun8

What Is Data Science and Machine Learning? A Short Guide for the Unsure

 What is data science, and what is machine learning? This is a short overview for someone who has never heard of either.

What Is Data Science?

 In the abstract, data science is an interdisciplinary field that seeks to use algorithms to organize, process, and analyze data. It represents a shift towards using computer programing, specifically machine learning algorithms, and other, related computational tools to process and analyze data.

By 2008, companies starting using the term data scientists to refer to a growing group of professionals utilizing advanced computing to organize and analyze large datasets,[i] and thus from the get-go, the practical needs of professional contexts have shaped the field. Data science combines strands from computer science, mathematics (particularly statistics and linear algebra), engineering, the social sciences, and several other fields to address specific real-world data problems.

On a practical level, I consider a data scientist someone who helps develop machine learning algorithms to analyze data. Machine learning algorithms form the central techniques/tools around what constitutes data science. For me personally, if it does not involve machine learning, it is not data science.

What Is Machine Learning?

 Machine learning is a complex term: What to say that a machine “learns”? Overtime data scientists have provided many intricate definitions of machine learning, but its most basic, machine learning algorithms are algorithms that adapt/modify how their approach to a task based on new data/information overtime.

Herbert Simon provides a commonly used technical definition: “Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the task or tasks drawn from the same population more efficiently and more effectively the next time.”[ii] As this definition implies, machine learning algorithms adapt by iteratively testing its performance against the same or similar data. Data scientists (and others) have developed several types of machine learning algorithms, including decision tree modeling, neural networks, logistic regression, collaborative filtering, support vector machines, cluster analysis, and reinforcement learning among others.

Data scientists generally split machine learning algorithms into two categories: supervised and unsupervised learning. Both involve training the algorithm to complete a given task but differ on how they test the algorithm’s performance. In supervised learning, the developer(s) provide a clear set of answers as a basis for whether the prediction is correct; while for unsupervised learning, whether the algorithm’s performance is much more open-ended. I liken the difference to be like the exams teachers gave us in school: some tests, like multiple choice exams, have clear, right and wrong answers or solutions, but other exams, like essays, are open-ended with qualitative means of determining goodness. Just like the nature of the curriculum determines the best type of exam, which type of learning to performs depends on the project context and nature of the data.

Here are four instances where machine learning algorithms are useful in these types of tasks:

  1. Autonomy: To teach computers to do a task without the direct aid/intervention of humans (e.g. autonomous vehicles)
  2. Fluctuation: Help machines adjust when the requirements or data change over time
  3. Intuitive Processing: Conduct (or assist in) tasks humans do naturally but are unable to explain how computationally/algorithmically (e.g. image recognition)
  4. Big Data: Breaking down data that is too large to handle otherwise

Machine learning algorithms have proven to be a very powerful set of tools. See this article for a more detailed discussion of when machine learning is useful.

[i] Berkeley School of Information. (2019). What is Data Science? Retrieved from https://datascience.berkeley.edu/about/what-is-data-science/.

[ii] Simon in Kononenko, I., & Kukar, M. (2007). Machine Learning and Data Mining. Elsevier: Philadelphia.

Photo credit #1: Frank V at https://unsplash.com/photos/zbLW0FG8XU8

Photo credit #2: Brett Jordan at https://unsplash.com/photos/HzOclMmYryc

Recently Published Article: “Anthropology by Data Science”

tea set and newspaper placed on round table near comfortable chair
Photo by Ekrulila on Pexels.com

I am pleased to announce that the Annals of Anthropological Practice has accepted my article “Anthropology by Data Science.” https://anthrosource.onlinelibrary.wiley.com/doi/10.1111/napa.12169. In it, I reflect on the relationship anthropologist have cultivated with data science as a discipline and the importance of integrating machine learning techniques into ethnographic practice.

Annals of Anthropological Practice is overseen by the National Association for the Practice of Anthropology (NAPA) within the American Anthropological Association. Thank you, NAPA, for publishing my article and thank you to all the unnamed editors and reviewers in the process.

Interdisciplinary Anthropology and Data Science Master’s Thesis: A Quick and Dirty Project Summary

This is a quick and dirty summary of my master’s practicum research project with Indicia Consulting over the summer of 2018. For anyone interested in more detail, here is a more detailed report, and here is the final report with Indicia. 

Background

My practicum was the sixth stage of a several year-long research project. The California Energy Commission commissioned this larger project to understand the potential relationship between individual energy consumption and technology usage. In stages one through five, we isolated certain clusters of behavior and attitudes around new technology adoption – which Indicia called cybersensitivity – and demonstrated that cybersensitivity tended to associate with a willingness to adopt energy-saving technology like smart meters.

This led to a key question: How can one identify cybersensivity among a broader population such as a community, county, or state? Answering this question was the main goal of my practicum project.

In the past stages of the research project, the team used ethnographic research to establish criteria for whether someone was a cybersensitive based on several hours of interviews and observations about their technology usage. These interviews and observations certainly helped the research team analyze behavioral and attitudinal patterns, determine what patterns were significant, and develop those into the concept of cybersensitivity, but they are too time- and resource-intensive to perform with an entire population. One generally does not have the ability to interview everyone in a community, county, or state. I sought to address this directly in my project.

TaskTimelineTask NameResearch TechniqueDescription
Task 1June 2015-Sept 2018General Project TasksAdministrative (N/A)Developed project scope and timeline, adjusting as the project unfolds
Task 2July 2015 – July 2016Documenting and analyzing emerging attitudes, emotions, experiences, habits, and practices around technology adoptionSurveyConducted survey research to observe patterns of attitudes and behaviors among cybersensitives/awares.
Task 3Sept 2016 – Dec 2016Identifying the attributes and characteristics and psychological drivers of cybersensitivesInterviews and Participant-ObservationConducted in-depth interviews and observations coding for psych factor, energy consumption attitudes and behaviors, and technological device purchasing/usage.
Task 4*Sept 2016 – July 2017Assessing cybersensitives’ valence with technologyStatistical AnalysisTested for statistically significant differences in demographics, behaviors, and beliefs/attitudes between cyber status groups
Task 5Aug 2017 – Dec 2018  Developing critical insights for supporting residential engagement in energy efficient behaviorsStatistical AnalysisAnalyzed utility data patterns of study participants, comparing it with the general population.
Task 6March 2018 – Aug 2018Recommending an alternative energy efficiency potential modelDecision Tree ModelingConstructed decision tree models to classify an individual’s cyber status

Project Goal

The overall goal for the project was to produce a scalable method to assess whether someone exhibits cybersensitivity based on data measurable across an entire population. In doing this, the project also helped address the following research needs:

  1. Created a method to further to scale across a larger population, assessing whether cybersensitives were more willing to adopt energy saving technologies across a community, county, or state
  2. Provided the infrastructure to determine how much promoting energy-saving campaigns targeting cybersensitives specifically would reduce energy consumption in California
  3. Helped the California Energy Commission determine the best means to reach cybersensitives for specific energy-saving campaigns

The Project

I used machine learning modeling to create a decision-making flow to isolate cybersensitives in a population. Random forests and decision trees produced the best models for Indicia’s needs: random forests in accuracy and robustness and decision trees in human decipherability. Through them, I created a programmable yet human-comprehensible framework to determine whether an individual is cybersensitive based on behaviors and other characteristics that an organization could be easily assess within a whole population. Thus, any energy organization could easily understand, replicate, and further develop the model since it was both easy for humans to read and encodable computationally. This way organizations could both use and refine it for their purposes.

Conclusion

This is a quick overview of my master’s practicum project. For more details on what modeling I did, how I did it, what results it produced, and how it fit within the wider needs of the multi-year research project, please see my full report.

I really appreciated the opportunity it posed to get my hands dirty integrating ethnography and data science to help address a real-world problem. This summary only scratches the surface of what Indicia did with the Californian Energy Commission to encourage sustainable energy usage societally. Hopefully, though, it will inspire you to integrate ethnography and data science to address whatever complex questions you face. It certainly did for me.

Thank you to Susan Mazur-Stommen and Haley Gilbert for your help in organizing and completing the project. I would like to thank my professorial committee at the University of Memphis – Dr. Keri Brondo, Dr. Ted Maclin, Dr. Deepak Venugopal, and Dr. Katherine Hicks – for their academic support as well.

Evaluating the Effectiveness of Part of Speech Augmentation in Next Word Predictors

The following was a project I completed for a graduate course in Artificial Intelligence I took at the University of Memphis in the spring of 2019. For the project, I analyzed whether part of speech evaluation could modulate Markov Chain-based next word predictors. In particular, I developed and tested two different strategies for incorporating part of speech predictions, which I termed excluder and multiplier. The multiplier method performed better than the excluder and matched the performance of the control. Hopefully, this is a helpful exploration into ways to use lexical information to improve next word predictors.

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Photo Credit: Brett Jordan from https://unsplash.com/photos/EvJ7uvqQb3E

The Anthropology of Machine Learning

In the spring of 2018, I researched how anthropologists and related social scholars have analyzed data science and machine learning for my Master’s in Anthropology at the University of Memphis. For the project, I assessed the anthropological literature on data science and machine learning to date and explore potential connections between anthropology and data science, based on my perspective as a data scientist and anthropologist. Here is my final report.

Thank you, Dr. Ted Maclin, for your help overseeing and assisting this project.

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Machine Stories: Machine Learning as Computerized Narrative Design

This is a presentation I gave at the 2018 Annual Conference of the American Society for Cybernetics. I won the Heinz von Foerster Award for the innovative research.

I hope you enjoy.

Machine Stories Machine Learning as Computerized Narrative Design [429.57 KB]

Anthropology by Data Science: The EPIC Project with Indicia Consulting as an Exploratory Case Study

This is my practicum report with Indicia Consulting. In lieu of a master’s thesis, the University of Memphis Department of Anthropology required that we master’s students conduct a practicum project. For this, we had to partner with an organization and complete a 300+ hour anthropological research project based on the organization’s needs and our skills and interests. My practicum project was Indicia’s EPIC Project with the California Energy Commission (see this link and this link for more details on the EPIC Project). In this report, I outline potential ways to integrate ethnographic/anthropological and data science research in professional settings.

In November 2019, the American Anthropological Association’s Committee for the Anthropology of Science, Technology, and Computing (CASTAC) awarded me the David Hakken Graduate Student Prize for innovative science and technology scholarship.

Full Report:

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The Anthropology Department also required that you publicly present your practicum research to the University of Memphis campus. This PowerPoint summarizes my practicum project. If you are not keen to read the 99 page full report, this is a much shorter alternative:

Download [1.05 MB]

If you are interested in learning more about the project, please check out the following:

  1. Indicia Consulting’s Final Research Report with the California Energy Commission
  2. My Presentation at the 2019 Memphis Data Conference for Data Scientists Specifically

Computerized Knowledge Production: Machine Learning Models as Social Actors

The following is a presentation I gave at the Society for Applied Anthropology’s 2018 annual conference in Philadelphia, PA. In it, I describe how I think anthropologists should understand, analyze, and relate to machine learning and data science.

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