What Machine Learning Practitioners Actually Do, by Rachel Thomas, fast.ai

By Rachel Thomas, founder, fast.ai and Asst. Professor, Data Institute, University of San Francisco There are frequent media headlines about both the scarcity of machine learning talent and about the promises of companies claiming their products automate machine learning and eliminate the need for ML expertise altogether. In his recent keynote at the TensorFlow DevSummit, Google’s head of AI Jeff […]

By Rachel Thomas, founder, fast.ai and Asst. Professor, Data Institute, University of San Francisco

There are frequent media headlines about both the scarcity of machine learning talent and about the promises of companies claiming their products automate machine learning and eliminate the need for ML expertise altogether. In his recent keynote at the TensorFlow DevSummit, Google’s head of AI Jeff Dean estimated that there are tens of millions of organizations that have electronic data that could be used for machine learning but lack the necessary expertise and skills. I follow these issues closely since my work at fast.ai focuses on enabling more people to use machine learning and on making it easier to use.

Rachel Thomas, founder, fast.ai

In thinking about how we can automate some of the work of machine learning, as well as how to make it more accessible to people with a wider variety of backgrounds, it’s first necessary to ask, what is it that machine learning practitioners do? Any solution to the shortage of machine learning expertise requires answering this question: whether it’s so we know what skills to teach, what tools to build, or what processes to automate.

This post is the first in a 3-part series. It will address what it is that machine learning practitioners do, with Part 2 explaining AutoML and neural architecture search (which several high profile figures have suggested will be key to decreasing the need for data scientists) and Part 3 will cover Google’s heavily-hyped AutoML product in particular.

Building Data Products is Complex Work

While many academic machine learning sources focus almost exclusively on predictive modeling, that is just one piece of what machine learning practitioners do in the wild. The processes of appropriately framing a business problem, collecting and cleaning the data, building the model, implementing the result, and then monitoring for changes are interconnected in many ways that often make it hard to silo off just a single piece (without at least being aware of what the other pieces entail). As Jeremy Howard et al. wrote in Designing Great Data Products, Great predictive modeling is an important part of the solution, but it no longer stands on its own; as products become more sophisticated, it disappears into the plumbing.

A team from Google, D. Sculley et al., wrote the classic Machine Learning: The High-Interest Credit Card of Technical Debt, about the code complexity and technical debt often created when using machine learning in practice. The authors identify a number of system-level interactions, risks, and anti-patterns, including:

  • glue code: massive amount of supporting code written to get data into and out of general-purpose packages
  • pipeline jungles: the system for preparing data in an ML-friendly format may become a jungle of scrapes, joins, and sampling steps, often with intermediate files output
  • re-use input signals in ways that create unintended tight coupling of otherwise disjoint systems
  • risk that changes in the external world may make models or input signals change behavior in unintended ways, and these can be difficult to monitor

The authors write, A remarkable portion of real-world “machine learning” work is devoted to tackling issues of this form… It’s worth noting that glue code and pipeline jungles are symptomatic of integration issues that may have a root cause in overly separated “research” and “engineering” roles… It may be surprising to the academic community to know that only a tiny fraction of the code in many machine learning systems is actually doing “machine learning”. (emphasis mine)

When machine learning projects fail

In a previous post, I identified some failure modes in which machine learning projects are not effective in the workplace:

  • The data science team builds really cool stuff that never gets used. There’s no buy-in from the rest of the organization for what they’re working on, and some of the data scientists don’t have a good sense of what can realistically be put into production.
  • There is a backlog with data scientists producing models much faster than there is engineering support to put them in production.
  • The data infrastructure engineers are separate from the data scientists. The pipelines don’t have the data the data scientists are asking for now, and the data scientists are under-utilizing the data sources the infrastructure engineers have collected.
  • The company has definitely decided on feature/product X. They need a data scientist to gather some data that supports this decision. The data scientist feels like the PM is ignoring data that contradicts the decision; the PM feels that the data scientist is ignoring other business logic.
  • The data science team interviews a candidate with impressive math modeling and engineering skills. Once hired, the candidate is embedded in a vertical product team that needs simple business analytics. The data scientist is bored and not utilizing their skills.

I framed these as organizational failures in my original post, but they can also be described as various participants being overly focused on just one slice of the complex system that makes up a full data product. These are failures of communication and goal alignment between different parts of the data product pipeline.

So, what do machine learning practitioners do?

As suggested above, building a machine learning product is a multi-faceted and complex task. Here are some of the things that machine learning practitioners may need to do during the process:

Understanding the context:

  • identify areas of the business that could benefit from machine learning
  • communicate with other stakeholders about what machine learning is and is not capable of (there are often many misconceptions)
  • develop understanding of business strategy, risks, and goals to make sure everyone is on the same page
  • identify what kind of data the organization has
  • appropriately frame and scope the task
  • understand operational constraints (e.g. what data is actually available at inference time)
  • proactively identify ethical risks, including how your work could be mis-used by harassers, trolls, authoritarian governments, or for propaganda/disinformation campaigns (and plan how to reduce these risks)
  • identify potential biases and potential negative feedback loops

Read the source post at fast.ai.

How to Map Out a Career Path in This Early Stage of AI

By Stephanie Glass, Head of Product Marketing, AI Solutions, Aera Technology Artificial intelligence is already reshaping society as we know it in both business and consumer realms. Early use cases with Alexa, autonomous vehicles and AI-driven supply chains provide just a glimpse of the disruption that AI is poised to deliver in the near future and […]

By Stephanie Glass, Head of Product Marketing, AI Solutions, Aera Technology

Artificial intelligence is already reshaping society as we know it in both business and consumer realms. Early use cases with Alexa, autonomous vehicles and AI-driven supply chains provide just a glimpse of the disruption that AI is poised to deliver in the near future and for years to come.

Stephanie Glass

Yet despite all the AI hype and initial successes, it remains in its infancy. That makes now the ideal time for young people to build the knowledge, skill sets and connections they need to capitalize on the fast-growing market for AI jobs and build a strong AI career.

One reason is simply practical. Gartner predicts that AI may eliminate 1.8 million jobs by 2020, yet is on track to create 2.3 million new positions. It clearly makes sense to map out an AI career path as new roles emerge that focus on problem-solving, collaboration and strategic decision-making.

The second reason is that AI offers a truly exciting opportunity to make an impact on our world. AI is an ideal career path for undergraduates and recent graduates with a passion for technology and an entrepreneurial spirit who relish a challenge and a front-line role in game-changing innovation.

AI has been called “the next Industrial Revolution,” and I think that analogy is spot on. The convergence of machine learning, big data and internet-scale cloud compute power is opening opportunities to transform healthcare, grow the economy, reduce waste, save energy, improve education, strengthen security and decrease poverty.

There’s virtually no area of society that will be untouched by AI. Given the myriad possibilities, how does a young person chart his or her course in the AI field?

Practical steps to an AI career

One first step is to simply narrow down key interests and strengths. AI offers technological roles in data science, machine learning development and architecting an AI technology stack. And it offers plenty of positions as a user or analyst with AI software, or in sales and marketing, HR, customer support and more.

Company type is another important consideration, as B2B and B2C can be distinctly different work environments. Candidates should also target industries they gravitate toward, be it technology, manufacturing, retail, higher education, services or others. To narrow down interests, hands-on research and learning is available through avenues such as:

AI and data science courses. Many free and low-cost online learning opportunities in AI can be found at providers such as Udacity, Coursera, Codeacademy and fast.ai. These courses are a great way to build knowledge and enrich a resume.

Industry and company conferences. AI experts and thought leaders discuss trends at conferences hosted by NIPS, Re-Work, O’Reilly, Gartner, AI World and others, while AI vendors also sponsor conferences. Students and young professionals are often able to gain free or discounted admission.

Meetup.com. The website lists many regional in-person meetups among AI enthusiasts, as well as online groups and discussions on topical areas including machine learning, Python, R, data science and advanced statistics.

General immersion. Keeping up-to-date on fast-moving AI news and trends pays off. So does using AI products, sharing code on GitHub or entering a code contest on the global Kaggle community. In short, would-be AI professionals should learn as much as they can, as fast as they can.

With this knowledge, young people are better able to address two key questions that can guide AI careers:

  1. What’s my prospective employer’s technology stack?
  2. What are the employer’s leadership motivations and potential social impact?

As a Stanford graduate now working in artificial intelligence, those were critical considerations for me as I looked to move from traditional software companies into the AI space. I had decided to pursue an AI career because of the positive change that AI promises to unleash. It was a matter of finding an employer that aligned with my interests and skills.

Read the source article in Entrepreneur.

How to Land a Data Scientist Job at Your Dream Company — My Journey to Airbnb

By Kelly Peng, Data Scientist, Airbnb I just started my new job at Airbnb as a data scientist a month ago, and I still feel that I’m too lucky to be here. Nobody knows how much I wanted to join this company — I had pictures of Airbnb office stuck in front of my desk; I had […]

By Kelly Peng, Data Scientist, Airbnb

I just started my new job at Airbnb as a data scientist a month ago, and I still feel that I’m too lucky to be here. Nobody knows how much I wanted to join this company — I had pictures of Airbnb office stuck in front of my desk; I had my iPhone wallpaper set as a photo of me standing in front of the Airbnb logo; I applied for positions at Airbnb four times, but only heard back from the recruiter in the last time…

Kelly Peng, data scientist, Airbnb

In the past, when people asked me, “Which company do you want to work for the most?” I dare not to say “Airbnb” because when I said that, they replied to me, “Do you know how many people want to work there? How many of them eventually got in? Come on, be realistic.”

The result proves that nothing is impossible. Since many of my friends asked me to share about my job search experience, I think it might be helpful to write it down and share with people.

Some data…

To provide an overview of my job search process:

  • Applications: 475
  • Phone interviews: 50
  • Finished data science take-home challenges: 9
  • Onsite interviews: 8
  • Offers: 2
  • Time spent: 6 months

As you probably can see from the data, I’m not a strong candidate because, otherwise, I would just apply for a few positions and receive a bunch of offers. Yes, I used to be super weak; I used to be the kind of candidates who are wasting interviewers’ time. But “who you were months ago doesn’t matter, what matters is who you are becoming.”

The road less traveled to a data scientist job

A little bit about my background, I obtained a Bachelor’s degree in Economics from a university in China and a Master’s degree in Business Administration from the University of Illinois at Urbana-Champaign. After graduated, I worked as a data analyst for two years, with 7 months as a contractor at Google, and another 1 year 4 months at a startup. My work was mostly about writing SQL queries, building dashboards, and give data-driven recommendations.

After realizing that I was not learning and growing as expected, I left my job and applied for Galvanize Data Science Immerse program, which is a 12-week boot camp in San Francisco. I failed the statistics interview to enter the boot camp program for 4 times, got admitted after taking the statistics interview for the fifth time.

The content taught at Galvanize was heavy on Python and machine learning, and they assume you already have a strong foundation in statistics. Unsurprisingly, I struggled a lot in the beginning, because I didn’t know much about programming, nor was I strong in statistics. I had no choice but to work really hard. During my time at Galvanize, I had no break, no entertainment, no dating, nothing else but more than 12 hours study every day. And I got much more comfortable with the courses later on.

However, I still embarrassed myself for uncountable times in interviews when I first started the job search process. Because the gap between a real data scientist and I was so huge that even though I was hardworking, the 12-week study was far from enough to make a career transformation. So I applied, interviewed, failed, applied again, interviewed again, failed again. The good thing is, each time I got to learn something new, and became a little bit stronger.

In March 2018, I have been unemployed for almost a year since I quitted my previous job. With only ~$600 in my bank account, I had no idea how to pay for the next month’s rent. What’s even worse, if I couldn’t find a job by the end of April 2018, I have to leave the U.S. because my visa will expire.

Luckily, after so much practice and repetition, I’ve grown from someone who doesn’t know how to introduce herself properly, doesn’t remember which one of Lasso and Ridge is L1, knows nothing about programming algorithms, into someone who knows she is ready to get what she wants.

When I entered the final interview at Airbnb, I had one data scientist offer in hand; thus, I was not nervous at all. My goal for the final interview was to be the best version of myself and leave no regret. The interview turned out to be the best one I have ever had. They gave me the offer, and all the hard work and sleepless nights paid off.

Read the source blog post at Toward Data Science.

The Mathematics of Machine Learning

By Wale Akinfaderin, PhD candidate in Physics at Florida State University In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I’ve observed that some actually […]

By Wale Akinfaderin, PhD candidate in Physics at Florida State University

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I’ve observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post.

Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

WHY WORRY ABOUT THE MATHS?

There are many reasons why the mathematics of Machine Learning is important and I’ll highlight some of them below:

1. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.

2. Choosing parameter settings and validation strategies.

3. Identifying underfitting and overfitting by understanding the Bias-Variance tradeoff.

4. Estimating the right confidence interval and uncertainty.

WHAT LEVEL OF MATHS DO YOU NEED?

The main question when trying to understand an interdisciplinary field such as Machine Learning is the amount of maths necessary and the level of maths needed to understand these techniques. The answer to this question is multidimensional and depends on the level and interest of the individual. Research in mathematical formulations and theoretical advancement of Machine Learning is ongoing and some researchers are working on more advance techniques. I’ll state what I believe to be the minimum level of mathematics needed to be a Machine Learning Scientist/Engineer and the importance of each mathematical concept.

1. Linear Algebra: A colleague, Skyler Speakman, recently said that “Linear Algebra is the mathematics of the 21st century” and I totally agree with the statement. In ML, Linear Algebra comes up everywhere. Topics such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigendecomposition of a matrix, LU Decomposition, QR Decomposition/Factorization, Symmetric Matrices, Orthogonalization & Orthonormalization, Matrix Operations, Projections, Eigenvalues & Eigenvectors, Vector Spaces and Norms are needed for understanding the optimization methods used for machine learning. The amazing thing about Linear Algebra is that there are so many online resources. I have always said that the traditional classroom is dying because of the vast amount of resources available on the internet. My favorite Linear Algebra course is the one offered by MIT Courseware (Prof. Gilbert Strang).

2. Probability Theory and Statistics: Machine Learning and Statistics aren’t very different fields. Actually, someone recently defined Machine Learning as ‘doing statistics on a Mac’. Some of the fundamental Statistical and Probability Theory needed for ML are Combinatorics, Probability Rules & Axioms, Bayes’ Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions, Maximum Likelihood Estimation (MLE), Prior and Posterior, Maximum a Posteriori Estimation (MAP) and Sampling Methods.

3. Multivariate Calculus: Some of the necessary topics include Differential and Integral Calculus, Partial Derivatives, Vector-Values Functions, Directional Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution.

4. Algorithms and Complex Optimizations: This is important for understanding the computational efficiency and scalability of our Machine Learning Algorithm and for exploiting sparsity in our datasets. Knowledge of data structures (Binary Trees, Hashing, Heap, Stack etc), Dynamic Programming, Randomized & Sublinear Algorithm, Graphs, Gradient/Stochastic Descents and Primal-Dual methods are needed.

5. Others: This comprises of other Math topics not covered in the four major areas described above. They include Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions, Limits), Information Theory (Entropy, Information Gain), Function Spaces and Manifolds.

Read the source post at Dataconomy.

Here Are 10 Free Must-Read Books for Machine Learning and Data Science

By Matthew Mayo, KDnuggets Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started. It’s time for another collection of free machine learning and data science books to kick off […]

By Matthew Mayo, KDnuggets

Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started.

1. Python Data Science Handbook
By Jake VanderPlas

The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A Whirlwind Tour of Python: it’s a fast-paced introduction to the Python language aimed at researchers and scientists.

  1. Neural Networks and Deep Learning

By Michael Nielsen

Neural Networks and Deep Learning is a free online book. The book will teach you about:

– Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data

– Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.

  1. Machine Learning & Big Data

By Kareem Alkaseer

This is a work in progress, which I add to as time allows. The purpose behind it is to have a balance between theory and implementation for the software engineer to implement machine learning models comfortably without relying too much on libraries. Most of the time the concept behind a model or a technique is simple or intutivei but it gets lost in details or jargon. Also, most of the time existing libraries would solve the problem at hand but they are treated as black boxes and more often than not they have their own abstractions and architectures that hide the underlying concepts. This book’s attempt is to make the underlying concepts clear.

5. Statistical Learning with Sparsity: The Lasso and Generalizations
By Trevor Hastie, Robert Tibshirani, Martin Wainwright

During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. This book descibes the important ideas in these areas in a common conceptual framework.

  1. Statistical inference for data science

By Brian Caffo

This book is written as a companion book to the Statistical Inference Coursera class as part of the Data Science Specialization. However, if you do not take the class, the book mostly stands on its own. A useful component of the book is a series of YouTube videos that comprise the Coursera class.

The book is intended to be a low cost introduction to the important field of statistical inference. The intended audience are students who are numerically and computationally literate, who would like to put those skills to use in Data Science or Statistics. The book is offered for free as a series of markdown documents on github and in more convenient forms (epub, mobi) on LeanPub and retail outlets.

Read the complete source list at KDnuggets.com.

High School Grads: Now You Can Major in AI and Become a Very Hot Job Candidate

It’s a college major that sounds straight out of science fiction: Starting this fall, at least two U.S. schools are offering degrees focused on artificial intelligence. Carnegie Mellon University in Pittsburgh announced on May 10 that the school will launch a bachelor of science program in artificial intelligence this fall. “Specialists in artificial intelligence have never been more important, in shorter supply or in greater […]

It’s a college major that sounds straight out of science fiction: Starting this fall, at least two U.S. schools are offering degrees focused on artificial intelligence.

Carnegie Mellon University in Pittsburgh announced on May 10 that the school will launch a bachelor of science program in artificial intelligence this fall.

“Specialists in artificial intelligence have never been more important, in shorter supply or in greater demand by employers,” said Andrew Moore, dean of the School of Computer Science, in a statement.

Students in the computer-science school can enter the degree program in their second year. The course of study will include the same computer science and math courses as other students in the school, but will “focus more on how complex inputs — such as vision, language and huge databases — are used to make decisions or enhance human capabilities,” the statement says. Additional course work will focus on “AI-related subjects such as statistics and probability, computational modeling, machine learning, and symbolic computation.”

And good news for those who have seen The Terminator a few too many times.  Reid Simmons, research professor of robotics and computer science and director of the new AI degree program, says the program will emphasize ethics and social responsibility. It will include “independent study opportunities in using AI for social good, such as improving transportation, health care or education.”

Carnegie Mellon isn’t alone: In January, the Milwaukee School of Engineering announced it will offer a computer science degree focused on artificial intelligence beginning in fall 2018. (Hat tip to Engadget for noticing.)

“Artificial intelligence and deep learning have reached a golden age thanks to recent hardware and software advancements,” Dr. Derek Riley, program director of the new computer science degree, said in a statement. “Our students will apply computer science theory, machine learning algorithms, and software engineering practices to produce computing solutions for a wide variety of problems in many industries.”

“Like all other MSOE programs, computer science students will be taught an industry-driven curriculum in an application-oriented environment, ensuring that they are prepared to hit the ground running upon graduation,” said Dr. John Walz, president of MSOE.

No question, it’s a timely degree. The fast-growing field needs trained employees, Microsoft Learning group program manager Matt Winkler told Indian news agency PTI in an article published Sunday.

“A lot of folks are very, very excited about (AI) and then they want to go and make that real,” Winkler said. “And when they go to make that real, there’s a really large skills shortage.”

Artificial intelligence goes well beyond the in-home digital assistance such as Google Assistant and Amazon’s Alexa. The concept is pushing beyond the tech industry into firms of all types. From aiding in recruiting to fraud detection to inventory control, the use of AI in business is expected only to continue to grow.

Read the source article at Inc.com.

This High School Student is Shaking Up AI With OpenAI Project

Since its founding by Elon Musk and others nearly two years ago, nonprofit research lab OpenAI has published dozens of research papers. One posted online recently is different: Its lead author is still in high school. The wunderkind is Kevin Frans, a senior currently working on his college applications. He trained his first neural net—the kind of system that tech […]

Since its founding by Elon Musk and others nearly two years ago, nonprofit research lab OpenAI has published dozens of research papers. One posted online recently is different: Its lead author is still in high school.

The wunderkind is Kevin Frans, a senior currently working on his college applications. He trained his first neural net—the kind of system that tech giants use to recognize your voice or face—two years ago, at the age of 15. Inspired by reports of software mastering Atari games and the board game Go, he has since been reading research papers and building pieces of what they described. “I like how you can get computers to do things that previously you would think were impossible,” Frans says, flashing his ready smile. One of his creations is an interactive webpage that automatically colors in line drawings, in the style of manga comics.

Frans landed at OpenAI after taking on one of the lab’s list of problems in need of new ideas. He made progress, but got stuck and emailed OpenAI researcher John Schulman for advice. After some back and forth on the matter of trust region policy optimization, Schulman checked out Frans’s blog and got a surprise. “I didn’t expect from those emails that he was in high school,” he says.

Frans later met Schulman when he interviewed for an internship at OpenAI. When he turned up for work in San Francisco’s Mission District this summer, Frans was the only intern without a degree or studying in grad school. He started working on a tricky problem that holds back robots and other AI systems—how can machines tap what they’ve previously learned to solve new problems?

Humans do this without a second thought. Even if you’re making a recipe for the first time, you don’t have to re-learn how to caramelize onions or sift flour. By contrast, machine-learning software generally has to repeat its lengthy training process for every new problem—even when they have common elements.

Frans’s new paper, with Schulman and three others affiliated with the University of California Berkeley, reports new progress on this problem. “If it could get solved it could be a really big deal for robotics but also other elements of AI,” Frans says. He developed an algorithm that helped virtual legged robots learn which limb movements could be applied to multiple tasks, such as walking and crawling. In tests, it helped virtual robots with two and four legs adapt to new tasks, including navigating mazes, more quickly. A video released by OpenAI shows an ant-like robot in those tests. The work has been submitted to ICLR, one of the top conferences in machine learning. “Kevin’s paper provides a fresh approach to the problem, and some results that go beyond anything demonstrated previously,” Schulman says.

Frans grapples with challenging motion problems away from computers, too, as a black belt in Tae Kwon Do. Some of his enthusiasm for AI may come just from inhaling the air on his way to Gunn High School in Palo Alto, California, the heart of Silicon Valley. Frans says he works on his AI projects without help from his parents, but he isn’t the only computer whiz in the house. His father works on silicon-chip design at publicly listed semiconductor company Xilinx.

As you may have guessed, Frans is an outlier. Olga Russakovsky, a professor at Princeton who works on machine vision, says making research contributions in machine learning so young is unusual. In general, it’s harder for school kids to try machine learning and AI than subjects such as math or science with a long tradition of extra-curricular competitions and mentoring, she says. Access to computing power can be a hurdle as well. When Frans’s desktop computer wasn’t powerful enough to test one of his ideas, he pulled out his debit card and opened an account with Google’s cloud-computing service to put his code through its paces. He advises other kids interested in machine learning to give it a shot. “The best thing to do is to go out and try it, make it yourself from your own hands,” he says.

Russakovsky is part of a movement among AI researchers trying to get more high schoolers tinkering with AI systems. One motivation is a belief that the field is currently too male, well-off, and white. “AI is a field that’s going to revolutionize everything in our society, and we can’t have it be built by people from a homogenous group that doesn’t represent society as a whole,” Russakovsky says. She co-founded AI4ALL, a foundation that organizes camps that give high-school students from diverse backgrounds a chance to work with and learn from AI researchers.

Read the source article in Wired.

AI Courses Now Available from Microsoft

Microsoft has launched an online AI course for developers who want to expand their knowledge of machine learning, and an online entry-level software development class. Not enough data scientists and machine learning developers are available to fulfill the current demand. Thus a number of large companies have started to teach the fundamentals of these disciplines […]

Microsoft has launched an online AI course for developers who want to expand their knowledge of machine learning, and an online entry-level software development class.

Not enough data scientists and machine learning developers are available to fulfill the current demand. Thus a number of large companies have started to teach the fundamentals of these disciplines to their existing employees; anybody can take the AI courses that Microsoft first developed for its own employees.

The Microsoft Professional Program for Artificial Intelligence is available for free on edX.org, though you can also opt to pay for a certificate. Each course runs three months and starts at the beginning of the quarter. Unsurprisingly, there’s a bit of a focus on Azure and Microsoft’s Cognitive Services here (and you need an Azure account), but otherwise the course is agnostic to the operating system you run.

The overall program consists of 10 courses that range from introductions to AI and Python for data science to a class on ethics for AI developers and lots of hands-on work with training models. Most of the 10 required courses should take about eight to 16 hours to complete.

In addition to the AI class, Microsoft also announced a similar program for entry-level software developers. This edX-based program consists of 13 courses that teach students the basics of software development, with a focus on Python and JavaScript. What’s maybe more important than just teaching those languages, though, is that the course also looks at basics like data structures and how to use GitHub and other tools to write code professionally.

These two new courses join a growing number of similar programs in Microsoft’s so-called “Professional Program” (why they don’t just call it the Microsoft Academy is beyond me, but I’m not a marketer…). These existing courses range from front-end development classes to a program for cloud admins and a course for IT support professionals.

Read the source article at TechCrunch.

Careers in 2018: Tips from a Top Data Analytics Recruiter

Demand for data scientists and other data and analytics professionals continues to grow in the job marketplace as enterprise organizations look to build out their business infrastructures for a new era. But even data scientists need to stay on top of new trends and technologies to stay relevant. That’s according to top data science and […]

Demand for data scientists and other data and analytics professionals continues to grow in the job marketplace as enterprise organizations look to build out their business infrastructures for a new era. But even data scientists need to stay on top of new trends and technologies to stay relevant.

That’s according to top data science and analytics recruiter Linda Burtch, founder of Burtchworks Executive Recruiting, which also publishes regular salary research reports for data and analytics professionals. Burtch outlined some key points for career-minded data pros and aspiring data pros in a recent interview with UBM Tech.

Is your analytics career ready for the future?

For seasoned data professionals Burtch offers this advice: “Keep learning. Technology, as we all know, is changing very quickly, and it’s really dangerous for you to ignore that as a mid-level or senior-level person.”

Burtch recommends data pros attend classes on new techniques, sign up for MOOCs (massive online open courses), go to conferences, go to meetups, get involved with local American Statistical Association or other quantitative professional organizations.

A Master’s degree is nearly a requirement for many positions now, she said. But for those who already hold an advanced degree, it’s still important to keep learning through free online courses or other options such as boot camps. Look at your skills gap to figure out which one to attend, Burtch said.

Is that boot camp any good?

Not all boot camps are alike, however. Before you sign up, you should ask the school where their graduates have gone. Find out who their industry contacts are for helping to place graduates. You may even want to check the boot camp’s references. Find out how many of their graduates got jobs and where they are working.

Make sure the boot camp gives you practice working with data, data sets, and messy data, using a variety of tools.

While boot camps may help you fill a skills gap, they may not be enough by themselves to get you into the top technology companies. Companies like Amazon will still be looking for candidates with advanced degrees.

But not every data scientist wants to work for Google, Facebook, or Amazon, or move to California, and not everybody wants to do analytics in support of advertising and targeting.

“There are really interesting problems to solve in the Internet of Things (IoT) as well as Natural Language Processing (NLP) and a lot of the industries that use these kinds of tools include legacy businesses — energy, manufacturing, insurance, medical,” Burtch said. You don’t need to move to Silicon Valley to pursue a career in one of these industries.

Burtch said another trend she sees is people gravitating towards “mission-driven careers.” For instance, one company is looking for an NLP expert to monitor online behavior of K-12 population to look for evidence of bullying behavior, sexting, predatory behavior, and suicidal markers.

“There’s just so many more options now than there every have been,” Burtch said.

Read the source article at Information Week.

Here is a Non-Technical Introduction to Machine Learning

Machine learning is a field that threatens to both augment and undermine exactly what it means to be human, and it’s becoming increasingly important that you—yes, you—actually understand it. I don’t think you should need to have a technical background to know what machine learning is or how it’s done. Too much of the discussion about […]

Machine learning is a field that threatens to both augment and undermine exactly what it means to be human, and it’s becoming increasingly important that you—yes, you—actually understand it.

I don’t think you should need to have a technical background to know what machine learning is or how it’s done. Too much of the discussion about this field is either too technical or too uninformed, and, through this blog, I hope to level the playing field.

This is for smart, ambitious people who want to know more about machine learning but who don’t care about the esoteric statistical and computational details underlying the field. You don’t need to know any math, statistics, or computer science to read and understand it.

By the end of this post, you’ll:

  1. Understand the basic logical framework of machine learning (ML).
  2. Be able to define important relevant terms and concepts that anyone interested in this field should know. These terms are highlighted in boldface.
  3. Know which high-level decisions go into building statistical models, and understand some of the implications of these decisions.
  4. Be able to better analyze the question of when we should use the results of ML to make big decisions, such as determining public policy.

This overview is in no way comprehensive. Huge portions of the field are left out, either because they are too rare to merit study by non-technical decision makers, because they’re difficult to explain, or both.

What is machine learning?

The field itself: ML is a field of study which harnesses principles of computer science and statistics to create statistical models. These models are generally used to do two things:

  1. Prediction: make predictions about the future based on data about the past
  2. Inference: discover patterns in data

Difference between ML and AI: There is no universally agreed upon distinction between ML and artificial intelligence (AI). AI usually concentrates on programming computers to make decisions (based on ML models and sets of logical rules), whereas ML focuses more on making predictions about the future.

They are highly interconnected fields, and, for most non-technical purposes, they are the same.

What’s a statistical model?

Models: Teaching a computer to make predictions involves feeding data into machine learning models, which are representations of how the world supposedly works. If I tell a statistical model that the world works a certain way (say, for example, that taller people make more money than shorter people), then this model can then tell me who it thinks will make more money, between Cathy, who is 5’2”, and Jill, who is 5’9”.

Read the source article at the SafeGraph blog.