AI News, Transitioning from Software Engineering to Artificial Intelligence
Transitioning from Software Engineering to Artificial Intelligence
In order to understand Machine Learning, a solid knowledge of statistics fundamentals is essential.
It can seem like a daunting task at first, so a good way to start is to look up a paper that already has code attached to it (on GitXiv for example) and try to understand the implementation in depth.
Data work comes in many aspects, and falls within a few categories: The best way to get familiar with data wrangling is to grab a dataset in the wild and try to use it.
Following the steps above, a good way to learn is to: Debugging Machine Learning algorithms that fail to converge or to give sensible results involves a very different process from debugging code.
In the same vein, finding the right architecture and hyperparameters requires solid theoretical fundamentals, but also good infrastructure work to be able to test different configurations out.
These skills include: For more details on some of the software skills we recommend acquiring to become a quality Machine Learning Engineer, check out our post dedicated to transitioning to Applied AI from Academia.
But the field of Applied AI changes extremely quickly, and the best way to learn, is to get your hands dirty and actually try to build out an end-to-end solution to solve a real problem.
7 key skills required for Machine Learning jobs
Machine Learning is usually associated with artificial intelligence (AI) that provides computers with the ability to do certain tasks, such as recognition, diagnosis, planning, robot control, prediction, etc., without being explicitly programmed.
It focuses on the development of algorithms that can teach themselves to grow and change when exposed to new data.
A good candidate should have a deep understanding of a broad set of algorithms and applied math, problem solving and analytical skills, probability and statistics and programming languages such as Python/C++/R/Java.
Beyond all, Machine Learning requires innate curiosity, so if you never lost the curiosity you had when you were a child, you’re a natural candidate for Machine Learning.
You will need to understand subjects such as gradient decent, convex optimization, lagrange, quadratic programming, partial differential equations and alike.
Expanding the Expertise in Unix Tools: You should also master all of the great unix tools that were designed for this: cat, grep, find, awk, sed, sort, cut, tr, and more.
Different types of problems need various solutions, you may be able to utilize really cool advance signal processing algorithms such as: wavelets, shearlets, curvelets, contourlets, bandlets.
It also means being aware of the news regarding the development to the tools (changelog, conferences, etc.), theory and algorithms (research papers, blogs, conference videos, etc.).
Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.
The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.
Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.
Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.
Machine Learning’s inroads into our collective consciousness have been both history making (as when AlphaGo won 4 of 5 Go matches against the world’s best Go player!) and hysterical (Machine Learning Algorithm Identifies Tweets Sent Under The Influence Of Alcohol), but regardless how you discovered it, one thing is clear: Machine Learning has arrived.
Let’s say you’re working for a grocery chain, and the company wants to start issuing targeted coupons based on things like the past purchase history of customers, with a goal of generating coupons that shoppers will actually use.
Computer science fundamentals important for Machine Learning engineers include data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.), computability and complexity (P vs.
Closely related to this is the field of statistics, which provides various measures (mean, median, variance, etc.), distributions (uniform, normal, binomial, Poisson, etc.) and analysis methods (ANOVA, hypothesis testing, etc.) that are necessary for building and validating models from observed data.
Data modeling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns (correlations, clusters, eigenvectors, etc.) and/or predicting properties of previously unseen instances (classification, regression, anomaly detection, etc.).
scikit-learn, Theano, Spark MLlib, H2O, TensorFlow etc.), but applying them effectively involves choosing a suitable model (decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc.), a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods), as well as understanding how hyperparameters affect learning.
You also need to be aware of the relative advantages and disadvantages of different approaches, and the numerous gotchas that can trip you (bias and variance, overfitting and underfitting, missing data, data leakage, etc.).
You need to understand how these different pieces work together, communicate with them (using library calls, REST APIs, database queries, etc.) and build appropriate interfaces for your component that others will depend on.
Software engineering best practices (including requirements analysis, system design, modularity, version control, testing, documentation, etc.) are invaluable for productivity, collaboration, quality and maintainability.
Machine Learning techniques are already being applied to critical arenas within the Healthcare sphere, impacting everything from care variation reduction efforts to medical scan analysis.
David Sontag, an assistant professor at New York University’s Courant Institute of Mathematical Sciences and NYU’s Center for Data Science, gave a talk on Machine Learning and the Healthcare system, in which he discussed “how machine learning has the potential to change health care across the industry, from enabling the next-generation electronic health record to population-level risk stratification from health insurance claims.”
You Could Become an AI Master Before You Know It. Here’s How.
His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.
The insurance company’s work with DataRobot hints at how artificial intelligence might have to evolve in the next few years if it is to realize its enormous potential.
Beyond spectacular demonstrations like DeepMind’s game-playing software AlphaGo, AI does have the power to revolutionize entire industries and make all sorts of businesses more efficient and productive.
And it isn’t as simple as building a more user-friendly interface on top of things, because engineers often have to apply judgment and know-how when crafting and tweaking their code.
But AI researchers and companies are now trying to address this by essentially turning the technology on itself, using machine learning to automate the trickier aspects of developing AI algorithms.
Barton first tried using the platform by inputting a bunch of insurance data to see if it could predict a specific dollar value.
This report concludes that artificial intelligence, especially machine learning, may overhaul big industries, including manufacturing, finance, and health care, potentially adding up to $126 billion to the U.S. economy by 2025.
He loads up a public data set of loan applications and payments, and then he has the system develop a bunch of models to see if there are any patterns in why people default.
Without a close examination, it’s hard to know how well the system automates some of the trickier aspects of data science, like data cleaning and feature engineering, but it seems to take care of a surprising amount.
“You’re not going to move the needle.” The shortage of data scientists is inspiring many others to work on automating machine learning.
And going beyond simple tools for image or text classification will mean automating more of the work involved in training machine-learning models.
“So anybody could say ‘Build me a predictive model’ and it goes off and does it.” Earlier this year, the company announced some significant progress toward this goal, demonstrating an experimental way to automate the process of tuning deep-learning neural networks (see “AI Software Learns to Make AI Software”).
“The barrier to entry is just too high.” Xing has created a company, Petuum, to develop the OS, and it has already created a series of tools aimed at bringing machine learning to medicine.
There are already concerns about machine-learning algorithms inadvertently absorbing biases from training data, and some models are simply too opaque to examine carefully (see “The Dark Secret at the Heart of AI”).
“To do machine learning really well, you need a PhD and about five years of experience,” says Rich Caruana, a senior researcher at Microsoft who has been doing data science for about 20 years.
Does your algorithm expire after six months, and is it interpretable?” Caruana believes it should be possible to automate some of the steps a data scientist needs to take in order to guard against such problems—something similar to a pilot’s pre-flight checklist.
- On Wednesday, March 20, 2019
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