At Accelirate, an automation startup, few newcomers to the IT staff claim to be experts in critical areas like robotic process automation (RPA) or machine learning, but everyone has the chance to become one. The Edison, N.J.-based company, which was launched last year to assist companies on the automation track, is now up to 120 employees, 90% residing in IT, and it has debuted on Computerworld's annual Best Places to Work in IT list as the No. 11 small organization. Since RPA and related technologies are treading new ground, Accelirate found itself facing a dearth of expert talent, which could put a damper on its plan for fast-paced growth. The solution: building an in-house, three-month training program that gets all new IT hires, both first-time job holders and seasoned veterans, quickly up to speed. "Not too many people have prior experience with the platforms or technologies we were working with -- finding someone who'd done RPA before was few and far between," says Ahmed Zaidi, Accelirate's chief automation officer.

Students at the Pearl Technology/ Richwoods Township STEM Academy had the opportunity to learn from, and operate cars of the future. The program focuses in on autonomous 1/18th- scale cars developed by Amazon Web Services. The cars- called AWS DeepRacers– learn through rewards and students' controls. It's a platform that only some engineers and developers have had an opportunity to experience. "In my day we didn't have these opportunities, but when you see kids that are fifth through eighth grade actually teaching cars how to drive themselves and that their thought process can get wrapped around artificial intelligence, it's an amazing thing to watch," said Dave Johnson, President of Pearl Technology.

Recently, I've been touring different conferences presenting a talk about best practices for implementing large scale machine learning solutions. The idea is to present a series of non-obvious ideas that result incredibly practical in the implementation of machine intelligence applications in the real world. All the lessons have been based on our experiences at Invector Labs working with large organizations and ambitious startups in the implementation of machine learning capabilities. During those exercises, we quickly realized that many of our assumptions of machine learning apps were really flawed and that there was a huge gap between the advancements in AI research and the practical viability of those ideas. In this two-part article, I would like summarize some of those ideas that hopefully will result valuable to machine learning practitioners and aspirational data scientists.

The world's biggest companies are coming to India for data analytics, artificial intelligence (AI) and machine learning (ML) skills. But renowned mathematicians believe the country needs to significantly improve its mathematics capabilities to be able to use these technologies to create really innovative and robust solutions – both for itself and the world. Manjul Bhargava, mathematics professor at Princeton University and winner of the Fields Medal, one of the highest honours in math, says India can't hope to lead the fourth industrial revolution, "if we don't have strong mathematical talent coming up very soon." Srinivasa Varadhan, mathematics professor at New York University, agrees. He says if you want to provide some guarantee that a certain machine learning algorithm will work the way it's supposed to, then you have to do the math.

I am pursuing Artificial Intelligence Microsoft Professional Program on edx & it's a struggle to get the right help every time I get stuck with a problem. Peers help works sometime but most of the time I end up spending lot of time, trying to figure things out on my own.

None of the candidates could give a satisfactory answer. May be, they thought becoming a data scientist has nothing to do with following them. Think back, when you were a kid and played sports, didn't you admire any sports player and aimed to be like him / her, when you grow up? The path to becoming a data scientist is exhausting, just like a marathon. To ensure you don't fall out, it is important that you keep seeking motivation from what others are doing.

Let's face it: attention spans are short these days. People are bombarded with content every second of every day and don't have the time or patience to read a long article. Add to that the difficulty text posts have standing out in a crowded feed of funny and interesting videos - it can be hard to connect with people. That's why videos are so important for your business; not only are they easy to consume, but they're fun to watch and people love them. Videos are becoming the most effective way to engage users and drive traffic to your website.

This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 128 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and application scenarios (e.g., autonomous driving, machine translation). The paper also analyses trends concerning datasets, research trends, and research focus, concluding with research challenges and promising research directions in ML testing.

Neural networks are vulnerable to adversarial examples, i.e. inputs that are imperceptibly perturbed from natural data and yet incorrectly classified by the network. Adversarial training, a heuristic form of robust optimization that alternates between minimization and maximization steps, has proven to be among the most successful methods to train networks that are robust against a pre-defined family of perturbations. This paper provides a partial answer to the success of adversarial training. When the inner maximization problem can be solved to optimality, we prove that adversarial training finds a network of small robust train loss. When the maximization problem is solved by a heuristic algorithm, we prove that adversarial training finds a network of small robust surrogate train loss. The analysis technique leverages recent work on the analysis of neural networks via Neural Tangent Kernel (NTK), combined with online-learning when the maximization is solved by a heuristic, and the expressiveness of the NTK kernel in the $\ell_\infty$-norm.

We study three fundamental statistical-learning problems: distribution estimation, property estimation, and property testing. We establish the profile maximum likelihood (PML) estimator as the first unified sample-optimal approach to a wide range of learning tasks. In particular, for every alphabet size $k$ and desired accuracy $\varepsilon$: $\textbf{Distribution estimation}$ Under $\ell_1$ distance, PML yields optimal $\Theta(k/(\varepsilon^2\log k))$ sample complexity for sorted-distribution estimation, and a PML-based estimator empirically outperforms the Good-Turing estimator on the actual distribution; $\textbf{Additive property estimation}$ For a broad class of additive properties, the PML plug-in estimator uses just four times the sample size required by the best estimator to achieve roughly twice its error, with exponentially higher confidence; $\boldsymbol{\alpha}\textbf{-R\'enyi entropy estimation}$ For integer $\alpha>1$, the PML plug-in estimator has optimal $k^{1-1/\alpha}$ sample complexity; for non-integer $\alpha>3/4$, the PML plug-in estimator has sample complexity lower than the state of the art; $\textbf{Identity testing}$ In testing whether an unknown distribution is equal to or at least $\varepsilon$ far from a given distribution in $\ell_1$ distance, a PML-based tester achieves the optimal sample complexity up to logarithmic factors of $k$. With minor modifications, most of these results also hold for a near-linear-time computable variant of PML.