The application of "machine learning" and "artificial intelligence" has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we aim to clarify the relationship between these terms and, in particular, to specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents. Hence, we seek to provide more terminological clarity and a starting point for (interdisciplinary) discussions and future research.

This work focuses on classification over time series data. When a time series is generated by non-stationary phenomena, the pattern relating the series with the class to be predicted may evolve over time (concept drift). Consequently, predictive models aimed to learn this pattern may become eventually obsolete, hence failing to sustain performance levels of practical use. To overcome this model degradation, online learning methods incrementally learn from new data samples arriving over time, and accommodate eventual changes along the data stream by implementing assorted concept drift strategies. In this manuscript we elaborate on the suitability of online learning methods to predict the road congestion level based on traffic speed time series data. We draw interesting insights on the performance degradation when the forecasting horizon is increased. As opposed to what is done in most literature, we provide evidence of the importance of assessing the distribution of classes over time before designing and tuning the learning model. This previous exercise may give a hint of the predictability of the different congestion levels under target. Experimental results are discussed over real traffic speed data captured by inductive loops deployed over Seattle (USA). Several online learning methods are analyzed, from traditional incremental learning algorithms to more elaborated deep learning models. As shown by the reported results, when increasing the prediction horizon, the performance of all models degrade severely due to the distribution of classes along time, which supports our claim about the importance of analyzing this distribution prior to the design of the model.

In this paper, we propose a method of distributed stochastic gradient descent (SGD), with low communication load and computational complexity, and still fast convergence. To reduce the communication load, at each iteration of the algorithm, the worker nodes calculate and communicate some scalers, that are the directional derivatives of the sample functions in some \emph{pre-shared directions}. However, to maintain accuracy, after every specific number of iterations, they communicate the vectors of stochastic gradients. To reduce the computational complexity in each iteration, the worker nodes approximate the directional derivatives with zeroth-order stochastic gradient estimation, by performing just two function evaluations rather than computing a first-order gradient vector. The proposed method highly improves the convergence rate of the zeroth-order methods, guaranteeing order-wise faster convergence. Moreover, compared to the famous communication-efficient methods of model averaging (that perform local model updates and periodic communication of the gradients to synchronize the local models), we prove that for the general class of non-convex stochastic problems and with reasonable choice of parameters, the proposed method guarantees the same orders of communication load and convergence rate, while having order-wise less computational complexity. Experimental results on various learning problems in neural networks applications demonstrate the effectiveness of the proposed approach compared to various state-of-the-art distributed SGD methods.

I've been doing this for a year, and it's the most helpful learning technique I've found in 14 years of computer programming. I didn't go to school for it. I just learned by necessity because I started a website that just kept growing and growing, and I couldn't afford to hire a programmer, so I picked up a few books on PHP, SQL, Linux, and Apache, learned just enough to make it work, then used that little knowledge for years. But later, when I worked along side a real programmer, I was blown away by his vocabulary! We were using the same language, but he had memorized so much of it, that I felt like a child next to a university professor.

According to a Brookings Institution report, "Automation and Artificial Intelligence: How machines are affecting people and places," roughly 25 percent of U.S. jobs are at a high risk of automation. Among the most vulnerable jobs are those with routine physical and cognitive tasks such as office administration, production, transportation and food preparation. The jobs that are the least vulnerable to automation are generally classified as abstract and manual occupations -- "those that involve tasks that are … difficult to codify or take place in physical environments that are difficult to control." According to the report, "abstract roles -- typically in management, technology or finance -- tend to require more formal education and skills such as creativity, persuasion, intuition and problem solving." The report predicts what automation does not replace, it will complement -- as will be the case with many technology workers.

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Data Scientist is the most promising job in the U.S according to LinkedIn. Also, the demand for Data Scientists is growing exponentially in all the industries. Out of all the openings, 19% of data science professionals jobs are secured by the Finance Industry. Python statistics is one of the most important python built-in libraries developed for descriptive statistics. Python statistics is all about the ability to describe, summarize, and represent data visually through comprehensive python statistics libraries.

A free online course developed by The University of Helsinki and the Reaktor agency aims to demystify and educate people in artificial intelligence. Artificial intelligence plays a major role in our everyday lives. In its many forms, it is responsible for the quality of photos we take with our smartphones or for the comfort we feel while shopping online. To help people better understand its basics, the University of Helsinki and the Reaktor agency launched a series of free online courses called Elements of AI. The program aims to explain what artificial intelligence means, what it can and can't do, how it works, and how it will affect us in the future.

I started down the AI rabbit hole two years ago. Looking back, I never felt like I was studying -- I was having fun. To me, AI was simply much cooler than most things at work or at school. It was real-life science fiction, and I felt like I was binge-watching good TV. Artificial Intelligence should not be intimidating.

"I thought at first it was a sign of insanity, speaking to a little thing like that and him talking back!" says 92-year-old John Winward of the first time he tested a smart speaker. The former head teacher was one of a group of residents at an elderly care home in Bournemouth, England who recently took part in a half-year academic experiment designed to test if artificial intelligence voice technologies could help tackle loneliness. He was a fast convert. "I was so surprised... it was such fun!" he says, explaining that several months later he remains an active user of his Google Home device. He asks the speaker for news and weather updates, music and audio book tips and crossword puzzle clues.