Many young professionals, who have started their journey into data science, and machine learning, face a common problem -- they have completed one or two basic online course, done some programming lessons, put up a couple of projects on Github, and then… then what? Where to find focused resources? In one of my previous articles on Medium (published by the TDS Team), I discussed, at length, where you can find MOOC (Massive Open Online Course) for jump-starting your journey into data science and machine learning. That article assumed the reader to be a beginner and covers essential MOOCs, which are optimized for basic and intermediate learning. How to choose effective MOOCs for machine learning and data science?

San Francisco (CNN Business)A new artificial intelligence system is so good at composing text that the researchers behind it said they won't release it for fear of how it could be misused. Created by nonprofit AI research company OpenAI (whose backers include Tesla CEO Elon Musk and Microsoft), the text-generating system can write page-long responses to prompts, mimicking everything from fantasy prose to fake celebrity news stories and homework assignments. It builds on an earlier text-generating system the company released last year. Researchers have used AI to generate text for decades with varying levels of success. In recent years, the technology has gotten particularly good.

Make sure you're following these industry leaders on Twitter. The world of AI and robotics is evolving all the time, with job opportunities and new products springing up constantly. It's never been a better time for someone interested in building a career in AI. While the jobs, companies and skills may vary, the wider industry trends are always worth keeping an eye on if you're serious about an AI career. One of the best ways to keep yourself immersed in the AI world is by following the leaders, experts and influencers in the field.

Accordingly, a prime step in text mining applications is to extract interesting patterns and features, from this supply of unstructured data. Feature extraction can be considered as the core of social media mining tasks such as sentiment analysis, event detection, and news recommendation [2]. In the literature, sentiment analysis tends to be used to refer to the task of classifying the polarity of a given piece of text at the document, sentence, feature, or aspect level [23]. There are various applications on a variety of domains which utilize sentiment analysis, in this regard one can mention applying the sentiment analysis for political reviews to estimate the general viewpoint of the parties [43], predicting stock market prices based on sentiment analysis by utilizing the different financial news data [5], and making use of the sentiment analysis to recognize the current medical and psychological status for a community [23]. Machine learning algorithms and statistical learning techniques have been rising in a variety of scientific fields [9, 10]. A number of machine learning techniques have been proposed to perform the task of sentiment analysis. As one of the powerful sub-domains of machine learning in recent years, deep learning models are emerging as a persuasive computational tool, they have affected many research areas and can be traced in many applications. With respect to the deep learning, textual deep representation models attempt to discover and present intricate syntactic and semantic representations of texts, automatically from data without any handmade feature engineering.

In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment. First, unlike traditional aggregated web search that merely presents multi-sourced results in the first page, this new task may present aggregated results in all pages and has to dynamically decide which source should be presented in the current page. Second, as pointed out by many existing studies, it is not trivial to rank items from heterogeneous sources because the relevance scores from different source systems are not directly comparable. To address these two issues, we decompose the task into two subtasks in a hierarchical structure: a high-level task for source selection where we model the sequential patterns of user behaviors onto aggregated results in different pages so as to understand user intents and select the relevant sources properly; and a low-level task for item presentation where we formulate a slot filling process to sequentially present the items instead of giving each item a relevance score when deciding the presentation order of heterogeneous items. Since both subtasks can be naturally formulated as sequential decision problems and learn from the future user feedback on search results, we build our model with hierarchical reinforcement learning. Extensive experiments demonstrate that our model obtains remarkable improvements in search performance metrics, and achieves a higher user satisfaction.

Perform advanced analytics on your data using neural networks, without programming or building block diagrams. Track the behaviour of a system and operate it at peak level. Identify who is interested in your products and services. Analyze market moving events to predict your future sales.

"This is a very exciting day as we kick-off testing of autonomous vehicles, putting Rhode Island on the map as a leader in this new high-tech field in transportation. And we'll do it in a careful and safe manner partnering with institutions of higher education to carefully study and evaluate the service and its integration on Rhode Island roads," said Governor Gina Raimondo. The vehicles are being tested this week on low-volume roads in the park as the beginning phase of a pilot project scheduled to launch in Providence in the spring of 2019. The testing period in Quonset will be followed by similar testing in Providence, prior to the start of service. Between the two locations, the vehicles will undergo 500 miles of testing.

Applying just a bit of strain to a piece of semiconductor or other crystalline material can deform the orderly arrangement of atoms in its structure enough to cause dramatic changes in its properties, such as the way it conducts electricity, transmits light, or conducts heat. Now, a team of researchers at MIT and in Russia and Singapore have found ways to use artificial intelligence to help predict and control these changes, potentially opening up new avenues of research on advanced materials for future high-tech devices. The findings appeared in early February in the Proceedings of the National Academy of Sciences, in a paper authored by MIT professor of nuclear science and engineering and of materials science and engineering Ju Li, MIT Principal Research Scientist Ming Dao, and MIT graduate student Zhe Shi, with Evgenii Tsymbalov and Alexander Shapeev at the Skolkovo Institute of Science and Technology in Russia, and Subra Suresh, the Vannevar Bush Professor Emeritus and former dean of engineering at MIT and current president of Nanyang Technological University in Singapore. Already, based on earlier work at MIT, some degree of elastic strain has been incorporated in some silicon processor chips. Even a 1% change in the structure can in some cases improve the speed of the device by 50 percent, by allowing electrons to move through the material faster.

Two major future of work trends impacting the workplace are the introduction of a new generation, and the acceleration of automation and artificial intelligence (AI). While these trends are already impacting today's workforce, they will continue to shape the future of work. Let's start with the newest generation of workers, Generation Z, born since 1995 (though the exact birth year starting Gen Z is under debate). This generation is just starting to enter the workforce, with the oldest turning 22-years old this year. There isn't multitudes of information about this generation in the workplace yet as they are just getting started, but we do know they will bring change.

If human beings are as Hamlet suggested, "noble in reason, infinite in faculty," then why are we facing so many problems? In many ways, people are better off than ever before: reduced infant mortality, longer lifespans, less poverty, fewer epidemic diseases, even fewer deaths per capita due to violence. And yet global threats abound and by nearly all measures they are getting worse: environmental destruction and wildlife extinction, ethnic and religious hatred, the specter of nuclear war, and above all, the disaster of global climate change. For some religious believers, the primary culprit is original sin. For ideologues of left, right, and otherwise, it's ill-functioning political structures.