Human Factors in Hypertext (HUMAN) Opinion Mining, Summarization and Diversification Narrative and Hypertext I attended the Opinion Mining, Summarization and Diversification workshop. The workshop started with a talk titled: "On Reviews, Ratings and Collaborative Filtering," presented by Dr. Oren Sar Shalom, principal data scientist at Intuit, Israel. Next, Ophélie Fraisier, a PhD student studying stance analysis on social media at Paul Sabatier University, France, presented: "Politics on Twitter: A Panorama," in which she surveyed methods of analyzing tweets to study and detect polarization and stances, as well as election prediction and political engagement. He showed how collective opinion mining can help capture the drivers behind opinions as opposed to individual opinion mining (or sentiment) which identifies single individual attitudes toward an item. I thank a million people! https://t.co/I3quPp6nw3 He also discussed a phenomenon in which people are likely to lie to pollsters (social desirability bias) but are honest to Google ("Digital Truth Serum") because Google incentivizes telling the truth. The paper sessions followed the keynote with two full papers and a short paper presentation. Google search data as "digital truth serum" - while reporting of child abuse go down at the recession time, Google search data indicates that real child abuse increases https://t.co/DQQoAotZqB However, it feels more like a research talk rather than a #keynote. Though still interesting, I'd rather hear about a #vision for this area of #research.

Netcore, a global Marketing Technology Company that offers solutions for enterprises Digital Marketing, organized a corporate training programme on Artificial Intelligence (AI) and Machine Learning (ML) for marketers to understand how to implement new age technology in their marketing campaigns. Marketers today have moved from a'batch & blast' approach to a behavior-based approach in their marketing automation strategy. By deploying analytics tools, marketers are able to set smart triggers based on various criteria such as RFM (Recency, Frequency, & Monetary analysis) combined with demographic & category affinity. With the advent of Artificial Intelligence, these professionals can derive greater value from their strategies with hyper-personalize campaigns aimed at creating 1:1 customer experiences. These technologies also enable a multi-fold increase in the Customer Life Cycle as AI allows one to harness data, and analyze it to generate insights in response to unpredictable situations, and that too in real time.

The race to become the global leader in artificial intelligence (AI) has officially begun. In the past fifteen months, Canada, China, Denmark, the EU Commission, Finland, France, India, Italy, Japan, Mexico, the Nordic-Baltic region, Singapore, South Korea, Sweden, Taiwan, the UAE, and the UK have all released strategies to promote the use and development of AI. No two strategies are alike, with each focusing on different aspects of AI policy: scientific research, talent development, skills and education, public and private sector adoption, ethics and inclusion, standards and regulations, and data and digital infrastructure. This article summarizes the key policies and goals of each strategy, as well as related policies and initiatives that have announced since the release of the initial strategies. It also includes countries that have announced their intention to develop a strategy or have related AI policies in place. I plan to continuously update this article as new strategies and initiatives are announced. If a country or policy is missing (or if something in the summary is incorrect), please leave a comment and I will update the article as soon as possible. I also plan to write an article for each country that provides an in-depth look at AI policy. Once these articles are written, I will include a link to the bottom of each country's summary. June 28: Publication of original article, included Australia, Canada, China, Denmark, EU Commission, Finland, France, Germany, India, Japan, Singapore, South Korea, UAE, US, and UK.

The graph represents a network of 2,727 Twitter users whose tweets in the requested range contained "#iot OR "internet of things"", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 19 July 2018 at 20:39 UTC. The requested start date was Thursday, 19 July 2018 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 13-day, 0-hour, 11-minute period from Thursday, 05 July 2018 at 07:32 UTC to Wednesday, 18 July 2018 at 07:43 UTC.

Machine Learning Apps are fast invading into our everyday lives as the technology is progressing towards delivering smarter mobile-centric solutions. Embedding mobile apps with Machine Learning, a promising segment of AI, is spelling out a lot of advantages for the adopting companies to stand out amidst the clutter and rake in sizeable profits. Many organizations are investing heavily in Machine Learning to reap its benefits. Based on a prediction, Machine Learning as a service market will touch $5,537 million by 2023 while growing at a CAGR of 39 per cent from 2017-2023. Machine Learning Applications refer to a set of apps with Artificial Intelligence mechanisms that are designed to create a universal approach throughout the web to solve similar problems. The ML apps are based on a continuous learning process and provide end users with the exceptional user experience.

Designing hierarchical reinforcement learning algorithms that induce a notion of safety is not only vital for safety-critical applications, but also, brings better understanding of an artificially intelligent agent's decisions. While learning end-to-end options automatically has been fully realized recently, we propose a solution to learning safe options. We introduce the idea of controllability of states based on the temporal difference errors in the option-critic framework. We then derive the policy-gradient theorem with controllability and propose a novel framework called safe option-critic. We demonstrate the effectiveness of our approach in the four-rooms grid-world, cartpole, and three games in the Arcade Learning Environment (ALE): MsPacman, Amidar and Q*Bert. Learning of end-to-end options with the proposed notion of safety achieves reduction in the variance of return and boosts the performance in environments with intrinsic variability in the reward structure. More importantly, the proposed algorithm outperforms the vanilla options in all the environments and primitive actions in two out of three ALE games.

When it comes to disruptive technologies, nothing is more on trend right now than Artificial Intelligence or AI as it's commonly known because it's one of those technologies that we know will impact business, economic and social models as well as our own personal lives. AI is just one part of the larger field of Data Science, where at its simplest, is the art of'extracting value or business insights from data'. While Artificial Intelligence is a term first coined by John McCarthy in 1956, the concept of computers performing cognitive functions to mirror those of humans is around for decades. English mathematician Alan Turing's paper'Computing Machinery and Intelligence' published in 1950 posed the question'can machines think?' and introduced the'Turing test', a model for measuring intelligence. Called'the Imitation Game', it gave notion to the idea of machines being able to move beyond just logical thinking and into the realm of cognitive thinking using skills like learning, reasoning, remembering, understanding and deduction/inference.

Careers in data science, artificial intelligence, machine learning, and related technologies are considered among the best choices to pursue in an uncertain future economy where many jobs may end up automated and performed by robots and AI. Yet in spite of the likely strong and secure future of these careers, the job marketplace remains fundamentally unbalanced. There are still many more jobs open and available than there are qualified applicants to fill those jobs. Just do a search on Monster for the keyword machine learning and you will find thousands of job openings across the country. Whether you are just starting out in your IT career or you are watching high-profile IT layoffs and considering the best new skills to learn, chances are you are wondering what the best skills are to emphasize on your LinkedIn profile and the best skills to focus on in the next online course you take.

How machine learning creates room for continuous business model innovation. A 35-year veteran of the industry, Weinstein sensed that the practice of accounting--issuing financial statements three months after the fact--while still necessary, was losing relevance in the real-time, data-driven economy. So he organized a three-day partner meeting to consider how machine-learning capabilities in particular might remake the traditional accounting firm for the digital era, enabling it to help its clients look into the future rather than simply reporting on the past. Weinstein invited a partner in charge of global innovation at a big-four accounting firm (not a direct competitor) to talk about the moves his firm was making. As the visitor spoke, it became plain to Weinstein that there was little time to waste. "That meeting was a watershed moment; it created a united mindset for the firm around deciding to lead, not follow, when it came to leveraging technology in the accounting space," says Weinstein.

Systematic compositionality is the ability to recombine meaningful units with regular and predictable outcomes, and it's seen as key to humans' capacity for generalization in language. Recent work has studied systematic compositionality in modern seq2seq models using generalization to novel navigation instructions in a grounded environment as a probing tool, requiring models to quickly bootstrap the meaning of new words. We extend this framework here to settings where the model needs only to recombine well-trained functional words (such as "around" and "right") in novel contexts. Our findings confirm and strengthen the earlier ones: seq2seq models can be impressively good at generalizing to novel combinations of previously-seen input, but only when they receive extensive training on the specific pattern to be generalized (e.g., generalizing from many examples of "X around right" to "jump around right"), while failing when generalization requires novel application of compositional rules (e.g., inferring the meaning of "around right" from those of "right" and "around").