Many experts called 2016 "the year of the chatbots." Thousands of chatbots already help businesses improve customer service, sell more, and increase earnings. This paper reports a Data Monsters overview of research on the best-known platforms for building chatbots. The relevance of this research is proved by the massive deployment of chatbots. Indeed, today chatbots are used to solve a number of business tasks across many industries like E-Commerce, Insurance, Banking, Healthcare, Finance, Legal, Telecom, Logistics, Retail, Auto, Leisure, Travel, Sports, Entertainment, Media and many others.

The vast majority of respondents to the 2014 Future of the Internet canvassing anticipate that robotics and artificial intelligence will permeate wide segments of daily life by 2025, with huge implications for a range of industries such as health care, transport and logistics, customer service, and home maintenance. But even as they are largely consistent in their predictions for the evolution of technology itself, they are deeply divided on how advances in AI and robotics will impact the economic and employment picture over the next decade. We call this a canvassing because it is not a representative, randomized survey. Its findings emerge from an "opt in" invitation to experts who have been identified by researching those who are widely quoted as technology builders and analysts and those who have made insightful predictions to our previous queries about the future of the Internet. The economic impact of robotic advances and AI--Self-driving cars, intelligent digital agents that can act for you, and robots are advancing rapidly. Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025? Half of these experts (48%) envision a future in which robots and digital agents have displaced significant numbers of both blue- and white-collar workers--with many expressing concern that this will lead to vast increases in income inequality, masses of people who are effectively unemployable, and breakdowns in the social order.

Deep learning is the field of machine learning that studies and develops artificial neural networks capable of learning several layers of representation (features) from raw data. These methods have delivered new levels of performance in the field of computer vision. More recently, they have become popular in medical imaging systems, such as for the segmentation of various types of tissues in medical imagery. In this tutorial, we will provide an introduction to deep learning, covering both theory and practice. On the theory side, we will describe the most common concepts found in today's deep learning research, with a focus on convolutional neural networks.

Language translation is important to Facebook's mission of making the world more open and connected, enabling everyone to consume posts or videos in their preferred language -- all at the highest possible accuracy and speed. Today, the Facebook Artificial Intelligence Research (FAIR) team published research results using a novel convolutional neural network (CNN) approach for language translation that achieves state-of-the-art accuracy at nine times the speed of recurrent neural systems.1 Additionally, the FAIR sequence modeling toolkit (fairseq) source code and the trained systems are available under an open source license on GitHub so that other researchers can build custom models for translation, text summarization, and other tasks. Originally developed by Yann LeCun decades ago, CNNs have been very successful in several machine learning fields, such as image processing. However, recurrent neural networks (RNNs) are the incumbent technology for text applications and have been the top choice for language translation because of their high accuracy. Though RNNs have historically outperformed CNNs at language translation tasks, their design has an inherent limitation, which can be understood by looking at how they process information.

Convolutional encoders for neural MT go as far back as (Kalchbrenner, Blunsom 2013) and convolutional encoders decoders in LM and MT appear first in (Kalchbrenner et al, 2016) and with pooling also in (Bradbury et al, 2016).

A new working paper finds that the arrival of one new industrial robot in a local labor market coincides with an employment drop of 5.6 workers. These papers have not been peer-reviewed, but are circulated by their authors for comment and discussion. With the NBER's blessing, Making Sen$e is pleased to feature these summaries regularly on our page. The following summary was written by the NBER and doesn't necessarily reflect the views of Making Sen$e. With America's workers already squeezed by forces ranging from international competition to offshoring to new information technologies, concern is growing about the impact of robots on jobs and wages.

As artificial intelligence becomes more human, to co-exist, does human intelligence need to become more artificial? We've spent a lot of time philosophizing about where Artificial Intelligence is going to take us, how far we are to achieving general AI and the implications it will have on humanity – all not without the sky net scenarios! Hype aside, there are companies out there who are focusing on how we can use artificially intelligent applications to improve the human experience, sustain life on our planet and significantly boost the economy. This pioneering technology could well see the next world-changing scientific discovery hailing from Silicon Valley, especially considering the significant increase in investment over the past few years. According to the Alzheimer's Association, there are more than 5 million Americans living with Alzheimer's today, with a predicted 16 million by 2050, and a further 850,000 people with dementia in the UK.

Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn. Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly. It has become a key obstacle in making learning protocols realistic in applications. In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work. We start from the comparison of world knowledge with domain-specific knowledge, and then introduce three key problems in using world knowledge in learning processes, i.e., explicit and implicit feature representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision. Finally we discuss the future directions of this research topic.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well. The primary motivation and driving force for these areas of study, and for developing these techniques further, is that the solutions required to solve certain problems are incredibly complicated, not well understood, nor easy to determine manually.

The digital revolution is democratizing societal change, evolving human progress by helping people & organizations innovate in ways not previously possible. Intelligent machines are increasingly complementing human reasoning to augment and enrich our experience and competencies. Machine learning Computers continuously learn from new data to evolve into advanced, intelligent systems. Human language technologies Computers and humans communicate using speech recognition, language modeling and understanding, and spoken language and dialog systems. Perception and sensing Computers and devices recognize what their visual sensors detect, facilitating tasks ranging from autonomous driving to medical image analysis.