We address the challenge of effective exploration while maintaining good performance in policy gradient methods. As a solution, we propose diverse exploration (DE) via conjugate policies. DE learns and deploys a set of conjugate policies which can be conveniently generated as a byproduct of conjugate gradient descent. We provide both theoretical and empirical results showing the effectiveness of DE at achieving exploration, improving policy performance, and the advantage of DE over exploration by random policy perturbations.

Nowadays with a growing number of online controlling systems in the organization and also a high demand of monitoring and stats facilities that uses data streams to log and control their subsystems, data stream mining becomes more and more vital. Hoeffding Trees (also called Very Fast Decision Trees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for classification and regression problems showed good performance in handling facing challenges and making the possibility of any-time prediction. Although these methods outperform other methods e.g. Artificial Neural Networks (ANN) and Support Vector Regression (SVR), they suffer from high latency in adapting with new concepts when the statistical distribution of incoming data changes. In this article, we introduced a new algorithm that can detect and handle concept drift phenomenon properly. This algorithms also benefits from fast startup ability which helps systems to be able to predict faster than other algorithms at the beginning of data stream arrival. We also have shown that our approach will overperform other controversial approaches for classification and regression tasks.

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future may have a possible dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers may need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Many learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, many of these workers are shooting themselves in the feet. In this paper, we propose Human-in-the-loop Artificial Intelligence (HitAI) as a fairer paradigm for AI systems. Recognizing that any AI system has humans in the loop, HitAI will reward these aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Merry Men, HitAI researchers should fight for a fairer Robin Hood Artificial Intelligence that gives back what it steals. This article is part of the special track on AI and Society.

Judging solely by headlines and quotes from CEOs, banks are in desperate need of engineers with experience working with artificial intelligence. J.P. Morgan recently made waves by poaching AI specialists from Google, Facebook and two leading research universities in the U.S. Goldman Sachs created an equal splash by hiring away a machine learning guru from Amazon to run its AI efforts. Still, there doesn't appear to be a dramatic need for rows and rows of AI engineers at big U.S. banks. Active job postings that require a background in artificial intelligence are far from overwhelming, though some of that could be due to the apparent difficulty of finding and hiring capable AI engineers. J.P. Morgan, for example, recently hosted an open recruiting event attended by its top AI minds in an attempt to meet and potentially lure talent away from big Silicon Valley tech companies.

In the past two years, Swiggy, the Naspers, DST Global and Bessemer Ventures-funded restaurant aggregator, has been on a tear. The number of interactions on its platform since October 2017 has gone from 2 billion (across consumers, riders and restaurants) to 40 billion in January 2019. In that time, Swiggy has gone from a business working with 12,000 restaurants to over 55,000; from seven cities to 70; from delivery staff of 15,000 to 120,000. The Bengaluru-based venture has become far more valuable, too -- from $700 million in February 2018 to $3.3 billion by the end of the year. This dizzying growth has meant that Swiggy, a firm founded as recently as 2014, has to look beyond human intervention to keep pace.

Is the art world ready for AI? Christie's held the first-ever auction of art created by artificial intelligence. By Thomas Mulier Four months ago, Christie's said it held the first-ever auction of art created by artificial intelligence. The $432,500 sale sparked a controversy among critics over whether it's really AI-generated if a human was involved in making the portrait. Next month, a new Sotheby's sale in London may end the dispute and could even presage a boom in AI-generated art, which until now has been relatively scarce. The firm will take bids for a piece made by German computer scientist Mario Klingemann on March 6 in London.

Choose "Yes, I have!" or "Never heard of it.". AI is progressing exponentially year after year and the media often likes to exaggerate what this technology can do. But the truth is that, although this technology is actually making great strides forward, the use of an AI-only workforce is still in doubt. There are those that have spoken out about the potential for future AI applications in industry. Kai-Fu Lee, the author of bestselling book AI Superpowers: China, Silicon Valley, and the New World Order, told Dailymail.com

With the expansion in the sphere and scale of human activities triggered by globalization, artificial intelligence (AI) constitutes the future of mankind. Unlike traditional hardware or software, artificial intelligence tries to emulate human intelligence in a non-human body by taking decisions without human guidance. They serve our various needs and in turn require the least amount of effort or energy from our end. As the science and technology of Artificial Intelligence is developing, smart artifacts are already being deployed as we speak (You may already have heard about Google's'DeepMind', IBM's'Watson' or Tesla's autopilot system in their electric cars in the news). The future highlights an important fact: AI will become more general, more capable and more efficient than a large group of humans in a very short period of time.

Can the minds of machines teach us something new about what it means to be human? When it comes to the intricate story of our species' complex origins and evolution, it appears that they can. A recent study used machine learning technology to analyze eight leading models of human origins and evolution, and the program identified evidence in the human genome of a "ghost population" of human ancestors. The analysis suggests that a previously unknown and long-extinct group of hominins interbred with Homo sapiens in Asia and Oceania somewhere along the long, winding road of human evolutionary history, leaving behind only fragmented traces in modern human DNA. The study, published in Nature Communications, is one of the first examples of how machine learning can help reveal clues to our own origins.

Machine learning has changed the game for email marketers. Once hyped as the'next big thing', it is now being put into practice by a wide range of businesses to improve the effectiveness of email. Here are just six case studies that demonstrate its success. Language plays a huge part in why consumers respond to marketing (and why they don't). To figure out why its email engagement rates were declining, Dell partnered with Persado in 2016.