Humans are unique in their ability to learn from, understand the world through and communicate with language… Or are they? Perhaps not for long, if Dr. Layla El Asri, a Research Manager at Microsoft Research Montreal, has a say in it. She wants you to be able to talk to your machine just like you'd talk to another person. The hard part is getting your machine to understand and talk back to you like it's that other person. Today, Dr. El Asri talks about the particular challenges she and other scientists face in building sophisticated dialogue systems that lay the foundation for talking machines. She also explains how reinforcement learning, in the form of a text game generator called TextWorld, is helping us get there, and relates a fascinating story from more than fifty years ago that reveals some of the safeguards necessary to ensure that when we design machines specifically to pass the Turing test, we design them in an ethical and responsible way. Layla El Asri: In a video game, most of the time you only have a few actions that you can take. You just need to learn when you should go right, when you should go left, when you should go up, when you should go down. But when it comes to dialogue, you need to learn how to make a sentence that is grammatically correct, and then you need to learn how to make a sentence that makes sense in the global context of the dialogue, or a sentence that brings new information in the dialogue that is going to make the person you are talking to satisfied with the sentence. Your action space is just huge because it's not just up/down, right/left, it's all the sentences you could imagine! Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Host: Humans are unique in their ability to learn from, understand the world through and communicate with language… Or are they? Perhaps not for long, if Dr. Layla El Asri, a Research Manager at Microsoft Research Montreal, has a say in it. She wants you to be able to talk to your machine just like you'd talk to another person.

In a new paper, NIST researchers demonstrate that deep learning algorithms -- a form of artificial intelligence -- are significantly better than a commonly used, less sophisticated method for detecting when offshore radars are operating. Improved radar detection would enable commercial users to know when they must yield the so-called 3.5 Gigahertz (3.5 GHz) Band. In 2015, the FCC adopted rules for the Citizens Broadband Radio Service (CBRS) to permit commercial LTE (long-term evolution) wireless equipment vendors and service providers to use the 3.5 GHz Band when not needed for radar operations. Companies such as AT&T, Google, Nokia, Qualcomm, Sony and Verizon have been eager to access this band (between 3550 and 3700 MHz) because it will expand product markets and give end users better coverage and higher data rate speeds in a variety of environments where service is traditionally weak. NIST helped develop 10 standard specifications that enable service providers and other potential users to operate in the 3.5 GHz Band under FCC regulations while assuring the Navy that the band can be successfully shared without RF interference.

Next-generation 5G technology is only just making its way to market after a decade of development, but Donald Trump is already demanding the rollout of 6G in the United States. The US President did not elaborate on what 6G might involve, with even his understanding of 5G appearing basic in a series of tweets on Thursday. He described it as "far more powerful, faster and smarter" than current 4G technology, while also revealing his concerns that the US is lagging behind in the deployment of 5G. "I want 5G, and even 6G, technology in the United States as soon as possible... American companies must step up their efforts, or get left behind," Trump tweeted. His comments come just days after the founder of Chinese technology giant Huawei – who are widely regarded as one of the pioneers of 5G – said the US risks falling behind the rest of the world when it comes to 5G rollout.

Modern technologies are enabling increased automation across multiple markets and enhancing everyday life. Artificial intelligence and machine learning are reshaping the way we live through the advent of automated and autonomous vehicles, smart cities, smart factories and much more. Modern technologies are enabling increased automation across multiple markets with the help with rugged, robust, reliable systems from Crystal Group. Early adopters and continued investors in AI and ML, military organizations and defense contractors helped to pioneer autonomous vehicles, which rely upon AI and ML capabilities. Critical infrastructure sectors – including power, oil and gas, telecommunications, and more – are undergoing modernization and digitization, and in turn, increasingly relying on AI, ML, and rugged, reliable systems to increase automation, efficiency, safety, and security.

Inter-Cell Interference Coordination (ICIC) is a promising way to improve energy efficiency in wireless networks, especially where small base stations are densely deployed. However, traditional optimization based ICIC schemes suffer from severe performance degradation with complex interference pattern. To address this issue, we propose a Deep Reinforcement Learning with Deterministic Policy and Target (DRL-DPT) framework for ICIC in wireless networks. DRL-DPT overcomes the main obstacles in applying reinforcement learning and deep learning in wireless networks, i.e. continuous state space, continuous action space and convergence. Firstly, a Deep Neural Network (DNN) is involved as the actor to obtain deterministic power control actions in continuous space. Then, to guarantee the convergence, an online training process is presented, which makes use of a dedicated reward function as the target rule and a policy gradient descent algorithm to adjust DNN weights. Experimental results show that the proposed DRL-DPT framework consistently outperforms existing schemes in terms of energy efficiency and throughput under different wireless interference scenarios. More specifically, it improves up to 15% of energy efficiency with faster convergence rate.

Artificial intelligence – also commonly known as AI – has revolutionized the technology world. Companies both inside and outside the tech circle are introducing AI into their work suite. AI takes the basic principles of computing and processing and applies intelligent environment analysis on top of it. For industries, AI analyzes the data they generate and provides them with insights based on its findings. AI can also apply machine learning to examine historical data in order to perform tasks without human input.

In this paper, we propose and study opportunistic contextual bandits - a special case of contextual bandits where the exploration cost varies under different environmental conditions, such as network load or return variation in recommendations. When the exploration cost is low, so is the actual regret of pulling a sub-optimal arm (e.g., trying a suboptimal recommendation). Therefore, intuitively, we could explore more when the exploration cost is relatively low and exploit more when the exploration cost is relatively high. Inspired by this intuition, for opportunistic contextual bandits with Linear payoffs, we propose an Adaptive Upper-Confidence-Bound algorithm (AdaLinUCB) to adaptively balance the exploration-exploitation trade-off for opportunistic learning. We prove that AdaLinUCB achieves O((log T)^2) problem-dependent regret upper bound, which has a smaller coefficient than that of the traditional LinUCB algorithm. Moreover, based on both synthetic and real-world dataset, we show that AdaLinUCB significantly outperforms other contextual bandit algorithms, under large exploration cost fluctuations.

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain.

The founder of Huawei has said that the firm can withstand attempts by foreign governments to shut out the Chinese technology giant. Ren Zhengfei said US was attempting to "crush" his company by encouraging allies not to use Huawei-made equipment. He warned that by turning their back on Huawei they risked falling behind in areas like 5G rollout, which Huawei has helped pioneer in recent years. "There's no way the US can crush us," he told the BBC. "The world cannot leave us because we are more advanced. Even if they persuade more countries not to use us temporarily, we can always scale things down a bit."

To work toward closing the AI gap, the study found several priorities on which Europe should focus. First, the continent must continue the development of a "vibrant ecosystem" of firms that will leverage AI technology. Veteran firms must also move ahead with digital transformations, rather than relying on their younger counterparts. Focus should also remain on the digital single market, which covers marketing, e-commerce, and telecommunications. Finally, by searching for and cultivating a talented and skilled workforce – be it through education or retraining – as well as finding paths on which to "guide societies through the potential disruption" (such as unease about potential unemployment), Europe can and will position itself more advantageously among the industry's international leaders.