Artificial intelligence and machine learning have emerged in the marketing industry as a pathway to competitive advantage. The best marketers are identifying, evaluating and testing AI-driven applications to make better sense of their data, create personalized customer experiences and accelerate revenue growth. In fact, 84% of marketing organizations either implemented or expanded AI and machine learning experiments and implementations in 2018. While it's no doubt that artificial intelligence has helped marketing teams improve their productivity, with most brands spending between 25 and 43% of their marketing budget on content, it's important to understand how AI can impact this specific department. The truth is, artificial intelligence has actually had an active presence in the content marketing industry for years.

It's been a big week for 5G, the next generation of wireless networks. Samsung announced its first 5G capable phone, the S10, on Wednesday. Qualcomm announced a new 5G modem on Tuesday. But President Trump is aiming higher. "I want 5G, and even 6G, technology in the United States as soon as possible," Trump wrote in a tweet urging carriers to pick up their pace.

Training complex machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach, SwitchML, reduces the volume of exchanged data by aggregating the model updates from multiple workers in the network. We co-design the switch processing with the end-host protocols and ML frameworks to provide a robust, efficient solution that speeds up training by up to 300%, and at least by 20% for a number of real-world benchmark models.

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.