It's hard not to like a Nest Thermostat, but as the folks at Google learned over the years, the primary reason people cite for not buying one is they're too expensive. It's not as slick or sophisticated as the top-of-the-line Nest Learning Thermostat, but it carries enough of that device's DNA to be an excellent value at $130. Buy one and you'll get the familiar round form factor, a bright display with sharp visuals, and the ease of installation and day-to-day use that made the original product such as star. You'll adjust your HVAC system's target temperatures and the new Nest Thermostat's various settings using its outer ring, too. But instead of spinning a mechanism, you'll slide and tap your fingertip on the responsive touch-sensitive surface of the thermostat's outer bezel.

Rasa, the customizable open source machine learning framework to automate text and voice based AI assistants, has released version 2.0 with significant improvements to dialogue management, training data format, and interactive documentation. In addition, the latest release reduces the learning curve to get started while expanding configuration options for advanced users. Rasa Open Source 2.0 simplifies dialogue policy configuration, draws a clearer distinction between policies that use rules and those that use machine learning, and makes it easier to enforce business logic. Previously, rule-based logic in Rasa Open Source was controlled by a combination of 3 or more dialogue policies. The new RulePolicy allows users to implement forms, map actions to intents, and specify fallback logic, using a single policy.

Deep learning has proved an effective means to capture the non-linear associations of user preferences. However, the main drawback of existing deep learning architectures is that they follow a fixed recommendation strategy, ignoring users' real time-feedback. Recent advances of deep reinforcement strategies showed that recommendation policies can be continuously updated while users interact with the system. In doing so, we can learn the optimal policy that fits to users' preferences over the recommendation sessions. The main drawback of deep reinforcement strategies is that are based on predefined and fixed neural architectures. To shed light on how to handle this issue, in this study we first present deep reinforcement learning strategies for recommendation and discuss the main limitations due to the fixed neural architectures. Then, we detail how recent advances on progressive neural architectures are used for consecutive tasks in other research domains. Finally, we present the key challenges to fill the gap between deep reinforcement learning and adaptive neural architectures. We provide guidelines for searching for the best neural architecture based on each user feedback via reinforcement learning, while considering the prediction performance on real-time recommendations and the model complexity.

In this paper, we aim to mine the cue of user preferences in resource-limited recommendation tasks, for which purpose we specifically build a large used car transaction dataset possessing resource-limitation characteristics. Accordingly, we propose an interest-behavior multiplicative network to predict the user's future interaction based on dynamic connections between users and items. To describe the user-item connection dynamically, mutually-recursive recurrent neural networks (MRRNNs) are introduced to capture interactive long-term dependencies, and meantime effective representations of users and items are obtained. To further take the resource limitation into consideration, a resource-limited branch is built to specifically explore the influence of resource variation on user preferences. Finally, mutual information is introduced to measure the similarity between the user action and fused features to predict future interaction, where the fused features come from both MRRNNs and resource-limited branches. We test the performance on the built used car transaction dataset as well as the Tmall dataset, and the experimental results verify the effectiveness of our framework.

Online recommendation is an essential functionality across a variety of services, including e-commerce and video streaming, where items to buy, watch, or read are suggested to users. Justifying recommendations, i.e., explaining why a user might like the recommended item, has been shown to improve user satisfaction and persuasiveness of the recommendation. In this paper, we develop a method for generating post-hoc justifications that can be applied to the output of any recommendation algorithm. Existing post-hoc methods are often limited in providing diverse justifications, as they either use only one of many available types of input data, or rely on the predefined templates. We address these limitations of earlier approaches by developing J-Recs, a method for producing concise and diverse justifications. J-Recs is a recommendation model-agnostic method that generates diverse justifications based on various types of product and user data (e.g., purchase history and product attributes). The challenge of jointly processing multiple types of data is addressed by designing a principled graph-based approach for justification generation. In addition to theoretical analysis, we present an extensive evaluation on synthetic and real-world data. Our results show that J-Recs satisfies desirable properties of justifications, and efficiently produces effective justifications, matching user preferences up to 20% more accurately than baselines.

Artificial intelligence (AI) is everywhere. In a typical day, people likely use AI multiple times without even knowing it: Alexa and Siri, Google Maps, Uber and Lyft, autopilot on commercial flights, spam filters, and smart email categorization (so anyone using Gmail, Yahoo, or Office 365/outlook), mobile check deposits, plagiarism checkers, online searches, personalized recommendations, Facebook, Instagram, and Pinterest are all examples of AI. But what happens when people are being introduced to a new AI technology? How likely are they to trust the new technology? With an interdisciplinary team of researchers from the University of Kansas, we set to find out.

Machine learning is one of the most promising technologies for the coming decades, a branch of artificial intelligence that studies how to make machines learn, and that could completely change the world as we know it today. If we manage to develop machines capable of learning by themselves, they will probably do so much faster than humans. They will also be able to make more efficient use of the information acquired, getting closer and closer to executive intelligence. Machine learning promises to bring more intelligence to all the software of the machines and devices that surround us, from a smartphone to a coffee machine or a home device, such as Amazon's Echo or Google Home. With that ability to learn, machines will gradually acquire new skills and abilities, achieving previously unthinkable things.

Amazon is making it easier for you to connect devices to your Alexa ecosystem with its latest feature. When Device Discovery is enabled on your Alexa app, you can go to the More section then Add a Device. You'll see products on your WiFi network that you can link to the voice assistant. If you'd rather Alexa didn't link to a certain device that it finds, you don't need to do anything else. Otherwise, setting it up should be straightforward.

NLPNatural Language Processing is used so commonly today that we take it for granted. We use it with Amazon's Alexa, Google Home and Translate, voice-to-text dictation on our phones etc. It is practically everywhere and makes our interactions with devices faster, convenient, and easier. NLP is a branch of Artificial Intelligence (AI) that uses Machine Learning (ML) to understand a text or a voice command's meaning. "For instance, people ask questions in different ways (word choice, tone of voice, etc.) - One customer might ask," Can you update me on my last order status?",

Conversational recommendation systems have recently gain a lot of attention, as users can continuously interact with the system over multiple conversational turns. However, conversational recommendation systems are based on complex neural architectures, thus the training cost of such models is high. To shed light on the high computational training time of state-of-the art conversational models, we examine five representative strategies and demonstrate this issue. Furthermore, we discuss possible ways to cope with the high training cost following knowledge distillation strategies, where we detail the key challenges to reduce the online inference time of the high number of model parameters in conversational recommendation systems