On Wednesday, at Amazon's re:MARS conference, the tech giant announced that it's making Alexa more conversational so the voice assistant can fluidly hop from one topic to the next without you having to call out "Alexa" before each command. Dubbed Alexa Conversations, part of the new rollout of Alexa capabilities includes the option for Alexa to plan a night out for you, according to Rohit Prasad, vice president and head scientist for Alexa at the artificial intelligence event in Las Vegas. Based on the demo of Alexa's upcoming abilities, Amazon is working with several companies to bring the new features to life, including the movie ticket service Atom Tickets, ride-hailing company Uber and dinner reservation app OpenTable. During a display of how the new technology will work, a human voice asked Alexa for Saturday showtimes for the movie "Dark Phoenix." Apple unveils $1,000 Pro Display stand.

New details about Google's much-anticipated Stadia game streaming service have leaked just hours before the firm is set to host an event about the platform. Stadia is reportedly set to launch in November and users will be able to choose between two different subscription plans, according to Canadian news site La Presse. For $11.99 CAD (roughly $9) a month, users will get access to some games with 4K resolution that run at 60 frames per second. Google will also launch a free subscription plan, called'Stadia Base,' that'll give users access to games with 1080p resolution. Stadia builds on Google's'Project Stream' initiative, which let users play high-quality games in Google Chrome.

"In the past, large volumes of data made us sweat". So said Pari Bajpay, vice president of Next Generation Enablement at CenturyLink, during a presentation titled "Can AI deliver its promise of a cost-effective, improved experience in telecom?" at the TM Forum's recent Digital Transformation World event in Nice. "We didn't have the networking, compute and storage capacity to cope. A lot of the data would be turned off and you would only work on the critical aspects of the data because what you had on the other end of it was humans that could not process such large volumes," noted Bajpay. However, as big data technology has matured, Bajpay and his team at CenturyLink have grappled with the issue and are now leveraging AI to extract more value from their data.

Continuing with the previous story, in this post we are going to go over an example of text preparation of the sentiment analysis of a movie review dataset. The dataset is classified into positive reviews and negative review; all positive reviews are stored in pos directory, and all negative in neg directory. Each review is in a separate file. First, we need to split the data into training data and testing data prior to any data preparation. This means that any knowledge in the test set that could help us better prepare the data is unavailable during the preparation of the data and the training of the model.

In October of 2018, Christie's auctioned off the first work of art ever created by an algorithm. The painting, titled Edmond de Belamy, was a portrait of a gentleman dressed in black. It was expected to sell for around $10,000, and instead garnered a staggering $432,500. Just a few months before that, Nature Morte, a contemporary art gallery in New Delhi, held an exhibition that featured seven artists working with artificial intelligence. AI-written novels, choreographers predicting dance sequences, and other forms of machine learning are also being readily explored within the creative arts.

Hollywood legend Robert Downey Jr. is putting down his Iron Man suit and taking up the bigger challenge of cleaning up the Earth. Iron Man's on-screen nemesis Thanos had a radical way of reducing humanity's impact on the Earth, but Downey Jr. hopes to use a more humane solution. Appearing on-stage at Amazon's new re:MARS (Machine Learning, Automation, Robotics and Space) conference in Las Vegas, Downey Jr. launched an initiative called Footprint Coalition. "I don't pretend to understand the complexities we face as a species, just because I portrayed a genius in my professional life. My scholastic achievement peaked at a correctional finishing," Downey Jr. joked, in reference to being incarcerated on drug charges and never making it past high school.

This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in the different categories. Her preferences about product attributes as well as her price sensitivity vary across products and are in general correlated across products. We build on techniques from the machine learning literature on probabilistic models of matrix factorization, extending the methods to account for time-varying product attributes and products going out of stock. We evaluate the performance of the model using held-out data from weeks with price changes or out of stock products. We show that our model improves over traditional modeling approaches that consider each category in isolation. One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data. Using held-out data, we show that our model can accurately distinguish which consumers are most price sensitive to a given product. We consider counterfactuals such as personally targeted price discounts, showing that using a richer model such as the one we propose substantially increases the benefits of personalization in discounts.

Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output.

IBM (NYSE:IBM) has announced the launch of Watson Ads Builder, an AI-powered ad developer that creates a dialogue between the brand and consumer. The platform employs natural language processing to enable fast and accurate conversation with the consumer. "Consumer expectations are shifting, and people expect the ability to communicate with brands on demand. Watson Ads Builder can change where, when and how brands connect with consumers - helping marketers increase loyalty and purchase consideration." Accenture (NYSE:ACN) has teamed with auto company Faurecia and tech firm Affectiva to develop the Connected Car Lab - "a digital product and service innovation facility that fits into a car ... to ideate, test and develop applications and experiences for the car cabin of the future".

AI is the de facto gold rush at the moment. Venture capitalists say AI startups in Thailand are in the initial stage of the AI journey. Artificial intelligence (AI) is one of the hottest buzzwords in the startup scene, with AI companies attracting 15-50% more money in funding rounds. Even larger corporations, many not even tech companies, must still pay lip service and claim to use AI or risk looking like they are unprepared for an AI-driven future. But how much of this so-called AI technology is actually of any value and not just empty buzzwords?