New technology devices and apps pop up as abundantly as summer weeds here in Silicon Valley. Chip-enhanced products offer to satisfy almost every need imaginable. Prompts from your smart refrigerator tell you to buy more milk. With a voice command, music plays to facilitate meditation, thanks to your smart -- always on -- helper who listens for your next query from a canister on your kitchen counter; you know, the one with a woman's voice and name. In this glut of offerings, how do you select what is truly useful from what is simply the latest "smart" thing?

DENVER, June 17, 2019 (GLOBE NEWSWIRE) -- RLH Corporation (NYSE:RLH) announced today that they have successfully deployed an industry-first Artificial Intelligence Virtual Agent for Central Reservations to support all 1,400 system hotels. This announcement follows the successful completion of a three-month program pilot originally announced on April 24, 2019. "The results of our pilot have been very positive," said RLH Corporation Director of Telephony Optimization & Analytics, April Weatherly. "We're seeing travelers engage with the technology at a rate higher than we anticipated and are very pleased with the Virtual Agent's ability to resolve traveler questions autonomously, reducing the cost of non-revenue related calls." In concert with the systemwide rollout of the Virtual Agent, new features and capabilities have been deployed to further support travelers.

The Bots Next Door follows the domestic lives of Roni and Rana Robot - a pair of sentient, artificial intelligence droids, trying to get on in a world dominated by humans. The Palo Alto treaty has decreed that creative limiters are now mandatory, to ensure artificial intelligence robots remain focused on their programmed function, and not anything else. But before they can be integrated into society to live among us, all bots must be'assigned' a lifelong menial vocation and'unified' into couples... Recently robo-coupled, trainbot R.O.N.I. and carebot R.A.N.A. are moving out of Skid Ro-bot into a home of their own. Things get worse when Roni unexpectedly finds himself both'unassigned' and able to have creative thoughts, putting Rana's social climbing aspirations in jeopardy.

Synthetic Data is fast becoming an essential component of autonomous driving and computer vision AI systems. By bringing together techniques from the movie and gaming industries (simulation, CGI) together with emerging generative neural networks (GANs, VAE's), we are now able to engineer perfectly-labeled, realistic datasets and simulated environments at scale. There is virtually no incremental cost of additional generated images and since the Synthetic Data is created all the attributes are known to pixel-perfect precision. Key labels such as depth, 3D position and partially obstructed objects are all provided by design. Application of this technology could allow important safety features to be brought to market quickly and cost-effectively, from crash prevention software to predictive maintenance, onboard diagnostics, and location insights.

The AI & Big Data Expo Europe, the leading Artificial Intelligence and big data conference and exhibition event, will take place on June 19 – 20, 2019, at the RAI, Amsterdam. It is a showcase of next generation technologies and strategies from the world of Artificial Intelligence and big data -- an opportunity to explore and discover the practical and successful implementation of AI and big data in driving forward your business in 2019 and beyond. The AI & Big Data Expo will bring together 2,000 visitors over the two days, including IT decision makers, developers and designers, heads of innovation, chief data officers, chief data scientists, brand managers, data analysts, start-ups and innovators, tech providers, C-level executives, and venture capitalists. This track covers the full spectrum of AI technologies -- how they are being developed and the real life scenarios where they are being utilized. Expect to hear about chatbots, visual recognition technologies, robotics, and machine learning.

Apps were smart but they are getting smarter with the addition of newer technologies like AI, AR, VR, and ML. With the enterprise and business facing crucial competition, they are striving to add every possible innovative addition that provides them a competitive edge and something extraordinary than the usual Android apps. Looks like AI has some significant impact on the business and enterprise apps. According to the reports of Statista AI is expected to cross $17 Billion by the end of 2020. I bet you want to be a part of this booming industry.

As electronically stored data grow in daily life, obtaining novel and relevant information becomes challenging in text mining. Thus people have sought statistical methods based on term frequency, matrix algebra, or topic modeling for text mining. Popular topic models have centered on one single text collection, which is deficient for comparative text analyses. We consider a setting where one can partition the corpus into subcollections. Each subcollection shares a common set of topics, but there exists relative variation in topic proportions among collections. Including any prior knowledge about the corpus (e.g. organization structure), we propose the compound latent Dirichlet allocation (cLDA) model, improving on previous work, encouraging generalizability, and depending less on user-input parameters. To identify the parameters of interest in cLDA, we study Markov chain Monte Carlo (MCMC) and variational inference approaches extensively, and suggest an efficient MCMC method. We evaluate cLDA qualitatively and quantitatively using both synthetic and real-world corpora. The usability study on some real-world corpora illustrates the superiority of cLDA to explore the underlying topics automatically but also model their connections and variations across multiple collections.

Multi-hop question answering requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. In this paper, we show that in the multi-hop HotpotQA (Yang et al., 2018) dataset, the examples often contain reasoning shortcuts through which models can directly locate the answer by word-matching the question with a sentence in the context. We demonstrate this issue by constructing adversarial documents that create contradicting answers to the shortcut but do not affect the validity of the original answer. The performance of strong baseline models drops significantly on our adversarial evaluation, indicating that they are indeed exploiting the shortcuts rather than performing multi-hop reasoning. After adversarial training, the baseline's performance improves but is still limited on the adversarial evaluation. Hence, we use a control unit that dynamically attends to the question at different reasoning hops to guide the model's multi-hop reasoning. We show that this 2-hop model trained on the regular data is more robust to the adversaries than the baseline model. After adversarial training, this 2-hop model not only achieves improvements over its counterpart trained on regular data, but also outperforms the adversarially-trained 1-hop baseline. We hope that these insights and initial improvements will motivate the development of new models that combine explicit compositional reasoning with adversarial training.

Just off the top of my head, I would say this is a multilabeling problem, and if it isn't too big, just create an output vector that contains all the possible vector values, and put 0 in the things that don't apply, and train away. I believe a fully connected network will work, and use binary_crossetropy as your loss function and a sigmoid activation in the output layer.

The question is posted on Cross Validated (haven't figured out how to write formulas in reddit). Feel free to leave your comments either here or at Cross Validated.