We present VASTA, a novel vision and language-assisted Programming By Demonstration (PBD) system for smartphone task automation. Development of a robust PBD automation system requires overcoming three key challenges: first, how to make a particular demonstration robust to positional and visual changes in the user interface (UI) elements; secondly, how to recognize changes in the automation parameters to make the demonstration as generalizable as possible; and thirdly, how to recognize from the user utterance what automation the user wishes to carry out. To address the first challenge, VASTA leverages state-of-the-art computer vision techniques, including object detection and optical character recognition, to accurately label interactions demonstrated by a user, without relying on the underlying UI structures. To address the second and third challenges, VASTA takes advantage of advanced natural language understanding algorithms for analyzing the user utterance to trigger the VASTA automation scripts, and to determine the automation parameters for generalization. We run an initial user study that demonstrates the effectiveness of VASTA at clustering user utterances, understanding changes in the automation parameters, detecting desired UI elements, and, most importantly, automating various tasks. A demo video of the system is available here: http://y2u.be/kr2xE-FixjI

Police in Florida believe recordings from a murder suspect's Amazon Echo may contain crucial information as they investigate an alleged argument at the man's home that ended in his girlfriend's death. Adam Reechard Crespo, 43, is charged with murder in connection to the July death of Silvia Galva, who died after suffering a stab wound to the chest. The Broward County Sheriff's Office believes Crespo's Echo - a smart speaker that connects to the Amazon voice-activated personal assistant Alexa - may have been a witness to the crime and obtained search warrants for all the device's recordings. Hallandale Beach Police Department spokesman Sgt Pedro Abut told the Sun-Sentinel that the department has received the recordings and is "in the process of analysing the information that was sent to us". The police department did not immediately return NBC News' request for comment on Saturday.

From Google and Amazon to Apple and Microsoft, every major tech company is dedicating resources to breakthroughs in artificial intelligence. Personal assistants like Siri and Alexa have made AI a part of our daily lives. Meanwhile, revolutionary breakthroughs like self-driving cars may not be the norm, but are certainly within reach. As the big guys scramble to infuse their products with artificial intelligence, other companies are hard at work developing their own intelligent technology and services. Here are 32 artificial intelligence companies and AI startups you may not know today, but you will tomorrow.

Facebook has been the Uber of 2018. They had a negative breaking headline every other week, centered around issues of privacy, social engineering, and national security. The reason for this is the decade long dissemination of content without any regulation under the legal construct for a platform. By not acknowledging itself as a media company they evaded all the rules that come along with it. Just think, would content flow with such irreverence on any other traditional media platform, and that too without any editorial oversight? Even now the reason for the outrage is because half of the country was pissed at the election result.

There is for all intents and purposes no industry that has stayed immaculate by the effect of Artificial Intelligence, be it Education, e-Commerce, Agriculture or Employment. The savvy approach it equips any business with, without a doubt empowers these organizations to be progressively effective at providing to their clients. Furthermore, something beyond being one more of the main tech-trends of these years, m-commerce has demonstrated to be a growing popular modern way for shopping. Artificial Intelligence has allowed m-commerce with new trends in 2019 to make more pleasant and comfortable shopping experience for shoppers. Supported by Artificial Intelligence, the e- commerce and m-commerce platforms are prepared to use the extensive data related to the customer behavior.

Neural network based models for collaborative filtering have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences where variational autoencoders were shown to produce state-of-the-art results. However, there are some potentially problematic characteristics of the current variational autoencoder for CF. The first is the too simplistic prior that VAEs incorporate for learning the latent representations of user preference. The other is the model's inability to learn deeper representations with more than one hidden layer for each network. Our goal is to incorporate appropriate techniques to mitigate the aforementioned problems of variational autoencoder CF and further improve the recommendation performance. Our work is the first to apply flexible priors to collaborative filtering and show that simple priors (in original VAEs) may be too restrictive to fully model user preferences and setting a more flexible prior gives significant gains. We experiment with the VampPrior, originally proposed for image generation, to examine the effect of flexible priors in CF. We also show that VampPriors coupled with gating mechanisms outperform SOTA results including the Variational Autoencoder for Collaborative Filtering by meaningful margins on 2 popular benchmark datasets (MovieLens & Netflix).

Summary: Contextually intelligent, NLP-based interactive assistants are one of the next big things for AI/ML. The tech is already here from recommendation engines. The need to be more efficient and to become AI-augmented in our decision making is now. Getting the contextual awareness is the hard part. Last week we took the position that from a technical standpoint, 'deeply inclusive and contextually sensitive' AI is one of the two'next big things' in AI.

Artificial intelligence (AI) is a technological advancement that has revolutionised the global market completely. More and more companies and governments are investing in AI to aim for a better and more technologically driven future. In order to gain knowledge in this field, young individuals have started opting for artificial intelligence courses, which prove to be very helpful. Companies also provide AI training to their employees to increase their skill sets in attempts to improve their business plans. The year 2018 saw many changes in the trends followed in AI.

Despite the progress that many other industries have made, healthcare is likely to be the one market where AI can truly have an impact that goes beyond convenience and positively affects human lives. Today, more than ever, many technology vendors are making significant investments in AI to ensure they are able to offer products and services that can use the technology. Microsoft, Google, Apple, IBM and Amazon, to name a few, have all adopted and fully committed to AI and are already providing these services to consumers. Anytime a new technology enters healthcare, there are a number of challenges it faces. Common setbacks of AI in healthcare include a lack of data exchange, regulatory compliance requirements and patient and provider adoption.

Regrettably, many leads are left unexplored due to the reality that Salespeople only have so many hours to spend on each lead before moving their attention elsewhere. Knowing this, some organizations hire Business Development Reps (BDRs) to help manage the influx of leads by evaluating, qualifying and nurturing leads before handing them off to a Salesperson to close. Still, the process isn't perfect since BDRs (much like Salespeople) will naturally cherry-pick leads based on their own assumptions. Not to mention, many businesses do not have BDR teams to leverage.