Artificial intelligence is no longer a sci-fi movie trope--it's here and it's at the forefront of the fourth industrial revolution. Businesses are wising up, hiring smart and investing in artificial intelligence, a Salesforce director told Santa Rosa Junior College students and World Affairs Council of Sonoma County (WACSC) members Oct. 25. Jonathan Miranda is the director of strategy of Salesforce's technology division.That's just his job title, though. He is a futurist who studies where technology trends are headed. "Our team is aimed at the next two, three, five years" he said.

A developer has trained artificial intelligence software to be able to dream up its own Halloween masks and the results are downright terrifying. Matt Reed, a creative technologist at Nashville-based advertising agency Red Pepper, trained a neural network by supplying it with 5,000 images of popular Halloween masks, like Jason Voorhees from the 1980's horror film'Friday the 13th.' A developer has trained a neural network to be able to create its own Halloween masks. The AI uses a general adversarial network, a type of algorithm that is used in automated machine learning software. It'pits two networks against each other' to be able to improve over time.

It is driven by proprietary quantum machine learning algorithms and methodologies and provides technology services to address complex AI optimization problems. Based on the Quantum Intelligence as a Service (QIaaS) hybridized platform architecture, QIT supports quantum computers, classical computers, and digital quantum simulators on the hardware layer. Current providers include D-Wave, AWS and AliCloud, etc. With QIT in place, business users will be able to conduct R&D and effectively build applications online through QIaaS. Inside QIT, they can interact directly with API layers for authentication, modeling and prediction, and evoke machine learning algorithms from classical models and Qindom's QI-enhanced ones.

Submissions should generally be about Artificial Intelligence and its applications. If you think your submission could be of interest to the community, feel free to post it. Please note that just because something else is a technology buzzword (e.g. We've had such a problem with blockchain posts that they will now need to be manually approved by a mod before they become visible. If your post is primarily about another technology (like blockchain), please make the relation to AI abundantly and immediately clear (e.g. through writing a comment).

Now, an AI system developed by MIT students Ziv Epstein and Michael Groh adds spooky figures to your photos, no reality TV show required. Called AI Spirits, it's a website that lets you upload empty landscapes, to be haunted with humanoid shadows. "In the world all around us, many people have lived full lives and passed on. Yet they are still with us emotionally, spiritually, and intellectually," says Epstein. "In the business of daily life, we can forget them and only see the empty scenes all around us. AI Spirits is a visualization of saudade: the presence of absence."

Halloween is no ordinary day. It is a day of fright and horror! And what is more terrifying than the development of AI? Don't agree? The most well-known stories are about Artificial Super Intelligence (ASI) taking over our decisions, rendering us humans obsolete, or eradicating us all together. We have HAL from 2001: A Space Odyssey and the terminators as good examples. But the most horrifying story I ever read was the one about the Paperclip maximizer.

Compared to a few years ago, the AI market is starting to solidify around real-world applications with the pace of change being faster than it has ever been before, as startups and technology providers rush to create platforms and targeted niche solutions for solving specific enterprise problems. The industry is churning and evolving quickly as merger and acquisition (M&A) activities abound, and it is homing in on areas of focus. Tractica's 2018 AI ecosystem report covers a wide range of companies in the growing AI space, drawing on Tractica's internal knowledge base of the competitive environment, as well as external sources. Tractica has identified and categorized a total of 1,000 AI companies and provided more in-depth profiles of 200 key industry players that our analysis has revealed to be the most notable and representative examples of AI technology providers, solution providers, platforms, service providers, hardware vendors, and other players who each in their own way are helping to propel the sector forward. These companies span the globe and cover a spectrum of technologies and end-market segments.

For one, it has all the smarts of the Google Assistant built into its interface, so you can ask it questions, play games, set reminders, broadcast messages, and access smart home controls with your voice. And with a 7-inch display, it's also much smaller than some of the other smart displays that have come out in recent months, so it fits into tight corners or on narrow countertops much better than its 8- or 10-inch counterparts. But it's the Home Hub's abilities as a digital photo frame that makes it so appealing to me. Digital photo frames were all the rage a decade ago, though they were often fussy to set up and maintain. You'd have to either tether it to your computer with a USB cable to transfer over files or load up an SD Card whenever you wanted to add or change the images on display.

Sound source separation has attracted attention from Music Information Retrieval(MIR) researchers, since it is related to many MIR tasks such as automatic lyric transcription, singer identification, and voice conversion. In this paper, we propose an intuitive spectrogram-based model for source separation by adapting U-Net. We call it Spectrogram-Channels U-Net, which means each channel of the output corresponds to the spectrogram of separated source itself. The proposed model can be used for not only singing voice separation but also multi-instrument separation by changing only the number of output channels. In addition, we propose a loss function that balances volumes between different sources. Finally, we yield performance that is state-of-the-art on both separation tasks.

Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user's immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like GRU4REC, factorized Markov model approaches such as FISM or FOSSIL, as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today's more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms.