In 2016, artificial intelligence (AI) reached its climax. Research and advisory firm Tractica predicted that the annual worldwide AI revenue will grow from $643.7 million in 2016 to $38.8 billion by 2025. The revenue for enterprise AI applications will increase from $358 million in 2016 to $31.2 billion by 2025, representing a compound annual growth rate (CAGR) of 64.3%. Thus, IT and business decision makers must face up to the potentials of AI already today. For each kind of organization this leads to the question, which type of technologies or infrastructure they can leverage to operate an AI-ready enterprise stack.

Initially, say Prisma's co-founders, they'll be offering an SDK for developers wanting to add effects like style transfer and selfie lenses to their own apps -- likely launching an API mid next week. The wave of augmented reality apps that are coming down the smartphone pipe, driven by more powerful hardware and active encouragement from mobile platforms, could also help generate demand for Prisma's effects, reckons Moiseenkov, as they can offer object tracking as well as face tracking via APIs or an SDK. "We want to explore the CV [computer vision] area and help companies also produce a greater user experience with AI -- helping people to communicate easier, to solve their tasks," adds Moiseenkov. The app achieved its effects not by applying filters to the photo but by utilizing neural networks and deep learning to process the original photo in the chosen style -- generating a new image that combined both input sources.

The startup behind the Prisma style transfer app is shifting focus onto the b2b space, building tools for developers that draw on its expertise using neural networks and deep learning technology to power visual effects on mobile devices. It's launched a new website, Prismalabs.ai, detailing this new offering. Initially, say Prisma's co-founders, they'll be offering an SDK for developers wanting to add effects like style transfer and selfie lenses to their own apps -- likely launching an API mid next week. Then, in the "next month or so", they also plan to offer another service for developers wanting help to port their code to mobile. This was, after all, how the co-founders originally came up with the idea for the Prisma app -- having seen a style transfer effect working (slowly) on a desktop computer and realized how much potential it would have if it could be made to work in near real-time on mobile.

Where you start depends on what you already know. The prerequisites for understanding and applying deep learning are linear algebra, calculus and statistics, as well as programming and some machine learning. In the case of Deeplearning4j, you should know Java well and be comfortable with tools like the IntelliJ IDE and the automated build tool Maven. Below you'll find a list of resources. The sections are roughly organized in the order they will be useful.

Do you want to explore the various arenas of machine learning and deep learning by creating insightful and interesting projects? If yes, then this Learning Path is ideal for you! Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Machine learning and deep learning gives you unimaginably powerful insights into data. Both of these fields are increasingly pervasive in the modern data-driven world.

The State Government of South Australia announced their contract with Solar Reserve to build a 150MW solar thermal power plant for Port Augusta, South Australia. This is an addition to the state-owned gas plant and the world's largest lithium ion battery recently announced contract with Tesla. According to State Premier Jay Weatherhill, this solar thermal plant "biggest of its kind in the world" and "will help make our energy grid more secure." This Aurora Solar Energy Project will be ready in 2020 and is expected to supply 100% of the government's anticipated power needs. IntelAI has been collaborating with NASA FDL's Lunar Water and Volatiles team in a 9-week program this year. Working with Intel's team and their deep learning technologies, Intel Nervana, NASA is looking to accelerate the development of a software solution to take AI to the moon.

For example, when Google DeepMind's AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go earlier this year, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won. Another algorithmic approach from the early machine-learning crowd, Artificial Neural Networks, came and mostly went over the decades. Today, image recognition by machines trained via deep learning in some scenarios is better than humans, and that ranges from cats to identifying indicators for cancer in blood and tumors in MRI scans. Deep Learning has enabled many practical applications of Machine Learning and by extension the overall field of AI.

To learn more about conversational AI, check out Yishay Carmiel's session Applications of neural-based models for conversational speech at the Artificial Intelligence Conference in San Francisco, Sept. 17-20, 2017. The dream of speech recognition is a system that truly understands humans speaking--in different environments, with a variety of accents and languages. For decades, people tackled this problem with no success. Pinpointing effective strategies for creating such a system seemed impossible. In the past years, however, breakthroughs in AI and deep learning have changed everything in the quest for speech recognition.

This course is for the absolute beginner to Artificial Intelligence (AI), Machine Learning, Deep Learning, and Data Science. If you are feeling overwhelmed by either the tsunami of data that you are tasked with trying to make sense out of, or overwhelmed by the tsunami of media coverage around Artificial Intelligence, Deep Learning, Data Science, and Machine Learning, I am here to share a competitive advantage. There is an AI and Data Discovery platform that can be constructed and configured with clicks instead of code. The disruptive power of this is that Artificial Intelligence is now available to the masses, and not just to the quants and data scientists among us. You can now not only catch the competition, but leap frog past them, by leveraging Salesforce Einstein as your On-Demand Data Scientist.

As a researcher on Computer Vision, I come across new blogs and tutorials on ML (Machine Learning) every day. However, most of them are just focussing on introducing the syntax and the terminology relavant to the field. While people are able to copy paste and run the code in these tutorials and feel that working in ML is really not that hard, it doesn't help them at all in using ML for their own purposes. For example, they never introduce you to how you can run the same algorithm on your own dataset. Or, how do you get the dataset if you want to solve a problem.