May 14, 2018 (Heraldkeeper via COMTEX) -- New York, May 14, 2018: Market Research Engine has published a new report titled as "Automotive Artificial Intelligence Market By Technology (Context Awareness, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing); By Offering (Hardware, Software); By Process (Data Mining, Image Recognition, Signal Recognition); By Application (Autonomous Vehicle, Human-Machine Interface, Semi-Autonomous Driving) and by Regional Analysis – Global Forecast by 2017 – 2024". The Automotive Artificial Intelligence Market is segmented on the lines of its offering, application, technology, process and regional. On the basis of offering, the market can be categorised into software and hardware. On the basis of application, Automotive Artificial Intelligence Market can be segmented into Human-Machine Interface, Semi-Autonomous Driving and Autonomous Vehicle. On the basis of technology can be segmented into Deep Learning, Machine Learning, Computer Vision, Context Awareness and Natural Language Processing.

Imagine unleashing the power of artificial intelligence to automate a critical component of biomedical research, expediting life-saving research in the treatment of almost every disease from rare disorders to the common cold. This could soon be a reality, thanks to the fourth Data Science Bowl, a 90-day competition in which, for the very first time, participants trained deep learning models to examine images of cells and identify nuclei, regardless of the experimental setup--and without human intervention. Algorithms developed in this competition could save researchers hundreds of thousands of hours of effort per year. This year, the competition brought together nearly 18,000 global participants, the most ever for the Data Science Bowl. Collectively, they submitted more than 68,000 algorithms and worked an estimated 288,000 hours to automate the vital, but time-consuming, process of nuclei detection.

The company Zebra Medical Vision developed a new platform called Profound, with algorithm-based analysis of all types of medical imaging reports that is able to find every sign of potential conditions such as osteoporosis, breast cancer, aortic aneurysms and many more with a 90 percent accuracy rate. And its deep learning capabilities have been trained to check for hidden symptoms of other diseases that the health care provider may not have been looking for in the first place. Other deep learning networks even earned a 100 percent accuracy score when detecting the presence of some especially lethal forms of breast cancer in biopsy slides.

Not surprisingly, the topics and tools around deep learning (DL) still top the list of big trends, and top-notch research in math and computation are driving progress across vision, speech, and text. Many in the DataWorks audience are already developing cutting-edge deep learning systems, while others are just beginning to start playing with DL. Either way, I suggest attending Magnus Hyttsten's talk on getting started with TensorFlow. As you read this article, a new DL framework might already be baking and being open-sourced. It's getting harder and harder to keep track of all the new DL frameworks and their capabilities.

There are only 300k AI developers all over the world and most of them are still in college and studies have shown that we need millions of AI developers to realize the true potential of the AI. So, the challenge that AI industry is facing is how it can create AI developers fast enough to fulfill that gap. This is what deep cognition is trying to fulfill by developing a platform named Deep Learning Studio. What I believe is that it requires a lot of time almost a year to learn concepts of AI and programming from scratch so that one can build a model to solve the real-world problem but lots of people doesn't have that time because they can't leave their jobs/work and focus on it full time. That is why there are not so much AI developers in the industry.

Chatbot's services could range from a number of things from functional services to fun. Some examples of chatbots can be found in major products like Facebook Messenger, Slack, and Telegram. While some chatbots are designed with sophisticated natural language processing systems, most of the widely adopted chatbots scan and identify keywords within the input and pull a reply with the most matching keyword from its database. Chatbots developed with the most sophisticated algorithms use machine learning techniques to improve their accuracy of their natural language processing capabilities. The end-users' interaction with the chatbot helps it to leverage deep learning programming evolution and better predicts the responses while communicating with the end user.

WIRE)-- 8x8, Inc. (NYSE:EGHT), a leading provider of cloud phone, meeting, collaboration and contact center solutions, today announced the acquisition of MarianaIQ (MIQ), a high-growth Silicon Valley startup, as part of the strategic investments it has been making in AI and Machine Learning. MIQ brings deep learning capabilities to the newly announced X Series to transform both employee and customer experience. The MIQ team, including founders Soumyadeb Mitra and Venkat Nagaswamy, have been leaders in applying AI and deep learning to practical business problems since 2013, and join 8x8 to strengthen AI capabilities for enterprise communications. "8x8 has continuously evolved itself to provide best-in-class enterprise communications to our customers. With the acquisition of MarianaIQ, we are fundamentally transforming how customers and employees interact through one system of engagement, and how companies optimize valuable moments of customer engagement with one set of data in one system of intelligence," said Dejan Deklich, Chief Product Officer, 8x8.

When Google DeepMind researchers trained a neural network to tackle a virtual maze, it spontaneously developed digital equivalents to the specialized neurons called grid cells that mammals use to navigate. Not only did the resulting AI system have superhuman navigation capabilities, the research could provide insight into how our brains work. Grid cells were the subject of the 2014 Nobel Prize in Physiology or Medicine, alongside other navigation-related neurons. These cells are arranged in a lattice of hexagons, and the brain effectively overlays this pattern onto its environment. Whenever the animal crosses a point in space represented by one of the corners these hexagons, a neuron fires, allowing the animal to track its movement.

Deep Learning is a type of Artificial Intelligence where we give the computer the ability to learn, rather than tell it what to learn. Here at MPCR, we look at Deep Learning as a member of multiple fields, if not every field. AI has its roots in Psychology and Biology, and we strive to remain true to those origins when we consider Deep Learning as a Theory of the Brain. However, it is also highly computational and is an important tool for today's Data Scientists. Deep Learning has already begun to answer questions in fields such as Medicine, Biology, Chemistry, and Engineering, and it is gaining momentum.

Very recently, the outbreak of AI-doctored pornographic videos in which the faces of the original actors had been swapped with those of celebrities and politicians caused panic over the implications of artificial intelligence applications blurring the line between what's real and what's not. Similar concerns surfaced when Google demonstrated an AI technology that could create human voice patterns that were indistinguishable from humans at last week's I/O Conference. For the most part, the concerns are well-placed. Thanks to advances in machine learning and deep learning, AI applications are becoming extremely convincing at reproducing human appearance and behavior. There are already several applications that can convincingly synthesize a person's face, voice, handwriting, and even conversation style.