A treasure hunter went missing in the Rocky Mountains, and a computer algorithm found him ... Drive.ai wants to give self-driving cars more brainpower, personality Drive.ai

Under, behind and inside many of the apps we use every day, a revolution is underway. It's a revolution that started decades ago but today is empowering companies to deliver better, smarter services with greater ease and on broader scales than ever before. At Singularity University's inaugural Global Summit, Neil Jacobstein, chair of Artificial Intelligence and Robotics, provided a primer showing how artificial intelligence literally transforms everything it touches. First of all, it's critical to define the scope of artificial intelligence (AI), which can be categorized into four areas: techniques in pattern recognition, software agency (that is, software that acts like real users), an exponential technology that is accelerating other exponential technologies, and a vision of a future superhuman intelligence (that fortunately hasn't happened yet). Anyone who has seen a science fiction film is likely familiar with this last area, but it's the other three areas where AI is making huge strides at a revolutionary pace.

You can add one more name to the constantly expanding list of companies that want a slice of that autonomous driving pie, as a new company named Drive.ai The new company, which also announced that it has added former General Motors Vice Chairman and Board Member Steve Girsky to its Board of Directors, is looking to put its stamp on the self-driving space with its own deep learning algorithms. These full stack deep learning algorithms, Drive.ai CEO Sameep Tandon says that the team at Drive.ai has been working on these deep learning applications since the company was founded in 2015. For now, the company says it will offer a retrofitted system that can be used in existing vehicle fleets.

Will Artificial Intelligence help Big Data deliver on its promise? One of the major trends I have been researching recently, has been the shift in interest towards Artificial Intelligence (AI) in its multiple forms and guises, and the potential it has to analyse vast quantities of data and quickly derive actionable insights. As we all know, AI, Machine Learning and Deep Learning are not new. However, there has been huge investment in the space in recent years and the ability to automatically apply complex mathematical calculations to Big Data – over and over, faster and faster – is a recent development. With steady advances in digitisation and cheap computing power, no wonder people are excited about the possibilities.

Carol Reiley, president and cofounder of autonomous car tech startup Drive.ai, "How do you create a robot that people can trust? That's what we're working on," says co-founder and president Carol Reiley, 34, a Johns Hopkins University PhD candidate who paused her studies to move west and wrangle her Stanford-trained peers into startup mode. This tidy formula of study hall, garage start-up, technological disruption, is well known and beloved in Silicon Valley. Larry Page and Sergey Brin started Google in a garage near their Palo Alto, Calif.

MUMBAI: Gurgaon-based startup Staqu Technologies has entered into a research partnership with Indraprastha Institute of Information Technology, Delhi to boost research in the field of artificial intelligence and deep learning. "This partnership will be the first of its kind in India as we aim to develop cutting edge deep learning based algorithms to tackle problems currently plaguing consumers worldwide," said Atul Rai, co-founder & CEO of Staqu Technologies in a statement. Experts from both the organizations will be working on creating innovative solutions in the domains of video and image understanding. IIIT Delhi is one of the leading institutes in the field of Artificial Intelligence and computer vision, with Infosys granting it a large amount to advance research and technology. Dr. Chetan Arora of the CSE Department from IIIT Delhi will be the Principal Investigator on this collaboration.

The concept of machine learning is not new. Attempts at systems emulating intelligent behavior, like expert systems, go as far back as the early 1980's. Three latest development are pushing forward "Machine Learning": As in many other fields, development of Machine Learning is also seeing development on algorithms that take advantage of the new hardware capabilities. According Wikipedia, Deep Learning is "a part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations are better than others at simplifying the learning task".

Earlier this year, Nervana Systems CEO Naveen Rao was asked what would happen if Intel began attacking the fast-growing market for chips designed specifically for running "deep learning" software. Now, Rao will be a key player in Intel's attempt to catch up in one of the most promising new silicon markets to emerge since the smartphone. Intel revealed Tuesday that it is buying Nervana and its deep-learning hardware and software for an undisclosed amount. The acquisition marks a departure for Intel and comes at a crucial moment. The company became the world's largest chip maker with a single-minded strategy to make its x86 microprocessors the standard for running a huge swath of applications, from solitaire to massive payroll systems.

You might know Google Deep Dream from the trippy, layered images it produces--some are like a digital cross between Dali and Van Gogh on acid. The Deep Dream Generator is a computer vision platform that allows users to input photos into the program and transform them through an artificial intelligence algorithm. This video by Computerphile, an educational Youtube channel about videos, explains just how Deep Dream works. The platform uses convolutional neural networks--a machine term for a forward-fed artificial neural network where neuron connectivity patterns respond to overlapping regions in the visual field--in order to enhance photo patterns with surreal effects. In simple terms, many levels of neural networks process the images input into the program.

Major medical centers and technology companies recently announced new projects aimed at harnessing artificial learning to improve detection, diagnosis, treatment, and management of diseases. Google's DeepMind Health, a Silicon Valley-based artificial intelligence project, and Moorfields Eye Hospital, a leading center for eye research in London, have teamed up for a five-year project to determine if machine learning can speed up and improve diagnosis of eye diseases by developing machine learning approaches to automatically review eye scans. The deal was announced in July. NVIDIA, the Silicon Valley-based developer of graphics processing unit techniques for use in scientific, engineering, and consumer products, is collaborating with Massachusetts General Hospital in Boston to apply machine learning initially in radiology and pathology, areas rich in images and data. The organizations in April announced they eventually will expand the research into genomics and electronic health records.