Augmented Reality: Where Mind and Matter Meet
February 7, 2011 – 4:14 am | 7 Comments

Costs can be lowered not only because augmented reality is visual and intuitive but also it can be configured at will. Product returns will be low when customers are able to virtually experience their ability to meet their needs. Transaction costs will be lower as searches can be done virtually on mobile or computer screens. Marketing and advertising costs will be lowered as customers share the immersive content that goes with them. Complex products are easier to sell when their utility is simulated in real life situations with ease. New products can be launched more easily as their use cases can be tested virtually and successful solutions communicated to customers in the context of their own environments and activities.

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Home » Artificial Intelligence, Blogs

Knowing the unknown by digging deep

Submitted by on April 10, 2018 – 4:27 pmNo Comment

Kishore Jethanandani

Deep learning, referred to as neural network algorithms, is a lot like solving a crossword puzzle–the unknowns in gargantuan data stores are knowable only by their relationships with the known. Unsupervised deep learning goes further and does not presume, at the outset, any knowledge of the interdependencies in the data.

Supervised deep learning is analogous to searching for an undersea destination like an oil well with the knowledge of the coastline alone. It reads the known relationships in the geophysical data in the layers underneath the seashore to reach, progressively, the oil well. Unsupervised learning first establishes whether a relationship exists between the contours of the coastline and the subterranean topography.

We spoke to Dr. Charles H Martin, a long-time expert in machine learning and the founder of Calculation Consulting, about the prospects for enterprise applications of supervised and unsupervised deep learning. “Many in the business world recognize the vast potential of applications of deep learning and the technology has matured for widespread adoption,” Dr. Martin surmised. “The most hospitable culture for machine learning is scientific and open to recurring experimentation with ideas and evolving business models, the legacy enterprise fixation on engineering and static processes is a barrier to its progress,” Dr. Martin underscored.

Unstructured data abounds, and the familiar methods of analyzing them with categories and correlations do not necessarily exist. The size and variety of such databases can elude modeling. These unstructured databases have valuable information like social media conversations about brands, video from traffic cameras, sensor data of factory equipment, or trading data from exchanges that are akin to finding a needle in a haystack. Deep learning algorithms find the brand value from positive and negative remarks on social media, elusive fugitives in the video from traffic cameras, the failing equipment from the factory data, or the investment opportunity in the trading data.

“Unsupervised deep learning helps in detecting patterns and hypothesis formulation while supervised deep learning is for hypothesis testing and deeper exploration,” Dr. Martin concluded.  “Unsupervised deep learning has proved to be useful for fraud detection, and oil exploration—anomalies in the data point to cybercrime and oil respectively,” he explained. “The prediction of corporate performance using granular data such as satellite imagery of traffic in the parking lots of retail companies is an example of the second generation of supervised deep learning,” Dr. Martin revealed.

Early detection of illnesses from medical imaging is one category of problems that deep learning is well suited to address. Citing the example of COPD (Chronic Obstructive Pulmonary Disease), Dave Sullivan, the CEO of Ersatz Labs, a cloud-based deep learning company based in San Francisco, told us, “the imaging data shows nodules and not all of them indicate COPD. It is hard for even a trained eye to tell one from another. Deep learning techniques evolve as they are calibrated and recalibrated (trained) on vast volumes of data gathered in the past, and they learn to distinguish with a high degree of accuracy for individual cases.”

Clarifai has democratized access to its deep learning with its API, which allows holders of data to analyze and benefit from the insights.  We spoke to Matthew Zeiler, the CEO and Founder of Clarifai, to understand how its partners use the technology.  One of them is France-based i-nside.com, a healthcare company, which employs smartphones to conduct routine examinations of the mouth, ear, and throat to generate data for diagnosis. “In developing countries where doctors are scarce, the analysis of the data points to therapies that are reliable,” Zeiler told us. “In developed countries, the analysis of the data supports the judgment of doctors, and they have reported satisfactory results,” Zeiler added.

Enterprise is not the only place where deep learning has found a home—consumer applications like Google Now, Microsoft’s Cortana and Assistant are available in the market. Folks are often anxious and distracted, at work or play, when they are unable to keep track of critical events that could affect them or their family. Home surveillance watches pets, the return of young children from school, elderly relatives falling, the arrival of critical packages and more. What matters is an alert on an unusual event. Camio uses the camera of a handheld phone or any other home device like a computer to capture video of happenings at home. When something irregular happens, IFTTT sends alerts.

Deep learning mimics the neurons of the brain to sift the meaningful relationships otherwise lost in the clutter of humongous streams or data stores. Machines can do it faster when the correlations are known. When they are unknown, it helps to discern the patterns before deciding to invest time in deeper investigations.

 

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