global bigdata conference


Global Bigdata Conference

#artificialintelligence

"We believe that current computing solutions don't stack up for running neural networks (i.e., deep learning) at scale in resource-constrained environments," said Orr Danon, CEO, Hailo Technologies. "Our observation is that the key deficiency is in the architecture of the computer, which was designed for running classical rule-based software. With our technology, it will be possible to bring state-of-the-art deep learning into devices outside the data center at reasonable power and cost. We believe this will enable many interesting use cases, automotive being a leading one."


Global Bigdata Conference

#artificialintelligence

Good results come from a brain-inspired system that uses the IBM TrueNorth neuromorphic chips together with a pair of vision sensors that act like eyes. Together the system can respond to changes in the environment to provide imagery in stereo with a sense of depth. Simply put, they're able to hone in on the action, and ignore extraneous visual noise. In the research paper "A Low Power, High Throughput, Fully Event-Based Stereo System" the team reported results of 200x less power per pixel than a comparable DVS system while achieving competitive accuracies.


Global Bigdata Conference

#artificialintelligence

Tom, a recent job candidate, had no idea how prevalent artificial intelligence (AI) had become until he was deep into his job search. Even though he wondered at times what he could do to get his résumé in front of more live hiring leaders, bots that he had encountered were making the application process faster and surprisingly fun. It's great news for employers, as HR experts are constantly looking for new technology to increase productivity and improve the candidate experience. According to a Korn Ferry Global survey, of the nearly 800 HR professionals surveyed, 63% said that AI has already changed the way recruiting is done today. Let's take a look at a few successful AI implementations and see how job seekers can keep up with evolving technology.


Global Bigdata Conference

#artificialintelligence

The term "big data" gets thrown around a lot, especially taking into account its importance for driving AI technology. Finding ways to build scalable systems that provide valuable insights into what you're doing well and what you could be doing better is imperative to maintain a competitive edge. And, as big data, artificial intelligence and machine learning become more advanced and interconnected each year, these scalable systems become more and more valuable. When PicsArt was founded in 2011, the online landscape and the world of data collection, management and analysis were much less sophisticated. Since then, many startups have risen while others have faltered, and those that have found success were largely companies that were able to adapt to an increasingly data-driven marketplace.


Global Bigdata Conference

#artificialintelligence

A Chinese aphorism says that "the fire burns highest when everyone adds wood to it." It's an apt way to describe the way that industrial design and product development are becoming a collaborative undertaking. Cities like Shenzhen, long known as factory towns that churn out low-end toys and shoes, are embracing a new identity as creative meccas for design. This trend is gathering steam worldwide, for one main reason: design tools are starting to function less like inanimate objects and more like colleagues or assistants. As people and machines begin working together in new ways, the field of design will turn into a team sport, one where human ingenuity combines with artificial intelligence and automation to broaden the possibilities of how society shapes the world.


Global Bigdata Conference

#artificialintelligence

Machine learning offers great potential for business and for researchers, unearthing unseen patterns in data, making inferences, and with boosting artificial intelligence. But what exactly is machine learning and what can it do?


Global Bigdata Conference

#artificialintelligence

But, what if these devices could also learn about you, hold a conversation, and interact with you on an entirely new level? The future of personal assistants is changing, and artificial intelligence is behind the evolving technology. We've seen it in Hollywood entertainment for many years, most recently with the Netflix Original Series, Black Mirror. The problem with each of these "smart assistants" is that it's not smart for you. At least that's what Voss believes.


Global Bigdata Conference

#artificialintelligence

Describing deep learning as a black box is not meant to denigrate the practice. After, all, we're thrilled that, when we build a convolutional neural network with hundreds of input variables and more than a thousand hidden layers (as the biggest CNNs are), it just works. We don't exactly know how it works, but we're grateful that it does work. If we had we been required to explicitly code a program to do the same thing as the CNN does, it likely would be a complete disaster. We simply could not build the decision-making systems we're building today without the benefit of self-learning machines.


Global Bigdata Conference

#artificialintelligence

Bedi says one way to do this is to look at whether an activity has a ranking, rating or forecast. It it does it's a candidate for machine learning and building up that competency. That means not using the same tools to solve business problems they have always used. Consequently, CIOs have to move from being technology experts into sales people who can present the benefits of new ways of doing things effectively. As those changes can be threatening to businesses CIOs need to be able to help their colleagues through the change journey as they promise a better future.


Global Bigdata Conference

#artificialintelligence

To help get a better understanding of how machine learning algorithms arrive at their decisions, the IBM team created a system for "contrastive explanations" - looking for information that was missing in order to better understand how a machine learning model arrived at its conclusion. What this means in practice, is that, for example, if a machine learning model is identifying photos of a dog, this method can be used to show not only what the machine model is using to identify a dog (like fur and eyes) but also what things have to be absent for the model to identify a dog (like it doesn't have wings.)