Classic work on associative memories following Hopfield 1982 focused on issues of capacity and performance, usually considering random memories embedded as stable attractors of a dynamical system. Such work usually led to capacities which scale linearly with the size of the network. The present work proposes a neural architecture which is able to reach exponential capacities at the cost of introducing specific low-dimensional structure into the stored patterns. The authors propose a bi-partite architecture of pattern and constraint neurons corresponding to patterns and clusters, and a two-tiered algorithm, based on within and between-module processing aimed at retrieving clean versions of noise corrupted the patterns. The intra-module algorithm operates iteratively based on forward and backward iterations, based on a belief variable.

Founded in 2008, by Harsh Jain and Bhavit Sheth, Dream Sports is a sports technology company with brands such as Dream 11, FanCode, DreamX, and DreamSetGo. "Across all our brands at Dream Sports, we implemented data-driven engineering with ML, AI and analytics very early in our automation journey, even though they were considered relatively new in the tech industry." In a recent conversation with Analytics India Magazine(AIM), Amit Sharma, Chief Technology Officer of Dream Sports explained how the sports technology company is leveraging artificial intelligence(AI) and machine learning(ML) technologies to better its product and services delivery for users. An alumnus of Santa Clara University and the University of Massachusetts Dartmouth, Amit has earlier worked at Netflix and Yahoo. Amit: At Dream Sports, one of our core culture pillars is being data-obsessed, and every decision we make is backed by data and technology.

The world is the witness of how artificial intelligence has transformed the digital landscape for good. The technology is crucial enough that every organisation must shelter it. It has the power to yield better outcomes for an organisation while enhancing efficiency and productivity. Therefore, its adoption becomes must for those who desire to excel with technological excellence. Amit Gautam, who has been an innovator all along and has patents in various fields including Career Ethernet, HCI, Sensor networks and more, has brought a set of technologies that are crucial for the adoption of AI.

You are face to face with a robot on a narrow sidewalk. You are in the city of San Francisco. Two LED eyes stare back. Are you facing off against R2-D2 or a T-1000 from the Terminator? The robot is named Serve, and it is a shopping cart-shaped invention that delivers food and packages across town.

In the last year, the hype around AI has been deafening. Despite a long hiatus in the AI research community without any major wins, we've made some amazing progress lately. From headlines about AI protecting our digital identities to driving our cars to even diagnosing our maladies, it seems like AI has been everywhere. Unless you have been living under a rock, chances are your feed has been littered with references to deep learning, convolutional neural networks (CNNs), recurrent neural nets (RNNs), or TensorFlow, each accompanied by a bold proclamation technology is about to solve everything from world hunger to health care. Ok, if you're not breaking out the champagne, I understand.

Another way to expand your pool of "qualified" candidates is to look for individuals with nontraditional backgrounds. When companies are looking to hire talent like operations or salespeople, industry-specific experience is a definite benefit. While competition for the best candidate in these fields is still intense, the arenas are well-established enough to offer a more robust pool of suitable candidates, so the challenge is more manageable. But when it comes to a field as new as ML and AI, limiting your search to individuals with years of experience will quickly whittle the applicant pool down to next to nothing. Unless you're ready to shell out $500k as a starting salary, good luck trying to outcompete the Facebooks and Googles of the world. Instead, recruiters should seek out people who have crossed over industries, changed roles, or have, through a diversity of experiences, demonstrated that they are connectors and integrators. These candidates offer the adaptive thinking necessary to build the future of artificial intelligence as it unfurls. And, most people will completely overlook them.

We've been told that the self-driving car will lead to better cities, that it could dock with your apartment to add more space, or be an office on the way to work. With companies like Uber investing heavily in the technology, it's easy to see the future of these vehicles as a shared urban resource, as readily available as cabs once were. But for the most part, none of this would fundamentally change life in cities--other than never needing to find a parking spot again. San Francisco design firm NewDealDesign--known for commercial projects like the Fitbit and unabashed provocations like Scrip--has a different view of the future. In a concept it calls Autonomics, the studio considers how autonomous, electric vehicles might change suburban and even rural life.

Even if you're not a machinist, you've probably had a crisis at home or with your car where you only needed one weird, tiny screw to fix the problem. You bring the part to a store and stand in line only to discover the part isn't in stock. So they call it in, and the part that arrives maybe a week later is the wrong one. Then the whole process starts over again. It all sounds terribly inefficient!

Kristina: We're seeing more businesses use chatbots for customer service. How are consumers using chatbots? Amit Sharma, CEO, Narvar: Shoppers have an increasing appetite for chatbots and AI-powered technology, given the customized, data-driven, contextual experience they provide. An analysis of conversations with the Narvar chatbot on Facebook Messenger found that 65 percent of people communicate with bots as they would a human by acknowledging the bot with a thank you or thumbs up. Communicating with consumers in a natural, human way without being in the same physical location is a key benefit of chatbots and conversational tools.

Mathematics has always attracted those brilliant few who see more than ones and zeroes. Some mathematicians -- like Alon Amit co-founder and VP of product at the marketing analytics platform Origami Logic -- look beyond figures and see stories. These stories are the bread and butter of a data scientists, individuals who thrive in the sea of data that has swallowed many marketers by bringing context to numbers. "A lot of the work of data science, machine learning, algorithms is not in the making of algorithms themselves, but in the handling and transforming of data [and] getting data to the right place and making sure it's clean and consistent," Amit says. This is the supreme challenge of the modern marketer, taking disparate datasets from multiple sources and channels, and making that data make sense.