Collaborating Authors

Deep Learning for Business Managers: Neural Networks in R


You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right? You've found the right Neural Networks course! Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in R using Keras and Tensorflow libraries and analyze their results. How this course will help you?

Exploring the transformational impact of AI and advanced analytics


AI and advanced analytics can have a transformational impact on every aspect of a business, from the contact centre or supply chain to the overall business strategy. With the new challenges caused by coronavirus, companies are in a growing need of more advice, more data and visibility to minimise the business impact of the virus. However, long before the disruption caused by Covid-19, data was recognised as an essential asset in delivering improved customer service. And yet, businesses of all sizes have continued to struggle with gaining more tangible value from their vast hoards of data to improve the employee and customer experience. Data silos, creaking legacy systems and fast-paced, agile competitors have made the need to harness an organisations data to drive value of paramount importance.

When it Comes to Neural Networks, We've Only Scratched the Surface


Jeremy Fain is the CEO and co-founder of Cogntiv. With over 20 years of interactive experience across agency, publisher, and ad tech management, Jeremy led North American Accounts for Rubicon Project before founding Cognitiv. At Rubicon Project, Jeremy was in charge of global market success of over 400 media organizations and 500 demand partners through Real-Time-Bidding, new product development, as well as other revenue strategies, ensuring interactive buyers and sellers could take full advantage of automated transactions. Prior to Rubicon Project, Jeremy served as Director of Network Solutions for CBS Interactive. With oversight of a $30 million P&L, Jeremy was in charge of development, execution and management of data-driven solutions across CBS Interactive's network of branded websites, including audience targeting, private exchange, and custom audience solutions.

Emerging Job Roles for Successful AI Teams - AI Trends


Many job descriptions across organizations will require at least some use of AI in the coming years, creating opportunities for the savvy to learn about AI and advance their careers regardless of discipline. New job titles have and will emerge to help the organization execute on AI strategy. Machine learning engineers have cemented a leading role on the AI team, for example, taking first place on best jobs listed on Indeed last year, according to a recent rapport in CIO. And AI specialists were the top job in LinkedIn's 2020 Emerging Jobs report, with 74% annual growth in the last four years. This was followed by robot engineer and data scientist.

Machine Learning Helps Robot Swarms Coordinate


To test their new systems, Chung's and Yue's teams implemented GLAS and Neural-Swarm on quadcopter swarms of up to 16 drones and flew them in the open-air drone arena at Caltech's Center for Autonomous Systems and Technologies (CAST). The teams found that GLAS could outperform the current state-of-the-art multi-robot motion-planning algorithm by 20 percent in a wide range of cases. Meanwhile, Neural-Swarm significantly outperformed a commercial controller that cannot consider aerodynamic interactions; tracking errors, a key metric in how the drones orient themselves and track desired positions in three-dimensional space, were up to four times smaller when the new controller was used. Their research appears in two recently published studies. "GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning" was published in IEEE Robotics and Automation Letters on May 11 by Chung, Yue, Rivière, and Hönig. "Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions" was published in Proceedings of IEEE International Conference on Robotics and Automation on June 1 by Chung, Yue, Shi, and Hönig.

Social Media AI for Better Marketing


Artificial intelligence powered platform that combines Advanced Data Analytics and Behavioral Science to study data available on Social Media for helping our customers to Reduce Costs and Increase Sales by Conducting best-performing Social Media and Influencer Marketing Campaigns.

Webinar: The Future of AI-Driven Customer Service


Bots are now a key starting point for conversations with customers, so it's vital that companies think through how they use them. Artificial intelligence is a technology that has already transformed how consumers interact with their home devices, with brands, even with their cars. It has shown benefits both for companies and customers, but what's next for virtual agents and their kin? In this webinar, P.V. Kannan, coauthor of "The Future of Customer Service Is AI-Human Collaboration," discusses how virtual agents are proving themselves as a technology and the ways AI-driven customer service will empower contact center agents to provide great customer experiences. Get periodic email updates on upcoming webinars, panel discussions, and other special events.

20+ Machine Learning Datasets & Project Ideas - KDnuggets


To Build a perfect model, you need a large amount of data. But finding the right dataset for your machine learning and data science project is sometimes quite a challenging task. There are many organizations, researchers, and individuals who've shared their work, and we will use their datasets to build our project. So in this article, we are going to discuss 20 Machine learning and Data Science dataset and project ideas that you can use for practicing and upgrading your skills. The Enron Dataset is popular in natural language processing.

em Palm Springs /em ' Theoretical Physicist on the Science Behind the Movie's Twists


This interview contains spoilers for Palm Springs, including the ending. There are plenty of romantic comedies in which a couple gets a second chance at love--but what about a million chances? In Palm Springs, two wedding guests played by Andy Samberg and Cristin Milioti find themselves stuck in a temporal rut, reliving the same day over and over again, and having company on their endless journey doesn't always make the going easier. Viewers raised on Groundhog Day will expect that the story is building towards a karmic out, but extricating themselves turns out to involve a dose of hard science, provided courtesy of science advisor Clifford Johnson. Johnson, a PhD who teaches at the University of Southern California, helped fine-tune the time-travel mechanics of Avengers: Endgame, and he's also written and drawn a nonfiction graphic novel, The Dialogues, that features "conversations about the nature of the universe."

Is AI Getting Easier?


About seven or eight years ago I realized that the analytics and big data research, writing, and teaching I was doing was going to turn into AI research, writing, and teaching. I could have been earlier to come to this awareness, but at least I wasn't totally clueless about it. But AI itself keeps changing. Over that time we've seen a lot of evolution in how enterprises think about AI as a business resource. I was motivated to reflect on this topic by the release of the 2020 Deloitte State of Enterprise AI survey, which is available here.