Contentgine, the world leader in content-based marketing, today released its latest "Top 5" research ranking the most popular artificial intelligence (AI) content consumed by B2B decision makers and analyzed by its Content Indication Platform (CIP). To determine the category leaders, Contentgine's CIP employed machine learning and AI to examine content consumption across more than 3000 AI case studies, research papers, and eBooks syndicated from the world's largest B2B library. "AI software is not only a category in and of itself, but it is also a core component of other categories," said "Top 5 in 15" Series Host Robert Rose, best-selling author and chief strategy advisor for the Content Marketing Institute. "We're talking about the core component of AI software that may or may not be embedded into other solutions to achieve advanced automation, decision insights, predictive measurement, targeting, personalization, content management, and conversational interfaces. Given the vast interest in this topic today, it's wonderful to see so many well performing assets available to decision makers."
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One of the best places to start when building a smart home ecosystem is smart lights. Not only are they relatively affordable compared to other IoT gadgets, often costing between $10 and $50 a bulb, but they can also completely change the feel of your home. You can go from boring and analogue to colorful and automated within minutes, and there are endless possibilities when it comes to creating funky-colored light scenes, setting schedules and more. But like the rest of the smart home space over the last few years, there are now more players in smart lighting than ever before. We tested out some of the most popular smart lights on the market and found that most of them are quite good, but there are differences in compatibility, color quality and mobile app usability that are worth considering before deciding which system will be right for your home.
Whether in the form of Robotic Process Automation, chatbots, or some other type of digital assistants, the presence of intelligent bots is substantially increasing across the data ecosystem … in more ways than one. The diversification of the number of tasks these bots can perform is multiplying, as is the intrinsic complexity of those jobs, which unambiguously benefits knowledge workers worldwide. Whether dynamically engaging in natural language interactions with contact center agents, for example, or issuing and answering queries from a certified knowledge base, intelligent bots are integral for not only automating these data exchanges, but also implementing the ensuing action required to complete workflows. "Over the next one to two years we'll see tens of thousands more knowledge workers deploy digital assistants to reduce complexity, achieve error-free work, help their customers by drastically reducing their'on-hold' times and, most importantly, eliminate the frustration that arises from performing repetitive, manual tasks," presaged Automation Anywhere CTO Prince Kohli. These capabilities, of course, are naturally augmented by coupling intelligent bots with the sundry of Artificial Intelligence manifestations that are more pervasive today than they ever were before.
One of the most common challenges in an e-commerce business to build a well-performing product recommender and categorisation model. A product recommender is used to recommend similar products to users so that total time and money spent on platform per user will be increased. There is also a need to have a model to categorise products correctly since there might be some wrongly categorised products in those platforms especially where most of content is generated by users as in case of classified websites. A product categorisation model is used to catch those products and place them back into their right categories to improve overall user experience on the platform. This article has 2 main parts.
Silicon Valley CEOs usually focus on the positives when announcing their company's next big thing. In 2007, Apple's Steve Jobs lauded the first iPhone's "revolutionary user interface" and "breakthrough software." Google CEO Sundar Pichai took a different tack at his company's annual conference Wednesday when he announced a beta test of Google's "most advanced conversational AI yet." Pichai said the chatbot, known as LaMDA 2, can converse on any topic and had performed well in tests with Google employees. He announced a forthcoming app called AI Test Kitchen that will make the bot available for outsiders to try.
When Hugging Face first announced itself to the world five years ago, it came in the form of an iPhone chatbot app for bored teenagers. It shared selfies of its computer-generated face, cracked jokes and gossiped about its crush on Siri. It hardly made any money. The viral moment came in 2018--not among teens, but developers. The founders of Hugging Face had begun to share bits of the app's underlying code online for free.
Robots are not limited to industrial works anymore! Thanks to the integration of artificial intelligence and voice recognition, robots are slowly invading our smart homes embedded with devices like wireless security cameras, Smart TVs, Amazon's Alexa, Amazon Echo, Google Assistant, Philips Hue lightbulbs, Ecobee4, etc. And it is not a secret that machine learning software development is on rise now. A lot of clients are coming to develop personalized ML solutions for their businesses. ABI Research predicts that this integration will grow, and by 2024 that over 79 million homes in the world will have a robot in the house.
While dating apps were once seen as taboo, they're now one of the main ways that singletons find love around the world. But if you have a profile on a dating app, a new study may encourage you to reassess which pictures you include. Researchers from the University of Colorado have revealed that men who pose topless on Tinder are seen as less competent and more promiscuous. The first dating app can be traced back to 1995 when Match.com was first launched. The website allowed single people to upload a profile, a picture and chat to people online.
It is utopian to rule out any form of anthropomorphism when addressing a conversational assistant because of the use of language as a vector of exchange. Designers, therefore, must limit these shortcomings with the implementation of these design rules, thus reducing the risks of deception and dependency, and giving confidence in these systems.