If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Founded in 2008 by business school friends Robert Gentz and David Schneider, German-headquartered "etailer" Zalando is as much of a tech company as it is a retailer. One reason the company can provide a personalized user experience to its 27 million customers is because of the way it uses artificial intelligence (AI) and machine learning just like the other 28 percent of retailers who used artificial intelligence in 2018. Here are only a few of the ways Zalando uses machine learning and artificial intelligence today. One of the ways Zalando uses technology to improve the user experience is through its Algorithmic Fashion Companion (AFC), a digital outfit recommendation tool that can generate outfit recommendations in real-time. The algorithm's recommendations are based on products that the customer has put in their "wish list," expressed interest in or purchased before.
As brick-and-mortar retailers continue to struggle against online competitors, some are seeking out services that leverage big data and personalization to increase e-commerce sales. "During the rise of big data, it was said that data was the new oil," Brian Solis, principal analyst at Altimeter, told TechRepublic. "In an era of AI and machine learning however, personalized data is the new competitive advantage and will only become standard CX on the horizon." Indeed, 72% of retailers reported that AI will be a "competitive necessity" in the next five years, according to a recent Oxford Economics survey. One such tech option for retailers looking to fight off the competition is uSizy, a recommendation technology for fashion apparel and footwear businesses, which unveiled its latest product, uSizy Smart Business, on Wednesday.
With all the excitement and hype about AI that's "just around the corner"--self-driving cars, instant machine translation, etc.--it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you're already using--right now? In the process of navigating to these words on your screen, you almost certainly used AI. You've also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases. We distinguish between AI and machine learning (ML) throughout this article when appropriate. At Emerj, we've developed concrete definitions of both artificial intelligence and machine learning based on a panel of expert feedback. To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI.
Digital experience technology is at its best when it disappears. When it melts into the customer's day. Its history has been one of incrementally removing friction from experiences until you forget the digital interface is there. Consider the humble checkout: from a multi-field form-fill for every purchase, to auto-populating upon login, to one-click, to a passing comment at a virtual assistant. The e-shopping example is an embodiment of the pursuit of the digital experience holy trinity: convenience, speed and usability.
If you are a small or medium business owner who wants to expand his/her business online, then chatbots can play a significant role in your success story. Chatbots can deliver the two essential requirements for any online business today – quality products and market presence. With the chatbot business solution, you can use smart AI technologies to keep your customers engaged in meaningful business to business conversations. This will enhance your knowledge base and help you in developing better products for your clients. Furthermore, you will be able to offer a quicker and more accurate support service to customers, enhancing your brand's reputation.
The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build. Pure Storage launched a bevy of artificial intelligence tools, including the AI Data Hub, which are designed to meld storage and AI workflows from design to deployment. The storage company said it co-developed the AI Data Hub with Nvidia to break down the data silos that hamper analytics and model development. Storage vendors have been increasingly focused on AI workloads and managing data workflows instead of just storing it. Pure has been using its AI-Ready Infrastructure (AIRI) and expertise in solid-state storage via its Flashblade infrastructure to grab next-gen workloads.
SAN FRANCISCO--Guiding Oracle's development of its latest generation of cloud applications are three main business imperatives: help customers innovate rapidly, create nimble processes, and make the most of their mobile, social, and other communications channels. Speaking at Oracle OpenWorld, Steve Miranda, executive vice president of applications development, emphasized the considerable work the company has done incorporating machine learning algorithms into its comprehensive, tightly integrated suites of cloud applications. "We're ready to run your business in the cloud," Miranda said. At Oracle OpenWorld, Steve Miranda, Oracle's executive vice president for applications development, outlines machine learning capabilities in the company's cloud applications. An intuitive, easy-to-use, voice-enabled user interface that runs on various computing platforms but is especially suited to mobile devices.
However, now, your user profiles can go way beyond this and can include a deeper insight into your customers based on their online browsing habits, social posts, device preferences, hobbies and interests, and much more! With machine learning, true personalization – also called individualization – can be done at scale. These tools can process huge amounts of data in microseconds and make the most relevant, up-to-date decisions based on this data, which in turn, helps you present the most relevant experience to each and every visitor. This deeper understanding of your audience is what's going to grab their attention and help you develop that long-term, meaningful relationship between brand and customer.
Oracle announced availability of its AI-trained voice with Oracle Digital Assistant. Now, enterprise customers can use voice commands to communicate with their enterprise applications to drive desired actions and outcomes, enriching the user experience with conversational AI, simplifying interactions and improving productivity. "Enterprises are demanding an AI-powered voice assistant that understands their specific vocabulary and enables naturally expressive interactions for its users," said Suhas Uliyar, vice president, AI and Digital Assistant, Oracle. "Most of all though, enterprises value a highly secure AI-powered voice assistant that stores their business' sensitive data in Oracle's second generation cloud infrastructure." Built on Oracle's next-generation infrastructure, Oracle Digital Assistant applies AI with deep semantic parsing for natural language processing (NLP), natural language understanding (NLU) and custom machine learning (ML) algorithms.
G4 instances provide the industry's most cost-effective machine learning inference for applications, like adding metadata to an image, object detection, recommender systems, automated speech recognition, and language translation. G4 instances also provide a very cost-effective platform for building and running graphics-intensive applications, such as remote graphics workstations, video transcoding, photo-realistic design, and game streaming in the cloud. Machine learning involves two processes that require compute – training and inference. Training entails using labeled data to create a model that is capable of making predictions, a compute-intensive task that requires powerful processors and high-speed networking. Inference is the process of using a trained machine learning model to make predictions, which typically requires processing a lot of small compute jobs simultaneously, a task that can be most cost-effectively handled by accelerating computing with energy-efficient NVIDIA GPUs.