AI tech can make retail storefronts relevant and engaging to the modern consumer. With the continued dominance of artificial intelligence in business applications, we've started to see a dramatic shift in how people shop for and purchase products. At least 60% of the U.S. population have made mobile purchases; 82% of mobile phone users use their devices while in-store to help them make a product decision. As mobile commerce continues to grow, retail stores will need to adopt new technologies to stay afloat. Consumer reliance on smart devices will only become greater, so brick-and-mortar stores must act quickly if they don't want to become outdated.

If you want to be successful in retail, you have to use technology at an early stage and put the customer at the center of all decisions. The digitization and technical progress, especially artificial intelligence (AI), are changing retail market in numerous industrial sectors, including tech, agriculture, studios, product testing, inspection, certification, insurance, and most important, retail. Customers are buying more and more online instead of from traditional stores, having a presence across all relevant sales channels has long been part of everyday life, and innovative business models are becoming increasingly popular. AI enables business models that were barely conceivable just a few years ago. Retail companies should use the changes strategically to get as close as possible to their customers. As a 2019 survey by EY and Microsoft shows, the gap between companies that embrace AI and those that are hesitant is widening.

Creating a good customer experience increasingly means creating a good digital experience. But metrics like pageviews and clicks offer limited insight into how much customers actually like a digital product. That's the problem the digital optimization company Amplitude is solving. Amplitude gives companies a clearer picture into how users interact with their digital products to help them understand exactly which features to promote or improve. "It's all about using product data to drive your business," says Amplitude CEO Spenser Skates '10, who co-founded the company with Curtis Liu '10 and Stanford University graduate Jeffrey Wang.

Evolving customer expectations and business needs have fueled a digital transformation in China, marked by the integration of cloud computing and artificial intelligence ("AI") into companies' operations. "To survive in this new world, businesses must learn to observe, think, and operate differently," reads Deloitte China's webpage about digital transformation (https://nnw.fm/8rH63). "Digital transformation, the cross-disciplinary power comprising digital, analytics, cloud, cybersecurity, and regulatory compliance, is about embracing digital disruption and unlocking exponential value." Interestingly, Infobird Software (NASDAQ: IFBD), a Software-as-a-Service (SaaS) company offering AI-enabled end-to-end customer engagement solutions in China, has packaged the aspects of digital transformation mentioned above, i.e., cloud computing, analytics, cybersecurity, and digital, into its robust proprietary solutions and is using them to help companies around China adapt to the changing times and transform digitally. IFBD's customer engagement solutions, which integrate the needs of both the customers and the businesses into a single platform, stimulate companies' market performance and growth and enable them to improve their infrastructure.

Although the revealing of Google's Recommendation AI has already been done during the company's Cloud Next event in 2019, Google is now launching its beta version for its customers. A fully managed service -- Google's Recommendation AI -- targeting retail businesses, has been designed to help in delivering personalised recommendation of products to customers at scale. According to the blog post written by the product manager, Pallav Mehta, the move has been taken in sync with the ongoing shift of retail companies towards data-driven strategies and the increasing customer demand. To keep up their relevance in this competitive scenario, the retail companies now require to provide an ultimate personalised experience to customers. And one such way of enhancing the experience is by recommending them products matching their interest, preferences and need.

Have you ever thought about how your email inbox is so smart that it can filter spam, tag important emails or conversations, and segregate promotional, social, and primary messages? In this post, we will explain how Machine Learning algorithms work and how we can take advantage of them for the benefit of app development companies. There is a complex algorithm for this type of prediction and this algorithm is within the broad spectrum of Machine Learning. What the algorithm does is an analysis of the words in the subject line, the links included in the email, and the patterns in the recipient list. Now, this method is definitely helping the email provider business, and such predictive and prescriptive algorithms can help all kinds of companies.

Artificial intelligence (AI) software has always received most of the attention, however, as the computational resources required to process this software skyrocket, a new generation of hardware is being created endowed with artificial intelligence. Some experts have named this evolution "Cambrian explosion", referring to the current period of fervent innovation. Today, AI's range of innovative hardware accelerator architectures continues to expand. Although you tend to think that graphics processing units (GPUs) are the most advanced dominant AI hardware architecture, that's far from true. Over the past few years, both start-ups and established vendors have introduced an impressive generation of new hardware architectures optimized for machine learning, deep learning, natural language processing and other much more advanced Artificial Intelligence workloads.

Shopping has never been so easy. Gone are the days when you would have to take long shopping lists to the stores and come back home only to realize that you forgot to buy toothpaste. Today, you could just find the right one in a matter of seconds online through an online tech giant. And this dominating giant tech company that is strategically taking over the industry and growing exponentially is Amazon. The company has already established itself as a leader in the online retail space -- according to eMarketer, it was expected to account for almost half of the total U.S. e-commerce dollars spent in 2018.

For decades retail companies have been exploiting analytics within the different segments of their businesses, including marketing and operations. Such analytics are dusty, however, and have now come to an end. Traditional analytical methods are outdated; they require a lot of manual steps and the insights extracted cannot be easily generalized. Using analytics ultimately provides a low return if you include the amount of manpower needed allocating to run them. Machine learning (ML) can be viewed as an extension of analytics.

Computer vision technology is one of the more remarkable AI applications. It actually helped spark the current AI revolution in 2012, when researchers used a deep neural network to radically improve the ability to recognize and classify objects in images. Since then, neural nets have surpassed even human abilities in many areas. Companies are now investing billions of dollars in computer vision research and product development. These include security cases, like detecting human faces with startling accuracy, even from lamp-posts as they speed down the highway in their cars; to ecommerce, where retail companies can let you search for a pair of jeans your favorite celebrity was wearing -- simply by uploading an image. Computer vision's potential is suddenly clear across a broad swath of industries, making it one of the fastest-growing trends.