A recent study by the Capgemini Research Institute examines the multi-billion dollar opportunity that artificial intelligence (AI) offers for retail. AI deployments among large retailers have increased exponentially in recent years, but many use cases have yet to be tapped. With feedback from over 400 retailers, analysis of more than 40 real-world use cases, and secondary research of the top 250 retailers' current state of AI deployment, the report not only investigates the status quo, but also offers a practical framework of use cases for companies that are just starting their AI journey. Input from retailers lends some much-needed realism to the discussion about AI deployments. Retailers are slowly realizing that AI and machine learning (ML) are not silver bullets.

He goes by the name "Marty." Tall, slow-moving and gray, he has big cartoonish eyes that disguise something unique about the newest employee at Giant Food Stores: Marty is deliberate and relentless, and -- unlike his fellow employees -- he has the ability to work a seemingly endless number of hours without pay. Though he doesn't say much, a small message is always plastered to his slender trunk: "This store is monitored by Marty for your safety," it reads. "Marty is an autonomous robot that uses image capturing technology to report spills, debris and other potential hazards to store employees to improve your shopping experience." After a pilot program that kicked off in several Pennsylvania stores this past fall, Giant Food Stores announced Monday that it will place Martys in each of the supermarket chain's 172 stores across Pennsylvania, Maryland, Virginia and West Virginia.

He goes by the name "Marty." Tall, slow-moving and gray, he has big cartoonish eyes that disguise something unique about the newest employee at Giant Food Stores: Marty is deliberate and relentless, and -- unlike his fellow employees -- he has the ability to work a seemingly endless number of hours without pay. Though he doesn't say much, a small message is always plastered to his slender trunk: "This store is monitored by Marty for your safety," it reads. "Marty is an autonomous robot that uses image capturing technology to report spills, debris and other potential hazards to store employees to improve your shopping experience." After a pilot program that kicked off in several Pennsylvania stores this past fall, Giant Food Stores announced Monday that it will place Martys in each of the supermarket chain's 172 stores across Pennsylvania, Maryland, Virginia and West Virginia.

End-to-end task-oriented dialogue is challenging since knowledge bases are usually large, dynamic and hard to incorporate into a learning framework. We propose the global-to-local memory pointer (GLMP) networks to address this issue. In our model, a global memory encoder and a local memory decoder are proposed to share external knowledge. The encoder encodes dialogue history, modifies global contextual representation, and generates a global memory pointer. The decoder first generates a sketch response with unfilled slots. Next, it passes the global memory pointer to filter the external knowledge for relevant information, then instantiates the slots via the local memory pointers. We empirically show that our model can improve copy accuracy and mitigate the common out-of-vocabulary problem. As a result, GLMP is able to improve over the previous state-of-the-art models in both simulated bAbI Dialogue dataset and human-human Stanford Multi-domain Dialogue dataset on automatic and human evaluation.

Due to the emerging technologies and the rapid integration of software development, many online retailers opt to design attractive webpages in order to influence consumer perceptions of the web environment and thus leading to more sales and profits [1]. An online 2017 UPS Pulse of the Online Shopper study with more than 6,400 European respondents from France, Germany, Italy, Poland, Spain, and the UK, discovered that 84% of shoppers will shop in the physical store instead of online [2]. The major reason is that they can look around and touch and feel the products. In the in-store environment, McCabe and Nowlis [3] found that the shoppers are less likely to pick up products with geometric properties as they highly rely on the sense of vision, which is believed to provide sufficient information of the products. For some of the products with geometric properties, especially packaged products, shoppers tends to evaluate the product through visually glancing instead of reach out or touch on the product [4]. The geometry properties of an agricultural product is often indicated by its weight as it is relatively easy and quick to be measured with a digital scale. An alternative solution to obtain the weight of the product is to measure its volume as the volume is a quantity defined by mass per unit density. There are several works published to measure the volume of the objects by fusing the computer vision technique with some mathematical modeling and derivations.

I have written many times before about how AI is changing the landscape of marketing. It gives marketers the opportunity to reach more people while delivering personalized, relevant and timely content to them. Particularly interesting, is the use of AI in the retail industry. Many people fear that e-commerce giants threaten the existence of local retailers, but, brick-and-mortar stores aren't dying, they're simply evolving. The use of technology is enhancing customer connectivity and experience at every touchpoint, both online and in-store.

A six-hour stand-off between police and a 40-year-old man threatening to set alight a convenience store was brought to a peaceful end by a robot carrying a vape pen. Local media reports described a tense situation on Saturday morning in Novato, California that led to the closure of a Safeway store and McDonald's amid fears that the suspect may have been armed. The incident was caught on tape, including the moment the robot handed the man (identified by police as Juan Roman) a smartphone for communications. The assistant manager at the Circle K convenience store told KGO that the suspect "took a gas can from his pick up and poured it all over the store" following a payment dispute over the pumps. He then attempted to unsuccessfully set the floor mat ablaze before fleeing to the Safeway location in a pickup truck.

Cashierless stores are all the rage. You need look no further for evidence than Amazon's rapidly expanding Amazon Go chain and the tens of millions of VC dollars startups like Trigo Vision and Zippin have snatched up in the past year. Some, like Grabango, offer plug-and-play systems designed to accommodate warehouse-style installations -- i.e., stores up to 100,000 square feet. But checkout-free platforms come in all shapes and sizes, and retailers on the hunt for tech with a smaller footprint can take advantage of AiFi's latest innovation. AiFi, a Santa Clara startup that emerged from stealth in February and beat out more than 900 submissions in Visa's 2018 Everywhere contest, today unveiled the NanoStore.

Technology has penetrated in every sphere of our lives. We live, love, eat and sleep on #technology now. Each year, we see technology moving deeper and deeper into our existence. Good because it helps us in doing more in less time and efforts. Bad because interweaving of tech in our lives has left us dependent, vulnerable and very anxious.

In the current field of computer vision, automatically generating texts from given images has been a fully worked technique. Up till now, most works of this area focus on image content describing, namely image-captioning. However, rare researches focus on generating product review texts, which is ubiquitous in the online shopping malls and is crucial for online shopping selection and evaluation. Different from content describing, review texts include more subjective information of customers, which may bring difference to the results. Therefore, we aimed at a new field concerning generating review text from customers based on images together with the ratings of online shopping products, which appear as non-image attributes. We made several adjustments to the existing image-captioning model to fit our task, in which we should also take non-image features into consideration. We also did experiments based on our model and get effective primary results.