A visit to DMart or Reliance Retail in India on any given day would make one think about Black Friday sales. The limited manpower in stores often falls short to tend to the swarm of shoppers in Indian retail stores. To solve the issue, Scribble Data strives to provide automated and customised solutions for retail businesses to tend to the demand and needs of every customer that walks in through their door. The startup offers retail chains real-time inventory management, identifies customer shopping trends, and provides personalised recommendations. Scribble Data helps businesses build machine learning (ML) applications for making their daily operations hassle free and for creating more market-worthy ML features.
This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.
Cresta Intelligence, a California-based AI startup, makes businesses radically more productive by using Expertise AI to help sales and service teams unlock their full potential. Cresta is bringing together world-renowned AI thought-leaders, engineers, and investors to create a real-time coaching and management solution that transforms sales and increases service productivity, weeks after application deployment. Cresta enables customers such as Intuit, Cox Communications, and Porsche to realize a 20% improvement in sales conversion rate, 25% greater average order value, and millions of dollars in additional annual revenue. This post discusses Cresta's journey as they moved from a multi-cloud environment to consolidating their machine learning (ML) workloads on AWS. It also gives a high-level view of their legacy and current training and inference architectures.
And walk right out again, without stopping to check out. In just the past three months, California-based AiFi has helped Choice Market increase sales at one of its Denver stores by 20 percent among customers who opted to skip the checkout line. It allowed Żabka, a Polish convenience store chain, to provide faster checkout for morning train commuters. It helped pro-racing team Penske and Verizon run a dinky 200-square-foot store at the Indy500, so race fans could quickly get back to the action. And on Wednesday AiFi announced an expanded partnership with Loop Neighborhood to introduce its computer vision, camera-only platform into stores in California, starting with two Bay Area locations.
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
AI technology which includes advanced analytics, deep learning, machine learning and other cognitive solutions, is a new digital transformation moving towards successful business in the retail market. Infinite Analysis was founded in 2012 with an intention of becoming the premier AI and personalization engine in retail and e-commerce search. By making use of Natural Language Processing (NLP), Machine Learning and a lot of Predictive analysis, Infinite analysis predicts users behaviour for retail and e-commerce applications. Standard cognition is a software development company based in San Francisco. They use artificial Intelligence technology, that enables consumers to buy and checkout without waiting in line for scan or pay.
Machine learning MLSys 2021: Bridging the divide between machine learning and systems Amazon distinguished scientist and conference general chair Alex Smola on what makes MLSys unique -- both thematically and culturally. Email Alex Smola, Amazon vice president and distinguished scientist The Conference on Machine Learning and Systems ( MLSys), which starts next week, is only four years old, but Amazon scientists already have a rich history of involvement with it. Amazon Scholar Michael I. Jordan is on the steering committee; vice president and distinguished scientist Inderjit Dhillon is on the board and was general chair last year; and vice president and distinguished scientist Alex Smola, who is also on the steering committee, is this year's general chair. As the deep-learning revolution spread, MLSys was founded to bridge two communities that had much to offer each other but that were often working independently: machine learning researchers and system developers. Registration for the conference is still open, with the very low fees of $25 for students and $100 for academics and professionals. "If you look at the big machine learning conferences, they mostly focus on, 'Okay, here's a cool algorithm, and here are the amazing things that it can do. And by the way, it now recognizes cats even better than before,'" Smola says. "They're conferences where people mostly show an increase in capability. At the same time, there are systems conferences, and they mostly care about file systems, databases, high availability, fault tolerance, and all of that. "Now, why do you need something in-between? Well, because quite often in machine learning, approximate is good enough. You don't necessarily need such good guarantees from your systems.
The ubiquity of smart voice assistants has made conversational shopping commonplace. This is especially true for low consideration segments like grocery. A central problem in conversational grocery is the automatic generation of short product titles that can be read out fast during a conversation. Several supervised models have been proposed in the literature that leverage manually labeled datasets and additional product features to generate short titles automatically. However, obtaining large amounts of labeled data is expensive and most grocery item pages are not as feature-rich as other categories. To address this problem we propose a pre-training based solution that makes use of unlabeled data to learn contextual product representations which can then be fine-tuned to obtain better title compression even in a low resource setting. We use a self-attentive BiLSTM encoder network with a time distributed softmax layer for the title compression task. We overcome the vocabulary mismatch problem by using a hybrid embedding layer that combines pre-trained word embeddings with trainable character level convolutions. We pre-train this network as a discriminator on a replaced-token detection task over a large number of unlabeled grocery product titles. Finally, we fine tune this network, without any modifications, with a small labeled dataset for the title compression task. Experiments on Walmart's online grocery catalog show our model achieves performance comparable to state-of-the-art models like BERT and XLNet. When fine tuned on all of the available training data our model attains an F1 score of 0.8558 which lags the best performing model, BERT-Base, by 2.78% and XLNet by 0.28% only, while using 55 times lesser parameters than both. Further, when allowed to fine tune on 5% of the training data only, our model outperforms BERT-Base by 24.3% in F1 score.
In the past year, lockdowns and other COVID-19 safety measures have made online shopping more popular than ever, but the skyrocketing demand is leaving many retailers struggling to fulfill orders while ensuring the safety of their warehouse employees. Researchers at the University of California, Berkeley, have created new artificial intelligence software that gives robots the speed and skill to grasp and smoothly move objects, making it feasible for them to soon assist humans in warehouse environments. The technology is described in a paper published online today (Wednesday, Nov. 18) in the journal Science Robotics. Automating warehouse tasks can be challenging because many actions that come naturally to humans -- like deciding where and how to pick up different types of objects and then coordinating the shoulder, arm and wrist movements needed to move each object from one location to another -- are actually quite difficult for robots. Robotic motion also tends to be jerky, which can increase the risk of damaging both the products and the robots.
In the past year, lockdowns and other COVID-19 safety measures have made online shopping more popular than ever, but the skyrocketing demand is leaving many retailers struggling to fulfill orders while ensuring the safety of their warehouse employees. Researchers at the University of California, Berkeley, have created new artificial intelligence software that gives robots the speed and skill to grasp and smoothly move objects, making it feasible for them to soon assist humans in warehouse environments. The technology is described in a paper published online today (Wednesday, Nov. 18) in the journal Science Robotics. Automating warehouse tasks can be challenging because many actions that come naturally to humans--like deciding where and how to pick up different types of objects and then coordinating the shoulder, arm and wrist movements needed to move each object from one location to another--are actually quite difficult for robots. Robotic motion also tends to be jerky, which can increase the risk of damaging both the products and the robots. "Warehouses are still operated primarily by humans, because it's still very hard for robots to reliably grasp many different objects," said Ken Goldberg, William S. Floyd Jr. Distinguished Chair in Engineering at UC Berkeley and senior author of the study.