Amazon is soft-launching a new supermarket Thursday, with a shopping cart that tallies up items as it enters the basket and enables instant checkout, and Alexa stations throughout the store you can ask questions. Amazon's first Fresh store is a 35,000 square foot traditional supermarket, opening in a strip mall in Woodland Hills, California, next to a See's Candy. Woodland Hills is a Los Angeles suburb in the heart of the San Fernando Valley. Customers can enter the store the traditional way, but if they want to use the cart, they need to open up their Amazon app and swipe it. Jeff Helbling, Amazon's vice-president of the Fresh Stores, says the shopping cart uses "a combination of computer vision algorithms and sensor fusion," within the cart to identify and tally up the items.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
Ahead of this year's AI Summit London, part of London Tech Week, we invited three Enterprise AI experts to join us for the London Tech Week Digital Series, to discuss responsible AI for business. This webinar is moderated by Aditya Kaul, Research Director at Tractica, who has 12 years' experience in technology market research with a primary focus on artificial intelligence & robotics. Joining Aditya is Ivana Bartoletti, Founder of the Women Leading in AI Network, and Udai Chilamkurthi, Lead Architect for Retail & Logistics at one of the UK's largest supermarket chains, Sainsbury's. In this on-demand webinar, you'll learn how your business can build an ethical framework for responsible AI & unlock the full potential of AI & Machine Learning to transform your enterprise. By accessing this free on-demand webinar by the AI Summit, you'll automatically receive a 20% discount to the upcoming AI Summit San Francisco (Palace of Fine Arts, 25 - 26 September 2019).
We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.
In this thesis, we leverage the neural copy mechanism and memory-augmented neural networks (MANNs) to address existing challenge of neural task-oriented dialogue learning. We show the effectiveness of our strategy by achieving good performance in multi-domain dialogue state tracking, retrieval-based dialogue systems, and generation-based dialogue systems. We first propose a transferable dialogue state generator (TRADE) that leverages its copy mechanism to get rid of dialogue ontology and share knowledge between domains. We also evaluate unseen domain dialogue state tracking and show that TRADE enables zero-shot dialogue state tracking and can adapt to new few-shot domains without forgetting the previous domains. Second, we utilize MANNs to improve retrieval-based dialogue learning. They are able to capture dialogue sequential dependencies and memorize long-term information. We also propose a recorded delexicalization copy strategy to replace real entity values with ordered entity types. Our models are shown to surpass other retrieval baselines, especially when the conversation has a large number of turns. Lastly, we tackle generation-based dialogue learning with two proposed models, the memory-to-sequence (Mem2Seq) and global-to-local memory pointer network (GLMP). Mem2Seq is the first model to combine multi-hop memory attention with the idea of the copy mechanism. GLMP further introduces the concept of response sketching and double pointers copying. We show that GLMP achieves the state-of-the-art performance on human evaluation.
We often frame new automation technology as a grave and immediate threat to the jobs and livelihoods of the humans whose tasks the machines take over. Tell that to the custodians at Sea-Tac airport who no longer have to spend their nights scrubbing floors, or sales associates at your local supermarket who will no longer have to schlep carts full of products throughout the store thanks to BrainCorps' smart scrubbers and tugs. BrainCorp is an AI software developer based in San Diego, California. Founded in 2009, the company spent half a decade developing its computer vision and automation technology before pivoting into the floor care industry. This service sector "hadn't seen a lot of automation yet or at least successful automation of the products," John Black, Brain Corp's Senior Vice President of New Product Development, told Engadget.
At first glance, the black and white Robomart vehicle, with its minimalist design and rounded body, looks like a vision of the future. But if you ignore the lack of a steering wheel and human driver, the electric, grocery-filled machine -- about the size of a minivan -- is actually something of a throwback. For much of U.S. history, perishable kitchen items such as produce, milk, eggs and ice arrived outside people's homes on a daily basis, first by horse-drawn wagon and later by truck. This curbside service would eventually fall victim to refrigeration, automobiles and the rise of the supermarket, making weekly shopping trips the modern American norm, according to Boston Hospitality Review. Now Robomart -- a Santa Clara, Calif.-based start-up -- seeks to merge the old with the new.
Grocery store chain Stop & Shop announced today that it will begin testing driverless grocery vehicles in Boston starting this spring, combining the hype of autonomous delivery cars, cashier-less stores, and meal kits into one experimental pilot. The launch is part of a partnership with San Francisco-based startup Robomart, whose vehicles will cart around Stop & Shop items like produce, convenience items, and meal kits to customers' doorsteps. The electric vehicles will be temperature-controlled to keep produce fresh, and controlled remotely from a Robomart facility. Customers can hail the mini grocery stores via an app, on an interface which feels a lot like calling an Uber. Once the vehicle arrives, customers can unlock the doors, and the items they grab are tracked with RFID and computer vision technology.
Amazon is set to test its cashier-less checkouts in bigger stores, according to the latest report. The firm is already testing the Amazon Go system in small convenience stores which are less than 2,500 square feet (232 square metres) large in Seattle, San Francisco and Chicago. However, reports suggest the firm would like to start implementing the checkout-free system in Whole Foods stores, which are typically 40,000 square feet (3,700 square metres) large. In September it was revealed Amazon was looking to open 3,000 of its cashier-less stores by 2021. Amazon is set to test its cashier-less checkouts in bigger stores, according to the latest report (file photo).
Amazon's cashier-less shopping tech could come to a full supermarket. As reported by the Wall Street Journal, the tech giant is experimenting with its Amazon Go technology at a larger store. SEE ALSO: What it's like to shop at Amazon Go At the company's seven Amazon Go stores in Seattle, Chicago, and San Francisco, shoppers scan in with their mobile devices, then pick their product and walk out. The technology uses a mix of computer vision, sensor fusion and deep learning to register what you're buying, then charges your Amazon account when you leave. According to sources who spoke to the news outlet, Amazon is testing its technology at a space in Seattle, which has been arranged like a large store.