This week brought a bunch of deals on new gadgets, including Amazon's rotating Echo Show 10 and Google's Nest Hub. The former dropped to a new all-time low while the latter remains 20 percent off at various retailers. AirPods Pro are more than $50 off right now, and Amazon Prime members can snag the Fire TV Stick Lite for only $20. Here are the best tech deals from this week that you can still get today. The Nest Audio smart speaker is still $20 off across the web, bringing to down to $80.

Declaring "it's no longer a question of if...but when" autonomous vehicles are used in retail, President and CEO of Walmart (NYSE:WMT) U.S. John Furner announced the retail titan's intention to invest in General Motors' (NYSE:GM) Cruise self-driving car company in a press release today. Furner said the move will "aid our work toward developing a last mile delivery ecosystem that's fast, low-cost and scalable." The Walmart investment brings the total of Cruise's most recent funding round to $2.75 billion, though neither GM nor Cruise provides specifics on how much each individual company contributes to the whole, CNBC reports. Other investors in the subsidiary include GM itself, Microsoft, Honda Motor, and institutional investors. Among other projects, Cruise intends to roll out self-driving taxis in Dubai within the next two years.

Walmart is signaling its commitment to autonomous deliveries with a new investment in self-driving company Cruise. The two already have a cozy relationship, having recently worked together on a delivery pilot in Scottsdale, Arizona. Walmart was so impressed with Cruise's "differentiated business, unique tech and unmatched driverless testing" that it decided to take part in the GM subsidiary's $2.75 billion funding round. The investment will see Cruise become an important part of the retailer's "last mile delivery ecosystem" -- industry parlance for the final journey from warehouse to customer. Walmart has struck additional partnerships on driverless deliveries with companies including Google's Waymo, Ford and Udelv.

Retailers are now applying AI, ML, and robotics in significant parts of the value chain. Above all, AI technologies could eliminate many manual activities in assortments, promotions, and supply chains. The three most remarkable opportunities in the short to medium term are promotions, arrangement, and replenishment. Significant retailers are trying different things with AI around these areas. "Digital native" e-commerce organizations are driving the way, using AI to anticipate trends, optimize advanced warehousing and logistics, set costs, and customize advancements and promotions.

In 2017, the Economist stated that the world's most valuable resource is no longer oil, but data. Four years later, this concept is only increasing in truth. Thanks to the revolutionary promises of 5G, artificial intelligence (AI) and machine learning (ML) possibilities are transforming the value of the data collected on consumers and our habits every single day. With 5G usage predicted to explode in coming years with over 1 billion 5G connections by 2023, the possibilities of AI and ML solutions are seemingly becoming limitless. Gone are the days when your mobile phone or laptop are the only devices collecting your data.

Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customer needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12 percent in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.

As companies welcome more autonomous robots and other heavy equipment into the workplace, we need to ensure equipment can operate safely around human teammates. In this post, we will show you how to build a virtual boundary with computer vision and AWS DeepLens, the AWS deep learning-enabled video camera designed for developers to learn machine learning (ML). Using the machine learning techniques in this post, you can build virtual boundaries for restricted areas that automatically shut down equipment or sound an alert when humans come close. For this project, you will train a custom object detection model with Amazon SageMaker and deploy the model to an AWS DeepLens device. Object detection is an ML algorithm that takes an image as input and identifies objects and their location within the image.

In 2019, AWS unveiled Amazon SageMaker Debugger, a SageMaker capability that enables you to automatically detect a variety of issues that may arise while a model is being trained. SageMaker Debugger captures model state data at specified intervals during a training job. With this data, SageMaker Debugger can detect training issues or anomalies by leveraging built-in or user-defined rules. In addition to detecting issues during the training job, you can analyze the captured state data afterwards to evaluate model performance and identify areas for improvement. This task is made easier with the newly launched XGBoost training report feature.

Without doubt, personalisation has huge benefits. When done well, the return on investment is significant. Recent research conducted by Wunderman Thompson Technology revealed that 35% of consumers are willing to give more of their personal data to brands if it improves the online experience. They value their data being used to provide loyalty-based discounts (55%), to be shown products tailored to their requirements (36%) and to be served offers based on their preferences (33%). When first party data is willingly exchanged, providing customers with these kinds of personalised experiences clearly adds value.

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.