A few days after watching a small army of Sawyer robots drop fake apples into plastic bowls, we're back at UC Berkeley's Sutardja Dai Hall to witness another approach to robotic learning. The UCB research team has deemed the robot BRETT -- that's the Berkeley Robot for Elimination of Tedious Tasks. Like the Sawyer robots, BRETT is here to learn -- and hopefully offer researchers some valuable insight into how we can teach robots how to perform dull and repetitive tasks without a lot of programming. In this case, the job is picking and placing -- a decidedly tedious warehouse task that has become extremely demanding as online retailers like Amazon have put the crunch on logistics companies. In some scenarios, robots are taught to execute the process through human demonstrations.

A few days after watching a small army of Sawyer robots drop fake apples into plastic bowls, we're back at UC Berkeley's Sutardja Dai Hall to witness another approach to robotic learning. The UCB research team has deemed the robot BRETT -- that's the Berkeley Robot for Elimination of Tedious Tasks. Like the Sawyer robots, BRETT is here to learn -- and hopefully offer researchers some valuable insight into how we can teach robots how to perform dull and repetitive tasks without a lot of programming. In this case, the job is picking and placing -- a decidedly tedious warehouse task that has become extremely demanding as online retailers like Amazon have put the crunch on logistics companies. In some scenarios, robots are taught to execute the process through human demonstrations.

"We are interested in understanding how bodies change shape over time," according to the survey. The invite comes from Amazon's new 3-D body scanning unit, an outgrowth of its acquisition last year of computer vision startup Body Labs. Accurately predicting how a pair of jeans or a suit will fit is a Holy Grail for retail. Technology to model a human body and how clothing will look on it has a wide range of applications, from being able to prevent returns of ill-fitting garments to on-demand printing and production. Startups and research teams have sprung up around the world to tackle the problem.

The retail industry has benefited greatly from the advancements of machine learning (ML), which have the ability to improve a company's bottom line. The technology can do so by improving the retail experience for consumers with a better user interface, a personalized recommendation engine, the optimization of stocking and inventory and to more accurately price an object. Many companies have already shifted towards a more digitized platform in order to have a better understanding of when to push products more aggressively and when to use more tact with customers. There's no telling how far ML will go in revolutionizing the retail world, but a recent study by McKinsey suggests that U.S. retailers that have adopted data and analytics into their supply chain have experienced up to a 10% increase in operating margin over the last five year. Attaining data and developing the right smart solutions platform with predictive capabilities have been key to boosting businesses' ROIs.

Today, we are launching support for Random Cut Forest (RCF) as the latest built-in algorithm for Amazon SageMaker. RCF is an unsupervised learning algorithm for detecting anomalous data points or outliers within a dataset. This blog post introduces the anomaly detection problem, describes the Amazon SageMaker RCF algorithm, and demonstrates the use of the Amazon SageMaker RCF on an example real-world dataset. Suppose you have collected data on traffic volume over a period of time across multiple city blocks. Can you predict if a spike in traffic volume represents a collision or just the usual rush hour?

Most people are familiar with shopping at Amazon. As with most online retailers, you visit its website, shop for items, place them in your cart, pay for them – and then Amazon ships them to you. Right now, Amazon's business model is "shopping then shipping." During the shopping process, Amazon's artificial intelligence offers suggestions of items that it predicts you will want to buy. The AI does a reasonable job.

In this blog post we'll show you how you can use Amazon Translate, Amazon Comprehend, Amazon Kinesis, Amazon Athena, and Amazon QuickSight to build a natural-language-processing (NLP)-powered social media dashboard for tweets. These conversations are a low-cost way to acquire leads, improve website traffic, develop customer relationships, and improve customer service. In this blog post, we'll build a serverless data processing and machine learning (ML) pipeline that provides a multi-lingual social media dashboard of tweets within Amazon QuickSight. We'll leverage API-driven ML services that allow developers to easily add intelligence to any application, such as computer vision, speech, language analysis, and chatbot functionality simply by calling a highly available, scalable, and secure endpoint. These building blocks will be put together with very little code, by leveraging serverless offerings within AWS.

The rapid pace of innovation in the e-commerce sector propelled by the trend'bricks to clicks' is increasingly shifting consumers to online shopping. A major disadvantage for offline retail stores is their lack of knowledge on customers entering their premises. Here, artificial intelligence (AI) opens up a big opportunity to predict the purchasing behaviour of in-store customers. AI through its sub-technologies such as machine learning and deep learning can enable offline retailers to derive actionable insights from consumer data (structured and unstructured) to offer predictive and precise decisions for better customer experience. AI practices incorporated by global offline retailers The global offline retail industry has been moving toward increased automation, cashless transactions and self-checkout stores based on consumer behaviour patterns, and demand for increased convenience.

Most advances in Artificial Intelligence (AI) have so far been confined to software. Today's AI computer programmes are vast users of data. They sift through these data and use methods such as pattern recognition. For instance, an online retailer like Amazon looks at your past history of browsing for a particular product online and then "matches" this use pattern to effectively target advertisements to you through sites like Facebook and Google so that you are enticed to buy. This is simple enough, but a similar method sits behind more advanced uses of AI such as self-driving vehicles.

There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world's leading companies. Here are 27 amazing practical examples of AI and machine learning. Using natural language processing, machine learning and advanced analytics, Hello Barbie listens and responds to a child. A microphone on Barbie's necklace records what is said and transmits it to the servers at ToyTalk. There, the recording is analyzed to determine the appropriate response from 8,000 lines of dialogue.