ML is no longer just an aspirational technology exclusive to academic and research institutions; it has evolved into a mainstream technology that has the potential to benefit organizations of all sizes. However, a lack of transparency in the ML process and the black box nature of resulting models is a hindrance for improved ML adoption in industries such as financial services and healthcare. For a team developing ML models, the responsibility to explain model predictions increases as the impact of predictions on business outcomes increase. For example, consumers are likely to accept a movie recommendation from an ML model without needing an explanation. The consumer may or may not agree with the recommendation, but the need to justify the prediction is relatively low on the model developers.

Analyzing fashion trends is essential in the fashion industry. Current fashion forecasting firms, such as WGSN, utilize the visual information from around the world to analyze and predict fashion trends. However, analyzing fashion trends is time-consuming and extremely labor intensive, requiring individual employees' manual editing and classification. To improve the efficiency of data analysis of such image-based information and lower the cost of analyzing fashion images, this study proposes a data-driven quantitative abstracting approach using an artificial intelligence (A.I.) algorithm. Specifically, an A.I. model was trained on fashion images from a large-scale dataset under different scenarios, for example in online stores and street snapshots. This model was used to detect garments and classify clothing attributes such as textures, garment style, and details for runway photos and videos. It was found that the A.I. model can generate rich attribute descriptions of detected regions and accurately bind the garments in the images. Adoption of A.I. algorithm demonstrated promising results and the potential to classify garment types and details automatically, which can make the process of trend forecasting more cost-effective and faster.

Amazon Web Services (AWS) is happy to announce the general availability of Notebooks within Amazon SageMaker Studio. Amazon SageMaker Studio supports on-the-fly selection of machine learning (ML) instance types, optimized and pre-packaged Amazon SageMaker Images, and sharing of Jupyter notebooks. You can switch a notebook from using a kernel on one instance type to another, for example from ml.t3.medium to ml.p3.2xlarge, without interrupting your work or managing infrastructure. Moving from one instance to another is seamless, and you can continue working while the instance launches. Your notebooks and data are available instantly on the new instance due to the Amazon Elastic File System (Amazon EFS) that is created for your Amazon SageMaker Studio domain.

Documents are a primary tool for record keeping, communication, collaboration, and transactions across many industries, including financial, medical, legal, and real estate. For example, millions of mortgage applications and hundreds of millions of tax forms are processed each year. Documents are often unstructured, which means the content's location or format may vary between two otherwise similar forms. Unstructured documents require time-consuming and complex processes to enable search and discovery, business process automation, and compliance control. When using machine learning (ML) to automate processing of these unstructured documents, you can now build in human reviews to aid in managing sensitive workflows that require human judgment.

Today's guest Barbara Martin Coppola is the Chief Digital Officer for Ingka Group, the strategic partner in the IKEA franchise system, operating IKEA Retail in 30 countries. She is part of the process of moving a very physical brand to one that naturally integrates highly digital and data-driven experiences for its customers. IKEA will have a more in-depth focus than just becoming digital its processes. Barbara talks about the need to deliver digital and data as smooth and natural extensions of the experiences that consumers want now and will expect to want in ten years. Not only will stores have the ability to simulate your own home in the store with virtual rooms, but you will also be able to place items in the store virtually inside your home.

Battlesnake is an AI competition in which you build AI-powered snakes. Battlesnake's rules are similar to the traditional snakes game. Your goal is to be the last surviving snake when competing against other snakes. Developers of all levels build snakes using techniques ranging from unique heuristic-based strategies to state-of-the-art deep reinforcement learning (RL) algorithms. You can use the SageMaker Battlesnake Starter Pack to build your own snake and compete in the Battlesnake arena.

If one was to explain ecommerce personalization in a simple manner, this could be it. When you walk into a physical brick and mortar store, what really impresses you? The ways in which the store engages you and treats you like you are their most special customer, isn't it? If you get what you are looking for, easily and quickly, without searching for it, then certainly the shopping experience is a breeze. If the owner is able to understand your preferences and shows you products according to your likes and thus, helps you save on time and effort, wouldn't you like to visit the store again and again?

Amazon A2I helps developers add human review for model predictions to new or existing applications using reviewers from Mechanical Turk, third party vendors, or their own employees. Amazon A2I makes it easier for developers to build the human review system, structure the review process, and manage the human review workforce. For example, developers could use Amazon A2I to quickly spin up and manage a workforce of humans to review and validate the accuracy of machine learning predictions for an application that extracts financial information from scanned mortgage documents or an application that uses image recognition to identify counterfeit items online, so that the quality of results improve over time. There are no upfront commitments to use Amazon A2I, and users pay only for each review needed. Today, machine learning provides highly accurate predictions (known as "inferences") for a variety of use cases, including identifying objects in images, extracting text from scanned documents, or transcribing and understanding spoken language.

Bots are social media accounts which are controlled by artificial software rather than by humans and serve a variety of purposes from news aggregation to automated customer assistance for online retailers. However, bots have recently been under the spotlight as they are regularly employed as part of large-scale efforts on social media to manipulate public opinion, such as during electoral campaigns. A new study in Frontiers in Physics has revealed the presence of short-term behavioral trends in humans that are absent in social media bots, providing an example of a'human signature' on social media which could be leveraged to develop more sophisticated bot detection strategies. The research is the first study of its kind to apply user behavior over a social media session to the problem of bot detection. "Remarkably, bots continuously improve to mimic more and more of the behavior humans typically exhibit on social media. Every time we identify a characteristic we think is prerogative of human behavior, such as sentiment of topics of interest, we soon discover that newly-developed open-source bots can now capture those aspects," says co-author Emilio Ferrara, Assistant Professor of Computer Science and Research Team Leader at the University of Southern California Information Sciences Institute.