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Associations

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Associations are the specific measurable constraints on interestingness used in association rule learning. Regardless of the rules being employed to classify new data, the associations need to be defined by constraints to determine what is both interesting and relevant. Support – How frequently the pattern/items occur in the dataset. Confidence – How often the rule being used has been true (conditional probability). Lift – Actual success rate of the target model (rule) over the expected success from random chance.


Artificial Intelligence vs. Machine Learning: The Costs of Innovation

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Artificial intelligence (AI) has been making serious technical progress over the last several years, but not in the political sense. Tech giants like Microsoft and Google, and online retailers like Amazon, have found new ways to accelerate their products using AI-driven algorithms. AI isn't exactly the correct term, however -- at least not in the sense that general consumers know it, in regard to machines like HAL from 2001: A Space Odyssey or Skynet in the Terminator movies. There's a difference between AI and Machine Learning, but it's such a new concept for the zeitgeist, the two are easily confused. AI is a broad term encompassing technology that employs advanced computer intelligence, but it's Machine Learning (ML) that really gives computers that human-like intellect seen in science fiction.


Optimizing I/O for GPU performance tuning of deep learning training in Amazon SageMaker

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GPUs can significantly speed up deep learning training, and have the potential to reduce training time from weeks to just hours. Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. In this post, we focus on general techniques for improving I/O to optimize GPU performance when training on Amazon SageMaker, regardless of the underlying infrastructure or deep learning framework. You can typically see performance improvements up to 10-fold in overall GPU training by just optimizing I/O processing routines. A single GPU can perform tera floating point operations per second (TFLOPS), which allows them to perform operations 10–1,000 times faster than CPUs.


How Stitch Fix used AI to personalize its online shopping experience

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Online retailers have long lured customers with the ability to browse vast selections of merchandise from home, quickly compare prices and offers, and have goods conveniently delivered to their doorstep. But much of the in-person shopping experience has been lost, not the least of which is trying on clothes to see how they fit, how the colors work with your complexion, and so on. Companies like Stitch Fix, Wantable, and Trunk Club have attempted to address this problem by hiring professionals to choose clothes based on your custom parameters and ship them out to you. You can try things on, keep what you like, and send back what you don't. Stitch Fix's version of this service is called Fixes.


Omnichannel + Voice Commerce = The Perfect Reopening Strategy -- Jetson - Grow with Voice Commerce

#artificialintelligence

As we progress from "the new normal" to "the next normal," businesses are working hard to revise their marketing strategy and develop an omnichannel presence to improve sales in the brave new world of e-commerce. Read on to learn everything you ever wanted to know (and then some) about omnichannel, including what it is, how to build yours with voice commerce, and how to use it to maximize reach and fulfill consumer needs. Recently, Forrester hosted the Engage Digital conference, a virtual adaptation of its annual user event. According to Brendan Witcher, Principal Analyst for Digital Business Strategy, "the next normal" will "involve advances in omnichannel." During his presentation, Witcher noted that it will be necessary for retailers to switch gears to be prepared for the next phase of e-commerce.


Building a custom Angular application for labeling jobs with Amazon SageMaker Ground Truth

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As a data scientist attempting to solve a problem using supervised learning, you usually need a high-quality labeled dataset before starting your model building. Amazon SageMaker Ground Truth makes dataset building for a different range of tasks, like text classification and object detection, easier and more accessible to everyone. Ground Truth also helps you build datasets for custom user-defined tasks that let you annotate anything. For complex labeling tasks, such as complex taxonomy classification, extreme multi-class classifications, or autonomous driving labeling tasks, you may need to build a more complex front-end application for your labeling workforce. Front-end frameworks like Angular are helpful in these cases because they bring useful design patterns like model-view-controller (MVC), which makes your codebase more robust and maintainable for a larger team composed of UX/UI designers and software developers.


Get two Amazon Echo Shows for $140 in an HSN bundle

Engadget

Those who want to outfit a room or two with smart displays can get a couple of Amazon's smaller Echo Shows for less at HSN. The online retailer has a bundle that includes one Echo Show 5 and one Echo Show 8 for $140, which is a great price and close to the sale prices we saw for both of those devices back in May. If you were to buy each smart speaker separately right now, you'd spend $170 -- and that's with both the Show 5 and the Show 8 technically being on sale. In May, Amazon dropped the prices of both smart speakers to their Black Friday lows. If you had purchased one of each then, you would have paid $130.


How AI Helps Digital Enterprises Streamline Operations

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Artificial intelligence (AI) is transforming how enterprises analyze and process information. It is also shifting from theoretical to real-world technology. Companies are deploying AI technologies to boost efficiency, reduce costs, and grow sales and profitability. The technology can also reduce marketing waste by predicting what works. It is the most impactful innovation of our lifetime, and it will create new winners and losers across entire industries.


Kinaxis to Acquire AI-based Retail and CPG Demand Planning Provider, Rubikloud

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OTTAWA, ON, June 15, 2020 /CNW/ - Kinaxis Inc. (TSX: KXS), the authority in driving agility for fast, confident decision-making in an unpredictable world, has signed a definitive agreement to acquire Toronto-based Rubikloud, a disruptive, emerging provider of AI solutions that automate supply chain prescriptive analytics and decision-making in the retail and consumer packaged goods (CPG) industries. Globally-recognized retailers and CPG manufacturers in the health and beauty, household and grocery segments use Rubikloud's AI-based products today. Their offerings include demand forecasting and automation to manage and optimize trade promotions, pricing and assortment to drive product demand and dramatically improve financial results. Kinaxis will enhance RapidResponse's demand planning capabilities with the Rubikloud offerings, anticipating initial opportunities in the company's rapidly-growing CPG customer base and over time for other industries such as life sciences. The acquisition also offers Kinaxis a springboard into the enterprise retail industry.


10 Present and Future Use Cases of AI in Retail

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Which AI applications are playing a role in automation or augmentation of the retail process? How are retail companies using these technologies to stay ahead of their competitors today, and what innovations are being pioneered as potential retail game-changers over the next decade? Innovation is a double-edged sword, and as with any innovation, results are a mixed bag. While many AI applications have yielded increased ROI -- this case study of AI in retail marketing segmentation is one example -- others have been tried and failed to meet expectations, shining a light on barriers that still need to be overcome before such innovations become industry drivers. Below are 10 brief use cases across five retail domains or phases.