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"Talent and technology are the keys to unlocking our future in this industry-- finding ways for tech to come in and do a better job than people can in roles people have traditionally done," said MDC Partners global president Julia Hammond in explaining AI's value to her holding company. "The challenge with that is it's completely contradictory to the agency model, which has been built around people, so there's been a reluctance to build out AI and machine learning. We're actively pursuing it, in how we resource, how we scale and how we serve clients." Progress is being made elsewhere to find a happy middle ground. Last week, GroupM agency Wavemaker went public with its AI-driven media planning tool, Maximize, which the company claims is generating plans faster and more effectively than human planning teams alone. "It's a question of complexity of the problem solved.

When it comes to statistics in machine learning, a common approach to accept or reject a null hypothesis is to check for the p-values and give a result without really having an idea of what goes on in the background. Without getting into any kind of fancy jargons or mathematical technicalities, this article attempts to sum up the intuition behind statistics using some real life examples especially for people from a non-statistics background. Why do we need hypothesis testing? But what if suddenly, Dunkin' happens to shut down because Krispe Kreme claims the weight of their donuts is less than what Dunkin' claims. How do we choose sides?

Now similar experiments are spreading as companies double down on plans to reduce social contact in stores. A survey published by McKinsey & Co. in July found that most consumers in the U.S. and China who changed their shopping habits during the pandemic expect the change to stick after the crisis. Get weekly insights into the ways companies optimize data, technology and design to drive success with their customers and employees. Giant Eagle Inc. recently hired cashier-free technology company Grabango Co. to introduce checkout-free shopping at a GetGo convenience store in Pittsburgh. And Mastercard is introducing its Shop Anywhere system, built on Accel Robotics Corp.'s computer vision technology, in the final quarter of this year.

Are you one of those writers who struggles to find topics to write about? I hear that a lot: "I just don't have any ideas!" So I decided to take a few minutes and find some article ideas for you. Seriously, I did not spend hours gathering the ideas for this post. I collected press releases for a week that come in my inbox due to my long history as a business reporter, and then took perhaps 5 minutes apiece to think on them.

Called Deep Brew, this isn't about a flavor of coffee or tea. Deep Brew is the company's artificial intelligence (AI) program, and it's all about "nurturing humanity," as Johnson put it. If this sounds really strange, maybe rethinking AI would be helpful. At its core, it's about teaching computers to find patterns in data sets too big for humans to analyze. For example, AI company Atomwise looks for new compounds to be used in drugs to better treat diseases.

Starbucks (NASDAQ:SBUX) CEO Kevin Johnson is determined to deliver a company culture centered around meaningful human interaction. This month, he announced a new "brew" that figures to be a big part of this culture. Called Deep Brew, this isn't about a flavor of coffee or tea. Deep Brew is the company's artificial intelligence (AI) program, and it's all about "nurturing humanity," as Johnson put it. If this sounds really strange, maybe rethinking AI would be helpful.

Starbucks (NASDAQ:SBUX) CEO Kevin Johnson is determined to deliver a company culture centered around meaningful human interaction. This month, he announced a new "brew" that figures to be a big part of this culture. Called Deep Brew, this isn't about a flavor of coffee or tea. Deep Brew is the company's artificial intelligence (AI) program, and it's all about "nurturing humanity," as Johnson put it. If this sounds really strange, maybe rethinking AI would be helpful.

Financial transactions can be considered edges in a heterogeneous graph between entities sending money and entities receiving money. For financial institutions, such a graph is likely large (with millions or billions of edges) while also sparsely connected. It becomes challenging to apply machine learning to such large and sparse graphs. Graph representation learning seeks to embed the nodes of a graph into a Euclidean vector space such that graph topological properties are preserved after the transformation. In this paper, we present a novel application of representation learning to bipartite graphs of credit card transactions in order to learn embeddings of account and merchant entities. Our framework is inspired by popular approaches in graph embeddings and is trained on two internal transaction datasets. This approach yields highly effective embeddings, as quantified by link prediction AUC and F1 score. Further, the resulting entity vectors retain intuitive semantic similarity that is explored through visualizations and other qualitative analyses. Finally, we show how these embeddings can be used as features in downstream machine learning business applications such as fraud detection.

The restaurant industry has long been an early adopter of technology. With technology evolving, the restaurant has a new tool in its technology toolkit--artificial intelligence (AI). AI has the power to revolutionize every aspect of the restaurant industry and improve the customer experience. Consider the reality of voice-activated ordering. Dunkin' Donuts just announced its integration with Amazon's Alexa.