CPG executives are painfully aware that they're investing billions of dollars in trade promotions each year, but as many as 72% fail to break even¹. It's clear that promotions have become more complex and harder to manage as CPGs must respond to changing consumer behavior, increasing demands from retailers and blurring of physical and online channels. Traditional forecasting and promotion-planning systems are unable to provide real-time, accurate insights to help managers understand the big picture. Below, we'll explore seven ways in which AI can help CPG companies more effectively plan promotional events, measure outcomes and make adjustments. You can read more in the companion paper on how AI transforms promotional trade funds management.

Running Machine Learning code on mobile devices is the next big thing. PyTorch, in the latest release PyTorch 1.3, added PyTorch Mobile for deploying machine learning models on Android and iOS devices. Here we will look into creating an Android Application for object detection inside an image; like the GIF shown below. We will be using a pre-trained ResNet18 model for this tutorial. ResNet18 is the state of the art computer vision model with 1000 classes for classification.

The rapid growth in the size of neural networks is outpacing the ability of hardware to keep up, said Naveen Rao, vice president and general manager of Intel's AI Products Group, at the company's AI Summit yesterday. Solving the problem will require rethinking how processing, network, and memory work together, he said. "Over the last 20 years we've gotten a lot better at storing data," Rao said during a one-hour presentation at Intel's AI Summit 2019 in San Francisco Tuesday. "We have bigger data sets than ever before. Moore's Law has led to much greater compute capability in a single place. And that allowed us to build better and bigger…neural network models. This is kind of a virtuous cycle and it's opened up new capabilities."

Deep learning is all the rage these days in enterprise circles, and it isn't hard to understand why. Whether it is optimizing ad spend, finding new drugs to cure cancer, or just offering better, more intelligent products to customers, machine learning -- and particularly deep learning models -- have the potential to massively improve a range of products and applications. The key word though is'potential.' While we have heard oodles of words sprayed across enterprise conferences the last few years about deep learning, there remain huge roadblocks to making these techniques widely available. Deep learning models are highly networked, with dense graphs of nodes that don't "fit" well with the traditional ways computers process information.

AI and machine learning model architectures developed by Alphabet's DeepMind have substantially improved the Google Play Store's discovery systems, according to Google. In a blog post this morning, DeepMind detailed a collaboration to bolster the recommendation engine underpinning the Play Store, the app and game marketplace that's actively used by over two billion Android users monthly. It claims that as a result, app recommendations are now more personalized than they used to be. In an email, a Google spokesperson told VentureBeat that the new system was deployed this year. It's not the first time the DeepMind team has contributed its expertise to the Android side of Google's business, it's worth noting.

Imagine your body is like a loaf of sliced bread. During an MRI scan, a powerful magnet and radio waves create detailed images of each "slice" of your body, then a computer puts the slices together to show a full picture of your anatomy. But before the slicing comes the choosing. Before an MRI technologist can scan a patient, they have to manually specify the slices they want the MRI to acquire. This process can take several minutes of tweaking and adjusting, leaving a patient waiting anxiously in the MRI scanner and adding unnecessary steps to set up each scan.

It's no secret that big tech companies like Amazon (AMZN), Microsoft (MSFT), and Alphabet (GOOG), the parent company of Google, are investing in digital healthcare. The market opportunity is pretty enticing when you consider that the U.S. alone spent $3.65 trillion on healthcare just last year. Google made the latest headline-grabbing move when it announced that it would buy wearables-maker Fitbit (FIT) in a deal valued at $2.1 billion. Analysts have noted that the acquisition is part of the company's overall strategy to build an ambient intelligent system where Google is omnipresent. Another motive behind the purchase – pending regulatory approvals – is that Fitbit gives Google access to a treasure trove of healthcare data that it can feed to its London-based AI lab DeepMind or its life sciences subsidiary Verily, which is already collaborating on at least one AI healthcare device for remote patient monitoring.

Github has become the goto source for all things open-source and contains tons of resource for Machine Learning practitioners. We bring to you a list of 10 Github repositories with most stars. TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

AI has touched just about every industry and discipline known to mankind, but how about the arts? Enter Artrendex and its ArtPI, a new interface or API driven by artificial intelligence that's poised to transform the way art gets discovered, displayed, and sold. It promises to transform art discovery the way Shazam transformed music discovery. ArtPI is the first public API designed and optimized for art. It uses AI (artificial intelligence) and deep learning models trained over 1 million artworks.

Researchers believe that the industry will contribute more than $5.6 trillion to the economy by 2025 as well. Much of this revenue comes from the medical research field, which is responsible for improving drug research, disease diagnosis and treatment protocols. Major research companies are collaborating with software development services to integrate deep learning technology into their investigations. Deep learning promises to transform the way that doctors review medical tests and make diagnoses, helping them identify diseases and start treatment quicker. The technology will also help pharmaceutical companies develop life-saving drugs in a shorter amount of time.