Of all the machine learning algorithms, the most fascinating are neural networks. They don’t require statistical hypothesis or rigorous data preparation save for normalization.

The power of a neural network lies in its architecture, its activation functions, its regularization, etc.

Here’s an interesting article exploring a particular kind of neural network: the autoencoder.

Computer vision may be just what retail needs to stay competitive with online giants and increasing costs. While still in its infancy, computer vision is quickly becoming a part of everyday life. Over the next few years, this technology will continue to evolve and scale at and its impact will be felt by in a variety of industries.

Here’s an interesting article in a retail trade magazine about what computer vision means to the industry.

Savvy retailers are beginning to experiment with applications of this next-gen technology, which will help them evolve with ever-changing shopping habits. Today 3% of retailers have computer vision technology in place, according to RIS’ “29th Annual Retail Technology Study: Retail Accelerates.” However, 40% plan to start or finish implementing the tech within the next two years.

Computer vision plays an important role in facial recognition, cashierless stores, inventory visibility, and visual search, four vital areas retailers are already putting the technology to work in. Here we take a deeper look at each of these applications and what they mean for both retailers already utilizing the tech and companies looking to adapt computer vision in the future.

AI will is disrupting every business in every industry. Usually, this process has been labelled by some as “Digital Transformation.” Working with customers has shown me that, while many want to be ready for the Age of AI, most have not taken the steps necessary to truly transform into a data driven organization.

Here’s a post outlining signs that your company may have stalled in its transformation.

The ongoing and amorphous nature of digital transformation change efforts can make progress hard to gauge – sometimes leading to stagnation. As Korn Ferry senior client partner Melissa Swift has pointed out, digital transformation is a marathon, not a sprint. So how do you know when you’re running a productive marathon or when you’ve hit a hill that the team is truly stuck on?

Individual digital project metrics, while important to track the performance of digital transformation programs, may not provide a complete picture of progress. However, IT leaders can keep an eye out for other qualitative signs that indicate their organization’s effort may be stalled.

There’s a new Cognitive Service in town and it detects anomalies. From the article in TechTarget.

The Azure Anomaly Detector service, now in preview, is an addition to Azure Cognitive Services. It takes in customers’ time-series data — information collected at and stamped with specific points in time — and applies the most efficient algorithm for the particular use case from a library of pretrained models. Time-series data presents a historical baseline from which the system can more easily spot deviations. Customers can fine-tune the algorithm’s sensitivity to reduce false-positive results. People with no background in machine learning can use Anomaly Detector thanks to the abstraction layer it provides, according to Microsoft.