Adobe and Microsoft have joined forces, combining Adobe's Sensei artificial intelligence (AI) with Microsoft's data to give Adobe customers more automated, intelligence-based business feedback. By plugging into Microsoft's ecosystem, Adobe can pull rich insights from Microsoft's customer relationship management (CRM) and data visualization tools, among other platforms. Adobe Sensei is now armed with a new trove of data from Microsoft Dynamics 365, Microsoft Power BI, and Microsoft Azure, from which it can draw on its algorithm-based recommendations. Both companies are also working to add Adobe Sensei to Microsoft tools; however, there is no timeline for when this reverse integration will be available. Here's an excellent example of how Adobe Sensei and Microsoft work together today: A financial services client working within Adobe Analytics can take existing Adobe data, combine it with Microsoft's data ecosystem, and then feed all of that information into Sensei.

Although our skies are now filled with quadcopter drones, fixed-wing aircraft have them beat in both speed and endurance; that's why the military's drones don't look like the ones you'd buy on Amazon. One of the biggest drawbacks with fixed-wing planes is that they tend to require a long runway for landing. However, drone makers are searching for a better way, and it turns out nature solved the problem millions of years ago. Now, we're trying to steal its secret. They can land on a dime by swooping in at low altitude then angling their wings upwards and spreading their feathers to act as air brakes.

The issue of sustainability has never been more prominent. Around the world, headlines are full of warnings on the dangers of climate change as companies, people and governments campaign for greener policies and practices. One of the sectors most affected by this drive is chemical processing and manufacturing. Every year, more than a thousand new chemical substances are introduced into the U.S. For each one, the potential applications need to be weighed against myriad potential health and environmental impacts across a broad range of metrics, such as energy consumption, toxicity or biodegradability across product lifecycles. If chemical processors and manufacturers are able to shift toward more sustainable practices – e.g., processes that are more energy efficient, require lower input volumes and are more environmentally and biologically-friendly – the benefits for both the industry and the environment would be significant.

As counsellors and consultants, replicating the "scale" issue as it relates to the respective data, information, and network security problems is a challenge. Unfortunately, "layperson" directors and officers of public companies, along with executives in government, tend to view "scale" (as it pertains to data protection) as a bad thing (and even a scary thing). Part of the challenge here is that there are few practical ways to explain to those holding these positions that an organization's security operations center may receive upwards of one million "incidents "every day and, at the same time, adequately deal with, and investigate, the potential peril inherent in such incidents, and reasonably assure that not even one of these small incidents slips between the cracks.

Although our skies are now filled with quadcopter drones, fixed-wing aircraft have them beat in both speed and endurance--that's why the military's drones don't look like the ones you'd buy on Amazon. But one of the biggest drawbacks with fixed-wing planes is that they tend to require a long runway for landing. But drone makers are searching for a better way, and it turns out nature solved the problem millions of years ago. Now, we're trying to steal its secret. They can land on a dime by swooping in at low altitude then angling their wings upwards and spreading their feathers to act as air brakes.

At Adobe Summit, Adobe (Nasdaq:ADBE) introduced significant enhancements to the Adobe Cloud Platform, the underlying, cross-cloud architecture that unifies content and data and leverages Adobe Sensei, Adobe's AI and machine learning framework. Advancements announced today include new Sensei capabilities for enterprise customers as well as new tools and partner integrations to help developers reduce time to market and better integrate Creative Cloud assets into enterprise workflows. Adobe also announced Adobe Experience Cloud (see separate press release) as well as the availability of the first set of solutions that integrate with Microsoft's Azure, Dynamics 365 and PowerBI offerings (see separate press release). Advancements in cloud services have fundamentally altered the computing landscape. The next decade will bring even more disruptive changes in how brands create, immerse and engage their customers in their experiences.

Want to work for one of the most recognizable companies in the world? Chances are someone in your network uses their products on a daily basis. This office in Silicon Valley is an automotive R&D environment that is working on cutting edge technology to be featured in the next wave of their vehicles 3-5 years down the road. One of their most recent projects include developing autonomous vehicles and automotive IoT devices that will become the the future of the automotive space. You will be a contributing member of Machine Learning & Predictive User Experience team doing algorithm tuning, iterative development, and developing software for a production automotive project.

Building Issues in local government may in the future be handled automatically by Artificial Intelligence (AI).

The 2017 New Rules for the Digital Age report from Deloitte found that only 5 percent of the companies surveyed said they have strong digital leadership development programs and a clear majority (65 percent) said they have no significant program to drive digital leadership skills. Josh Bersin, a principal at the Bersin by Deloitte research group, says the challenge is that companies don't realize how much more complicated digital transformation is than simply acquiring new technology. "Digital technology is easy to buy, but once you turn it on it changes the way you work and how you deliver products and services," Bersin told CIO.com. "From the CIO's perspective, it may seem relatively easy to implement artificial intelligence (AI), social media and other new technology, but these things have a disruptive impact on the workplace." For example, the study found that companies feel 31 percent "less ready" to redesign their organization around digital business models than they did last year.

A few months ago I wrote a tutorial on how to classify images using Convolutional Neural Networks (specifically, VGG16) pre-trained on the ImageNet dataset with Python and the Keras deep learning library. The pre-trained networks inside of Keras are capable of recognizing 1,000 different object categories, similar to objects we encounter in our day-to-day lives with high accuracy. Back then, the pre-trained ImageNet models were separate from the core Keras library, requiring us to clone a free-standing GitHub repo and then manually copy the code into our projects. This solution worked well enough; however, since my original blog post was published, the pre-trained networks (VGG16, VGG19, ResNet50, Inception V3, and Xception) have been fully integrated into the Keras core (no need to clone down a separate repo anymore) -- these implementations can be found inside the applications sub-module. Because of this, I've decided to create a new, updated tutorial that demonstrates how to utilize these state-of-the-art networks in your own classification projects.