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.

Self-driving cars operate using machine learning. Machine learning algorithms have become commoditized, even open source. Data, not algorithms, is what confers competitive advantage. Currently, Tesla (NASDAQ:TSLA) has access to vastly more driving data than any other company. The Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL) subsidiary Waymo, once widely recognized as the leader in self driving, had a cumulative 2 million miles of driving data in October 2016. Tesla's cars with "full self-driving hardware" (HW2) are currently driving over 1 million miles per day.

Machine learning couldn't be hotter. A type of artificial intelligence that enables computers to learn to perform tasks and make predictions without explicit programming, machine learning has caught fire among the hip tech set, but remains a somewhat futuristic concept for most enterprises. But thanks to technological advances and emerging frameworks, machine learning may soon hit the mainstream. Consulting firm Deloitte expects to see a big increase in the use and adoption of machine learning in the coming year. This is in large part because the technology is becoming much more pervasive.

Bosch's car control computer will make use of Artificial Intelligence (AI) to enable autonomous driving even in complex traffic situations such as urban environments with heterogeneous traffic participants. "We are teaching the car how to maneuver through road traffic by itself," said Volkmar Denner, chairman of the Bosch board of management, at the event currently taking place in Berlin. Using AI, the car will be able to correctly interpret traffic situations and predict the behavior of other traffic participants at high accuracy. For building the core onboard computer, Bosch plans to collaborate with U.S. technology company Nvidia. The company will supply Bosch with a chip that stores algorithms, generated with machine learning methods.

Wal-Mart Stores Inc. is creating a technology-startup incubator in Silicon Valley to identify changes that will reshape the retail experience, including virtual reality, autonomous vehicle and drone delivery and personalized shopping. The incubator will be called Store No. 8, a reference to a Wal-Mart location where the company experimented with new store layouts. Marc Lore, chief executive officer of Wal-Mart's e-commerce operations, announced the incubator Monday at the ShopTalk conference in Las Vegas. The world's biggest retailer has been overhauling its online team to better challenge Amazon.com Lore founded Jet.com, which Wal-Mart purchased in September for about $3.3 billion in pursuit of Amazon in the e-commerce race.

About David: David Asboth is a Data Scientist with a software development background. He's had many different job titles over the years, with a common theme: he solves human problems with computers and data. This post originally appeared on his blog, davidasboth.com In Part 1, I introduced the concept of Self-Organising Maps (SOMs). Now in Part 2 I want to step through the process of training and using a SOM – both the intuition and the Python code.

Virtually any data science experiment that uses a new machine learning algorithm requires testing across different scenarios. Simulated data allows one to do this in a controlled and systematic way that is usually not possible with real data. A convenient way to implement and re-use data simulation in Azure Machine Learning (AML) Studio is through a custom R module. Custom R modules combine the convenience of having an R script packaged inside a drag and drop module, with the flexibility of custom code where the user has the freedom of adding and removing functionality parameters, seen as module inputs in the AML Studio GUI, as needed. A custom R module has identical behavior to native AML Studio modules.

At their current, exponential rate of improvement, it is predicted that physical limitations in the human body such as strength and endurance will be vastly reduced due to nanotechnology modifying our genome and reprogramming our bodily systems. The human brain will have been entirely reverse-engineered allowing for mass replication, and artificial intelligence will be powerful enough to tackle problems as general as the human brain can, only with input & output speeds that are millions of times faster. I'll be 60 by then, which will probably be half the life expectancy of the average person. School children could outperform Olympic athletes. Families could grow to enormous groups of people spanning many generations.

The use of artificial intelligence will add impetus to the digital change in the financial market. An award-winning fund manager this week will launch a fund that is under the full control of artificial intelligence. Artificial intelligence and highly intelligent computers will revolutionize the world of asset management to an extent that there will be no place for human beings in this part of the industry. This analysis is not new. The term artificial intelligence dates back to the mid-twentieth century and has been repeated with every new step in the development.