In an engaging TED talk recorded recently, economist David Autor points out that in the 45 years since the introduction of Automated Teller Machines (ATMs), the number of human bank tellers doubled from a quarter of a million to half a million. He argues that this demonstrates that automation does not cause unemployment – rather, it increases employment. He says ATMs achieved this feat by making it cheaper for banks to open new branches. The number of tellers per branch dropped by a third, but the number of branches increased by 40%. The ATMs replaced a big part of the previous function of the tellers (handing out cash) but the tellers were liberated to do more value-adding tasks, like selling insurance and credit cards.

It is hardly possible in real life to develop a good machine learning model in a single pass. ML modeling is an iterative process and it is extremely important to keep track of your steps, dependencies between the steps, dependencies between your code and data files and all code running arguments. This becomes even more important and complicated in a team environment where data scientists' collaboration takes a serious amount of the team's effort. Today, we are pleased to announce the beta version release of new open source tool -- data version control or DVC. DVC is designed to help data scientists keep track of their ML processes and file dependencies in the simple form of git-like commands: "dvc run python train_model.py

Apache Spark has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these models to a production environment? How do I embed what I have learned into customer facing data applications? In this latest Data Science Central webinar, we will discuss: Best practices on how customers productionize machine learning models Case studies with actual customers Live tutorials of a few example architectures and code in Python, Scala, Java and SQL Speaker: Richard Garris, Principal Solutions Architect -- Databricks Inc. Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

The Mirai is Toyota's car of the future. It runs on hydrogen fuel cells, gets 312 miles on a full tank and only emits water vapor. So, to target tech and science enthusiasts, the brand is running thousands of ads with messaging crafted based on their interests. The campaign was written by IBM's supercomputer, Watson. After spending two to three months training the AI to piece together coherent sentences and phrases, Saatchi LA began rolling out a campaign last week on Facebook called "Thousands of Ways to Say Yes" that pitches the car through short video clips.

Java is a programming language expressly designed for use in the distributed environment of the Internet. It was designed to have the "look and feel" of the C language, but it is simpler to use than C and enforces an object-oriented programming model. The exercises and presentation of content where extremely helpful. This is the first instruction manual I've used where I actually found myself reading all of the lessons instead of just skipping ahead to the exercises. Syntax is discussed artfully, leaving more room for an exploration of concepts and practices - meaning that someone new to OOP will understand not only what to do but the best way to do it. Highly recommended for people ready to learn how to program.

Watching this week's I/O livestream I came away somewhat awestruck by Google's vision. Gone are the days of talking about tablets and phones. I hope I'm not alone in feeling that this was one of the more complex keynotes at the annual conference for developers. A times it felt like sitting in on a first year university engineering class. The big picture seems to be either H.G. Wells utopia or Orwellian dystopia.

I believe that there is a misconception that Artificial Intelligence is - or will be - a single piece of technology that should be bolted onto a business process to make it'smart' and/or "independent" from human intervention. My experience to date is that the answer is more complex and more interesting. Rather than a single solution, the real "intelligence" is in how a set of technologies are combined to create a solution. It is similar to our perception of human intelligence – this isn't built on a single element, it's a combination of senses, experiences, and knowledge. I looked at a variety of solutions that are deemed to display artificial intelligence and concluded that they had 5 attributes in common based on a fusion of smart processes and intelligent automation.

An engineer at IBM once told me that the future of information technology could be summed up in a few key terms: mobility, cloud computing, the internet of things, and automation and artificial intelligence. It is worth bearing in mind that automation and artificial intelligence are becoming more and more prominent topics within the public sphere, especially with the emergence of powerful AI like IBM's Watson and DeepMind's AlphaGo. These forms of artificial intelligence, also referred to as robots or bots for short, don't necessarily take up physical space. Instead, they are programs, stored on a desktop or a cloud, that have the ability to learn and adapt to different situations as opposed to earlier programs that were more rigid. Because of their learning capabilities, these robots can perform tasks that were deemed impossible by earlier programmers: Robots can write stories, they can understand human speech, and they can diagnose a patient better than their own doctor can.

Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. CEO Sundar Pichai and other top executives brought Google's audacious ambition into sharper focus Wednesday at an annual conference attended by more than 7,000 developers who design apps to work with its wide array of digital services. Among other things, Google unveiled new ways for its massive network of computers to identify images, as well as recommend, share, and organize photos. It also is launching an attempt to make its voice-controlled digital assistant more proactive and visual while expanding its audience to Apple's iPhone, where it will try to outwit an older peer, Siri. The push marks another step toward infusing nearly all of Google's products with some semblance of artificial intelligence – the concept of writing software that enables computers to gradually learn to think more like humans.