AI-powered business applications will start to infiltrate companies other than technology firms. Employees, teams and entire departments will champion process re-engineering efforts with these intelligent systems whether they realize it or not. As each individual app eliminates a task, employees will automate many of the mundane parts of their jobs and assemble their own stack of AI-powered apps. Teammates eager to be productive and stay competitive will follow, along with team managers who are looking to execute on cost-cutting efforts.

Computers that draw and analyze data from the Internet are ubiquitous in many industries. But the new wave of deep-learning machines makes this technology far more compelling for military use. This is attributed to the proliferation of data collectors like drones and smart devices -- known as the "Internet of things" -- combined with advances in software algorithms and the vast computing power available in the cloud. How the Pentagon could use smart machines to gain an edge on the battlefield is now the subject of many closed-door conversations and exchanges with the intelligence community and Silicon Valley firms.

Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks. At the same time, every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient descent (e.g. These algorithms, however, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This blog post aims at providing you with intuitions towards the behaviour of different algorithms for optimizing gradient descent that will help you put them to use. We are first going to look at the different variants of gradient descent. We will then briefly summarize challenges during training. Subsequently, we will introduce the most common optimization algorithms by showing their motivation to resolve these challenges and how this leads to the derivation of their update rules.

The latest trend in data science is artificial intelligence. It has been all over the news for tackling a bunch of interesting questions. Almost all of these applications are based (at some level) on using variations on neural networks and deep learning. These models are used like any other statistical or machine learning model. They involve a prediction function that is based on a set of parameters. Using a training data set, you estimate the parameters.

Today, Google announced the beta release of its Google Cloud Vision API. The API was designed to empower applications to both see and understand images submitted to the API. With powerful features such as label/entity detection, optical character recognition, safe search detection, facial detection, landmark detection, and logo detection; the Cloud Vision API gives applications unprecedented ability to comprehend the situation within an image. With the new API, Google enters a rapidly developing market where both startups and major enterprises are producing cutting edge technology. From Microsoft, with its Project Oxford, to niche startups like Cognitec and Lambda Labs; image analysis is proving to be an attractive space as it appeals across industries from marketing to security.

On March 23rd, 2016, Microsoft released a chunk of artificial intelligence onto the Internet. Dubbed "Tay," this was a bot1 designed to chat with real human beings, simulating a 19-year-old female, learning from those humans how to act more human. On March 24th, less than 24 hours later, Microsoft put Tay to sleep. She was spewing neo-Nazi, xenophobic, racist tweets. Apparently, Tay had been learning from the wrong humans--those who had chosen to teach her.

This publication is not an official publication of Colorado State University, but is published by an independent corporation using the name'The Rocky Mountain Collegian' pursuant to a license granted by CSU.

Artificial intelligence is in the news on a daily basis--some estimates suggest millions of knowledge workers will be replaced by AI in the coming decade. Should we celebrate or worry? Artificial intelligence, dreamed about (or feared) for decades, will undoubtedly be one of the defining technologies of our generation. Elements of AI are already working their way into our daily life--self-driving cars, financial robo-advisors, and "personal assistants" that suggest what we should be doing this afternoon. Vast sums have already been invested in AI: more than 1 billion in VC investment since 2010; 1 billion in AI R&D by Toyota alone; and the Open AI initiative, which has pledged 1 billion. It seems the promise of AI is being realized as we speak--while at the same time, some of our leading technologists and policy makers are ringing alarm bells about the uncontrolled development of these new capabilities.

At Vin65, we like to be progressive. We try new things; sometimes they work, sometimes they don't. The Vin65 support team is now powered by Alexa (Amazon's artificial intelligence device). You can ask it anything that you would have asked the previous team. As you ask additional questions, Alexa learns, which means that every day, every hour, every minute, Alexa learns.

Conversation fillers such as "hmm" and "uh-huh" may seem like insignificant parts of human conversation, but they're critical to improving communication between humans and artificial intelligence. So argues Alan Black, a professor in the Language Technologies Institute at the Carnegie Mellon School of Computer Science, who specializes in speech synthesis and ways to make artificially intelligent speech sound more real. Both Siri and Cortana incorporate aspects of Black's work, he says. But for the most part, such technologies still boil down to a pretty simple pattern: The human speaks, then the machine processes that speech and answers. "It's not really how humans interact," Black said in an interview on Friday. Key to making such conversations more natural are pauses, fillers, laughs and the ability of speakers to anticipate and complete each other's sentences -- all of which help build rapport and trust.