Artificial intelligence (AI) stands out as a transformational technology of our digital age--and its practical application throughout the economy is growing apace. For this briefing, Notes from the AI frontier: Insights from hundreds of use cases (PDF–446KB), we mapped both traditional analytics and newer "deep learning" techniques and the problems they can solve to more than 400 specific use cases in companies and organizations. Drawing on McKinsey Global Institute research and the applied experience with AI of McKinsey Analytics, we assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. Our findings highlight the substantial potential of applying deep learning techniques to use cases across the economy, but we also see some continuing limitations and obstacles--along with future opportunities as the technologies continue their advance. Ultimately, the value of AI is not to be found in the models themselves, but in companies' abilities to harness them.

With significant growth in investments in Artificial Intelligence (AI) technology, its advancement has never been better. It all began decades ago. Although AI is not new, a broad set of powerful technologies are emerging under it – Robotics, Deep Learning, Reinforcement Learning and Facial Recognition are just a few to count. NLP is a branch of computer science and AI that works on the machine's ability to understand human language – on understanding the sentence structure, its meaning, sentiment and the intent behind it. ServiceNow, a platform to automate business processes across enterprises, has recently acquired Parlo for their NLP tech – to bring NLP and understanding into enterprise systems.

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Artificial intelligence (AI) and machine learning (ML) are terms being used in various industries to explain their latest foray into next-generation technology. Add deep learning (DL) to the mix and things start to get really confusing. While AI and ML can be used interchangeably in many contexts, there are some serious differences between them. For businesses looking to embark on exploring new AI solutions, distinguishing one from the other is critical to identifying which your business needs and what can help it the most. Everything from Apple's Siri voice assistant to self-driving cars falls under AI.

Artificial intelligence (AI is intelligence displayed by machines. An example of this is machine learning. There are two categories of machine learning: supervised (learning by example) and unsupervised (self-learning). Supervised machine learning requires "labels" in the data. "Labels" are the answers that enable learning by example, such as malware.

Lobe is an easy-to-use visual tool that lets you build custom deep learning models, quickly train them, and ship them directly in your app without writing code.

At Sentiance, we developed a platform that takes in smartphone sensor data such as accelerometer, gyroscope and location information, and extracts behavioral insights. Our AI platform learns about the user's patterns and is able to predict and explain why and when things happen, allowing our customers to coach their users and engage with them in the right way, at the right time. An important component of our platform is the venue mapping algorithm. The goal of the venue mapper is to figure out what venue you are visiting, given an often inaccurate location measurement coming from the smartphone's location subsystem. Figure 1: Left: Venue mapping means estimating which of the neighboring venues a user was actually visiting. Right: Human intuition helps us to quickly discard unlikely venues, such as the lifeguard station when a user is visiting the beach. Although venue mapping is a difficult problem altogether and will be material for a future blog post, a simple sense of human intuition based on the surrounding geography of the area often goes a long way.

Is the algorithm that runs the police surveillance system in my city paranoid? Marvin the android in Douglas Adams' Hitchhikers Guide to the Galaxy had a pain in all the diodes down his left-hand side. Is that how my toaster feels? This all sounds ludicrous until we realize that our algorithms are increasingly being made in our own image. As we've learned more about our own brains, we've enlisted that knowledge to create algorithmic versions of ourselves.

Suddenly, artificial intelligence is everywhere. Are you AI ready if not then be ready to be read in history books. Are we not missing the fact that artificial intelligence is about the people, not the machines. Technology and non technology companies are now investing and brining out the real and materialistic values of Artificial Intelligence to the real world. Its almost after a frustrating and hard work of decade AI has started delivering values.

This programming library written for Java offers a computing framework with a wide support for deep learning algorithms. Considered as one of the most innovative contributors to the Java ecosystem, it is an open source distributed deep learning library brought together with an intention to bring deep neural networks and deep reinforcement learning together for business environments. It usually serves as a DIY tool for JAVA and has the ability to handle virtually limitless concurrent tasks. It is extremely useful for identifying patterns and sentiment in speech, sound and text. It can also be used for detection of anomalies in time series data like financial transactions, clearly showcasing that it is designed to be used business environments rather than as a research tool.