Thomas Dinsmore is the Director of Product Marketing for Data Science at Cloudera. Previously, as an independent consultant, he provided machine learning market insight to private clients seeking intelligence about the machine learning marketplace. Before launching his consultancy in 2015, he served as an analytics expert for The Boston Consulting Group; Director of Product Management for Revolution Analytics (Microsoft); Solution Architect for IBM Big Data, SAS and PriceWaterhouseCoopers. In a thirty-year career, he has led or contributed to analytic solutions for more than five hundred clients across vertical markets and around the world. Thomas holds an MBA in Accounting and Decision Sciences from the University of Pennsylvania - The Wharton School and a BA from Boston University.

Data's emergence as a critical business asset has been a persistent theme in every Tech Trends report, from the foundational capabilities needed to manage its exploding volumes and complexity to the increasingly sophisticated analytics tools techniques available to unearth business insights from data troves. By harnessing analytics to illuminate patterns, insights, and opportunities hidden within ever-growing data stores, companies have been able to develop new approaches to customer engagement; to amplify employee skills and intelligence; to cultivate new products, services, and offerings; and to explore new business models. Today, more and more CIOs are aggressively laying the foundations needed for their organizations to become more insight-driven. Artificial intelligence (AI)--technologies capable of performing tasks normally requiring human intelligence--is becoming an important component of these analytics efforts. Yet AI is only one part of a larger, more compelling set of developments in the realm of cognitive computing. The bigger story is machine intelligence (MI), an umbrella term for a collection of advances representing a new cognitive era. We are talking here about a number of cognitive tools that have evolved rapidly in recent years: machine learning, deep learning, advanced cognitive analytics, robotics process automation, and bots, to name a few.

For decades, the world's smartest game-playing humans have been racking up losses to increasingly sophisticated forms of artificial intelligence. The defeats began in the 1990s when IBM's Deep Blue computer conquered chess master Garry Kasparov. More recently, Ke Jie -- until then the world's best player of the ancient Chinese board game "Go" -- was defeated by a Google computer program in May. Now the AI supergamers have moved into the world of e-sports. Last week, an artificial intelligence bot created by the Elon Musk-backed start-up OpenAI defeated some of the world's most talented players of Dota 2, a fast-paced, highly complex, multiplayer online video game that draws fierce competition from all over the globe.

This talk will demonstrate how to harness a deep-learning framework such as Tensorflow, together with Pandas and Numpy, to implement recommendation models. EVENT: PyData, London, 2017 SPEAKER: Guillaume Allain ATTRIBUTION: Original content of this video was published under the Creative Commons Attribution license (reuse allowed).

DeepMind, a British artificial intelligence firm acquired by Google in 2014, is building an AI capable of "imagination" and understanding the consequences of previous actions. In two research papers submitted last week, DeepMind describes how the AI would be able to "construct a plan" and remember information that may be important in the future. "What differentiates these agents is that they learn a model of the world from noisy sensory data, rather than rely on privileged information such as a pre-specified, accurate simulator," said the DeepMind research team to Wired. "Imagination-based approaches are particularly helpful in situations where the agent is in a new situation and has little direct experience to rely on, or when its actions have irreversible consequences and thinking carefully is desirable over spontaneous action." Like most of DeepMind's research, it used video games to test the AI's proficiency.

Deep learning is one of the most exciting elements of artificial intelligence, but it's also one of the slowest moving. IBM Fellow Hillery Hunter calls deep learning, which enables computers to extract meaning from images and sounds with no human intervention, a "rarified thing, off in an ivory tower," but a recent breakthrough by the company promises to make it more accessible -- and a lot faster too. Last week IBM Corp. (NYSE: IBM) announced that its software was able to take the speed of training deep neural networks down from weeks to hours, or hours to minutes depending on the use case, while also improving the accuracy. It accomplished this by increasing the scalability of its training applications across 256 Nvidia Corp. (Nasdaq: NVDA) GPUs in 64 IBM Power systems. IBM was looking specifically at image recognition and was able to train its model in 50 minutes.

According to multiple reports over the past week, Facebook recently shut down its AI-fueled chatbots after they created a secret language for communicating--a language that was not of human origin. This is part of the Facebook Artificial Intelligence Research (FAIR) project designed to create bots that can "negotiate" with other bots and humans. While this sounds freakishly ominous, Facebook is far from alone in this debate. Google's DeepMind recently experienced a similar event when it spawned a new shorthand language, and a number of research labs (including Elon Musk's OpenAI) are currently working on similar negotiation systems, considered a key step in the development of AI. Why did this particular announcement gain traction?

Consumers have spoken, artificial intelligence is a profitable industry. From Amazon to Google to Apple, major tech companies have made inroads, crafting intelligent software -- housed in sleek, accessible hardware -- that has drawn massive customer attention. This trend is set to soon move out of home devices, like Echo and Google Home, and onto the streets, where self-driving cars leverage major breakthroughs in computer vision so passengers can ride easy, knowing their vehicles will "see" and react to objects and road signs in real time without their input. In fact, cars with these features are already popular with consumers, and by 2020 10 million cars with self-driving attributes will be on roadways. But while there are plenty of ways for consumers to leverage AI, enterprises are asking themselves how they can get in on this wave of innovation. And a big part of the answer lies at the crossroads of computer vision and an emerging field known as transfer learning.

The Deep Learning Summit is the next revolution in artificial intelligence. How can multiple levels of representation and abstraction help to make sense of data such as images, sound, and text.

There are over 6500 articles about AI/ML/DL written on medium, and if you would want to read them all, you would need to spend a full month reading 14 hours a day. So … this is why we have data and machines that can do this for us. After downloading 6 million posts from medium.com in the winter of 2016, i was able to filter just the posts that are tagged with either AI/ML or Deep Learning, and sort them by the number of recommends. In total, i got 6,546 articles, written by 3,528 users. All of this articles have a total of 153,567 recommends.