Deep learning continues to gather steam in corporate computing, and IBM and Nvidia are teaming up to help accelerate the foundation behind artificial intelligence breakthroughs. The two companies are collaborating on a deep learning solution that melds technology from both companies in a bid to speed up the process of training computers to think and learn more like humans do. A new software toolkit available today called IBM PowerAI is designed to run on the recently announced IBM server built for artificial intelligence that features Nvidia NVLink technology optimized for IBM's Power Architecture. The hardware-software solution provides more than two times the performance of comparable servers with four graphics processing units (GPUs) running AlexNet with Caffe. The same four-GPU Power-based configuration running AlexNet with BVLC Caffe can also outperform 8-M40 GPU-based x86 configurations, making it the world's fastest commercially available enterprise systems platform on two versions of a key deep learning framework, the companies said.

With growing interest in neural networks and deep learning, individuals and companies are claiming ever-increasing adoption rates of artificial intelligence into their daily workflows and product offerings. Coupled with breakneck speeds in AI-research, the new wave of popularity shows a lot of promise for solving some of the harder problems out there. That said, I feel that this field suffers from a gulf between appreciating these developments and subsequently deploying them to solve "real-world" tasks. A number of frameworks, tutorials and guides have popped up to democratize machine learning, but the steps that they prescribe often don't align with the fuzzier problems that need to be solved. This post is a collection of questions (with some (maybe even incorrect) answers) that are worth thinking about when applying machine learning in production.

Tom Davenport: It's common to say that AI handles tasks that were previously only addressable by humans. But I think to exclude more traditional forms of automation, you also have to define it as performing tasks requiring a high level of expertise, insight or perception. Davenport: I found that while many companies are still doing a good bit of work in analytics and big data, they are less interested in hearing or reading about those topics. And to me, AI and cognitive technologies are a straightforward extension of analytics in most cases. Most of the models are statistical in nature, and analytical people are logical candidates to push AI forward in organizations.

Google's DeepMind AI Research News & Update: Company Works With Blizzard To Use'Starcraft ... Artificial Intelligence Could Not Replace CEOs...Yet, Study Says Back to the Future 2.1 – Will Amazon Echo & Google Home play a big part in lives in 2017? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Facebook's deep-learning artificial intelligence systems have learned to recognize your friends in your photos, and Google's AI has learned to anticipate what you'll be searching for. But there's no need to feel left out, even if your company's computers haven't learned much lately. A growing number of tech giants and startups have begun offering machine learning as a cloud service. That means other companies and startups do not need to develop their own specialized hardware or software to apply deep learning--the high-powered version du jour of machine learning--to their specific business needs. "Deep-learning algorithms dominate other machine-learning methods when data sets are large," says Zachary Chase Lipton, a deep-learning researcher in the Artificial Intelligence Group at the University of California, San Diego, who has examined cloud AI services from companies such as Amazon and IBM.

You can now hold neural nets in the palm of your hand. Last week, Facebook unveiled a tool called "style transfer" that applies visual effects to live phone video in real time. Making your clips look like an episode of The Simpsons or a Van Gogh painting may seem gimmicky, but the artificial intelligence required to do this would usually need to run on massive servers. Google squeezed a neural network into its Google Translate app last year. Now, Facebook has developed a deep learning system called Caffe2Go that is condensed enough to run directly in mobile apps on iOS and Android.

The next frontier for artificial intelligence may involve teaching computers to distinguish albacore tuna from its yellowfin cousin. The Nature Conservancy, an environmental non-profit, is working with several Pacific Island nations and a big tuna fishing company to more easily count and identify fish caught at sea using cutting edge technology. The goal is to use trendy artificial intelligence techniques like deep learning to help fishermen reduce the number of protected animals like sharks and turtles that are accidentally caught along with the tuna. The Nature Conservancy hopes that the program could prevent overfishing and help threatened and endangered sea life recover without putting fishermen out of work. "We have real optimism that data science community can help us differentiate a turtle from a tuna, and flag when a shark comes on board," said Mark Zimring, a project director for The Nature Conservancy.

Just over five years ago, IBM's Watson supercomputer crushed opponents in the televised quiz show Jeopardy. It was hard to foresee then, but artificial intelligence is now permeating our daily lives. Since then, IBM has expanded the Watson brand to a cognitive computing package with hardware and software used to diagnose diseases, explore for oil and gas, run scientific computing models, and allow cars to drive autonomously. The company has now announced new AI hardware and software packages. The original Watson used advanced algorithms and natural language interfaces to find and narrate answers.

Google will buy London-based artificial intelligence company DeepMind. The Information reports that the acquisition price was more than $500 million, and that Facebook was also in talks to buy the startup late last year. DeepMind confirmed the acquisition to us, but couldn't disclose deal terms. The acquisition was originally confirmed by Google to Re/code. Google's hiring of DeepMind will help it compete against other major tech companies as they all try to gain business advantages by focusing on deep learning.

Developed back in the 50s by Rosenblatt and colleagues, this extremely simple algorithm can be viewed as the foundation for some of the most successful classifiers today, including suport vector machines and logistic regression, solved using stochastic gradient descent. The convergence proof for the Perceptron algorithm is one of the most elegant pieces of math I've seen in ML. Most useful: Boosting, especially boosted decision trees. This intuitive approach allows you to build highly accurate ML models, by combining many simple ones. Boosting is one of the most practical methods in ML, it's widely used in industry, can handle a wide variety of data types, and can be implemented at scale.