Deep learning is in the early stages of unlocking tremendous economic value outside its impact in large technology companies. While the algorithms have revolutionized consumer experiences in domains as varied as speech interfaces, image search, language translation and game AI, enterprises are still in the early stages with efforts to apply these algorithms to other areas - such as improving automotive speech interfaces, agricultural robotics, finding anomalies in IoT data, and more. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. The team at Nervana Systems (recently acquired by Intel) aims to change this, and have built a deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. At the 2016 Deep Learning Summit in London, Arjun Bansal, Co-founder and VP of Algorithms at Nervana, presented Catalyzing Deep Learning's Impact in the Enterprise.

The last few years have seen IBM Corp. apply its Watson artificial intelligence to a wide variety of areas ranging from speech recognition to drug research. But the company can't address every single use case alone, a limitation that it's now looking to remove. IBM today unveiled a standalone implementation of the machine learning technology powering Watson that will enable organizations to adapt its capabilities for their specific requirements. The IBM Machine Learning platform aims to reduce the amount of effort it takes to develop, train and deploy a custom analytics model. "We are telling clients that you can get the power of machine learning across any type of data, whether its data in a warehouse, a database, unstructured content, email you name it, we are bringing machine learning everywhere," Rob Thomas, general manager of platform development at IBM Analytics, said in an interview today with theCUBE, SiliconANGLE Media Inc.'s mobile video studio.

As organizations plan their customer strategies they foresee an onslaught of customer interactions coming their way. The fallacy of believing that adding self-service options will decrease customer requests is now known. When your organization opens channels for customer to interact with you, even with self-service options, customers will interact with you more. This engagement is a good thing. But the only way to handle all of your volume – in an effective manner without adding more staff – is by leveraging Artificial Intelligence. Listening to John Pisarek of Interactions at Call Center Week Winter the scenario of about projecting more customer interaction volume and not getting additional staff to handle it is a common reality for many contact center leaders.

Not all core concepts from software engineering translate into the machine learning universe. Here are some differences I've noticed. Divide and Conquer A key technique in software engineering is to break a problem down into simpler subproblems, solve those subproblems, and then compose them into a solution to the original problem. Arguably, this is the entire job, recursively applied until the solution can be expressed in a single line in whatever programming language is being used. The canonical pedagogical example is the Tower of Hanoi.

Note: For training purposes, I highly recommend building a training/validation set using a steering wheel controller, and you'll want a labeled set of about 40K samples (though I have heard you can get by with much fewer, even unaugmented - my sample set actually used augmentation of about 8k real samples to boost it up to around 40k). You'll also want to use GPU and/or a generator or some other batch processing for training (otherwise, you'll run out of memory post-haste).

Ford Motor is spending $1 billion to take over a budding robotics startup to acquire more expertise needed to reach its ambitious goal of having a fully driverless vehicle on the road by 2021. The big bet announced Friday comes just a few months after the Pittsburgh startup, Argo AI, was created by two alumni of Carnegie Mellon University's robotics program, Bryan Salesky and Peter Rander. The alliance between Argo and Ford is the latest to combine the spunk and dexterity of a technologically savvy startup with the financial muscle and manufacturing knowhow of a major automaker in the race to develop autonomous vehicles. Last year rival General Motors paid $581 million (roughly Rs. 3,890 crores) to buy Cruise Automation, a 40-person software company that is testing vehicles in San Francisco. The Argo deal marks the next step in Ford's journey toward building a vehicle without a steering wheel or brake pedal by 2021 - a vision that CEO Mark Fields laid out last summer.

The convergence of major trends in the digital landscape is a phenomenon that redefines the future of the IT industry. However, these changes must be embraced with agility and diligence. Artificial Intelligence or AI is the most intriguing development ever made in this field. It is a vast concept that involves many disruptive technologies including machine learning. Now, the researchers are eyeing an iconic change to evolve by the combination of big data and AI. AI has become pervasive in every industry.

Despite more than 50 years of experimentation with artificial general intelligence (AGI), mankind still hasn't achieved the goal of creating a single machine that can perform all cognitive tasks as well as humans can. But what if there was a way to attain AGI-level artificial intelligence (AI) using today's technology? The fact is that artificial narrow intelligence (ANI) systems are already available that can approximate the capabilities of AGI by employing multiple cognitive engines in a parallel-processing architecture. And with the number of commercially-available cognitive engines expanding exponentially, this approach will enable ANI to rapidly overtake and even surpass human capabilities. Moreover, with the AI market set to expand by nearly a factor of 60 from 2016 to 2022, this method represents the best way for companies to capitalize on the industry's growth potential.

It's relatively easy to develop a drone that can fly on its own, but it's another matter developing one that can navigate the many obstacles of real life. That's where Microsoft thinks it can help. It just published an open source simulator, the Aerial Informatics and Robotics Platform, that helps designers test and train autonomous machines in realistic conditions without wrecking expensive prototypes. The tool has vehicles move through randomized environments filled with the minutiae you see on a typical street, such as power lines and trees -- if your drone can't dodge a tree branch, you'll find out quickly. You can see what the vehicle would see (including simulated sensor data), and the software ties into both existing robotic hardware platforms and machine learning systems to speed up development.

Around the time Raj Bhan injured himself, he decided to part ways with the data team at Netflix. He'd been training for a half marathon and plucked a regimen from the web. Unfortunately, he had grouped himself into the wrong bucket and hurt his leg. "That struck a chord," he says. "People can suffer adverse effects from being in the wrong program at the wrong time. Fitness programs really need to be personalized."