Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. Making an approach to pursue the most advanced technology takes a lot of innovation and it is exactly what Mantra Labs has been doing. If you are keen to solve real world problem using AI, Drop us a line hello@mantra.ai

This article is by Featured Blogger Daniel Newman from his LinkedIn page. Decades ago, artificial intelligence was a distant concept meant for future generations. We're seeing advancements every day that will make our lives easier and more efficient. And as a way to make big data useful, AI might be a game changer. Data collection has always been an important facet of business, but never before have we been able to access it in such a responsive and useful manner.

Ravi is a Senior Managing Consultant at IBM (Analytics Platform, North American Lab Services). Ravi is a Distinguished IT Specialist (Open Group certified) with more than 23 years of I/T experience. He has a Masters degree in Business Administration (MBA) from University of Nebraska, Lincoln. He had contributed to 7 other redbooks in the areas of Database, Analytics Accelerator and Information Management tools. IBM SPSS Modeler is a powerful analytic tool that supports all phases of data analytics process, including data preparation, model building, deployment, and model maintenance.

The development of Artificial Intelligence (AI) has been remarkable and, in recent years, has experienced several breakthroughs. Will 2017 continue this trend? Commercial enterprises are procuring and developing systems that use AI to aid in their corporate goals, enhancing their performance through the use of autonomy. By exploiting advancements in big data analytics, processing power and clearer computer systems and networks, companies can use automation and AI to augment their current work processes and generate methods of long-term value creation. The stage is set, however, for AI to rise higher than ever before as large players including Apple, Facebook, Google and Microsoft all open-source or share their latest research in AI, to advance collective advancement.

When Google's AlphaGo algorithm beat the Go world champion in 2016, it became apparent that machine learning had arrived and would significantly shape the future. As a new breed of software that is able to learn without being explicitly programmed, machine learning will be able to access and analyze structured as well as unstructured data at a level of complexity that human minds find difficult to grasp. Looking at the quality of today's voice recognition and image recognition software, as well as at the capabilities of self-driving cars, we can already see how self-learning algorithms may influence our lives. Computer scientists have been pursuing artificial intelligence since the 1950s. Now, thanks to recent advances in technology, including Big Data processing, increased computing power, and better algorithms, computers have begun to compete with, or even surpass, abilities once considered exclusive to humans.

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).