NEW DELHI: Software jobs are still pretty lucrative: Only that robotics, artificial intelligence and Big Data now rake in the money, while mature application services sink fast in the pronounced migration of value toward emerging technologies. Initially built around cost benefits and an improving telecom infrastructure that would allow Indians to follow the US workday, technology is now battling a sweeping wave of de-globalization and automation: Emerging technologies where talent is scarce will likely be the winners when valuemigration is complete, according to hiring and compensation data. At least five search firms and HR heads at technology companies told ET that the demand for professionals with expertise in robotics, machine learning, artificial learning and data science has increased by up to 50% over the last year. Hirings at the top end of the technology spectrum will replace jobs that are repetitive in structure and content, creating a recruitment market premised largely on emerging technologies. "While low-skilled jobs will drop by 30%, automation is expected to increase the middle-skilled jobs by 8% and high-skilled recruitments will rise by 56%," said Debashis Patnaik, senior director, human resources, Dell EMC.

Parascript has introduced a data location, extraction and verification software solution that deploys template-less, neural network-based document extraction. Parascript says it has'productised' it's machine learning platform to support custom-developed recognition projects with much quicker turnaround than traditional rules-based approaches. The result is significantly faster production with more reliable and refined results. "Machine learning offers a whole new set of opportunities for organisations across many industries to more precisely streamline their operations and deliver rapid, accurate data to their clients," said Greg Council, Vice President of Marketing and Product Management. Traditional recognition and capture solutions often successfully use business rules to process information.

This is a replay of IBM's Machine Learning Event which took place in New York City on February 15, 2017. To learn more about IBM Machine Learning, please visit: https://ibm.biz/machinelearning In this video: Event Kickoff -- Machine Learning launch Overview -- Rob Thomas, General Manager, IBM Analytics Top Trends in Machine Learning & Cognitive Applications -- Mike Gualtieri, VP, Principal Analyst, Forrester Build a Better World -- Tooraj Arvajeh, Chief Engineering Officer, Blocpower Cognitive Analytics and Machine Learning for the Enterprise Town Hall -- Dinesh Nirmal, VP, IBM Analytics Platform Development, Stephanie Mitchko, Chief Technology Officer, Cadent Network, and Randy Halley, Executive Vice President, America First Credit Union Live Product Demonstration on Machine Learning -- Dr. Avijit Chatterjee, STSM Analytics IBM and Daniel Hernandez, Vice President Offering Management, IBM

There are seven key megatrends driving the future of enterprise IT. You can remember them all with the helpful mnemonic acronym CAMBRIC, which stands for Cloud Computing, Artificial Intelligence, Mobility, Big Data, Robotics, Internet of Things, CyberSecurity. In this post we dive deeper into Artificial Intelligence. Artificial Intelligence is the discipline of thinking machines. The field is growing dramatically with the proliferation of high powered computers into homes and businesses and especially with the growing power of smartphones and other mobile devices.

As organizations struggle with finding the delicate balance of cybersecurity and customer convenience, Mordecai Rosen of CA Technologies says behavioral analytics and machine learning will help. Rosen is senior vice president and general manager for the cybersecurity business at CA Technologies. He joined CA in August 2015 when it acquired Xceedium, where he was the COO. He has more than 25 years of technology experience, including founding and leading an early stage venture management firm. Rosen formerly served as senior vice president of corporate development and strategy at NetSec, a leading managed security services provider acquired by MCI/Verizon.

The world of Artificial Intelligence today is still centered around the aspiration for machines to understand: from virtual assistants capable of anticipating our needs and helping us drafting emails or handling our complex schedule, to self-driving cars, and personalized medicine. These are just some examples of how machines need to acquire, demonstrate, and apply understanding. Today's investments in public research and in the private sector are directed at cracking the nut of machines that understand. But the breadth of techniques goes well beyond what it did even ten years ago. In light of today's relevance of Cognitive Artificial Intelligence, we are happy to announce the Cognitive Artificial Intelligence Meetup #CAIM ("Kay-im").

Although artificial intelligence (as a set of technologies, not in the sense of mimicking human intelligence) is here since a long time in many forms and ways, it's a term that quite some people, certainly IT vendors, don't like to use that much anymore – but artificial intelligence is real, for your business too. Instead of talking about artificial intelligence (AI) many describe the current wave of AI innovation and acceleration with – admittedly somewhat differently positioned – terms and concepts such as cognitive computing or focus on several real-life applications of artificial intelligence that often start with words such as "smart", "intelligent", "predictive" and, indeed, "cognitive", depending on the exact application – and vendor. Despite the term issues, artificial intelligence is essential for and in, among others, information management, medicine/healthcare, data analysis, digital transformation, security (cybersecurity and others), various consumer applications, scientific advances, FinTech, predictive systems and so much more. There are many reasons why several vendors doubt using the term artificial intelligence for AI solutions/innovations and often package them in another term (trust us, we've been there). Artificial intelligence (AI) is a term that has somewhat of a negative connotation in general perception but also in the perception of technology leaders and firms. One major issue is that artificial intelligence – which is really a broad concept/reality, covering many technologies and realities – has become like a thing we talk about and also seem to need to have an opinion/feeling about, with thanks to, among others, popular culture.

"Ambiguity is pervasive" – true to its definition, as increasingly data getting generated, system connectivity reaching its peak, data and outcome are diverging. IT systems are evolving from "BIG DATA" to "BIGGER DATA" systems. Not all of this data is structured and easily consumable, thus challenge is posed by nexus of technology & "Data Greed". Having said this, fact is that future is found in ambiguity and chaos. We will never have complete and perfect information or a full understanding of data, system, experts, people, process and "partially hidden" technology.

If you play video games, imagine how much you sometimes empathize with the character you're controlling. You may even forget the separation between the two of you, experiencing the world as that character. Consider whether your control in these moments is different, both at a high level and in tiny movements, than what the character would do if you had simply written down a set of rules for it to act by. In that difference lies the promise of this method for developing character AI. In psychology research methodology, there's a broad consensus that if you want to know what someone would do in a situation, you don't ask them what they would do.

Facial recognition technology is something that most of us take for granted. We've casually noticed that our smartphones now organize photos by people, or that Facebook somehow always knows the right friends to tag. But until recently, most people haven't realized that this technology is less of a "cool trick" and will actually significantly shape the way we do business in the next five to ten years. The technology is already being tested out in many different industries for vastly different purposes. For example, security scanners at the airport use it to allow e-passport holders to clear customs more easily; as facial recognition improves, Customs and Border Protection will be able to weed out travelers with fake passports more easily.