Big Data


Big data, big change

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Robots, in the past, were limited to performing some assigned prototype jobs by machines. These machines worked on preloaded instructions, and many were open-loop systems wherein, corrective measures were not included in the coded instructions. As the generations of robots are evolving, feedback systems with artificial intelligence (AI) have become an integral part for the robots. This new trend has resulted in a revolution in robotics which includes machine learning as a part of AI and robotics. Robotics is now not just limited to factories, industries and corporates to perform pre-assigned jobs, but have entered every aspect of life, including social life.


Big data, big change

#artificialintelligence

Robots, in the past, were limited to performing some assigned prototype jobs by machines. These machines worked on preloaded instructions, and many were open-loop systems wherein, corrective measures were not included in the coded instructions. As the generations of robots are evolving, feedback systems with artificial intelligence (AI) have become an integral part for the robots. This new trend has resulted in a revolution in robotics which includes machine learning as a part of AI and robotics. Robotics is now not just limited to factories, industries and corporates to perform pre-assigned jobs, but have entered every aspect of life, including social life.



Global Big Data Conference

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I keep having the same conversation with companies that want to brief me on their latest AI-related news. It goes something like this: They have some new feature or improved stats or fresh achievement that's specific to their business. Then we get to the AI part, and I start to salivate, asking them to tell me more. The reply is increasingly some version of "We use [a mostly unexciting AI tool] to do it." Being accustomed to covering technology for technology's sake, I always feel a little let down.


TCSHUG is BACK - Check out our first meeting!

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Edge2AI Talk Details Machine learning models find patterns in data and can automate decisions that are critical for businesses today. Please join us for a chat with Jordan Birdsell from phData and our usual great pizza, drinks, and conversation! Jordan Birdsell will provide you with a 4 step action plan for how to build an enterprise Machine Learning application that can solve real business problems. The tutorial introduces you to building an end-to-end machine learning solution using MiNiFi, NiFi, Kafka, Kudu, Impala, Spark, and CDSW. With more than 10 years of experience launching successful Analytics and Data Science organizations at large enterprises, Jordan Birdsell now brings his expertise and knowledge to our clients around the globe.


Big Data And AI Could Make Traditional Developers…Extinct?

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Back in 2010, a team of self-described "data nerds" founded Sumo Logic. The vision was to build a platform to deliver–as a cloud native service–machine data analytics for anybody. It definitely turned out to be the right focus. Consider that today Sumo Logic analyzes 100 petabytes of data on a daily basis (to put this into perspective, a petabyte consists of 1,000 terabytes or about five years of the Earth Observing System). The platform also performs 30 million searches and handles a whopping 500 trillion record queries every day.


Financial Institutions Must Adapt and Innovate or Be Left Behind

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Outdated systems are preventing one in four Asia Pacific banks from investing in disruptive technologies, says Greg Watson, citing a recent Fenergo survey. New financial technologies are completely transforming the finance industry, shaking up traditional ways of thinking and reshaping financial services as we know it. As banks begin to explore the benefits of new technologies such as big data analytics and artificial intelligence (AI) they will soon reap the rewards of increased efficiencies and improvements to client life cycle management (CLM). However, not all banks across APAC are getting on board. A recent survey showed that legacy infrastructure is preventing almost one in four (24%) banks in APAC from investing in new, disruptive technologies.


Wells Fargo: Artificial intelligence and machine learning a 'double-edged sword' 7wData

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Although a bank's business has basically stayed the same over the last couple of decades, how a bank operates has drastically shifted. The fundamental pillars of a bank, according to Mike Telang, executive vice president and head of enterprise architecture at Wells Fargo, now focus on mostly security, regulation, and innovation. Everything you need to know about AI An executive guide to artificial intelligence, from machine learning and general AI to neural networks. When it comes to technology trends in the financial services industry, Telang said its impact on a bank depends heavily on where the bank is in its maturity lifecycle. "I think it depends on the lifecycle of the maturity of where you are as a bank at that point in time and you've got to find a solution for that ... you have a lot of trends like blockchain, you have a lot of trends like security, so we are applying the technology to the use case that suits us best," Telang said during a panel session at VMworld 2019 in San Francisco last week.


Navigating The High Seas Of Data

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After the term "big data" became mainstream, the related concept of a "data lake" also gained currency, applying to repositories of information so large they were reminiscent of vast bodies of water. Not unlike the secret denizens of a mysterious Scottish loch, hidden in the murky depths or threading unobserved among the currents coursing through its farthest volumes, there could be elusive secrets of great value lurking undiscovered. However, according to 2016 Forrester data, nearly 75% of all data retained by an organization is never analyzed or used. I've witnessed this process firsthand, having worked in the data storage industry since 1990 -- at the first dedicated fileserver company (Auspex Systems), and then during the glory days (1995–2007) at NetApp before embarking on a series of startups and a stint at EMC (now Dell). Through it all, I saw compounding data growth on platforms and in circumstances that expanded at astonishing rates to keep up.


The Role of AI & Big Data Analytics in the Future of Healthcare

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As the world is booming with technology, why we should leave the healthcare sector behind. As technologies like Artificial Intelligence and Augmented Reality is revolutionizing various industry sectors, it definitely plays a significant role in healthcare future. As healthcare is one of the world's fastest as well as largest growing industry and adding technology will make a bigger and better difference. However, reports suggest that the healthcare IT market is currently a USD 187.6 billion market. With a 15.8% forecast CAGR growth, the Healthcare IT market is expected to reach 390.7 USD billion market shares by 2024.