information technology services


Why Learn Machine Learning and Artificial Intelligence?

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Machine learning, artificial intelligence (ML & AI) and big data form up a new niche area that is seeing a fast-paced growth rate in India. To clarify terminologies for a layperson, AI is basically all about mimicking human intelligence in machines, ML is a sub-set of AI and is about techniques that enable these machines to continuously learn on their own through data and perform a desired set of processes. Big Data analytics is about extracting huge data and observing unanticipated patterns from the same, while ML uses the same to provide incremental data/information to help the machine learn on its own. Data science and big data industry in India is growing at 33per cent CAGR (Compounded annual growth rate) and stood at $2.71 Billion in 2018. While the Finance & Banking industry leads the share in the analytics market, travel-hospitality and healthcare saw the fastest growth in recent years, in terms of analytics-use.


Three Ways Brands Can Leverage AI For Predictive Advertising

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We live in a world that is becoming more personalized every day. Consumers have come to expect experiences that are tailored for them -- especially when it comes to engaging with brands. When you open your Uber app, it now suggests your home address; online shopping is increasingly personalized, and, of course, so is advertising. You expect to see ads that reflect your interests and buying patterns and, in fact, are more likely to engage with those ads.We have artificial intelligence (AI) to thank for our increasingly personalized world. As the demand for personalization increases, so too does the buzz around AI. AI is a term that is becoming ubiquitous -- and potentially overused -- as an umbrella term relating to any action a machine takes based on a set of rules in order to mimic human intelligence.



How Silicon Valley's whiz-kids finally ran out of friends John Naughton

The Guardian

Remember the time when tech companies were cool? Once upon a time, Silicon Valley was the jewel in the American crown, a magnet for high IQ – and predominately male – talent from all over the world. Palo Alto was the centre of what its more delusional inhabitants regarded as the Florence of Renaissance 2.0. Parents swelled with pride when their offspring landed a job with the Googles, Facebooks and Apples of that world, where they stood a sporting chance of becoming as rich as they might have done if they had joined Goldman Sachs or Lehman Brothers, but without the moral odium attendant on investment backing. I mean to say, where else could you be employed by a company to which every president, prime minister and aspirant politician craved an invitation?


Machine Learning And The Changing Face Of Today's Data Centers

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Machine learning and Artificial intelligence have taken over data centers by storm. As racks begin to fill with ASICs, FPGAs, GPUs, and supercomputers, the face of the hyper-scale server farm seems to change. These technologies are known to provide exceptional computing power to train machine learning systems. Machine learning is a process that involves tremendous amounts of data-crunching, which is a herculean task in itself. The ultimate goal of this tiring process is to create applications that are smart and also to improve services that are already in everyday use.


Opinion Algorithms Won't Fix What's Wrong With YouTube

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Whether that's the everyday life of improbably rich young millionaires like Jake Paul, a high school dropout from Westlake, Ohio, or PewDiePie, a skinny, fast-talking Swede whose real name is Felix Arvid Ulf Kjellberg, YouTube seeks to serve a need. It does so through "the algorithm" -- YouTube's recommendation engine. It's a black box that YouTube introduced to keep us watching, but which has become a thorn in its side as the platform grows at an astronomically grand scale. YouTube's recommendation algorithm is a set of rules followed by cold, hard computer logic. It was designed by human engineers, but is then programmed into and run automatically by computers, which return recommendations, telling viewers which videos they should watch.


Using Google's Speech Recognition And Natural Language APIs To Thematically Analyze Television

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Television news coverage is typically thought of as a visual medium, yet most of the narrative we consume from television comes in the form of spoken narration. Watching a news show with the audio muted and closed captioning off reinforces that the visual elements of television act more as enrichment than primary information conveyor. This means that quantifying this spoken narrative is imperative to understanding what television news is paying attention to and how it is framing and covering those events. Using Google's Cloud Speech-to-Text API to transcribe a week of television news coverage and annotating it with Google's Natural Language API, what might we learn about how television news covers the world? In the United States, most television stations provide closed captioning for their news programming, meaning they already come with a textual human-produced transcript.


How to Disable Google Assistant on your Android smartphone

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Google Assistant is a handy tool to have on your smartphone, but if you intend never to use it then you can disable it. This is how to disable Google Assistant on your Android smartphone. If you don't like talking to your smartphone you can disable Google Assistant in spite of it being useful. This is how you can do it. This process will turn off the Google Assistant on your Android smartphone.


It's Time To Demystify Machine Learning

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The hype machine is cranked up to an 11 on the topic of machine learning (sometimes called artificial intelligence, though I don't call it that because AI is not really intelligence and there's nothing artificial about it). Machine learning will either empower the world or take it over, depending on what you read. But before you get swept away by the gust of hot air coming from the technology industry, it's important to pause in order to put things into perspective. Maybe just explaining it in reasonable terms will help. Shortly after the first caveman figured out how to make fire, the second caveman wanted to learn how to make fire, too.


Amazon's next big thing may redefine big

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"I see Amazon as a technology company that just happened to do retail," begins Werner Vogels, Amazon's chief technology officer. "When Jeff [Bezos] started Amazon, he wasn't thinking about starting a bookshop. He was really fascinated by the internet." Only "mortal humans", he tells me in an interview, ever saw Amazon as merely a retailer. So the question now is: what will Amazon become next?