Collaborating Authors

How can India make its technology policy powerful, innovative, and secure?


Can we ever rein in the Big Tech firms to foster indigenous innovation, stimulate balanced growth, and protect national sovereignty? Can we have a balanced set of rules and a clear framework to safeguard larger public interest? Can we check the weaponisation of the internet with balanced cybersecurity and secure data governance framework to make Google (Alphabet); Apple; Facebook (Meta); Amazon; and Microsoft, besides others, more responsible and resilient? Look around, Big Tech run most of the digital services that are integral and ubiquitous to our life. Our minds, economy, national security, democracy, and progress are invisibly controlled by a few technology firms.

Data Centers Need to Go Green - And AI Can Help


Climate change is here, and it's set to get much worse, experts say – and as a result, many industries have pledged to reduce their carbon footprints in the coming decades. Now, the recent jump in energy prices due mainly to the war in Ukraine, also emphasizes the need for development of cheap, renewable forms of energy from freely available sources, like the sun and wind – as opposed to reliance on fossil fuels controlled by nation-states. But going green is easier for some industries than for others,- and one area where it is likely to be a significant challenge is in data centers, which require huge amounts of electricity to cool off, in some cases, the millions of computers deployed. Growing consumer demand to reduce carbon output, along with rules that regulators are likely to impose in the near future, require companies that run data centers to take immediate steps to go green. And artificial intelligence, machine learning, neural networks, and other related technologies can help enterprises of all kinds achieve that goal, without having to spend huge sums to accomplish it.

Hai Robotics eyes global expansion as warehouse automation boots up


Hai Robotics makes robots for moving and sorting boxes in warehouses, a market estimated to reach US$41 billion globally by 2027.

Lifelike Medical Robot Actually Bleeds: Only Created to Suffer?


Pediatric HAL is a well-known medical robot that really bleeds, cries, urinates, and mimics further Human behavior very well. Medical college students use HAL to learn how to diagnose and deal with illness earlier than operating with actual patients. Pediatric HAL is a part of a line of robots from a company known as Gaumard. Gaumard additionally makes robots that simulate pregnant people, newborns, and trauma wounds. Heading into 2019, buyers are being plagued with the aid of using a laundry listing of concerns.

Skills and security continue to cloud the promise of cloud-native platforms


Joe McKendrick is an author and independent analyst who tracks the impact of information technology on management and markets. As an independent analyst, he has authored numerous research reports in partnership with Forbes Insights, IDC, and Unisphere Research, a division of Information Today, Inc. The KubeCon and CloudNativeCon events just wrapped up in Europe, and one thing has become clear: the opportunities are outpacing organizations' ability to leverage its potential advantages. Keith Townsend, who attended the conference, observed in a tweet that "talent and education is the number one challenge. I currently don't see a workable way to migrate thousands of apps without loads of resources. Information technology gets more complex every day, and there is no shortage of demand for monitoring and automation capabilities the build and manage systems. Cloud-native platforms are seen as remedies for not only improved maintenance, monitoring, and automation, but also for modernizing ...

Traditional vs Deep Learning Algorithms in the Telecom Industry -- Cloud Architecture and Algorithm Categorization


The unprecedented growth of mobile devices, applications and services have placed the utmost demand on mobile and wireless networking infrastructure. Rapid research and development of 5G systems have found ways to support mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Moreover inference from heterogeneous mobile data from distributed devices experiences challenges due to computational and battery power limitations. ML models employed at the edge-servers are constrained to light-weight to boost model performance by achieving a trade-off between model complexity and accuracy. Also, model compression, pruning, and quantization are largely in place.

Data Scientist - Associate Vice President


Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Lead Data Scientist


Since 2002, Quantium have combined the best of human and artificial intelligence to power possibilities for individuals, organisations and society. Whether it be building forecasting engines that are driving down food wastage or creating mapping tools to support targeted measures in combatting human trafficking, Quantium believes in better goods, services, experiences, and championing the benefits of data for a brighter future. Q-Telco is the new joint venture between Quantium and Telstra to unlock the full potential of data and AI for Telstra and its customers. We'll do this by combining our market leading data science and AI capabilities with Telstra's customer, product and network data assets. This new partnership will not only provide personalised and data-enabled products and offers for Telstra's customers, but it will also embed proactive and predictive AI and machine learning across Telstra's core business.

Interview with Daniel Shearly, VP of Products at GfK


Daniel Shearly answers questions about data-centricity, trusted data, and how data-driven intelligence can help in better decision-making for future-proofing businesses. What specific trends do you see that will shape the future of its adoption? Every organization has its own reasons for adopting AI which have historically ranged from a real desire to answer complex problems and uncover insights from large data sets that would be impossible to process through traditional statistical methods to driving operational efficiencies and even just a company's desire to appear cutting edge. As AI is now much more mainstream it's less of a buzzword and badge of innovation and more of something to be applied practically to real-world problems. The main trends I see emerging are the broader adoption of it by companies big and small as knowledge and talent build and barriers to adoption decrease.

Natural Language Processing in TensorFlow


If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network.