DoT Blog

  Image by Negative Space  (Pexels) Ok, so generative AI continues to dominate headlines, but what’s more telling is how quickly it’s cementing itself in real business strategies. Well, according to a recent Yahoo Finance report , the global chatbot market is expected to hit $15.5 billion by 2028, driven largely by the ubiquity of conversational AI tools. The numbers themselves are striking, but even more so is what they represent: a shift from AI as an “innovation experiment” to AI as a structural pillar of modern organizations. Customer service, sales enablement, and education are just a few of the areas being reshaped by chatbots and conversational platforms. In many ways, the chatbot market is just the tip of the iceberg. Recent surveys suggest that 95 percent of U.S. companies already use generative AI in some capacity, and production-level use cases have doubled in a short timeframe. We no longer talk about cautious pilots in innovation labs; we’re seeing generative AI t...

  Image by geralt (Pixabay) When I first read the Light Reading article “ AI looks increasingly useless in telecom and anywhere else,”  I had to pause. Not because the argument was new—I’ve seen plenty of skepticism about AI—but because of the tone. It doesn’t just question AI’s utility, but it paints a picture of a lobotomized society , drifting into an “AI psychosis” where people see machines as sentient companions. Boy, it’s an arresting way to start, but also, perhaps, too convenient a metaphor. The author compares our intellectual reliance on AI to muscles wasting away from disuse, citing early studies that show people who lean too much on generative AI may grow less critical, less precise, and even a little sloppy. It’s a provocative analogy, but one that, in my view, overreaches. Yes, there are legitimate concerns: copy-pasting AI outputs without scrutiny is a real problem, and treating chatbots as friends, or worse, as oracles, can be dangerous, but to equate this with...

  In recent years, the term “AI agent” has gained significant attention within the technology industry, as well as the broader public discourse. While concepts like artificial intelligence (AI) and machine learning have become increasingly familiar, AI agents represent a quite distinct and evolving area of development in the AI sector. But what the heck are they? Well, the purpose of this article aims to provide a clear, foundational understanding of what AI agents are, where they come from, and why they are considered a crucial step forward in artificial intelligence research and application. It will also discuss current uses, potential benefits, key technical underpinnings, and the societal and ethical considerations that come into play as AI agents become more integrated into our infrastructures and daily lives. Defining AI Agents Image by geralt ( Pixabay ) A First Definition Let us start with a basic definition. So, in essence, AI agents are autonomous software entities, des...

In a world where we might think is being ruled and controlled by tech geeks and data scientists, during meetings and phone calls with customers I’m still, often, being hit with honest and candid questions about any given topic about the data and analytics and give my personal take on them. In virtue of this, I’ve decided to take a shot and a series of posts to answer, as plainly as I possibly can, common questions I receive in my day-to-day life as a consultant and analyst. Starting with my most popular question nowadays: WTF is machine learning? So, here we go... Machine Learning in a Tiny Nutshell The discipline of  machine learning evolved as part of larger disciplines including data mining and artificial intelligence (AI) and, in many ways, evolving side by side with traditional statistics and data mining and other mathematical disciplines. So, simply put, machine learning cares about the development of mathematical models and algorithms with the ability to “ lea...

“Companies are looking for a unified and open approach to help them accelerate and expand the flow of data across their data landscapes for all users. SAP Data Hub bridges the gap between Big Data and enterprise data, enabling companies to build applications that extract value from data across the organization, no matter if it lies in the cloud or on premise, in a data lake or the enterprise data warehouse, or in an SAP or non-SAP system.” This is part of what Bernd Leukert, SAP’s member of the executive board for products & innovation mentioned during SAP’s Big Data Event held at the SAP Hudson Yards office in New York City as part of the new SAP Data Hub announcement and one that, in my view, marked the beginning of a small yet important trend within analytics consisting on the launch or renewed and integrated software platforms for analytics, BI and data science. This movement, marked by other important announcements including Teradata’s New Analytics Platform as well ...

As we get deeper into an era of new software platforms both, big players and newcomers are industriously working to reshape or launch their proposed new-generation analytics platforms, especially aiming to appeal to the growing community of new information workers or “data scientists” ㅡa community always eager to attain the best possible platform to “crunch the numbers”ㅡ, examples include those including Teradata with its new analytics platform or Cloudera with its Data Science Workbench . So now the turn is for IBM, which recently unveiled its Integrated Analytics System . IBM’s new offering represents the company’s unified data system aimed to provide organizations with easy, yet sophisticated platform for the development of data science within data from on-premises, private, public of hybrid cloud environments. The new offering coming from the “Big Blue” company is set to incorporate a myriad of data science tools and functionality features as well as the proper data managem...

As Teradata continues to expand its Teradata Everywhere initiative, major announcements came from within its 2017 Partners conference, so along with the announcement of its brand new analytics platform , the company also unveiled a new comprehensive software portfolio that adds the data management power needed behind the analytics scenario. According to Teradata, IntelliSphere is “ a comprehensive software portfolio that unlocks a wealth of key capabilities for enterprises to leverage all the core software required to ingest, access, deploy and manage a flexible analytical ecosystem ”. (Image courtesy of Teradata) Meanwhile, Teradata IntelliSphere is intended to complement the ongoing Teradata Everywhere initiative and be a natural companion for the Teradata Analytics Platform and, an important tool to enable users across the organization to use their preferred analytic tools and engines across data sources at scale, while having all the necessary components to ensure ...

(Image Courtesy of Teradata) In a recent announcement made during its 2017 Partners conference data management software provider, Teradata made an important new addition to its global Teradata Everywhere initiative with a brand new analytics platform . The new offering to be available for early access later this year will aim to enable users to use the analytics environment of their choice. According to the company, the new analytics platform is planned to enable access to a myriad of analytics functions and engines so users can develop full analytics processes and business solutions using the tools of their choice so initially, the new platform, will natively integrate with Teradata and Aster technology (Figure 1) and in a near future will enable integration with leading analytics engines including Spark , TensorFlow , Gluon , and Theano . Figure 1.  Aster Analytics Functions (Courtesy of Teradata) As corporate data is increasingly captured and stored in a wider ...