BENGALURU: With increased adoption of Artificial Intelligence and Machine Learning, companies are now taking advantage of such emerging technologies to drive growth. Dell Technologies is expecting to see a significant impact of these technologies. "The utilization of AI and ML is becoming more widespread as companies seek to take advantage of the benefits these technologies offer. We have been exploring this field and currently have multiple projects, products focused on using AI to improve our business," Ramesh Jampula, VP, IT, India and APJC Regional CIO, Dell Technologies, told this newspaper. Talking about emerging tech such as IoT, multi-cloud, Jampula said businesses have varying needs that influence their IT strategies, but they all share common goals of reducing costs, increasing flexibility and creating new value.

As the chief engineer and head of the department for digital transformation of manufacturing technologies at the Laboratory for Machine Tools and Production Engineering (WZL) within RWTH Aachen University, I've seen a lot of technological advancements in the manufacturing industry over my tenure. I hope to help other manufacturers struggling with the complexities of AI in manufacturing by summarizing my findings and sharing some key themes. The WZL has been synonymous with pioneering research and successful innovations in the field of production technology for more than a hundred years, and we publish over a hundred scientific and technical papers on our research activities every year. The WZL is focused on a holistic approach to production engineering, covering the specifics of manufacturing technologies, machine tools, production metrology and production management, helping manufacturers test and refine advanced technology solutions before putting them into production at the manufacturing edge. In my team, we have a mix of computer scientists, like me, working together with mathematicians and mechanical engineers to help manufacturers use advanced technologies to gain new insights from machine, product, and manufacturing data.

The meager supply and high salaries of data scientists have led to a decision among many companies totally in keeping with artificial intelligence ― to automate whatever is possible. Case in point is machine learning. A Forrester study found that automated machine learning (AutoML) has been adopted by 61% of data and analytics decision makers in companies using AI, with another 25% of companies saying they'll do so in the next year. Automated machine learning (AutoML) automates repetitive and manual machine learning tasks. That's no small thing, especially when data scientists and data analysts now spend a majority of their time cleaning, sourcing, and preparing data.

In its debut in the industry MLPerf benchmarks, NVIDIA Orin, a low-power system-on-chip based on the NVIDIA Ampere architecture, set new records in AI inference, raising the bar in per-accelerator performance at the edge. Overall, NVIDIA with its partners continued to show the highest performance and broadest ecosystem for running all machine-learning workloads and scenarios in this fifth round of the industry metric for production AI. In edge AI, a pre-production version of our NVIDIA Orin led in five of six performance tests. It ran up to 5x faster than our previous generation Jetson AGX Xavier, while delivering an average of 2x better energy efficiency. NVIDIA Orin is available today in the NVIDIA Jetson AGX Orin developer kit for robotics and autonomous systems.

Here's a look at how HPC, AI, and other technologies are being used throughout the world by organizations to enhance healthcare research, drug development, public health, and patient outcomes. The ability to gather, process, and analyze data from genomics, bioinformatics, microscopy, medical imaging, and other areas in the life sciences has been supercharged with HPC systems and artificial intelligence (AI) algorithms. Researchers can sequence vast quantities of DNA data faster than ever before with supercomputer resources and use AI to identify patterns and make predictions. They can now use these available and affordable technologies to study genes and proteins, to predict health events, automate imaging analysis, and generate ideas for improving healthcare delivery. Here's a look at how HPC, AI, and other technologies are being used throughout the world by organizations to enhance healthcare research, drug development, public health, and patient outcomes.

Artificial intelligence (AI) is the darling of businesses and governments because it not only promises to add tens of trillions to the gross domestic product (GDP), but it comes with all the excitement of action-packed movies or dopamine-drenched gaming. We are mesmerized by computer vision, natural language processing, and the uncanny predictions of recommendation engines.…

To help the company and its customers gain value from this data deluge, the Dell IT organization manages a massive data lake and a world-class set of tools for data analytics, machine learning, deep learning and artificial intelligence. At the heart of this data environment is a Greenplum database, a massively parallel data platform for structured data analytics, machine learning and AI. In a typical use case, this raw data gets parsed in Hadoop into a structured format, and then that structured data gets pumped into the Greenplum database, so business and IT users can consume it in analytics applications. The data is used by Dell Technologies employees and customers in the Americas, Europe, the Middle East, Asia and other geographic regions, according to Darryl Smith, chief data platform architect and distinguished engineer at Dell Technologies. For the full story, see the Dell Technologies case study "Analytics and AI in a massive data lake."

Businesses that want to be digital leaders in their markets need to embrace automation, not only to augment existing capabilities or to reduce costs but to position themselves to successfully maneuver the rapid expansion of IT demand ushered in through digital innovation. "It's a scale issue," says John Roese, global chief technology officer at Dell Technologies. "Without autonomous operations, it becomes impossible to keep up with the growing opportunity to become a more digital business using human effort alone." The main hurdle to autonomous operations, says Roese, is more psychological than technological. "You have got to be open-minded to this concept of rebalancing the work between human beings and the machine environments that exist both logically and physically," he says. "If you're not embracing and wanting it to happen and you're resisting it, all the products and solutions we can deliver to you will not help." Technology and infrastructure-driven AI and machine-learning discussions are expanding beyond IT into finance and sales--meaning, technology has direct business implications. "Selling is a relationship between you and your customer, but there's a third party--data and artificial intelligence-- that can give you better insights and the ability to be more contextually aware and more responsive to your customer, says Roese. "Data, AI, and ML technologies can ultimately change the economics and the performance of all parts of the business, whether it be sales or services or engineering or IT." And as companies gather, analyze, and use data at the edge, autonomous operations become even more of a business necessity. "Seventy percent of the world's data is probably going to be created and acted upon outside of data centers in the future, meaning in edges," says Roese. "Edge and distributed topologies have huge impacts on digital transformation, but we also see that having a strong investment in autonomous systems, autonomous operations at the edge is actually almost as big of a prerequisite … to make it work."

The pandemic situation has changed the way we do business. Digitalization has accelerated in many ways. We see many businesses moving to online, we see cryptocurrencies emerging and metaverses being created. With Facebook's recent announcement of rebranding to Meta, this trend has only gained speed. Artificial intelligence (AI) has now become more important than ever.

The job of an IT architect or administrator becomes more complex with each passing day. It hasn't been that long since an IT staff only had to deal with the resources found inside the walls of the enterprise. IT organizations must address the needs of a remote workforce, multi-cloud integration, and the rapid rise of edge computing and artificial intelligence. Edge computing places compute and storage capabilities into the real world, allowing an enterprise to generate insights and deliver value where it is most needed. Edge computing extends the reach of enterprise IT right to the point that data is generated, enabling new and impactful use cases that change the way that many businesses operate.