WIRE)--Verta, the Operational AI company, today released findings from the 2023 AI/ML Investment Priorities study, which surveyed more than 460 AI and machine learning (ML) practitioners to benchmark AI/ML spending plans across industry sectors in light of evolving technology trends, industry developments, and macroeconomic conditions. The study was conducted by Verta Insights, the research practice of Verta Inc., and found that nearly two-thirds of organizations are planning to either increase or maintain their spending on AI/ML technology and infrastructure despite economic headwinds in the broader market. "We currently are experiencing an inflection point for the AI/ML industry, with technologies like ChatGPT and Stable Diffusion driving heightened interest in how companies can leverage machine learning models to significantly automate human-based activities with very innovative and game-changing capabilities. Findings from our research study confirm that organizations are continuing to make significant investments in AI/ML technology and talent, despite turbulence in the market, as they orient their business strategies around creating intelligent experiences for their customers," said Conrado Silva Miranda, Chief Technology Officer of Verta. In the research study, 31% of respondents said that their organizations would increase AI/ML spending in 2023 due to the current economic conditions, while 32% said that they would maintain 2022 spending levels for AI/ML technology and infrastructure.

IT and IT services companies in India have stepped up reskilling of employees as they race to grab business in the post-pandemic environment where clients are demanding new digital expertise. Companies such as Infosys, Wipro, Accenture, Zensar Technologies, among others, have embarked on a massive upskilling drive to make sure their workforce in India is ready for the next-level jobs. The training being imparted is in the fields of cloud technology, artificial intelligence, machine learning, data analytics, cyber security, Internet of Things (IoT), user experience (UX) and digital networking, among others. Infosys, which has around 240,000 employees, has seen a 1.5 times increase in reskilling of workforce in the April-June period compared with the previous quarter. Krishnamurthy Shankar, executive vice president at Infosys, told ET that "the need for reskilling has been exacerbated in the post-Covid scenario with new-age digital skills becoming indispensable" to clients.

Leading organizations recognize the potential for artificial intelligence and machine learning to transform work and society. The technologies offer companies strategic new opportunities and integrate into a range of business processes -- customer service, operations, prediction, and decision-making -- in scalable, adaptable ways. As with other major waves of technology, AI requires organizations and managers to shed old ways of thinking and grow with new skills and capabilities. "The AI & Machine Learning Imperative," an Executive Guide from MIT SMR, offers new insights from leading academics and practitioners in data science and AI. The guide explores how managers and companies can overcome challenges and identify opportunities across three key pillars: talent, leadership, and organizational strategy.

These perspectives are flat-out wrong and/or miss the point of what digital transformation is. For starters, the relatively small minority of consumers--25%--who plan to make less (or no) use of branches going forward is a pretty weak argument for transformation. Who says that they won't start using the phone to make voice calls to call centers more often? Second, banks are not "moving quickly" to providing "personalized solutions that can be delivered seamlessly." That simply doesn't happen in a three-month time frame, and especially during one when financial institutions are scrambling to adapt to a crisis.

And for in-house teams, labeling data can be the proverbial bottleneck, limiting a company's ability to quickly train and validate machine learning models. By its very definition, artificial intelligence refers to computer systems that can learn, reason, and act for themselves, but where does this intelligence come from? For decades, the collaborative intelligence of humans and machines has produced some of the world's leading technologies. And while there's nothing glamorous about the data being used to train today's AI applications, the role of data annotation in AI is nonetheless fascinating. Imagine reviewing hours of video footage – sorting through thousands of driving scenes, to label all of the vehicles that come into frame, and you've got data annotation.

How are data-fueled technologies such as AI widening competitive advantage? In an age of collaboration between humans and machines--what we call the "Age of With"--organizations can gain an advantage by designing systems in which humans and machines work together to improve the speed and quality of decision-making based on data-driven insights. By leveraging that type of information in their strategy, companies can identify opportunities to streamline operations, better understand their employees' needs, and increase business efficiency. These benefits combined improve both the bottom line and ability to attract and retain employees – uniquely positioning them to beat out competitors in the areas of both profit and talent. But not every organization is optimizing the opportunities available in the Age of With.

Amazon Web Services (AWS) has connected the Middle East to its global network with the launch of its Bahrain AWS region. The cloud supplier already has infrastructure in the region, but the launch of the Bahrain AWS region, with three datacentres, will connect to its global network. This will bring the Middle East region up to par with its other global AWS regions as the Middle East accelerates its digital transformation. Andy Jassy, CEO at AWS, said the cloud could unlock digital transformation in the Middle East. "Today, we are launching advanced and secure technology infrastructure that matches the scale of our other AWS regions around the world and are already seeing strong demand in the Middle East for AWS technologies like artificial intelligence (AI) and machine learning, data analytics, IoT [internet of things] and much more," he said.

Along with virtual and augmented reality and the Internet of Things, self-driving cars have been one of tech experts' go-to applications when explaining the utility of edge computing. Until not too long ago, Dean Nelson, who just left his job as head of computing infrastructure at Uber, also assumed autonomous vehicles, which can generate tens of terabytes of data per day, would shuffle much of that data to and from servers in their vicinity, relying on the processing power at the edge (and ultra-fast wireless networks) to augment their onboard computing muscle. But, after spending a few years up close and personal with the technology infrastructure that powers one of the front-running companies in the race to make driverless cars viable, he's no longer sure edge computing will play a big role. "In the last couple years I changed my mind with autonomous vehicles," Nelson said Thursday while sitting on a panel at DCD San Francisco, a data center industry conference. Earlier that day, he had announced in post on LinkedIn that he was leaving Uber to start his own consultancy and devote more time to family and the activities of Infrastructure Masons, the industry group for data center professionals he founded in 2016.

U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing. U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing. These and many other insights are from the McKinsey Global Institute's study The Age of Analytics: Competing In A Data-Driven World published in collaboration with McKinsey Analytics this month.

U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing. U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing. These and many other insights are from the McKinsey Global Institute's study The Age of Analytics: Competing In A Data-Driven World published in collaboration with McKinsey Analytics this month.