By running simulations of different scenarios, a digital twin offers an accurate reflection of the current state versus the future state and can provide the insights needed for making evidence-based decisions. Digital twins have become a major part of operational excellence in recent years. Originally developed as a way to replicate the physical world in virtual form, they now play a prominent role in forecasting and planning future scenarios. Their adoption is also extremely varied, from manufacturing plants identifying bottlenecks in production, contact centers allocating resources to manage surges in demand, hospitals looking to reduce a backlog of appointments, or indeed any type of business looking to combat operational inefficiencies – there's a twin for them all. So much so that it's become something of a buzzword, but there are other relevant buzzwords here too. For one, every new piece of technology now seems to have artificial intelligence and machine learning as part of its engine, developing highly targeted insights on which to build more personalized or sophisticated services.

In this week's real-time analytics news: Several companies announced generative AI offerings or enhancements to their product lines. Keeping pace with news and developments in the real-time analytics market can be a daunting task. We want to help by providing a summary of some of the important real-time analytics news items our staff came across this week. Salesforce launched Einstein GPT, a generative AI CRM technology, which delivers AI-created content across every sales, service, marketing, commerce, and IT interaction. Einstein GPT will infuse Salesforce's proprietary AI models with generative AI technology from an ecosystem of partners and real-time data from the Salesforce Data Cloud.

An important part of the process is to bring in people from across disciplines, even if they have conflicting perspectives. A few years back, experts and pundits alike were predicting the highways of the 2020s would be packed full of autonomous vehicles. One glance and it's clear there are still, for better or worse, mainly human drivers out there on the roads, as driverless vehicles have hit many roadblocks. Their ability to make judgements in unforeseen events is still questionable, as is the ability of human riders to adapt and trust their robot drivers. Autonomous vehicles are just one example of the greater need for human-centered design, the theme of the recent Stanford Human-Centered Artificial Intelligence fall conference, in which experts urged more human involvement from the very start of AI development efforts.

… computing Google healthcare ibm IIoT industrial IoT Industry 4.0 Internet of Things iot IoT security Kubernetes machine learning Observability …

These deep learning algorithms are able to train on medical data to uncover patterns that are frequently presented in a positive heart disease …

Most businesses are new to artificial intelligence and face daunting challenges when trying to scale their efforts that seek to derive business value from data. Get artificial intelligence right, and generate $460 billion in additional revenues. That's the estimated gains today's companies may see if they do three things: improve data practices, trust in advanced AI, and integrate AI with business operations. However, most companies have not gotten the memo. That's the word from Infosys Knowledge Institute, which finds in its latest study that while the potential for AI-driven gains are significant, most companies are still struggling to "convert data science to business value."

The latest artificial intelligence code research these days is quickly translated by big tech and startups into commercial developer tools. When it comes to artificial intelligence, the past year has been one of constructive progress, versus glitz and glam. That's the word from two leading venture capitalists, Nathan Benaich of Air Street Capital and Ian Hogarth of Plural, who released their annual summary of the state of AI, observing that while investment in AI ventures declined in 2022, there has been impressive work with emerging intelligent applications. For example, DeepMind, Alphabet's AI subsidiary, is now delivering greatly enhanced protein models for emerging scientific and agricultural research. "The company has now deployed the system to predict the 3D structure of 200 million known proteins from plants, bacteria, animals and other organisms," Benaich and Hogarth report.

According to the FWHA, there is a need for early-stage research to support emerging advances in artificial intelligence to help solve complex issues in highway transportation. A new Federal Highway Administration (FHWA) program aims to transform highways with artificial intelligence. Some of the areas where the technology is expected to play a future role include improved safety, environmental mapping, bridge capacity insights, and smart parking. The Exploratory Advanced Research (EAR) Program is currently soliciting proposals up until December 5. It will then award contracts or engage in cooperative agreements.

AIOps will enable the use of network analytics to enhance the customer experience for use cases such as energy efficiency improvement, network planning, and roaming cost reduction. The telecom industry is at a turning point: many communication service providers (CSPs) continue to sell connectivity as bandwidth demands increase and average revenue per user (ARPU) decreases, while others begin to take full advantage of 5G and expand their value proposition. As pressures around 5G cost, scale and quality-of-service accelerate, CSPs are challenged to quickly change to new operational and business models or attempt to succeed with cost-cutting measures alone. It is the intersection of groundbreaking technologies such as edge computing, artificial intelligence (AI), machine learning (ML), the cloud, and the Internet of Things. Further, CSPs face urgent pressures to strengthen their value proposition in the digital ecosystem as hyperscalers push deeper into the telecom space.

With the potential to democratize AI and ML, AutoML is the answer many enterprises across industry verticals have been seeking to take AI projects from pilots to scaled deployments. Adopting Artificial Intelligence (AI) is no longer just to gain competitive advantage; it has become table stakes for mere business survival. However, today's acute shortage of data scientists combined with the continuous effort to automate laborious tasks is posing unprecedented challenges for enterprises. Automated machine learning (AutoML) is poised to help. Why? Traditional machine learning (ML) is a time-consuming, arduous, and iterative task that involves data cleansing and preparation, algorithm training, validation, etc., to imitate the way that humans learn to make predictions or decisions without being explicitly programmed to do so.