This year, U.S. rail carrier Amtrak will be installing two novel inspection gateways from Duos Technologies along its busy Northeast Corridor. The barn-like Duos structures straddle railway tracks; as passenger trains speed through at up to 125 miles per hour, 97 cameras and dozens of LED lights arrayed around the sides, top, and bottom of the tracks will capture thousands of high-resolution images of the railcars. These images are aggregated and processed on site in real time to present a complete, 360-degree, highly detailed view of the train. Artificial intelligence (AI) algorithms running on Nvidia GPUs will analyze the images locally; if the model flags a potential structural or mechanical flaw, train personnel will be notified in less than a minute. The Duos portal is one of many new examples of what is loosely categorized as edge AI, or the deployment and operation of AI models outside of massive cloud datacenters.

What started as a simple home repair project ended with multiple trips to the hardware store, cursing in the aisles, and a vow to never buy from a specific manufacturer ever again. A single defective bolt, which had evaded quality inspection and been packaged, shipped, and unfortunately purchased by me. The product packaging, installation instructions, and final functionality were all exemplary. But a single defective bolt, which costs only pennies in the product's bill of materials, was enough to sour me on the whole experience. Manufacturers and brand owners are under tremendous pressure to ensure premium end-to-end product quality, especially as consumers increasingly demand perfection.

We believe Artificial Intelligence is a technology platform that will transform every industry across the global economy. Despite its promise, the technology is often misunderstood. Moreover, it is often portrayed as an existential threat to humanity. With AI Arena, we want to educate the world about AI, inspire the next generation of researchers, and prove that an AI powered world can enhance the richness of human experience. In short, we want to bring AI to the masses.

Even before countries began rolling out their vaccination campaigns, Pfizer, Moderna and AstraZeneca's announcements had already proved fortifying shots. Stocks rallied and healthcare workers celebrated in the wake of the vaccine news late last year. But months on, that early euphoria has evaporated, replaced by uncertainty and debate over vaccine safety, possible side effects and varying degrees of citizen reluctance. Artificial intelligence (AI) researchers and health experts modeling COVID-19's spread have warned that for vaccines to be useful in curbing the pandemic, a significant percentage of the population must be vaccinated to reach herd immunity. But, as SMU's Vice Provost of Research Professor Archan Misra pointed out at an AI-centered panel discussion, held in conjunction with the SMU- Global Young Scientists Summit (GYSS) on 15 January 2021, from a purely self-interested point of view, each person would be best served if all the others got vaccinated and they themselves did not have to vaccinate--because that would stop the spread of the virus without their having to take on the possible risks of side effects. To account for these considerations, Professor Misra explained, the most powerful AI-based epidemiology models actually need to incorporate concepts from the behavioral sciences and game theory.

Organisations of all sizes are looking for new ways to innovate, but in ways that won't break the bank. Proprietary solutions can offer some answers, but in uncertain times, vendor lock-in is undesirable and expensive. To remain agile, open-source solutions are offering agnostic and low-cost ways to continue and even accelerate the rate of digitisation across the APAC and Australasia. For enterprise-grade open-source solutions that are making a practical difference today, many turn to SUSE as the perfect partner that offers business-centricity with an open-source ethos. The company behind a global community of thousands of developers is offering a series of webinars that show companies and businesses of all sizes and industries just how business outcomes can be improved using technology.

The trend toward the use of massive AI models to power a large number of tasks is changing how AI is built. At Microsoft Build 2020, we shared our vision for AI at Scale utilizing state-of-the-art AI supercomputing in Azure and a new class of large-scale AI models enabling next-generation AI. The advantage of large scale models is that they only need to be trained once with massive amounts of data using AI supercomputing, enabling them to then be "fine-tuned" for different tasks and domains with much smaller datasets and resources. The more parameters that a model has, the better it can capture the difficult nuances of the data, as demonstrated by our 17-billion-parameter Turing Natural Language Generation (T-NLG) model and its ability to understand language to answer questions from or summarize documents seen for the first time. Natural language models like this, significantly larger than the state-of-the-art models a year ago, and many orders of magnitude the size of earlier image-centric models, are now powering a variety of tasks throughout Bing, Word, Outlook, and Dynamics.

Machine learning, a sub-component of artificial intelligence, is not new to the enterprise. But with techniques like deep learning, emulating human brain actions, increasingly gaining traction, businesses are identifying new and potentially transformative deployments of digitally disruptive technologies. According to Algorithmia's 2020 report, the main use cases for machine learning translate to customer service (i.e. But machine learning has applications far and wide. Dynamic pricing or surge pricing is essentially ML models that learn from corresponding factors that include customer interest, demand and history to adjust prices and entice purchases.

Ensuring we have access to healthy and tasty food for the future means lots of people are working hard across food industry supply chains on a global scale. Agtech, food origins, alternative proteins, native foods, food waste, personal food choices, and more are up for discussion. Prof Andy Lowe examines the solutions being put in place today to put tomorrow's meal on your table.

No enterprise wants to be a dinosaur when it comes to innovation, and today, AI is on the cutting edge. With an estimated 80 percent of enterprises already using AI in some form, the transition to AI seems as widespread as the transition from typewriters to PCs. Despite the hype, enterprises'sense' the challenge: in a recent study, 91 percent of companies foresee significant barriers to AI adoption, including a lack of IT infrastructure, and shortage of AI experts to guide the transition. Still, few organizations truly understand what lies ahead of them, and what it really takes to transition out of the AI Jurassic era. Let's look more closely at the underlying realities of AI adoption that your internal AI group or consultant will never tell you. To paint a picture, let's consider a hypothetical company, Global Heavy Industry Corporation (GHIC).

Excel's power comes from its simplicity. At its core, Excel is three things: cells of data laid out in rows and columns, a powerful calculation engine, and a set of tools for working with the data. The result is an incredibly flexible app that hundreds of millions of people use daily in a wide variety of jobs and industries around the world. Today, we're pleased to announce four new artificial intelligence (AI) features that make Excel even more powerful: Ideas is an AI-powered insights service that helps people take advantage of the full power of Office. Proactively surfacing suggestions that are tailored to the task at hand, Ideas helps users create professional documents, presentations, and spreadsheets in less time.