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Redefining Support with AI and Human Assistants

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In the age of AI, the question isn’t whether technology can replace humans—it’s whether it should. At AtlasIED, that debate goes beyond the theoretical, delving into how businesses can balance the efficiency of AI with the empathy and nuance of human touch. While AI assistants offer lightning-fast scheduling and data retrieval, human assistants bring judgment, discretion, and emotional intelligence to complex interactions. The conversation isn’t about replacing one with the other, but about designing collaborations where each plays to its strengths. In the end, the smartest assistants—whether silicon or flesh—are the ones that know when to lean on the other.



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AI hype has just shaken up the world’s rich list. What if the boom is really a bubble?

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Just for a moment this week, Larry Ellison, co-founder of US cloud computing company Oracle, became the world’s richest person. The octogenarian tech titan briefly overtook Elon Musk after Oracle’s share price rocketed 43% in a day, adding about US$100 billion (A$150 billion) to his wealth.

The reason? Oracle inked a deal to provide artificial intelligence (AI) giant OpenAI with US$300 billion (A$450 billion) in computing power over five years.

While Ellison’s moment in the spotlight was fleeting, it also illuminated something far more significant: AI has created extraordinary levels of concentration in global financial markets.

This raises an uncomfortable question not only for seasoned investors – but also for everyday Australians who hold shares in AI companies via their superannuation. Just how exposed are even our supposedly “safe”, “diversified” investments to the AI boom?

The man who built the internet’s memory

As billionaires go, Ellison isn’t as much of a household name as Tesla and SpaceX’s Musk or Amazon’s Jeff Bezos. But he’s been building wealth from enterprise technology for nearly five decades.

Ellison co-founded Oracle in 1977, transforming it into one of the world’s largest database software companies. For decades, Oracle provided the unglamorous but essential plumbing that kept many corporate systems running.

The AI revolution changed everything. Oracle’s cloud computing infrastructure, which helps companies store and process vast amounts of data, became critical infrastructure for the AI boom.

Every time a company wants to train large language models or run machine learning algorithms, they need huge amounts of computing power and data storage. That’s precisely where Oracle excels.

When Oracle reported stronger-than-expected quarterly earnings this week, driven largely by soaring AI demand, its share price spiked.

That response wasn’t just about Oracle’s business fundamentals. It was about the entire AI ecosystem that has been reshaping global markets since ChatGPT’s public debut in late 2022.

The great AI concentration

Oracle’s story is part of a much larger phenomenon reshaping global markets. The so-called “Magnificent Seven” tech stocks – Apple, Microsoft, Alphabet, Amazon, Meta, Tesla and Nvidia – now control an unprecedented share of major stock indices.

Year-to-date in 2025, these seven companies have come to represent approximately 39% of the US S&P500’s total value. For the tech-heavy NASDAQ100, the figure is a whopping 74%.

This means if you invest in an exchange-traded fund that tracks the S&P500 index, often considered the gold standard of diversified investing, you’re making an increasingly concentrated bet on AI, whether you realise it or not.

Are we in an AI ‘bubble’?

This level of concentration has not been seen since the late 1990s. Back then, investors were swept up in “dot-com mania”, driving technology stock prices to unsustainable levels.

When reality finally hit in March 2000, the tech-heavy Nasdaq crashed 77% over two years, wiping out trillions in wealth.

Today’s AI concentration raises some similar red flags. Nvidia, which controls an estimated 90% of the AI chip market, currently trades at more than 30 times expected earnings. This is expensive for any stock, let alone one carrying the hopes of an entire technological revolution.

Yet, unlike the dot-com era, today’s AI leaders are profitable companies with real revenue streams. Microsoft, Apple and Google aren’t cash-burning startups. They are established giants, using AI to enhance existing businesses while generating substantial profits.

This makes the current situation more complicated than a simple “bubble” comparison. The academic literature on market bubbles suggests genuine technological innovation often coincides with speculative excess.

The question isn’t whether AI is transformative; it clearly is. Rather, the question is whether current valuations reflect realistic expectations about future profitability.

President and chief executive of Nvidia Corporation, Jensen Huang.
Chiang Ying-ying/AP

Hidden exposure for many Australians

For Australians, the AI concentration problem hits remarkably close to home through our superannuation system.

Many balanced super fund options include substantial allocations to international shares, typically 20–30% of their portfolios.

When your super fund buys international shares, it’s often getting heavy exposure to those same AI giants dominating US markets.

The concentration risk extends beyond direct investments in tech companies. Australian mining companies, such as BHP and Fortescue, have become indirect AI players because their copper, lithium and rare earth minerals are essential for AI infrastructure.

Even diversifying away from technology doesn’t fully escape AI-related risks. Research on portfolio concentration shows when major indices become dominated by a few large stocks, the benefits of diversification diminish significantly.

If AI stocks experience a significant correction or crash, it could disproportionately impact Australians’ retirement nest eggs.

A reality check

This situation represents what’s called “systemic concentration risk”. This is a specific form of systemic risk where supposedly diversified investments become correlated through common underlying factors or exposures.

It’s reminiscent of the 2008 financial crisis, when seemingly separate housing markets across different regions all collapsed simultaneously. That was because they were all exposed to subprime mortgages with high risk of default.

This does not mean anyone should panic. But regulators, super fund trustees and individual investors should all be aware of these risks. Diversification only works if returns come from a broad range of companies and industries.



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Virginia Tech expands faculty in AI, systems, and manufacturing

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The Grado Department of Industrial and Systems Engineering at Virginia Tech has added four new faculty members for 2025, part of a strategy to expand expertise in high-demand fields such as data-driven decision making, advanced manufacturing, and artificial intelligence.

The department prepares students for careers in engineering through research, teaching, and industry collaboration.

New faculty join the department

The new appointments are Alkim Avsar, assistant professor in systems engineering; Eric Brubaker, assistant professor in systems engineering; Kelsey Coleman, associate professor of practice and Learning Factory director; and Yuhao Zhong, assistant professor in manufacturing engineering.

Eileen Van Aken, professor and department head, says: “We’re thrilled to welcome these outstanding faculty to our team. Our students will benefit from their perspectives, and their work will expand our capabilities in areas critical to the future of engineering and the success of our graduates.”

Research and teaching areas

Avsar, who joins from Arizona State University, earned a Ph.D. in systems engineering from Stevens Institute of Technology. Her work combines systems engineering, behavioral science, game theory, and human factors to examine how social factors influence engineering decision making. She is currently teaching a graduate course in systems engineering.

Brubaker, previously a senior complex systems engineer at NASA, holds a Ph.D. in mechanical engineering from Stanford University. His research addresses human-AI collaboration and designs for complex systems that tackle challenges in water, energy, transportation, and healthcare. He is teaching AI for systems engineering.

Coleman, formerly at Eaton Corporation, manages workforce development programs and directs the Learning Factory, which provides students with hands-on training in areas such as additive manufacturing, robotics, and digital factory systems.

Zhong, who completed his Ph.D. at Texas A&M University, focuses on data science applications in manufacturing. His research develops explainable AI and computer vision tools to improve quality, safety, and performance in Industry 5.0 environments.

The ETIH Innovation Awards 2026



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How Malawi is taking AI technology to small-scale farmers who don’t have smartphones

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MULANJE, Malawi (AP) — Alex Maere survived the destruction of Cyclone Freddy when it tore through southern Malawi in 2023. His farm didn’t.

The 59-year-old saw decades of work disappear with the precious soil that the floods stripped from his small-scale farm in the foothills of Mount Mulanje.

He was used to producing a healthy 850 kilograms (1,870 pounds) of corn each season to support his three daughters and two sons. He salvaged just 8 kilograms (17 pounds) from the wreckage of Freddy.

“This is not a joke,” he said, remembering how his farm in the village of Sazola became a wasteland of sand and rocks.

Freddy jolted Maere into action. He decided he needed to change his age-old tactics if he was to survive.

He is now one of thousands of small-scale farmers in the southern African country using a generative AI chatbot designed by the non-profit Opportunity International for farming advice.

AI suggests potatoes

The Malawi government is backing the project, having seen the agriculture-dependent nation hit recently by a series of cyclones and an El Niño-induced drought. Malawi’s food crisis, which is largely down to the struggles of small-scale farmers, is a central issue for its national elections next week.

More than 80% of Malawi’s population of 21 million rely on agriculture for their livelihoods and the country has one of the highest poverty rates in the world, according to the World Bank.

The AI chatbot suggested Maere grow potatoes last year alongside his staple corn and cassava to adjust to his changed soil. He followed the instructions to the letter, he said, and cultivated half a soccer field’s worth of potatoes and made more than $800 in sales, turning around his and his children’s fortunes.

“I managed to pay for their school fees without worries,” he beamed.

AI, agriculture and Africa

Artificial intelligence has the potential to uplift agriculture in sub-Saharan Africa, where an estimated 33-50 million smallholder farms like Maere’s produce up to 70-80% of the food supply, according to the U.N.’s International Fund for Agricultural Development. Yet productivity in Africa — with the world’s fast-growing population to feed — is lagging behind despite vast tracts of arable land.

As AI’s use surges across the globe, so it is helping African farmers access new information to identify crop diseases, forecast drought, design fertilizers to boost yields, and even locate an affordable tractor. Private investment in agriculture-related tech in sub-Saharan Africa went from $10 million in 2014 to $600 million in 2022, according to the World Bank.



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