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Artificial intelligence helps diagnose long Covid and chronic fatigue syndrome with 90% accuracy – study

Published

1 month ago

July 25, 2025

Scientists have made a step forward in diagnosing complex diseases such as long Covid and myalgic encephalomyelitis (ME), more commonly known as chronic fatigue syndrome. To do this, they used a new artificial intelligence platform that demonstrates up to 90% accuracy in detecting ME using conventional laboratory tests. This is reported by the Financial Times, writes UNN.

Details

The study, published in Nature Medicine, is based on data from 249 people, 153 of whom were ME patients. The team analyzed biological changes in gut bacteria, immune responses, and metabolism, finding links between the microbiome, immune system, and chemicals involved in maintaining vital functions.

“Our goal is to create a detailed map of how the immune system interacts with gut bacteria and the chemicals they produce,” said Julia Oh, a microbiologist at Duke University. “By connecting these dots, we can begin to understand what is driving the disease and pave the way for truly precise medicine that has long been out of reach.”

Researchers found that ME patients have impaired interaction between the microbiome, metabolites, and the immune system, as well as reduced levels of butyrate — a substance that plays an important role in gut function. Symptoms of ME include prolonged fatigue after exertion, sleep disturbances, difficulty concentrating and memory — similar to the manifestations of long Covid.

According to Derya Unutmaz, a professor of immunology at Jackson, the lack of clear laboratory markers has led some doctors to doubt that ME is a real disease.

The problem may not be in one broken component, but in a disrupted connection between systems

– said Janet Scott, clinical lecturer in infectious diseases at the MRC-University of Glasgow Centre for Virus Research.

At the same time, researchers warn: despite success in identifying physiological abnormalities, there are still many unresolved questions, particularly regarding the causes and effective treatment of ME.

“Patients are often diagnosed some time after the onset of the disease, which means that the causes are very difficult to determine at the molecular level,” said Daniel Davis, professor of immunology at Imperial College London. “The search for effective treatments is ongoing, but the basic knowledge contained in this analysis can be used for many years to come.”

Not all scientists believe that the research provides definitive answers. Some emphasize the limitations of the sample and the diversity of conditions among patients.

“At best, these (studies) are small incremental steps that are not reproducible,” said Alan Carson, professor of neuropsychiatry at the University of Edinburgh. “We are still very far from understanding the biology of ME.”

According to the WHO, about 6% of those who have had Covid-19 subsequently face long-term consequences. And artificial intelligence can become one of the keys to solving this global medical problem.

AI-powered microscope predicts brain disease formation – study24.07.25, 16:44 • 3122 views

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Tech giants pay talent millions of dollars

Published

27 minutes ago

September 6, 2025

The Editors

Meta CEO Mark Zuckerberg offered $100 million signing bonuses to top OpenAI employees.

David Paul Morris | Bloomberg | Getty Images

The artificial intelligence arms race is heating up, and as tech giants scramble to come out on top, they’re dangling millions of dollars in front of a small talent pool of specialists in what’s become known as the AI talent war.

It’s seeing Big Tech firms like Meta, Microsoft, and Google compete for top AI researchers in an effort to bolster their artificial intelligence divisions and dominate the multibillion-dollar market.

Meta CEO Mark Zuckerberg recently embarked on an expensive hiring spree to beef up the company’s new AI Superintelligence Labs. This included poaching Scale AI co-founder Alexander Wang as part of a $14 billion investment into the startup.

OpenAI’s Chief Executive Sam Altman, meanwhile, recently said the Meta CEO had tried to tempt top OpenAI talent with $100 million signing bonuses and even higher compensation packages.

If I’m going to spend a billion dollars to build a [AI] model, $10 million for an engineer is a relatively low investment.

Alexandru Voica

Head of Corporate Affairs and Policy at Synthesia

Google is also a player in the talent war, tempting Varun Mohan, co-founder and CEO of artificial intelligence coding startup Windsurf, to join Google DeepMind in a $2.4 billion deal. Microsoft AI, meanwhile, has quietly hired two dozen Google DeepMind employees.

“In the software engineering space, there was an intense competition for talent even 15 years ago, but as artificial intelligence became more and more capable, the researchers and engineers that are specialized in this area has stayed relatively stable,” Alexandru Voica, head of corporate affairs and policy at AI video platform Synthesia, told CNBC Make It.

“You have this supply and demand situation where the demand now has skyrocketed, but the supply has been relatively constant, and as a result, there’s the [wage] inflation,” Voica, a former Meta employee and currently a consultant at the Mohamed bin Zayed University of Artificial Intelligence, added.

Voica said the multi-million dollar compensation packages are a phenomenon the industry has “never seen before.”

Here’s what’s behind the AI talent war:

Building AI models costs billions

The inflated salaries for specialists come hand-in-hand with the billion-dollar price tags of building AI models — the technology behind your favorite AI products like ChatGPT.

There are different types of AI companies. Some, like Synthesia, Cohere, Replika, and Lovable, build products; others, including OpenAI, Anthropic, Google, and Meta, build and train large language models.

“There’s only a handful of companies that can afford to build those types of models,” Voica said. “It’s very capital-intensive. You need to spend billions of dollars, and not a lot of companies have billions of dollars to spend on building a model. And as a result, those companies, the way they approach this is: ‘If I’m going to spend a billion dollars to build a model, $10 million for an engineer is a relatively low investment.'”

Anthropic’s CEO Dario Amodei told Time Magazine in 2024 that he expected the cost of training frontier AI models to be $1 billion that year.

Stanford University’s AI Institute recently produced a report that showed the estimated cost of building select AI models between 2019 and 2024. OpenAI’s GPT-4 cost $79 million to build in 2023, for example, while Google’s Gemini 1.0 Ultra was $192 million. Meta’s Llama 3.1-405B cost $170 million to build in 2024.

“Companies that build products pay to use these existing models and build on top of them, so the capital expenditure is lower and there isn’t as much pressure to burn money,” Voica said. “The space where things are very hot in terms of salaries are the companies that are building models.”

AI specialists are in demand

The average salary for a machine learning engineer in the U.S. is $175,000 in 2025, per Indeed data.

Pixelonestocker | Moment | Getty Images

Machine learning engineers are the AI professionals who can build and train these large language models — and demand for them is high on both sides of the Atlantic, Ben Litvinoff, associate director at technology recruitment company Robert Walters, said.

“There’s definitely a heavy increase in demand with regards to both AI-focused analytics and machine learning in particular, so people working with large language models and people deploying more advanced either GPT-backed or more advanced AI-driven technologies or solutions,” Litvinoff explained.

This includes a “slim talent pool” of experienced specialists who have worked in the industry for years, he said, as well as AI research scientists who have completed PhDs at the top five or six universities in the world and are being snapped up by tech giants upon graduating.

It’s leading to mega pay packets, with Zuckerberg reportedly offering $250 million to a 24-year-old AI genius Matt Deitke, who dropped out of a computer science doctoral program at the University of Washington.

Meta directed CNBC to Zuckerberg’s comments to The Information, where the Facebook founder said there’s an “absolute premium” for top talent.

“A lot of the specifics that have been reported aren’t accurate by themselves. But it is a very hot market. I mean, as you know, and there’s a small number of researchers, which are the best, who are in demand by all of the different labs,” Zuckerberg told the tech publication.

“The amount that is being spent to recruit the people is actually still quite small compared to the overall investment and all when you talk about super intelligence.”

Litvinoff estimated that, in the London market, machine learning engineers and principal engineers are currently earning six-figure salaries ranging from £140,000 to £300,000 for more senior roles, on average.

In the U.S., the average salary for a machine learning engineer is $175,000, reaching nearly $300,000 at the higher end, according to Indeed.

Startups and traditional industries get left behind

As tech giants continue to guzzle up the best minds in AI with the lure of mammoth salaries, there’s a risk that startups get left behind.

“Some of these startups that are trying to compete in this space of building models, it’s hard to see a way forward for them, because they’re stuck in the space of: the models are very expensive to build, but the companies that are buying those models, I don’t know if they can afford to pay the prices that cover the cost of building the model,” Voica noted.

Mark Miller, founder and CEO of Insurevision.ai, recently told Startups Magazine that this talent war was also creating a “massive opportunity gap” in traditional industries.

“Entire industries like insurance, healthcare, and logistics can’t compete on salary. They need innovation but can’t access the talent,” Miller said. “The current situation is absolutely unsustainable. You can’t have one industry hoarding all the talent while others wither.”

Voica said AI professionals will have to make a choice. While some will take Big Tech’s higher salaries and bureaucracy, others will lean towards startups, where salaries are lower, but staff have more ownership and impact.

“In a large company, you’re essentially a cog in a machine, whereas in a startup, you can have a lot of influence. You can have a lot of impact through your work, and you feel that impact,” Voica said.

Until the price of building AI models comes down, however, the high salaries for AI talent are likely to remain.

“As long as companies will have to spend billions of dollars to build the model, they will spend tens of millions, or hundreds of millions, to hire engineers to build those models,” Voica added.

“If all of a sudden tomorrow, the cost to build those models decreases by 10 times, the salaries I would expect would come down as well.”

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Generative vs. agentic AI: Which one really moves the customer experience needle?

Published

2 hours ago

September 6, 2025

The Editors

Artificial intelligence, first coined by John McCarthy in 1956, lay dormant for decades before exploding into a cultural and business phenomenon post-2012. From predictive algorithms to chatbots and creative tools, AI has evolved rapidly. Now, two powerful paradigms are shaping its future: generative AI, which crafts content from text to art, and agentic AI, which acts autonomously to solve complex tasks. But should businesses pit generative AI against agentic AI or combine them to innovate? The answer isn’t binary, because these technologies aren’t competing forces. In fact, they often complement each other in powerful ways, especially when it comes to transforming customer engagement.

The rise of generative AI: Creativity meets scale

Generative AI is all about creation; it represents the imaginative side of artificial intelligence. From producing marketing copy and designing campaign visuals to generating product descriptions and chat responses, generative AI has unlocked new possibilities for enterprises looking to scale content and personalisation like never before.

Fuelled by powerful models like ChatGPT, DALL·E, and MidJourney, these systems have entered the enterprise stack at speed. Marketing teams are using them to brainstorm ideas and accelerate go-to-market efforts. Customer support teams are deploying them to enhance chatbot interactions with more human-like language. Product teams are using generative AI to auto-draft FAQs or documentation. And sales teams are experimenting with tailored email pitches generated from past deal data.

At the heart of this capability is the model’s ability to learn from massive datasets, analysing and replicating patterns in text, visuals, and code to produce new, relevant content on demand. This has made generative AI a valuable tool in customer engagement workflows where speed, relevance, and personalisation are paramount. But while generative AI can start the conversation, it rarely finishes it. That’s where its limitations show up.

For instance, it can draft a beautifully written response to a billing query, but it can’t resolve the issue by accessing the customer’s account, applying credits, or triggering workflows across enterprise systems. In other words, it creates the message but not the outcome. This creative strength makes generative AI a powerful enabler of customer engagement but not a complete solution. To drive real business value, measured in resolution rates, retention, and revenue, enterprises need to go beyond content generation and toward intelligent action. This is where agentic AI comes into play.

How agentic AI is redefining enterprise and consumer engagement

As the need for deeper automation grows, agentic AI is taking centre stage. Agentic AI is built to act; it makes decisions, takes autonomous actions, and adapts in real time to achieve goals. For businesses, this marks a transformative shift. Generative AI has empowered enterprises to accelerate communication, generate insights, and personalise engagement. Agentic AI, on the other hand, goes beyond assistance to autonomy. Imagine a virtual enterprise assistant that doesn’t just draft emails but manages entire customer service workflows — triggering follow-ups, updating CRM systems, and escalating issues when needed.

In industries like supply chain, finance, and telecom, agentic AI can dynamically reconfigure networks, detect anomalies, or reroute deliveries—all with minimal human input. It’s a new era of AI-driven execution. On the consumer front, agentic AI takes engagement from passive response to proactive assistance. Think of a digital concierge that not only understands your intent but acts on your behalf — tracking shipments or negotiating a better mobile plan based on usage patterns.

A new layer of intelligence — with responsibility

The increased autonomy of agentic AI raises important questions around trust, governance, and accountability. Who’s liable when an agentic system makes an error or an ethically questionable decision? Enterprises adopting such systems will need to ensure alignment with human values, transparency in decision-making, and robust fail-safes.

Generative and agentic AI are not rivals — they’re complementary forces that, together, enable a new era of intelligent enterprise and consumer engagement.

When generative meets agentic AI

Generative AI and agentic AI may serve different functions. However, rather than operating in isolation, these technologies frequently collaborate, enhancing both communication and execution.

Take, for example, a virtual customer service agent. The agentic AI manages the flow of interaction, makes decisions, and determines next steps, while generative AI crafts clear, personalised responses tailored to the conversation in real time.

This collaborative dynamic also plays out in robotics. Imagine a robot chef: generative AI could invent creative recipes based on user tastes and available ingredients, while agentic AI would take over the cooking, executing the recipe with precision and adapting to real-time conditions in the kitchen.

Summing Up

As AI continues to evolve, the boundaries between generative and agentic systems will become increasingly fluid. We’re heading toward a future where AI doesn’t just imagine possibilities but also brings them to life, merging creativity with execution in a seamless loop. This fusion holds immense promise across industries, from streamlining healthcare operations to revolutionising manufacturing workflows.

However, with such transformative power comes great responsibility. Ethical development, transparency, and accountability must remain non-negotiable, especially when it comes to safeguarding consumer data. As these systems take on more autonomous roles, ensuring privacy, security, and user consent will be critical to building trust.

By understanding the distinct roles and combined potential of generative and agentic AI, we can shape a future where technology enhances human capability responsibly, meaningfully, and with integrity at its core.

This article is authored by Harsha Solanki, VP GM Asia, Infobip.