Mobile phones had long been an integral part of our daily lives when April 2000 arrived. People took them everywhere because they were a must-have for younger users. Reporter Lindsey Fallow looked closely at how these phones were on the verge of becoming something major, such as having continual access to email and the internet right in the palm of your hand.

Lindsey starts with checking mobile email. Anyone with a phone that was less than two years old could send and receive text messages. There were services that would forward emails from your regular email account to your phone as text messages, and the greatest part was that registration was free, however each downloaded message cost approximately 6 pence ($.15 today). To respond, you would need to construct a text message, include a specific code at the beginning, and submit it to your service provider. Typing on such tiny keypads took a long time, and the expense quickly mounted up.

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She demonstrates with a short exchange, beginning with an incoming message that reads “Can you meet me for lunch to talk about the report? Can you find a restaurant sushi?” she asks, wondering where to eat. She pulls out a WAP phone, which she refers to as a “mobile with internet built in,” and we can see why: previous attempts to get phones to access the internet failed because the whole web requires a large color screen, and most mobiles at the time only had a couple of inches of screen space.

WAP phones changed all that by rewriting web material specifically for small screen sizes. Pages had to be recoded, so the entire internet remained out of reach. Still, useful sites existed. Fallow navigates to the BBC’s pages and to H2G2—a user-edited guide inspired by The Hitchhiker’s Guide to the Galaxy, full of searchable entries anyone could contribute to. She searches for lunch spots and locates a sushi restaurant right around the corner. The screen shows basic text results, no images or fancy layouts, but the information arrives where she needs it.

These WAP phones were retailing for about £130 ($334 today) with a contract, and more were on their way. Services were also constantly expanding, and Lindsey highlights both progress and problems. When a follow-up email arrives stating that lunch has been canceled and that the report should be sent instead, responding with only text messages is inconvenient and can take hours to complete.

Following that came the early smartphones. Lindsey tries out a prototype with a much bigger screen. It includes a full web browser for WAP material, a calendar, and a note feature, as well as handwriting recognition on a touch-sensitive surface. If the handwriting does not work out, a little keyboard appears that you can use. Navigation is a lot speedier and easier on the eyes. These devices promised to combine the power of the web with organization and communication, all in one convenient package. They were expected to hit the shelves that summer for between £300 to £400 ($770 to $1,029 today) with a contract.

The investment marks a significant moment for the organisation as it prepares to advance its ‘Real World Model’.

Stanhope AI, a London-based deep-tech start-up, has announced the closure of an $8m seed funding round. The round attracted a transatlantic cohort of investors led by Frontline Ventures, with participation from Paladin Capital Group and Auxxo Female Catalyst Fund, as well as follow-on investment from UCL Technology Fund and MMC Ventures.

A 2023 spin-out from University College London and King’s College London, Stanhope AI was founded by Irish computational neuroscientist Prof Rosalyn Moran and theoretical neurobiologist Prof Karl Friston. 

The team at Stanhope AI has been building a new AI model for autonomous systems that allows machines to “mimic the human brain”, drawing from Friston’s ‘Free Energy Principle’ – a framework developed to explain how intelligent systems minimise uncertainty through continuous perception and action.

According to the start-up, this “brain-inspired paradigm”, known as active inference, enables machines to learn and adapt on the move, which Stanhope AI believes is a crucial capability missing from large language model-based systems that rely on large static datasets.

Stanhope AI’s technology is currently being tested in autonomous drone and robotics applications with international partners, with the goal of teaching machines to behave more intelligently in unpredictable, real-world environments.

According to the organisation, the investment marks a significant milestone as Stanhope AI advances its ‘Real World Model’, which it described a next-generation framework for adaptive intelligence, “designed to function in dynamic, physical environments beyond the limitations of large language models”.

“We’re moving from language-based AI to intelligence that possesses the ability to act to understand its world, a system with a fundamental agency,” said Moran, who is also the company’s CEO. “Our approach doesn’t just process words, it understands context, uncertainty and physical reality.”

In a post on LinkedIn, she explained that the investment is about more than just fresh capital, stating it is a “clear point of technology maturity”.

“Over the past two years in London, we’ve progressed from foundational research and early prototypes to production-grade systems operating in real customer environments, engineered for explainability and scalability,” she said. “The round is also a validation of that journey and evidence that our technology performs beyond the lab.

“We’re proud to be building from London, a deep-tech ecosystem increasingly global in its reach, and equally proud to be backed by investors spanning the UK, US and Europe. That transatlantic support reflects both the ambition of the technology and the scale of the opportunity ahead.”

She added that the funding will accelerate deployments, expand the team and advance the “next phase of applied AI via active inference”.

In other AI start-up funding news, on Tuesday (10 February), Dublin-based property management AI start-up Marc raised $1m from angel investors in a pre-seed funding round. The platform uses AI to analyse fragmented sources of vendor contract and invoice data related to property units and consolidates the information for use by owners and managers to help identify discrepancies leading to overpayments.

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I spend a lot of time talking with teams that are trying to expand their AI efforts, and I’ve noticed a consistent pattern: AI pilots are multiplying across the board, but a majority of them fail to see the light of production.

Data tells a similar story: Only 26% of leaders report more than half of their pilots scaling to production. Meanwhile, 69% of practitioners (the front-line teams embedding AI into workflows) say most of their pilots are never scaled.