Development Archives | Page 233 of 539 | Digital Watch Observatory

Small business revival could hinge on AI-driven tools

If AI is to matter in the economy, it must first matter to small businesses. Firms employ over 61 million people, nearly half the private workforce, yet most run on outdated technology. While smartphones update monthly, many small businesses still use systems built a decade ago.

Search fund entrepreneurs bridge this gap by upgrading established firms with modern tech. One deal turned a 50-person roadside assistance firm into Asurion, now a global tech-care provider. Others have scaled compliance firms into nationwide SaaS platforms.

Generative AI now accelerates these transformations, cutting work times by over 60% across supply chains, compliance, and document processing functions. Complex tasks can now be completed in hours, unlocking double-digit productivity gains and allowing small businesses to focus on growth.

Search funds are not the only path forward. AI consulting firms, tech studios, and AI-powered roll-up strategies bring enterprise-grade tools to family-run firms. For communities that have relied on traditional playbooks, decades of growth can be compressed into months.

The cost of AI has never been lower, and the opportunity is wide open. Once deployed at scale, AI could power a wave of productivity on Main Street, helping small businesses compete and strengthening the economy for half of their workforce.

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AI and AFM deliver real-time macrophage phenotyping

Macrophages drive immune responses, including inflammation, tissue repair, and tumour growth. Identifying their polarisation states is key for diagnosis and immunotherapy, but current methods, such as RNA sequencing and flow cytometry, are expensive, slow, and unsuitable for real-time use.

Atomic force microscopy (AFM) has emerged as a powerful tool for decoding mechanobiological signatures of cells. Combined with AI, AFM data can be rapidly analysed, but macrophage phenotyping has been relatively underexplored using this approach.

Researchers led by Prof Li Yang at the Shenzhen Institutes of Advanced Technology have now developed a label-free, non-invasive method combining AFM with deep learning. The system accurately profiles human macrophage mechanophenotypes and identifies polarisation states in real-time.

The AI model was trained on well-characterised macrophage subtypes and validated using flow cytometry. Results showed that pseudovirus stimulation mainly produced M1 macrophages, with smaller populations of M2 and mixed phenotypes, closely matching the model’s predictions.

The study, published in Small Methods, offers a promising diagnostic tool that could be extended beyond macrophages to other cell types. It could support new approaches in cancer, fibrosis, and infectious disease diagnostics based on mechanophenotypes.

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NotebookLM turns notes into flashcards podcasts and quizzes

Google’s learning-focused AI tool NotebookLM has gained a major update, making studying and teaching more interactive.

Instead of offering only static summaries, it now generates flashcards that condense key information into easy-to-remember notes, helping users recall knowledge more effectively.

Reports can also be transformed into quizzes with customisable topics and difficulty, which can then be shared with friends or colleagues through a simple link.

The update extends to audio learning, where NotebookLM’s podcast-style Audio Overviews are evolving with new formats. Instead of a single style, users can now create Brief, Debate, or Critique episodes, giving greater flexibility in how material is explained or discussed.

Google is also strengthening its teaching tools. A new Blog Post format offers contextual suggestions such as strategy papers or explainers, while the ability to create custom report formats allows users to design study resources tailored to their needs.

The most significant addition, however, is the Learning Guide. Acting like a personal tutor, it promotes deeper understanding by asking open-ended questions, breaking problems into smaller steps, and adapting explanations to suit each learner.

With these features, NotebookLM is moving closer to becoming a comprehensive learning assistant, offering a mix of interactive study aids and adaptable teaching methods that go beyond simple note-taking.

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Google Cloud scraps transfer fees to win multicloud users

Google Cloud has rolled out a new Data Transfer Essentials plan, allowing customers to move workloads across multiple clouds without paying transfer fees. The move comes ahead of the EU Data Act on 12 September 2025, intended to strengthen competition across the cloud market.

Under the new rules, providers must charge data transfer fees only ‘at cost.’ While Microsoft and Amazon have already taken steps to comply, Google has gone further by removing the charge altogether.

The company said the change is aimed at supporting businesses with multicloud strategies, offering more flexibility, and reducing downtime in critical workloads.

The initiative also positions Google as more aligned with regulatory goals, particularly compared with Microsoft, which has faced scrutiny over restrictive licensing practices. Google said qualifying traffic will now be billed at zero cost, while other transfers remain charged at existing rates.

The announcement follows strong growth in Google Cloud’s business, especially from AI firms like OpenAI and Anthropic. Alphabet’s cloud contracts are valued at around $106 billion, with CEO Thomas Kurian projecting $58 billion in revenue conversion within two years.

Alphabet’s stock price rose 2.47% following the update, reaching $239.94, as investors responded positively to both growth prospects and regulatory positioning.

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Photonic chips open the path to sustainable AI by training with light

A team of international researchers has shown how training neural networks directly with light on photonic chips could make AI faster and more sustainable.

A breakthrough study, published in Nature, involved collaboration between the Politecnico di Milano, EPFL Lausanne, Stanford University, the University of Cambridge, and the Max Planck Institute.

The research highlights how physical neural networks, which use analogue circuits that exploit the laws of physics, can process information in new ways.

Photonic chips developed at the Politecnico di Milano perform mathematical operations such as addition and multiplication through light interference on silicon microchips only a few millimetres in size.

By eliminating the need to digitise information, these chips dramatically cut both processing time and energy use. Researchers have also pioneered an ‘in-situ’ training technique that enables photonic neural networks to learn tasks entirely through light signals, instead of relying on digital models.

The result is a training process that is faster, more efficient and more robust.

Such advances could lead to more powerful AI models capable of running directly on devices instead of being dependent on energy-hungry data centres.

An approach that paves the way for technologies such as autonomous vehicles, portable intelligent sensors and real-time data processing systems that are both greener and quicker.

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Oracle and OpenAI drive record $300B investment in cloud for AI

OpenAI has finalised a record $300 billion deal with Oracle to secure vast computing infrastructure over five years, marking one of the most significant cloud contracts in history. The agreement is part of Project Stargate, OpenAI’s plan to build massive data centre capacity in the US and abroad.

The two companies will develop 4.5 gigawatts of computing power, equivalent to the energy consumed by millions of homes.

Backed by SoftBank and other partners, the Stargate initiative aims to surpass $500 billion in investment, with construction already underway in Texas. Additional plans include a large-scale data centre project in the United Arab Emirates, supported by Emirati firm G42.

The scale of the deal highlights the fierce race among tech giants to dominate AI infrastructure. Amazon, Microsoft, Google and Meta are also pledging hundreds of billions of dollars towards data centres, while OpenAI faces mounting financial pressure.

The company currently generates around $10 billion in revenue but is expected to spend far more than that annually to support its expansion.

Oracle is betting heavily on OpenAI as a future growth driver, although the risk is high given OpenAI’s lack of profitability and Oracle’s growing debt burden.

A gamble that rests on the assumption that ChatGPT and related AI technologies will continue to grow at an unprecedented pace, despite intense competition from Google, Anthropic and others.

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Canadian news publishers clash with OpenAI in landmark copyright case

OpenAI is set to argue in an Ontario court that a copyright lawsuit by Canadian news publishers should be heard in the United States. The case, the first of its kind in Canada, alleges that OpenAI scraped Canadian news content to train ChatGPT without permission or payment.

The coalition of publishers, including CBC/Radio-Canada, The Globe and Mail, and Postmedia, says the material was created and hosted in Ontario, making the province the proper venue. They warn that accepting OpenAI’s stance would undermine Canadian sovereignty in the digital economy.

OpenAI, however, says the training of its models and web crawling occurred outside Canada and that the Copyright Act cannot apply extraterritorially. It argues the publishers are politicising the case by framing it as a matter of sovereignty rather than jurisdiction.

The dispute reflects a broader global clash over how generative AI systems use copyrighted works. US courts are already handling several similar cases, though no clear precedent has been established on whether such use qualifies as fair use.

Publishers argue Canadian courts must decide the matter domestically, while OpenAI insists it belongs in US courts. The outcome could shape how copyright laws apply to AI training and digital content across borders.

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Google launches AI Plus to expand access to Gemini tools

Google has introduced a new mid-tier subscription called AI Plus, designed to make its Gemini AI tools more accessible at a lower price. Positioned between the free and premium Pro plans, AI Plus offers broader access to Gemini 2.5 Pro, productivity features across Gmail, Docs, and Sheets, and 200GB of Google One cloud storage.

Subscribers will benefit from a larger 128,000-token context window, compared with 32,000 for free users, and tools such as Veo 3 Fast for video creation, Google Flow for video generation, and Whisk for image-to-video conversion. The plan also includes expanded use of NotebookLM, Google’s AI-powered research assistant.

The service has launched first in Indonesia at Rp. 75,000 ($4.56) per month, a fraction of the AI Pro plan’s price of Rp. 309,000 ($18.79). Google has not set a timeline for global rollout but said AI Plus will cost under $20 per month in other markets, with prices adjusted regionally.

After months of vague descriptions, the new tier follows Google’s move to publish clearer guidelines on Gemini’s usage limits across free, Pro, and Ultra plans. The update aims to bring greater transparency as the company pushes deeper into the competitive AI subscription market.

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Harvard develops AI to identify life-changing gene-drug combinations

Researchers at Harvard Medical School unveiled an AI designed to match genes and drugs to combat disease in cells. The system, called PDGrapher, aims to tackle conditions ranging from Parkinson’s and Alzheimer’s to rare disorders like X-linked Dystonia-Parkinsonism.

Unlike traditional tools that only detect correlations, PDGrapher forecasts which gene-drug pairings can restore healthy cellular function and explains their mechanisms. It may speed up research, lower expenses, and point to novel treatments.

Early tests suggest that PDGrapher can identify known effective combinations and propose new ones that have yet to be validated. If validated in trials, the technology could move medicine towards personalised treatments.

The debut of PDGrapher reflects a broader trend of AI transforming biotechnology. Innovations in AI are accelerating research by mapping biological systems with unprecedented speed, showing how machine learning can decode complex biological systems faster than ever before.

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Ransomware 3.0 raises alarm over AI-generated cyber threats

Researchers at NYU’s Tandon School of Engineering have demonstrated how large language models can be utilised to execute ransomware campaigns autonomously. Their prototype, dubbed Ransomware 3.0, simulated every stage of an attack, from intrusion to the generation of a ransom note.

The system briefly raised an alarm after cybersecurity firm ESET discovered its files on VirusTotal, mistakenly identifying them as live malware. The proof-of-concept was designed only for controlled laboratory use and posed no risk outside testing environments.

Instead of pre-written code, the prototype embedded text instructions that triggered AI models to generate tailored attack scripts. Each execution created unique code, evading traditional detection methods and running across Windows, Linux, and Raspberry Pi systems.

The researchers found that the system identified up to 96% of sensitive files and could generate personalised extortion notes, raising psychological pressure on victims. With costs as low as $0.70 per attack using commercial AI services, such methods could lower barriers for criminals.

The team stressed that the work was conducted ethically and aims to help defenders prepare countermeasures. They recommend monitoring file access patterns, limiting outbound AI connections, and developing defences against AI-generated attack behaviours.

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