Technology Tales

Current AI systems, particularly large language models, face persistent issues such as hallucinations and unreliable performance despite significant investment in scaling models. These systems rely on statistical pattern recognition from vast datasets, which limits their ability to understand abstract rules or apply knowledge to novel situations, leading to errors in reasoning and logic.

Scaling models has proven inefficient, costly and ethically problematic, with diminishing returns on reliability. An alternative approach, neurosymbolic AI, integrates neural networks with symbolic reasoning to extract and apply abstract rules, enhancing reliability, efficiency and explainability.

This method allows systems to generalise beyond training data, reduce computational demands and support verifiable decision-making, addressing critical gaps in current AI capabilities. By combining the flexibility of learning with the precision of logical reasoning, neurosymbolic AI offers a more robust framework for developing trustworthy and adaptable systems, and represents a potential shift in the broader evolution of artificial intelligence.

Cybercriminals are increasingly exploiting generative artificial intelligence to enhance the sophistication and efficiency of their attacks. The technology has thus far primarily improved productivity rather than creating entirely new attack methods.

Attackers are leveraging generative AI to craft more convincing phishing emails with personalised content drawn from social media and other sources. They are also developing malware with detailed code documentation suggesting AI assistance, and accelerating vulnerability discovery and exploitation. According to one study, this has reduced the time from disclosure to exploit from 47 days to just 18 days.

More concerning developments include the emergence of AI-orchestrated espionage campaigns that automate approximately 80 per cent of attack activities. Unregulated large language models such as WormGPT and FraudGPT, built without safety guardrails, have also appeared, alongside the theft of cloud credentials to hijack costly LLM resources for criminal purposes.

Attackers are employing deepfakes for social engineering through voice and video impersonation, and using generative AI to create fraudulent advertising campaigns that impersonate legitimate brands. They are additionally poisoning AI model memories with malicious data and compromising AI infrastructure through supply chain attacks on servers and dependencies.

Whilst these tools lower barriers to entry for less skilled criminals and enable faster execution of traditional attack methods, AI-generated attacks still face fundamental limitations. Security experts note that such attacks have yet to produce completely novel exploit techniques. Defensive applications of AI combined with robust identity management and anomaly detection therefore remain effective countermeasures against these evolving threats.

Anthropic's introduction of Claude Code Security, an AI-driven tool that scans code for vulnerabilities and suggests patches, has sparked significant discussion within the cybersecurity community. The feature is currently in a limited research preview and requires human oversight for final approval.

It joins a growing list of AI-powered security initiatives by companies such as Amazon, Microsoft, Google and OpenAI, each developing tools to identify and address software flaws. These systems leverage large language models to detect vulnerabilities at scale, though they remain context-aware and rely on human judgement to validate fixes.

Industry experts acknowledge the potential benefits of such tools in improving code quality and security, but also highlight ongoing challenges. These include the need for transparency in performance metrics, the risk of false positives and the necessity of human involvement to ensure accuracy and mitigate potential oversights.

PumasAI is a pharmaceutical technology company based in Dover, Delaware, that develops data analytics tools designed to support drug development and healthcare delivery. Its flagship software, Pumas, has been used in over 26 successful regulatory submissions, and the company claims to have saved its 60-plus clients a combined total of one billion dollars. The firm has recently released version 2.8 of its platform, which includes a new feature called PumasAide, and has been recognised at the Biotechnology Awards for its contributions to the pharmaceutical industry. Alongside its software products, the company offers consulting services aimed at helping clients navigate the regulatory approval process, as well as complimentary access to its modelling tools for those engaged in non-commercial research and education.

The notion that AI will decimate the job market for developers is greatly exaggerated. In fact, AI represents a platform shift that's changing what it looks like to build software and ushering in a period of enormous demand for ambitious, innovative and highly specialised code. This demand is driven by the imagination engine of the human mind, which constantly comes up with better ways of doing things. Each imagined future requires software to become reality, leading to new jobs and new approaches to existing ones. The changing nature of development work means that developers are shifting from writing every line of code by hand to orchestrating AI agents that generate code. New roles are emerging, such as AI orchestrators, prompt engineers and human-AI collaboration architects, which require an in-depth understanding of both traditional computer science fundamentals and how to work effectively with AI tools.

Data Hub is an AI-native data platform developed by Datopian that allows users to discover, publish and interact with datasets through agentic workflows. It offers a curated selection of regularly maintained core datasets covering areas such as country codes, S&P 500 companies, airport codes and geographic boundaries, alongside enterprise-grade data solutions including postal codes, logistics data and country reference data.

For those unable to find what they need independently, a Premium Data Service connects users with data experts who will source, verify and integrate customised datasets on their behalf. The platform also supports easy publishing of data in a Markdown-based format with optional GitHub synchronisation and a free entry tier. A companion blog covers practical topics such as decoupling frontend architecture from CKAN-based portals and modernising open data infrastructure for organisations ranging from government bodies to private enterprises.

Claude has been integrated into Microsoft Excel as a beta feature available to Pro, Max, Team and Enterprise subscribers. The AI assistant can analyse entire workbooks, including complex formulas and dependencies across multiple tabs, whilst providing explanations with specific cell references for verification.

Users can test different scenarios by updating assumptions throughout their models without disrupting existing formulas, with all changes clearly highlighted and explained. The tool can also identify and help resolve common spreadsheet errors such as reference errors, value errors and circular dependencies by tracing them back to their origin.

Claude can generate draft financial models based on user requirements, and can populate existing templates with new data whilst preserving all formulas and structural elements. These capabilities make it a practical aid for both building and maintaining complex spreadsheet-based workflows.

Following user feedback that Claude Code was being applied to non-coding tasks, Anthropic has introduced Cowork, a simplified version designed for general productivity work rather than software development. Available initially as a research preview for Claude Max subscribers on macOS, Cowork allows users to grant Claude access to specific folders on their computers where it can read, edit and create files autonomously. The system can handle tasks such as reorganising downloads, generating spreadsheets from screenshots or drafting reports from notes, operating with greater independence than standard conversational interactions by making plans and executing them whilst keeping users informed of progress. Users can enhance Cowork's capabilities through existing connectors and newly added skills for document creation, and can combine it with Claude in Chrome for browser-based tasks.

Whilst users maintain control by selecting which folders Claude can access and receive prompts before significant actions occur, the system carries risks including potential file deletion through misinterpreted instructions and vulnerability to prompt injection attacks where malicious content might alter Claude's behaviour. The company plans to expand availability to other subscription tiers, add cross-device synchronisation and Windows support, and continue developing safety features based on feedback from this early release.

After open-weight language models made it cheaper and more practical to run capable systems outside proprietary platforms, many teams have found that hosting them locally still demands extreme hardware. Attention has therefore shifted to specialist API providers that charge by tokens and remove most of the infrastructure burden.

Several providers are compared using benchmark and live performance observations, with the assessment focusing on speed, latency, cost, accuracy and reliability. Cerebras stands out for very high throughput on large models, whilst Fireworks AI and Groq emphasise very low latency suited to interactive and real-time agent use. Together.ai aims for broadly strong, steady production performance on conventional GPU infrastructure, and Clarifai targets enterprise needs with hybrid deployment control and cost management. DeepInfra is presented as a lower-cost option suitable for batch or non-critical workloads, with the trade-off of weaker reliability compared to the leading services.

As software systems grow more complex and delivery cycles shorten, requirements engineering is under pressure to stay rigorous whilst moving faster. A recent article argues that artificial intelligence is increasingly being used to support that shift.

It outlines how AI can help teams capture and refine requirements earlier through meeting transcription and summarisation, automatic drafting of user stories and acceptance criteria, clustering and sentiment analysis to surface disagreement and themes, and live translation to improve collaboration in distributed teams. It also describes tools that generate diagrams, detect duplicates, suggest tests, support traceability and predict change impacts, alongside general-purpose assistants that help analysts brainstorm, rephrase and review specifications.

Alongside these potential gains in efficiency and consistency, the article stresses the need for careful governance around bias, privacy and transparency. Human oversight, clear accountability and compliance measures such as GDPR are described as remaining essential.