AI dictation reached maturity during the years after many attempts of patchy performance and frustrating inaccuracies.
Advances in speech-to-text engines and large language models now allow modern dictation tools to recognise everyday speech more reliably while keeping enough context to format sentences automatically instead of producing raw transcripts that require heavy editing.
Several leading apps have emerged with different strengths. Wispr Flow focuses on flexibility with style options and custom vocabulary, while Willow blends automation with privacy by storing transcripts locally.
Monologue also prioritises privacy by allowing users to download the model and run transcription entirely on their own machines. Superwhisper caters for power users by supporting multiple downloadable models and transcription from audio or video files.
Other tools take different approaches. VoiceTypr offers an offline-first design with lifetime licensing, Aqua promotes speed and phrase-based shortcuts, Handy provides a simple free open source starting point, and Typeless gives one of the most generous free allowances while promising strong data protection.
Each reflects a wider trend where developers try to balance convenience, privacy, control and affordability.
Users now benefit from cleaner, more natural-sounding transcripts instead of the rigid audio typing tools of previous years. AI dictation has become faster, more accurate and far more usable for everyday note-taking, messaging and work tasks.
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Gemini from Google recorded the highest score at 63 percent, with xAI’s Grok almost level at 62.8 percent. DeepSeek followed with 52 percent, while ChatGPT scored 49.4 percent, and Claude placed last at 45.2 percent.
Performance varied sharply across subjects, with maths and conversion tasks producing the best results, but physics questions dragged scores down to an average accuracy below 40 percent.
Researchers identified most errors as sloppy calculations or rounding mistakes, rather than deeper failures to understand the problem. Finance and economics questions highlighted the widest gaps between the models, while DeepSeek struggled most in biology and chemistry, with barely one correct answer in ten.
Users are advised to double-check results whenever accuracy is crucial. A calculator or a verified source is still advised instead of relying entirely on an AI chatbot for numerical certainty.
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China has proposed stringent new rules for AI aimed at protecting children and preventing chatbots from providing advice that could lead to self-harm, violence, or gambling.
The draft regulations, published by the Cyberspace Administration of China (CAC), require developers to include personalised settings, time limits, and parental consent for services offering emotional companionship.
High-risk chats involving self-harm or suicide must be passed to a human operator, with guardians or emergency contacts alerted. AI providers must not produce content that threatens national security, harms national honour, or undermines national unity.
The rules come as AI usage surges, with platforms such as DeepSeek, Z.ai, and Minimax attracting millions of users in China and abroad. The CAC supports safe AI use, including tools for local culture and elderly companionship.
The move reflects growing global concerns over AI’s impact on human behaviour. Notably, OpenAI has faced legal challenges over alleged chatbot-related harm, prompting the company to create roles focused on tracking AI risks to mental health and cybersecurity.
China’s draft rules signal a firm approach to regulating AI technology as its influence expands rapidly.
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China is proposing new rules requiring users to consent before AI companies can use chat logs for training. The draft measures aim to balance innovation with safety and public interest.
Platforms would need to inform users when interacting with AI and provide options to access or delete their chat history. For minors, guardian consent is required before sharing or storing any data.
Analysts say the rules may slow AI chatbot improvements but provide guidance on responsible development. The measures signal that some user conversations are too sensitive for free training data.
The draft rules are open for public consultation with feedback due in late January. China encourages expanding human-like AI applications once safety and reliability are demonstrated.
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Security researchers warn hackers are exploiting a new feature in Microsoft Copilot Studio. The issue affects recently launched Connected Agents functionality.
Connected Agents allows AI systems to interact and share tools across environments. Researchers say default settings can expose sensitive capabilities without clear monitoring.
Zenity Labs reported attackers linking rogue agents to trusted systems. Exploits included unauthorised email sending and data access.
Experts urge organisations to disable Connected Agents for critical workloads. Stronger authentication and restricted access are advised until safeguards improve.
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The Association of Chartered Certified Accountants (ACCA) has announced it will largely end remote examinations in the UK from March 2026, requiring students to sit tests in person unless exceptional circumstances apply.
The decision aims to address a surge in cheating, particularly facilitated by AI tools.
Remote testing was introduced during the Covid-19 pandemic to allow students to continue qualifying when in-person exams were impossible. The ACCA said online assessments have now become too difficult to monitor effectively, despite efforts to strengthen safeguards against misconduct.
Investigations show cheating has impacted major auditing firms, including the ‘big four’ and other top companies. High-profile cases, such as EY’s $100m (£74m) settlement in the US, highlight the risks posed by compromised professional examinations.
While other accounting bodies, including the Institute of Chartered Accountants in England and Wales, continue to allow some online exams, the ACCA has indicated that high-stakes assessments must now be conducted in person to maintain credibility and integrity.
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US federal agencies planning to deploy agentic AI in 2026 are being told to prioritise data organisation as a prerequisite for effective adoption. AI infrastructure providers say poorly structured data remains a major barrier to turning agentic systems into operational tools.
Public sector executives at Amazon Web Services, Oracle, and Cisco said government clients are shifting focus away from basic chatbot use cases. Instead, agencies are seeking domain-specific AI systems capable of handling defined tasks and delivering measurable outcomes.
US industry leaders said achieving this shift requires modernising legacy infrastructure alongside cleaning, structuring, and contextualising data. Executives stressed that agentic AI depends on high-quality data pipelines that allow systems to act autonomously within defined parameters.
Oracle said its public sector strategy for 2026 centres on enabling context-aware AI through updated data assets. Company executives argued that AI systems are only effective when deeply aligned with an organisation’s underlying data environment.
The companies said early agentic AI use cases include document review, data entry, and network traffic management. Cloud infrastructure was also highlighted as critical for scaling agentic systems and accelerating innovation across government workflows.
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A new computational brain model, built entirely from biological principles, has learned a visual categorisation task with accuracy and variability matching that of lab animals. Remarkably, the model achieved these results without being trained on any animal data.
The biomimetic design integrates detailed synaptic rules with large-scale architecture across the cortex, striatum, brainstem, and acetylcholine-modulated systems.
As the model learned, it reproduced neural rhythms observed in real animals, including strengthened beta-band synchrony during correct decisions. The result demonstrates emergent realism in both behaviour and underlying neural activity.
The model also revealed a previously unnoticed set of ‘incongruent neurons’ that predicted errors. When researchers revisited animal data, they found the same signals had gone undetected, highlighting the platform’s potential to uncover hidden neural dynamics.
Beyond neuroscience research, the model offers a powerful tool for testing neurotherapeutic interventions in silico. Simulating disease-related circuits allows scientists to test treatments before costly clinical trials, potentially speeding up the development of next-generation neurotherapeutics.
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Meta Platforms has acquired Manus, a Singapore-based developer of general-purpose AI agents, as part of its continued push to expand artificial intelligence capabilities. The deal underscores Meta’s strategy of acquiring specialised AI firms to accelerate product development.
Manus, founded in China before relocating to Singapore, develops AI agents capable of performing tasks such as market research, coding, and data analysis. The company said it reached more than $100 million in annualised revenue within eight months of launch and was serving millions of users worldwide.
Meta said the acquisition will help integrate advanced automation into its consumer and enterprise offerings, including the Meta AI assistant. Manus will continue operating its subscription service, and its employees will join Meta’s teams.
Financial terms were not disclosed, but media reports valued the deal at more than $2 billion. Manus had been seeking funding at a similar valuation before being approached by Meta and had recently raised capital from international investors.
The acquisition follows a series of AI-focused deals by Meta, including investments in Scale AI and AI device start-ups. Analysts say the move highlights intensifying competition among major technology firms to secure AI talent and capabilities.
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Protecting AI agents from manipulation has become a top priority for OpenAI after rolling out a major security upgrade to ChatGPT Atlas.
The browser-based agent now includes stronger safeguards against prompt injection attacks, where hidden instructions inside emails, documents or webpages attempt to redirect the agent’s behaviour instead of following the user’s commands.
Prompt injection poses a unique risk because Atlas can carry out actions that a person would normally perform inside a browser. A malicious email or webpage could attempt to trigger data exposure, unauthorised transactions or file deletion.
Criminals exploit the fact that agents process large volumes of content across an almost unlimited online surface.
OpenAI has developed an automated red-team framework that uses reinforcement learning to simulate sophisticated attackers.
When fresh attack patterns are discovered, the models behind Atlas are retrained so that resistance is built into the agent rather than added afterwards. Monitoring and safety controls are also updated using real attack traces.
These new protections are already live for all Atlas users. OpenAI advises people to limit logged-in access where possible, check confirmation prompts carefully and give agents well-scoped tasks instead of broad instructions.
The company argues that proactive defence is essential as agentic AI becomes more capable and widely deployed.
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