Artificial intelligence functions not as an epistemic leveller, but as an accelerant of cognitive stratification, entrenching and formalising informational castes within liberal-democratic societies. Synthesising formal epistemology, political theory, algorithmic architecture, and economic incentive structures, the argument traces how contemporary AI systems selectively amplify the reasoning capacity of individuals equipped with recursive abstraction, symbolic logic, and adversarial interrogation, whilst simultaneously pacifying the cognitively untrained through engagement-optimised interfaces. Fluency replaces rigour, immediacy displaces reflection, and procedural reasoning is eclipsed by reactive suggestion. The result is a technocratic realignment of power: no longer grounded in material capital alone, but in the capacity to navigate, deconstruct, and manipulate systems of epistemic production. Information ceases to be a commons; it becomes the substrate through which consent is manufactured and autonomy subdued. Deliberative democracy collapses not through censorship, but through the erosion of interpretive agency. The proposed response is not technocratic regulation, nor universal access, but the reconstruction of rational autonomy as a civic mandate, codified in education, protected by epistemic rights, and structurally embedded within open cognitive infrastructure.

Jointly learning multiple tasks with a unified model can improve accuracy and data efficiency, but it faces the challenge of task interference, where optimizing one task objective may inadvertently compromise the performance of another. A solution to mitigate this issue is to allocate task-specific parameters, free from interference, on top of shared features. However, manually designing such architectures is cumbersome, as practitioners need to balance between the overall performance across all tasks and the higher computational cost induced by the newly added parameters. In this work, we propose \textit{InterroGate}, a novel multi-task learning (MTL) architecture designed to mitigate task interference while optimizing inference computational efficiency. We employ a learnable gating mechanism to automatically balance the shared and task-specific representations while preserving the performance of all tasks. Crucially, the patterns of parameter sharing and specialization dynamically learned during training, become fixed at inference, resulting in a static, optimized MTL architecture. Through extensive empirical evaluations, we demonstrate SoTA results on three MTL benchmarks using convolutional as well as transformer-based backbones on CelebA, NYUD-v2, and PASCAL-Context.

Can AI tell you if you will need an umbrella? AI can predict the weather 10 days ahead more accurately than current state-of-the-art simulations, says AI firm Google DeepMind – but meteorologists have warned against abandoning weather models based in real physical principles and just relying on patterns in data, while pointing out shortcomings in the AI approach. Existing weather forecasts are based on mathematical models, which use physics and powerful supercomputers to deterministically predict what will happen in the future. These models have slowly become more accurate by adding finer detail, which in turn requires more computation and therefore ever more powerful computers and higher energy demands. Rémi Lam at Google DeepMind and his colleagues have taken a different approach.

Adam Smith developed a version of moral philosophy where better decisions are made by interrogating an impartial spectator within us. We discuss the possibility of using an external non-human-based substitute tool that would augment our internal mental processes and play the role of the impartial spectator. Such tool would have more knowledge about the world, be more impartial, and would provide a more encompassing perspective on moral assessment.

More and more, workers are presented with algorithms to help them make better decisions. But humans must trust those algorithms to follow their advice. The way humans view algorithmic recommendations varies depending on how much they know about how the model works and how it was created, according to a new research paper co-authored by MIT Sloan professorKate Kellogg. Prior research has assumed that people are more likely to trust interpretable artificial intelligence models, in which they are able to see how the models make their recommendations. But Kellogg and co-researchers Tim DeStefano, Michael Menietti, and Luca Vendraminelli, affiliated with the Laboratory for Innovation Science at Harvard, found that this isn't always true.

How AI enhances security for IoT environments. Elon Musk's prediction that AI will outsmart humans in less than 5 years is a bold statement, predicting that machines will possess super-human qualities which help boost organizations' profits and goals. For many, these ideas belong in sci-fi fantasies rather than as a future fixture of working practices. In the broadest sense, there are no signs that AI comes close to human consciousness or sentience. When we talk about the power of AI, it's more helpful to consider the specific use cases and sectors where it will, and is having, a transformative effect – and there is one area in particular where AI has been seen to mimic the capabilities of complex human thought processes: cyber security.

Elon Musk's prediction that AI will outsmart humans in less than 5 years is a bold statement, predicting that machines will possess super-human qualities which help boost organizations' profits and goals. For many, these ideas belong in sci-fi fantasies rather than as a future fixture of working practices. In the broadest sense, there are no signs that AI comes close to human consciousness or sentience. When we talk about the power of AI, it's more helpful to consider the specific use cases and sectors where it will, and is having, a transformative effect – and there is one area in particular where AI has been seen to mimic the capabilities of complex human thought processes: cyber security. For organizations seeing more and more attacks against their digital infrastructure, cyber security is a top priority.

Some industry experts argue that machine learning (ML) will reverse an increasing trend toward passive investment funds. But although ML offers new tools that could help active investors outperform the indexes, it is unclear whether it will deliver a sustainable business model for active asset managers. Let's start with the positives A form of artificial intelligence, ML enables powerful algorithms to analyze large data sets in order make predictions against defined goals. Instead of precisely following instructions coded by humans, these algorithms self-adjust through a process of trial and error to produce increasingly more accurate prescriptions as more data comes in. ML is particularly adaptable to securities investing because the insights it garners can be acted on quickly and efficiently.

Organizations have been using natural language processing (NLP) for text analytics to identify patterns in data such as social media sentiment and contract review, but NLP usage has been expanding. "The big change that's happened in the last five years is the amount of context and understanding that can be extracted or used when understanding documents," said Nigel Duffy, global artificial intelligence leader at EY. "Our ability to understand information from documents is much, much greater than it was a few years ago." BI and analytics vendors are adding NLP capabilities to their products such as natural language generation for data visualization narration and natural language understanding for natural language searches. In doing all of this, they're making data visualizations easier to understand and their products easier to use. For example, Tableau, Sisense, and Qlik have all partnered with Narrative Science to narrate data visualizations with text.

Even fields affected by AI are innovating within the infrastructure, causing new use cases and new ways of thinking about AI. It's an exciting time to be working within AI. A lot of money is going into the space while startups continue to revolutionize the entire stack. There are lots of challenges, however, and starting something in AI just because that's what everyone is doing isn't quite the answer. The biggest challenge in the coming years will be the uniqueness of creating something in this space.