The FutureHouse Platform, launched in May 2025, is a domain-specific AI environment designed to support various stages of scientific research. It provides researchers with access to four specialised agents — each tailored to a particular task in the knowledge production pipeline: concise information retrieval (Crow), deep literature synthesis (Falcon), precedent detection
Can we truly distinguish between text produced by artificial intelligence and that written by a human author? As large language models become increasingly sophisticated, the boundary between machine-generated and human-crafted writing is growing ever more elusive. Although a range of detection tools claim to identify AI-generated text with high precision,
As scientific research became increasingly data-intensive and fragmented across disciplines, the limitations of traditional research workflows became more apparent. In response to these structural challenges, FutureHouse — a nonprofit backed by Eric Schmidt — launched a platform in May 2025 featuring four specialised AI agents. Designed to support literature analysis, hypothesis development,
Can a large language model accurately solve a university-level microeconomics exercise on health insurance? We tested Manus AI on a multi-part problem involving demand curves, list prices, out-of-pocket prices, and the calculation of social loss under various insurance schemes – including full insurance, coinsurance, and copayment plans. Not only did the
Generative AI has made extraordinary strides in recent years, reshaping how researchers write, code, and even analyse data. But a key question remains for academics hoping to automate more of their daily work: Can popular GenAI models like Copilot, Claude 3.7 Sonnet, Gemini 2.0 Flash, GPT-4o, Grok 3
In our previous blog post, we examined how useful publicly available Custom GPTs are for academic tasks. While many showed promise, especially for brainstorming or outlining, they often fell short in areas where accuracy, source verification, and methodological transparency are crucial. In short: if your work demands high standards of
Manus is a general-purpose AI agent designed to carry out multi-step tasks on behalf of users. It promises to understand instructions, break down complex goals, and deliver results—whether that means writing code, automating research workflows, or, in our case, retrieving public data from the web. Touted as the world’
Can large language models return accurate results when asked to find real academic texts written by specific philosophers on a specific topic? In this case, the topic was Marxist theory — and the instruction was to list peer-reviewed publications in which Gilles Deleuze or Michel Foucault offer a critique of it.
Can a generative AI model systematically identify and structure philosophical arguments from classic political texts? In this post, we put OpenAI’s o3 model to the test by applying it to a foundational work in early modern political thought: John Locke’s Second Treatise of Government. Focusing on Chapter II
What if philosophy students could go beyond reading about Kant and Hume — and instead step into their minds? What if, instead of summarising the Critique of Pure Reason, they became Hume and challenged Kant directly on causality, synthetic a priori knowledge, or the limits of metaphysics? This blog introduces a
As generative AI tools become increasingly integrated into academic practice, researchers are beginning to explore the use of Custom GPTs—personalised AI variants that operate according to predefined instructions, tone, or tasks. These agents can be configured for specific roles or workflows, such as teaching support, literature exploration, or data
One of the key promises of recent generative AI models is their ability to process not only text and images but also audio and video content. GPT-4.5, with its apparent support for MP3 and MP4 file uploads, seems like an ideal tool for tasks such as transcription — particularly for
