From information gaps to global access — how AI is reshaping the pursuit of knowledge.
Updated
January 8, 2026 6:33 PM
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Paper cut-outs of robots sitting on a pile of books. PHOTO: FREEPIK
Encyclopaedias have always been mirrors of their time — from heavy leather-bound volumes in the 19th century to Wikipedia’s community-edited pages online. But as the world’s information multiplies faster than humans can catalogue it, even open platforms struggle to keep pace. Enter Botipedia, a new project from INSEAD, The Business School for the World, that reimagines how knowledge can be created, verified and shared using artificial intelligence.
At its core, Botipedia is powered by proprietary AI that automates the process of writing encyclopaedia entries. Instead of relying on volunteers or editors, it uses a system called Dynamic Multi-method Generation (DMG) — a method that combines hundreds of algorithms and curated datasets to produce high-quality, verifiable content. This AI doesn’t just summarise what already exists; it synthesises information from archives, satellite feeds and data libraries to generate original text grounded in facts.
What makes this innovation significant is the gap it fills in global access to knowledge. While Wikipedia hosts roughly 64 million English-language entries, languages like Swahili have fewer than 40,000 articles — leaving most of the world’s population outside the circle of easily available online information. Botipedia aims to close that gap by generating over 400 billion entries across 100 languages, ensuring that no subject, event or region is overlooked.
"We are creating Botipedia to provide everyone with equal access to information, with no language left behind", says Phil Parker, INSEAD Chaired Professor of Management Science, creator of Botipedia and holder of one of the pioneering patents in the field of generative AI. "We focus on content grounded in data and sources with full provenance, allowing the user to see as many perspectives as possible, as opposed to one potentially biased source".
Unlike many generative AI tools that depend on large language models (LLMs), Botipedia adapts its methods based on the type of content. For instance, weather data is generated using geo-spatial techniques to cover every possible coordinate on Earth. This targeted, multi-method approach helps boost both the accuracy and reliability of what it produces — key challenges in today’s AI-driven content landscape.
Additionally, the innovation is also energy-efficient. Its DMG system operates at a fraction of the processing power required by GPU-heavy models like ChatGPT, making it a sustainable alternative for large-scale content generation.
By combining AI precision, linguistic inclusivity and academic credibility, Botipedia positions itself as more than a digital library — it’s a step toward universal, unbiased access to verified knowledge.
"Botipedia is one of many initiatives of the Human and Machine Intelligence Institute (HUMII) that we are establishing at INSEAD", says Lily Fang, Dean of Research and Innovation at INSEAD. "It is a practical application that builds on INSEAD-linked IP to help people make better decisions with knowledge powered by technology. We want technologies that enhance the quality and meaning of our work and life, to retain human agency and value in the age of intelligence".
By harnessing AI to bridge gaps of language, geography and credibility, Botipedia points to a future where access to knowledge is no longer a privilege, but a shared global resource.
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How a Korean biotech startup is using AI to move drug discovery from trial-and-error to precision design
A close up of a protein structure model. PHOTO: UNSPLASH
For decades, drug discovery has relied on trial and error, with scientists testing thousands of molecules to find one that works. Galux, a South Korean biotech startup, is changing that by using AI to design proteins from scratch. This method, called “de novo” design, makes it possible to build precise new therapies instead of searching through existing ones.
The company recently announced a US$29 million Series B funding round, bringing its total capital to US$47 million.This significant investment attracted a substantial roster of institutional backers, including the Korea Development Bank (KDB), Yuanta Investment, SL Investment and NCORE Ventures. These firms joined existing investors such as InterVest, DAYLI Partners and PATHWAY Investment, as well as new participants including SneakPeek Investments, Korea Investment & Securities and Mirae Asset Securities.
At the core of the company’s work is a platform called GaluxDesign. Unlike many AI tools that only predict how existing proteins fold, this system uses deep learning and physics to create entirely new therapeutic antibodies. This “from scratch” approach lets the team go after so-called “undruggable” proteins. These are targets that traditional small-molecule drugs can’t reach because they lack clear binding pockets. By designing proteins to fit these complex shapes, Galux aims to unlock treatments that have stayed out of reach for decades. And that’s exactly why investors are paying attention.
The pharmaceutical industry is actively looking for faster and more efficient ways to develop new drugs, and Galux is built for exactly that. The company connects its AI platform directly to its own wet lab, where designs can be tested in real time. Each result feeds straight back into the system, sharpening the next round of models. This continuous loop speeds up discovery and improves precision at every step. It’s also why partners like Celltrion, LG Chem and Boehringer Ingelheim are already working with Galux.
Galux is no longer just trying to make drugs that stick to a target. The company now wants its AI to design medicines that actually work in the body and can be made at scale. In simple terms, a drug has to do more than bind to a disease—it must be stable, safe and strong enough to change how the illness behaves. Galux is moving into tougher targets such as ion channels and GPCRs. These play key roles in heart function and sensory signals. Ultimately, the goal is to show that AI-driven design can turn complex biology into real treatments. And instead of hunting blindly for a solution, the team is building exactly what they need.