Family offices , increasingly sophisticated in their investment strategies, are turning to direct investments to achieve higher returns,...

In AI research, as in life, sometimes the most effective path to a desired outcome isn't a direct one. This is the essence of obliquity , a concept popularized by John Kay in his book "Obliquity: Why Our Goals Are Best Achieved Indirectly." It suggests that when faced with complex, nonlinear problems , pursuing our goals head-on can often be counterproductive, while approaching them indirectly, with a broader perspective and focusing on related activities, can yield surprisi

The pursuit of Artificial Intelligence has largely been defined by mirroring and replicating human cognitive capabilities . However, a burgeoning area of research is challenging this anthropocentric paradigm by looking beyond the human mind and delving into the rich tapestry of non-human intelligence found in the natural world. This exploration promises to not only broaden our understanding of intelligence itself but also inspire novel and potentially more robust AI archite

Artificial Intelligence is rapidly evolving, permeating various aspects of our lives, from healthcare and finance to entertainment and transportation. However, beneath the surface of sophisticated algorithms and vast datasets lies a subtle but pervasive problem: anthropocentric bias . This is the tendency to view the world solely from a human perspective, projecting human assumptions, values, and limitations onto non-human entities, including AI. This article is about intri

Making an initial investment is just the first step and smart VCs understand the power of follow-on investments , strategic allocations of capital to companies already in their portfolio. These aren't just arbitrary top-ups; they represent a deliberate and nuanced strategy to maximize returns and support portfolio companies through different stages of growth. This article explores the intricacies of follow-on strategies in VC, providing concrete examples to illustrate their

The world is witnessing an explosive growth in artificial intelligence. But transforming cutting-edge research into impactful, commercially viable products remains a significant challenge. Traditional academic research labs often lack the resources and expertise for commercialization, while conventional venture capital struggles to understand and nurture AI startups in their earliest stages. This is where the AI research lab venture studio model emerges as a compelling solu

For Limited Partners , the world of venture capital is a complex tapestry of funds, strategies, and potential returns. While fund investments remain the bedrock of most LP portfolios, a growing trend is emerging: direct investments into Venture Studios . These unique entities, distinct from traditional venture funds, offer a compelling avenue to access early-stage innovation and potentially generate significant returns. This article delves into the compelling reasons why LP

Artificial Intelligence has evolved significantly by integrating insights from neuroscience, cognitive science, and information theory. Among the most intriguing developments are Active Inference  and Active Learning , two paradigms that, while distinct, can synergistically enhance AI systems. Their combination enables intelligent agents to efficiently learn from data while dynamically adapting to uncertain environments. This article explores their interplay, highlighting ho

DeepSeek-V3's position at the forefront of open-source large language models isn't solely a product of scaling or raw data. While those elements are undoubtedly essential, the model's alignment with human values , its ability to reason effectively, and its exceptional performance in domains like coding and mathematics are significantly shaped by a carefully designed reinforcement learning (RL) post-training process. This article pulls back the curtain on DeepSeek-V3's RL im

Venture Capital is an exciting, high-stakes world promising astronomical returns. But beneath the glossy headlines and revolutionary narratives lies a fundamental, often uncomfortable truth: every VC investment is essentially binary. It either works, delivering a multiple of the invested capital that justifies the risk, or it doesn't, resulting in a total or near-total loss. Understanding this binary nature is crucial for anyone involved in the startup ecosystem, from founder

A world characterized by constant change and unpredictable disruptions, the concept of antifragility  has gained prominence, particularly in the realm of business. Coined by Nassim Nicholas Taleb in his book "Antifragile: Things That Gain from Disorder," antifragility goes beyond resilience and robustness. It's not just about withstanding shocks; it's about actively benefiting from them.   This article looks into how venture studios can leverage antifragility principles to

Portfolio construction in venture capital is less about precision and more about informed bets and strategic diversification. Unlike public markets, VC involves highly illiquid assets , early-stage companies with high failure rates, and long investment horizons. Therefore, a well-constructed portfolio aims to capture significant upside while mitigating the inherent risks. Let's break down the key dimensions: Sector (Industry) Focus: Thematic Investing:   Many VCs specializ

MAML (Model-Agnostic Meta-Learning ) is a meta-learning algorithm that focuses on learning a good parameter initialization  for a model. Think of it as finding the right "pre-training" so that subsequent fine-tuning on new tasks can be extremely efficient. Instead of learning to directly solve tasks, we're learning how to start  learning tasks. Core Concepts Tasks and Task Distribution:  MAML operates on the idea of tasks. Each task is a specific learning problem, like classi

One of the most pressing challenges in artificial intelligence has been the need for vast amounts of labeled data to train effective models . Traditional machine learning algorithms, especially deep learning models, are notorious for their "data hunger." This means they require thousands, or even millions, of examples to learn a specific task accurately. However, real-world scenarios often present situations where obtaining such vast datasets is impractical or impossible. Th

Data is often messy, incomplete, and riddled with "noise"—irrelevant or erroneous information that can obscure the underlying patterns we want AI models to learn. Noise tolerance, therefore, is the ability of an AI system to maintain its performance even when dealing with noisy data. It's the capacity to extract meaningful signals from a chaotic environment. Think of it like trying to hear a friend at a crowded concert. The loud music, chatter, and general commotion repres