Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision. However, they face a fundamental limitation: given enough data, the number of nodes in decision trees will grow exponentially with depth. For certain applications, for example on mobile or embedded processors, memory is a limited resource, and so the exponential growth of trees limits their depth, and thus their potential accuracy. This paper proposes decision jungles, revisiting the idea of ensembles of rooted decision directed acyclic graphs (DAGs), and shows these to be compact and powerful discriminative models for classification. Unlike conventional decision trees that only allow one path to every node, a DAG in a decision jungle allows multiple paths from the root to each leaf. We present and compare two new node merging algorithms that jointly optimize both the features and the structure of the DAGs efficiently. During training, node splitting and node merging are driven by the minimization of exactly the same objective function, here the weighted sum of entropies at the leaves. Results on varied datasets show that, compared to decision forests and several other baselines, decision jungles require dramatically less memory while considerably improving generalization.

"How to evaluate de novo designs proposed by a generative model?" Despite the transformative potential of generative deep learning in drug discovery, this seemingly simple question has no clear answer. The absence of standardized guidelines challenges both the benchmarking of generative approaches and the selection of molecules for prospective studies. In this work, we take a fresh $- \textit{critical}$ and $\textit{constructive} -$ perspective on de novo design evaluation. We systematically investigate widely used evaluation metrics and expose key pitfalls ('traps') that were previously overlooked. In addition, we identify tools ('treasures') and strategies ('ways out') to navigate the complex 'jungle' of generative drug discovery, and strengthen the connections between the molecular and deep learning fields along the way. Our systematic and large-scale results are expected to provide a new lens for evaluating the de novo designs proposed by generative deep learning approaches.

Planning in a text-based environment continues to be a major challenge for AI systems. Recent approaches have used language models to predict a planning domain definition (e.g., PDDL) but have only been evaluated in closed-domain simulated environments. To address this, we present Proc2PDDL , the first dataset containing open-domain procedural texts paired with expert-annotated PDDL representations. Using this dataset, we evaluate state-of-the-art models on defining the preconditions and effects of actions. We show that Proc2PDDL is highly challenging, with GPT-3.5's success rate close to 0% and GPT-4's around 35%. Our analysis shows both syntactic and semantic errors, indicating LMs' deficiency in both generating domain-specific prgorams and reasoning about events. We hope this analysis and dataset helps future progress towards integrating the best of LMs and formal planning.

Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision. However, they face a fundamental limitation: given enough data, the number of nodes in decision trees will grow exponentially with depth. For certain applications, for example on mobile or embedded processors, memory is a limited resource, and so the exponential growth of trees limits their depth, and thus their potential accuracy. This paper proposes decision jungles, revisiting the idea of ensembles of rooted decision directed acyclic graphs (DAGs), and shows these to be compact and powerful discriminative models for classification. Unlike conventional decision trees that only allow one path to every node, a DAG in a decision jungle allows multiple paths from the root to each leaf. We present and compare two new node merging algorithms that jointly optimize both the features and the structure of the DAGs efficiently. During training, node splitting and node merging are driven by the minimization of exactly the same objective function, here the weighted sum of entropies at the leaves. Results on varied datasets show that, compared to decision forests and several other baselines, decision jungles require dramatically less memory while considerably improving generalization.

Creativity is core to being human. Generative artificial intelligence (GenAI) -- including ever more powerful large language models (LLMs) -- holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on GenAI ideas. We study the causal impact of GenAI ideas on the production of a short story in an online experimental study where some writers could obtain story ideas from a GenAI platform. We find that access to GenAI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, GenAI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: with GenAI, individual writers are better off, but collectively a narrower scope of novel content may be produced. Our results have implications for researchers, policy-makers and practitioners interested in bolstering creativity.

Pixar's 1998 movie, A Bug's Life, brought tiny CGI ants to the world's largest screens. The only thing digital about the critters featured in the Disney series, though, is the technology filming them. But like its animated counterpart, the show explores the world they live in and their adventures in ways we've never seen before. With its focus on insects, A Real Bug's Life isn't limited to specific remote habitats. But thanks to a series of innovations, we see these worlds from entirely new perspectives.

The team behind ETHforestAI strongly believes that education should be accessible, engaging, and empowering. By combining cutting-edge technology with a focus on gamification, they are creating a platform that not only aims to teach users about Web3 but also fosters their growth and development in the space. Armed with an AI chatbot, the team further aims to provide a fun way of getting personalized recommendations and answers to a wide variety of Web3 and Crypto related questions. At ETHForestAI, the team is motivating both creators and users to engage with Learn-To-Earn, Real Yield and Creator Economy! Join us and discover the future of digital education!

Pixelz AI Art Generator enables you to create incredible art from text. Stable Diffusion, CLIP Guided Diffusion & PXL·E Realistic Algorithms available!