Mikel Landajuela

Mikel Landajuela

Senior Staff Scientist at Lawrence Livermore National Laboratory

Posts by Tags

AI for science

Grounding ChatGPT-like systems in the real world with Wolfram Alpha

3 minute read

Published:

In this blog post, we will explore the potential of integrating ChatGPT with a computational knowledge engine like Wolfram Alpha. In his recent blog post Wolfram Alpha as the Way to Bring Computational Knowledge Superpowers to ChatGPT, Stephen Wolfram describes how ChatGPT excels in the “human-like parts” of language processing but may struggle with precise answers. By connecting ChatGPT with Wolfram Alpha and its vast computational knowledge, we aim to bridge this gap and provide ChatGPT with the “grounding” into the real world that large language models like GPT are missing. We will create reports with real data and visuals to showcase the potential of this integration. Let’s get started!

Neural-Guided Search for Scientific Discovery

3 minute read

Published:

In recent years, the field of scientific discovery has seen a surge of interest in the application of machine learning techniques. One promising approach is Deep Symbolic Optimization (DSO), a computational framework for scientific discovery that treats the discovery problem as a sequential decision-making task. In this blog post, we provide an overview of the DSO framework and its applications to scientific discovery.

Machine Learning for Cardiac Electrocardiography

9 minute read

Published:

In this blog, we explore the possibility of using machine learning to reconstruct electroanatomical maps at clinically relevant resolutions using only standard 12-lead electrocardiograms (ECGs) as input. The blog post is also available in Medium.

AI research

DisCo-DSO: Joint Optimization in Hybrid Discrete-Continuous Spaces

5 minute read

Published:

In this blog post, we introduce DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach for joint optimization in hybrid discrete-continuous spaces. DisCo-DSO leverages autoregressive models and deep reinforcement learning to optimize discrete tokens and continuous parameters simultaneously. This unified approach leads to more efficient optimization, robustness to non-differentiable objectives, and superior performance in tasks like decision tree learning and symbolic regression. Let’s dive into the key innovations, applications, and results of DisCo-DSO.

decision trees

DisCo-DSO: Joint Optimization in Hybrid Discrete-Continuous Spaces

5 minute read

Published:

In this blog post, we introduce DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach for joint optimization in hybrid discrete-continuous spaces. DisCo-DSO leverages autoregressive models and deep reinforcement learning to optimize discrete tokens and continuous parameters simultaneously. This unified approach leads to more efficient optimization, robustness to non-differentiable objectives, and superior performance in tasks like decision tree learning and symbolic regression. Let’s dive into the key innovations, applications, and results of DisCo-DSO.

education

Wolfram Demonstrations Project

less than 1 minute read

Published:

The Wolfram Demonstrations Project is a collection of interactive examples in the Wolfram Language to illustrate different concepts in mathematics, physics, engineering, and other fields. During the last years, I contributed with some of them. I decided to collect all my Wolfram Demonstrations in a single place.

generative design

DisCo-DSO: Joint Optimization in Hybrid Discrete-Continuous Spaces

5 minute read

Published:

In this blog post, we introduce DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach for joint optimization in hybrid discrete-continuous spaces. DisCo-DSO leverages autoregressive models and deep reinforcement learning to optimize discrete tokens and continuous parameters simultaneously. This unified approach leads to more efficient optimization, robustness to non-differentiable objectives, and superior performance in tasks like decision tree learning and symbolic regression. Let’s dive into the key innovations, applications, and results of DisCo-DSO.

large language models

Grounding ChatGPT-like systems in the real world with Wolfram Alpha

3 minute read

Published:

In this blog post, we will explore the potential of integrating ChatGPT with a computational knowledge engine like Wolfram Alpha. In his recent blog post Wolfram Alpha as the Way to Bring Computational Knowledge Superpowers to ChatGPT, Stephen Wolfram describes how ChatGPT excels in the “human-like parts” of language processing but may struggle with precise answers. By connecting ChatGPT with Wolfram Alpha and its vast computational knowledge, we aim to bridge this gap and provide ChatGPT with the “grounding” into the real world that large language models like GPT are missing. We will create reports with real data and visuals to showcase the potential of this integration. Let’s get started!

reinforcement learning

DisCo-DSO: Joint Optimization in Hybrid Discrete-Continuous Spaces

5 minute read

Published:

In this blog post, we introduce DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach for joint optimization in hybrid discrete-continuous spaces. DisCo-DSO leverages autoregressive models and deep reinforcement learning to optimize discrete tokens and continuous parameters simultaneously. This unified approach leads to more efficient optimization, robustness to non-differentiable objectives, and superior performance in tasks like decision tree learning and symbolic regression. Let’s dive into the key innovations, applications, and results of DisCo-DSO.

symbolic regression

DisCo-DSO: Joint Optimization in Hybrid Discrete-Continuous Spaces

5 minute read

Published:

In this blog post, we introduce DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach for joint optimization in hybrid discrete-continuous spaces. DisCo-DSO leverages autoregressive models and deep reinforcement learning to optimize discrete tokens and continuous parameters simultaneously. This unified approach leads to more efficient optimization, robustness to non-differentiable objectives, and superior performance in tasks like decision tree learning and symbolic regression. Let’s dive into the key innovations, applications, and results of DisCo-DSO.

wolfram language

Grounding ChatGPT-like systems in the real world with Wolfram Alpha

3 minute read

Published:

In this blog post, we will explore the potential of integrating ChatGPT with a computational knowledge engine like Wolfram Alpha. In his recent blog post Wolfram Alpha as the Way to Bring Computational Knowledge Superpowers to ChatGPT, Stephen Wolfram describes how ChatGPT excels in the “human-like parts” of language processing but may struggle with precise answers. By connecting ChatGPT with Wolfram Alpha and its vast computational knowledge, we aim to bridge this gap and provide ChatGPT with the “grounding” into the real world that large language models like GPT are missing. We will create reports with real data and visuals to showcase the potential of this integration. Let’s get started!

Wolfram Demonstrations Project

less than 1 minute read

Published:

The Wolfram Demonstrations Project is a collection of interactive examples in the Wolfram Language to illustrate different concepts in mathematics, physics, engineering, and other fields. During the last years, I contributed with some of them. I decided to collect all my Wolfram Demonstrations in a single place.