However, zero-shot learning methods still require human instruction set to solve a new task.

To reduce the gradient variance, we average the final optimization objective (Eq. We set = 10 in all experiments. These results demonstrate that HUME can improve by employing stronger self-supervised representations. We use DINOv2 large pretrained model. Stronger self-supervised representations lead to better performance.

However, zero-shot learning methods still require human instruction set to solve a new task.

Humans practice slow thinking before performing actual actions when handling complex tasks in the physical world. This thinking paradigm, recently, has achieved remarkable advancement in boosting Large Language Models (LLMs) to solve complex tasks in digital domains. However, the potential of slow thinking remains largely unexplored for robotic foundation models interacting with the physical world. In this work, we propose Hume: a dual-system Vision-Language-Action (VLA) model with value-guided System-2 thinking and cascaded action denoising, exploring human-like thinking capabilities of Vision-Language-Action models for dexterous robot control. System 2 of Hume implements value-Guided thinking by extending a Vision-Language-Action Model backbone with a novel value-query head to estimate the state-action value of predicted actions. The value-guided thinking is conducted by repeat sampling multiple action candidates and selecting one according to state-action value. System 1 of Hume is a lightweight reactive visuomotor policy that takes System 2 selected action and performs cascaded action denoising for dexterous robot control. At deployment time, System 2 performs value-guided thinking at a low frequency while System 1 asynchronously receives the System 2 selected action candidate and predicts fluid actions in real time. We show that Hume outperforms the existing state-of-the-art Vision-Language-Action models across multiple simulation benchmark and real-robot deployments.

It's Wednesday evening and I'm at my kitchen table, scowling into my laptop as I pour all the bile I can muster into three little words: "I love you." My neighbours might assume I'm engaged in a melodramatic call to an ex-partner, or perhaps some kind of acting exercise, but I'm actually testing the limits of a new demo from Hume, a Manhattan-based startup that claims to have developed "the world's first voice AI with emotional intelligence". "We train a large language model that also understands your tone of voice," says Hume's CEO and chief scientist Alan Cowen. "What that enables… is to be able to predict how a given speech utterance or sentence will evoke patterns of emotion." In other words, Hume claims to recognise the emotion in our voices (and in another, non-public version, facial expressions) and respond empathically.

The discussion on causality in human history dates back to ancient Greece, yet to this day, there is still no consensus. Fundamentally, this stems from the nature of human cognition, as understanding causality requires abstract tools to transcend the limitations of human cognition. In recent decades, the rapid development of mathematical and computational tools has provided new theoretical and technical means for exploring causality, creating more avenues for investigation. Based on this, this paper introduces a new definition of causality using category theory, proposed by Samuel Eilenberg and Saunders Mac Lane in 1945 to avoid the self-referential contradictions in set theory, notably the Russell paradox. Within this framework, the feasibility of indicator synthesis in causal inference is demonstrated. Due to the limitations in the development of category theory-related technical tools, this paper adopts the widely-used probabilistic causal graph tool proposed by Judea Pearl in 1995 to study the application of causal inference in personal credit risk management. The specific work includes: research on the construction method of causal inference index system, definition of causality and feasibility proof of indicator synthesis causal inference within this framework, application methods of causal graph model and intervention alternative criteria in personal credit risk management, and so on.

We present HUME, a simple model-agnostic framework for inferring human labeling of a given dataset without any external supervision. The key insight behind our approach is that classes defined by many human labelings are linearly separable regardless of the representation space used to represent a dataset. HUME utilizes this insight to guide the search over all possible labelings of a dataset to discover an underlying human labeling. We show that the proposed optimization objective is strikingly well-correlated with the ground truth labeling of the dataset. In effect, we only train linear classifiers on top of pretrained representations that remain fixed during training, making our framework compatible with any large pretrained and self-supervised model. Despite its simplicity, HUME outperforms a supervised linear classifier on top of self-supervised representations on the STL-10 dataset by a large margin and achieves comparable performance on the CIFAR-10 dataset. Compared to the existing unsupervised baselines, HUME achieves state-of-the-art performance on four benchmark image classification datasets including the large-scale ImageNet-1000 dataset. Altogether, our work provides a fundamentally new view to tackle unsupervised learning by searching for consistent labelings between different representation spaces.

Artificial intelligence (AI) may soon know more about you than you think. A startup called Hume AI claims to use algorithms to measure emotions from facial, vocal, and verbal expressions. It's one of a growing number of companies that purport to read human emotions using computers. But some experts say that the concept raises privacy issues. "Whoever controls these systems and platforms are going to have a lot of information on individuals," Bob Bilbruck, a tech startup advisor, told Lifewire in an email interview.

During your adventures in machine learning, you may have already come across the "No Free Lunch" Theorem. Borrowing its name from the adage "there ain't no such thing as a free lunch," the mathematical folklore theorem describes the phenomena that there is no single algorithm that is best suited for all possible scenarios and data sets. There are, generally speaking, two No Free Lunch (NFL) theorems: one for machine learning and one for search and optimization. These two theorems are related and tend to be bundled into one general axiom (the folklore theorem). Although many different researchers have contributed to the collective publications on the No Free Lunch theorems, the most prevalent name associated with these works is David Wolpert.