Unfortunately, learning systems struggle with compositional generalization because they often build on features that are correlated with class labels even if they are not "essential" for the class.

People easily recognize new visual categories that are new combinations of known components. This compositional generalization capacity is critical for learning in real-world domains like vision and language because the long tail of new combinations dominates the distribution. Unfortunately, learning systems struggle with compositional generalization because they often build on features that are correlated with class labels even if they are not essential for the class. This leads to consistent misclassification of samples from a new distribution, like new combinations of known components. Here we describe an approach for compositional generalization that builds on causal ideas. First, we describe compositional zero-shot learning from a causal perspective, and propose to view zero-shot inference as finding which intervention caused the image?. Second, we present a causal-inspired embedding model that learns disentangled representations of elementary components of visual objects from correlated (confounded) training data. We evaluate this approach on two datasets for predicting new combinations of attribute-object pairs: A well-controlled synthesized images dataset and a real world dataset which consists of fine-grained types of shoes. We show improvements compared to strong baselines.

Unfortunately, learning systems struggle with compositional generalization because they often build on features that are correlated with class labels even if they are not "essential" for the class.

Large Language Models have emerged many intellectual capacities. While numerous benchmarks assess their intelligence, limited attention has been given to their ability to explore, an essential capacity for discovering new information and adapting to novel environments in both natural and artificial systems. The extent to which LLMs can effectively explore, particularly in open-ended tasks, remains unclear. This study investigates whether LLMs can surpass humans in exploration during an open-ended task, using Little Alchemy 2 as a paradigm, where agents combine elements to discover new ones. Results show most LLMs underperform compared to humans, except for the o1 model, with those traditional LLMs relying primarily on uncertainty driven strategies, unlike humans who balance uncertainty and empowerment. Representational analysis of the models with Sparse Autoencoders revealed that uncertainty and choices are represented at earlier transformer blocks, while empowerment values are processed later, causing LLMs to think too fast and make premature decisions, hindering effective exploration. These findings shed light on the limitations of LLM exploration and suggest directions for improving their adaptability.

People easily recognize new visual categories that are new combinations of known components. This compositional generalization capacity is critical for learning in real-world domains like vision and language because the long tail of new combinations dominates the distribution. Unfortunately, learning systems struggle with compositional generalization because they often build on features that are correlated with class labels even if they are not "essential" for the class. This leads to consistent misclassification of samples from a new distribution, like new combinations of known components. Here we describe an approach for compositional generalization that builds on causal ideas.

Digital Transformation quickly became one of the most popular buzzwords of the COVID era as leaders struggled to navigate the applications, virtualized experiences and new practices required to fully enable and manage a remote workforce. Yet, even as many organizations have returned to on-site or hybrid operations, the need for defining, executing, and leveling how a business adapts to today's digital reality has never been more important. Jean - Luc Robert, CEO of Kyriba, sees digital transformation to achieving new business outcomes ... [ ] with a new combination of people, processes, and data At its simplest, Jean - Luc Robert, CEO of Kyriba, sees digital transformation to achieving new business outcomes with a new combination of people, processes, and data. As the CEO of a leading cloud-based finance and IT solution company, Jean -Luc values transformation projects that move the company forward with a compelling ROI, enabled by hyper-automation and composable technology that unlock a holistic view of data from across the enterprise to drive strategy with precision and confidence. The CEO leads the company to achieve a more competitive and profitable future state and is supported by visionary executives who collectively enable various parts of the CEO's plan.

GAN, Generative adversarial networks- simply put are advanced algorithms based on neural networks that can learn data and generate entirely new data based on the features learnt from data. Grab a coffee, let's understand this with an example.

You are free to share this article under the Attribution 4.0 International license. To more quickly identify drug combinations, such as those that might treat COVID-19, researchers have come up with an artificial intelligence platform called IDentif.AI. Traditionally, when dangerous new bacterial and viral infections emerge, the response is to develop a treatment that combines several different drugs. However, this process is laborious and time-consuming, with drug combinations chosen sub-optimally, and selection of doses a matter of trial and error. This costly and inefficient way of developing a treatment presents problems when a rapid response becomes crucial to tackle a global pandemic and resources need to be conserved.

IBM has trained an AI to help humans develop new fragrances for perfumes and other products. The system is called Philyra, and it involves machine learning algorithms that find new combinations of scents to produce novel fragrances. According to IBM, its AI can comb through existing combinations to spot "whitespaces" in which entirely new formulas can be formed. IBM named its AI after the Greek goddess of fragrance and created it to augment the work of human experts. The system is able to sift through hundreds of thousands of formulas, as well as thousands of raw fragrance materials, according to the company.

Not so long ago the difficulty in working with data stemmed from the fact it came from different places in different forms, much of it was unstructured or at best semi structured. Getting this data into a shape where it could be analysed and used to provide insights was a tedious process, data cleaning and preparation can be time consuming processes. I have mentioned elsewhere about the "The dirty little secret of big data," being that fact, "that most data analysts spend the vast majority of their time cleaning and integrating data-- not actually analysing it." In the recent past the routines to get inside the data were repetitive and time time consuming, people were doing those very tasks that software was good at. The trouble was the software tools we tended to use did not have high levels of flexibility.