Large language models (LLMs) have demonstrated remarkable Large language models (LLMs) have demonstrated performance across a wide range of reasoning remarkable reasoning capabilities across tasks, including mathematical problems (Cobbe et al., 2021; diverse domains. Recent studies have shown that Hendrycks et al., 2021a), programming (Chen et al., 2021; increasing test-time computation enhances LLMs' Zhuo et al., 2024) and logical reasoning (Han et al., 2024; reasoning capabilities. This typically involves extensive Liu et al., 2020). One of the key techniques enabling these sampling at inference time guided by an strong reasoning capabilities is Chain-of-Thought (CoT) external LLM verifier, resulting in a two-player prompting (Wei et al., 2022), which allows LLMs to address system. Despite external guidance, the effectiveness complex tasks by generating a series of intermediate of this system demonstrates the potential of reasoning steps. As a result, many early efforts focus on finetuning a single LLM to tackle complex tasks. Thus, we LLMs using large-scale, high-quality CoT reasoning pose a new research problem: Can we internalize chains, either through human annotation (Hendrycks et al., the searching capabilities to fundamentally 2021a; Yue et al., 2024) or by distilling synthetic data from enhance the reasoning abilities of a single LLM? more advanced models (Yu et al., 2024; Toshniwal et al., This work explores an orthogonal direction focusing 2024a; Ding et al., 2024). However, human annotation is on post-training LLMs for autoregressive extremely labor intensive, and distillation often limits the searching (i.e., an extended reasoning process model's reasoning capabilities to certain level.

Augmented Reality assistance are increasingly popular for supporting users with tasks like assembly and cooking. However, current practice typically provide reactive responses initialized from user requests, lacking consideration of rich contextual and user-specific information. To address this limitation, we propose a novel AR assistance system, Satori, that models both user states and environmental contexts to deliver proactive guidance. Our system combines the Belief-Desire-Intention (BDI) model with a state-of-the-art multi-modal large language model (LLM) to infer contextually appropriate guidance. The design is informed by two formative studies involving twelve experts. A sixteen within-subject study find that Satori achieves performance comparable to an designer-created Wizard-of-Oz (WoZ) system without relying on manual configurations or heuristics, thereby enhancing generalizability, reusability and opening up new possibilities for AR assistance.

Knowledge base population seeks to expand knowledge graphs with facts that are typically extracted from a text corpus. Recently, language models pretrained on large corpora have been shown to contain factual knowledge that can be retrieved using cloze-style strategies. Such approach enables zero-shot recall of facts, showing competitive results in object prediction compared to supervised baselines. However, prompt-based fact retrieval can be brittle and heavily depend on the prompts and context used, which may produce results that are unintended or hallucinatory.We propose to use textual entailment to validate facts extracted from language models through cloze statements. Our results show that triple validation based on textual entailment improves language model predictions in different training regimes. Furthermore, we show that entailment-based triple validation is also effective to validate candidate facts extracted from other sources including existing knowledge graphs and text passages where named entities are recognized.

Satori is an Analytics Agency made with one simple vision: to give clarity in decision making through data analytics. Whether it's a cloud-based big data ecosystem for a global fintech or a machine learning model predicting churn for a leading airline group, Satori develops cutting edge analytics solutions, providing real value to its clients. Services cover the whole data lifecycle from ingestion and warehousing to ML and AI applications. Satori is a scale-up, well on track to become the leading data and analytics company in South-Eastern Europe and already a 65 specialized tech team, consisting of Data Engineers, Data Scientists, Software Engineers, QAs, and CRM Specialists, delivering innovative data, tech, and customer experience solutions. The majority of our clients are household names and global brands in FMCG, retail, ecommerce, financial services, and travel, with a footprint in Greece but also across Europe and beyond.

Artificial Intelligence may seem transient, yet it always managed to have a controversial presence. Recently it raised concerns about its sustainability. In June 2019, the University of Massachusetts at Amherst study discovered that a single large (213 million parameters) Transformer-based neural network built using NAS (commonly used in machine translation) has produced around 626,000 pounds of carbon dioxide. This amount is equivalent to five times more than an average car produces in its lifespan. These massive consumption numbers are because of the energy needed to run specialized hardware like GPUs and TPUs for AI training and development.

IBM designed Summit, the fastest supercomputer on Earth, to run the calculation-intensive models that power modern artificial intelligence (AI). Now MIT is about to get a slice. IBM pledged earlier this year to donate an $11.6 million computer cluster to MIT modeled after the architecture of Summit, the supercomputer it built at Oak Ridge National Laboratory for the U.S. Department of Energy. The donated cluster is expected to come online this fall when the MIT Stephen A. Schwarzman College of Computing opens its doors, allowing researchers to run more elaborate AI models to tackle a range of problems, from developing a better hearing aid to designing a longer-lived lithium-ion battery. "We're excited to see a range of AI projects at MIT get a computing boost, and we can't wait to see what magic awaits," says John E. Kelly III, executive vice president of IBM, who announced the gift in February at MIT's launch celebration of the MIT Schwarzman College of Computing.

IBM designed Summit, the fastest supercomputer on Earth, to run the calculation-intensive models that power modern artificial intelligence (AI). Now MIT is about to get a slice. IBM pledged earlier this year to donate an $11.6 million computer cluster to MIT modeled after the architecture of Summit, the supercomputer it built at Oak Ridge National Laboratory for the U.S. Department of Energy. The donated cluster is expected to come online this fall when the MIT Stephen A. Schwarzman College of Computing opens its doors, allowing researchers to run more elaborate AI models to tackle a range of problems, from developing a better hearing aid to designing a longer-lived lithium-ion battery. "We're excited to see a range of AI projects at MIT get a computing boost, and we can't wait to see what magic awaits," says John E. Kelly III, executive vice president of IBM, who announced the gift in February at MIT's launch celebration of the MIT Schwarzman College of Computing.