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Category: robotics/AI – Page 133

Introduction Artificial intelligence has rapidly evolved over the last decade, leading to breakthroughs in natural language processing (NLP), machine learning, and multimodal applications. OpenAI’s O1 model exemplifies this innovation, offering capabilities that extend beyond traditional AI models. O1 is not just a tool; it is a revolutionary framework that brings advanced language understanding, multimodal integration, and real-time adaptability to the table. This comprehensive guide explores the intricacies of OpenAI’s O1 model, its applications, benefits, limitations, and how to optimize related content for search engine visibility.

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Artificial intelligence, AI, is rapidly transforming work also in the financial sector. Conducted at the University of Eastern Finland, a recent study explored how integrating AI into the work of sales teams affects the interpersonal communication competence required of sales managers. The study found that handing routine tasks over to AI improved efficiency and freed up sales managers’ time for more complex tasks. However, as the integration of AI progressed, sales managers faced new kind of communication challenges, including those related to overcoming fears and resistance to change.

“Members of sales teams needed encouragement in the use AI, and their self-direction also needed support. Sales managers’ contribution was also vital in adapting to constant digital changes and in maintaining trust within the team,” says Associate Professor Jonna Koponen of the University of Eastern Finland.

The longitudinal study is based on 35 expert interviews conducted over a five-year period in 2019–2024, as well as on secondary data gathered from one of Scandinavia’s largest financial groups. The findings show that besides traditional managerial interpersonal communication competence, consideration of ethical perspectives and adaptability were significant when integrating AI into the work of sales teams.

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Image classification is one of AI’s most common tasks, where a system is required to recognize an object from a given image. Yet real life requires us to recognize not a single standalone object but rather multiple objects appearing together in a given image.

This reality raises the question: what is the best strategy to tackle multi-object ? The common approach is to detect each object individually and then classify them. But new research challenges this customary approach to multi-object classification tasks.

In an article published today in Physica A: Statistical Mechanics and its Applications, researchers from Bar-Ilan University in Israel show how classifying objects together, through a process known as Multi-Label Classification (MLC), can surpass the common detection-based classification.

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Globally, approximately 139 million people are expected to have Alzheimer’s disease (AD) by 2050. Magnetic resonance imaging (MRI) is an important tool for identifying changes in brain structure that precede cognitive decline and progression with disease; however, its cost limits widespread use.

A new study by investigators from Massachusetts General Hospital (MGH), a founding member of the Mass General Brigham health care system, demonstrates that a simplified, low magnetic field (LF) MRI machine, augmented with machine learning tools, matches conventional MRI in measuring brain characteristics relevant to AD. Findings, published in Nature Communications, highlight the potential of the LF-MRI to help evaluate those with cognitive symptoms.

“To tackle the growing, global health challenge of dementia and cognitive impairment in the aging population, we’re going to need simple, bedside tools that can help determine patients’ underlying causes of cognitive impairment and inform treatment,” said senior author W. Taylor Kimberly, MD, Ph.D., chief of the Division of Neurocritical Care in the Department of Neurology at MGH.

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Researchers at Lawrence Livermore National Laboratory (LLNL) have developed a new approach that combines generative artificial intelligence (AI) and first-principles simulations to predict three-dimensional atomic structures of highly complex materials.

This research highlights LLNL’s efforts in advancing machine learning for materials science research and supporting the Lab’s mission to develop innovative technological solutions for energy and sustainability.

The study, recently published in Machine Learning: Science and Technology, represents a potential leap forward in the application of AI for materials characterization and inverse design.

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Yongcui Mi has developed a new technology that enables real-time shaping and control of laser beams for laser welding and directed energy deposition using laser and wire. The innovation is based on the same mirror technology used in advanced telescopes for astronomy.

In a few years, this new technology could lead to more efficient and reliable ways of using lasers for welding and directed energy deposition with laser and wire. The manufacturing industry could benefit from new opportunities to build more robust processes that meet stringent quality standards.

“We are the first to use deformable technology for this application. The mirror optics can handle multi-kilowatt laser power, and with the help of computer vision and AI, the laser beam can be shaped in real time to adapt to variations in joint gaps,” explains Yongcui, a newly minted Ph.D. in Production technology from University West.

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Researchers have developed XLuminA, an AI framework for the automated discovery of super-resolution microscopy techniques. With 10,000x faster optimization than traditional methods, it discovers unexplored designs breaking the diffraction limit.

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Researchers at Lawrence Livermore National Laboratory (LLNL) have developed a new approach that combines generative artificial intelligence (AI) and first-principles simulations to predict three-dimensional (3D) atomic structures of highly complex materials.

This research highlights LLNL’s efforts in advancing machine learning for materials science research and supporting the Lab’s mission to develop innovative technological solutions for energy and sustainability.

The study, recently published in Machine Learning: Science and Technology, represents a potential leap forward in the application of AI for materials characterization and inverse design.

Read more | >

Initially a variant of LSTM known as AWD LSTM was pre trained (unsupervised pre training) for language modelling task using wikipedia articles. In the next step the output layer was turned into a classifier and was fine tuned using various datasets from IMDB, yelp etc. When the model was tested on unseen data, sate of the art results were obtained. The paper further went on to claim that if a model was built using 10,000 rows from scratch then fine tuning the above model (transfer learning) would give much better results with 100 rows only. The only thing to keep in mind is they did not used a transformer in their architecture. This was because both these concepts were researched parallely (transformers and transfer learning) so researchers on both the sides had no idea of what work the other was doing. Transformers paper came in 2017 and ULMFit paper (transfer learning) came in early 2018.

Now architecture wise we had state of the art architecture i.e. Transformers and training wise we have a very beautiful and elegant concept of Transfer Learning. LLMs were the outcome of the combination of these 2 ideas.

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