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Category: robotics/AI – Page 709

In a recent study published in Nutrients, a group of researchers investigated the interactions between individual diets and the gut microbiome in seven volunteers, leveraging technological advancements and machine learning to inform personalized nutrition strategies and potential therapeutic targets.

Study: Unraveling the Gut Microbiome–Diet Connection: Exploring the Impact of Digital Precision and Personalized Nutrition on Microbiota Composition and Host Physiology. Image Credit: ART-ur/Shutterstock.com.

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The chatbot’s reasoning was “at times medically implausible or inconsistent, which can lead to misinformation or incorrect diagnosis, with significant implications,” the report noted.

The scientists also admitted some shortcomings with the research. The sample size was small, with 30 cases examined. In addition, only relatively simple cases were looked at, with patients presenting a single primary complaint.

It was not clear how well the chatbot would fare with more complex cases. “The efficacy of ChatGPT in providing multiple distinct diagnoses for patients with complex or rare diseases remains unverified.”

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Today’s blog is from guest contributors Alaric Wilson, Senior ISV Partner Development Manager, and Michael Gillett, Partner Technology Strategy Manager.

In the era of AI, every app has the potential to be intelligent. Independent Software Vendors (ISVs) are facing increasing pressure from customers to deliver innovative solutions that meet their demands with a more dynamic user experience. To stay competitive, ISVs are turning to cutting-edge technologies like generative AI to unlock new possibilities for their software development process. Azure OpenAI Service, powered by OpenAI’s advanced language models, is revolutionizing how ISVs innovate, providing them with unprecedented capabilities to create intelligent, adaptive, and highly customized applications.

In today’s blog, we’re sharing recent resources and examples, to help ISV partners learn more about the opportunities to leverage generative AI on Azure OpenAI Service and fuel customers’ innovation efforts.

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Mapping molecular structure to odor perception is a key challenge in olfaction. Here, we use graph neural networks (GNN) to generate a Principal Odor Map (POM) that preserves perceptual relationships and enables odor quality prediction for novel odorants. The model is as reliable as a human in describing odor quality: on a prospective validation set of 400 novel odorants, the model-generated odor profile more closely matched the trained panel mean (n=15) than did the median panelist. Applying simple, interpretable, theoretically-rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.

One-Sentence Summary An odor map achieves human-level odor description performance and generalizes to diverse odor-prediction tasks.

The authors have declared no competing interest.

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Artificial intelligence (AI) large language models (LLM) like OpenAI’s hit GPT-3, 3.5, and 4, encode a wealth of information about how we live, communicate, and behave, and researchers are constantly finding new ways to put this knowledge to use.

A recent study conducted by Stanford University researchers has demonstrated that, with the right design, LLMs can be harnessed to simulate human behavior in a dynamic and convincingly realistic manner.

The study, titled “Generative Agents: Interactive Simulacra of Human Behavior,” explores the potential of generative models in creating an AI agent architecture that remembers its interactions, reflects on the information it receives, and plans long-and short-term goals based on an ever-expanding memory stream. These AI agents are capable of simulating the behavior of a human in their daily lives, from mundane tasks to complex decision-making processes.

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Are large language models sentient? If they are, how would we know?

As a new generation of AI models have rendered the decades-old measure of a machine’s ability to exhibit human-like behavior (the Turing test) obsolete, the question of whether AI is ushering in a generation of machines that are self-conscious is stirring lively discussion.

Former Google software engineer Blake Lemoine suggested the large language model LaMDA was sentient.

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Mind mastery refers to intentionally developing self-awareness and discipline to take control of your thought patterns, emotional responses, and behaviors. Rather than operating on autopilot or being swept away by negativity, you respond consciously in alignment with your values and goals. Benefits of mind mastery include reduced stress, achieving ambitions, fulfilled relationships, and overall life satisfaction.

Mastering your mind requires commitment, but small, consistent steps to steward your thoughts and manage your emotions will compound to impact your mental health and empower your life profoundly. Here are key techniques:

Practice observing your thoughts like clouds passing by without reacting or judging. Creating this mental space between stimulus and response allows you to gain perspective. Ask what evidence supports or contradicts anxious thoughts.

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Advances in artificial intelligence have prompted extensive public concern about its capacity to contribute to the spread of misinformation, bias, and cybersecurity breaches—and its potential existential threat to humanity. But, if anything, AI can aid human beings in making decisions aimed at improving social equality, safety, productivity—and mitigate some existential threats.

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