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Category: robotics/AI – Page 167

From the article:

When most analysts discuss Tesla, they focus on new vehicles or the electric vehicle company’s advancements in autonomy.

Yet, according to Launch i/o CEO Jeff Lutz, one of the most significant—and under-discussed—developments at Tesla is happening not in its design studios or on the road, but in its factories.

Lutz, a former executive at Google and Motorola, argues that Tesla’s true innovation isn’t just the electric vehicles or robots it’s building, but how those products are being made.

The company’s first-principles approach to manufacturing is a radical departure from the industry norm, focusing not just on cheap labor or existing models, but on rethinking the entire production process.

Continue reading “The Musk-Led Manufacturing Revolution Nobody Is Talking About” | >

“However, chatbot answers were largely difficult to read and answers repeatedly lacked information or showed inaccuracies, possibly threatening patient and medication safety,” they add.

The researchers also noted that a major drawback was the chatbot’s inability to understand the underlying intent of a patient question.

“Despite their potential, it is still crucial for patients to consult their healthcare professionals, as chatbots may not always generate error-free information. Caution is advised in recommending AI-powered search engines until citation engines with higher accuracy rates are available,” the researchers concluded.

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This presents another challenge: convincing patients to allow the use of their data. Some 70% of Americans have expressed concerns about data privacy, with 56% admitting they find AI in healthcare “scary.”

It isn’t helped by the growing number of data breaches in the healthcare space, with 88 million patients having had their personal health information compromised in data breaches last year alone. Undoubtedly, if AI-powered healthcare is to maintain its trajectory, the sector will need to address these cybersecurity concerns.

AI is no longer a prospect but a reality today. It’s already being deployed in doctors’ offices and hospitals to analyze patient data, handle back-office tasks and assist surgeons. Anticipated to decrease administrative costs by up to 30%, free up hundreds of thousands of hours of physicians’ time and cut surgical waiting times—for the millions of Americans currently suffering in silence, whether due to affordability or accessibility, AI will offer a lifeline.

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This ultrathin device safely delivers gene therapies to the inner ear, offering hope for hearing restoration.

Elon Musk’s xAI announced Monday it raised $6 billion in a Series C funding round, putting the company’s value at more than $40 billion as it continues to strengthen its AI products and infrastructure.

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Urban construction land expansion damages natural ecological patches, changing the relationship between residents and ecological land. This is widespread due to global urbanization. Considering nature and society in urban planning, we have established an evaluation system for urban green space construction to ensure urban development residents’ needs while considering natural resource distribution. This is to alleviate the contradiction of urban land use and realize the city’s sustainable development. Taking the Fengdong New City, Xixian New Area as an example, the study used seven indicators to construct an ecological source evaluation system, four types of factors to identify ecological corridors and ecological nodes using the minimum cumulative resistance model, and a Back Propagation neural network to determine the weight of the evaluation system, constructing an urban green space ecological network. We comprehensively analyzed and retained 11 ecological source areas, identified 18 ecological corridors, and integrated and selected 13 ecological nodes. We found that the area under the influence of ecosystem functions is 12.56 km2, under the influence of ecological demands is 1.40 km2, and after comprehensive consideration is 22.88 km2. Based on the results, this paper concludes that protecting, excavating, and developing various urban greening factors do not conflict with meeting the residents’ ecological needs. With consideration of urban greening factors, cities can achieve green and sustainable development. We also found that the BP neural network objectively calculates and analyzes the evaluation factors, corrects the distribution value of each factor, and ensures the validity and practicability of the weights. The main innovation of this study lies in the quantitative analysis and spatial expression of residents’ demand for ecological land and the positive and negative aspects of disturbance. The research results improve the credibility and scientificity of green space construction so that urban planning can adapt and serve the city and its residents.

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The world of artificial intelligence (AI) has made remarkable strides in recent years, particularly in understanding human language. At the heart of this revolution is the Transformer model, a core innovation that allows large language models (LLMs) to process and understand language with an efficiency that previous models could only dream of. But how do Transformers work? To explain this, let’s take a journey through their inner workings, using stories and analogies to make the complex concepts easier to grasp.

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Apple’s latest machine learning research could make creating models for Apple Intelligence faster, by coming up with a technique to almost triple the rate of generating tokens when using Nvidia GPUs.

One of the problems in creating large language models (LLMs) for tools and apps that offer AI-based functionality, such as Apple Intelligence, is inefficiencies in producing the LLMs in the first place. Training models for machine learning is a resource-intensive and slow process, which is often countered by buying more hardware and taking on increased energy costs.

Earlier in 2024, Apple published and open-sourced Recurrent Drafter, known as ReDrafter, a method of speculative decoding to improve performance in training. It used an RNN (Recurrent Neural Network) draft model combining beam search with dynamic tree attention for predicting and verifying draft tokens from multiple paths.

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