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Category: robotics/AI – Page 526

Why Do Neural Networks Hallucinate (And What Are Experts Doing About It)?

Originally published on Towards AI.

AI hallucinations are a strange and sometimes worrying phenomenon. They happen when an AI, like ChatGPT, generates responses that sound real but are actually wrong or misleading. This issue is especially common in large language models (LLMs), the neural networks that drive these AI tools. They produce sentences that flow well and seem human, but without truly “understanding” the information they’re presenting. So, sometimes, they drift into fiction. For people or companies who rely on AI for correct information, these hallucinations can be a big problem — they break trust and sometimes lead to serious mistakes.

So, why do these models, which seem so advanced, get things so wrong? The reason isn’t only about bad data or training limitations; it goes deeper, into the way these systems are built. AI models operate on probabilities, not concrete understanding, so they occasionally guess — and guess wrong. Interestingly, there’s a historical parallel that helps explain this limitation. Back in 1931, a mathematician named Kurt Gödel made a groundbreaking discovery. He showed that every consistent mathematical system has boundaries — some truths can’t be proven within that system. His findings revealed that even the most rigorous systems have limits, things they just can’t handle.

Meta announces the construction of its largest data center to date

The company has unveiled plans for a new data center in Richland Parish, Louisiana, marking a significant expansion of its global infrastructure. The $10 billion investment will be Meta’s largest data center to date, spanning a massive 4 million square feet. This state-of-the-art facility will be a crucial component in the company’s ongoing efforts to support the rapid growth of artificial intelligence (AI) technologies. Meta did not disclose an estimated completion or operational date for this facility.

The new Richland Parish data center will create over 500 full-time operational jobs, providing a substantial boost to the local economy. During peak construction, the project is expected to employ more than 5,000 workers.

Meta’s decision to build in Richland Parish was driven by several factors, including the region’s robust infrastructure, reliable energy grid, and business-friendly environment. The company also cited the strong support from local community partners, which played a critical role in facilitating the development of the data center.

Google DeepMind’s Breakthrough “AlphaQubit” Closing in on the Holy Grail of Quantum Computing

The dream of building a practical, fault-tolerant quantum computer has taken a significant step forward.

In a breakthrough study recently published in Nature, researchers from Google DeepMind and Google Quantum AI said they have developed an AI-based decoder, AlphaQubit, which drastically improves the accuracy of quantum error correction—a critical challenge in quantum computing.

“Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers,” researchers wrote.

Building and Training Your First Neural Network with TensorFlow and Keras

AI has gone so far now, and various state-of-the AI models are evolving that are used in Chatbots, Humanoid Robots, Self-driving cars, etc. It has become the fastest-growing technology, and Object Detection and Object Classification are trendy these days.

In this blog post, we will cover the complete steps of building and training an Image Classification model from scratch using Convolutional Neural Network. We will use the publicly available Cifar-10dataset to train the model. This dataset is unique because it contains images of everyday seen objects like cars, aeroplanes, dogs, cats, etc. By training the neural network to these objects, we will develop intelligent systems to classify such things in the real world. It contains more than 60,000 images of size 32×32 of 10 different types of objects. By the end of this tutorial, you will have a model which can determine the object based on its visual features.

Learn how to build and train your first Image Classification model with Keras and TensorFlow using Convolutional Neural Network.

Explore the world of artificial intelligence with online courses from MIT

Through MIT OpenCourseWare, MITx, and MIT xPRO learn about machine learning, computational thinking, deepfakes, and more, all for free.

With the rise of artificial intelligence, the job landscape is changing — rapidly. MIT Open Learning offers online courses and resources straight from the MIT classroom that are designed to empower learners and professionals across industries with the competencies essential for succeeding in an increasingly AI-powered world.

OpenAI’s O1 Model: A Detailed Exploration into the Future of AI

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.

Adoption of AI calls for new kind of communication competence from sales managers

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.

Multi-label classification in AI: A new path for object recognition

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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