In this work, we study the implications of the implicit bias of gradient flow on generalization and adversarial robustness in ReLU networks. We focus on a setting where the data consists of clusters and the correlations between cluster means are small, and show that in two-layer ReLU networks gradient flow is biased towards solutions that generalize well, but are vulnerable to adversarial examples. Our results hold even in cases where the network is highly overparameterized. Despite the potential for harmful overfitting in such settings, we prove that the implicit bias of gradient flow prevents it. However, the implicit bias also leads to non-robust solutions (susceptible to small adversarial $\ell_2$-perturbations), even though robust networks that fit the data exist.

When you purchase through links in our articles, we may earn a small commission. Amazon's new OS for Fire TV players is a double-edged sword Priced at just $40, the Fire TV Stick 4K Select is powered by Vega OS, a speedy new platform that only allows Amazon-sanctioned apps. Amazon is going in a new direction with its latest 4K Fire TV streaming stick, and it's a direction that Fire TV sideloaders probably won't like. Making its debut during the company's big fall hardware event in New York City on Tuesday and slated to ship in October, the Fire TV Stick 4K Select will rank as Amazon's third 4K-capable streaming stick, alongside the Fire TV Stick 4K and the Fire TV Stick 4K Max. The newer Fire TV Stick 4K Select offers pared-down audio and video specifications compared to its siblings.

In this work, we study the implications of the implicit bias of gradient flow on generalization and adversarial robustness in ReLU networks. We focus on a setting where the data consists of clusters and the correlations between cluster means are small, and show that in two-layer ReLU networks gradient flow is biased towards solutions that generalize well, but are vulnerable to adversarial examples. Our results hold even in cases where the network is highly overparameterized. Despite the potential for harmful overfitting in such settings, we prove that the implicit bias of gradient flow prevents it. However, the implicit bias also leads to non-robust solutions (susceptible to small adversarial \ell_2 -perturbations), even though robust networks that fit the data exist.

In this work, we study the implications of the implicit bias of gradient flow on generalization and adversarial robustness in ReLU networks. We focus on a setting where the data consists of clusters and the correlations between cluster means are small, and show that in two-layer ReLU networks gradient flow is biased towards solutions that generalize well, but are vulnerable to adversarial examples. Our results hold even in cases where the network is highly overparameterized. Despite the potential for harmful overfitting in such settings, we prove that the implicit bias of gradient flow prevents it. However, the implicit bias also leads to non-robust solutions (susceptible to small adversarial \ell_2 -perturbations), even though robust networks that fit the data exist.

Large language models (LLMs) like ChatGPT demonstrate the remarkable progress of artificial intelligence. However, their tendency to hallucinate -- generate plausible but false information -- poses a significant challenge. This issue is critical, as seen in recent court cases where ChatGPT's use led to citations of non-existent legal rulings. This paper explores how Retrieval-Augmented Generation (RAG) can counter hallucinations by integrating external knowledge with prompts. We empirically evaluate RAG against standard LLMs using prompts designed to induce hallucinations. Our results show that RAG increases accuracy in some cases, but can still be misled when prompts directly contradict the model's pre-trained understanding. These findings highlight the complex nature of hallucinations and the need for more robust solutions to ensure LLM reliability in real-world applications. We offer practical recommendations for RAG deployment and discuss implications for the development of more trustworthy LLMs.

Deep neural networks (DNNs) are known to be sensitive to adversarial input perturbations, leading to a reduction in either prediction accuracy or individual fairness. To jointly characterize the susceptibility of prediction accuracy and individual fairness to adversarial perturbations, we introduce a novel robustness definition termed robust accurate fairness. Informally, robust accurate fairness requires that predictions for an instance and its similar counterparts consistently align with the ground truth when subjected to input perturbations. We propose an adversarial attack approach dubbed RAFair to expose false or biased adversarial defects in DNN, which either deceive accuracy or compromise individual fairness. Then, we show that such adversarial instances can be effectively addressed by carefully designed benign perturbations, correcting their predictions to be accurate and fair. Our work explores the double-edged sword of input perturbations to robust accurate fairness in DNN and the potential of using benign perturbations to correct adversarial instances.

A 12-year-old boy who scored the maximum in an IQ test says football still comes first. Rory Bidwell, from Great Torrington, Devon, joined the ranks of Mensa after acing the Cattell III B test with 162, the top score for children. This is above what is believed to be a score of 160 for Albert Einstein, and in the top 2% of the population. Rory is also a keen sportsman and said he would prefer a career in football because it is "what I love".Image source, Family pictureImage caption, The keen sportsman also enjoys gaming and going out to the park Rory said he felt "really good" and "fantastic" after taking the test which his mother had suggested he take. "I knew nothing about Mensa before the test, no preparation," he said.

In this work, we study the implications of the implicit bias of gradient flow on generalization and adversarial robustness in ReLU networks. We focus on a setting where the data consists of clusters and the correlations between cluster means are small, and show that in two-layer ReLU networks gradient flow is biased towards solutions that generalize well, but are highly vulnerable to adversarial examples. Our results hold even in cases where the network has many more parameters than training examples. Despite the potential for harmful overfitting in such overparameterized settings, we prove that the implicit bias of gradient flow prevents it. However, the implicit bias also leads to non-robust solutions (susceptible to small adversarial $\ell_2$-perturbations), even though robust networks that fit the data exist.

"Lois & Clark" star Dean Cain shares what he sees as the good and bad about artificial intelligence, but he has some worries about a "Terminator"-like future. Dean Cain sees the good and bad when it comes to artificial intelligence. "AI is a weird thing," he told Fox News Digital. "I look at someone like [Tesla CEO] Elon Musk who knows a lot more about it, and I think [there] would be some great uses for AI." The actor says he hasn't tried any of the programs available but is interested in their capabilities.

ChatGPT has answers for almost everything, but there's one answer we may not know for a while: will this tool turn out to be the genie its creators regret taking out of the bottle over unintended consequences in cybersecurity? BlackBerry surveyed 1,500 IT decision makers across North America, UK, and Australia and half (51 percent) predicted we're less than a year away from a cyberattack credited to ChatGPT. Three-quarters of respondents believe foreign states are already using ChatGPT for malicious purposes against other nations. The survey also exposed a perception that, while respondents see ChatGPT as being used for'good' purposes, 73 percent acknowledge its potential threat to cybersecurity and are either very or fairly concerned, proving Artificial Intelligence (AI) is a double-edged sword. The emergence of chatbots and AI-powered tools presents new challenges in cybersecurity, especially when they end up in the wrong hands.