Dr. Harvey Castro said he's less concerned about AI being developed by big corporations because there are safeguards, but it can be created without safeguards and sold. Artificial intelligence – specifically large language models like ChatGPT – can theoretically give criminals information needed to cover their tracks before and after a crime, then erase that evidence, an expert warns. Large language models, or LLMs, make up a segment of AI technology that uses algorithms that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. ChatGPT is the most well known LLM, and its successful, rapid development has created unease among some experts and sparked a Senate hearing to hear from Sam Altman, the CEO of ChatGPT maker OpenAI, who pushed for oversight. Corporations like Google and Microsoft are developing AI at a fast pace. But when it comes to crime, that's not what scares Dr. Harvey Castro, a board-certified emergency medicine physician and national speaker on artificial intelligence who created his own LLM called "Sherlock."
AI image generators Midjourney and Stable Diffusion trained their models with the works of countless artists without their permission or compensation, artist says. AI-generated images that mimic an artist's style is a form of identity theft and compete with the very creatives whose work was used to train the models, a fine artist suing two artificial intelligence firms told Fox News. AI platforms like Midjourney and Stable Diffusion use text and images from across the internet and other sources to train their machines to create images for their consumers. "Somebody is able to mimic my work because a company let them," Ortiz told Fox News. "It feels like some sort of industrial-level identity theft."
Fox News correspondent Madeleine Rivera has more on the rise of artificial intelligence as the federal government looks to address concerns and overcome the learning curve. Artificial intelligence tech has the ability to crack any kind of seven-character password in just six minutes, a new study has found. The research, shared by identity theft prevention company Home Security Heroes, said the same was true even if the password contains symbols. The company used a generative AI service called PassGAN to run through 15,680,000 common passwords from the Rockyou dataset to determine how long it would take to crack them. Rockyou is a data group used to train intelligent systems on password analysis.
Evaluating new techniques on realistic datasets plays a crucial role in the development of ML research and its broader adoption by practitioners. In recent years, there has been a significant increase of publicly available unstructured data resources for computer vision and NLP tasks. However, tabular data -- which is prevalent in many high-stakes domains -- has been lagging behind.
We introduce Logical Credal Networks (or LCNs for short) - an expressive probabilistic logic that generalizes prior formalisms that combine logic and probability. Given imprecise information represented by probability bounds and conditional probability bounds on logic formulas, an LCN specifies a set of probability distributions over all its interpretations. Our approach allows propositional and first-order logic formulas with few restrictions, e.g., without requiring acyclicity. We also define a generalized Markov condition that allows us to identify implicit independence relations between atomic formulas. We evaluate our method on benchmark problems such as random networks, Mastermind games with uncertainty and credit card fraud detection. Our results show that the LCN outperforms existing approaches; its advantage lies in aggregating multiple sources of imprecise information.
Fraud causes substantial losses to telecommunication carriers. Detec(cid:173) tion systems which automatically detect illegal use of the network can be used to alleviate the problem. Previous approaches worked on features derived from the call patterns of individual users. In this paper we present a call-based detection system based on a hierarchical regime-switching model. The detection problem is formulated as an inference problem on the regime probabilities.
Fintech is one of the industries that is skyrocketing due to the growing number of internet users. To increase the speed, security, and scalability of the financial industry, several technologies function in the background. One of the technologies that have significantly changed the financial industry in 2023 and beyond is artificial intelligence (AI). Financial organizations are focused on leveraging AI, which would be introduced in areas such as mobile banking, customer experience, cyber security, social banking, payments, branch automation, and operational efficiency. Due to its remarkable advantages, such as more effective business operations, superior financial analysis, and more consumer engagement, artificial intelligence (AI) and machine learning (ML) are increasingly being used in the finance industry. Artificial intelligence is not going out of trend anytime soon. But, what are the best use cases of AI in the fintech industry, how does it change the finance industry, and how can you profit from this new technology? This blog will address the technical aspects of bringing AI/ML to the finance industry and outline every aspect of AI in the finance industry. But before proceeding further, please go through the interesting stats.
Evaluating new techniques on realistic datasets plays a crucial role in the development of ML research and its broader adoption by practitioners. In recent years, there has been a significant increase of publicly available unstructured data resources for computer vision and NLP tasks. However, tabular data -- which is prevalent in many high-stakes domains -- has been lagging behind. To bridge this gap, we present Bank Account Fraud (BAF), the first publicly available 1 privacy-preserving, large-scale, realistic suite of tabular datasets. The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized,real-world bank account opening fraud detection dataset.