VeraAI text analysis tools for fact-checking EBU Technology & Innovation

An advantage of this dataset is that it ensures a classifier learns to separate stylistic patterns rather than merely overfitting to different challenges. We use the 2017 GCJ dataset, which consists of 1,632 C++ files from 204 authors, solving the same eight challenges. For our analysis we use the dataset published by.15 We focus on vulnerabilities related to buffers (CWE-119) and obtain 39,757 source code snippets of which 10,444 (26 %) are labeled as containing a vulnerability. The ground-truth labels required ChatGPT for classification tasks are inaccurate, unstable, or erroneous, affecting the overall performance of a learning-based system. On the contrary, it is a reflective effort that shows how subtle pitfalls can have a negative impact on progress of security research, and how we—as a community—can mitigate them adequately. This was a couple years before ChatGPT was released publicly – if you can remember those times – and the natural language processing capabilities Reifschneider was working with were more rudimentary.

Microsoft’s approach uses a combination of advanced object detection and OCR (optical character recognition) to overcome these hurdles, resulting in a more reliable and effective parsing system. OmniParser is essentially a powerful new tool designed to parse screenshots into structured elements that a vision-language model (VLM) can understand and act upon. As LLMs become more integrated into daily workflows, Microsoft recognized the need for AI to operate seamlessly across varied GUIs. The OmniParser project aims to empower AI agents to see and understand screen layouts, extracting vital information such as text, buttons, and icons, and transforming it into structured data. It’s important to note that most models listed here, even those with traditionally open-source licenses like Apache 2.0 or MIT, do not meet the Open Source AI Definition (OSAID). This gap is primarily due to restrictions around training data transparency and usage limitations, which OSAID emphasizes as essential for true open-source AI.

• For example, over-optimistic results can be easily produced by calibrating the detection threshold on the test data instead of the training data.
• In an empirical analysis, we further demonstrate how individual pitfalls can lead to unrealistic performance and interpretations, obstructing the understanding of the security problem at hand.
• Here is a detailed look at some of the top NLP tools and libraries available today, which empower data scientists to build robust language models and applications.
• When assessing the pitfalls in general, the authors especially agree that lab-only evaluations (92 %), the base rate fallacy (77 %), inappropriate performance measures (69 %), and sampling bias (69 %) frequently occur in security papers.
• The release of OmniParser is part of a broader competition among tech giants to dominate the space of AI screen interaction.

In September 2023, Musk went to the border crossing in Eagle Pass, Texas — a place Republicans had already singled out as emblematic of the migrant crisis. Musk’s commentary on noncitizens voting is based on a “weak to non-existent” understanding of election law, said David Schultz, a professor of political science at Hamline University in St. Paul, Minnesota. Federal law bars non-US citizens from voting in presidential elections, and voters must legally swear, under penalty of criminal prosecution, that they’re eligible to cast a ballot.

Interest in statistical learning in developmental studies stems from the observation that 8-month-olds were able to extract words from a monotone speech stream solely using the transition probabilities (TP) between syllables (Saffran et al., 1996). A simple mechanism was thus part of the human infant’s toolbox for discovering regularities in language. Since this seminal study, observations on statistical learning ChatGPT App capabilities have multiplied across domains and species, challenging the hypothesis of a dedicated mechanism for language acquisition. Here, we leverage the two dimensions conveyed by speech –speaker identity and phonemes– to examine (1) whether neonates can compute TPs on one dimension despite irrelevant variation on the other and (2) whether the linguistic dimension enjoys an advantage over the voice dimension.

These help find patterns, adjust inputs, and thus optimize model accuracy in real-world applications. Morphology, or the form and structure of words, involves knowledge of phonological or pronunciation rules. These provide excellent building blocks for higher-order applications such as speech and named entity recognition systems. Other accounts whose anti-immigrant posts he frequently responds to include @KanekoaTheGreat, a proponent of the baseless QAnon conspiracy movement, and Michael Benz, a former Trump State Department official who has routinely promoted racist conspiracy theories online.

4 Network Intrusion Detection

Data were reference averaged and normalised within each epoch by dividing by the standard deviation across electrodes and time. During the last two decades, many studies have extended this finding by demonstrating sensitivity to statistical regularities in sequences across domains and species. Non-human animals, such as cotton-top tamarins (Hauser et al., 2001), rats (Toro and Trobalón, 2005), dogs (Boros et al., 2021), and chicks (Santolin et al., 2016) are also sensitive to TPs. Detecting network intrusions is semantic text analysis one of the oldest problems in security and it comes at no surprise that detection of anomalous network traffic relies heavily on learning-based approaches. However, challenges in collecting real attack data has often led researchers to generate synthetic data for lab-only evaluations (P9). Here, we demonstrate how this data is often insufficient for justifying the use of complex models (for example, neural networks) and how using a simpler model as a baseline would have brought these shortcomings to light (P6).

The evidence is convincing, although an additional experimental manipulation with conflicting linguistic and non-linguistic information as well as further discussion about the linguistic vs non-linguistic nature of the stimulus materials would have strengthened the manuscript. The findings are highly relevant for researchers working in several domains, including developmental cognitive neuroscience, developmental psychology, linguistics, and speech pathology. Bloomberg ran a machine learning model on the posts to identify subjects that Musk most often discusses on X, and found that about 1,300 of Musk’s posts in 2024 revolved around immigration and voter fraud. Reporters then manually reviewed hundreds of them to ensure they were properly categorized. Posts were provided by researchers at Clemson University’s Media Forensics Hub and the data platform Bright Data.

Skills in deep models like RNNs, LSTMs, transformers, and the basics of data engineering, and preprocessing must be available to be competitive in the role. The 0.5 s epochs were concatenated chronologically (2 minutes of Random, 2 minutes of long Structured stream, and 5 minutes of short Structured blocks). The same analysis as above was performed in sliding time windows of 2 minutes with a 1 s step. A time window was considered valid if at least 8 out of the 16 epochs were free of motion artefacts.

Understanding licensing in open-source AI models

Segments containing samples with artefacts defined as bad data in more than 30% of the channels were rejected, and the remaining channels with artefacts were spatially interpolated. But video content is much more complex for AI models to analyze than text is, with more contextual factors at play. A travel ad makes a lot more sense juxtaposed against a culture-specific cooking show, for example, so only showing food-related ads on food-related channels would miss out on that possible connection point. NLP is one of the fastest-growing fields in AI as it allows machines to understand human language, interpret, and respond.

Each tool set possesses unique strengths, enabling developers to tailor their environments for specific project needs. For example, Stability Diffusion by Stability AI employs the Creative ML OpenRAIL-M license, which includes ethical restrictions that deviate from OSAID’s requirements for unrestricted use. Similarly, Grok by xAI combines proprietary elements with usage limitations, challenging its alignment with open-source ideals. When organizations require real-time updates, advanced security, or specialized functionalities, proprietary models can offer a more robust and secure solution, effectively balancing openness with the rigorous demands for quality and accountability.

Immigration and voter fraud has become, by far, the entrepreneur’s favorite and most popular policy topic online, according to a large-scale Bloomberg analysis of his posts on X, the social network he owns, where he has more than 200 million followers. The success of the boxplot method also shows how simple methods can reveal issues with data generated for lab-only evaluations (P9). In the Mirai dataset the infection is overly conspicuous; an attack in the wild would likely be represented by a tiny proportion of network traffic. Current feature sets for authorship attribution include these templates, such that models are learned that strongly focus on them as highly discriminative patterns. However, this unused duplicate code leads to features that represent artifacts rather than coding style which are spurious correlations.

These correlations pose a problem once results are interpreted and used for drawing general conclusions. Without knowledge of spurious correlations, there is a high risk of overestimating the capabilities of an approach and misjudging its practical limitations. As an example, §4.2 reports our analysis on a vulnerability discovery system indicating the presence of notable spurious correlations in the underlying data. It works with a range of vision-language models, including GPT-4V, Phi-3.5-V, and Llama-3.2-V, making it flexible for developers with a broad range of access to advanced foundation models. While the concept of GUI interaction for AI isn’t entirely new, the efficiency and depth of OmniParser’s capabilities stand out. Previous models often struggled with screen navigation, particularly in identifying specific clickable elements, as well as understanding their semantic value within a broader task.

A common source of recent mobile data is the AndroZoo project,2 which collects Android apps from a large variety of sources, including the official GooglePlay store and several Chinese markets. At the time of writing it includes more than 11 million Android applications from 18 different sources. As well as the samples themselves, it includes meta-information, such as the number of antivirus detections. Although AndroZoo is an excellent source for obtaining mobile apps, we demonstrate that experiments may suffer from severe sampling bias (P1) if the peculiarities of the dataset are not taken into account. Please note that the following discussion is not limited to the AndroZoo data, but is relevant for the composition of Android datasets in general. Although these findings point to a serious problem in research, we would like to remark that all of the papers analyzed provide excellent contributions and valuable insights.

As an example, several authors (44 %) neither agree nor disagree on whether data snooping is easy to avoid, emphasizing the importance of clear definitions and recommendations. The survey consists of a series of general and specific questions on the identified pitfalls. First, we ask the authors whether they have read our work and consider it helpful for the community. Second, for each pitfall, we collect feedback on whether they agree that (a) their publication might be affected, (b) the pitfall frequently occurs in security papers, and (c) it is easy to avoid in most cases. To quantitatively assess the responses, we use a five-point Likert scale for each question that ranges from strongly disagree to strongly agree.

Bloomberg analyzed more than 53,000 posts sent from Musk’s account on X, formerly known as Twitter, between December 2011 and October 2024 to determine which topics he remarked on most, and tracked which of his posts received the most engagement. The analysis also revealed patterns in how Musk links immigrants to voter fraud, and reaches a wider audience. The majority of the time, he’s reacting to others’ content with a simple emoji reaction or comment, adding his endorsement without directly repeating it. And his posts generally do not receive notes from X’s fact-checking system, even if demonstrably false. The classification performance of the autoencoder ensemble compared to the boxplot method is shown in Table 3.

To support this claim, we analyze the prevalence of these pitfalls in 30 top-tier security papers from the past decade that rely on machine learning for tackling different problems. To our surprise, each paper suffers from at least three pitfalls; even worse, several pitfalls affect most of the papers, which shows how endemic and subtle the problem is. Although the pitfalls are widespread, it is perhaps more important to understand the extent to which they weaken results and lead to over-optimistic conclusions. To this end, we perform an impact analysis of the pitfalls in four different security fields. NLP ML engineers focus primarily on machine learning model development for various language-related activities. Their areas of application lie in speech recognition, text classification, and sentiment analysis.

Image generation models

Understandably, this results from the nature of the competition, where participants are encouraged to solve challenges quickly. These templates are often not used to solve the current challenges but are only present in case they might be needed. As this deviates from real-world settings, we identify a sampling bias in the dataset.

We find that all of the pitfalls introduced in §2 are pervasive in security research, affecting between 17 % and 90 % of the selected papers. Each paper suffers from at least three of the pitfalls and only 22 % of instances are accompanied by a discussion in the text. While authors may have even deliberately omitted a discussion of pitfalls in some cases, the results of our prevalence analysis overall suggest a lack of awareness in our community. First, most authors agree that there is a lack of awareness for the identified pitfalls in our community. Second, they confirm that the pitfalls are widespread in security literature and that there is a need for mitigating them.

While NLTK and TextBlob are suited for beginners and simpler applications, spaCy and Transformers by Hugging Face provide industrial-grade solutions. AllenNLP and fastText cater to deep learning and high-speed requirements, respectively, while Gensim specializes in topic modelling and document similarity. Choosing the right tool depends on the project’s complexity, resource availability, and specific NLP requirements. On X, Musk has promoted a program called Community Notes to add context to posts using a network of thousands of volunteers. According to Bloomberg’s analysis, since Musk purchased Twitter in late 2022, his activity on the platform has skyrocketed.

Audio models

The same statistical structures were used for both Experiments, only changing over which dimension the structure was applied. The 10 short structured streams lasted 30 seconds each, each duplet appearing a total of 200 times (10 × 20). Parsing based on statistical information was revealed by steady-state evoked potentials at the duplet rate observed around 2 min after the onset of the familiarisation stream and by different ERPs to Words and Part-words presented during the test in both experiments. Despite variations in the other dimension, statistical learning was possible, showing that this mechanism operates at a stage when these dimensions have already been separated along different processing pathways. Our results, thus, revealed that linguistic content and voice identity are calculated independently and in parallel. With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas.

At the other end of the spectrum, when languages have the same roots, such as French and Spanish, AI translation can better utilize parallel structures to produce a more fluent and nuanced translation. Additionally, the more stylistically rich the source text, as is the case with literature, the less fluent and accurate the output, but more so when languages are structurally distant. AI translation nuance mishaps are more frequent in literary texts, such as fiction or poetry works.

Open-source generative models are valuable for developers, researchers, and organizations wanting to leverage cutting-edge AI technology without incurring high licensing fees or restrictive commercial policies. AllenNLP, developed by the Allen Institute for AI, is a research-oriented NLP library designed for deep learning-based applications. It offers a comprehensive set of tools for text processing, including tokenization, stemming, tagging, parsing, and classification. Ad revenue grew 56% YOY even without some of Reddit’s shiny new ad products, including generative AI creative tools and in-comment ads, being fully integrated into its platform. Samba is already developing its own AI tools to better analyze video content, as Navin demonstrated during the IAB NewFronts in May.

With a few rare exceptions, researchers develop learning-based approaches without exact knowledge of the true underlying distribution of the input space. Instead, they need to rely on a dataset containing a fixed number of samples that aim to resemble the actual distribution. While it is inevitable that some bias exists in most cases, understanding the specific bias inherent to a particular problem is crucial to limiting its impact in practice. Drawing meaningful conclusions from the training data becomes challenging, if the data does not effectively represent the input space or even follows a different distribution. Despite its great success, the application of machine learning in practice is often non-trivial and prone to several pitfalls, ranging from obvious flaws to minor blemishes.

Let’s break down how this technology works and why it’s gaining traction so quickly. During SlatorPod episode #207, Dr Woodstein broached the matter of nuance in literary choices, especially in children’s literature. She explained that in her Swedish into English translation work, for example, she has seen content that is acceptable for children’s literature in Sweden, like depictions of nudity and weapons, but require adaptations for English-speaking audiences because of cultural differences. Literary experts like Dr B.J. Woodstein have seen first-hand how certain aspects of language can be mishandled in translation when the broader context is not present, including cultural knowledge, whether the translation is done by humans or machines.

The task of identifying the developer based on source code is known as authorship attribution.8 Programming habits are characterized by a variety of stylistic patterns, so that state-of-the-art attribution methods use an expressive set of such features. These range from simple layout properties to more unusual habits in the use of syntax and control flow. In combination with sampling bias (P1), this expressiveness may give rise to spurious correlations (P4) in current attribution methods, leading to an overestimation of accuracy. Throughout the learning procedure, it is common practice to generate different models by varying hyperparameters. The best-performing model is picked and its performance on the test set is presented.

Inappropriate performance measures are a long-standing problem in security research, particularly in detection tasks. While true and false positives, for instance, provide a more detailed picture of a system’s performance, they can also disguise the actual precision when the prevalence of attacks is low. Additionally, an OCR module extracts text from the screen, which helps in understanding labels and other context around GUI elements. By combining detection, text extraction, and semantic analysis, OmniParser offers a plug-and-play solution that works not only with GPT-4V but also with other vision models, increasing its versatility. At its core, OmniParser is an open-source generative AI model designed to help large language models (LLMs), particularly vision-enabled ones like GPT-4V, better understand and interact with graphical user interfaces (GUIs).

We investigated (1) the main effect of test duplets (Word vs. Part-word) across both experiments, (2) the main effect of familiarisation structure (Phoneme group vs. Voice group), and finally (3) the interaction between these two factors. We used non-parametric cluster-based permutation analyses (i.e. without a priori ROIs) (Oostenveld et al., 2011). The manuscript provides important new insights into the mechanisms of statistical learning in early human development, showing that statistical learning in neonates occurs robustly and is not limited to linguistic features but occurs across different domains.

Moreover, the OCR component’s bounding box precision can sometimes be off, particularly with overlapping text, which can result in incorrect click predictions. These challenges highlight the complexities inherent in designing AI agents capable of accurately interacting with diverse and intricate screen environments. The release of OmniParser is part of a broader competition among tech giants to dominate the space of AI screen interaction. Recently, Anthropic released a similar, but closed-source, capability called “Computer Use” as part of its Claude 3.5 update, which allows AI to control computers by interpreting screen content. Apple has also jumped into the fray with their Ferret-UI, aimed at mobile UIs, enabling their AI to understand and interact with elements like widgets and icons. Released relatively quietly by Microsoft, OmniParser could be a crucial step toward enabling generative tools to navigate and understand screen-based environments.

SpaCy is a fast, industrial-strength NLP library designed for large-scale data processing. You can foun additiona information about ai customer service and artificial intelligence and NLP. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Our vision is that AI-generated data and insights will yield better-performing and better-quality ads,” Navin said of the trend. Contextual targeting that’s based on content, in contrast, would instead serve up ads more relevant to the tech publication’s purview. From there, Samba TV plans to integrate its video data fully into Semasio’s platform, allowing clients like Acxiom and National Media to access better contextual relevance across digital, mobile and CTV.

An interesting mix of programming, linguistics, machine learning, and data engineering skills is needed for a career opportunity in NLP. Whether it is a dedicated NLP Engineer or a Machine Learning Engineer, they all contribute towards the advancement of language technologies. Preprocessing is the most important part of NLP because raw text data needs to be transformed into a suitable format for modelling. Major preprocessing steps include tokenization, stemming, lemmatization, and the management of special characters. Being a master in handling and visualizing data often means one has to know tools such as Pandas and Matplotlib.