Lie Detector Kit

[Gwenda Gronert]

Visualize3D dose distribution and find the best myQA SRS setup to effectively evaluate multiple targets at once, and import arbitrary dose planes for myQA SRS.

It is now possible to efficiently measure and evaluate multiple targets at any rotational angle with myQA SRS.

myQA SRS combines the best of both worlds: unrivalled accuracy and film-class resolution of film QA, with the proven efficiency of the digital detector array workflow. Stephan Drge, a chief medical physicist from DGD Lungenklinik Hemer, shares his clinical experience with myQA SRS and the benefits associated with its use for patient QA.

Symmetry S3 uses a customised CMOS sensor and fibre optics to unlock a unique and powerful combination of speed, sensitivity, and diffraction pattern detail. The S3, in combination with the AZtec software, delivers exceptional performance on all materials and for all measurements. The highest analysis speed of the Symmetry S3, in excess of 5700 pps, enables texture and grain size characterisation in a matter of seconds, yet this is achieved without requiring high beam currents or sacrificing pattern resolution. This means that these high speeds can be achieved even on challenging, real-world samples such as multiphase light metal alloys or deformed steels.

In addition, the Symmetry S3 can collect distortion-free, megapixel resolution EBSPs for detailed strain and phase analyses. This is a detector to suit all applications, enhanced by innovative features such as software controlled tilting (with dynamic calibration) and a unique proximity sensor.

The performance of the Symmetry S3 detector is enabled by the powerful AZtecHKL acquisition platform, backed up by the fastest and most modern EBSD data processing software, AZtecCrystal. The comparison table below will help you to choose the best software package to deliver the flexibility and functionality that you require.

Learn about a new phosphor screen for the Oxford Instruments CMOS EBSD detector range that uses an optical interference filter to block out the infrared signal during high temperature EBSD experiments. This new technology enables faster and more sensitive analyses of microstructural changes measured in-situ at high temperatures.

Recorded live from our Spotlight session, at the virtual meeting M&M 2020. In this session, expert Dr. Pat Trimby introduces the previous generation Symmetry S2 fibre-optically coupled EBSD detector.

Uniden R-Series radar detectors offer some of the best performing radar/laser detectors available in the market today. Our radar/laser detectors feature cutting edge technologies, such as industry-leading radar detection range and accuracy with dual horn antennas providing directional arrows to indicate radar signal source (R8). Other features include a low-noise amplifier and DSP chip to analyze signal received in matter of nano seconds; an internal GPS receiver to automatically or manually mark/mute locations, advanced K/Ka band filtering to ignore false alerts and vehicle blind spot monitoring/collision avoidance systems. All radar detectors come with free updates for the firmware and the Speed/Red-light Camera location database allowing you to keep your radar detector up-to-date.

The granular detail provided by GPTZero allows administrators to observe AI usage across the institution. This data is helping guide us on what type of education, parameters, and policies need to be in place to promote an innovative and healthy use of AI.

GPTZero is the leading AI detector for checking whether a document was written by a large language model such as ChatGPT. GPTZero detects AI on sentence, paragraph, and document level. Our model was trained on a large, diverse corpus of human-written and AI-generated text, with a focus on English prose. To date, GPTZero has served over 2.5 million users around the world, and works with over 100 organizations in education, hiring, publishing, legal, and more.

Simply paste in the text you want to check, or upload your file, and we'll return an overall detection for your document, as well as sentence-by-sentence highlighting of sentences where we've detected AI. Unlike other detectors, we help you interpret the results with a description of the result, instead of just returning a number.

Our users have seen the use of AI-generated text proliferate into education, certification, hiring and recruitment, social writing platforms, disinformation, and beyond. We've created GPTZero as a tool to highlight the possible use of AI in writing text. In particular, we focus on classifying AI use in prose.

Overall, our classifier is intended to be used to flag situations in which a conversation can be started (for example, between educators and students) to drive further inquiry and spread awareness of the risks of using AI in written work.

The nature of AI-generated content is changing constantly. As such, these results should not be used to punish students. We recommend educators to use our behind-the-scene Writing Reports as part of a holistic assessment of student work. There always exist edge cases with both instances where AI is classified as human, and human is classified as AI. Instead, we recommend educators take approaches that give students the opportunity to demonstrate their understanding in a controlled environment and craft assignments that cannot be solved with AI.

The accuracy of our model increases as more text is submitted to the model. As such, the accuracy of the model on the document-level classification will be greater than the accuracy on the paragraph-level, which is greater than the accuracy on the sentence level.

The accuracy of our model also increases for text similar in nature to our dataset. While we train on a highly diverse set of human and AI-generated text, the majority of our dataset is in English prose, written by adults.

Firstly, at GPTZero, we don't believe that any AI detector is perfect. There always exist edge cases with both instances where AI is classified as human, and human is classified as AI. Nonetheless, we recommend that educators can do the following when they get a positive detection:

Our model is trained on millions of documents spanning various domains of writing including creating writing, scientific writing, blogs, news articles, and more. We test our models on a never-before-seen set of human and AI articles from a section of our large-scale dataset, in addition to a smaller set of challenging articles that are outside its training distribution.

Our API returns a document_classification field which indicates the most likely classification of the document. The possible values are HUMAN_ONLY, MIXED, and AI_ONLY. We also provide a probability for each classification, which is returned in the class_probabilities field. The keys for this field are human, ai or mixed. To get the probability for the most likely classification, the predicted_class field can be used. The class probability corresponding to the predicted class can be interpreted as the chance that the detector is correct in its classification. I.e. 90% means that 90% of the time on similar documents our detector is correct in the prediction it makes. Lastly, each prediction comes with a confidence_category field, which can be high, medium, or low. Confidence categories are tuned such that when the confidence_categoryfield is high 99.1% of human articles are classified as human, and 98.4% of AI articles are classified as AI.

Additionally, we highlight sentences that been detected to be written by AI. API users can access this highlighting through the highlight_sentence_for_ai field. The sentence-level classification should not be solely used to indicate that an essay contains AI (such as ChatGPT plagiarism). Rather, when a document gets a MIXED or AI_ONLY classification, the highlighted sentence will indicate where in the document we believe this occurred.

No. We do not store or collect the documents passed into any calls to our API. We wanted to be overly cautious on the side of storing data from any organizations using our API.

However, we do store inputs from calls made from our dashboard. This data is only used in aggregate by GPTZero to further improve the service for our users. You can refer to our privacy policy for more details.

No one wants to fear false positives that can lead to false accusations. We tested over 20k human-written papers, and the rate of false positives was 0.2%, the lowest false positive rate of any ai detector available.

When a Language Model writes a sentence, it probes all of its pre-training data to output a statistically generated sentence, which is simply not how a human writes. It becomes apparent when analyzed against a vast corpus of human writing.

The chance for content written by a human to be falsely labeled as AI-generated content is 0.2%, the lowest of any AI detector available. Nevertheless, we strive to inspire authenticity and digital trust by creating secure environments to share ideas and learn confidently, and that comes with the responsibility to ensure complete accuracy, particularly around false accusations. To address this, we have taken several precautions, including:

Yes. In July 2023, four researchers from across the globe published a study on the Cornell Tech-owned arXiv, declaring Copyleaks AI Detector the most accurate for checking and detecting Large Language Models (LLM) generated text. Since then, additional independent third-party studies have been released, each one highlighting the accuracy and efficiency of the AI Detector.

For example, Grammarly has a genAI-driven feature that rewrites your content to help improve it, shorten it, etc. As a result, this reworked content could get flagged as AI since it was rewritten by genAI.

However, the Copyleaks Writing Assistant does not get flagged as AI or any content that Grammarly changed to fix grammatical errors, mechanical issues, etc., because it does not use or uses minimal genAI to power these features or functionalities.

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