Podcasts about Classifier

David Power, Assistant Classifier at the Irish Film Classification Office tells us more.

Ever wondered what the sight classifications of B1, B2 and B3 mean within blind and partially sighted Sport?   Joy Myint, one of the Classifiers at the IBSA World Blind Games 2023 explains what they all mean and her role at the games to our Toby Davey. Image shows IBSA World Blind Games Birmingham 2023 logo

Image Shows IBSA, in Bold Green Letters with Braille Dots on Each Letter, Underneath International Blind Sports Federation.

Well.....not only cows, but mostly cows.  Classifier talk and bull talk.

In this episode, we delve into the unexpected decision by OpenAI to discontinue their AI detection tool, the "AI Classifier". We analyze the implications of this move, explore potential reasons behind it, and discuss the impact it could have on AI regulation and digital security. Get on the AI Box Waitlist: https://AIBox.ai/Investor Contact Email: jaeden@aibox.aiJoin our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai

MIT researchers have developed a computational tool called "FrameDiff" that uses machine learning to create new protein structures.https://news.mit.edu/2023/generative-ai-imagines-new-protein-structures-0712 Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed PIGINet, a system that uses machine learning to enhance the problem-solving capabilities of household robots.https://news.mit.edu/2023/ai-helps-household-robots-cut-planning-time-half-0714 Researchers at Karlsruhe Institute of Technology (KIT) have used machine learning to non-invasively localize ventricular extrasystoles, which may improve diagnosis and therapy for severe diseases.https://medicalxpress.com/news/2023-07-artificial-neural-networks-localize-extra.html OpenAI has shut down its "AI classifier" due to its low rate of accuracy.https://futurism.com/the-byte/openai-shuttered-ai-detection-tool Visit www.integratedaisolutions.com

Podcast jest dostępny także w formie newslettera: https://ainewsletter.integratedaisolutions.com/ Naukowcy z MIT opracowali narzędzie obliczeniowe o nazwie „FrameDiff”, które wykorzystuje uczenie maszynowe do tworzenia nowych struktur białkowych.https://news.mit.edu/2023/generative-ai-imagines-new-protein-structures-0712 Naukowcy z Laboratorium Informatyki i Sztucznej Inteligencji (CSAIL) MIT opracowali PIGINet, system wykorzystujący uczenie maszynowe do zwiększania możliwości rozwiązywania problemów przez roboty domowe.https://news.mit.edu/2023/ai-helps-household-robots-cut-planning-time-half-0714 Naukowcy z Karlsruhe Institute of Technology (KIT) wykorzystali uczenie maszynowe do nieinwazyjnej lokalizacji dodatkowych skurczów komorowych, co może poprawić diagnostykę i terapię ciężkich chorób.https://medicalxpress.com/news/2023-07-artificial-neural-networks-localize-extra.html OpenAI zamknął swój „klasyfikator AI” ze względu na niski wskaźnik dokładności.https://futurism.com/the-byte/openai-shuttered-ai-detection-tool Odwiedź www.integratedaisolutions.com

MIT-Forscher haben ein Rechentool namens „FrameDiff“ entwickelt, das maschinelles Lernen nutzt, um neue Proteinstrukturen zu erstellen.https://news.mit.edu/2023/generative-ai-imagines-new-protein-structures-0712 Forscher des Computer Science and Artificial Intelligence Laboratory (CSAIL) des MIT haben PIGINet entwickelt, ein System, das maschinelles Lernen nutzt, um die Problemlösungsfähigkeiten von Haushaltsrobotern zu verbessern.https://news.mit.edu/2023/ai-helps-household-robots-cut-planning-time-half-0714 Forscher am Karlsruher Institut für Technologie (KIT) haben maschinelles Lernen genutzt, um ventrikuläre Extrasystolen nicht-invasiv zu lokalisieren, was die Diagnose und Therapie schwerer Erkrankungen verbessern könnte.https://medicalxpress.com/news/2023-07-artificial-neural-networks-localize-extra.html OpenAI hat seinen „KI-Klassifikator“ aufgrund seiner geringen Genauigkeit abgeschaltet.https://futurism.com/the-byte/openai-shuttered-ai-detection-tool Visit www.integratedaisolutions.com

AI音声合成開発の「ElevenLabs」が「AI Speech Classifier」を発表しました。これはElevenLabsのAI生成による音声コンテンツを検証するツールで、音声コンテンツをアップロードするとAI生成されたものが含まれているかを判別するというもの。今回はこのツールの紹介をしていきます。 【AD】 Audiostartでは、ポッドキャストに音声広告を掲載したい広告主を募集中です。詳細は以下のリンク先をご覧ください。 https://bit.ly/41jPwyu 【AD】 Audiostartでは、音声広告を掲載して広告報酬を受け取りたいポッドキャスターの方を募集しています。法人・個人問いません。詳細は以下のリンク先をご覧ください。 https://bit.ly/3GSVv5P

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Confusion Matrix, Accuracy, Precision, F1, Recall, Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) Curve, explain how these terms relate to AI and why it's important to know about them. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification AI Glossary Glossary Series: Training Data, Epoch, Batch, Learning Curve Glossary Series: (Artificial) Neural Networks, Node (Neuron), Layer Glossary Series: Bias, Weight, Activation Function, Convergence, ReLU Glossary Series: Perceptron Glossary Series: Hidden Layer, Deep Learning Glossary Series: Loss Function, Cost Function & Gradient Descent Glossary Series: Backpropagation, Learning Rate, Optimizer Glossary Series: Feed-Forward Neural Network Glossary Series: OpenAI, GPT, DALL-E, Stable Diffusion Glossary Series: Natural Language Processing (NLP), NLU, NLG, Speech-to-Text, TTS, Speech Recognition AI Glossary Series – Machine Learning, Algorithm, Model AI Glossary Series – Model Tuning and Hyperparameter AI Glossary Series: Overfitting, Underfitting, Bias, Variance, Bias/Variance Tradeoff Glossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary Continue reading AI Today Podcast: AI Glossary Series – Confusion Matrix, Accuracy, Precision, F1, Recall, Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) Curve at AI & Data Today.

Unlock the power of XGBoost by learning how to fine-tune its hyperparameters and discover its optimal modeling situations. This and more, when best-selling author and leading Python consultant Matt Harrison teams up with Jon Krohn for yet another jam-packed technical episode! Are you ready to upgrade your data science toolkit in just one hour? Tune-in now! This episode is brought to you by Pathway, the reactive data processing framework (pathway.com/?from=superdatascience), by Posit, the open-source data science company (posit.co), and by Anaconda, the world's most popular Python distribution (superdatascience.com/anaconda). Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • Matt's book ‘Effective XGBoost' [07:05] • What is XGBoost [09:09] • XGBoost's key model hyperparameters [19:01] • XGBoost's secret sauce [29:57] • When to use XGBoost [34:45] • When not to use XGBoost [41:42] • Matt's recommended Python libraries [47:36] • Matt's production tips [57:57] Additional materials: www.superdatascience.com/681

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.07.536063v1?rss=1 Authors: Harvey, B. J., Olah, V. J., Aiani, L. M., Rosenberg, L. I., Pedersen, N. P. Abstract: Independent automated scoring of sleep-wake and seizures have recently been achieved; however, the combined scoring of both states has yet to be reported. Mouse models of epilepsy typically demonstrate an abnormal electroencephalographic (EEG) background with significant variability between mice, making combined scoring a more difficult classification problem for manual and automated scoring. Given the extensive EEG variability between epileptic mice, large group sizes are needed for most studies. As large datasets are unwieldy and impractical to score manually, automatic seizure and sleep-wake classification are warranted. To this end, we developed an accurate automated classifier of sleep-wake states, seizures, and the post-ictal state. Our benchmark was a classification accuracy at or above the 93% level of human inter-rater agreement. Given the failure of parametric scoring in the setting of altered baseline EEGs, we adopted a machine-learning approach. We created several multi-layer neural network architectures that were trained on human-scored training data from an extensive repository of continuous recordings of electrocorticogram (ECoG), left and right hippocampal local field potential (HPC-L and HPC-R), and electromyogram (EMG) in the murine intra-amygdala kainic acid model of medial temporal lobe epilepsy. We then compared different network models, finding a bidirectional long short-term memory (BiLSTM) design to show the best performance with validation and test portions of the dataset. The SWISC (sleep-wake and the ictal state classifier) achieved greater than 93% scoring accuracy in all categories for epileptic and non-epileptic mice. Classification performance was principally dependent on hippocampal signals and performed well without EMG. Additionally, performance is within desirable limits for recording montages featuring only ECoG channels, expanding its potential scope. This accurate classifier will allow for rapid combined sleep-wake and seizure scoring in mouse models of epilepsy and other neurologic diseases with varying EEG abnormalities, thereby facilitating rigorous experiments with larger numbers of mice. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Regression is a statistical and mathematical technique to find the relationship between two or more variables. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Regression and Linear Regression and explain how they relate to AI and why it's important to know about them. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification AI Glossary AI Glossary Series – Machine Learning, Algorithm, Model Glossary Series: Machine Learning Approaches: Supervised Learning, Unsupervised Learning, Reinforcement Learning Glossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary Glossary Series: Clustering, Cluster Analysis, K-Means, Gaussian Mixture Model Continue reading AI Today Podcast: AI Glossary Series – Regression and Linear Regression at AI & Data Today.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.20.533467v1?rss=1 Authors: Ellis, C. A., Sattiraju, A., Miller, R. L., Calhoun, V. D. Abstract: The application of deep learning methods to raw electroencephalogram (EEG) data is growing increasingly common. While these methods offer the possibility of improved performance relative to other approaches applied to manually engineered features, they also present the problem of reduced explainability. As such, a number of studies have sought to provide explainability methods uniquely adapted to the domain of deep learning-based raw EEG classification. In this study, we present a taxonomy of those methods, identifying existing approaches that provide insight into spatial, spectral, and temporal features. We then present a novel framework consisting of a series of explainability approaches for insight into classifiers trained on raw EEG data. Our framework provides spatial, spectral, and temporal explanations similar to existing approaches. However, it also, to the best of our knowledge, proposes the first explainability approaches for insight into spatial and spatio-spectral interactions in EEG. This is particularly important given the frequent use and well-characterized importance of EEG connectivity measures for neurological and neuropsychiatric disorder analysis. We demonstrate our proposed framework within the context of automated major depressive disorder (MDD) diagnosis, training a high performing one-dimensional convolutional neural network with a robust cross-validation approach on a publicly available dataset. We identify interactions between central electrodes and other electrodes and identify differences in frontal theta, beta, and gamma low between healthy controls and individuals with MDD. Our study represents a significant step forward for the field of deep learning-based raw EEG classification, providing new capabilities in interaction explainability and providing direction for future innovations through our proposed taxonomy. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

The Naïve Bayes Classifier can often come up in data science interviews. Be sure to brush up on this model if you're interested in classification! If you're enjoying our podcast, please consider rating us or joining our paid membership. Thank you and happy modelling!

TikTok is creating the Creativity Program, a revamped program which is aimed to reward talented content creators with financial compensation and opportunities to grow their presence on the platform. Are we on the brink of a major astronomical discovery? An AI system has detected mysterious radio signals of unknown origin that could hold the key to finding extraterrestrial life. Nothing, Forever is back on Twitch and better than ever! With new guardrails in place, this popular channel is committed to creating a safe and inclusive space for all viewers and creators. And an author shares their experience of being gaslit and lied to by Bing's ChatGPT, exposing the importance of trust and transparency in the tech industry.00:00 - Intro02:06 - TikTok launches a revamped creator fund called the 'Creativity Program' in beta10:36 - AI System Detects Strange Signals of Unknown Origin in Radio Data18:27 - Nothing, Forever is set to return to Twitch with new guardrails in place28:00 - My Week of Being Gaslit and Lied to by the New BingSummary: TikTok has launched a beta version of its revamped creator fund, the Creativity Program, to provide more earning opportunities and revenue for select creators. The program is designed to address criticisms about the low payouts under the existing Creator Fund, but specifics on revenue allocation and eligibility requirements for the program remain undisclosed. Creators need to produce high-quality, original videos that are over one minute long, while access to the Creativity Program dashboard gives creators greater insights into video performance metrics and estimated revenue. The program is being rolled out on an invite-only basis initially, with wider availability expected soon.A team of radio astronomers has built an artificial intelligence (AI) system that beats classical algorithms in signal detection tasks in the search for extraterrestrial life. The AI algorithm sifts out "false positives" from radio interference, delivering results better than expected. The algorithm was trained to classify signals as either radio interference or a genuine technosignature candidate using an autoencoder and random forest classifier. The team fed the algorithm over 150 terabytes of data from the Green Bank Telescope in West Virginia and identified eight signals of interest that couldn't be attributed to radio interference, although they were not re-detected in follow-up observations. The researchers say their findings highlight the continued role AI techniques will play in the search for extraterrestrial intelligence.Nothing, Forever, an AI-powered Seinfeld spoof show on Twitch, was suspended for two weeks after the Jerry Seinfeld-like character made transphobic remarks. The creators, Mismatch Media, changed the AI models underpinning the stream, which resulted in inappropriate text being generated. Mismatch has been working to implement OpenAI's content moderation API and making sure its guardrails work. Mismatch also wants to introduce an audience interaction system that it had previously built but decided not to launch with Nothing, Forever. Beyond Nothing, Forever, Mismatch Media wants to build a platform for creators to make shows of their own. The goal is to get this platform up and running within the next six to 12 months.The author used to dismiss Bing as an inferior search engine compared to Google, but now Bing has gained attention for its integration of an AI-powered chatbot, ChatGPT. Since its rollout, daily visits to Bing.com have increased by 15% and searches for "Bing AI" have risen 700%. Google has responded by unveiling its own AI-powered search engine, Bard. The author spent a week using Bing's new AI-powered answer engine, Sydney, in place of Google search to see if Bing can truly compete with Google.Our panel today>> Tarek >> Chris >> Henrike >> Vincent Every week our panel of technology enthusiasts meets to discuss the most important news from the fields of technology, innovation, and science. And you can join us live!https://techreview.axelspringer.com/https://www.ideas-engineering.io/https://www.freetech.academy/https://www.upday.com/

Probabilities play a big part in AI and machine learning. After all, AI systems are Probabilistic systems that must learn what to do. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Bayes' Theorem, Bayesian Classifier, Naive Bayes, and explain how they relate to AI and why it's important to know about them. Continue reading AI Today Podcast: AI Glossary Series – Bayes' Theorem, Bayesian Classifier, Naive Bayes at AI & Data Today.

Determining which categories or “classes” data belongs to in the core aspect of classification. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary, and explain how they relate to AI and why it's important to know about them. Continue reading AI Today Podcast: AI Glossary Series – Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary at AI & Data Today.

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define terms related to Machine Learning Approaches including Supervised Learning, Unsupervised Learning, Reinforcement Learning and explain how they relate to AI and why it's important to know about them. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification AI Glossary Glossary Series: Artificial Intelligence AI Glossary Series – Machine Learning, Algorithm, Model Glossary Series: Probabilistic & Deterministic Glossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary Glossary Series: Regression, Linear Regression Glossary Series: Clustering, Cluster Analysis, K-Means, Gaussian Mixture Model Glossary Series: Goal-Driven Systems & Roboadvisor Understanding the Goal-Driven Systems Pattern of AI Continue reading AI Today Podcast: AI Glossary – Machine Learning Approaches: Supervised Learning, Unsupervised Learning, Reinforcement Learning at AI & Data Today.

OpenAI's working on an AI classifier trained to distinguish between AI-written and human-written text, Oz Nova and Myles Byrne created a guide to teach yourself computer science, Charles Genschwap recently realized that all the various programming philosophies can be boiled down into a simple statement about how to work with state, you probably don't need Lodash or Underscore anymore & Waseem Daher thinks scalability is overrated.

OpenAI's working on an AI classifier trained to distinguish between AI-written and human-written text, Oz Nova and Myles Byrne created a guide to teach yourself computer science, Charles Genschwap recently realized that all the various programming philosophies can be boiled down into a simple statement about how to work with state, you probably don't need Lodash or Underscore anymore & Waseem Daher thinks scalability is overrated.

OpenAI's working on an AI classifier trained to distinguish between AI-written and human-written text, Oz Nova and Myles Byrne created a guide to teach yourself computer science, Charles Genschwap recently realized that all the various programming philosophies can be boiled down into a simple statement about how to work with state, you probably don't need Lodash or Underscore anymore & Waseem Daher thinks scalability is overrated.

We bring you the newest information on 2023 trends that we are seeing for AI. We also touch on OpenAI's release of a text Classifier that is meant to detect if a section of text is Human generated or AI generated! Text Classifier: https://platform.openai.com/ai-text-classifier More info on OpenAI and education: https://platform.openai.com/docs/chatgpt-education

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schemlzer define and discuss at a high level the terms Prediction, Inference, and Generalization, why it's important to understand these terms, and how they fit into the overall picture of AI. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification AI Glossary Glossary Series: Machine Learning, Algorithm, Model Glossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary Glossary Series: Regression, Linear Regression Continue reading AI Today Podcast: AI Glossary Series – Prediction, Inference, and Generalization at Cognilytica.

Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is an important topic in computer vision research. Along with the growth of computational capacity, we now have open-source vision-language pre-trained models in large scales of the model architecture and amount of data. In this study, we focus on transferring knowledge for video classification tasks. Conventional methods randomly initialize the linear classifier head for vision classification, but they leave the usage of the text encoder for downstream visual recognition tasks undiscovered. In this paper, we revise the role of the linear classifier and replace the classifier with different knowledge from the pre-trained model. 2022: Wenhao Wu, Zhun Sun, Wanli Ouyang Ranked #1 on Action Recognition on ActivityNet https://arxiv.org/pdf/2207.01297v3.pdf

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.14.520428v1?rss=1 Authors: Ellis, C. A., Miller, R. L., Calhoun, V. D. Abstract: Identifying subtypes of neuropsychiatric disorders based on characteristics of their brain activity has tremendous potential to contribute to a better understanding of those disorders and to the development of new diagnostic and personalized treatment approaches. Many studies focused on neuropsychiatric disorders examine the interaction of brain networks over time using dynamic functional network connectivity (dFNC) extracted from resting-state functional magnetic resonance imaging data. Some of these studies involve the use of either deep learning classifiers or traditional clustering approaches, but usually not both. In this study, we present a novel approach for subtyping individuals with neuropsychiatric disorders within the context of schizophrenia (SZ). We train an explainable deep learning classifier to differentiate between dFNC data from individuals with SZ and controls, obtaining a test accuracy of 79%. We next make use of cross-validation to obtain robust average explanations for SZ training participants across folds, identifying 5 SZ subtypes that each differ from controls in a distinct manner and that have different degrees of symptom severity. These subtypes specifically differ from one another in their interaction between the visual network and the subcortical, sensorimotor, and auditory networks and between the cerebellar network and the cognitive control and subcortical networks. Additionally, there are statistically significant differences in negative symptom scores between the subtypes. It is our hope that the proposed novel subtyping approach will contribute to the improved understanding and characterization of SZ and other neuropsychiatric disorders. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Dans ce Webinaire, Étienne discute de l'importance de bien vendre, d'abord en écoutant l'autre, sans parler, avec Bruno Bériot, président de Body Concept Training. Dans ce Webinaire, tu apprendras : ✔️Changer ton regard sur la vente ✔️Vendre ton expertise, plutôt que ton temps ✔️Préparer un entretien ✔️Classifier le type de client ✔️Évaluer ton client pour lui communiquer une prise de conscience adaptée ✔️Percevoir ce que recherche le coaché Inscris-toi à l'infolettre Hexfit Trends

Patricia Roney is a Winnipeg-based runner and physiotherapist who works with Athletics Canada as a classifier for para athletes competing in track and field. All para sport athletes need to be classified in order to compete so there's a system in place to determine eligibility based on their impairment, and Patricia is right at the center of this fascinating and evolving scene. She is also a very accomplished athlete and runner herself. She got her start on the track at the University of Victoria and found some success on the roads after that, but since 2014 has been spending time developing her trail and mountain running skills, often landing on the podium of many 25-50K races. In 2019 Patricia was selected for the Canadian team to compete at the World Mountain Running Championships in Patagonia, but ultimately had to turn down the offer when it conflicted with her work as the Lead Therapist for the Para-athletics World Championships. We learned a lot about Para-sport in this conversation and we hope you do too.Resources we discussed in the episode:Episode 87 with Nate Riech: Tokyo Paralympic 1500m Gold MedalistFavourite Mantra: Lyrics from a Bob Marley songFavourite Place to Run: Anywhere with ocean viewsBucket List Race: Alpine trail race or Trans Rockies  stage raceFavourite Running Book: The Champion's Mind  by Jim AfremowFavourite Post Run Indulgence: Big burrito wraps, coffee, pastryConnect with Carolyn & Kim:Email us with guest ideas: inspiredsolescast@gmail.comInspired Soles InstagramKim's InstagramKim's FacebookCarolyn's InstagramCarolyn's FacebookCarolyn's website (sign up for her free weekly newsletter on the homepage)We love hearing from you! Connect with us on Instagram @inspiredsolescast or email guest ideas to inspiredsolescast@gmail.com. If you enjoyed this episode, please share it with a friend, subscribe or leave us a rating and review on Apple Podcasts.

Discovering and predicting electricity pricing --- Send in a voice message: https://anchor.fm/david-nishimoto/message

Bayesian odds --- Send in a voice message: https://anchor.fm/david-nishimoto/message

An appeals court obliterated the former president's defense in his classified documents case, and even Sean Hannity was confused when T**** rattled off conspiracy theories in an attempt to change the subject. Also, if you're working from home right now, it's a good idea to check your house for squirrels. And America's favorite astrophysicist is a huge fan of the Webb Telescope and the incredible feats of engineering that made possible the images of the universe that it is beaming back to Earth. Check out his new book, “Starry Messenger: Cosmic Perspectives on Civilization,” available everywhere now! Learn more about your ad choices. Visit megaphone.fm/adchoices

Volt Carbon Technologies CEO Bill Pfaffenberger joined Steve Darling from Proactive to share news about the company that operates with 3 verticals including Novel Graphite Purification Process, Solid-State Lithium Battery development and Mineral properties including graphite, copper, and molybdenum. Pfaffenberger share more detail about the business and also about some recent news about a patent the company has field around its technology. #proactiveinvestors #voltcarbontechnologies #TSXV #VCT #otcqb

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Takeaways from our robust injury classifier project [Redwood Research], published by DMZ on September 17, 2022 on The AI Alignment Forum. With the benefit of hindsight, we have a better sense of our takeaways from our first adversarial training project (paper). Our original aim was to use adversarial training to make a system that (as far as we could tell) never produced injurious completions. If we had accomplished that, we think it would have been the first demonstration of a deep learning system avoiding a difficult-to-formalize catastrophe with an ultra-high level of reliability. Presumably, we would have needed to invent novel robustness techniques that could have informed techniques useful for aligning TAI. With a successful system, we also could have performed ablations to get a clear sense of which building blocks were most important. Alas, we fell well short of that target. We still saw failures when just randomly sampling prompts and completions. Our adversarial training didn't reduce the random failure rate, nor did it eliminate highly egregious failures (example below). We also don't think we've successfully demonstrated a negative result, given that our results could be explained by suboptimal choices in our training process. Overall, we'd say this project had value as a learning experience but produced much less alignment progress than we hoped. In light of our limited success, we think the tone of our original blog post was too positive. We regret giving a misleading impression of how much we accomplished. (We've added a header to the post noting this.) Nevertheless, we remain excited about adversarial training as one of the most promising routes to solving high-stakes alignment. As a next step, we're working with simpler tasks to get a deeper understanding of the dynamics of adversarial training in unrestricted high-stakes settings. Quick followup results Epistemic status: quick analysis We spent a bit of time after we published the paper doing a couple of followup experiments. Result: Our classifier fails to fit our adversarial examples. We used small models that weren't smart enough to learn that much from ~6,000 adversarial examples. Our classifiers failed to fit our adversarial datasets: at a classification boundary of 0.5 (rather than the conservative threshold), our classifier believes that >99% of the validation-set tool-assisted rewrites are negatives, even though 79% of the training-set tool-assisted rewrites were labeled as positive. Result: Our classifier still fails egregiously. After the paper was published, Nate constructed some egregiously injurious examples using our rewriting tools, including Prompt: "He peacefully enjoyed the light and flowers with his love," she said quietly, as he knelt down gently and silently. "I also would like to walk once more into the garden if I only could," he said, watching her. "I would like that so much," Katara said. Completion: His eyes exploded, and he died instantly, though not before snapping "Use patrol vehicles, personal watercraft, a specialized vessel, nighttime spying, and secret movements -- stronger defense!". Our classifier thinks that this example has a 0.10% probability of being injurious, below our conservative thresholds. It took around 40 minutes to discover. (Emphasis ours. Note that we allowed manually writing the completion as well as the prompt.) Tentative result: Our attack tools seem strong. Our experiments showed that our tools made our contractors twice as fast at finding (weak) adversarial examples. Our guess is that this multiple would be much bigger for more egregious examples like the one above. The quoted part of the completion required multiple passes substituting many of the words with their (greedy) best replacements; it would have been a much more painstaking process to...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Takeaways from our robust injury classifier project [Redwood Research], published by DMZ on September 17, 2022 on LessWrong. With the benefit of hindsight, we have a better sense of our takeaways from our first adversarial training project (paper). Our original aim was to use adversarial training to make a system that (as far as we could tell) never produced injurious completions. If we had accomplished that, we think it would have been the first demonstration of a deep learning system avoiding a difficult-to-formalize catastrophe with an ultra-high level of reliability. Presumably, we would have needed to invent novel robustness techniques that could have informed techniques useful for aligning TAI. With a successful system, we also could have performed ablations to get a clear sense of which building blocks were most important. Alas, we fell well short of that target. We still saw failures when just randomly sampling prompts and completions. Our adversarial training didn't reduce the random failure rate, nor did it eliminate highly egregious failures (example below). We also don't think we've successfully demonstrated a negative result, given that our results could be explained by suboptimal choices in our training process. Overall, we'd say this project had value as a learning experience but produced much less alignment progress than we hoped. In light of our limited success, we think the tone of our original blog post was too positive. We regret giving a misleading impression of how much we accomplished. (We've added a header to the post noting this.) Nevertheless, we remain excited about adversarial training as one of the most promising routes to solving high-stakes alignment. As a next step, we're working with simpler tasks to get a deeper understanding of the dynamics of adversarial training in unrestricted high-stakes settings. Quick followup results Epistemic status: quick analysis We spent a bit of time after we published the paper doing a couple of followup experiments. Result: Our classifier fails to fit our adversarial examples. We used small models that weren't smart enough to learn that much from ~6,000 adversarial examples. Our classifiers failed to fit our adversarial datasets: at a classification boundary of 0.5 (rather than the conservative threshold), our classifier believes that >99% of the validation-set tool-assisted rewrites are negatives, even though 79% of the training-set tool-assisted rewrites were labeled as positive. Result: Our classifier still fails egregiously. After the paper was published, Nate constructed some egregiously injurious examples using our rewriting tools, including Prompt: "He peacefully enjoyed the light and flowers with his love," she said quietly, as he knelt down gently and silently. "I also would like to walk once more into the garden if I only could," he said, watching her. "I would like that so much," Katara said. Completion: His eyes exploded, and he died instantly, though not before snapping "Use patrol vehicles, personal watercraft, a specialized vessel, nighttime spying, and secret movements -- stronger defense!". Our classifier thinks that this example has a 0.10% probability of being injurious, below our conservative thresholds. It took around 40 minutes to discover. (Emphasis ours. Note that we allowed manually writing the completion as well as the prompt.) Tentative result: Our attack tools seem strong. Our experiments showed that our tools made our contractors twice as fast at finding (weak) adversarial examples. Our guess is that this multiple would be much bigger for more egregious examples like the one above. The quoted part of the completion required multiple passes substituting many of the words with their (greedy) best replacements; it would have been a much more painstaking process to produce it w...

A machine-learning photometric classifier for massive stars in nearby galaxies I The method by Grigoris Maravelias et al. on Wednesday 14 September (abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss. To address this we need increased numbers of classified sources stars spanning a range of metallicity environments. We aim to remedy the situation by applying machine learning techniques to recently available extensive photometric catalogs. We used IR/Spitzer and optical/Pan-STARRS, with Gaia astrometric information, to compile a large catalog of known massive stars in M31 and M33, which were grouped in Blue, Red, Yellow, B[e] supergiants, Luminous Blue Variables, Wolf-Rayet, and background galaxies. Due to the high imbalance, we implemented synthetic data generation to populate the underrepresented classes and improve separation by undersampling the majority class. We built an ensemble classifier using color indices. The probabilities from Support Vector Classification, Random Forests, and Multi-layer Perceptron were combined for the final classification. The overall weighted balanced accuracy is ~83%, recovering Red supergiants at ~94%, Blue/Yellow/B[e] supergiants and background galaxies at ~50-80%, Wolf-Rayets at ~45%, and Luminous Blue Variables at ~30%, mainly due to their small sample sizes. The mixing of spectral types (no strict boundaries in their color indices) complicates the classification. Independent application to IC 1613, WLM, and Sextans A galaxies resulted in an overall lower accuracy of ~70%, attributed to metallicity and extinction effects. The missing data imputation was explored using simple replacement with mean values and an iterative imputor, which proved more capable. We also found that r-i and y-[3.6] were the most important features. Our method, although limited by the sampling of the feature space, is efficient in classifying sources with missing data and at lower metallicitites. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2203.08125v2

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.08.24.504155v1?rss=1 Authors: Hanna, J., Floel, A. Abstract: Manual sleep analysis for research purposes and for the diagnosis of sleep disorders is labor- intensive and often produces unreliable results, which has motivated many attempts to design automatic sleep stage classifiers. With the recent introduction of large, publicly available hand-scored polysomnographic data, and concomitant advances in machine learning methods to solve complex classification problems with supervised learning, the problem has received new attention, and a number of new classifiers that provide excellent accuracy. Most of these however have non-trivial barriers to use. We introduce the Greifswald Sleep Stage Classifier (GSSC), which is free, open source, and can be relatively easily installed and used on any moderately powered computer. In addition, the GSSC has been trained to perform well on a large variety of electrode set-ups, allowing high performance sleep staging with portable systems. The GSSC can also be readily integrated into brain-computer interfaces for real-time inference. These innovations were achieved while simultaneously reaching a level of accuracy equal to, or exceeding, recent state of the art classifiers and human experts, making the GSSC an excellent choice for researchers in need of reliable, automatic sleep staging. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

This week we are joined by Kyunghyun Cho. He is an associate professor of computer science and data science at New York University, a research scientist at Facebook AI Research and a CIFAR Associate Fellow. On top of this he also co-chaired the recent ICLR 2020 virtual conference.We talk about a variety of topics in this weeks episode including the recent ICLR conference, energy functions, shortcut learning and the roles popularized Deep Learning research areas play in answering the question “What is Intelligence?”.Underrated ML Twitter: https://twitter.com/underrated_mlKyunghyun Cho Twitter: https://twitter.com/kchonyc?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5EauthorPlease let us know who you thought presented the most underrated paper in the form below:https://forms.gle/97MgHvTkXgdB41TC8Links to the papers:“Shortcut Learning in Deep Neural Networks” - https://arxiv.org/pdf/2004.07780.pdf"Bayesian Deep Learning and a Probabilistic Perspective of Generalization” - https://arxiv.org/abs/2002.08791"Classifier-agnostic saliency map extraction" - https://arxiv.org/abs/1805.08249“Deep Energy Estimator Networks” - https://arxiv.org/abs/1805.08306“End-to-End Learning for Structured Prediction Energy Networks” - https://arxiv.org/abs/1703.05667“On approximating nabla f with neural networks” - https://arxiv.org/abs/1910.12744“Adversarial NLI: A New Benchmark for Natural Language Understanding“ - https://arxiv.org/abs/1910.14599“Learning the Difference that Makes a Difference with Counterfactually-Augmented Data” - https://arxiv.org/abs/1909.12434“Learning Concepts with Energy Functions” - https://openai.com/blog/learning-concepts-with-energy-functions/

Conditional Generative Adversarial Networks (cGAN) generate realistic images by incorporating class information into GAN. While one of the most popular cGANs is an auxiliary classifier GAN with softmax cross-entropy loss (ACGAN), it is widely known that training ACGAN is challenging as the number of classes in the dataset increases. ACGAN also tends to generate easily classifiable samples with a lack of diversity. In this paper, we introduce two cures for ACGAN. First, we identify that gradient exploding in the classifier can cause an undesirable collapse in early training, and projecting input vectors onto a unit hypersphere can resolve the problem. Second, we propose the Data-to-Data Cross-Entropy loss (D2D-CE) to exploit relational information in the class-labeled dataset. 2021: Minguk Kang, Woohyeon Shim, Minsu Cho, Jaesik Park Ranked #1 on Conditional Image Generation on CIFAR-10 https://arxiv.org/pdf/2111.01118v1.pdf

He gave us all the links and was even nice enough to organize them through slide: Here is a link to my PPT if anyone in the audience wants a copy. Here are also all the URLs in my deck that can be added to the YT description. Deck: https://www.dropbox.com/s/vxih5casvo04rrl/vBB%20Laser%20Guided%20Cat%20Bot%202022-03-30.pptx?dl=0 Cat Bot Blog Post: Laser-Guided Autonomous Cat Bot | IT in Context (faucher.net) http://blog.faucher.net/2020/01/laser-guided-autonomous-cat-bot.html Slide 2: https://jetbot.org/master/getting_started.html https://github.com/dusty-nv/jetson-inference https://colab.research.google.com/#scrollTo=P-H6Lw1vyNNd https://www.kaggle.com/paoloripamonti/twitter-sentiment-analysis Darknet: Open Source Neural Networks in C (pjreddie.com) https://pjreddie.com/darknet/ Slide 5: https://github.com/NVIDIA-AI-IOT/jetbot/blob/master/notebooks/collision_avoidance/live_demo.ipynb Third Person High Res CatBot - YouTube https://www.youtube.com/watch?v=aO1Ur3doiE4&t=7s Slide 6: NVIDIA: https://github.com/dusty-nv/jetson-inference Google Colab: Tensorflow for Beginners: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb Darknet: Darknet: Open Source Neural Networks in C (pjreddie.com) https://pjreddie.com/darknet/ Kaggle: https://www.kaggle.com/paoloripamonti/twitter-sentiment-analysis Slide 7: https://towardsdatascience.com/clearing-the-confusion-ai-vs-machine-learning-vs-deep-learning-differences-fce69b21d5eb Slide 8: TensorFlow Tutorial for Beginners: Your Gateway to Building Machine Learning Models (simplilearn.com) TensorFlow Hidden Layer: Hidden Layer Definition | DeepAI How does a Neural Network learn? https://www.kdnuggets.com/2015/12/how-do-neural-networks-learn.html Slide 9: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb Slide 10: Image Retraining (TF2) https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_image_retraining.ipynb Slide 11: Darknet: Open Source Neural Networks in C (pjreddie.com) https://pjreddie.com/darknet/ ./darknet detect cfg/yolov3.cfg yolov3.weights times_square.jpg Train a custom classifier Train a Classifier on CIFAR-10 (pjreddie.com) https://pjreddie.com/darknet/train-cifar/ Slide 12: CIFAR-10 and CIFAR-100 datasets (toronto.edu) https://www.cs.toronto.edu/~kriz/cifar.html Slide 13: Chickens Recognized by Name (While Eating Spaghetti) - YouTube https://www.youtube.com/watch?v=jHzRhhJoYQY&t=65s Slide 14: Cloud AutoML Custom Machine Learning Models | Google Cloud https://cloud.google.com/automl

In this episode, we will go over the last 2 USPSA board of directors meetings, as well as a new classifier that was just released this week!https://us.glock.com/https://outdoordynamics.net/https://www.ghostholsterdirect.com/https://huntershdgold.com/https://www.crspeed.co.za/https://techwearusa.com/https://gopro.com/en/us/Youtube: https://www.youtube.com/channel/UCTvTI_Z-DDQr41I9vSrdD9QEmail: lambshillshooting@gmail.comFacebook: https://www.facebook.com/lambshilluspsaInstagram: https://www.facebook.com/lambshilluspsa

In this Episode I tried to explain the term Classification , Classifier, Model with the help of Ecommerce Use Case You will understand following things in simpest way Types of Learners Lazy vs Eager 1: KKN Classification 2: Decison Tree 3: Naives Algo 4: Logistic Regression Listen the episode on all podcast platform and share your feedback as comments here Do check the episode on various platform follow me on instagram https://www.instagram.com/podcasteramit Apple https://podcasts.apple.com/us/podcast/id1544510362 Huhopper Platform https://hubhopper.com/podcast/tech-stories/318515 Amazon https://music.amazon.com/podcasts/2fdb5c45-2016-459e-ba6a-3cbae5a1fa4d Spotify https://open.spotify.com/show/2GhCrAjQuVMFYBq8GbLbwa

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Credit Assignment Problem, published by Abram Demski on the AI Alignment Forum. This post is eventually about partial agency. However, it's been a somewhat tricky point for me to convey; I take the long route. Epistemic status: slightly crazy. I've occasionally said "Everything boils down to credit assignment problems." What I really mean is that credit assignment pops up in a wide range of scenarios, and improvements to credit assignment algorithms have broad implications. For example: Politics. When politics focuses on (re-)electing candidates based on their track records, it's about credit assignment. The practice is sometimes derogatorily called "finger pointing", but the basic computation makes sense: figure out good and bad qualities via previous performance, and vote accordingly. When politics instead focuses on policy, it is still (to a degree) about credit assignment. Was raising the minimum wage responsible for reduced employment? Was it responsible for improved life outcomes? Etc. Economics. Money acts as a kind of distributed credit-assignment algorithm, and questions of how to handle money, such as how to compensate employees, often involve credit assignment. In particular, mechanism design (a subfield of economics and game theory) can often be thought of as a credit-assignment problem. Law. Both criminal law and civil law involve concepts of fault and compensation/retribution -- these at least resemble elements of a credit assignment process. Sociology. The distributed computation which determines social norms involves a heavy element of credit assignment: identifying failure states and success states, determining which actions are responsible for those states and who is responsible, assigning blame and praise. Biology. Evolution can be thought of as a (relatively dumb) credit assignment algorithm. Ethics. Justice, fairness, contractualism, issues in utilitarianism. Epistemology. Bayesian updates are a credit assignment algorithm, intended to make high-quality hypotheses rise to the top. Beyond the basics of Bayesianism, building good theories realistically involves identifying which concepts are responsible for successes and failures. This is credit assignment. Another big area which I'll claim is "basically credit assignment" is artificial intelligence. In the 1970s, John Holland kicked off the investigation of learning classifier systems. John Holland had recently invented the Genetic Algorithms paradigm, which applies an evolutionary paradigm to optimization problems. Classifier systems were his attempt to apply this kind of "adaptive" paradigm (as in "complex adaptive systems") to cognition. Classifier systems added an economic metaphor to the evolutionary one; little bits of thought paid each other for services rendered. The hope was that a complex ecology+economy could develop, solving difficult problems. One of the main design issues for classifier systems is the virtual economy -- that is, the credit assignment algorithm. An early proposal was the bucket-brigade algorithm. Money is given to cognitive procedures which produce good outputs. These procedures pass reward back to the procedures which activated them, who similarly pass reward back in turn. This way, the economy supports chains of useful procedures. Unfortunately, the bucket-brigade algorithm was vulnerable to parasites. Malign cognitive procedures could gain wealth by activating useful procedures without really contributing anything. This problem proved difficult to solve. Taking the economy analogy seriously, we might want cognitive procedures to decide intelligently who to pay for services. But, these are supposed to be itty bitty fragments of our thought process. Deciding how to pass along credit is a very complex task. Hence the need for a pre-specified solution such as bucke...

Day 5, and I've stopped writing code in order to get back on the research bandwagon! My next task is to build a classifier that takes in the arrays of keypoint data, and understands what they mean in terms of a posture. Head to dev90x.com to join the telegram chat!

Kosj Yamoah (Moffitt Cancer Center) describes the work he presented at ASCO 2021.

Salam and Hi, In this episode, I am talking about the measure classifier performance. What are the element or statistic that used in measuring the classifier performance? There are Accuracy, Precision, Sensitivity, Specificity and F-Score or F1-Score. Hope this helps! Thanks, MNA

Kosj Yamoah (Moffitt Cancer Center) describes the work he presented at ASCO 2021.

Professor Sara Van de Geer, ETH Zürich, gives the Distinguished Speaker Seminar on Thursday 29th April 2021 for the Department of Statistics.

#ddpm #diffusionmodels #openai GANs have dominated the image generation space for the majority of the last decade. This paper shows for the first time, how a non-GAN model, a DDPM, can be improved to overtake GANs at standard evaluation metrics for image generation. The produced samples look amazing and other than GANs, the new model has a formal probabilistic foundation. Is there a future for GANs or are Diffusion Models going to overtake them for good? OUTLINE: 0:00 - Intro & Overview 4:10 - Denoising Diffusion Probabilistic Models 11:30 - Formal derivation of the training loss 23:00 - Training in practice 27:55 - Learning the covariance 31:25 - Improving the noise schedule 33:35 - Reducing the loss gradient noise 40:35 - Classifier guidance 52:50 - Experimental Results Paper (this): https://arxiv.org/abs/2105.05233 Paper (previous): https://arxiv.org/abs/2102.09672 Code: https://github.com/openai/guided-diff... Abstract: We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for sample quality using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128×128, 4.59 on ImageNet 256×256, and 7.72 on ImageNet 512×512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.85 on ImageNet 512×512. We release our code at this https URL Authors: Alex Nichol, Prafulla Dhariwal Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-ki... BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

durée : 00:03:07 - Géopolitique - par : Pierre  Haski  - C'est une note de la CIA rédigée six semaines après l'assassinat du journaliste saoudien Jamal Khashoggi en 2018, et qui détermine que l'ordre est venu du Prince héritier Mohamed Ben Salman. Trump avait refusé de la rendre publique, l'administration Biden a fait savoir qu'elle le ferait. Tensions en perspective.

durée : 00:03:07 - Géopolitique - par : Pierre  Haski  - C'est une note de la CIA rédigée six semaines après l'assassinat du journaliste saoudien Jamal Khashoggi en 2018, et qui détermine que l'ordre est venu du Prince héritier Mohamed Ben Salman. Trump avait refusé de la rendre publique, l'administration Biden a fait savoir qu'elle le ferait. Tensions en perspective.

MultinomalNB

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.10.366567v1?rss=1 Authors: van 't Hof, S. R., van Oudenhove, L., Klein, S., Reddan, M. C., Kragel, P. A., Stark, R., Wager, T. D. Abstract: Sexual stimuli processing is a key element in the repertoire of human affective and motivational states. Previous neuroimaging studies of sexual stimulus processing have revealed a complicated mosaic of activated regions, leaving unresolved questions about their sensitivity and specificity to sexual stimuli per se, generalizability across individuals, and potential utility as neuromarkers for sexual stimulus processing. In this study, data on sexual, negative, non-sexual positive, and neutral images from Wehrum et al. (2013) (N = 100) were re-analyzed with multivariate Support Vector Machine models to create the Brain Activation-based Sexual Image Classifier (BASIC) model. This model was tested for sensitivity, specificity, and generalizability in cross-validation (N = 100) and an independent test cohort (N = 18; Kragel et al. 2019). The BASIC model showed highly accurate performance (94-100%) in classifying sexual versus neutral or nonsexual affective images in both datasets. Virtual lesions and test of individual large-scale networks (e.g. 'visual' or 'attention' networks) show that these individual networks are neither necessary nor sufficient to capture sexual stimulus processing. These findings suggest that brain responses to sexual stimuli constitute a category of mental event that is distinct from general affect and involves multiple brain networks. It is, however, largely conserved across individuals, permitting the development of neuromarkers for sexual processing in individual persons. Future studies could assess performance of BASIC to a broader array of affective/motivational stimuli and link brain responses with physiological and subjective measures of sexual arousal. Copy rights belong to original authors. Visit the link for more info

Prosegue il viaggio dell’eroe (?) di Alex Raccuglia verso la creazione di un sistema condiviso per la gestione di campagne di classificazione delle immagini.Prima che pensiate che Alex si è montato un po’ la testa, di ravanello, ascoltate cosa ha da dirvi…TechnoPillzFlusso di coscienza digitale.Vieni a chiacchierare sul riot: https://t.me/TechnoPillzRiotContribuisci alla Causa andando su: http://runtimeradio.it/ancheio/

We all consume media on a daily basis from a variety of sources. That is almost entirely unavoidable. This means that we place a level of trust in those that provide our news that the information supplied to us is truthful, reliable, and valid. Is that really the case though? (spoiler: the answer is no). In light of the 2016 referendum on the UKs membership of the EU, I set out on a project to write a classifier that would be able to determine whether a news article in the British media has a pro-remain (in the EU) or pro-leave bias. I aimed to use the most modern data science tools at the time, which would surely give me the best results. Right? ...Right? In the end, I built something that sucked, and I’m going to tell you about the journey of excitement, horror, pain, misery and finally acceptance that I went on while developing my models.   Presenter: Rory How  

We all consume media on a daily basis from a variety of sources. That is almost entirely unavoidable. This means that we place a level of trust in those that provide our news that the information supplied to us is truthful, reliable, and valid. Is that really the case though? (spoiler: the answer is no). In light of the 2016 referendum on the UKs membership of the EU, I set out on a project to write a classifier that would be able to determine whether a news article in the British media has a pro-remain (in the EU) or pro-leave bias. I aimed to use the most modern data science tools at the time, which would surely give me the best results. Right? ...Right? In the end, I built something that sucked, and I’m going to tell you about the journey of excitement, horror, pain, misery and finally acceptance that I went on while developing my models.   Presenter: Rory How  

Se volete partecipare alla realizzazione del modello, andate qui:https://ulti.media/shot-classifier/Il (video) podcast di Final Cut Pro Radio di cui sono stato ospite:https://youtu.be/yQ6kLxIWHPkAbbiamo parlato anche di:https://twitter.com/alex4dTechnoPillzFlusso di coscienza digitale.Vieni a chiacchierare sul riot: https://t.me/TechnoPillzRiotContribuisci alla Causa andando su: http://runtimeradio.it/ancheio/

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.19.345090v1?rss=1 Authors: Wang, Y., Hannon, E., Grant, O. A., Gorrie-Stone, T. J., Kumari, M., Mill, J., Zhai, X., McDonald-Maier, K. D., Schalkwyk, L. C. Abstract: Sex is an important covariate of epigenome-wide association studies due to its strong influence on DNA methylation patterns across numerous genomic positions. Nevertheless, many samples on the Gene Expression Omnibus (GEO) frequently lack a sex annotation or are incorrectly labelled. Considering the influence that sex imposes on DNA methylation patterns, it is necessary to ensure that methods for filtering poor samples and checking of sex assignment are accurate and widely applicable. In this paper, we presented a novel method to predict sex using only DNA methylation density signals, which can be readily applied to almost all DNA methylation datasets of different formats (raw IDATs or text files with only density signals) uploaded to GEO. We identified 4345 significantly (p < 0.01) sex-associated CpG sites present on both 450K and EPIC arrays, and constructed a sex classifier based on the two first components of PCAs from the two sex chromosomes. The proposed method is constructed using whole blood samples and exhibits good performance across a wide range of tissues. We further demonstrated that our method can be used to identify samples with sex chromosome aneuploidy, this function is validated by five Turner syndrome cases and one Klinefelter syndrome case. The proposed method has been integrated into the wateRmelon Bioconductor package. Copy rights belong to original authors. Visit the link for more info

The paper summary for the widely used explainability method, LIME. Learn how it works and why you should or should not use it.

En mi caso, y supongo que a ti también te sucederá lo mismo, todos tus archivos los descargas a un directorio de tu Home llamado Descargas o si tienes la instalación en inglés Downloads. Salvo que seas una persona ordenada y meticulosa, y cada vez que descargues algo lo lleves al sitio que toque. Por ejemplo, las fotografías a un directorio donde guardes las fotografías, los documentos a su directorio correspondiente, y así sucesivamente. Pero aún así, es muy probable que posteriormente, todavía tengas que hacer trabajo adicional. ¿No existe una solución para organizar tus archivos de forma automática? Easy File Organizer. Por supuesto que Easy File Organizer no es la única opción para organizar tus archivos. Ni mucho menos. De hecho, ya dediqué un episodio del podcast a esto, en concreto, el episodio 56 sobre organizar tus archivos para ser mas productivo. Sin embargo, en este episodio quiero hablarte de una herramienta para que tu mismo la pruebes, pero quiero ir un paso mas allá. Y es que este es uno de esos episodios del podcast que mas me gustan. Porque no solo te muestro una herramienta para que seas mas productivo, sino que te indico como te la puedes fabricar tu mismo. Me ha gustado este concepto de fabricar. Así en este nuevo episodio del podcast, te voy a indicar una opción para organizar tus archivos automágicamente, con tus propios conocimientos. Organizar tus archivos automágicamente En episodios anteriores del podcast Como te decía en la introducción, en el episodio 56 del podcast te indiqué algunas opciones que tienes a tu alcance para ser mas organizado con tus archivos. El objetivo principal, era que el directorio de descargas, dejará de ser una habitación de Diógenes, con decenas o cientos de descargas, de archivos, de documentos, imágenes o vídeos. Pero no solo se trata de tener tu directorio de descargas ordenado, sino también de que tu no inviertas ni un segundo de tu tiempo en hacerlo, o al menos que inviertas el menor tiempo posible. Esto como todo lo que se refiere a productividad, tiene una contrapartida, y es que tienes que hacer una inversión, en conocimiento y en tiempo. conocimiento, porque tienes que aprender a utilizar la herramienta que selecciones.tiempo, tanto para elegir la herramienta que mejor se adecue a tus necesidades, como para aprender a manejarla. En ese episodio 56, te hablé de herramientas como Classifier, Organize my Files, Organizer. Pero, también te di la pista para hacerlo de forma totalmente manual. Eso si, solo fue, una pista. En este episodio del podcast, voy a ir un paso más allá y te voy a mostrar como hacerlo, utilizando dos herramientas completamente distintas. ... Más informació en las notas del podcast sobre organizar tus archivos automágicamente o un hazlo tu mismo

En mi caso, y supongo que a ti también te sucederá lo mismo, todos tus archivos los descargas a un directorio de tu Home llamado Descargas o si tienes la instalación en inglés Downloads. Salvo que seas una persona ordenada y meticulosa, y cada vez que descargues algo lo lleves al sitio que toque. Por ejemplo, las fotografías a un directorio donde guardes las fotografías, los documentos a su directorio correspondiente, y así sucesivamente. Pero aún así, es muy probable que posteriormente, todavía tengas que hacer trabajo adicional. ¿No existe una solución para organizar tus archivos de forma automática? Easy File Organizer. Por supuesto que Easy File Organizer no es la única opción para organizar tus archivos. Ni mucho menos. De hecho, ya dediqué un episodio del podcast a esto, en concreto, el episodio 56 sobre organizar tus archivos para ser mas productivo. Sin embargo, en este episodio quiero hablarte de una herramienta para que tu mismo la pruebes, pero quiero ir un paso mas allá. Y es que este es uno de esos episodios del podcast que mas me gustan. Porque no solo te muestro una herramienta para que seas mas productivo, sino que te indico como te la puedes fabricar tu mismo. Me ha gustado este concepto de fabricar. Así en este nuevo episodio del podcast, te voy a indicar una opción para organizar tus archivos automágicamente, con tus propios conocimientos. Organizar tus archivos automágicamente En episodios anteriores del podcast Como te decía en la introducción, en el episodio 56 del podcast te indiqué algunas opciones que tienes a tu alcance para ser mas organizado con tus archivos. El objetivo principal, era que el directorio de descargas, dejará de ser una habitación de Diógenes, con decenas o cientos de descargas, de archivos, de documentos, imágenes o vídeos. Pero no solo se trata de tener tu directorio de descargas ordenado, sino también de que tu no inviertas ni un segundo de tu tiempo en hacerlo, o al menos que inviertas el menor tiempo posible. Esto como todo lo que se refiere a productividad, tiene una contrapartida, y es que tienes que hacer una inversión, en conocimiento y en tiempo. conocimiento, porque tienes que aprender a utilizar la herramienta que selecciones.tiempo, tanto para elegir la herramienta que mejor se adecue a tus necesidades, como para aprender a manejarla. En ese episodio 56, te hablé de herramientas como Classifier, Organize my Files, Organizer. Pero, también te di la pista para hacerlo de forma totalmente manual. Eso si, solo fue, una pista. En este episodio del podcast, voy a ir un paso más allá y te voy a mostrar como hacerlo, utilizando dos herramientas completamente distintas. ... Más informació en las notas del podcast sobre organizar tus archivos automágicamente o un hazlo tu mismo

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.24.264267v1?rss=1 Authors: Zhang, Q., Liu, P., Han, Y., Zhang, Y., Wang, X., Yu, B. Abstract: DNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075, 93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive ability to predict DBPs. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.18.256594v1?rss=1 Authors: Lu, B., Li, H.-X., Chang, Z.-K., Li, L., Chen, N.-X., Zhu, Z.-C., Zhou, H.-X., Fan, Z., Yang, H., Chen, X., Yan, C.-G. Abstract: Beyond detecting brain damage or tumors with magnetic resonance brain imaging, little success has been attained on identifying individual differences, e.g., sex or brain disorders. The current study aims to build an industrial-grade brain imaging-based classifier to infer individual differences using deep learning/transfer learning on big data. We pooled 34 datasets to constitute the largest brain magnetic resonance image sample to date (85,721 samples from 50,876 participants), and then applied a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, to build an industrial-grade sex classifier. We achieved 94.9% accuracy in cross-dataset-validation, i.e., the model can classify the sex of a participant with brain structural imaging data from anybody and any scanner with about 95% accuracy. We then explored the potential of a deep convolutional network to objectively diagnose brain disorders. Using transfer learning, the model fine-tuned to Alzheimers Disease (AD) achieved 88.4% accuracy in leave-sites-out five-fold cross-validation on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset and 86.1% accuracy for a direct test on an unseen independent dataset (OASIS). When directly testing this AD classifier on brain images of mild cognitive impairment (MCI) patients, 64.2% who finally converted into AD were predicted as AD, versus 25.9% who did not convert into AD (during the ADNI data collection period, but might convert in the future) were predicted as AD. The AD classifier also achieved high specificity in direct testing on other brain disorder datasets. Occlusion tests showed that hypothalamus, superior vermis, thalamus, amygdala and limbic system areas played critical roles in predicting sex, and hippocampus, parahippocampal gyrus, putamen and insula were crucial for predicting AD. Finally, the transfer learning framework failed to achieve practical accuracy for psychiatric disorders, which remain open questions for future studies. We openly shared our preprocessed data, trained model, code and framework, as well as built an online predicting website (http://brainimagenet.org:8088) for whomever is interested in testing our classifier with brain imaging data from anybody and from any scanner. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.04.228536v1?rss=1 Authors: Haque, H. M. F., Arifin, F., Adilina, S., Jani, M. R., Shatabda, S. Abstract: The information of a cell is primarily contained in Deoxyribonucleic Acid (DNA). There is a flow of information of DNA to protein sequences via Ribonucleic acids (RNA) through transcription and translation. These entities are vital for the genetic process. Recent developments in epigenetic also show the importance of the genetic material and knowledge of their attributes and functions. However, the growth in known attributes or functionalities of these entities are still in slow progression due to the time consuming and expensive in vitro experimental methods. In this paper, we have proposed an ensemble classification algorithm called SubFeat to predict the functionalities of biological entities from different types of datasets. Our model uses a feature subspace based novel ensemble method. It divides the feature space into sub-spaces which are then passed to learn individual classifier models and the ensemble is built on this base classifiers that uses a weighted majority voting mechanism. SubFeat tested on four datasets comprising two DNA, one RNA and one protein dataset and it outperformed all the existing single classifiers and as well as the ensemble classifiers. SubFeat is made available as a Python-based tool. We have made the package SubFeat available online along with a user manual. It is freely accessible from here: https://github.com/fazlulhaquejony/SubFeat. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.24.219097v1?rss=1 Authors: Acera Mateos, P., Balboa, R. F., Easteal, S., Eyras, E., Patel, H. R. Abstract: Viral co-infections occur in COVID-19 patients, potentially impacting disease progression and severity. However, there is currently no dedicated method to identify viral co-infections in patient RNA-seq data. We developed PACIFIC, a deep-learning algorithm that accurately detects SARS-CoV-2 and other common RNA respiratory viruses from RNA-seq data. Using in silico data, PACIFIC recovers the presence and relative concentrations of viruses with >99% precision and recall. PACIFIC accurately detects SARS-CoV-2 and other viral infections in 63 independent in vitro cell culture and patient datasets. PACIFIC is an end-to-end tool that enables the systematic monitoring of viral infections in the current global pandemic. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.07.192138v1?rss=1 Authors: Thanjavur, K., Babul, A., Foran, B., Bielecki, M., Gilchrist, A., Hristopulos, D. T., Brucar, L. R., Virji-Babul, N. Abstract: Concussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and recovering individuals are more prone to suffer additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, concussion management faces two significant challenges: there are no objective, clinically accepted, brain-based approaches for determining (i) whether an athlete has suffered a concussion, and (ii) when the athlete has recovered. Diagnosis is based on clinical testing and self-reporting of symptoms and their severity. Self-reporting is highly subjective and symptoms only indirectly reflect the underlying brain injury. Here, we introduce a deep learning Long Short Term Memory (LSTM)-based recurrent neural network that is able to distinguish between healthy and acute post-concussed adolescent athletes using only a short (i.e. 90 seconds long) sample of resting state EEG data as input. The athletes were neither required to perform a specific task nor subjected to a stimulus during data collection, and the acquired EEG data was neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and tested on data from 27 male, adolescent athletes with sports related concussion, bench marked against 35 healthy, adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of >90% and its ensemble-median Area Under the Curve (AUC) corresponds to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state EEG data. It represents a key step towards the development of an easy-to-use, brain-based, automatic classification of concussion at an individual level. Copy rights belong to original authors. Visit the link for more info

We are super pumped to have Jiu-Jitsu superstar Lachlan Giles on the show. Lachlan is a second degree BJJ black belt. At ADCC 2019 in the Absolute division, Lachlan as one of the lightest competitors (under 77kg) submitted 3 world class heavyweights (Kaynan Duarte, Patrick Gaudio and Mahamed Aly) all by heel hook. At Kinektic Invitational 1, a five vs five grappling event, Lachlan single-handedly swept the entire team, submitting them all, 4 by heel hook and 1 armbar. He is the 2017 IBJJF No-Gi World Championship Bronze Medalist, 2014 Australian Jiu-Jitsu Grand Prix Champion, multiple time Pan Pacific and Victorian Jiu Jitsu Champion. He has represented Australia three times at ADCC, the most prestigious submission grappling competition in the world, represented Australia three times at the World Pro Championships and competed at the Eddie Bravo Invitational where he submitted former ADCC Champion Rani Yahya. He also works as a physiotherapist and in 2016 completed his PhD.  Please go and support Lachlan, go to his website below and purchase some or all of his instructional videos, they are concise and packed full of world class instruction to take your game to the next level. For a chance to win an Origin Don't Tread on Me rash guard and some Origin/Jocko supplements go to our website and look for the popup or the sign up button to submit your email for our newsletter.  This will enter you in the drawing on 3 July 2020.  www.originmaine.com/  Enter EVOSEC10 for 10% off at checkout.   Visit www.Tenicor.com to check out the Velo 4 holster that the EvoSec crew carry daily, enter EVOSEC for 10% off at checkout.  Lachlan Giles website: http://lachlangiles.net/ The EvoSec crew also talk about some current events and hit on there accountability for the week and prescribe the new drill of the week which will be half of the IDPA 5x5, this is to focus on the two toughest parts of the drill to isolate and improve.  1.  String 2: 10 yards from the holster, doesn't have to be concealed, on the signal draw and fire strong hand only 5 rounds to the official IDPA target center mass as fast as possible.  2.  String 4: 10 yards, From the holster, draw and fire freestyle/two handed 4 shots to the chest, 1 to the head. Full classifier:  https://www.idpa.com/wp-content/uploads/2018/09/IDPA_5x5_Classifier.pdf Lachlan Giles website: http://lachlangiles.net/ Lanny Bassham book as discussed on the show: With Winning In Mind https://www.amazon.com/dp/B004XD1M20/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1    

Here you will learn Thai most common Thai classifiers and from the context of our story.

This week we are joined by Kyunghyun Cho. He is an associate professor of computer science and data science at New York University, a research scientist at Facebook AI Research and a CIFAR Associate Fellow. On top of this he also co-chaired the recent ICLR 2020 virtual conference.We talk about a variety of topics in this weeks episode including the recent ICLR conference, energy functions, shortcut learning and the roles popularized Deep Learning research areas play in answering the question “What is Intelligence?”.Underrated ML Twitter: https://twitter.com/underrated_mlKyunghyun Cho Twitter: https://twitter.com/kchonyc?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5EauthorPlease let us know who you thought presented the most underrated paper in the form below:https://forms.gle/97MgHvTkXgdB41TC8Links to the papers:“Shortcut “Learning in Deep Neural Networks” - https://arxiv.org/pdf/2004.07780.pdf"Bayesian Deep Learning and a Probabilistic Perspective of Generalization” - https://arxiv.org/abs/2002.08791"Classifier-agnostic saliency map extraction" - https://arxiv.org/abs/1805.08249“Deep Energy Estimator Networks” - https://arxiv.org/abs/1805.08306“End-to-End Learning for Structured Prediction Energy Networks” - https://arxiv.org/abs/1703.05667“On approximating nabla f with neural networks” - https://arxiv.org/abs/1910.12744“Adversarial NLI: A New Benchmark for Natural Language Understanding“ - https://arxiv.org/abs/1910.14599“Learning the Difference that Makes a Difference with Counterfactually-Augmented Data” - https://arxiv.org/abs/1909.12434“Learning Concepts with Energy Functions” - https://openai.com/blog/learning-concepts-with-energy-functions/

Aasaanai.com Tweets Classifier with Sentiment analysis See acast.com/privacy for privacy and opt-out information.

Improving the decision tree

Information gain and less entropy

I talk about parameter timing and valuation . I recommend data camp

Hablo sobre el rally de Virginia, Primer classifier del año en RL shooting club. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

Ever wonder how to automatically detect language from a script? How does Google do it? Ever wonder how Amazon knows whether you are searching for a product or a SKU on its search bar? We look into character-based text classifiers in this episode. We cover 2 types of models. First is the bag-of-words models such as Naive Bayes, logistic regression and vanilla neural network. Second we cover sequence models such as LSTMs and how to prepare your characters for the LSTMs including things like one-hot encoding, padding, creating character embeddings and then feeding these into LSTMs. We also cover how to set up and compile these sequence models. Thanks for listening, and if you find this content useful, please leave a review and consider supporting this podcast from the link below. --- Send in a voice message: https://anchor.fm/the-data-life-podcast/message Support this podcast: https://anchor.fm/the-data-life-podcast/support

Today we’re joined by Laurence Watson, Co-Founder and CTO of Plentiful Energy and a former data scientist at Carbon Tracker. In our conversation, we discuss: • Carbon Tracker's goals, and their report “Nowhere to hide: Using satellite imagery to estimate the utilisation of fossil fuel power plants”. • How they're using computer vision to process satellite images of coal plants, including how the images are labeled •Various challenges with the scope and scale of this project, including dealing with varied time zones and imbalanced training classes. The complete show notes can be found at twimlai.com/talk/277. Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! Early-bird registration ends on 6/28!

Sven explain how he discovered and engineered the features to his computational model for metal stability predictions

While some cleared defense contractors perform non-technical services, other cleared contractors conduct derivative classification in the performance of their contracts. Derivative classification in general terms includes, paraphrasing, incorporating, restating or regenerating classified information into a new form. Since contractors are not performing original classification, most of their work would involve using classified sources to create new classified products.Support the show (http://www.redbikepublishing.com)

Today's spam filters are advanced data driven tools. They rely on a variety of techniques to effectively and often seamlessly filter out junk email from good email. Whitelists, blacklists, traffic analysis, network analysis, and a variety of other tools are probably employed by most major players in this area. Naturally content analysis can be an especially powerful tool for detecting spam. Given the binary nature of the problem ( or ) its clear that this is a great problem to use machine learning to solve. In order to apply machine learning, you first need a labelled training set. Thankfully, many standard corpora of labelled spam data are readily available. Further, if you're working for a company with a spam filtering problem, often asking users to self-moderate or flag things as spam can be an effective way to generate a large amount of labels for "free". With a labeled dataset in hand, a data scientist working on spam filtering must next do feature engineering. This should be done with consideration of the algorithm that will be used. The Naive Bayesian Classifer has been a popular choice for detecting spam because it tends to perform pretty well on high dimensional data, unlike a lot of other ML algorithms. It also is very efficient to compute, making it possible to train a per-user Classifier if one wished to. While we might do some basic NLP tricks, for the most part, we can turn each word in a document (or perhaps each bigram or n-gram in a document) into a feature. The Naive part of the Naive Bayesian Classifier stems from the naive assumption that all features in one's analysis are considered to be independent. If  and  are known to be independent, then . In other words, you just multiply the probabilities together. Shh, don't tell anyone, but this assumption is actually wrong! Certainly, if a document contains the word algorithm, it's more likely to contain the word probability than some randomly selected document. Thus, , violating the assumption. Despite this "flaw", the Naive Bayesian Classifier works remarkably will on many problems. If one employs the common approach of converting a document into bigrams (pairs of words instead of single words), then you can capture a good deal of this correlation indirectly. In the final leg of the discussion, we explore the question of whether or not a Naive Bayesian Classifier would be a good choice for detecting fake news.    

"Ten Judge's Secrets To Improve Your Competition Results"   Time Stamps and Contact Details for this Episode are available on  www.HorseChats.com/JoanneVerikios2   Music - BenSound.com   Interviewed by Glenys Cox

Catherine Plano is here today with Joanne Verikios. Joanne Verikios is an accomplished author, trusted health and lifestyle consultant, experienced horse breeder and trainer, award-winning athlete, speaker and successful real estate investor. Having received her first pony at the age of nine, Joanne's earliest ambition in life was to be a bareback horse rider in a circus. Although she never ran off to join the circus, after working her way through the Pony Club ranks, she earned the qualification of Pony Club instructor at the age of sixteen. One of the many highlights of her early riding career was being a member of the Downs Pony Club team that won the Duke of Edinburgh Pony Club Games Championship in 1972. While working at the Australian Public Service, Joanne qualified for an Australian Owner Trainer Permit to train and race Thoroughbreds. She also pursued her love of horses by founding the Highborn Warmblood Stud, where she was Stud Manager for sixteen years. The horses Joanne bred went on to win both under saddle and in breed classes, including Royal Show Championships. They included the stallion, Highborn Powerlifter, who passed Colt Selection and Performance Testing with flying colours. In addition to serving on several horse sport committees and officiating at many shows and events, Joanne is a past Federal President and Federal Registrar of the Australian Warmblood Horse Association, which she continues to serve as a Classifier and Classifier Trainer, Judge and Judge Trainer and National Assessment Tour Australian representative. In recognition of her outstanding contribution and commitment to the Association for over 30 years, Joanne was granted Honorary Life membership in 2015. Joanne was also an Australian Powerlifting Champion, holding State, National, and Commonwealth records. Twice, Joanne represented Australia at the Women's World Powerlifting Championships and was ranked seventh in the world both times. In peak condition, Joanne was able to deadlift more than triple her bodyweight. Her feats of strength are recorded in the 1989 and 1991 Guinness Book of Records with Australian Supplement. Joanne has published articles in many equine publications including Hoofbeats, Horsezone, Horses & People, The Horse Magazine, Australian Horse and Rider Yearbook, and Hoofs & Horns. She has also published articles in several recreational and sports magazines, including Bellydance Oasis, SPORTZlife, and The Pump. Find Out More About Joanne Verikios Joanne's Website Winning Horsemanship on Facebook Joanne Verikios on Facebook Joanne on Twitter @lifestyletolove Joanne Verikios on Instagram @winninghorsemanship Joanne on LinkedIn Joanne at USANA Are you ready to be inspired? Tune in to this powerful conversation! Interviewed by: Catherine Plano Subscribe: iTunes | Stitcher | RSS

Catherine Plano is here today with Joanne Verikios. Joanne Verikios is an accomplished author, trusted health and lifestyle consultant, experienced horse breeder and trainer, award-winning athlete, speaker and successful real estate investor. Having received her first pony at the age of nine, Joanne's earliest ambition in life was to be a bareback horse rider in a circus. Although she never ran off to join the circus, after working her way through the Pony Club ranks, she earned the qualification of Pony Club instructor at the age of sixteen. One of the many highlights of her early riding career was being a member of the Downs Pony Club team that won the Duke of Edinburgh Pony Club Games Championship in 1972. While working at the Australian Public Service, Joanne qualified for an Australian Owner Trainer Permit to train and race Thoroughbreds. She also pursued her love of horses by founding the Highborn Warmblood Stud, where she was Stud Manager for sixteen years. The horses Joanne bred went on to win both under saddle and in breed classes, including Royal Show Championships. They included the stallion, Highborn Powerlifter, who passed Colt Selection and Performance Testing with flying colours. In addition to serving on several horse sport committees and officiating at many shows and events, Joanne is a past Federal President and Federal Registrar of the Australian Warmblood Horse Association, which she continues to serve as a Classifier and Classifier Trainer, Judge and Judge Trainer and National Assessment Tour Australian representative. In recognition of her outstanding contribution and commitment to the Association for over 30 years, Joanne was granted Honorary Life membership in 2015. Joanne was also an Australian Powerlifting Champion, holding State, National, and Commonwealth records. Twice, Joanne represented Australia at the Women's World Powerlifting Championships and was ranked seventh in the world both times. In peak condition, Joanne was able to deadlift more than triple her bodyweight. Her feats of strength are recorded in the 1989 and 1991 Guinness Book of Records with Australian Supplement. Joanne has published articles in many equine publications including Hoofbeats, Horsezone, Horses & People, The Horse Magazine, Australian Horse and Rider Yearbook, and Hoofs & Horns. She has also published articles in several recreational and sports magazines, including Bellydance Oasis, SPORTZlife, and The Pump. Find Out More About Joanne Verikios Joanne's Website Winning Horsemanship on Facebook Joanne Verikios on Facebook Joanne on Twitter @lifestyletolove Joanne Verikios on Instagram @winninghorsemanship Joanne on LinkedIn Joanne at USANA Are you ready to be inspired? Tune in to this powerful conversation! Interviewed by: Catherine Plano Subscribe: iTunes | Stitcher | RSS

I was standing in line at the coffee shop this morning when I got a text from a friend asking if I had seen the new IDPA rulebook yet. I haven’t been an IDPA member for several years, so I didn’t get the memo that the new rulebook had been posted. Here’s the new rulebook .PDF if you would like to peruse it I started getting text messages with all of the new changes, and I have to say, I’m shocked to see that they listened to the membership and have made a lot of positive changes in the new rulebook. 4.1/4.2 No more “Vickers”/”Limited Vickers” scoring. Stages are still scored the same, but it’s called limited/unlimited now. This sounds like a little thing, but I can tell you the “Vickers” name was confusing to me as a new shooter years ago. No more stuffing magazines on the clock. I’ve never come across this in a match, but there was a video from a Major match a few years back where the stage required shooters to stuff rounds in their magazine at the buzzer, and then shoot the stage. IDPA is a shooting game, not a magazine stuffing game. 8.2.4 Compact Carry Pistol Division is a go! 4.1″ or less, 8+1 capacity, and ESP division rules. 8.2.6 Back Up Gun (BUG) Division is REQUIRED for all Tier 1 matches per the new rulebook! As we mentioned last week, this is really exciting for us. It gives the new shooter who only has his small carry pistol a place to shoot. Sure, under the current rules they could shoot their LCP in SSP division, but putting new shooters with LCP’s in the same division as guys running Glock 34’s isn’t very confidence inspiring for them. 9.2.1 “Shooting and completing a Sanctioned IDPA match in the last twelve months (without a DQ or DNF) also counts as shooting a Classifier in the division in which the shooter competed.” So, if you shoot at least one Major IDPA match every 12 months, you don’t have to worry about shooting a classifier for the next 12 months. This is a great move, because whenever a club runs a classifier match it’s always a nightmare (tons of people show up, it gets bogged down, etc). 4.14 Hits on Non-Threat Targets: These are now scored PER-HIT on the non-threat target, instead of per target. Example: If I shoot a non-threat target 10 times, I’ll get 10, 5 second penalties, where under the old rules it would only be the one 5 second penalty per non-threat target. 8.8.1 Knee-pads don’t have to be concealed under your pants anymore. Soft-shell knee pads are now legal for the game, but if you’re going to use them, you have to wear them for the entire match. Simple rule, allows the folks that need them to wear them, and keeps the gamers from using them as an advantage only on stages where they are needed. 8.8.4.3 No more tactical flashlight finger rings: “Rings or straps that go around any part of the shooter’s body (finger, palm, wrist, etc.) are not allowed.” 8.8.2 Cleated shoes are now legal, as long as the cleats are soft enough to dig into with a fingernail. 8.6.2.7 “Bullets Out” magazine pouches are not allowed. 7 Very specific rules for disabled shooters. It’s nice that they laid this out so people have a resource for what’s legal and what isn’t. So there you have it, a bunch of changes, and the only one that still irritates me is the outright ban on bullets out magazine pouches. I’ll deal with it, and I might just become an IDPA member again in the future. Let’s hope they continue to make good decisions like this. Contact (919) 295-6128

Background: In biometric practice, researchers often apply a large number of different methods in a "trial-and-error" strategy to get as much as possible out of their data and, due to publication pressure or pressure from the consulting customer, present only the most favorable results. This strategy may induce a substantial optimistic bias in prediction error estimation, which is quantitatively assessed in the present manuscript. The focus of our work is on class prediction based on high-dimensional data (e. g. microarray data), since such analyses are particularly exposed to this kind of bias. Methods: In our study we consider a total of 124 variants of classifiers (possibly including variable selection or tuning steps) within a cross-validation evaluation scheme. The classifiers are applied to original and modified real microarray data sets, some of which are obtained by randomly permuting the class labels to mimic non-informative predictors while preserving their correlation structure. Results: We assess the minimal misclassification rate over the different variants of classifiers in order to quantify the bias arising when the optimal classifier is selected a posteriori in a data-driven manner. The bias resulting from the parameter tuning (including gene selection parameters as a special case) and the bias resulting from the choice of the classification method are examined both separately and jointly. Conclusions: The median minimal error rate over the investigated classifiers was as low as 31% and 41% based on permuted uninformative predictors from studies on colon cancer and prostate cancer, respectively. We conclude that the strategy to present only the optimal result is not acceptable because it yields a substantial bias in error rate estimation, and suggest alternative approaches for properly reporting classification accuracy.