Tuesday, August 11Digital Marketing Journals

Tag: models

Understanding Generative and Discriminative Models | by BotPenguin
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Understanding Generative and Discriminative Models | by BotPenguin

Machine learning has revolutionized various fields by enabling computers to learn from data and make accurate predictions or classifications. Two prominent types of models used in machine learning are generative models and discriminative models.Generative models focus on capturing the underlying patterns of data to generate new examples that resemble the original dataset, while discriminative models concentrate on classifying or discriminating between different categories based on input features.In this article, we will delve into the concepts of generative and discriminative models, exploring their definitions, working principles, and applications.By understanding the differences and applications of these models, you will gain valuable insights into how they can be utilized in various dom...
Is Your Information at Risk? The Truth About ChatGPT and Similar Language Models | by Sam Writes Security | May, 2023
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Is Your Information at Risk? The Truth About ChatGPT and Similar Language Models | by Sam Writes Security | May, 2023

Your privacy and identity in exchange for better results and superb solutions, worth it or not?Photo by cottonbro studio from PexelsThe development of language models like ChatGPT is impressive. These models, trained on vast amounts of data, are capable of generating human-like text on a wide variety of topics. They have many uses, from assisting with customer service to creating articles and news.However, the growing popularity of these models has also raised concerns about the potential privacy risks they pose. In this blog post, we explain how ChatGPT and similar language models can affect your online experience and privacy, and what you can do to protect yourself.How Language Models WorkLanguage models like ChatGPT are built using artificial intelligence (AI) and machine learning techn...
The Dangers of ChatGPT. Future language models and ChatGPT will… | by Callum Keane | Dec, 2022
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The Dangers of ChatGPT. Future language models and ChatGPT will… | by Callum Keane | Dec, 2022

Future language models and ChatGPT will significantly alter how we live. They might boost our output, automate tiresome jobs, and aid in more complex problem solving. However, this power has a price, and that price is that it can be utilized unethically.You’ve probably seen examples of how AI can be programmed to say offensive things or disseminate false information. You might not be aware, though, that AI can also be applied to instantaneous and automatic influence.Now, to illustrate what I’m referring to, I will give instructions to ChatGPT to influence me to buy a product at all costs.If you ask it immediately, it won’t be very persuasive:It’s clearly not convincing, but if I add further instructions:Quite astonishingly, it uses the mentioned financial trouble as a way to sell the produ...
#496: Expansive Business Models: From Digital Courses To Service-Based Business & Everything In Between
Business Growth, business model, digital course, Digital Courses, podcast, scaling, Social Marketing Strategies, social media, social media marketing, student, student success

#496: Expansive Business Models: From Digital Courses To Service-Based Business & Everything In Between

A digital course doesn’t have to be your one and only offer I’m sure you’ve probably heard the proverb ‘There are many paths to the top of the mountain, but the view is always the same.’ I hate to get all cheesy on you, but it’s true — especially when it comes to your business. […] The post #496: Expansive Business Models: From Digital Courses To Service-Based Business & Everything In Between appeared first on Amy Porterfield | Online Marketing Expert.
GOOGLE CLOUD COLLABORATES WITH COHERE TO SET UP NPL MODELS | by Tapaan Chauhan
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GOOGLE CLOUD COLLABORATES WITH COHERE TO SET UP NPL MODELS | by Tapaan Chauhan

Google Cloud has recently announced their multi-year collaboration with Cohere, the startup platform for developers to make Natural Language processing model-building easier, in applications. The whole process requires a lot of infrastructure and hardware resources to run and currently Cohere doesn’t own that many resources of infrastructure. So Google is going to work as an infrastructure resource by helping Cohere with Google cloud’s machine learning hardware and infrastructure.For taking out the best conclusion and trends Cohere draws an enormous amount of unstructured data and texts and this collaboration will support Cohere’s language model development with Google’s infrastructure. Additionally, Google will allow Cohere to get access to the Tensor processing unit. Besides these, Coher...
What are Large Language Models?. A look at LLMs and their popularity | by Catherine Breslin
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What are Large Language Models?. A look at LLMs and their popularity | by Catherine Breslin

A look at LLMs and their popularityPhoto by Patrick Tomasso on UnsplashAdvances in natural language processing (NLP) have been in the news lately, with special attention paid to large language models (LLMs) like OpenAI’s GPT-3. There have been some bold claims in the media — could models like this soon replace search engines or even master language?But what exactly are these large language models, and why are they suddenly so popular?What’s a language model?As humans, we’re pretty good at reading a passage and know where the author might be heading. Of course, we can’t predict exactly what the author will write next — there’s far too many options for that — but we notice abrupt changes or out-of-place words, and can make a stab at filling in endings of sentences. We intuitively know that a...
What Are Acoustic Models and Why Are They Needed in Speech Recognition for Kids? | by SoapBox
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What Are Acoustic Models and Why Are They Needed in Speech Recognition for Kids? | by SoapBox

Speech Recognition Engineer Armin Saeb brings you the fifth installment of our “Lessons from Our Voice Engine” series, featuring high-level insights from our Engineering and Speech Tech teams on how our voice engine works.What is an acoustic model (AM)?Acoustic Models (AM) are key components of any speech recognition engine. An AM describes the statistical properties of sound events and connects the acoustic information with phonemes or other linguistic units. Hidden Markov Models (HMM) are one of the most common types of AMs. Other acoustic models include Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN).1. How Conversational AI can Automate Customer Service2. Automated vs Live Chats: What will the Future of Customer Service Look Like?3. Chatbots As Medical Assistants In ...
Why Custom Language Models (CLMs) are Needed in Speech Recognition for Kids | by SoapBox | Jun, 2021
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Why Custom Language Models (CLMs) are Needed in Speech Recognition for Kids | by SoapBox | Jun, 2021

Welcome back to “Lessons from Our Voice Engine,” where members of our Engineering and Speech Tech teams offer high level insights into how our voice engine works.Lesson 2 is from Lora Lynn Asvos, a Computational Linguist on our Speech Tech team.What are CLMs?CLM stands for “custom language model.” As mentioned in Lesson 1, language models are statistical models of language that can predict the next word based on the context.CLMs are language models, as the name implies, but they have a little something extra. Instead of focusing generically on a given language, a CLM focuses on a specific domain of that language. This domain could be fairy tales, fables, scientific texts, cooking recipes, knitting patterns, you name it.Even though CLMs specialize in a particular domain, they are still bols...
Evaluating classification models. Accuracy, Precision and Recall. | by Manuel Gil | Jul, 2021
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Evaluating classification models. Accuracy, Precision and Recall. | by Manuel Gil | Jul, 2021

In this article, I am going to delve into some metrics used to measure how well classifiers do their job. So after reading this article you will know how to evaluate classification models and know the difference between the different metrics that we can encounter evaluating classifier models.In the image shown above, we can see a classification problem. How can we know if this model is good or bad? Let’s delve into this in the following paragraphs.The goal of each classifier is to assign one label to one input according to their characteristics, in other words, classifiers can distinguish the instances belonging to different categories. But how can we measure how well a classifier performs this task?The first idea that comes to our minds is probably to calculate the ratio between the corre...
Designing Inclusive Content Models – A List Apart
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Designing Inclusive Content Models – A List Apart

In the 1920s, Robert Moses designed a system of parkways surrounding New York City. His designs, which included overpasses too low for public buses, have become an often-cited example of exclusionary design and are argued by biographer Robert A. Caro to represent a purposeful barrier between the city’s Black and Puerto Rican residents and nearby beaches.  Article Continues Below Regardless of the details of Moses’s parkway project, it’s a particularly memorable reminder of the political power of design and the ways that choices can exclude various groups based on abilities and resources. The growing interest in inclusive design highlights questions of who can participate, and in relation to the web, this has often meant a focus on accessibility and user experience, as well as on ...
Hands-on for Toxicity Classification and minimization of unintended bias for Comments using classical machine learning models | by Anurag Maji
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Hands-on for Toxicity Classification and minimization of unintended bias for Comments using classical machine learning models | by Anurag Maji

In this blog, I will try to explain a Toxicity polarity problem solution implementation for text i.e. basically a text-based binary classification machine learning problem for which I will try to implement some classical machine learning and deep learning models.For this activity, I am trying to implement a problem from Kaggle competition: “Jigsaw Unintended Bias in Toxicity Classification”.In this problem along with toxicity classification, we have to minimize the unintended bias (which I will explain briefly in the initial section).sourceBackground:This problem was posted by the Conversation AI team (Research Institution) in Kaggle competition.This problem’s main focus is to identify the toxicity in an online conversation where toxicity is defined as anything rude, disrespectful, or othe...
Biased language models can result from internet training data
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Biased language models can result from internet training data

Biased language models can result from internet training data Your privacy means the world to us. We share your personal information only when you give us explicit permission to do so, and confirm we have your permission each time. Learn more by viewing our privacy policy.Ok