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AI: The emerging Artificial General Intelligence debate

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Since Google's artificial intelligence (AI) subsidiary DeepMind published a paper a few weeks ago describing a generalist agent they call Gato (which can perform various tasks using the same trained model) and claimed that artificial general intelligence (AGI) can be achieved just via sheer scaling, a heated debate has ensued within the AI community. While it may seem somewhat academic, the reality is that if AGI is just around the corner, our society--including our laws, regulations, and economic models--is not ready for it. Indeed, thanks to the same trained model, generalist agent Gato is capable of playing Atari, captioning images, chatting, or stacking blocks with a real robot arm. It can also decide, based on its context, whether to output text, join torques, button presses, or other tokens. As such, it does seem a much more versatile AI model than the popular GPT-3, DALL-E 2, PaLM, or Flamingo, which are becoming extremely good at very narrow specific tasks, such as natural language writing, language understanding, or creating images from descriptions.


Language Models

Communications of the ACM

A transformer has strong language representation ability; a very large corpus contains rich language expressions (such unlabeled data can be easily obtained) and training large-scale deep learning models has become more efficient. Therefore, pre-trained language models can effectively represent a language's lexical, syntactic, and semantic features. Pre-trained language models, such as BERT and GPTs (GPT-1, GPT-2, and GPT-3), have become the core technologies of current NLP. Pre-trained language model applications have brought great success to NLP. "Fine-tuned" BERT has outperformed humans in terms of accuracy in language-understanding tasks, such as reading comprehension.8,17 "Fine-tuned" GPT-3 has also reached an astonishing level of fluency in text-generation tasks.3


Natural Language Processing

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By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you've completed course 3 - Natural Language Processing with Sequence Models - before starting this course. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization.


GitHub - salesforce/OmniXAI: OmniXAI: A Library for eXplainable AI

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OmniXAI (short for Omni eXplainable AI) is a Python machine-learning library for explainable AI (XAI), offering omni-way explainable AI and interpretable machine learning capabilities to address many pain points in explaining decisions made by machine learning models in practice. OmniXAI includes a rich family of explanation methods integrated in a unified interface, which supports multiple data types (tabular data, images, texts, time-series), multiple types of ML models (traditional ML in Scikit-learn and deep learning models in PyTorch/TensorFlow), and a range of diverse explaination methods including "model-specific" and "model-agnostic" methods (such as feature-attribution explanation, counterfactual explanation, gradient-based explanation, etc). For practitioners, OmniXAI provides an easy-to-use unified interface to generate the explanations for their applications by only writing a few lines of codes, and also a GUI dashboard for visualization for obtaining more insights about decisions. The following table shows the supported explanation methods and features in our library. We will continue improving this library to make it more comprehensive in the future, e.g., supporting more explanation methods for vision, NLP and time-series tasks.


Sentient? Google LaMDA feels like a typical chat bot

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LaMDA is a software program that runs on Google TPU chips. Like the classic brain in a jar, some would argue the code and the circuits don't form a sentient entity because none of it engages in life. Google engineer Blake Lemoine caused controversy last week by releasing a document that he had circulated to colleagues in which Lemoine urged Google to consider that one of its deep learning AI programs, LaMDA, might be "sentient." Google replied by officially denying the likelihood of sentience in the program, and Lemoine was put on paid administrative leave by Google, according to an interview with Lemoine by Nitasha Tiku of The Washington Post. There has been a flood of responses to Lemoine's claim by AI scholars. University of Washington linguistics professor Emily Bender, a frequent critic of AI hype, told Tiku that Lemoine is projecting anthropocentric views onto the technology. "We now have machines that can mindlessly generate words, but we haven't learned how to stop imagining a mind behind them," Bender told Tiku. In an interview with MSNBC's Zeeshan Aleem, AI scholar Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute, observed that the concept of sentience has not been rigorously explored. Mitchell concludes the program is not sentient, however, "by any reasonable meaning of that term, and the reason is because I understand pretty well how the system works."


Sentient? Google LaMDA feels like a typical chat bot

ZDNet

LaMDA is a software program that runs on Google TPU chips. Like the classic brain in a jar, some would argue the code and the circuits don't form a sentient entity because none of it engages in life. Google engineer Blake Lemoine caused controversy last week by releasing a document that he had circulated to colleagues in which Lemoine urged Google to consider that one of its deep learning AI programs, LaMDA, might be "sentient." Google replied by officially denying the likelihood of sentience in the program, and Lemoine was put on paid administrative leave by Google, according to an interview with Lemoine by Nitasha Tiku of The Washington Post. There has been a flood of responses to Lemoine's claim by AI scholars. University of Washington linguistics professor Emily Bender, a frequent critic of AI hype, told Tiku that Lemoine is projecting anthropocentric views onto the technology. "We now have machines that can mindlessly generate words, but we haven't learned how to stop imagining a mind behind them," Bender told Tiku. In an interview with MSNBC's Zeeshan Aleem, AI scholar Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute, observed that the concept of sentience has not been rigorously explored. Mitchell concludes the program is not sentient, however, "by any reasonable meaning of that term, and the reason is because I understand pretty well how the system works."


An easy tutorial about Sentiment Analysis with Deep Learning and Keras

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Get comfortable, it's going to take you several minutes to read but hopefully, you'll stick with me along the whole article. I'm gonna walk you through a foundational task that you as data scientist/machine learning engineer must know how to perform because at some point of your career you'll be required to do so. In the context of this article, I'll assume you have a basic understanding of what I'm going to talk in the next lines. I'll be stacking layers of concepts as I move forward, keeping a very low-level language -- don't worry if you fell a little lost between lines, later I will probably clarify your doubts. The main idea is for you to understand what I'll be explaining.


Neuron – Machine Learning & AI Startups HTML Template

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There are 24 unique pages with 3 different home pages included where you can find most type of pages. This template is suitable for any type of Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, Natural Language Processing (NLP), Face Recognition, Speech Analysis, Self Driving Car & any Startup Business Websites. This template include less file so you can change template color easily without any hassle. It's 100% fluid responsive & fits any device perfectly. By using this template you can easily build your own website just you like it.! Features: 03 Unique Awesome Home Pages 20 HTML Templates Available Product Demo pa Read more


Announcing .NET 7 Preview 5

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Today we released .NET 7 Preview 5. This preview of .NET 7 includes improvements to Generic Math which make the lives of API authors easier, a new Text Classification API for ML.NET that adds state-of-the-art deep learning techniques for natural language processing, various improvements to source code generators and a new Roslyn analyzer and fixer for RegexGenerator and multiple performance improvements in the areas of CodeGen, Observability, JSON serialization / deserialization and working with streams. If you're on macOS, we recommend using the latest Visual Studio 2022 for Mac preview. Now, let's get into some of the latest updates in this release. The goal of observability is to help you better understand the state of your application as scale and technical complexity increases. The exposed methods can be used in performance critical scenarios to enumerate the Tag objects without any extra allocations and with fast items access.


Artificial Intelligence Tutorial for Beginners

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This Artificial Intelligence tutorial provides basic and intermediate information on concepts of Artificial Intelligence. It is designed to help students and working professionals who are complete beginners. In this tutorial, our focus will be on artificial intelligence, if you wish to learn more about machine learning, you can check out this tutorial for complete beginners tutorial of Machine Learning. Through the course of this Artificial Intelligence tutorial, we will look at various concepts such as the meaning of artificial intelligence, the levels of AI, why AI is important, it's various applications, the future of artificial intelligence, and more. Usually, to work in the field of AI, you need to have a lot of experience. Thus, we will also discuss the various job profiles which are associated with artificial intelligence and will eventually help you to attain relevant experience. You don't need to be from a specific background before joining the field of AI as it is possible to learn and attain the skills needed. While the terms Data Science, Artificial Intelligence (AI) and Machine learning fall in the same domain and are connected, they have their specific applications and meaning. Simply put, artificial intelligence aims at enabling machines to execute reasoning by replicating human intelligence. Since the main objective of AI processes is to teach machines from experience, feeding the right information and self-correction is crucial. The answer to this question would depend on who you ask. A layman, with a fleeting understanding of technology, would link it to robots. If you ask about artificial intelligence to an AI researcher, (s)he would say that it's a set of algorithms that can produce results without having to be explicitly instructed to do so. Both of these answers are right.