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Amazon digs into ambient and generalizable intelligence at re:MARS

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Many, if not most, AI experts maintain that artificial general intelligence (AGI) is still many decades away, if not longer. And the AGI debate has been heating up over the past couple of months. However, according to Amazon, the route to "generalizable intelligence" begins with ambient intelligence. And it says that future is unfurling now.


What is Artificial Intelligence? How does AI work, Types, Trends and Future of it?

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Let's take a detailed look. This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.


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