The noise around conversational AI--and AI in general--is truly, truly exploding. I reckon 2023 would be a pathbreaking year for consumer-grade AI! Here are a few updates from just the beginning of this year: - CNET has started adding AI-written content: https://bit.ly/3Zz2cBM Apple unveils suite of AI-voiced audiobooks https://bit.ly/3W3nCny But one of the most astute observations I have come across on ChatGPT is this, via Fortune (https://bit.ly/3CFv9BS): "Like many, I had tended to view A.I. as a tool for calculating reliable, data-based predictions, point solutions, or probabilities.

API development can be a time-consuming and complex task, but it doesn't have to be. With the advancements in natural language processing and machine learning, we now have access to tools like ChatGPT that can greatly simplify the process. In this blog post, we'll be taking a step-by-step approach to using ChatGPT to generate a Flask REST API. We'll cover everything from setting up…

Large-scale Pre-Trained Language Models (PTLMs) capture knowledge from massive human-written data which contains latent societal biases and toxic contents. In this paper, we leverage the primary task of PTLMs, i.e., language modeling, and propose a new metric to quantify manifested implicit representational harms in PTLMs towards 13 marginalized demographics. Using this metric, we conducted an empirical analysis of 24 widely used PTLMs. Our analysis provides insights into the correlation between the proposed metric in this work and other related metrics for representational harm. We observe that our metric correlates with most of the gender-specific metrics in the literature. Through extensive experiments, we explore the connections between PTLMs architectures and representational harms across two dimensions: depth and width of the networks. We found that prioritizing depth over width, mitigates representational harms in some PTLMs. Large-scale Pre-Trained Language Models (PTLMs) such as BERT (Devlin et al., 2019) and GPT models (Radford et al., 2019; Brown et al., 2020) have recently achieved great success in varieties of Natural Language Processing (NLP) tasks. These large-scale PTLMs capture knowledge from massively labeled and unlabeled human written data which can potentially contain harmful contents and societal biases. The goal of a language model is to estimate the probability of a sequence of words for the given language. One can argue that, when the data from which the model was trained on is different than the desired behavior of the model at a semantic level, representational harms are present.

Since the release of the ChatGPT interactive AI assistant it has been surprising to see some of the snide, passive-aggressive reactions from some (not all) members of the software engineering community, in the style of "it's just inference from bad data". Let's get real, folks, it is truly game-changing. Basically, if you need a program element and can describe that need, the assistant will generate it for you. There is no particular restriction on the programming language that you choose, as long as its description and enough examples are available somewhere. The code will be pretty good.

Talk of ChatGPT was in. Emphasis on bitcoin was out.

Ukraine is on a list of nations unable to access OpenAI's ChatGPT. The war-torn country would like this to change, with the country's vice prime minister appealing to the chatbot's owners to provide access.

While there are some concerns about how generative AI chatbots such as ChatGPT can be used maliciously -- to craft phishing campaigns or write malware -- several companies are harnessing the power of conversational AI technology to enhance their product capabilities, including for security. ChatGPT, a large language model (LLM) developed by OpenAI, uses GPT 3 LLM and relies on large test data sets culled from multiple sources. ChatGPT, which can understand human language, provides detailed answers to simple questions and can handle complex tasks such as creating documents and writing code in response to user queries. It's an example of how conversational AI can be used to organize large volumes of information and enhance user experience and communications. For instance, a conversational AI tool -- whether that's ChatGPT or something else -- could serve as the back end of an information concierge that automates the use of threat intelligence in enterprise support, according to IT research and advisory firm Into-Tech Research.

MINNEAPOLIS – Since its public launch in November, ChatGPT has taken the world by storm. The free artificial intelligence chatbot has attracted the attention of giant companies like Microsoft, who will reportedly cut a potential $10 billion deal with the company who owns ChatGPT, OpenAI. "What's unique about this is that it presents information in a way that a human would," says Gene Munster, a managing partner for Loup Ventures, a technology research and investment firm. "It essentially has the ability to think." Artificial intelligence research has been happening for decades, but only recently has it gotten good enough that OpenAI released its ChatGPT chatbot to the public via an easy-to-use website.

You may have heard of a little thing called ChatGPT recently. It's quite simply an incredible tool - and one that opens a lot of possibilities These possibilities, however, have raised a lot of questions too - namely, if this bot is so damn good, can it actually do my job for me? There's a lot of debate right now amongst writers, authors, researchers, scientists, and well, just about anyone who works in an office job as to whether this tool - and AI as a whole - is a threat or not. There's a faint idea, or notion, that automation took all the blue-collar jobs, and now AI is coming for the white-collar ones. I don't believe, however, that there's much evidence for that based on how we work today.

ChatGPT is an artificial intelligence tool that can generate text on any topic. It is already causing problems because it can create text that is inaccurate or inappropriate. People are using it to create malware, cheat in school, and spam on dating apps. It is also being used to phish and scam people. The AI tool is very cool but can also be used for nefarious purposes.