GPT stands for Generative Pre-trained Transformer. It is a neural network machine learning model which is trained using data on the internet to generate any type of text. This sophisticated neural network is used to train large language models (LLMs) to simulate human communication. The model tracks words in a sequence, allowing it to learn both the context and meaning of the language. The GPT model focuses on text-only, allowing it to use artificial intelligence to analyze what the user is asking and effectively generate text.

Explaining a joke, as E.B. White once wrote, is like dissecting a frog: "the thing dies in the process and the innards are discouraging to any but the purely scientific mind." In fact, the large language model -- released on March 14 by OpenAI -- is surprisingly good at generating detailed explanations of why a joke is funny. And like its predecessor, ChatGPT, the AI can also generate jokes, though its go-to one-liners are simple and seem to have been scraped from the internet's corniest, punniest corners (Why don't scientists trust atoms? Because they make up everything!). GPT-4 seems better at explaining humor than its predecessor.

ChatGPT can give some impressive results, and also sometimes some very poor advice. But while it's free to talk with ChatGPT in theory, often you end up with messages about the system being at capacity, or hitting your maximum number of chats for the day, with a prompt to subscribe to ChatGPT Plus. Also, all of your queries are taking place on ChatGPT's server, which means that you need Internet and that OpenAI can see what you're doing. Fortunately, there are ways to run a ChatGPT-like LLM (Large Language Model) on your local PC, using the power of your GPU. The oobabooga text generation webui (opens in new tab) might be just what you're after, so we ran some tests to find out what it could -- and couldn't! Getting the webui running wasn't quite as simple as we had hoped, in part due to how fast everything is moving within the LLM space. There are the basic instructions in the readme, the one-click installers, and then multiple guides for how to build and run the LLaMa 4-bit models (opens in new tab).

The rise of ChatGPT has been nothing short of spectacular. Within two months of launch, the artificial intelligence (AI)-based application reached 100 million unique users. In January 2023 alone, ChatGPT registered about 590 million visits. In addition to AI, blockchain is another disruptive technology with increasing adoption. Decentralized protocols, applications and business models have matured and gained market traction since the Bitcoin (BTC) white paper was published in 2008.

On Tuesday, OpenAI announced the release of GPT-4, its latest, biggest language model, only a few months after the splashy release of ChatGPT. GPT-4 was already in action -- Microsoft has been using it to power Bing's new assistant function. The people behind OpenAI have written that they think the best way to handle powerful AI systems is to develop and release them as quickly as possible, and that's certainly what they're doing. Also on Tuesday, I sat down with Holden Karnofsky, the co-founder and co-CEO of Open Philanthropy, to talk about AI and where it's taking us. Karnofsky, in my view, should get a lot of credit for his prescient views on AI.

In this comprehensive guide, we'll explore the capabilities of Chat GPT4, a powerful AI language model developed by OpenAI, and its potential benefits for construction companies. By the end of this article, you'll clearly understand how this innovative technology can revolutionise construction businesses' operations.

Hype can be a dangerous thing. Too much of it can tank your shares, kill your product launch effectively and turn the excitement on its head. The wave of excitement around generative AI that OpenAI is riding has effectively become an introduction to LLMs for most of the world. When Altman first confirmed that OpenAI was in fact building the successor to its benchmark model GPT3, the AI community was excited. GPT3 was a state-of-the-art language model with 175 billion parameters – holding the record for the largest-ever AI model then. And since its release in 2020, speculation has been rife around GPT4.

The AI community has embraced multi-sensory or multi-modal approaches to advance this generation of AI models to resemble expected intelligent understanding. Combining language and imagery represents a familiar method for specific tasks like image captioning or generation from descriptions. This paper compares these monolithic approaches to a lightweight and specialized method based on employing image models to label objects, then serially submitting this resulting object list to a large language model (LLM). This use of multiple Application Programming Interfaces (APIs) enables better than 95% mean average precision for correct object lists, which serve as input to the latest Open AI text generator (GPT-4). To demonstrate the API as a modular alternative, we solve the problem of a user taking a picture of ingredients available in a refrigerator, then generating novel recipe cards tailored to complex constraints on cost, preparation time, dietary restrictions, portion sizes, and multiple meal plans. The research concludes that monolithic multimodal models currently lack the coherent memory to maintain context and format for this task and that until recently, the language models like GPT-2/3 struggled to format similar problems without degenerating into repetitive or non-sensical combinations of ingredients. For the first time, an AI chef or cook seems not only possible but offers some enhanced capabilities to augment human recipe libraries in pragmatic ways. The work generates a 100-page recipe book featuring the thirty top ingredients using over 2000 refrigerator images as initializing lists.

It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not match. Although transductive ZSL (TZSL) attempts to improve this by allowing the use of unlabelled examples from the unseen classes, there is still a high level of distribution shift. We propose a novel TZSL model (named as Bi-VAEGAN), which largely improves the shift by a strengthened distribution alignment between the visual and auxiliary spaces. The key proposal of the model design includes (1) a bi-directional distribution alignment, (2) a simple but effective L_2-norm based feature normalization approach, and (3) a more sophisticated unseen class prior estimation approach. In benchmark evaluation using four datasets, Bi-VAEGAN achieves the new state of the arts under both the standard and generalized TZSL settings. Code could be found at https://github.com/Zhicaiwww/Bi-VAEGAN

Building systems that are good for society in the face of complex societal effects requires a dynamic approach. Recent approaches to machine learning (ML) documentation have demonstrated the promise of discursive frameworks for deliberation about these complexities. However, these developments have been grounded in a static ML paradigm, leaving the role of feedback and post-deployment performance unexamined. Meanwhile, recent work in reinforcement learning has shown that the effects of feedback and optimization objectives on system behavior can be wide-ranging and unpredictable. In this paper we sketch a framework for documenting deployed and iteratively updated learning systems, which we call Reward Reports. Taking inspiration from various contributions to the technical literature on reinforcement learning, we outline Reward Reports as living documents that track updates to design choices and assumptions behind what a particular automated system is optimizing for. They are intended to track dynamic phenomena arising from system deployment, rather than merely static properties of models or data. After presenting the elements of a Reward Report, we discuss a concrete example: Meta's BlenderBot 3 chatbot. Several others for game-playing (DeepMind's MuZero), content recommendation (MovieLens), and traffic control (Project Flow) are included in the appendix.