Attri’s Generative AI Wiki: Comprehensive Guide on AI, Foundation Models, LLM & More
Paying ChatGPT users have access to GPT-4, which can write more naturally and fluently than the model that previously powered ChatGPT. In addition to GPT-4, OpenAI recently connected ChatGPT to the internet with plugins available in alpha to users and developers on the waitlist. One of the major business uses for generative AI is to automate or accelerate the development of software.
Generative AI allows people to maintain privacy using avatars instead of images. In addition, it can also help companies opt for impartial recruitment practices and research to present unbiased results. Enhancing images from old movies, upscaling them to 4k and beyond, generating more frames per second (e.g. 60 fps instead of 23) and adding color to black and white movies. Here is a video of a professional cameraman and photographer using Topaz’s video enhance AI to upscale low-quality videos. Nvidia created NGP Instant NeRF code for quickly transforming pictures into 3D images and content.
ChatGPT app is now available in 11 more countries
But since ChatGPT came off the starting block in late 2022, new iterations of gen AI technology have been released several times a month. In March 2023 alone, there were six major steps forward, including new customer relationship management solutions and support for the financial services industry. These breakthroughs notwithstanding, we are still in the early days of using generative AI to create readable text and photorealistic genrative ai stylized graphics. Early implementations have had issues with accuracy and bias, as well as being prone to hallucinations and spitting back weird answers. Still, progress thus far indicates that the inherent capabilities of this type of AI could fundamentally change business. Going forward, this technology could help write code, design new drugs, develop products, redesign business processes and transform supply chains.
Some might speculate that that imbalance is leading to a catastrophic collapse of the system, much as we see with poorly tuned GANs. They are useful in dimensionality reduction; that is, the vector serving as a hidden representation compresses the raw data into a smaller number of salient dimensions. Autoencoders can be paired with a so-called decoder, which allows you to reconstruct input data based on its hidden representation, much as you would with a restricted Boltzmann machine. Generative AI generating code has the potential to improve developer productivity.
Video Prediction & Generation
At a high level, attention refers to the mathematical description of how things (e.g., words) relate to, complement and modify each other. The breakthrough technique could also discover relationships, or hidden orders, between other things buried in the data that humans might have been unaware of because they were too complicated to express or discern. Google was another early leader in pioneering transformer AI techniques for processing language, proteins and other types of content. Microsoft’s decision to genrative ai implement GPT into Bing drove Google to rush to market a public-facing chatbot, Google Bard, built on a lightweight version of its LaMDA family of large language models. Google suffered a significant loss in stock price following Bard’s rushed debut after the language model incorrectly said the Webb telescope was the first to discover a planet in a foreign solar system. Meanwhile, Microsoft and ChatGPT implementations also lost face in their early outings due to inaccurate results and erratic behavior.
Dependency on previous token computations prevented them from being able to parallelize the attention mechanism. As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny. The results depend on the quality of the model—as we’ve seen, ChatGPT’s outputs so far appear superior to those of its predecessors—and the match between the model and the use case, or input. ChatGPT may be getting all the headlines now, but it’s not the first text-based machine learning model to make a splash. OpenAI’s GPT-3 and Google’s BERT both launched in recent years to some fanfare.
Additionally, diffusion models are also categorized as foundation models, because they are large-scale, offer high-quality outputs, are flexible, and are considered best for generalized use cases. However, because of the reverse sampling process, running foundation models is a slow, lengthy process. Generative AI has massive implications for business leaders—and many companies have already gone live with generative AI initiatives. In some cases, companies are developing custom generative AI model applications by fine-tuning them with proprietary data.
Both consist of multiple encoder blocks piled on top of one another, with the output of each becoming the input for the next. By iterating encoder layers, transformers utilize sequence-to-sequence learning, where a sequence of tokens predicts the next component of the output. Through attention or self-attention mechanisms, transformers can identify subtle ways that even distant data elements in a series create dependencies with one another. These techniques decipher the context around items within the input sequence, so rather than treating each element separately, the transformer attempts to bring meaning to each.
And automation of knowledge work is now in sight
Developed in the 1950s and 1960s, the first neural networks were limited by a lack of computational power and small data sets. It was not until the advent of big data in the mid-2000s and improvements in computer hardware that neural networks became practical for generating content. The Microsoft-backed think tank OpenAI has released a series of powerful natural language generation models under the name GPT (Generative Pre-trained Transformer). GPT-3 is a surprisingly powerful generative language model capable of emulating net new human speech in response to prompts.
I raised two kids and got a literature degree before I went into computer science, so I’m asking myself real questions about how educators measure success in a world where generative AI can write a pretty good eighth- or ninth-grade essay. Sure, or how hip-hop evolved in the Bronx with the use of the drum machine. That entire genre was advanced by this new backend tech development in music. A task-driven autonomous AI agent operates independently to achieve defined goals, adapt priorities, learn from previous actions, and execute without human intervention.
OpenAI announced the general availability of GPT-4
The Malaria No More charity and soccer star David Beckham used deep fake technology to translate his speech and facial movements into nine languages as part of an urgent appeal to end malaria worldwide. Microsoft released the chatbot TAY (thinking about you), which responded to questions submitted via Twitter. Users soon began tweeting inflammatory concepts to the chatbot, which quickly generated racist and sexually charged messages in response. Computer scientist and philosopher Judea Pearl introduced Bayesian network causal analysis, which provided statistical techniques for representing uncertainty that led to methods for generating content in a specific style, tone or length. Event analytics tool answers CX data queries using ChatGPTMixpanel aims to improve users’ CX strategy with its new generative AI-supported data query tool, which lets users type CX data-related questions and get answers in chart format. The Eliza chatbot created by Joseph Weizenbaum in the 1960s was one of the earliest examples of generative AI.
- Let’s say we’re trying to do something more banal than mimic the Mona Lisa.
- Generative AI refers to a set of deep-learning technologies that use existing content, such as text, audio, and images, to create new plausible content that previously would have required humans.
- Users soon began tweeting inflammatory concepts to the chatbot, which quickly generated racist and sexually charged messages in response.
- And the world was alerted to a new era of social engineering cyber attacks.
Elon Musk has expressed his concern about AI, but he has not expressed that concern simply enough, based on a clear analogy. Algorithms are learning faster than we are, just as we learn faster than the species we are driving to extinction. Both the generator and discriminator are often implemented as Convolutional Neural Networks (CNNs), in particular when they are designed to work with images.
The contest between the two neural networks takes the form of a zero-sum game, where one gain is at the other’s expense. The adversarial nature of GANs is due to the game theory scenario whereby the generator and discriminator compete. GANs can be considered successful when the generator begins to create samples convincing enough to fool both the discriminator and humans.