Generative AI: Definition, Tools, Models, Benefits & More

The authors suggest using a 2×2 matrix to identify the use cases with the lowest risk and highest demand. I think that we are obsessed with whether you’re an optimist or whether you’re a pessimist. And from where I stand, we can very clearly see that with every step up in the scale of these large language models, they get more controllable. Generative AI is a broad term that can be used for any AI system whose primary function is to generate content. Language models with hundreds of billions of parameters, such as GPT-4 or PaLM, typically run on datacenter computers equipped with arrays of GPUs (such as Nvidia’s H100) or AI accelerator chips (such as Google’s TPU). These very large models are typically accessed as cloud services over the Internet.

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 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.

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Generative AI is an exciting field that has the potential to revolutionize the way we create and consume content. It can generate new art, music, and even realistic human faces that never existed before. One of the most promising aspects of Generative AI is its ability to create unique and customized products for various industries. For example, in the fashion industry, Generative AI can be used to create new and unique clothing designs. In contrast, in interior design, it can help generate new and innovative home decor ideas.

New use cases are being tested monthly, and new models are likely to be developed in the coming years. As generative AI becomes increasingly, and seamlessly, incorporated into business, society, and our personal lives, we can also expect a new regulatory climate to take shape. As organizations begin experimenting—and Yakov Livshits creating value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk. When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole.

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This type of training is known as supervised learning because a human is in charge of “teaching” the model what to do. Through machine learning, practitioners develop artificial intelligence through models that can “learn” from data patterns without human direction. The unmanageably huge volume and complexity of data (unmanageable by humans, anyway) that is now being generated has increased the potential of machine learning, as well as the need for it. A Bayesian inference-based probabilistic graphical model, VAE seeks to understand the underlying probability distribution of the training data so that it can quickly sample new data from that distribution. In VAEs, the encoders aim to represent data more effectively, whereas the decoders regenerate the original data set more efficiently. Popular applications of VAEs include anomaly detection for predictive maintenance, signal processing and security analytics applications.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Generative AI models combine various AI algorithms to represent and process content. Similarly, images are transformed into various visual elements, also expressed as vectors. One caution is that these techniques can also encode the biases, racism, deception and puffery contained in the training data. Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.

The newest versions of Codex can now identify bugs and fix mistakes in its own code — and even explain what the code does — at least some of the time. The expressed goal of Microsoft is not to eliminate human programmers, but to make tools like Codex or CoPilot “pair programmers” with humans to improve their speed and effectiveness. On the how—I mean, like, I’m not going to go into too many details because it’s sensitive.

For example, such breakthrough technologies as GANs and transformer-based algorithms. To understand the idea behind generative AI, we need to take a look at the distinctions between discriminative and generative modeling. In the intro, we gave a few cool insights that show the bright future of generative AI. The potential of generative AI and GANs in particular is huge because this technology can learn to mimic any distribution of data. That means it can be taught to create worlds that are eerily similar to our own and in any domain.

Video generation

GPT, on the other hand, is a unidirectional transformer-based model primarily used for text generation tasks such as language translation, summarization, and content creation. ChatGPT can produce what one commentator called a “solid A-” essay comparing theories of nationalism from Benedict Anderson and Ernest Gellner—in ten seconds. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. Other generative AI models can produce code, video, audio, or business simulations. The first machine learning models to work with text were trained by humans to classify various inputs according to labels set by researchers. One example would be a model trained to label social media posts as either positive or negative.