What is Generative AI? Definition & Examples

A group from Stanford recently tried to “distill” the capabilities of OpenAI’s large language model, GPT-3.5, into its Alpaca chatbot, built on a much smaller model. The researchers asked GPT-3.5 to generate thousands of paired instructions and responses, and through instruction-tuning, used this AI-generated data to infuse Alpaca with ChatGPT-like conversational skills. Since then, a herd of similar models with names like Vicuna and Dolly have landed on the internet.

• This can result in lower labor costs, greater operational efficiency and new insights into how well certain business processes are — or are not — performing.
• And vice versa, numbers closer to 1 show a higher likelihood of the prediction being real.
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• Musenet – can produce songs using up to ten different instruments and music in up to 15 different styles.
• Most of today’s foundation models are large language models (LLMs) trained on natural language.
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Experts say that their interest is motivated by the latest improvements in this area and real benefits that generative AI can bring across multiple industries. Algorithms can be regarded as some of the essential building blocks that make up artificial intelligence. AI uses various algorithms that act in tandem to find a signal among the noise of a mountain of data and find paths to solutions that humans would not be capable of. AI makes use of computer algorithms to impart autonomy to the data model and emulate human cognition and understanding. Video is a set of moving visual images, so logically, videos can also be generated and converted similar to the way images can. If we take a particular video frame from a video game, GANs can be used to predict what the next frame in the sequence will look like and generate it.

Marketing and advertising

He recommends you feed your data through a generative AI to subtly deliver different versions of an ad that speaks to people where they are. While there is a hype cycle that generative AI will go through, these are tools that companies and brands can start using today to figure out revenue streams and find audiences in ways that work for them. Generative image AIs can create art from scratch, modify pictures (like removing objects), or add objects to a picture. Neural Radiance Fields (AI NeRF) are a new type of AI that produces 3D models from 2D pictures.

However, it also can introduce new risks, be they legal, financial or reputational. Many generative models, including those powering ChatGPT, can spout information that sounds authoritative but isn’t true (sometimes called “hallucinations”) or is objectionable and biased. Generative models can also inadvertently ingest information that’s personal or copyrighted in their training data and output it later, creating unique challenges for privacy and intellectual property laws.

Increases efficiency and productivity

That said, the music may change according to the atmosphere of the game scene or depending on the intensity of the user’s workout in the gym. So, if you show the model an image from a completely different class, for example, a flower, it can tell that it’s a cat with some level of probability. In this case, the predicted output (ŷ) is compared to the expected output (y) from the training dataset. Based on the comparison, we can figure out how and what in an ML pipeline should be updated to create more accurate outputs for given classes.

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.

Overall, generative AI has the potential to significantly impact a wide range of industries and applications and is an important area of AI research and development. On the flip side, there’s a continued interest in the emergent capabilities that arise when a model reaches a certain size. It’s not just the model’s architecture that causes these skills to emerge but its scale. Examples include glimmers of logical reasoning and the ability to follow instructions. Some labs continue to train ever larger models chasing these emergent capabilities. Encoder-decoder models, like Google’s Text-to-Text Transfer Transformer, or T5, combine features of both BERT and GPT-style models.

At the moment, there is no fact-checking mechanism built into this technology. Models don’t have any intrinsic mechanism to verify their outputs, and users don’t necessarily do it either. The speed, efficiency and ease of use permitted by generative AI is what makes Yakov Livshits it such an appealing tool to so many companies today. It’s why companies like Salesforce, Microsoft and Google are all scrambling to incorporate generative AI across their products, and why businesses are eager to find ways to fold it into their operations.

And if a business or field involves code, words, images or sound, there is likely a place for generative AI. Looking ahead, some experts believe this technology could become just as foundational to everyday life as the cloud, smartphones and the internet itself. Regardless of the approach, generative AI models must be evaluated after each iteration to determine how closely their generated data matches the training data. Teams can adjust parameters, add more training data and even introduce new data sets to accelerate the progress of generative AI models. In customer support, AI-driven chatbots and virtual assistants help businesses reduce response times and quickly deal with common customer queries, reducing the burden on staff.

Models can be applied to virtually any aspect of business, and developers are constantly finding new uses for the technology. Some current uses for AI models include chatbots and customer service, image, video, and music creation, drug research, marketing and advertising, architecture and engineering, and language translation. A streamlined pipeline is created to handle input data, process it through the generative model, and deliver the generated outputs. Transformers are another thing that played a big role in generative AI becoming mainstream. Sorry to disappoint you, but that doesn’t refer to the heroic Autobots of the media franchise.

Rather than being told specifically what to do every step of the way, generative AI is designed to create and innovate on its own, with minimal human intervention. The algorithms used in generative AI are trained on massive datasets and can create new, unique outputs based on the information that they’ve been fed. “The ability to connect generative tools to some of your data and your modeling systems is going to be incredibly exciting,” said Kaplan. Use generative AI to make variable data sets based on a specific volume of data you already have on your consumers or audience. You can also use generative AI to clean, filter, identify, and fill in data and even explore new insights.