Background: What is a Generative Model? Machine Learning

For a quick, one-hour introduction to generative AI, consider enrolling in Google Cloud’s Introduction to Generative AI. Learn what it is, how it’s used, and why it is different from other machine learning methods. Generative AI’s popularity is accompanied by concerns of ethics, misuse, and quality control. Because it is trained on existing sources, including those that are unverified on the internet, generative AI can provide misleading, inaccurate, and fake information. Even when a source is provided, that source might have incorrect information or may be falsely linked. AI generators like ChatGPT and DALL-E2 are gaining worldwide popularity.

A subset of generative modeling, deep generative modeling uses deep neural networks to learn the underlying distribution of data. These models can develop novel samples that have never been seen before by producing new samples that are similar to the input data but not exactly the same. Deep generative models come in many forms, including VAEs, GANs and autoregressive models. These models have shown to be promising in a wide range of applications, including text-to-image synthesis, music generation and drug discovery. Generative AI (GenAI) is a type of Artificial Intelligence that can create a wide variety of data, such as images, videos, audio, text, and 3D models.

What are the risks of generative AI?

3 min read – The process of introducing new features to the US Open digital experience has never been smoother than it was this year, thanks to watsonx. For more information, see how generative AI can be used to maximize experiences, decision-making and business value, and how IBM Consulting brings a valuable and responsible approach to AI. Since they are so new, we have yet to see the long-tail effect of generative AI models. This means there are some inherent risks involved in using them—some known and some unknown.

Other kinds of AI, in distinction, use techniques including convolutional neural networks, recurrent neural networks and reinforcement learning. These breakthroughs notwithstanding, we are still in the early days of using generative AI to create readable text and photorealistic 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.

Build new solutions and identify new use cases with the Generative AI Innovation Center

The transformer can generate text, computer code and even protein structures. Once the model is trained, it can be used to generate new data by sampling from the learned distribution. The generated data can be similar to the original data set, but with some variations or noise. For example, a data set containing images of horses could be used to build a model that can generate a new image of a horse that has never existed but still looks almost realistic. This is possible because the model has learned the general rules that govern the appearance of a horse. The more neural networks intrude on our lives, the more the areas of discriminative and generative modeling grow.

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 model

It has become essential for safeguarding personal data due to companies’ rising collection of that information. Businesses need accurate information to improve their products and services, but getting it may be at the expense of their consumers’ privacy. However, this issue may be resolved using creative, generative AI algorithms. and utilize generative AI to produce artificially generated information from real data, ensuring user privacy while keeping data authenticity for evaluating and creating machine learning models. These models have been applied in various fields such as computer vision, natural language processing and music generation.


As opposed to building custom NLP models for each domain, foundation models are enabling enterprises to shrink the time to value from months to weeks. In client engagements, IBM Consulting is seeing up to 70% reduction in time to value for NLP use cases such as call center transcript summarization, analyzing reviews and more. Large Language Models (LLMs) were explicitly trained on large amounts of text data for NLP tasks Yakov Livshits and contained a significant number of parameters, usually exceeding 100 million. They facilitate the processing and generation of natural language text for diverse tasks. Each model has its strengths and weaknesses and the choice of which one to use depends on the specific NLP task and the characteristics of the data being analyzed. Choosing the correct LLM to use for a specific job requires expertise in LLMs.

For example, a discriminative classifier like a decision
tree can label an instance
without assigning a probability to that label. Such a classifier would still be
a model because the distribution of all predicted labels would model the real
distribution of labels in the data. A transformer is made up of multiple transformer Yakov Livshits blocks, also known as layers. ChatGPT’s ability to generate humanlike text has sparked widespread curiosity about generative AI’s potential. For example, business users could explore product marketing imagery using text descriptions. They could further refine these results using simple commands or suggestions.

OpenAI’s GPT implementation powers it, and its second version, Dall-E 2, allows users to generate imagery in diverse styles based on human prompts. According to the company, it trained Stable Audio with “a dataset consisting of over 800,000 audio files containing music, sound effects, and single-instrument stems” and text metadata from stock music licensing company AudioSparx. By partnering with a licensing company, Stability AI says it has permission to use copyrighted material.

Generative AI datasets could face a reckoning The AI Beat – VentureBeat

Generative AI datasets could face a reckoning The AI Beat.

Posted: Mon, 21 Aug 2023 07:00:00 GMT [source]

When we say this, we do not mean that tomorrow machines will rise up against humanity and destroy the world. But due to the fact that generative AI can self-learn, its behavior is difficult to control. For example, in March 2022, a deep fake video of Ukrainian President Volodymyr Zelensky telling his people to surrender was broadcasted on Ukrainian news that was hacked. Though it could be seen to the naked eye that the video was fake, it got to social media and caused a lot of manipulation. Transformers work through sequence-to-sequence learning where the transformer takes a sequence of tokens, for example, words in a sentence, and predicts the next word in the output sequence.

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