Generative AI: What Is It, Tools, Models, Applications and Use Cases

Generate an image from text using generative AI

Make extraordinary images from just a description using Text to image feature in Adobe Express. Architects could explore different building layouts and visualize them as a starting point for further refinement. Subsequent research into LLMs from Open AI and Google ignited the recent enthusiasm that has evolved into tools like ChatGPT, Google Bard and Dall-E.

  • Many, many iterations are required to get the models to the point where they produce interesting results, so automation is essential.
  • It can generate new art, music, and even realistic human faces that never existed before.
  • The more neural networks intrude on our lives, the more the areas of discriminative and generative modeling grow.
  • From data sourcing to prompt engineering and enhancement, we can help accelerate your AI initiatives.
  • The model then decodes the low-dimensional representation back into the original data.

The impact of generative models is wide-reaching, and its applications are only growing. Listed are just a few examples of how Yakov Livshits is helping to advance and transform the fields of transportation, natural sciences, and entertainment. 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.

Venture Capital Partners, PR Partner and Media Partners

While the results from generative AI can be intriguing and entertaining, it would be unwise, certainly in the short term, to rely on the information or content they create. The responses might also incorporate biases inherent in the content the model has ingested from the internet, but there is often no way of knowing whether that’s the case. Both of these shortcomings have caused major concerns regarding the role of generative AI in the spread of misinformation. However, there are plenty of other AI generators on the market that are just as good, if not more capable, and that can be used for different requirements.

generative ai

Gartner recommends connecting use cases to KPIs to ensure that any project either improves operational efficiency or creates net new revenue or better experiences. Yakov Livshits is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds. Generative AI models can also be prone to biases present in the training data. Another challenge is the interpretability of the generated results, as it can be difficult to understand and explain the decision-making process of the models. Generative AI focuses on generating new content or data based on existing patterns, while cognitive AI aims to simulate human intelligence by understanding, reasoning, and learning from data.

Machine Learning & Generative AI

With that data in the system, it is possible that if someone enters the right prompt, the AI could potentially use your company’s data in response to a query. His is a text-to-image generator developed by OpenAI that generates images or art based on descriptions or inputs from users. Encoder-only models like BERT power search engines and customer-service chatbots, including IBM’s Watson Assistant.

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.

AI music generators are the hottest trend in AI right now, and with good reason. Imagine using AI chatbots to handle customer service inquiries, providing immediate responses and support. Or using AI to transcribe audio, making content more accessible to a wider audience. Generative AI can even assist in writing, from drafting email responses and resumes to creating compelling marketing copy. As AI-generated content becomes more prevalent, AI detection tools are being developed to detect and flag such content. Publishers or individuals using AI-wholesale may experience great reputational damage, especially if the AI-generated content is not clearly labeled as such.

AI existential risk: Is AI a threat to humanity?

It enhances imaging techniques, accelerates drug development, predicts disease onset, generates synthetic medical data, automates documentation, and tailors treatments to individuals. However, ethical and regulatory considerations must be addressed to ensure responsible implementation. Generative AI has the potential to transform healthcare by improving diagnostics, treatment, and patient care. As a new technology that is constantly changing, many existing regulatory and protective frameworks have not yet caught up to generative AI and its applications. A major concern is the ability to recognize or verify content that has been generated by AI rather than by a human being. Another concern, referred to as “technological singularity,” is that AI will become sentient and surpass the intelligence of humans.

Generative AI focuses on creating original content and generating human-like responses, while Conversational AI aims to facilitate natural and engaging interactions with users. By combining these technologies, businesses can provide highly personalized and contextually relevant experiences to their customers. The synergy between Generative AI and Conversational AI opens up never-before-seen possibilities, enabling virtual assistants and chatbots to deliver more authentic, intelligent, and satisfying customer experiences. Generative AI is revolutionizing cybersecurity by detecting threats, analyzing malware, assessing vulnerabilities, enhancing user authentication, and providing realistic training simulations. This technology strengthens defenses against evolving cyber threats, safeguarding data and systems.

Generative AI vs Natural Language Processing vs Large Language Models

A good instruction prompt will deliver the desired results in one or two tries, but this often comes down to placing colons and carriage returns in the right place. A prompt that works beautifully on one model may not transfer to other models. Transformers processed words in a sentence all at once, allowing text to be processed in parallel, speeding up training.

generative ai

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. So, instead of paying attention to each word separately, the transformer attempts to identify the context that brings meaning to each word of the sequence. Both the encoder and the decoder in the transformer consist of multiple encoder blocks piled on top of one another.

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