steven2358 awesome-generative-ai: A curated list of modern Generative Artificial Intelligence projects and services

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.

GrammarlyGo is Grammarly’s AI-powered content creation tool for brainstorming ideas, constructing outlines, drafting, and even giving your old work new life. If you’re looking for more coherent and engaging responses from your AI writing tool, Jasper might Yakov Livshits be your best bet. Jasper specializes in creating long-form content like blog articles, scripts, outlines, and more. New AI apps pop up at rates faster than you could ever imagine, but not every tool will reap the same benefits you may specifically need.

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It’s also about how people and businesses can use it to change their everyday jobs and creative work. This kind of AI lets systems learn and improve from experience without specific programming. Artificial Intelligence, or AI, is a broad term that refers to machines or software mimicking human intelligence.

Enterprises need a computing infrastructure that provides the performance, reliability, and scalability to deliver cutting-edge products and services while increasing operational efficiencies. NVIDIA-Certified Systems™ enables enterprises to confidently deploy hardware solutions that securely and optimally run their modern accelerated workloads—from desktop Yakov Livshits to data center to the edge. NVIDIA offers state-of-the-art community and NVIDIA-built foundation models, including GPT, T5, and Llama, providing an accelerated path to generative AI adoption. These models can be downloaded from Hugging Face or the NGC catalog, which allows users to test the models directly from the browser using AN AI playground.

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Text-based models, such as ChatGPT, are trained by being given massive amounts of text in a process known as self-supervised learning. Here, the model learns from the information it’s fed to make predictions and provide answers. ChatGPT has become extremely popular, accumulating more than one million users a week after launching. Many other companies have also rushed in to compete in the generative AI space, including Google, Microsoft’s Bing, and Anthropic. The buzz around generative AI is sure to keep on growing as more companies join in and find new use cases as the technology becomes more integrated into everyday processes.

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.

ChatGPT’s ability to generate humanlike text has sparked widespread curiosity about generative AI’s potential. A generative AI model starts by efficiently encoding a representation of what you want to generate. For example, a generative AI model for text might begin by finding a way to represent the words as vectors that characterize the similarity between words often used in the same sentence or that mean similar things.

This hands readers a unique opportunity to gain a comprehensive understanding of the generative AI market and the potential for new players to challenge established players like Google. OpenAI has the potential to become a massive business, earning a significant portion of all NLP category revenues as more killer apps are built — especially if their integration into Microsoft’s product portfolio goes smoothly. Given the huge usage of these models, large-scale revenues may not be far behind. Across app companies we’ve spoken with, there’s a wide range of gross margins — as high as 90% in a few cases but more often as low as 50-60%, driven largely by the cost of model inference.

Fine-tune large language models (LLMs) for specific applications by developing training datasets, running the fine-tuning process, and validating results. One common application is using generative models to create new art and music, either by generating completely new works from scratch or by using existing works as a starting point and adding new elements to them. For example, a generative model might be trained on a large dataset of paintings and then be used to generate new paintings that are similar to the ones in the dataset, but are also unique and original. So, it’s not yet obvious that selling end-user apps is the only, or even the best, path to building a sustainable generative AI business. Margins should improve as competition and efficiency in language models increases (more on this below). And there’s a strong argument to be made that vertically integrated apps have an advantage in driving differentiation.

For example, a call center might train a chatbot against the kinds of questions service agents get from various customer types and the responses that service agents give in return. An image-generating app, in distinction to text, might start with labels that describe content and style of images to train the model to generate new images. Neural networks, which form the basis of much of the AI and machine learning applications today, flipped the problem around. Designed to mimic how the human brain works, neural networks « learn » the rules from finding patterns in existing data sets. 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.

• Additionally, there are  also ongoing concerns about the ethical and societal implications of generative AI, and how to ensure that these technologies are used in a responsible and beneficial way.
• Generative AI produces new content, chat responses, designs, synthetic data or deepfakes.
• Autonomous content generation can be used for marketing campaigns, copywriting, true personalization, assessing user insights, and creating high-quality user content quickly.
• Semantic web applications could use generative AI to automatically map internal taxonomies describing job skills to different taxonomies on skills training and recruitment sites.
• Our CTI resources aim to provide support on what these tools are and how they work.