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

Posted on June 16, 2023

Wizelines Map of the Generative AI Landscape

This allows for swarms of drones to perform military operations and provide persistent aerial dominance across sea, air, and land, without risking the safety of human pilots. The Hivemind AI pilot reads and reacts to the battlefield, allowing for intelligent decision-making without preset behaviors or waypoints. The technology has already been deployed in combat since 2018 and continues to advance towards revolutionizing both military and commercial aviation. These use cases highlight how procurement is ripe for transformation from generative AI. But the speed at which they’re adopted will be largely dependent on a team’s ability to tune their AI models using proprietary company data.

First, advances in machine learning and natural language processing have made it possible for AI systems to generate high-quality, human-like content. Second, the growing demand for personalized and unique content, such as in the fields of art, marketing, and entertainment, has increased the need for Gen-AI platforms. Third, the availability of large amounts of data and powerful computational resources has made it possible to train and deploy these types of models at scale. Generative models are used in a variety of applications, including image generation, natural language processing, and music generation.

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They facilitate the efficient storage and retrieval of large, unstructured datasets required to train complex models like Transformers. The use of Azure Cosmos DB – Microsoft’s NoSQL database within Azure – by OpenAI for dynamically scaling the ChatGPT service underscores the need for databases that are both highly performant and scalable in the realm of generative AI. The application of generative AI spans across a wide range of industries, from entertainment and gaming to fashion and e-commerce. In this article, we will explore the generative AI application landscape and discuss the different use cases, challenges, and opportunities of this exciting technology. As AI technologies evolve at a breathtaking speed, founders have an unprecedented opportunity to leverage those tools to solve complex, meaningful, and pervasive problems. Antler is looking for the next wave of visionary founders committed to using AI to disrupt industries and improve how we live, work, and thrive as individuals, organizations, and economies.

  • By automating content generation, customer insights analysis, and personalized recommendations, Generative AI can significantly enhance marketing strategies and sales interactions.
  • Safety and security remain pressing concerns in the development of generative AI, and key players are incorporating human feedback to make the models safer from the outset.
  • Examples of open source models are Meta’s Llama 2, Databricks’ Dolly 2.0, Stability AI’s Stable Diffusion XL, and Cerebras-GPT.

For instance, it can help companies reduce costs, improve customer engagement, and optimize business processes. It can also enable businesses to develop new products and services that previously may have been too costly to pursue. NLP is a field Yakov Livshits of study in AI that focuses on the interactions between humans and computers using natural language. Generative AI models that incorporate NLP are being used to create chatbots that can provide customer service in a more human-like way.

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Although generative AI is a technology that has been developed for a long time, its reputation has increased rapidly in recent years. It is used in areas such as marketing, advertising, data analysis, and creating new data using existing data. Of course, current AI tools outside of NLP also provide significant advantages to businesses of all sizes. In some cases, AI powers the robotic process automation applications used to automate a variety of tedious and repetitive business processes.

the generative ai application landscape

The platform is popular for sharing and utilizing Transformer models, a neural network particularly effective for natural language processing tasks. In addition, it functions as a collaborative community where developers can upload, annotate, and employ a diverse range of machine learning models such as BERT, GPT-2, and RoBERTa, among others. The Hub’s comprehensive library of pre-trained models is easily accessible and comes with in-depth documentation and usage examples to facilitate understanding and efficient deployment. The benefits of using closed-source foundation models are their high accuracy, the production of high-quality content, scalability to meet the needs of many users and security against unauthorized access.

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.

Thus, striking a balance between leveraging existing resources and investing in new assets is key to achieving success in generative AI. Lastly, selecting compute hardware is one facet of building a generative AI application. Other considerations include the choice of your machine learning framework, data pipeline, and model architecture, among other factors. Also, remember to factor in the cost, availability, and expertise required to use compute hardware effectively, as these elements can also impact the successful implementation of generative AI apps. Frameworks like Hugging Face Transformers, PyTorch Lightning, and TensorFlow Hub significantly improve the accessibility and usability of these models. In addition, they offer libraries of open-source foundation models for various tasks such as text classification, text generation, question answering, and more.

Foundation models, including generative pretrained transformers (which drives ChatGPT), are among the AI architecture innovations that can be used to automate, augment humans or machines, and autonomously execute business and IT processes. Gartner sees generative AI becoming a general-purpose technology with an impact similar to that of the steam engine, electricity and the internet. The hype will subside as the reality of implementation sets in, but the impact of generative AI will grow as people and enterprises discover more innovative applications for the technology in daily work and life. On the other hand, Replicate is a versatile Model Hub that enables developers to share, discover, and reproduce machine learning projects across various domains.

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This type of conversion can also be used for manipulating the fundamental attributes of an image (such as a face, see the figure below), colorize them, or change their style. With its latest courses, NVIDIA Training is enabling organizations to fully harness the power of generative AI and virtual worlds, which are transforming the business landscape. When we launched the AI 50 almost five years ago, I wrote, “Although artificial general intelligence (AGI)… gets a lot of attention in film, that field is a long way off.” Today, that sci-fi future feels much closer. (3 – Sequoia Market Map and Manifesto) Written by Sonya Huang, Pat Grady, and GPT-3, Sequoia recently created a Generative AI Application Landscape article.

How to Tell if Your A.I. is Conscious – The New York Times

How to Tell if Your A.I. is Conscious.

Posted: Mon, 18 Sep 2023 09:00:42 GMT [source]

And just as the inflection point of mobile created a market opening for a handful of killer apps a decade ago, we expect killer apps to emerge for Generative AI. Midjourney is an independent research lab focused on exploring new mediums of thought and expanding the imaginative powers of the human species through design, human infrastructure, and AI. Their AI application is not described in detail, but it is mentioned that they are actively hiring to scale and build humanist infrastructure focused on amplifying the human mind and spirit. They also offer product support and have a Discord community for questions and support. Personalised suggestions for goods, content, or entertainment are another application for generative AI that excels because it takes into account user preferences and behaviour.

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There’s been an explosion of new startups leveraging GPT, in particular, for all sorts of generative tasks, from creating code to marketing copy to videos. Perhaps those companies are just the next generation of software rather than AI companies. As they build more functionality around things like workflow and collaboration on top of the core AI engine, they will be no more, but also no less, defensible than your average SaaS company. In particular, there’s an ocean of “single-feature” data infrastructure (or MLOps) startups (perhaps too harsh a term, as they’re just at an early stage) that are going to struggle to meet this new bar. The landscape is built more or less on the same structure as every annual landscape since our first version in 2012.

Think about it, with generative AI, a team of researchers can quickly analyze data and share their findings with just a click. In a recent Gartner webinar poll of more than 2,500 executives, 38% indicated that customer experience and retention is the primary purpose of their generative AI investments. This was followed by revenue growth (26%), cost optimization (17%) and business continuity (7%). But generative AI only hit mainstream headlines in late 2022 with the launch of ChatGPT, a chatbot capable of very human-seeming interactions. These application types represent different ways of applying generative AI techniques, and they all have their unique potential benefits and challenges.

Rotary Positional Embeddings instead of learned positional embeddings, as found in GPT models. Nvidia is a company that designs GPUs and APIs for data science and high-performance computing, and SoCs for mobile computing and the automotive market. Additionally, Nvidia’s CUDA API enables the creation of massively parallel programs Yakov Livshits that leverage GPUs. Chinchilla has 70B parameters (60% smaller than GPT-3) and was trained on 1,400 tokens (4.7x GPT-3). Just as mobile unleashed new types of applications through new capabilities like GPS, cameras and on-the-go connectivity, we expect these large models to motivate a new wave of generative AI applications.

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