Generative Artificial Intelligence: A New Chapter for Enterprise Business Applications

Jason Allen, the creator of Théâtre d’Opéra Spatial, explains that he spent 80 hours and created 900 images before getting to the perfect combination. Coupled with rapid progress in data infrastructure, powerful hardware and a fundamentally collaborative, open source approach to research, the transformer architecture gave rise to the Large Language Model (LLM) phenomenon. AI circles had been buzzing about GPT-3 since its release in June 2020, raving about a quality of text output that was so high that it was difficult to determine whether or not it was written by a human. Confluent, the public company built on top of the open-source streaming project Kafka, is also making interesting moves by expanding to Flink, a very popular streaming processing engine.

They will likely first integrate deeply into applications for leverage and distribution and later attempt to replace the incumbent applications with AI-native workflows. It will take time to build these applications the right way to accumulate users and data, but we believe the best ones will be durable and have a chance to become massive. This large-scale machine learning model is commonly trained on unlabeled data through the use of a Transformer algorithm. The training and fine-tuning process enables the foundation model to evolve into a versatile tool that can be adapted for a wide variety of tasks, to support the capabilities of various generative AI applications.

The ChatGPT list of lists: A collection of 3000+ prompts, examples, use-cases, tools, APIs…

Subsequently, Google also rushed to market its own ChatGPT competitor, the interestingly named Bard. This did not go well either, and Google lost $100B in market capitalization after Bard Yakov Livshits made factual errors in its first demo. However, Microsoft was forced by competition (or could not resist the temptation) to open Pandora’s box and add GPT to its Bing search engine.

As the models get smarter, partially off the back of user data, we should expect these drafts to get better and better and better, until they are good enough to use as the final product. Among content creators, 71% found that their followers responded positively to their AI-generated content, while only 10% found they reacted negatively. “It’s both a force multiplier and terrifying competition,” Gewirtz says, adding his video audience seems to like the slightly higher production value tacked on by the AI tools. But on the flip side, generative AI is also the same technology that can create deep fakes, which are images and videos that closely resemble the likeness of others to the point of proving hard to determine whether they’re real. APIs are an easy way for computer programs to call on someone else’s data or software services. Accessing airline schedules, using online checkouts, delivering comprehensive news, or securing and tracking international supply chains are just some of the thousands of online activities made possible by APIs.

FAQs on the Generative AI Applications Landscape

The recent emergence of open-source alternatives to proprietary generative AI models, such as Eleuther.ai’s GPT-NeoX-20B and StabilityAI’s Stable Diffusion, has greatly contributed to the rapid growth and widespread adoption of generative AI. These open-source models, launched in February and August of 2022, respectively, offer similar capabilities as their proprietary counterparts from OpenAI, such as text generation and image and video generation. Generative AI is one of the biggest changes to the Internet in recent years, after social media and crypto.

The first represents instances in which companies use foundation models largely as is within the applications they build—with some customizations. These could include creating a tailored user interface or adding guidance and a search index for documents that help the models better understand common customer prompts so they can return a high-quality output. Anthropic is a company that focuses on AI research and products that prioritize safety. Their AI assistant, Claude, is designed to assist with various tasks, regardless of their scale. Claude is a next-generation AI assistant that aims to make complex tasks easier and more efficient by integrating natural language processing and other advanced AI technologies. The company emphasizes the need for safety and responsibility in AI development, and their products reflect this philosophy.

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.

Leaders in the Generative AI Landscape

These tools not only help us with our projects, but also support us in making better decisions. “You’ll be hearing the term copilot a lot, and I think that’s the right way to think of it,” Johnson said. “This technology will allow everyone to focus on how they can better serve their customers and grow their business.” Below is a schematic that describes the platform layer that will power each category and the potential types of applications that will be built on top.

But the output from the generative AI tool could result in generic and low-quality stuff, especially when more people use it, and it all starts looking and sounding similar. From scriptwriting to video editing, AI can accompany a content creator throughout video production, as evidenced by the survey showing most creators use it to generate video and photo backgrounds. A good creator can combine the excellent generative AI tools available and use them as instruments to more easily create social media content, like text for their Instagram posts or even some graphics for their photos. The vast success of social media has resulted in its growth into a full-blown business model. A long way from your Myspace Top 8 and glitter GIFs, we’ve found a way to monetize and create an economic model from our social media habits. The initial focus will be on eliminating repetitive work, such as scaffolding, documenting and creating tests, but over time the overall architecture will improve too.

Blockchain Development

In video production, AI-driven tools assist in generating animations, special effects, and even automated video editing, streamlining the creative process and reducing production costs. New startups continue to enter the market at a swift pace, supported by advances in generative infrastructure like large language models and vector databases. Across 91 deals in 2023 so far, the space has already seen $14.1B in equity funding (including $10B to OpenAI). As with AI in general, dedicated generative AI services will certainly emerge to help companies fill capability gaps as they race to build out their experience and navigate the business opportunities and technical complexities. Existing AI service providers are expected to evolve their capabilities to serve the generative AI market.

Combined with cloud computing, including services that manage millions of API transactions securely and easily, APIs enabled a software ecosystem of common components, producing the critical means of business engagement in online life. This led to the development of middleware, which acted as a layer between computers and applications, facilitating business processes and data communication and integration. Storage plays a vital role in the training and inference phases of generative AI models, enabling the retention of vast amounts of training data, model parameters, and intermediate computations. Parallel storage systems enhance the overall data transfer rate by providing simultaneous access to multiple data paths or storage devices. This functionality allows large quantities of data to be read or written at a rate much faster than that achievable with a single path. Semiconductors enable the underlying hardware for computation, facilitating the processing and complex calculations required for generative AI models.

The best (or luckiest, or best funded) of those companies will find a way to grow, expand from a single feature to a platform (say, from data quality to a full data observability platform), and deepen their customer relationships. If there’s one thing the MAD landscape makes obvious year after year, it’s that the data/AI market is incredibly crowded. In recent years, the data infrastructure market was Yakov Livshits very much in “let a thousand flowers bloom” mode. As to the small group of “deep tech” companies from our 2021 MAD landscape that went public, it was simply decimated. As an example, within autonomous trucking, companies like TuSimple (which did a traditional IPO), Embark Technologies (SPAC), and Aurora Innovation (SPAC) are all trading near (or even below!) equity raised in the private markets.