With the launch of OpenAI’s Generative AI product ChatGPT in December 2022, we are experiencing an AI revolution in every field. Never before in history has AI been so accessible to humanity. For a long time, AI was only for science fiction lovers and scientists, but now it is a big part of our daily lives, especially our business lives. Generative AI can create a presentation about consumer behavior for us from start to finish, for example, right before a brainstorming session for our new product’s social media campaign.
Analytical AI, or traditional artificial intelligence, has been quietly present in our lives for a long time. The next video that TikTok will show us is completely determined by artificial intelligence. Machines can now easily understand whether a user is a real user or a bot from the behavior of users visiting a site. It is almost impossible to examine large data sets without the support of artificial intelligence.
However, until recently, we did not think that AI could compete with humans in any way in areas that required creativity. We used AI more as cognitive labor in tasks that were based on analysis and big data. The entry of Generative AI into the game has fundamentally changed our perspective on both AI itself and the potential we can achieve with AI.
The era of generative AI is just beginning, and it will take time for the benefits of this new technology to be fully realized. The competitive advantage is predicted to go to the organizations that are the first to integrate generative AI into their innovation, company growth, and workflow processes. A study conducted by McKinsey in the USA in mid-April 2023 shows that generative AI is on the board agenda of 28% of the organizations to which the participants belong. One-third of the organizations use generative AI in at least one business function.
What is Generative AI?
Generative AI can be defined as a subset of artificial intelligence that can create artifacts that mimic various aspects of human creativity and intelligence. Generative AI refers to artificial intelligence techniques and models that can perform original content production functions such as product design, visual production, copywriting, audio production, and code generation.
Generative AI starts with a prompt, which can be text, an image, a video, a design, musical notes, or any other input that the AI system can process. Various AI algorithms then create new and original content in response to that prompt.
Open AI’s release of ChatGPT, the most widespread generative AI overseas chinese in uk data application or interface of our time, is a technological development that is almost an era-making and annihilating for some. With the number of users increasing at a rate never seen before on any online platform, it has literally caused a revolution in the market for AI applications. This revolution has touched and affected all sectors from health to entertainment in one way or another. The potential of generative AI technologies is too great for any online business to ignore. ChatGPT broke the record by reaching 100 million active users in just 2 months.
What is Generative AI Used For?
The greatest power of generative AI is its ability to produce new ideas, designs, and solutions to problems that humans have never thought of before. All of these processes can be used to support creative processes in areas such as art, design, written content production, and engineering.
Generative AI can also significantly increase efficiency and productivity in many industries. Repetitive and time-consuming tasks can be automated with generative AI, freeing up time and resources for more important tasks. For example, instead of manually creating unique text for each product, an e-commerce company can have generative AI produce product descriptions based on the features of the products. In this way, marketing teams can only handle the process of checking product descriptions, and the rest of their time can be spent on more creative and strategic tasks.
At the same time, AI-powered technology can enable better business decisions. For example, businesses can use generative AI to uncover data that will guide their marketing strategy or decision-making processes for developing a new product.
Generative AI offers the ability to generate personalized content by understanding individual preferences. For example, in the fashion industry, generative AI can be used in personalized clothing design processes. Generative AI can increase customer satisfaction and loyalty by generating personalized solutions.
Generative AI can also play an inspiring role in supporting creative workflows, accelerating creative processes. In areas such as design, generative AI tools can help creative professionals work more efficiently.
Generative AI Applications
At this point, it is useful to mention the difference between Generative AI and Generative AI interfaces. Generative AI, as you know, refers to artificial intelligence models that can produce content in text, visual, audio and other formats. For example, models like GPT-4 can produce text, while DALL-E can produce images. Generative AI interfaces and applications are actually tools that work based on models like GPT-4 and make these models more accessible in a user-friendly way.
This way, any user can use powerful AI models without having to deal with technical details. For example, ChatGPT is actually a generative AI application that uses GPT-3 and GPT-4 models.
Some generative AI models allow users to use their interface directly. That's why you may see models and interfaces used synonymously from time to time. We can say that some of the applications you use open source code to developers through APIs in the background.
Especially with the spread of no-code applications, it is possible for businesses today to create artificial intelligence applications that will provide solutions to the problems they encounter in their business processes. In the simplest terms, companies that use applications such as Discord or Slack for internal communication can design bots that will facilitate their own workflows without much effort. Or, you can create your own AI content application by training open source applications on your own company policies. You can design personalized assistants that go one step beyond Chatbots for your customers. You can automate a job that you previously spent hours on by using only applications such as Google Sheets as an interface. The first step in this process is actually understanding the potential of AI and imagining an application that you can create by using AI models.
We can summarize some of the AI interfaces that we hear a lot about, which are products in their own right, as follows. Compared to the power of what can be done with productive AI models, these products are actually only a small part of the ecosystem. We plan to touch on these applications in more detail in a different content.
Content generation: GPT, Jasper, AI-Writer and Lex.
Image generation: Dall-E 2, Midjourney and Stable Diffusion.
Music production: Ampere, Dadabots and MuseNet.
Code generation: CodeStarter, Codex, GitHub Copilot and Tabnine.
Audio production: Descript, Listnr and Podcast.ai.
AI chip design: Synopsys, Cadence, Google and Nvidia.
Here we can see how the application and model layers interact with each other in generative AI
Here we can see how the application and model layers interact with each other in generative AI
How Does Generative AI Work?
Unlike traditional AI systems, generative AI uses advanced deep learning models and large language models (LLMs) to learn patterns or patterns from the data it has. The trained model then uses the learned patterns and structures to generate new content.
For example, a generative AI system could be presented with a large number of images of human faces. After discovering patterns in the dataset—facial features, outlines, skin color—the generative AI could then create new faces that do not belong to any human in the world.
Levis used models created entirely by AI in the product images on its e-commerce site.
Levis used models created entirely by AI in the product images on its e-commerce site.
Early versions of generative AI required data to be transferred through an API or other complex process. Developers also had to be familiar with these specialized tools and write applications using languages like Python. Today, AI pioneers are developing better user experiences that allow us to describe a request with just language. After Generative AI’s initial response, we can customize the output by getting feedback on the style, tone, and other elements of the generated content.
The first stage of the process of developing a generative AI begins with the collection and processing of a large dataset that includes a variety of the types of data you want to create. For example, if you have a dream of creating a Generative AI product related to text content creation, you need to compile a dataset consisting of various texts. The next step is to decide which generative AI model you will use.
Generative AI Models
There has been significant investment in the field of Generative AI in recent years from both open-source communities and large companies. All these advances are also revolutionizing areas such as natural language processing. At the same time, the ever-growing data sets we have and the developments in computer technology are enabling the emergence of larger and more sophisticated models.
Language model ecosystem as of July
Language model ecosystem as of July
Let’s take a closer look at some of the most popular generative AI models, including some technical details. Let’s imagine the products that can be created with generative AI.
1. Transformer Based Models
Transformer-based models are one of the important architects of the rapid development of artificial intelligence in recent years. First encountered in an article published by Google in 2017, transformer-based models can be defined as powerful neural networks that learn context and therefore meaning by monitoring the relationships between sequential data, such as words in a sentence. Transformer-based models can successfully perform tasks especially related to natural language processing (NLP). The best-known examples of transformers are GPT-4, BERT and LaMDA.
With the integration of ChatGPT into our lives, GPT-4, the converter we use most often, can write poetry, create emails from scratch, and even tell jokes.
BERT is another groundbreaking transformer-based model in the field of language processing. The most important feature of BERT is that it can act independently of word order while learning texts. This actually differentiates it from traditional transformer models.
