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Generative AI is all the rage now. Learn exactly what it is and why it has become so prominent.

Generative AI is taking the world by storm and marks a turning point in content creation. Generative AI refers to models and algorithms that produce new output trained from extensive amounts of data in various outputs such as text, audio, images, video, data, 3D renderings, and more.

Generative AI rose to prominence in 2022, most noticeably when OpenAI made Dall-E 2 available to anyone. Dall-E 2 generates images with text to graphics prompts and was initially only accessible to users on a long waiting list. Then came the launch of ChatGPT in late 2022, which grew by 132% in January 2023, and is now considered the best AI chatbot around. Since then, the interest in generative AI hasn’t wavered.

The economic potential and benefits of Generative AI
According to a McKinsey report, it is estimated that generative AI’s impact on productivity can contribute up to $4.4 trillion annually to the global economy by 2040. After all, generative AI tools reduce the money and time required for content creation. 

For instance, generative AI can be used to power chatbots and virtual assistants, which can optimise time and resources, increasing productivity for human employees. AI chatbots and virtual assistants allow for faster response time, which can boost conversions and in turn improve brand perception. 

Furthermore, generative AI sets the stage for new business models and applications, in which their development can be accelerated with code suggestions based on the original developers’ comments and code. Business operations can be improved through generative AI-powered tools that automatically extract and summarise data. 

In terms of automation, generative AI can be used to generate reports, summaries and more  – also saving time and reducing errors. Moreover, marketing content such as blogs and social media posts can be generated in real time as well. However, these possible use cases and benefits of generative AI barely scratch the surface of how it is single-handedly changing our world. 

Use cases of Generative AI
In addition to the seminal chatbots ChatGPT-4 and Google Bard, which have changed the writing game, other use cases of generative AI include Copilot, a tool that suggests code snippets to AI developers, and even in the healthcare field – AlphaFold is a tool that can predict the 3D structure of proteins, which plays a crucial role in understanding diseases. 

Generative AI is even equipped to take on the role of supply chain advisor and offers greater visibility across the supply chain, which ultimately aids the decision-making process. It can help maintain machinery by interpreting readings, as well as offering recommendations and solutions if an issue is detected. 

Adobe Photoshop also offers generative AI capabilities that can fill in elements of an image, as well as background-switching capabilities and more. 

The difference between traditional and generative AI
Traditional AI models are more specific and mainly analyse data while making predictions. On the other hand, generative AI analyses datasets and subsequently creates output similar to what it was trained with. 

Evaluating generative AI models
Assessing whether a generative AI model depends on three factors namely quality, diversity and speed. For AI apps that interact directly with users, high-quality generative output is crucial. In the case of speech generation, the output must be understandable and with images, the output must look realistic enough.

An effective generative AI model captures minority modes in its data distribution without generation quality, which lowers biases in the learned models. Furthermore, generative AI apps require real-time generation such as instant image editing, to smoothen content creation processes.

Jobs in generative AI
Generally, a position within the generative AI field requires prior knowledge in prompt engineering, which revolves around designing inputs that interact optimally with other inputs in a generative AI model. Here are some other roles that are generative AI-related: 

1. Generative AI Quality Engineer
Generative AI Quality Engineers drive the development and implementation of best practices for relevant end-to-end features powered by machine learning and foundation models. They are also responsible for building novel software prototypes, tools and applications which enhance the quality of operating systems using foundation models.

2. Data Scientist
Data scientists work with machine learning scientists and architects to research and develop cutting-edge generative AI algorithms that address real-world situations. They directly interact with customers to understand their pain points, guiding customers on adoption patterns and production paths.

3. AI Engineer
Part of the responsibilities of an AI Engineer include designing and developing production platforms that power the AI models - improving reliability and security at scale. They collaborate with researchers and product managers to bring new features and research capabilities to the world.

Generative AI courses to pursue in 2024
Several courses may need individuals to possess prior knowledge of AI-related modules. However, there are courses that individuals from all backgrounds can join. 

a. Generative AI for Business Professionals: ChatGPT, Media Synthesis, and Beyond
This course helps individuals understand the principles and concepts of generative AI. Individuals would be able to identify and evaluate the potential use cases of generative AI based on respective industries, as well as learn from generative AI case studies across these industries. 

Learn more about this course here: https://www.ntuclearninghub.com/-/course/generative-ai-for-business-professionals-chatgpt-media-synthesis-and-beyond

b. AI Innovation Unleashed: Navigating the Terrain of Prompt Engineering
With this course, individuals get to learn about AI innovation through prompt engineering. This course lets students explore the intricacies of crafting effective prompts that drive AI models. They will also gain a fundamental understanding of the principles of AI, with a GPT-specific focus.

Find out more about this course: https://www.ntuclearninghub.com/-/course/ai-innovation-unleashed-navigating-the-terrain-of-prompt-engineering

c. AI Tools for Creative Professionals
Participants will learn about how AI affects marketing and business strategies, as well as the right AI tools for marketing planning and execution. They will learn to create effective marketing content such as visuals and copy using AI software.

More on this course here: https://www.ntuclearninghub.com/-/course/ai-tools-for-creative-professionals

d. Empowering Digital Marketers with Essential AI Tools
Individuals from and beyond the creative industry can grasp an understanding of generative AI principles and concepts. They can learn to evaluate possible use cases of generative AI relating to their specific industries as well. Students can also learn to develop and use basic generative AI models using tools like ChatGPT.

Click this link for more information: https://www.ntuclearninghub.com/-/course/empowering-digital-marketers-with-essential-ai-tools

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