Since the unveiling of ChatGPT to the world, generative AI has stuck on the lips of everyone. Although there might be some concerns about these, the world is still awaiting its next phase.
The rise of artificial intelligence has made machines able to have some distinct behaviors of their own. Before now, humans had to continuously repeat certain tasks but with new AI models, they can do it easily without human supervision.
With a few minutes of your time, we will guide you through the amazing realm of generative AI. Let’s walk together!
Table of Contents
What is Generative AI?
Simply put, Generative AI is an application that helps users quickly generate solutions, answers, or content to most tasks, depending on the input given.
In other words, It is an umbrella word for every application whose algorithms can automatically generate new ideas or content. With this, we can arguably say machine learning and deep learning are types of these.
Additionally, its algorithms are first built on a foundation model. We can’t refute the importance of a good foundation in any area of life. Without a good foundation, a building cannot be built, likewise, without knowing the foundation of any field of study, it will be difficult to progress in it.
This is why these algorithms are built on a foundation model and these models are trained on volumes of unfiltered data, under supervised learning.
How does Generative AI work?
As said earlier, It is built on foundational models. But also, work by using machine learning algorithms, deep learning, and natural language processing (NLP), alongside neural networks, to identify trends in data and generate original content.
Nowadays, due to advances in artificial intelligence, these models can train themselves with unsupervised and semi-supervised learning methods. If you don’t remember what unsupervised or semi-supervised learning is, that’s fine, here is a brief recap:
- Semi-supervised Learning: This is a type of learning approach that contains elements of supervised learning and unsupervised learning. It involves training an AI model with few labeled data and more unlabeled data.
- Unsupervised Learning: uses unlabeled datasets to train AI models.
Now back to base; Because of these improvements, organizations around the world can leverage volumes of unlabeled data to build foundation models for its applications.
An example of a foundation model is the GPT-3.5, which was trained on vast unlabeled text data. The GPT-3.5 served as the foundation for the famous ChatGPT language model.
What can Generative AI do?
By giving certain prompts or instructions to a generative AI model, it can generate the necessary output. But what can it do exactly?
- Generate ideas for content: The internet today is filled with content and to get your niche across a wide audience, content must be created. It can help with ideas in any area you choose. It can go further to create the content for you. For instance, ChatGPT can write a blog post for you(Not our blogs though) or give topic ideas for writers. There are other applications that can help create videos for your YouTube channel.
- Enhancing Productivity and Efficiency: With how fast its applications create content, users can do more work in a short period of time. For example, students can research faster, and better (not promoting laziness and inauthenticity) and still have time to focus on studying.
- Streamlining Workflows: Organizations around the world incorporating AI in their workspaces, have seen a rise in productivity and decision-making of their employees. Also, it has enabled them to focus less on repetitive tasks and focus on other areas.
What is next for Generative AI?
The future of generative AI is still full of possibilities. With content being the norm today, the use of these will forever be important. Although its applications today are not able to imitate human responses accurately, they are close and with frequent use, they will get there eventually.
Recently (as at the time this post was written), Open AI has proposed that ChatGPT 5 will be able to imitate human intelligence and responses as closely as possible and many more. Isn’t this great?!
Although we have touched on the potential of generative AI, there are significant concerns about how it can affect the jobs of people. Will it take their jobs? they ask. Well, the answer to that is Yes and No.
While it might take some jobs it will open more opportunities for everyone. Also, like in every phase in technological evolution, take the computer era, for instance, some jobs were lost but more opportunities opened. Now we have jobs like prompt engineers for AI inputs.
Finally, let’s not undermine the power and creativity of humans. We will always need to fact-check the outputs of AI models. The future of generative AI is full of possibilities but for us humans, it’s limitless.
Conclusion
Generative AI applications will continue to shape and assist every user across the world. From fast content creation, video creation, image visualization, and email automation, to chatbots for virtual assistants, its possibilities are vast. Finally, although generative AI is amazing, it is nothing but a tool to help streamline our daily tasks.
Get Started with Macgence
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