Betterthistechs Article: Future Technologies for 2024


Michelle Zhou, Rajesh Patel, and Thomas Cho started BetterThisTechs in 2019 to make business printing technology better and more innovative. The business began as a small one in Charlotte, North Carolina, with a strong desire to make products that improve people’s lives. The business sells many different items to meet the needs and wants of many people. The company makes products like smart home appliances and modern smartphones meant to make people’s lives easier.

Demystifying Generative AI

As a type of artificial intelligence technology, generative AI can make different kinds of content, like text, images, sounds, and synthetic data. Recently, there has been a lot of talk about generative AI because it is easy to use new tools to make high-quality text, graphics, and videos in seconds.

The transformer network is one of the most well-known. When it comes to generative AI, it’s important to know how it works. Networks for transformers: Like recurrent neural networks, transformers are made to process sequential input data in a way that is not sequential. Self-attention and positional encodings are two features of transformers that make them great for text-based generative AI applications. These two technologies are used together to show time and let the algorithm focus on how words connect over long distances.

Betterthistechs’ Approach to Generative AI

For instance, GPT-2 and GPT-3 were made available following the 2017 release of the initial version. The model grew larger with each run, and at the conclusion, GPT-4 had an astounding one trillion parameters. Due to its increased size, GPT is now a formidable AI competitor and is much more proficient at many tasks.

Planning and Incremental Development 

One big reason for GPT’s success is that it trains models on huge amounts of text data ahead of time. This gives them a deep understanding of language. This model has already been trained, so it can be used as a place to begin. Then, it can be changed to do certain tasks, like making medical diagnoses or translating languages. GPT can be fine-tuned to work well in many areas and shine in certain tasks. This shows how useful and flexible it is.

Self-Supervised Learning 

A cool thing about GPT is that it lets you learn independently rather than being watched. By guessing and trying repeatedly, GPT learns how to write text that makes sense and is relevant to the situation. This is like how people understand. GPT can keep improving at making up language with this self-supervised method, pushing the limits of what is possible in NLP. This article from betterthistech wants to congratulate the GPT team on their achievement of understanding each other.

Large Model Training 

In the past few years, people have been working hard to make language models that are bigger and stronger. GPT has been leading the way (as we found when we researched for our betterthistechs article). GPT has reached new performance levels thanks to the huge amount of text data it processes during training and the exponential growth in model size. However, this push for scale brings up concerns about energy use and environmental damage, which shows how important it is for AI to be developed responsibly.

Fine-tuning for Specific Applications

GPT’s pre-trained model can do much, but it will only work at its best when fine-tuned for specific tasks. Developers can change the weights and parameters of the model to make GPT work well in business and healthcare. GPT can solve real-world problems very accurately and precisely thanks to this process of fine-tuning. This leads to new ideas in many areas. We’ll talk about more in the next betterthistechs article.

Challenges and Considerations 

Because generative AI content looks so real, it adds new risks to AI. More importantly, it makes it harder to tell when something is wrong and harder to spot content that AI made. This can be a big problem if we use the results of generative AI to write code or give medical advice. Many of the results of generative AI need to be clarified, making it hard to tell if they, for example, violate copyrights or if there are issues with the sources they use. You can only figure out why the AI might be wrong if you know how it came to its thoughts.

The Future of Generative AI

 The next generation of generative AI will progress in translating, finding new drugs, finding strange things, and creating new content, such as text, video, fashion design, and music. Even though these new one-time tools are useful, the most important thing that generative AI will do in the future is add these features directly to the tools we already use. It is tough to guess what generative AI will do in the future. But as we continue to use these tools to automate and improve human tasks, we must rethink what expertise is and how much it is worth.


So, the article from Better Hi-tech has fully explained that Generative Pre-trained Transformers are a big step forward in Natural Language Processing. GPT models have repeatedly shown that they can read and write text that sounds like a person wrote it. Smart use of GPT’s power will be important for getting the most out of it and making a world where people and machines can live together without problems when AI moves on to the next level.


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