Generative artificial intelligence (AI) is a type of machine learning system that can specifically create content such as text, images, audio, code, and videos.
The term generative AI become popular with the introduction of ChatGPT, a chatbot, and Dale E, an image generator by OpenAI in 2022 and 2021, respectively. These new tools have enabled us to create human like content using computers and our imagination.
By now generative AI is in the news headlines every day. It is the biggest digital disruption since the era of the internet, fundamentally changing the way we create content, work, and innovate.
Generative AI is powered by large language models (LLMs). These machine-learning models use neural networks that replicate the human brain structure. This enables them to understand and identify patterns and relationships from any data. As a result, it can understand the context between related terms, words, or any other information and use these findings to generate responses to users' requests. Unlike traditional algorithms that rely on explicit rules or predefined templates, generative AI models are closer to reasoning like humans, which no other technology has been able to do yet.
The general rule has been that the quality of the content produced is based on the quality of the training data and its size. That meant the more data you provide, the more refined and accurate the model became. However, the latest tests have shown that training the models with your data is difficult and very expensive. Therefore, there is a shift happening, which is using pre-trained foundational models with systems such as Retrieval Augmentation Generation to increase the relevancy and accuracy of the output.
Generative AI offers many benefits across industries. Here are some key benefits of generative AI for all businesses:
Currently, it is far from replacing employees, but enhancing their capabilities. How will the future look, we cannot know for certainty. But one thing is for sure generative AI has been integrated into many aspects of our lives and work, and the transition continues to grow.
There are many challenges businesses face when integrating generative AI into their workflows, such as:
The biggest concern is data protection. Sensitive information could potentially be mixed into LLM models. This is why many organizations are not permitting their teams to use private generative AI models.
There are no industries that are limited to benefit from generative AI. Essentially, all businesses can have some form of enhancement or improvement. Regardlessly, popular industries that can benefit from generative AI are automotive, manufacturing, defense, healthcare, and finance. For example, airline companies can generate new designs for more efficient engines.
When you choose API-based solutions or build the software infrastructure from the ground up it is expensive. The cost can easily spin up, even outperform the value of generative AI benefits.
There are many ways to reduce the costs of generative AI. But the main ones are choosing a generative AI software infrastructure platform as a service, and cost-efficient open-source LLMs, and running these systems locally in your data centers. Doing so you eliminate the need to hire expensive AI engineers (the platforms are a fraction of the cost of building it yourself) and avoid high overheads with API-based providers. As a result, you can lower your generative AI implementation costs up to 50-75%.
Ever since the introduction of ChatGPT and DALL-E by OpenAI, Generative AI has been in the news spotlight. No wonder why. Its capabilities to produce new content from human language are far from ordinary. It has completely revolutionized how businesses lower operational costs, serve their clients and innovate new products. All this is possible by making sense of the data and creating content such as text, images, and code, as well as videos and voices.