Generative AI, which is almost synonymous with ChatGPT these days, continues to make headlines around the world. “What are we doing with ChatGPT?” is a common question. The Wall Street Journal reports venture capitalists are pouring money into AI startups piggybacking on large language models, even when they lack clear business plans. Big players in the AI space all want to be seen as leading in the generative AI game.
What gets lost in all this noise is that proven open-source artificial intelligence/machine learning (AI/ML) models exist right now. These are not mysterious black boxes but are well-documented models. Companies can build successful solutions on these when they follow well-known software development and data engineering best practices.
That may not sound as exciting as a conversation with Cleopatra via ChatGPT. But the proven rigor of these practices is what will enable companies to drive faster, predictive and proactive decisioning by applying business discipline to their AI initiatives. In addition to setting realistic budgets and timelines, companies will work with transparent models and be able to reuse components to build on and extend their initial AI efforts for even greater utility and returns.
A disciplined approach to AI
In our experience, only about 10% of corporate AI projects actually get deployed. And while 68% of businesses, globally and across industries, have adopted AI/ML, according to our recent research, many are struggling to scale their AI initiatives and realize business value from these projects.
This trend may also apply to generative AI. In our September 2023 survey of senior business and technology decision makers in the US and UK, 75% said that while their organization understands the potential value of gen AI, they are stuck when it comes to next steps, such as implementation, testing and deployment. Such limited corporate use reflects the sense that AI is an experimental technology to play with vs. applying a structured, disciplined approach.
A disciplined approach uses proven data and software engineering frameworks as the foundation for training open-source AI/ML models. Because the frameworks are well established, we know their associated timelines and costs. The business applications of different open-source AI models are clear, such as whether they are better suited for finding and predicting patterns in images or in text. By building a well-structured and trained AI model, businesses can also generate desired results faster. Further, they can apply the model to other situations by training it with different data.
One of our clients, an aquaculture major in Norway, wanted a faster and more accurate way of understanding fish development. The company was curious about using computer vision to track growth and detect diseases and malformations. We helped the client train a convolutional neural network to identify salmon by weight and length. This type of open-source model excels at categorizing images, essentially “encoding” them in its internal connections.
With proper design, such as that enabled by our Learning Evolutionary Algorithm Framework, the model will be able to recognize additional patterns. Now, when the client wants to identify additional fish, it does not need to build a new model. Instead, it can use its existing model “off the shelf,” training it with different data sets about other fish species.