Fractal: Taking AI into the future One of the key things that Fractal does is approach problem-solving in a structured way using a framework called “solving today’s problem, tomorrow’s problem, and the day after’s problem.” The first category includes tasks such as fine-tuning GPT architectures and vision models and forecasting the next best actions, as these are the immediate problems that need to be addressed to make current models work more efficiently.
Fractal’s NLP and Vision team works on the next generation of AI problems, such as exploring quantum machine learning for tasks like protein folding and cognitive neuroscience. This involves understanding the brain’s behavior and decision-making and has led to forming Fractal’s cognitive neuroscience team. Another area of research is how AI models work in low-latency, low-resource environments, the aim of which is to develop models that are small enough to work on edge devices and in ambient AI environments. Finally, the team also considers embedding AI and ethics into the model rather than treating them as external considerations. While these fundamental problems are not application-specific, the belief is that, if we can find solutions to these challenges, we can create truly disruptive technologies. As AI continues to develop, it’s likely to spur new industries and new ways of using technology. However, it will take time for people to learn how to use AI effectively and for the technology itself to overcome its remaining hurdles. While the jury is still out on the ultimate impact of AI, we have reached a critical inflection point, and the next few years are likely to provide some concrete answers.
One of our main areas of focus is quantum computing, which could lead to an exponential change in computation.
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