2024 is set to witness a surge in the development and advancements of artificial intelligence (AI), with OpenAI’s highly anticipated GPT 4.5 expected to outperform human experts in various intelligence tests. This accelerating intelligence explosion has led to an exponential increase in funding and resources for AI research, with predictions indicating exponential revenue growth as well.
Renowned AI expert, Dr Alan Thompson, has revealed that OpenAI will release GPT 4.5 within days, taking AI capabilities beyond the reach of human experts. This breakthrough is projected to have a significant impact on a wide range of industries and sectors.
A recent survey conducted among thousands of AI experts yielded noteworthy insights. While the majority of experts do not expect AI to surpass human performance in all tasks until around 2047, the timeline has been brought forward by thirteen years compared to last year’s estimates. Moreover, the survey results indicate that AI is not anticipated to replace every human job until the years 2100-2150. However, it is worth highlighting that most experts believe AI will accelerate technological advancements by a factor of ten within the next five years. This unprecedented leap in progress raises questions about how technology will experience rapid acceleration without major world-altering effects.
Jim Fan, an expert from Nvidia, has forecasted that the utilization of synthetic data will become increasingly important in the field of AI in 2024. Techniques that were originally developed for board games and image generation have regained significance in the context of Language Model (LLM) research.
One groundbreaking approach is centered around the concept of self-play combined with Generative Adversarial Networks (GANs). Here’s how it works: In each iteration (t+1), an LLM engages in an adversarial game against its previous iteration (t). The objective of (t+1) is to correctly distinguish between outputs generated by the model and those produced by humans, while (t) aims to deceive its opponent by generating text that appears as realistic as possible. After each iteration, the parameters of (t+1) are copied to (t), thereby creating a self-improvement loop.
Significantly, this method does not rely on human preference data. Instead, a supervised finetuning dataset is sufficient to initiate the self-play process. The essence of this technique bears resemblance to GAIL (Generative Adversarial Imitation Learning), which has proven superior to simple behavior cloning for control tasks such as Robotics. GAIL also incorporates an adversarial game mechanism that drives self-improvement, albeit with the inclusion of a physics simulator.
As AI continues to evolve and captivate the attention of researchers, the possibilities become increasingly exciting. With the imminent release of OpenAI’s GPT 4.5, the boundaries of AI capabilities will be pushed beyond human expertise. As experts predict a future of amplified technological advancements driven by AI, synthetic data and innovative techniques like self-play and GANs hold promise for further breakthroughs in the field. While the prospect of AI replacing human jobs may seem distant, its role in accelerating progress across various industries is undeniably within reach.
In the coming years, the trajectory of AI’s growth and impact will be closely monitored. As technological advancements unfold at an unprecedented pace, it is essential for society to assess the ethical implications and potential consequences, ensuring that the potential benefits of AI are harnessed responsibly and for the greater good. Only time will tell how AI will transform the world we live in, but the future undoubtedly holds remarkable possibilities for human-AI collaboration and innovation.
