The Limitations of Today’s AI
As AI hype continues to skyrocket, fueled by claims of general artificial intelligence just around the corner, a recent research paper highlights the current limitations of popular transformer models. Published by three Google DeepMind scientists but not yet peer-reviewed, the findings serve as an important reminder that while exciting progress is being made, human-level artificial general intelligence remains elusive.
The paper examines OpenAI’s GPT-2 language model, focusing on its “transformer” architecture that allows inputs to be transformed into different outputs. This type of neural network has gained prominence as one pathway towards AGI due to its ability to draw intuitive connections like the human mind. However, after testing GPT-2 on tasks outside its training data, the researchers discovered a notable inability to extrapolate even for simple requests unrelated to what it had learned.
In other words, without direct exposure to a topic through its training dataset, the model struggled to apply its knowledge creatively. This challenges the narrative of AI systems progressing towards general problem-solving abilities independent of human expertise. For now, transformer models remain adept only within narrow domains defined by their programming, unable to reason beyond preconceived boundaries.
OpenAI’s latest GPT-3 is vastly more powerful thanks to its mammoth training size. Yet as the paper shows with an earlier version, scale alone does not guarantee intelligence will emerge organically. Instead of hype, a dose of realism is in order regarding the present limitations of AI. While future breakthroughs may one day achieve AGI, today’s most advanced models rely entirely on their training to function. Without representing the world abstractly in a human-like manner, machines cannot truly understand.
The findings also serve as a reminder of AI’s iterative nature. Incremental research will likely be needed to solve challenges like commonsense reasoning, conceptual learning, and fluid knowledge application — core abilities we take for granted but have thus far eluded even our most sophisticated AI. Progress depends on acknowledging present shortcomings, not denying them. Overall, the paper reinforces prudent skepticism towards unrealistic timelines for AGI and cautions against inflated claims of general problem-solving skills in existing systems.
As AI research presses forward, maintaining public trust requires honesty about technical realities — especially when commercial pressures Risk overpromising capabilities. While excitement for the field is justified by ongoing breakthroughs, overconfidence could undermine credibility. By shedding light on today’s limitations, this research contributes constructively to responsible development of advanced AI for the benefit of humanity.
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https://futurism.com/google-deepmind-researchers-transformers-agi
