The AI community is embracing a new perspective on the growth of artificial intelligence: the Sigmoid Curve. This S-shaped model suggests that after an initial period of rapid progress, advancements start to level off.
I think it is a big question whether a small language model is good enough at general reasoning, & hallucination free enough, to be able to run a phone.
We only have standard benchmarks for them, but those don't apply to real-world scenarios. Big models seem capable, but SLMs?
— Ethan Mollick (@emollick) August 6, 2024
The Sigmoid Curve is rooted in the idea that technological developments experience bursts of rapid growth followed by periods of plateau.
In the AI space, we’ve witnessed tremendous breakthroughs in machine learning, natural language processing, and large language models.
Is AI riding an exponential curve, or a sigmoid wave? ?
Our latest article in @thenewstack explores the challenges around data, energy, and economics for AI, and why looking for realistic progress is the silver lining for projects.https://t.co/HJJQekqGoH#DataStax
— DataStax (@DataStax) August 5, 2024
However, recent developments indicate we are approaching a phase where progress might not be as sensational as in the recent past. The implications of this shift are profound for developers, businesses, and policymakers.
During the exponential growth phase, the industry rode waves of hype, fueled by significant innovations and lofty promises. Companies invested heavily in AI, expecting continuous, groundbreaking advancements. However, signs now point towards a transition into a more sustainable phase.
Is AI being overhyped? I found the current versions of ChatGPT and Gemini very frequently making weird mistakes.https://t.co/s6ISvM44Y7
— Donald J. Robertson (@DonJRobertson) August 6, 2024
This doesn’t mean innovation is stalling, but rather that the focus is shifting towards refining existing technologies, improving AI quality, and addressing critical ethical considerations. For example, while large language models have drastically improved, issues like bias, energy consumption, and the need for massive datasets are coming to the forefront.
Embracing sustainable growth in AI
The community is now prioritizing responsible AI practices, seeking efficient algorithms that align better with ethical standards without compromising on performance. So, what does this mean for the future of AI? First, we’ll likely see more collaboration across various sectors to tackle the nuances of AI deployment.
Governments, tech companies, and academia need to work together to ensure AI technologies are not only powerful but also ethical and accessible. Second, there’s an increasing emphasis on education and upskilling. As the growth curve levels off, the demand for expertise in maintaining and improving existing systems will rise.
Continuous learning and adaptation will be crucial for anyone involved in the AI space. Lastly, the slower pace of radical innovation might give us the necessary breathing room to address the challenges that rapid growth brought along. This period allows for introspection and improvement, setting a stronger foundation for future AI advancements.
In conclusion, riding the sigmoid curve offers a more balanced perspective on AI growth. It reflects the maturation of the field, bringing about a phase of consolidation, quality enhancement, and ethical introspection. While the initial excitement might taper off, the journey ahead promises a more sustainable and inclusive evolution of artificial intelligence.
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