ChatGPT and other generative AI tools are rising in popularity. If you’ve ever used these tools, you might have realised that you’re revealing your thoughts (and possibly emotions) through your questions and interactions with the AI platforms. You can therefore imagine the huge amount of data these AI tools are gathering and the patterns that they’re able to extract from the way we think.
The impact of these business practices is crystal clear: a new AI economy is emerging through collecting, codifying, and monetising the patterns derived from our thoughts and feelings. Intrusions into our intimacy and cognition will be much greater than with existing social media and tech platforms.
We, therefore, risk becoming victims of ‘knowledge slavery’ where corporate and/or government AI monopolies control our access to our knowledge.
Let’s not permit this. We’ve ‘owned’ our thinking patterns since time immemorial, we should also own those derived automatically via AI. And we can do it!
One way to ensure that we remain in control is through the development of bottom-up AI, which is both technically possible and ethically desirable. Bottom-up AI can emerge through an open source approach, with a focus on high quality data.
Bottom-up AI challenges the dominant view that powerful AI platforms can be developed only by using big data, as it is the case with ChatGPT, Bard, and other large language models (LLMs).
According to a leaked document from Google titled ‘We have no Moat, and Neither Does OpenAI’, open source AI could outcompete giant models such as ChatGPT.
As a matter of fact, it is already happening. Open source platforms Vicuna, Alpaca, and LLama are getting closer in quality to ChatGPT and Bard, the leading proprietary AI platforms, as illustrated below.
Source: https://lmsys.org/blog/2023-03-30-vicuna/
Open source solutions are also more cost-effective. According to Google’s leaked document:
They are doing tings with $100 and 13B params that we struggle with at $10M and 540B. And they are doing so in weeks, not months.
Open source solutions are also faster, more modular, and greener in the sense that they demand less energy for data processing.
As algorithms for bottom-up AI become increasingly available, the focus is shifting to ensuring higher quality of data. Currently, the algorithms are fine-tuned mainly manually through data labelling performed mainly in low-cost English-speaking countries such as India and Kenya. For example, ChatGPT datasets are annotated in Kenya. This practice is not sustainable as it raises many questions related to labour law and data protection. It also cannot provide in-depth expertise, which is critical for the development of new AI systems.
At Diplo, the organisation I lead, we’ve been successfully experimenting with an approach that integrates data labelling into our daily operations, from research to training and management. Analogous to yellow markers and post-its, we annotate text digitally as we run courses, conduct research or develop projects. Through interactions around text, we gradually build bottom-up AI.
The main barrier in this bottom-up process is not technology but cognitive habits that often favour control over knowledge and information sharing. Based on our experience at Diplo, by sharing thoughts and opinions on the same texts and issues, we gradually increase cognitive proximity not only among us colleagues as humans, but also between us humans and AI algorithms. This way, while building bottom-up AI, we’ve also nurtured a new type of organisation which is not only accommodating the use of AI but also changing the way we work together.
ChatGPT triggered major governance fears, including a call by Elon Musk, Yuval Harari and thousands of leading scientists to pause AI development on account of big AI models triggering major risks for society, including high concentrations of market, cognitive, and societal power. Most of these fears and concerns could be addressed by bottom-up AI, which returns AI to citizens and communities.
By fostering bottom-up AI, many governance problems triggered by ChatGPT might be resolved through the mere prevention of data and knowledge monopolies. We will be developing our AI based on our data, which will ensure privacy and data protection. As we have control over our AI systems, we will also have control over intellectual property. In a bottom-up manner, we can decide when to contribute their AI patterns to wider organisations, from communities to countries and the whole of humanity.
Thus, many AI-related fears, including those raised in relation to the very survival of humanity (leaving aside whether they are realistic or not), will become less prominent by our ownership of AI and knowledge patterns.
Bottom-up AI will be essential for developing an inclusive, innovative, and democratic society. It can mitigate the risks of power centralisation, which is inherited from generative AI. Current legal, policy, and market mechanisms cannot deal with the risk of knowledge monopolies of generative AI. Thus, bottom-up AI is a practical way to foster a new societal ‘operating system’ built around the centrality of human beings, their dignity, free will, and realising creative potential, as Diplo proposed via our humAInism approach we began developing back in 2019.
Technological solutions for bottom-up AI are feasible today. Will we use them as an alternative to top-down AI? For the time being, it remains anyone’s guess. Some individuals and communities may have more incentives and abilities to experiment with bottom-up AI than others. Some may continue to rely on top-down AI out of sheer inertia. And the two approaches may even co-exist. But we owe it to ourselves and to humanity to question what is being served to us, and to both explore and encourage alternatives. And, ultimately, to make informed decisions.
