Why the Future of AI is Like Playing with Lego

The invention of Lego brought many young kids and adults both joy and mental development. Since the early days of Lego, the number of parts has quadrupled, enabling users to build and put pieces together in more than a billion new ways. However, the power of this ‘plastic gold’ lies not in the fact that there are so many bricks and colors available, but rather, that the fundamental principle always stays the same: bricks should always fit into each other and may be used in different ways to create something unique out of mere blocks. As a result, you could use bricks to create new narratives with untaught outcomes.

If we extend this analogy to AI, we can notice a few parallels. Most AI discoveries today, such as autonomous driving, protein folding, conversational AI, are also built on standards. For example, the most popular programming language in AI used today is Python. Along with that, the use of deep neural networks[1] has become its standard architecture. Not to forget, the major revolution of transformers[2] in the NLP domain. With transformers, you can basically use near-human level language understanding without the need to obtain the data or to train a model yourself, often applied in one line of code. All in all, these standardizations make it much easier to ‘connect’ them as ‘bricks’ and build new solutions.

Community-driven evolution of AI

It is there to say that there is a general trend in AI research that tends to build on the AI principles, models, architectures that show good results for a certain AI task. Many AI papers, therefore, are an alteration or an improvement on the existing AI model: take for example BERT. Not surprisingly, amongst all research areas, Artificial intelligence papers amass citations more than any other research topic.

I’m not saying AI research is easier, nor is it less groundbreaking. The point here is that the nature of AI research is different from most, as it opts for a more open and community-driven evolution. AI has become much more like standing on the shoulders of others. This analogy was also pointed out by Clément Delangue, founder of an open-source AI platform called Huggingface. HuggingFace, together with PapersWithCode and Kaggle, saw this trend and jumped into it by providing a platform for the community where you can easily share and continue building on new and better AI creations.

So what does this have to do with Lego? Just like the AI research community, the power of Lego is not entailed as a single entity. Neither the Lego Corporation nor a single AI institute alone is responsible for the majority of its success. Instead, it was accomplished by first setting a standard and then allowing it to be shared as creations worldwide. AI, just like Lego, has developed a narrative that may even be considered a way of communication. The narrative is that everyone in the community understands and attempts to design and build better variations of existing creations.

Let’s build something

Back to the customer service agent. Suppose a customer writes a long service question. You can use DistillBERT to detect the intent first and Albert to obtain potentially present information present in the question. As shown in the figure below, we need the key date, moving date, and new home’s address to completely set up a bridging contract for a customer.

You can ask any question on a given passage, and Albert understands what is being asked.

Future of AI and the race for superintelligence

Also, Lego understands this as well and launched Lego Ideas a few years ago. Lego Ideas is a platform where they ask the crowd to design and vote for creations. If a creation is found good enough by the community, Lego might release it as a set. It can clearly be concluded that the future of AI is bright as it continues to become more accessible to everyone.

‘Playing’ with AI in your organization

Just like Lego’s motto says, “Only the best is good enough.”

Written by Thomas Schijf

[1] Using deep neural networks in the form of CNN for Computer Vision or RNN/LSTM’s for NLP

[2] Transformers are pre-trained models for NLP solutions. Examples are BERT, DistillBERT, XLNet, GPT-3, etc.