As we predicted in our AI Trends 2020, NLP is the year’s leading trend. But research on self-regenerating structures has likewise been both surprising and fascinating. February was rich in news on self-regenerating machines and NLP-related events and research.
Microsoft trains T-NLG, a 17-billion-parameter language model
The tech behemoths are racing to build larger and more efficient language models, which are both costly and technologically challenging, if not daunting. Microsoft is a relatively new player in the game, and follows in the footsteps of Google with itsBERT model and OpenAI, which brought outGPT-2.
While Microsoft may not have been the first mover, its Turing Natural Language Generation (T-NLG) is now the largest model trained and published for language generation. It continues the line of work begun by BERT and GPT-2. Increasing neural network size once again proved successful. After pre-training on essentially all text from the Internet, T-NLG can be fine-tuned to solve various downstream tasks like answering questions, understanding documents or simply filling in as a conversational agent (like Meena from our last post). In order to achieve this new state of the art in large-scale deep learning, Microsoft came up with novel optimization techniques and tricks that make it feasible to train artificial neural networks with over 100 billion parameters.
To put this number into perspective: the human brain has around 100 trillion synapses (in a nutshell, their job is to connect neurons). That’s a thousand times more than 100 billion. Does this mean we can now train artificial neural networks that are 0.1% size of the human brain? Definitely not, biological neurons are much more complex than artificial ones and the recent discovery shows that a single biological neuron is able to compute XOR, a mathematical function that requires a multilayer artificial neural network to be computed.
Why it matters
With industry heavily focused and invested in NLP, the technology is one of deepsense.ai’s cutting edge trends in 2020. The new models being delivered support business and consumer services like chatbots and search engines.
Given these developments, any bigger and newer language model brings us closer to the day when natural communication between computers and humans a la Star Trek will be possible. In decidedly less galactic terms, it is also a convenient way to communicate with a computer when your hands are otherwise occupied – like when you’re cooking or driving a car. A simple “skip that song” makes a difference.
Growing Neural Cellular Automata
Apparently nature is the most brilliant engineer of all, with its unrivaled ability to reuse and repurpose building blocks to deliver a breathtaking set of incredibly diverse forms. With cells being the most basic building block of every creature alive, from the lowly germ to humans, it is both fascinating and inspiring to crack the process of making a single cell that not only functions but is specialized in a particular context.
Putting it simply – a single cell can be a separate organism or only a tiny part of a larger structure, designed to perform a specific role, without itself having the ability to survive. Apart from that, living organisms can regenerate themselves, sometimes regrowing lost limbs, sometimes just healing wounds. Cracking this process would deliver a significant boost for engineering.
What happened exactly?
Every living cell contains a DNA code that contains all the information about the organism in which it lives–its height, eye color, and other factors. In terms of programming, it can be seen as a list of instructions containing if-then statements, which tell the cell how it should operate in its given context and surroundings.
Research published on distill.pub tackles the matter by delivering images built with single “cells” that contain DNA-like information which, when rolled out using local updates, converges to a full image in global scale. Some parts of the image can be destroyed, and the image will rebuild itself with the lifelike process of spawning new cells to replace destroyed ones.
Also, more images can be spawned and built near the original one, effectively blending them to observe how cells interact with the context. In some cases, when blended and destroyed, the images rebuild in odd ways, delivering imperfect or twisted versions of previous projects. Scar tissue of some kind.
Why it matters
Currently, it is hard to come up with a practical application, but it brings science-fiction-like prospects that could result in unimaginable progress. Just to name a few:
Building megastructures – be it a Death Star, a Dyson Sphere or a planet-wide supercolony, making a centralized program to control the whole system might be challenging due to the very scale of that system and the latencies within it.
Also, when delivering a “smart gravel” that transforms into the desired type of material or form you need to build the structure, managing the construction or repairs would be much easier. A car wouldn’t have a spare tire and a toolbox, but a box containing smart-gravel (or any other currently non-existing smart material) infused with the DNA-like code of the car and used to repair what’s broken.
But that’s a distant future and practical appliances are yet to come – not unlike when neural networks and machine learning started from the creation of a computational model for neural networks in 1943. The technology is now transforming our daily lives in the form of machine learning, reinforcement learning or any other paradigm used to infuse our tools.
These lyrics don’t exist
The final news is more lifestyle related and lighter in mood than the rest. After the popularity of thispersondoesnotexist.com, the time has come for song lyrics. Pop on over to theselyriscsdontexist.com and see how you can choose the mood, theme and musical style–then read the lyrics the AI spits out. Perhaps a happy heavy metal song about eating burritos would suit your fancy? No problem. Or would a sad pop ballad about a steam locomotive be more to your liking? That’s no problem, either.
Why does it matter
It doesn’t, but data science is also about fun and neural networks can power both inspiring and powerful tech as well as silly toys. Because why not?