Tag: architecture

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

Google Brain researchers published this amazing paper, with accompanying GIF where they show the true power of AutoML.

AutoML stands for automated machine learning, and basically refers to an algorithm autonomously building the best machine learning model for a given problem.

This task of selecting the best ML model is difficult as it is. There are many different ML algorithms to choose from, and each of these has many different settings ([hyper]parameters) you can change to optimalize the model’s predictions.

For instance, let’s look at one specific ML algorithm: the neural network. Not only can we try out millions of different neural network architectures (ways in which the nodes and lyers of a network are connected), but each of these we can test with different loss functions, learning rates, dropout rates, et cetera. And this is only one algorithm!

In their new paper, the Google Brain scholars display how they managed to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. Using evolutionary principles, they have developed an AutoML framework that tailors its own algorithms and architectures to best fit the data and problem at hand.

This is AI research at its finest, and the results are truly remarkable!

GIF for the interpretation of the best evolved algorithm

You can read the full paper open access here: https://arxiv.org/abs/2003.03384 (quick download link)

The original code is posted here on github: github.com/google-research/google-research/tree/master/automl_zero#automl-zero

ArchiGAN: Designing buildings with reinforcement learning

I’ve seen some uses of reinforcement learning and generative algorithms for architectural purposes already, like these evolving blueprints for school floorplans. However, this new application called ArchiGAN blew me away!

ArchiGAN (try here) was made by Stanislas Chaillou as a Harvard master’s thesis project. The program functions in three steps:

building footprint massing
program repartition
furniture layout

Generation stack image — Stanislas’ three generation steps

Each of these three steps uses a TensorFlow Pix2Pix GAN-model (Christopher Hesse’s implementation) in the back-end, and their combination makes for a entire apartment building “generation stack” — according to Stanislas — which also allows for user input at each step.

The design of a building can be inferred from the piece of land it stands on. Hence, Stanislas fed his first model using GIS-data (Geographic Information System) from the city of Boston in order to generate typical footprints based on parcel shapes.

Model 1 results image — The inputs and outputs of model I

Stanislas’ second model was responsible for repartition and fenestration (the placement of windows and doors). This GAN took the footprint of the building (the output of model I) as input, along with the position of the entrance door (green square), and the positions of the user-specified windows.

Stanislas used a database of 800+ plans of apartments for training. To visualize the output, rooms are color-coded and walls and fenestration are blackened.

Model II results image — The inputs and outputs of model II

Finally, in the third model, the rooms are filled with appropriate furniture. What training data Stanislas has used here, he did not specify in the original blog.

Model III results image — The inputs and outputs of model III

Now, to put all things together, Stanislas created a great interactive tool you can play with yourself. The original NVIDEA blog contains some great GIFs of the tool being used:

Stanislas’ GAN-models progressively learned to design rooms and realistically position doors and windows. It took about 250 iterations to get some realistic floorplans out of the algorithm. Here’s how an example learning sequence looked like:

Architectural sequence image — Visualization of the training process

Now, Stanislas was not done yet. He also scaled the utilization of GANs to design whole apartment buildings. Here, he chains the models and processes multiple units as single images at each step.

Apartment building generation pipeline image — Generating whole appartment blocks using ArchiGAN

Stanislas did other cool things to improve the flexibility of his ArchiGAN models, about which you can read more in the original blog. Let these visuals entice you to read more:

GAN-enabled building layouts image — ArchiGAN scaled to handle whole appartment blocks and neighborhoods.

I believe a statistical approach to design conception will shape AI’s potential for Architecture. This approach is less deterministic and more holistic in character. Rather than using machines to optimize a set of variables, relying on them to extract significant qualities and mimicking them all along the design process represents a paradigm shift.
Stanislas Chaillou (via)

I am so psyched about these innovative applications of machine learning, so please help me give Stanislas the attention and credit he deserved.

Currently, Stanislas is Data Scientist & Architect at Spacemaker.ai. Read more about him in his NVIDEA developer bio here. He recently published a sequence of articles, laying down the premise of AI’s intersection with Architecture. Read here about the historical background behind this significant evolution, to be followed by AI’s potential for floor plan design, and for architectural style analysis & generation.

3D visual representations of common neural network architectures

Came across this awesome Youtube video that blew my mind. Definitely a handy resource if you want to explain the inner workings of neural networks. Have a look!