Feb 16, 2016 · guest post

Machines and Metaphors

This is a guest post by Gene Kogan, an artist and programmer who applies emerging technology into artistic and expressive contexts, and teaches courses and workshops on topics related to code and art.

Recent advances in deep learning research have renewed popular interest in machine intelligence. With new benchmarks set in tough problems (e.g., image classification and speech recognition), researchers are exploring unexpected and exciting applications, and eliciting public engagement and private investment. These recent breakthroughs have captured the attention of many for whom AI was previously obscure, as new capabilities spur applications of interest to wider public audiences.

But these advances have captured more than just our attention; they’ve captured our  imagination. Artists have been quick to apply these new techniques for novel creations, exploring the uncharted territories of machine creativity, slyly provoking questions of greater importance. What is creativity anyway? How do machines perceive, learn, and imitate?

When an algorithm is taught to paint the Mona Lisa in the style of van Gogh’s Starry Night, it doesn’t just demonstrate an ability to paint like van Gogh; it demonstrates a much more general ability to emulate human behavior. Taken further, it shows the capacity of an algorithm to take bits of seemingly disorganized and meaningless morsels of data–pixels, characters–and abstract knowledge from them which is quite meaningful to humans. Even the world’s foremost researchers are admittedly awestruck by these results. We may understand the math, but the intuition for why it works escapes us.

neural-style of Mona Lisa in cubist, expressionist, and impressionist form

Artistic applications can help mitigate this uncertainty, and naturally, many are initiated within the research community itself, sometimes with intentionally creative undertones. Deepdream and style transfer (or “neural style”, “#stylenet”) were publicly released by researchers, piquing the curiosity of many practitioners online (including myself) who riffed on the software to create countless artworks. Both received a great deal of mainstream press coverage [1][2][3][4][5][6], becoming among the first examples of machine-learned generative art being shown to wider audiences. Using Justin Johnson’s popular neural-style implementation, I produced a restyled version of a scene from Alice in Wonderland. At the time, the results seemed too good to be real, but I had a hunch that more clarity would arrive downstream.

The implementation of a deep convolutional generative adversarial network (DCGAN) published on Github by Alec Radford, Soumith Chintala, and Luke Metz is a good example of research partially motivated by creative goals. Describing their algorithm as “tripping” and packing the README with troves of machine-hallucinated psychedelia, they seemed to be deliberately inviting artists to repurpose their code. So I took the bait and trained a DCGAN to generate animated interpolations of handwritten Chinese characters. Others applied the technique to produce fake flowers and manga characters, eliciting praise from Yann LeCun and others.

excerpt from A Book from the Sky

Spurred by these and others, more creative machine learning hacks have appeared in recent months, including another project involving Chinese characters by @hardmaru. Here, the artist invented wholly new characters from a trained recurrent neural network. Continuing with the theme of written language aesthetics, Erik Bernhardsson explored the latent space of typography, training a network on 50,000 fonts. Another strain of research, combining convolutional and recurrent neural networks, has produced software capable of annotating or describing images with natural language [1][2], and even more experimentally, the reverse. These hint toward a future in which machines can autonomously process information multi-modally, exchanging text for images or sounds, and vice-versa. These implementations reflect a growing interest of those with more technical backgrounds to apply their research in artistic and design-focused ways.

excerpt from Erik Bernhardsson’s font-generating neural net

Conversely, those coming from artistic backgrounds have been delving deeper into scientific literature, reading public papers on, and designing their own deep neural networks using open-source frameworks like Caffe, Theano, Torch, and TensorFlow, which have dramatically eased the process of getting started. Even higher-level libraries on top of these frameworks have appeared, including Keras, Lasagne, Blocks, and others. The tools of the trade for artists and scientists alike have been converging, blurring the distinctions between them and facilitating new lines of inquiry and cross-disciplinary dialogue.

The rise of these powerful tools has precipitated an explosion in public discourse about machine learning. The ML subreddit, once a treasure chest of wild speculation, is now rife with students and amateurs asking technical questions and sharing project ideas. Many of the authors of the libraries mentioned above actively maintain blogs where they discuss the latest discoveries, often sharing code for how to reproduce them.

One researcher, Andrej Karpathy of Stanford and OpenAI, even released a Javascript implementation of a convolutional neural network, convnet.js. One may wonder why, since Javascript’s limitations give this library little applicability to scientific research. But that isn’t the point. Embedded in a web page, the neural network is instantly accessible: anyone with an internet connection can now interact with an algorithm that has revolutionized computer vision and speech processing.

As these new methods improve and mature, they will rapidly be applied in contexts that could have significant consequences for society, raising the stakes for their use. This creates more urgency for outreach and education, to inform the general public about these multifaceted and counterintuitive technologies. As exciting as they are, they also risk misuse. The example usually cited is how a self-driving car would choose which of two pedestrians to kill if it could only avoid one. Although this scenario is a bit exaggerated, it’s an example of a decision a machine would inevitably have to make. As implementation decisions along the way introduce biases and affect the outcome, the public should have a say in influencing their development.

Fortunately, the deep learning research community largely conducts research openly, embracing open-review platforms like arxiv and publishing open source software. Additionally, libraries wrapping machine learning functionality into creative coding toolkits like openFrameworks provide an avenue for artists to probe deeper.


images of animals clustered in 2D using openframeworks libraries ofxCcv and ofxTSNE

But the fact that tools are open source does not mean the public understands what those tools entail. Along the journey from initial scientific research and to mass deployment of machine intelligence, artists can help illuminate the gap, providing accessible and engaging cultural metaphors which are more readily understandable than the layers of abstraction in pure research. The machine metaphor has been successful in the past, helping to popularize computer vision in the context of interactive installations, celebrating its playful side while simultaneously raising caution about its lesser known properties. If present trends continue into the near future, machine intelligence could follow a similar path.

- Gene Kogan

Read more

Feb 18, 2016 · interview
Feb 3, 2016 · post

Latest posts

Sep 22, 2021 · post

Automatic Summarization from TextRank to Transformers

by Melanie Beck · Automatic summarization is a task in which a machine distills a large amount of data into a subset (the summary) that retains the most relevant and important information from the whole. While traditionally applied to text, automatic summarization can include other formats such as images or audio. In this article we’ll cover the main approaches to automatic text summarization, talk about what makes for a good summary, and introduce Summarize. – a summarization prototype we built that showcases several automatic summarization techniques. more
Sep 21, 2021 · post

Extractive Summarization with Sentence-BERT

by Victor Dibia · In extractive summarization, the task is to identify a subset of text (e.g., sentences) from a document that can then be assembled into a summary. Overall, we can treat extractive summarization as a recommendation problem. That is, given a query, recommend a set of sentences that are relevant. The query here is the document, relevance is a measure of whether a given sentence belongs in the document summary. How we go about obtaining this measure of relevance varies (a common dilemma for any recommendation system). more
Sep 20, 2021 · post

How (and when) to enable early stopping for Gensim's Word2Vec

by Melanie Beck · The Gensim library is a staple of the NLP stack. While it primarily focuses on topic modeling and similarity for documents, it also supports several word embedding algorithms, including what is likely the best-known implementation of Word2Vec. Word embedding models like Word2Vec use unlabeled data to learn vector representations for each token in a corpus. These embeddings can then be used as features in myriad downstream tasks such as classification, clustering, or recommendation systems. more
Jul 7, 2021 · post

Exploring Multi-Objective Hyperparameter Optimization

By Chris and Melanie. The machine learning life cycle is more than data + model = API. We know there is a wealth of subtlety and finesse involved in data cleaning and feature engineering. In the same vein, there is more to model-building than feeding data in and reading off a prediction. ML model building requires thoughtfulness both in terms of which metric to optimize for a given problem, and how best to optimize your model for that metric! more
Jun 9, 2021 ·

Deep Metric Learning for Signature Verification

By Victor and Andrew. TLDR; This post provides an overview of metric learning loss functions (constrastive, triplet, quadruplet, and group loss), and results from applying contrastive and triplet loss to the task of signature verification. A complete list of the posts in this series is outlined below: Pretrained Models as Baselines for Signature Verification -- Part 1: Deep Learning for Automatic Offline Signature Verification: An Introduction Part 2: Pretrained Models as Baselines for Signature Verification Part 3: Deep Metric Learning for Signature Verification In our previous blog post, we discussed how pretrained models can serve as strong baselines for the task of signature verification. more
May 27, 2021 · post

Pretrained Models as a Strong Baseline for Automatic Signature Verification

By Victor and Andrew. Figure 1. Baseline approach for automatic signature verification using pretrained models TLDR; This post describes how pretrained image classification models can be used as strong baselines for the task of signature verification. The full list of posts in the series is outlined below: Pretrained Models as Baselines for Signature Verification -- Part 1: Deep Learning for Automatic Offline Signature Verification: An Introduction Part 2: Pretrained Models as Baselines for Signature Verification Part 3: Deep Metric Learning for Signature Verification As discussed in our introductory blog post, offline signature verification is a biometric verification task that aims to discriminate between genuine and forged samples of handwritten signatures. more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)


In-depth guides to specific machine learning capabilities


Machine learning prototypes and interactive notebooks


A usable library for question answering on large datasets.

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.


Cloudera Fast Forward is an applied machine learning reseach group.
Cloudera   Blog   Twitter