In just a few years, the number of artworks produced by self-described AI artists has dramatically increased. Some of these works have been sold by large auction houses for dizzying prices and have found their way into prestigious curated collections. Initially spearheaded by a few technologically knowledgeable artists who adopted computer programming as part of their creative process, AI art has recently been embraced by the masses, as image generation technology has become both more effective and easier to use without coding skills.
The AI art movement rides on the coattails of technical progress in computer vision, a research area dedicated to designing algorithms that can process meaningful visual information. A subclass of computer vision algorithms, called generative models, occupies center stage in this story. Generative models are artificial neural networks that can be “trained” on large datasets containing millions of images and learn to encode their statistically salient features. After training, they can produce completely new images that are not contained in the original dataset, often guided by text prompts that explicitly describe the desired results. Until recently, images produced through this approach remained somewhat lacking in coherence or detail, although they possessed an undeniable surrealist charm that captured the attention of many serious artists. However, earlier this year the tech company Open AI unveiled a new model— nicknamed DALL·E 2—that can generate remarkably consistent and relevant images from virtually any text prompt. DALL·E 2 can even produce images in specific styles and imitate famous artists rather convincingly, as long as the desired effect is adequately specified in the prompt. A similar tool has been released for free to the public under the name Craiyon (formerly “DALL·E mini”).
The coming-of-age of AI art raises a number of interesting questions, some of which—such as whether AI art is really art, and if so, to what extent it is really made by AI—are not particularly original. These questions echo similar worries once raised by the invention of photography. By merely pressing a button on a camera, someone without painting skills could suddenly capture a realistic depiction of a scene. Today, a person can press a virtual button to run a generative model and produce images of virtually any scene in any style. But cameras and algorithms do not make art. People do. AI art is art, made by human artists who use algorithms as yet another tool in their creative arsenal. While both technologies have lowered the barrier to entry for artistic creation— which calls for celebration rather than concern—one should not underestimate the amount of skill, talent, and intentionality involved in making interesting artworks.
Like any novel tool, generative models introduce significant changes in the process of art-making. In particular, AI art expands the multifaceted notion of curation and continues to blur the line between curation and creation.
There are at least three ways in which making art with AI can involve curatorial acts. The first, and least original, has to do with the curation of outputs. Any generative algorithm can produce an indefinite number of images, but not all of these will typically be conferred artistic status. The process of curating outputs is very familiar to photographers, some of whom routinely capture hundreds or thousands of shots from which a few, if any, might be carefully selected for display. Unlike painters and sculptors, photographers and AI artists have to deal with an abundance of (digital) objects, whose curation is part and parcel of the artistic process. In AI research at large, the act of “cherry-picking” particularly good outputs is seen as bad scientific practice, a way to misleadingly inflate the perceived performance of a model. When it comes to AI art, however, cherry-picking can be the name of the game. The artist’s intentions and artistic sensibility may be expressed in the very act of promoting specific outputs to the status of artworks.
Second, curation may also happen before any images are generated. In fact, while “curation” applied to art generally refers to the process of selecting existing work for display, curation in AI research colloquially refers to the work that goes into crafting a dataset on which to train an artificial neural network. This work is crucial, because if a dataset is poorly designed, the network will often fail to learn how to represent desired features and perform adequately. Furthermore, if a dataset is biased, the network will tend to reproduce, or even amplify, such bias—including, for example, harmful stereotypes. As the saying goes, “garbage in, garbage out.” The adage holds true for AI art, too, except “garbage” takes on an aesthetic (and subjective) dimension.
In one example of the IC’s successful use of AI, after exhausting all other avenues—from human spies to signals intelligence—the US was able to find an unidentified WMD research and development facility in a large Asian country by locating a bus that traveled between it and other known facilities. To do that, analysts employed algorithms to search and evaluate images of nearly every square inch of the country, according to a senior US intelligence official who spoke on background with the understanding of not being named.
While AI can calculate, retrieve, and employ programming that performs limited rational analyses, it lacks the calculus to properly dissect more emotional or unconscious components of human intelligence that are described by psychologists as system 1 thinking.
AI, for example, can draft intelligence reports that are akin to newspaper articles about baseball, which contain structured non-logical flow and repetitive content elements. However, when briefs require complexity of reasoning or logical arguments that justify or demonstrate conclusions, AI has been found lacking. When the intelligence community tested the capability, the intelligence official says, the product looked like an intelligence brief but was otherwise nonsensical.
Such algorithmic processes can be made to overlap, adding layers of complexity to computational reasoning, but even then those algorithms can’t interpret context as well as humans, especially when it comes to language, like hate speech.
AI’s comprehension might be more analogous to the comprehension of a human toddler, says Eric Curwin, chief technology officer at Pyrra Technologies, which identifies virtual threats to clients from violence to disinformation. “For example, AI can understand the basics of human language, but foundational models don’t have the latent or contextual knowledge to accomplish specific tasks,” Curwin says.
“From an analytic perspective, AI has a difficult time interpreting intent,” Curwin adds. “Computer science is a valuable and important field, but it is social computational scientists that are taking the big leaps in enabling machines to interpret, understand, and predict behavior.”
In order to “build models that can begin to replace human intuition or cognition,” Curwin explains, “researchers must first understand how to interpret behavior and translate that behavior into something AI can learn.”
Although machine learning and big data analytics provide predictive analysis about what might or will likely happen, it can’t explain to analysts how or why it arrived at those conclusions. The opaqueness in AI reasoning and the difficulty vetting sources, which consist of extremely large data sets, can impact the actual or perceived soundness and transparency of those conclusions.
Transparency in reasoning and sourcing are requirements for the analytical tradecraft standards of products produced by and for the intelligence community. Analytic objectivity is also statuatorically required, sparking calls within the US government to update such standards and laws in light of AI’s increasing prevalence.
Machine learning and algorithms when employed for predictive judgments are also considered by some intelligence practitioners as more art than science. That is, they are prone to biases, noise, and can be accompanied by methodologies that are not sound and lead to errors similar to those found in the criminal forensic sciences and arts.
In recent years, researchers have used artificial intelligence to improve translation between programming languages or automatically fix problems. The AI system DrRepair, for example, has been shown to solve most issues that spawn error messages. But some researchers dream of the day when AI can write programs based on simple descriptions from non-experts.
On Tuesday, Microsoft and OpenAI shared plans to bring GPT-3, one of the world’s most advanced models for generating text, to programming based on natural language descriptions. This is the first commercial application of GPT-3 undertaken since Microsoft invested $1 billion in OpenAI last year and gained exclusive licensing rights to GPT-3.
“If you can describe what you want to do in natural language, GPT-3 will generate a list of the most relevant formulas for you to choose from,” said Microsoft CEO Satya Nadella in a keynote address at the company’s Build developer conference. “The code writes itself.”
Microsoft VP Charles Lamanna told WIRED the sophistication offered by GPT-3 can help people tackle complex challenges and empower people with little coding experience. GPT-3 will translate natural language into PowerFx, a fairly simple programming language similar to Excel commands that Microsoft introduced in March.
This is the latest demonstration of applying AI to coding. Last year at Microsoft’s Build, OpenAI CEO Sam Altman demoed a language model fine-tuned with code from GitHub that automatically generates lines of Python code. As WIRED detailed last month, startups like SourceAI are also using GPT-3 to generate code. IBM last month showed how its Project CodeNet, with 14 million code samples from more than 50 programming languages, could reduce the time needed to update a program with millions of lines of Java code for an automotive company from one year to one month.
Microsoft’s new feature is based on a neural network architecture known as Transformer, used by big tech companies including Baidu, Google, Microsoft, Nvidia, and Salesforce to create large language models using text training data scraped from the web. These language models continually grow larger. The largest version of Google’s BERT, a language model released in 2018, had 340 million parameters, a building block of neural networks. GPT-3, which was released one year ago, has 175 billion parameters.
Such efforts have a long way to go, however. In one recent test, the best model succeeded only 14 percent of the time on introductory programming challenges compiled by a group of AI researchers.