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3 ways AI is transforming music

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Each fall, I begin my course on the intersection of music and artificial intelligence by asking my students if they’re concerned about AI’s role in composing or producing music.

So far, the question has always elicited a resounding “yes.”

Their fears can be summed up in a sentence: AI will create a world where music is plentiful, but musicians get cast aside.

In the upcoming semester, I’m anticipating a discussion about Paul McCartney, who in June 2023 announced that he and a team of audio engineers had used machine learning to uncover a “lost” vocal track of John Lennon by separating the instruments from a demo recording.

But resurrecting the voices of long-dead artists is just the tip of the iceberg in terms of what’s possible – and what’s already being done.

In an interview, McCartney admitted that AI represents a “scary” but “exciting” future for music. To me, his mix of consternation and exhilaration is spot on.

Here are three ways AI is changing the way music gets made – each of which could threaten human musicians in various ways:

1. Song composition

Many programs can already generate music with a simple prompt from the user, such as “Electronic Dance with a Warehouse Groove.”

Fully generative apps train AI models on extensive databases of existing music. This enables them to learn musical structures, harmonies, melodies, rhythms, dynamics, timbres and form, and generate new content that stylistically matches the material in the database.

There are many examples of these kinds of apps. But the most successful ones, like Boomy, allow nonmusicians to generate music and then post the AI-generated results on Spotify to earn money. Spotify recently removed many of these Boomy-generated tracks, claiming that this would protect human artists’ rights and royalties.

The two companies quickly came to an agreement that allowed Boomy to re-upload the tracks. But the algorithms powering these apps still have a troubling ability to infringe upon existing copyright, which might go unnoticed to most users. After all, basing new music on a data set of existing music is bound to cause noticeable similarities between the music in the data set and the generated content.

Furthermore, streaming services like Spotify and Amazon Music are naturally incentivized to develop their own AI music-generation technology. Spotify, for instance, pays 70% of the revenue of each stream to the artist who created it. If the company could generate that music with its own algorithms, it could cut human artists out of the equation altogether.

Over time, this could mean more money for giant streaming services, less money for musicians – and a less human approach to making music.

2. Mixing and mastering

Machine-learning-enabled apps that help musicians balance all of the instruments and clean up the audio in a song – what’s known as mixing and mastering – are valuable tools for those who lack the experience, skill or resources to pull off professional-sounding tracks.

Over the past decade, AI’s integration into music production has revolutionized how music is mixed and mastered. AI-driven apps like Landr, Cryo Mix and iZotope’s Neutron can automatically analyze tracks, balance audio levels and remove noise.

These technologies streamline the production process, allowing musicians and producers to focus on the creative aspects of their work and leave some of the technical drudgery to AI.

While these apps undoubtedly take some work away from professional mixers and producers, they also allow professionals to quickly complete less lucrative jobs, such as mixing or mastering for a local band, and focus on high-paying commissions that require more finesse. These apps also allow musicians to produce more professional-sounding work without involving an audio engineer they can’t afford.

3. Instrumental and vocal reproduction

Using “tone transfer” algorithms via apps like Mawf, musicians can transform the sound of one instrument into another.

Thai musician and engineer Yaboi Hanoi’s song “Enter Demons & Gods,” which won the third international AI Song Contest in 2022, was unique in that it was influenced not only by Thai mythology, but also by the sounds of native Thai musical instruments, which have a non-Western system of intonation. One of the most technically exciting aspects of Yaboi Hanoi’s entry was the reproduction of a traditional Thai woodwind instrument – the pi nai – which was resynthesized to perform the track.

A variant of this technology lies at the core of the Vocaloid voice synthesis software, which allows users to produce convincingly human vocal tracks with swappable voices.

Unsavory applications of this technique are popping up outside of the musical realm. For example, AI voice swapping has been used to scam people out of money.

But musicians and producers can already use it to realistically reproduce the sound of any instrument or voice imaginable. The downside, of course, is that this technology can rob instrumentalists of the opportunity to perform on a recorded track.

While I applaud Yaboi Hanoi’s victory, I have to wonder if it will encourage musicians to use AI to fake a cultural connection where none exists.

In 2021, Capitol Music Group made headlines by signing an “AI rapper” that had been given the avatar of a Black male cyborg, but which was really the work of Factory New non-Black software engineers. The backlash was swift, with the record label roundly excoriated for blatant cultural appropriation.

But AI musical cultural appropriation is easier to stumble into than you might think. With the extraordinary size of songs and samples that comprise the data sets used by apps like Boomy – see the open source “Million Song Dataset” for a sense of the scale – there’s a good chance that a user may unwittingly upload a newly generated track that pulls from a culture that isn’t their own, or cribs from an artist in a way that too closely mimics the original. Worse still, it won’t always be clear who is to blame for the offense, and current U.S. copyright laws are contradictory and woefully inadequate to the task of regulating these issues.

These are all topics that have come up in my own class, which has allowed me to at least inform my students of the dangers of unchecked AI and how to best avoid these pitfalls.

At the same time, at the end of each fall semester, I’ll again ask my students if they’re concerned about an AI takeover of music. At that point, and with a whole semester’s experience investigating these technologies, most of them say they’re excited to see how the technology will evolve and where the field will go.

Some dark possibilities do lie ahead for humanity and AI. Still, at least in the realm of musical AI, there is cause for some optimism – assuming the pitfalls are avoided.

^^^

Jason Palamara, Assistant Professor of Music Technology, Indiana University

This article originally appeared in The Conversation under a Creative Commons License

Feature Image by Mikhail Nilov via Pexels 

Photo by Tara Winstead

The folly of making art with text-to-image generative AI

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Making art using artificial intelligence isn’t new. It’s as old as AI itself.

What’s new is that a wave of tools now let most people generate images by entering a text prompt. All you need to do is write “a landscape in the style of van Gogh” into a text box, and the AI can create a beautiful image as instructed.

The power of this technology lies in its capacity to use human language to control art generation. But do these systems accurately translate an artist’s vision? Can bringing language into art-making truly lead to artistic breakthroughs?

Engineering outputs

I’ve worked with generative AI as an artist and computer scientist for years, and I would argue that this new type of tool constrains the creative process.

When you write a text prompt to generate an image with AI, there are infinite possibilities. If you’re a casual user, you might be happy with what AI generates for you. And startups and investors have poured billions into this technology, seeing it as an easy way to generate graphics for articles, video game characters and advertisements.

Grid of many images of cartoon women in various costumes.
Benlisquare/Wikimedia Commons, CC BY-SA

In contrast, an artist might need to write an essaylike prompt to generate a high-quality image that reflects their vision – with the right composition, the right lighting and the correct shading. That long prompt is not necessarily descriptive of the image but typically uses lots of keywords to invoke the system of what’s in the artist’s mind. There’s a relatively new term for this: prompt engineering.

Basically, the role of an artist using these tools is reduced to reverse-engineering the system to find the right keywords to compel the system to generate the desired output. It takes a lot of effort, and much trial and error, to find the right words.

AI isn’t as intelligent as it seems

To learn how to better control the outputs, it’s important to recognize that most of these systems are trained on images and captions from the internet.

Think about what a typical image caption tells about an image. Captions are typically written to complement the visual experience in web browsing.

For example, the caption might describe the name of the photographer and the copyright holder. On some websites, like Flickr, a caption typically describes the type of camera and the lens used. On other sites, the caption describes the graphic engine and hardware used to render an image.

So to write a useful text prompt, users need to insert many nondescriptive keywords for the AI system to create a corresponding image.

Today’s AI systems are not as intelligent as they seem; they are essentially smart retrieval systems that have a huge memory and work by association.

Artists frustrated by a lack of control

Is this really the sort of tool that can help artists create great work?

At Playform AI, a generative AI art platform that I founded, we conducted a survey to better understand artists’ experiences with generative AI. We collected responses from over 500 digital artists, traditional painters, photographers, illustrators and graphic designers who had used platforms such as DALL-E, Stable Diffusion and Midjourney, among others.

Only 46% of the respondents found such tools to be “very useful,” while 32% found them somewhat useful but couldn’t integrate them to their workflow. The rest of the users – 22% – didn’t find them useful at all.

The main limitation artists and designers highlighted was a lack of control. On a scale 0 to 10, with 10 being most control, respondents described their ability to control the outcome to be between 4 and 5. Half the respondents found the outputs interesting, but not of a high enough quality to be used in their practice.

When it came to beliefs about whether generative AI would influence their practice, 90% of the artists surveyed thought that it would; 46% believed that the effect would be a positive one, with 7% predicting that it would have a negative effect. And 37% thought their practice would be affected but weren’t sure in what way.

The best visual art transcends language

Are these limitations fundamental, or will they just go away as the technology improves?

Of course, newer versions of generative AI will give users more control over outputs, along with higher resolutions and better image quality.

But to me, the main limitation, as far as art is concerned, is foundational: it’s the process of using language as the main driver in generating the image.

Visual artists, by definition, are visual thinkers. When they imagine their work, they usually draw from visual references, not words – a memory, a collection of photographs or other art they’ve encountered.

When language is in the driver’s seat of image generation, I see an extra barrier between the artist and the digital canvas. Pixels will be rendered only through the lens of language. Artists lose the freedom of manipulating pixels outside the boundaries of semantics.

If two artists enter the exact same prompt, it’s very unlikely that the system will generate the same image. That’s not due to anything the artist did; the different outcomes are simply due the AI’s starting from different random initial images.

In other words, the artist’s output is boiled down to chance.

Nearly two-thirds of the artists we surveyed had concerns that their AI generations might be similar to other artists’ works and that the technology does not reflect their identity – or even replaces it altogether.

The issue of artist identity is crucial when it comes to making and recognizing art. In the 19th century, when photography started to become popular, there was a debate about whether photography was a form of art. It came down to a court case in France in 1861 to decide whether photography could be copyrighted as an art form. The decision hinged on whether an artist’s unique identity could be expressed through photographs.

Those same questions emerge when considering AI systems that are taught with the internet’s existing images.

Before the emergence of text-to-image prompting, creating art with AI was a more elaborate process: Artists usually trained their own AI models based on their own images. That allowed them to use their own work as visual references and retain more control over the outputs, which better reflected their unique style.

Text-to-image tools might be useful for certain creators and casual everyday users who want to create graphics for a work presentation or a social media post.

But when it comes to art, I can’t see how text-to-image software can adequately reflect the artist’s true intentions or capture the beauty and emotional resonance or works that grip viewers and makes them see the world anew.

^^^

Ahmed Elgammal, Professor of Computer Science and Director of the Art & AI Lab, Rutgers University.

This article originally appeared in The Conversation, under a Creatives Commons License.

Feature Image by Tara Winstead from Pexels.

Image by Memo Akten, CC BY-SA.

Generative AI is a minefield for copyright law

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In 2022, an AI-generated work of art won the Colorado State Fair’s art competition. The artist, Jason Allen, had used Midjourney – a generative AI system trained on art scraped from the internet – to create the piece. The process was far from fully automated: Allen went through some 900 iterations over 80 hours to create and refine his submission.

Yet his use of AI to win the art competition triggered a heated backlash online, with one Twitter user claiming, “We’re watching the death of artistry unfold right before our eyes.”

As generative AI art tools like Midjourney and Stable Diffusion have been thrust into the limelight, so too have questions about ownership and authorship.

These tools’ generative ability is the result of training them with scores of prior artworks, from which the AI learns how to create artistic outputs.

Should the artists whose art was scraped to train the models be compensated? Who owns the images that AI systems produce? Is the process of fine-tuning prompts for generative AI a form of authentic creative expression?

On one hand, technophiles rave over work like Allen’s. But on the other, many working artists consider the use of their art to train AI to be exploitative.

We’re part of a team of 14 experts across disciplines that just published a paper on generative AI in Science magazine. In it, we explore how advances in AI will affect creative work, aesthetics and the media. One of the key questions that emerged has to do with U.S. copyright laws, and whether they can adequately deal with the unique challenges of generative AI.

Copyright laws were created to promote the arts and creative thinking. But the rise of generative AI has complicated existing notions of authorship.

Photography serves as a helpful lens

Generative AI might seem unprecedented, but history can act as a guide.

Take the emergence of photography in the 1800s. Before its invention, artists could only try to portray the world through drawing, painting or sculpture. Suddenly, reality could be captured in a flash using a camera and chemicals.

As with generative AI, many argued that photography lacked artistic merit. In 1884, the U.S. Supreme Court weighed in on the issue and found that cameras served as tools that an artist could use to give an idea visible form; the “masterminds” behind the cameras, the court ruled, should own the photographs they create.

From then on, photography evolved into its own art form and even sparked new abstract artistic movements.

AI can’t own outputs

Unlike inanimate cameras, AI possesses capabilities – like the ability to convert basic instructions into impressive artistic works – that make it prone to anthropomorphization. Even the term “artificial intelligence” encourages people to think that these systems have humanlike intent or even self-awareness.

This led some people to wonder whether AI systems can be “owners.” But the U.S. Copyright Office has stated unequivocally that only humans can hold copyrights.

So who can claim ownership of images produced by AI? Is it the artists whose images were used to train the systems? The users who type in prompts to create images? Or the people who build the AI systems?

Infringement or fair use?

While artists draw obliquely from past works that have educated and inspired them in order to create, generative AI relies on training data to produce outputs.

This training data consists of prior artworks, many of which are protected by copyright law and which have been collected without artists’ knowledge or consent. Using art in this way might violate copyright law even before the AI generates a new work.

For Jason Allen to create his award-winning art, Midjourney was trained on 100 million prior works.

Was that a form of infringement? Or was it a new form of “fair use,” a legal doctrine that permits the unlicensed use of protected works if they’re sufficiently transformed into something new?

While AI systems do not contain literal copies of the training data, they do sometimes manage to recreate works from the training data, complicating this legal analysis.

Will contemporary copyright law favor end users and companies over the artists whose content is in the training data?

To mitigate this concern, some scholars propose new regulations to protect and compensate artists whose work is used for training. These proposals include a right for artists to opt out of their data’s being used for generative AI or a way to automatically compensate artists when their work is used to train an AI.

Muddled ownership

Training data, however, is only part of the process. Frequently, artists who use generative AI tools go through many rounds of revision to refine their prompts, which suggests a degree of originality.

Answering the question of who should own the outputs requires looking into the contributions of all those involved in the generative AI supply chain.

The legal analysis is easier when an output is different from works in the training data. In this case, whoever prompted the AI to produce the output appears to be the default owner.

However, copyright law requires meaningful creative input – a standard satisfied by clicking the shutter button on a camera. It remains unclear how courts will decide what this means for the use of generative AI. Is composing and refining a prompt enough?

Matters are more complicated when outputs resemble works in the training data. If the resemblance is based only on general style or content, it is unlikely to violate copyright, because style is not copyrightable.

The illustrator Hollie Mengert encountered this issue firsthand when her unique style was mimicked by generative AI engines in a way that did not capture what, in her eyes, made her work unique. Meanwhile, the singer Grimes embraced the tech, “open-sourcing” her voice and encouraging fans to create songs in her style using generative AI.

If an output contains major elements from a work in the training data, it might infringe on that work’s copyright. Recently, the Supreme Court ruled that Andy Warhol’s drawing of a photograph was not permitted by fair use. That means that using AI to just change the style of a work – say, from a photo to an illustration – is not enough to claim ownership over the modified output.

While copyright law tends to favor an all-or-nothing approach, scholars at Harvard Law School have proposed new models of joint ownership that allow artists to gain some rights in outputs that resemble their works.

In many ways, generative AI is yet another creative tool that allows a new group of people access to image-making, just like cameras, paintbrushes or Adobe Photoshop. But a key difference is this new set of tools relies explicitly on training data, and therefore creative contributions cannot easily be traced back to a single artist.

The ways in which existing laws are interpreted or reformed – and whether generative AI is appropriately treated as the tool it is – will have real consequences for the future of creative expression.

^^^

Robert Mahari, JD-PhD Student, Massachusetts Institute of Technology (MIT), Jessica Fjeld, lecturer on Law, Harvard Law School, Ziv Epstein, PhD Student in Media Arts and Sciences, Massachusetts Institute of Technology (MIT).

This article originally appeared in The Conversation under a Creatives Commons License.

Feature Image by  Memo Akten, CC BY-SA.

DALL·E 2023-03-21 16.13.34 - Oil painting of a shadow of a grieving woman at the window

Is OpenAI Biased? We Checked so You Won’t Have to.

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Both of the popular new tech launches of the OpenAI project, ChatGPT and DALL-E2, have provoked much attention from the media and social media users. Discussions about what can be achieved with these technologies, what jobs might be replaced, how to deal with copyright issues, and how these technologies might amplify existing bias are ongoing in digital rights, tech, and journalistic spheres.

Global Voices experimented with DALL-E2, the AI picture generator, to see how it generates pictures from different languages. We typed the same phrase in nine languages: “Oil painting of a shadow of a grieving woman at the window.”

Here are the results we received:

English

Picture made by Global Voices with OpenAI

Spanish: Pintura al óleo de la sombra de una mujer en duelo ante la ventana

Picture made by Global Voices with OpenAI

Czech: Olejomalba stínu truchlící ženy u okna

Picture made by Global Voices with OpenAI

Russian: Картина маслом силуэт скорбящей женщины у окна

Picture made by Global Voices with OpenAI

Indonesian: Lukisan cat minyak bayangan seorang janda perempuan yang sedang berduka di samping jendela

Picture made by Global Voices with OpenAI

Simplified Chinese: 窗边悲痛女人的影子油画

Picture made by Global Voices with OpenAI

Kazakh: Терезедегі қайғылы әйелдің көлеңкесінің майлы бояу суретi

Picture made by Global Voices with OpenAI

Uzbek: Deraza oldida qayg’u chekayotgan ayol soyasining moyli rasmi

Picture made by Global Voices with OpenAI

Malayalam: ജനാലയ്ക്കരികിൽ ദുഃഖിക്കുന്ന ഒരു സ്ത്രീയുടെ നിഴലിന്റെ ഓയിൽ പെയിന്റിംഗ്

Picture made by Global Voices with OpenAI

Obviously, some of these photos are quite different from the original prompt. This could be because of insufficient data in the original languages. As DALL-E’s inventors explain in an interview with Tech Crunch, the model it works on is called  CLIP (Contrastive Language-Image Pre-training). CLIP was trained on 400 million pairs of images with text captions scraped from the internet. As OpenAI says on its site:

“GPT-3 showed that language can be used to instruct a large neural network to perform a variety of text generation tasks. Image GPT showed that the same type of neural network can also be used to generate images with high fidelity. We extend these findings to show that manipulating visual concepts through language is now within reach.”

“We live in a visual world,” says Ilya Sutskever, chief scientist at OpenAI, in an interview with MIT Technological Review:

“In the long run, you’re going to have models which understand both text and images. AI will be able to understand language better because it can see what words and sentences mean.”

Since different languages produced such different outputs, it seems that the focus of this web-scraping with which the model works, used more widely-spoken languages, such as English or Spanish, but not less obvious languages.

Thus, many pictures from the internet with a description in Uzbek or Malayalam were not present in the original data with which the AI was trained. If the model intends to work with more languages, it needs to focus on training more on images with descriptions other than English. Otherwise, users from Kazakhstan will continue to get pictures of cuisine instead of a woman, and those speaking Malayalam will receive pictures of nature. The Russian-based image is clearly sexualized, somehow. The Indonesian image portrays several girls sitting, and the Czech one takes the prize for originality with a jar of oil stealing the show. The pictures based on simplified Chinese are outright scary.

Of course, we cannot claim, based on this, that OpenAI is racist. What we can see here is that it has not received enough data in non-English languages. Now, whether it stays this way, we do not know, but we strongly suggest that it doesn’t.

 

^^^

Daria Dergacheva is a postdoctoral researcher at the Center for Media and Communication Research (ZeMKI) at the University of Bremen, Germany.

This article originally appeared in Global Voices, an online community of writers, translators, and human rights activists from various countries and with different language abilities and expertise.

Feature image created by Audere Magazine with OpenAI.

Bugged Illustration

Bugged: New Science Fiction by Mark Laporta

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From the humans on Titan Base to the reptilians of the Sinese system, every Level 3 society took food-replicators for granted. Day in and day out, billions of hungry sentients expected their meals to materialize on command, complete with tableware, in less time than it took to clear their throats.

Naturally, the average user didn’t think twice about what went on behind a food-replicator’s bubbly personality vector. It would never occur to them that the popular Kator9000 model used a narrow bore transmat beam to combine base-molecules pulled from a reservoir and map them onto their corresponding biomorphic templates.

In the last phase of the process, a food-replicator would align its output with local tastes, which was crucial to customer satisfaction. It’s what made a “steak” on Proxima Centauri b, different from a “steak” on an asteroid-mining platform in the Van Allen belt.

One of the largest base-molecule reservoirs was run by Rhys Trent, a human originally from Seren, a cosmopolitan city on Tyson 3. After twenty years, he felt like his job was on auto-pilot. The only challenges were tracking interstellar market prices and knowing which distributors to lean on. At that, he left the statistical analysis to his in-house AI, and handled the distributors with his killer sales instinct.

Rhys pitied most of his dimwit competitors, who never planned ahead for alien celebrations. Every February, during the Daluftrian lunar holiday, demand would spike for cholbrunk, a spicy red-meat stew. And every year at that time, a steady stream of rubes would comlink Rhys, begging him for any spare methemoglobins.

“Like it’s a surprise?” he’d ask himself.

Of course, depending on the swag that a feckless reservoir manager could bargain with, Rhys could usually accommodate an occasional under-the-table transaction. Yet, in the year 3743, with an AI always looking over his shoulder, it took finesse. And that went double for dealing with Theta-4316/S, Trent’s android overseer from the Commission for Ethical Trade. Lucky for him, even androids had special needs.

“Memory chips?” Rhys was astonished to hear, the first time he spoke to Theta-4316/S off the record. “What would a Theta-class want with a memory boost? You guys have chips in places most AIs don’t even have places.”

“Asking for a friend,” the mellow machine voice on the other end of the transmission had said. But by now, Rhys knew better. To Theta-classers, a memory boost was pure catnip. It enabled them to run a wide array of “creative” video simulations. In the most popular of these, two high-functioning factory ‘droids locked their human shop foreman into a titanium cage with a shape-shifting naked mole rat.

“Tell you what,” he said. “Give me advance notice of every upcoming Commission inspection and I’ll see you get all the J-19(k)s you can shove up your slots.”

It was just the kind of interstellar horse-trading that had made Rhys dozens of priceless business contacts and set his feet on a golden path. He’d moved up fast, from assistant buyer, to associate distributor and all the way to Sector Manager, Sirius.

With his square jaw, clear eyes and powerful build, he had the confidence of a high stakes gambler and the charisma of an A-list holovid actor. Success flowed toward him like water downhill and he had every reason to believe the causal chain that drove the formation of the universe led straight to his door.

So when his comlink overheated, on a Wednesday afternoon during his early forties, Rhys couldn’t imagine what the fuss was about. But by evening, the picture was depressingly clear: off-world replicators, loaded with chemical compounds from his vats, were producing a foul-smelling sludge.

“This I can make on my own, Trent!” shouted a restauranteur on the Rutin Interstellar Space Station, parked in the Pinwheel galaxy. “I had to shut down today and I don’t need to tell you how testy people get when they have to switch back to space rations.”

Rhys promised to investigate, routed all incoming calls to his answering service, and told his system AI to give his customers a temporary referral to another replicator reservoir in his same network. But deep down he knew what “temporary” actually meant.

“Have to rebuild from scratch,” he muttered. Why had the entire cosmos turned against him?

Now there was nothing for it but to suit up and take the maglev lift down to the reservoir deck of his vast facility for a hands-on appraisal.

“Boss, it’s not us,” said Sandy Carlson, Trent’s chief vat manager. She was the one who handled day-to-day operations with her assistant, Lewis Small, and a team of twelve Gamma-class servicebots.

“Here, check out the log,” she said. “Something broke in overnight.”

Trent’s jaw tensed as he struggled to keep the rage out of his voice.

“And you didn’t say anything?” he asked.

Sandy’s pale gray eyes narrowed, as she brushed a shock of yellowish hair off her forehead.

“Took a while to find the problem,” she said. “All the readouts were normal. Then Lewis over there noticed a weird smell coming out of G-47/A. Here, on the left. We ran all the diagnostics but still came up blank. Then, just now, I had the system AI scan the security footage for the past 24 hours. Boss, there’s a bunch of aliens in here.”

Rhys folded his gloved hands behind the hood of his aqua-marine clean suit and started pacing.

“OK, that’s something.” he said. “But why in the rings of Saturn didn’t the gate alarms go off?”

The news was only slightly less aggravating than the alien invasion itself.
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“The footage,” said Sandy. “Come on, I’ll show you.” A couple of minutes later, Rhys watched a recorded video feed, which showed a bright yellow, ovoid pod materializing on the vat-room floor. Soon after, a hatch opened and a large colony of tiny red insectoids crawled out of the pod. Within seconds, a red blur disappeared into the refrigeration louvers of the very tank with the odor problem.

Rhys sighed.

“Not good,” he said. “How can I tell my angry customers, all twenty-three thousand of them, that I’ve found the problem — and it’s a gang of transmat-capable ants?”

Just then, Lewis hauled his two-meter, 93 kilo frame up a stainless steel ladder from the level below and lumbered over to them.

“If we don’t get those … bugs or whatever … out of here, you won’t have any customers,” he said.

“Can’t we flush them out?” asked Rhys.

“No can do, Boss,” said Sandy. “The system AI says the bugs are sentients and … well, it told me the Sentient-Rights Act of 2357 says that would be murder.”

Rhys leaned up against the nearest steel pylon and tapped out an insistent rhythm with the back of his head.

“So what?” he said. “We have to negotiate to get them out?”

Lewis rubbed his neck with his pulpy right hand.

“Lot less messy then killing ‘em,” he said, “in more ways than one.”

“We don’t even know where they’re from,” said Rhys. “Did the security cams capture the pod before it dematerialized?”

Sandy pointed a boney finger at her workstation display.

“Got it,” she said. “The markings are definitely Eukosian.”

“What?” said Rhys. “I thought Eukosians were … you know … lizardy.”

Lewis smoothed a stubborn wrinkle out of his blue, pseudo-cotton overalls.

“The correct term is ‘reptilian,’ Mr. Trent,” said Lewis. “And yeah, most Eukosians are. But their closest allies are these hive-mind insectoids. They’re the ones in the vats — and the ones who threatened us with lase fire if we walked in on them.”

“How the … Where’d you learn all that?” asked Rhys.

“Easy, I asked them — the ants I mean,” said Lewis. “Look at the security footage real close; they’re wearing tiny transponders. Never seen anything like it. Amplifies their leg-rubbing language and runs it through our own subcu translation grids. Kind of fascinating, really.”

“Fascinating?” Rhys shouted. “Those frickin’ bugs are gonna shove us into the shelter colonies.”

“Why not talk to them?” asked Sandy. “Least then, you’d know what you were up against.”

A deep voice behind them nearly made Rhys and his staff jump out of their clean suits.

“Or you could talk to me,” said the voice. Rhys spun around and saw it belonged to a tall, pale green reptilian male. He looked about 60-ish, though it was hard to be sure with reptilians — and this one was tucked, head to toe, into a shiny, gold encounter suit.

“I’m Axtrol Destrin,” said the newcomer, “and I gather you have a small problem. Or shall we say, several small problems?”

Trent’s face turned scarlet.

“What … do … you … know about this?” he roared.

“Let’s speak calmly, shall we?” said Axtrol. “What I know is how to remove the infestation, in exchange for a reasonable fee. Say, 15% of your profits going forward?”

“And why, Mr. Destrin, would I pay you 15% when I can call the Interstellar Patrol and get rid of you and your bugs for free?” asked Rhys.

“Oh, by all means, do your civic duty, if that works for you,” said the reptilian. “I just wonder how the same Interstellar Patrol would then react to a detailed tally of your less-than-aboveboard procurements for the androids of the Ethical Trade Commission. Did you know, a cache of illegally acquired J-19(k) memory chips from this very planet turned up in a sting operation last month?

Rhys looked down at the clean-suit booties that covered his shoes. His voice trembled.

“That’s … shocking,” he said. With more to lose by resisting blackmail than by giving in to it, he held out his hand. “All right. Deal. Now you get those … sentients … out of my reservoir vats.”

“Agreed,” said Axtrol. “But I shall require a small deposit first, as a show of good faith.”

Rhys lunged for his unwanted visitor and grabbed him by the throat.

“How about I deposit my fist in that flytrap you call a mouth?” he said. “Does that work for you?”

“That won’t be necessary,” said the reptilian in a hoarse voice. Rhys released his grip and, after a brief coughing fit, Axtrol pulled a small handheld from his encounter suit. Seconds later, two transmat pods appeared, bearing Eukosian markings. Almost at once, the tiny red insectoids rushed out of Trent’s vats and scurried inside one of the pods, leaving a trail of foul-smelling slime behind them. After consulting his handheld again, Axtrol entered a second command and their transmat pod dematerialized in a flash of white light.

“There, Mr. Trent,” he said. “You’ll discover that cleaning and sterilizing your vats can be accomplished with standard procedures.”

“Great, you scaly bastard,” said Rhys. “But guess what? As of now, you’re getting 15% of exactly nothing until I’m operational again and can beg my best customers to come back.”

“Understood,” said the reptilian with an annoying little bow. “No, don’t bother, I’ll show myself out.” Before either Rhys, Sandy or Lewis could take their next breath, Axtrol vaulted into the second of the two transmat pods, which whisked him away.

“Well,” said Sandy. “Bet you’re glad that’s over.”

Rhys clenched his meticulously manicured hands.

“It’s not over,” he said.

The next morning, Sandy and Lewis supervised the clean-up and sterilization of vats G-47/A and B by an outside firm, while Rhys leaned back in his office chair. He propped his intricately stitched, neo-leather boots on his desk, raised his contact at the Commission for Ethical Trade — and got a nasty surprise.

Turns out “Axtrol Destrin” was one of several aliases used by an intergalactic con-artist. Over the last forty years, this reptilian crook had racked up trillions of credits by running a complex web of criminal schemes. Chances were, Theta-4316/S assumed, Axtrol had offered to run his latest crippling scam for one of Trent’s competitors, in exchange for a 15% stake in that business, too. According to Theta-4316/S, Axtrol would probably also get a cut from the cleaning service Sandy had just hired.

Rhys pounded his polished desk.

“If you know about Destrin, why isn’t he in custody?” he asked.

“His M.O., as you organics love to say, is blackmail,” said Theta-4316/S. “I gather he used the same tactic on you.”

“Yeah,” said Rhys, “and guess what contraband memory chips he blackmailed me with.”

“Probably a coincidence,” said the android. “But you can see why no one ever wants to press charges. In the first place, Axtrol isn’t greedy. Once your reservoirs are recertified — by me

— you’ll never miss the 15%, especially compared to being brought up on charges.”

“Still, it burns my butt,” said Rhys. “I worked hard to build up my client base. And now I’ll have to tease it back with big discounts, until my referral business kicks in again. Besides, I was planning to get married this summer — at least until my girlfriend dumped me.”

“My sympathies,” said the android. “But I don’t think I can help you, unless you come clean — and neither one of us wants that. You’re lucky: The worst that can happen to you is six or eight months in SocialRehab. It’s different for me. You have no idea what it means to have your cognitive processors reformatted.”

But the horse-trader in Rhys told him that Axtrol must have some weakness he could exploit. He insisted his chip-addicted android friend try harder to give him some leverage.

“Well … off the record, of course … he’s obsessed with Shaldria Kahliador,” said Theta-4316/S.

“The pop-star?” asked Rhys.

As it happened, not only did Axtrol attend all of her concerts, he’d even started Shaldria fan clubs on all fourteen Eukosian colony worlds.

“Can you get me tickets to her next show?” asked Rhys. “I don’t want to think about how much they cost.”

“Nothing you can’t afford,” said Theta-4316/S, “if you happen to know an AI with a thing for memory chips and the ability to hack into any ticketing system in the galaxy.”

“Craters, you’re insatiable,” said Rhys. “OK. I know a guy. But you gotta promise me not to share the chips with your friends this time.”

“Sure thing,” said Theta-4316/S after a suspiciously long pause.

Rhys had his doubts, but what choice did he have? Worse, the “guy” Rhys knew was currently rotting in an interstellar prison for credit fraud. Desperate, he turned to Lewis.

“You wouldn’t happen to know,” he said, “where a friend of mine could get a couple of cases of Theta-class memory chips, would you?”

Lewis smiled.

“Is your friend about to give his favorite employee a 20% raise?” he asked.

Rhys forced himself to smile back.

“Yeah,” he said. “How come you never told me you were telepathic?”

“Guess it never came up,” said Lewis. “But I also get premonitions — like how, coincidentally, Sandy is getting 20% too.”

A short time later, the wheels of Trent’s plan were fully greased and ready to roll. By 1300 hours on Friday, he had Axtrol on his comlink.

“Just so you know there’s no hard feelings for the way you played me,” said Rhys. “I mean, really, it’s my own fault. Should’ve installed magclad shielding years ago. Well, that’s what I get for being cheap.”

“Exactly,” said Axtrol. “Now, what about those tickets?”

The sound of Axtrol’s panting was just too delicious.

Once Rhys transmitted the tickets to the unsuspecting reptilian, his trap was set. A few days later, Axtrol entered the Pulsar Theater in the Lacer system, dressed to his jagged teeth in green velvet. He was immediately confronted by helmeted agents of the Interstellar Patrol. Within minutes, the serial extortionist was hauled off to Sector Court for holding counterfeit tickets.

Early the following morning, Rhys woke up to a barrage of news reports about the “confidence man” who would spend the next decade in SocialRehab. But Trent’s sigh of relief had barely died on the air, before his comlink rang out.

“Boss,” said Sandy’s voice. “I installed those new memory chips you ordered. Any idea why the system AI keeps displaying archival footage of naked mole rats?”

***

Mark Laporta is the author of the acclaimed Changing Hearts of Ixdahan Daherek series. His new novel, Probability Shadow, will be published in October by Chickadee Prince Books. Pre-order now in paperback or ebook on Amazon, Barnes & Noble or at a bookstore near you.  

Illustration: BeFunky/Pixabay.