A new artificial intelligence (AI) algorithm is testing just how important attractiveness is by attempting to figure out who you’ll find attractive and why you find that person attractive. A team comprised of researchers from the University of Helsinki and Copenhagen University generated images of fake faces that they then asked people to rate for attractiveness. It then used that feedback to further tune the AI algorithm, making it perform even better at generating fake attractive faces.

Adversarial Strengths

The application, which uses a machine learning development algorithm called generative adversarial network (GAN), creates fake faces by pitting two “adversarial” algorithms against one another. Adversarial means the two algorithms have differing goals: one, called the generator, creates images based on what it learned during its training phase, and the other (called the discriminator) tries to figure out which of the generated images are fake or real. The latter algorithm is tested with photos of real people and fake faces.

The two algorithms eventually start training each other in a loop that allows the generator and discriminator to improve their performance greatly with each cycle. The generator improves its ability to create realistic images, and the discriminator gets better at finding fake faces. This symbiotic relationship may first sound unproductive, but the algorithms work well together and also successfully test each other.

The GAN algorithm was trained on 200,000 images of celebrities, who usually have attractive faces—at least, according to Hollywood standards.

Testing Attractiveness

After the training phase, the generative algorithm developed hundreds of unique faces of people who it believed were of similar attractiveness as the celebrities it “knew” to be attractive. These fake faces were shown to real people who wore brain-computer interfacing equipment hooked up to an EEG (electroencephalography) reader. Using this data, the researchers could measure each person’s brain activity for each photo they saw, down to the neuron’s exact moment of firing.

When a participant saw an image of an attractive face, there was a marked increase in brain activity. This could be partially attributed to the fact that the participants were told to focus harder on faces they thought were attractive. The participants weren’t asked to articulate what specifically they found attractive about any of the images. Instead, the AI stored the EEG datapoints and found the commonalities within each photo.

Those commonalities could be big eyes, high cheekbones, a medium-sized nose, wide-set eyes, small ears, or any other facial feature. The AI found that most participants liked the same aspects of a face in an image. In other words, humans seem to favor most of the same facial features when asked about attractiveness.

Using the common features found by the algorithm, the team distilled this data back down in a format that could be fed to the GAN algorithm. The generative algorithm then took this new information as instruction in making its second batch of attractive faces. Now, the faces had more chiseled jawlines, darker and more mysterious eyes, curlier hair, and more features that we find conventionally more attractive.

Real Looks vs. Fake Faces

When this second round of generated photos was shown to participants, they were instructed to rate the face as attractive or unattractive. For 87% of the newly-generated photos, participants rated the face as attractive. The remaining 13% were either too perfect or there was something that seemed off about their facial features. Even though the participants were told to focus on attractiveness, they were unable to look past how some faces looked fake or off.

AI developers and AI ethics experts worry that this type of well-performing technology could be used to generate faces that look realistic for the purposes of deepfake videos or fake images. Not only do the faces not need to be real, they don’t need to be attractive to cause issues for people or even nations. And the consequences don’t need to be so far-reaching: even social media accounts used for a malicious purpose could use AI-generated fake faces to blend in with the crowd. They might even look normal and real at a quick glance. After all, how much detail can you see in a small circular avatar?

The Future of Dating?

The future of this type of technology extends far beyond dating and social media. It could be used for political gain or even start a war. The research team is interested in advancing the technology, and it has some ideas for how to use their application in a productive and non-malicious way. Associate professor at the University of Helsinki, Tuukka Ruotsalo, says that the team hopes to dig deeper into attractiveness, as well as explore stereotypes, biases, preferences, and individual differences.

Have you come across an AI-generated face that was attractive but looked off? How did it make you feel? Let us know in the comments below!

Chatbots are a natural fit for enhancing customer service, especially in an online or mobile setting, and we run into them on websites, in hospitals, on social media, and in mobile apps. There is room for this AI-enabled application in almost every business vertical, but there are pros and cons to be aware of when developing your first chatbot.

Chatbots in Customer Support

Customer support can be grueling. It requires businesses to provide friendly, responsive, and quick help at almost any time of the day. Because of the large investment involved in hiring people and building resources, it is difficult to build an excellent customer support team overnight.

It takes a lot of constant training and ongoing optimization to finetune your support representatives to meet all key performance indicators, like time-to-first-response, time-to-close-ticket, and customer satisfaction or feedback. Instead of skimping on one of your most important customer-facing departments, consider what a chatbot can unlock for you and your customers. Whether your company is a Fortune 500 company, doctor’s office, or a corner grocery store, your business can create and deploy a chatbot quickly and easily without a massive investment.

Benefits of Chatbots in Business

Increase Customer Happiness

Every company is in the business of making their customers happy. It’s what keeps profits steady and brand recognition strong. Thankfully, chatbots fit right into that notion as they are 100% customer-centric.

Chatbots work to answer user questions as fast as possible to reach a resolution on the question at hand. They help customers with the same efficiency and response rate, unlike humans which respond to various queries with different response times. This reliability can give customers more consistent expectations of interacting with your company.

If done right, chatbots can improve customer satisfaction. For example, an customer service representative based in Los Angeles may not be able to speak Japanese to help a customer, but a chatbot can easily switch to a different language on the fly. This unlocks the added benefit of reaching many more customers without having to hire interpreters or native speakers.

Leave an Impression on Customers

Besides serving up a consistent experience that customers can count on, chatbots offer customers the opportunity to “ooh” and “aah” over your company’s use of the most cutting-edge AI and machine learning technology. Many customers enjoy interacting with a chatbot, and engagement rates have been increasing steadily.

In 2019, nearly 40% of retail customers engaged with a chatbot in their search for an answer. When chatbots work well, they can bolster your brand image. They can also help customers see your company as an industry leader.

Available Around-the-Clock

Unlike people, chatbots can be “on” 24/7 without feeling exhausted or needing a break. They can help a customer whenever they need it and from wherever they’re located. Chatbots can stay on indefinitely and use customer feedback to continuously improve their success rates.

Generate Better Leads

Besides helping people with their questions and issues, chatbots can also generate leads. Add in a spam filter, and you can even generate better leads than your contact forms. In on-demand delivery apps, chatbots are often used to take orders and process payments from new customers. This can reduce the amount of time it takes to get a new lead to purchase a product from your company.

More Cost-Effective

Because chatbots don’t require healthcare or sick days, chatbots are much cheaper in the long run compared to a traditional customer support team. They use fewer resources like office space, money, and equipment. Chatbots cost a one-time investment fee, and they need minimal maintenance to continue performing.

Downsides of Chatbots

Talking to a Computer

At the end of the day, most chatbots can’t help customers shake the feeling that they’re talking to an automated AI application. For many customers, their problems can only be solved by a human, and this often includes problems with a purchase, shipping, or credit card charge. For these customers, interacting with a chatbot can feel futile and frustrating.

Although natural language processing technology has come a long way in the past few years, we still haven’t reached a point where chatbots feel human. Fluid language isn’t possible yet. Even the best chatbots can’t make customers feel like they’re talking to a human.

Difficult to Develop

Chatbots can be difficult to design and develop, especially if your company’s use case is complex. When adding machine learning technology into the mix, chatbot development can get downright arduous. As it stands, companies need to spend a lot of time, effort, and server space in creating their chatbot.

Most businesses need chatbots for simpler needs, however, like customer support which can run on pattern-based algorithms. There are already no-code or low-code platforms that offer brands the ability to create a custom chatbot. As technology keeps advancing, it’ll get easier and easier to create and deploy chatbots.

Ongoing Maintenance Schedules

Chatbots require updating and maintenance. Someone should look into the chatbot’s failed customer support tickets to make adjustments where necessary to avoid failing to resolve a ticket about the same topic again. As user questions and issues grow, take some time to add these new topics and their associated solutions into the chatbot’s knowledge bank. The time and effort will be worth it: this work can keep your brand image strong.

Developing Your Chatbot

Creating and deploying a chatbot is getting easier, and more companies are taking advantage of this technology by combining it with AI and machine learning. Don’t get left behind — a well-maintained chatbot with a high success rate in answering questions can strengthen your brand image and leave a great impression on your customers.

Have you interacted with an excellent chatbot? What company’s chatbot was it, and what did you take away from the experience? Let us know in the comments below!

We’ve barely just begun 2021, but it’s already shaping up to be an amazing year for Dogtown Media. We were recently featured as a top machine learning developer by Techreviewer.co! Thanks so much to our clients, team, and community for your continued support — you’re the ones who make this possible.

Techreviewer is a trusted B2B information platform that regularly conducts market research across numerous sectors, such as development, design, and marketing. Its meticulous methodology quickly helps companies identify high-quality services for a variety of complex technical tasks. Whether you’re searching for experts in AI app development, the Internet of Things, or business analysis, Techreviewer’s analysts can expedite the process of finding the best technology partner for your business needs.

For Techreviewer’s list of 2021’s top machine learning companies, the review hub evaluated each contender by examining its demonstrated expertise, experience, quality of services, and reliability to deliver products that went above and beyond clients’ requirements. That last component was especially crucial in elucidating the top players in this sector; the product must leverage key aspects of machine learning to help transform a client’s digital presence.

Over 500 companies were considered for this prestigious award. We’re extremely proud to be able to say that we made the cut after quite a few time-intensive assessments!

Besides being recognized as one of the best machine learning app developers of 2021, we’re also grateful to have been featured as one of 2021’s best iPhone app developers as well as a top mobile app developer in Los Angeles by Digital.com!

With this award, we couldn’t be more excited to see what else is in store for our company in the new year! Thanks to Techreviewer for recognizing our hard work. And thanks again to our clients, team, and community — we couldn’t have done this without you!

About TechReviewer.co

Techreviewer a trusted analytical hub that carries out studies and compiles the lists of top development, design and marketing companies. The platform helps to find the best companies that provide high-quality IT services for technical support, development, system integration, AI, Big Data, and business analysis. As a result of objective market analysis, the Techreviewer platform determines the most successful and reliable IT companies and makes top ratings for each of the service categories. Techreviewer’s ranking lists help organizations select the right technology partner for their business needs.

By now, we’re all aware that the development of artificial intelligence (AI) and machine learning will shape our future in several ways. But many of us do not know how these technologies will impact cybersecurity.

We find ourselves at a pivotal moment in this digital era — one in which our personal information is at unprecedented risk. The last decade alone was riddled with hundreds of massive data breaches and identity fraud incidents. Today, cyber criminals can achieve their objectives from anywhere in the world, at any time.

Our need for more progressive cybersecurity measures has never been more imperative than now. Fortunately, cybersecurity applications have received numerous technological advancements over the last few years. Chief among these game-changing developments is the introduction of AI and machine learning to the field. Let’s examine how these technologies are augmenting current cybersecurity endeavors.

1. Improving Cyber Threat Detection With Machine Learning

In cybersecurity, foresight is priceless. Detecting cyber attacks in advance can give organizations the time they need to successfully neutralize these incoming threats. And it turns out that the application of machine learning to data analysis can help immensely in identifying them.

Machine learning helps computers learn from and understand obtained data. In turn, systems can adjust and refine algorithms to reach optimal performance. In terms of cybersecurity, this means that machine learning can enable computers to detect threats and anomalies more accurately than any human is capable of doing.

Traditional technology relies too much upon previous results. Often, this leaves it unable to adapt fast enough to hackers’ latest techniques and strategies. And the sheer volume of cyber threats that people face every day is too immense for human-directed systems. On the other hand, AI allows computers to excel at improvisation by adapting faster than ever before.

2. AI-Fueled Phishing Detection and Prevention

Phishing is the fraudulent practice of sending fake messages. Hackers use this all the time; they pretend to be from reputable organizations or groups so that victims either reveal personal information like passwords or install malware. Phishing emails are so common that one in every 99 email messages is believed to be an attempted attack.

Luckily, AI and machine learning play an integral role in mitigating phishing attacks. Besides being able to respond much faster than a human can, these technologies can identify and track over 10,000 active phishing sources. They also allow for swift distinction between fake and valid websites. Because these technologies are now being employed around the world, AI’s knowledge of phishing campaigns isn’t relegated to only one geographic location.

3. Making Vulnerability Management Easier

Every modern business relies on information technology (IT). But keeping your IT safe can be difficult. Just this year, over 2,000 unique cybersecurity vulnerabilities have been recorded. Managing these with only humans would be practically impossible. Thankfully, AI opens up an easier approach.

AI- and machine learning-based systems can efficiently scan for potential flaws in corporate IT systems. And, by incorporating recent relevant information such as dark web forums, hacking trends, and more, these technologies make it simple to stay on top of the latest developments in this field. With all of these insights, you’ll not only know how your vulnerable targets may be attacked, but also when.

4. More Powerful Password Protection and Authentication

Passwords have always been one of the weakest components of security control. In fact, they’re often the only link between cyber criminal activity and our identities. Biometric authentication is seen as a potential alternative for the future, but it’s currently not the most convenient paradigm to employ. AI could change this.

Developers are leveraging AI to improve biometric authentication and eliminate any weaknesses so that it’s more robust. Apple’s facial recognition technology is a prime example of this. Known as Face ID, this system detects a user’s facial features via infrared sensors. Apple’s AI software then produces a sophisticated representation of the user’s face that allows it to recognize key similarities.

Apple is so confident in this technology that it believes hackers have a one-in-a-million probability of bypassing it. This system also works under different lighting conditions and can compensate for changes such as a new hairstyle or more facial hair.

5. Automated Network Security

Security policy development and organization network topography are two essential components of network security. Unfortunately, both take up a monumental amount of time and human effort to fulfill and manage.

Fortunately, AI can automate both of these processes to some degree. By analyzing network traffic dynamics, AI can generate and recommend policies and procedures to fit your unique situation. The amount of time, energy, and money this could save organizations can’t be overstated.

6. More Robust Behavioral Analytics

Similar to our other examples, AI and machine learning can also be employed to improve behavioral analytics by studying your patterns. This allows them to understand how you use your computer and other smart devices. Details can include but are not limited to your favorite online platforms, usual login times, as well as your texting and browsing patterns.

If an algorithm detects unusual actions that are outside your normal patterns, it can lock the culprit of this questionable activity out of your system. Massive shopping sprees, shipping products to addresses other than your own (e.g., why’d you ship that new game console to Beijing if you live in Los Angeles?), a sharp spike in uploads or downloads of files, and even a change in your typing pace can all alert AI to nefarious behavior.

AI and ML Make Smarter Cybersecurity Possible

We hope you’ve enjoyed this list of amazing ways that AI and machine learning are improving cybersecurity. As far as security goes, these emerging technologies have vast potential for sectors such as finance, retail, and healthcare.

Speaking of different industries, stay tuned for our follow-up post to this article! We’ll delve into how AI is preventing data breaches in three large sectors.

In the meantime, what do you think of AI and cybersecurity together? And what cybersecurity measures do you employ to protect your information? As always, let us know your thoughts in the comments below!

As the novel coronavirus (COVID-19) continues to wreak havoc around the world, doctors and medical researchers are desperately looking for medications to combat the illness effectively. And artificial intelligence (AI) development may help point them in the right direction.

Deep neural networks could help healthcare providers identify the right antivirals to fight COVID-19. And they’re not just looking at experimental drugs for a solution — the algorithms behind these networks are also considering already-existing compounds.

The Frantic Search for a Solution to COVID-19

If you’ve been following the news about coronavirus, you’ve probably heard of chloroquine by now. Based on a naturally-occurring compound found in certain South African trees, this anti-malaria drug was created by German medical developers in the 1930s. Since then, it has been saving lives across the globe.

As a last resort, Chinese physicians tried using chloroquine (along with numerous other drugs) on patients with severe COVID-19 cases. Some of them recovered. Many of them didn’t. Basically, nobody is sure if it really helped. And we won’t be able to confirm so without clinical trials, which are ongoing.

Alongside chloroquine, several other existing drugs are being investigated for potential efficacy in combating the coronavirus. Currently, “there are no definitively effective drugs,” according to Dr. Li Haichao, a respiratory and critical care doctor at Peking University First Hospital. Dr. Haichao is also a member of China’s emergency medical rescue team which was sent to Wuhan.

While these drug candidates may range in application, they do share one common characteristic: None of them are new. None of them were developed specifically to treat coronavirus patients. But they each have traits that could potentially combat the illness.

Repurposing already available drugs is one of the quickest ways to treat an outbreak. Developing new drugs is not only daunting but time-consuming — the process can take a decade. Existing drugs that have already been approved by regulatory agencies can spring into action much faster and start saving lives.

Before AI, scientists would be left to make educated guesses as to what works in crises like this. But a recently released preprint research paper looks at how deep neural networks could help. Not only do their algorithms scan new compounds, but they also consider already-approved medications for effectiveness.

Drug Repurposing May Be Our Best Shot

The preprint is one effort of many that are using AI tools and machine learning applications for drug discovery. AI can help this process in numerous ways; identifying new targets, searching for novel molecules and finding compounds with potential to pass through clinical trials and make it to market name just a few avenues.

Most AI-fueled drug discovery endeavors concentrate on new compounds. But COVID-19’s rapid damage to global health and economies has urged researchers to consider drug repurposing as a promising option.

The concept of applying a drug for one illness to another sounds strange and nonsensical on the surface level. After all, if it took a decade to develop that drug to work against one sickness, why would it be effective for something else? Two words: Biological similarity.

COVID-19 may be new to humans, but it’s not exactly unique to evolution. Because COVID-19 is a type of coronavirus (and virus in general), we have some idea of how it infects cells and transmits based on our dealings with similar viruses like SARS and MERS. Going deeper, we can essentially match up how our bodies respond to this illness on a molecular level by comparing it to the precedent of these other viruses.

In more technical terms, if a drug has a similar effect on gene expression profiles between two different circumstances (two infections, in this case), then perhaps the drug can be applied to the new infection. At least, that’s the logic here. But from a practicing physician’s perspective, this logic may not carry as much merit.

Chloroquine may have exhibited antiviral properties on cells in an isolated lab experiment, but “no acute virus infection has been successfully treated by chloroquine in humans.” Its use on COVID-19 patients was really a desperate attempt to save them. In the past, it seemed to help against SARS. But this was never truly confirmed.

Besides this, familiarity can backfire. A drug that was approved for one reason may not be questioned in terms of safety when it’s applied for another reason. But that can be dangerous. For example, the difference between a therapeutic dose of chloroquine and a toxic, potentially life-threatening dose is extremely narrow.

AI’s Role in All of This

It’s worth noting that AI does have the capability to explore drug effects on the molecular or genetic level much better than a human doctor can. And if the puzzle pieces align, there may be promise there. If an HIV drug triggers the same gene expression changes in COVID-19 patients as it does with HIV, perhaps it could work.

In the preprint’s case, the researchers based their hypothesis on SARS, a virus that carries many similarities to COVID-19 — genetically speaking, they have an 86% similarity. So, in theory, a drug that successfully works against SARS could be promising for combating COVID-19.

This is where AI comes in. A gene called COPB2 was found to be essential in helping SARS proliferate in the human body. The research team examined the genetic profile of cells without this gene; they are at least partially SARS-resistant and could be COVID-19-resistant. The team then screened through various chemical libraries to identify compounds that would basically eliminate the COPB2 gene in cells.

The researchers’ neural network ended up with a list of both experimental and approved compounds that matched this profile. For example, one chemical on the list was indeed previously found to reduce SARS replication in cells.

Not a Panacea, but a Useful Tool in Our Fight Against COVID-19

If all of this has left you with more questions than answers, you’re not alone. We’re just beginning to understand COVID-19. That means there’s little data on it to train AI with. The research team used SARS as a proxy, which, when considering its similarity to COVID-19, is logical. But vital questions, like if COPB2 is actually necessary for COVID-19 to proliferate, still need to be answered.

Everyone is eager to find a solution to COVID-19. Who wouldn’t be? From our home in Los Angeles to Venice, Italy, this illness has taken away too much from us already.

But letting hope outweigh truth and data could be detrimental in our progress. We must remember that AI is a tool, not a panacea. And just because a drug has been approved for one disorder doesn’t mean it will be effective against another similar one. We can’t let scientific objectivity take a backseat — not if we want to solve this crisis properly.

AI can help us immensely. But it’s ultimately up to clinical trials to validate a drug’s effectiveness against COVID-19.

Is artificial intelligence (AI) taking over everything audio-related?

Welcome back to our short series on AI’s role in the future of music! In our first entry, we delved into how AI is already impacting the current sonic landscape. We also explored AI tools that allow anyone to create music scores or provide an adaptable soundscape to your day. In case you missed it, check it out here.

For this second and final post, we’ll take a closer look at how musicians and songwriters are pushing their art forward with AI.

YACHT Is on Board With AI

Many people hold concerns that AI could “flatten” the musical landscape and make every popular song sound generic and similar. The fear that major record labels will use algorithms to concoct and cram simplistic, functional ditties down our ears is a real one.

But Claire Evans, lead singer of Los Angeles-based electropop duo YACHT, has a different perspective on the matter: She thinks that sort of heartless optimization already occurs in the music industry. “That algorithm exists and it’s called Dr. Luke,” Evans explains. She’s referring to Lukasz Gottwald, an American record producer who has utilized specific formulas to create massive pop hits for a variety of people such as Britney Spears, Rihanna, Nicki Minaj, and Kesha.

Instead of viewing AI with pessimism, forward-thinking musicians actually have the opportunity to fight against this dystopian flattened soundscape; they can use the technology to venture into new creative territory. This is exactly what YACHT did for their newest album, Chain Tripping.

The musical duo applied machine learning (ML) to their songwriting process. After training an ML system on their entire musical catalog, it output hours of new tunes. The band gleaned the most intriguing pieces from these results and meshed them together into coherent songs. Evans admits that learning the new music was challenging and time-consuming — mainly because the chord changes and riffs incorporated deviated from their usual inclinations.

“AI forced us to come up against patterns that have no relationship to comfort. It gave us the skills to break out of our own habits,” Evans explains. And it seems like the hard work paid off. Chain Tripping garnered the band their first-ever Grammy nomination.

AI & Audio Experimentation Around the World

Across the globe, AI is making a unique impact on many musicians’ work. For instance, Ash Koosha, a British-Iranian composer and tech entrepreneur, actually create an AI pop star. Named Yona, it writes music via generative software. Admittedly, many of Yona’s lyrics are nonsensical. But some of them are also surprisingly insightful and emotional.

Koosha thinks this already pushes past boundaries for most humans: “Being so blunt and so open — so emotionally naked — is not something most humans can do. I wouldn’t be able to be that honest unless something triggers me.”Another pertinent example of AI’s effect on audio experimentation is Berlin-based duo Dadabots. The pair utilizes neural networks to generate 24/7 live streams of music spanning genres such as death metal, free jazz, and skate punk. They’re currently in the middle of a residency in which they’re creating new AI tools.

Dadabots co-founder CJ Carr sees AI development as a trainer that can help musicians improve their craft. And part of the fun comes from seeing what AI cooks up. Carr says, “I want to see expressions and emotions and sounds that have never existed before.”

Besides creating the future of music, AI can also reinvent its past. Last summer, a mutated version of English singer and songwriter Jai Paul’s popular track “Jasmine” appeared online. Initially, it sounds the same as the original. But it quickly morphs into an infinite, spontaneous jam. This version was generated via AI by London-based development company Bronze.

Behind Bronze is scientist Mick Grierson and musicians Gwilym Gold and Lexx. “We wanted a system for people to listen to music in the same state it existed in our hands — as a constant, evolving form,” Gold explained in a recent interview with TIME. Bronze’s ability to capture the ephemerality of live music intrigued Venezuelan record producer Arca, who has worked on critically-acclaimed albums like Kanye West’s Yeezus and Björk’s Vulnicura.

Arca and Bronze’s team used AI to collaborate on an art piece by French artist Philippe Parreno. It currently resides in the lobby of New York’s Museum of Modern Art. The music transforms and the speakers swivel according to factors like crowd density and temperature, meaning no two minutes are the same. Arca already has a few other ideas she’d like to implement with Bronze’s technology. She thinks “it opens up a world of possibilities.”

A New Era of Audio’s Just Beginning

Despite all of these musical developments with AI, many still worry that the technology will eventually replace musicians. Koosha says that this sort of fear and concern have been a part of every major technological advancement in recent decades. While some musicians may have experienced displacement, the new developments always ushered in an era in which the barrier for entry was lowered to make impactful art.

We’re still very much in the early days of AI music experimentation. And there’s no telling what the future holds. But for optimistic innovators like Carr, they can’t wait to see what unfolds: “I want to see 14-year-old bedroom producers inventing music that I can’t even imagine.”

Thanks for tuning in to our series on AI’s effect on music! Where do you think the technology will go from here? Do you think more artists will utilize it in their work? Let us know your thoughts in the comments below!

Whether it’s potential job loss or the Terminator becoming a reality, there’s no shortage of fears surrounding the development of artificial intelligence (AI). But last November on Sean Carroll’s Mindscape podcast, the musician Grimes took things in a different direction regarding AI’s capabilities: “I feel like we’re in the end of art, human art. Once there’s actually AGI (Artificial General Intelligence), they’re gonna be so much better at making art than us.”

Is Grimes right? Are we on the cusp of an audio revolution where AI is both the main composer and frontman?

A New Era of Creativity?

Not long after Grimes’ comments, numerous artists took to Twitter and other social media platforms to voice their own opinions on the matter. While many lambasted the musician’s comments, some contributed another unique perspective on the subject: Maybe AI won’t end human art; maybe, it will augment it.

There’s a substantial amount of evidence to back up this argument. The past few years have seen several artists (Toro y Moi, Holly Herndon, and Arca name just a few) incorporating AI into their work to give it a fresh direction. Across the world right now, researchers and musicians are working on developing AI tools to make the technology more accessible to creatives.

Copyright complexities and other obstacles still need to be worked out. But many of these musicians working with AI hope that one day, it will not only be a democratizing force in the industry but an essential part of artistic endeavors. And for some of these people, this work is proving that the sky is still the limit.

“It’s provided me a sense of relief and excitement that not everything has been done — that there’s a wide-open horizon of possibility,” Arca told TIME in a recent interview. She’s a music producer who has worked with the likes of Björk and Kanye West on some of their most innovative albums.

The Relationship Between AI and Music

But as fresh as all of this feels to some artists, the relationship between AI and music goes back quite a few decades. In 1951, Alan Turing built a machine that could generate three melodies. And in the 90s, David Bowie employed a digital lyric randomizer for inspiration.

Around this time, a music theory professor was also training a computer program to compose new music in the style of Johann Sebastian Bach. When put to the test, an audience couldn’t differentiate between original Bach pieces and the imitations.

Of course, combining AI and music has come a long way since then. University research teams, major tech investments, and machine learning conferences such as NeurIPS have all played a part in this rapid advancement. And it has culminated in some unprecedented possibilities.

AI music innovator Francois Pachet released Hello, World in 2018, the first pop album composed with AI. And in 2019, singer-songwriter Holly Herndon harmonized with an AI version of herself on her critically-acclaimed album Proto.In spite of these achievements, many still believe that we’re far away from a hit song completely crafted by AI. “AI is simply not good enough to create a song that you will listen to and be like, ‘I would rather listen to this than Drake,'” explains Ole Stavitsky, CEO and co-founder of Endel, a sound environment-generating app.

While AI hasn’t smashed world records in the pop genre of music, it is making significant headway in other avenues.

AI Tools to Meet New Audio Demands

The explosion in popularity of streaming and social media platforms has caused the number of content creators to balloon in recent years. As a result, more music is needed than ever before. This problem became readily apparent early last decade.

While working on musical scores for films like The Dark Knight, composers Michael Hobe, Drew Silverstein, and Sam Estes flooded with requests for background music for an array of content like video games, TV, and more. To make matters more convoluted, many of these people could not afford original music and didn’t have time to make it themselves. And they certainly didn’t want to depend on stock music.

The trio of composers turned to AI to see how it could help. Eventually, they created Amper, an AI composition tool that lets anyone create new music by specifying parameters such as genre and tempo. The NYC- and Los Angeles-based company quickly became a smash hit; its music is now used in commercials, podcasts, and many other types of content.

On the other end of the spectrum, Berlin-based Endel is providing personalized soundscapes, another modern need. The concept of Endel came about when Stavitsky realized, “there’s no playlist or song that can adapt to the context of whatever’s happening around you.”

By accounting for real-time factors such as weather, the listener’s heart rate, and circadian rhythms, Endel generates music that can help you focus, relax, and sleep better. Stavitsky says that users have used Endel to combat problems like insomnia and ADHD. Last January, the app passed one million downloads.

Tune in to Our Follow-Up

AI may have not perfected a smash hit pop song yet. But Amper and Endel help fulfill the functional and experimental demands of the modern music industry. And this is just the beginning of a new audio era.

Tune in to our follow-up post, where we’ll take a closer look at how musicians are pushing their art forward with AI!