AI has reimagined films, used the voices of deceased musicians to create new tracks, and can now make the sound of a brand “liquid”. Using AI, we can now create a patentable “sonic identity” for any product or service. Moreover, “sonic identity” allows a brand to take any shape according to the desired customer experience.

AI enables sound to shift its shape in our high-speed, digitally connected world.  Amp is an AI-centred music company. It was recently acquired by Landor and Fitch, which is part of the WPP advertising group.

Amp generates all kinds of sounds. Short bursts of noises when an App launches, the sound of a credit card transaction on completion, compositions for podcasts, and social media posts.

Using machine learning the DNA of the “composition” is verified to ensure that it does not resemble an arrangement already in use. Then the algorithm checks if the signature patterns in the composition are likely to be memorable.

Once the DNA is established, the role of the AI is to allow the owner to generate infinite remixes from this base DNA using different tempos, durations, moods, and rests depending on the context. Brands are no longer mute.

Recently, using AI techniques similar to those used to create the artificial voices on the viral track “Heart on My Sleeve”, the Canadian lo-fi singer Grimes has created a digital likeness of her voice that can be used by anyone to create new music using a liberal licencing scheme.

“Over the Bridge” has created an album of AI-generated “lost tracks” by musicians who passed away at a young age. The tracks are not re-mixed versions of previous songs – they are new tracks created by computers and gifted audio engineers.

This new AI-generated album, “Lost Tapes of the 27 Club” features four tracks in the musical styles of Kurt Cobain, Amy Winehouse, Jimi Hendrix, and Jim Morrison. Using Magenta, “Over the Bridge” analysed MIDI files of selected works of the artists.

AI sifted out each artist’s melodies, harmonies, and rhythmic choices into synthesized recreations, and then small snippets were isolated and woven into new songs. More than ever, it is becoming increasingly critical to identify if content is AI-generated.

Shadow libraries are now at the heart of copyright infringement lawsuits and the unauthorized use of content to train some AI platforms. Shadow libraries are online databases that offer access to articles and texts that are out of print, hard to obtain, and those that are paywalled.

Shadow libraries are also named “pirate libraries” because they often infringe on copyrighted work. This has opened schools, universities, think tanks, and policy labs to the threat of AI-generated plagiarism.

It is not unlikely that there may be instances where AI is used to produce content but unbeknownst to the user it may be drawing upon copyrighted treatises, dissertations, books, and manuscripts housed in shadow libraries.

Governments have taken action against shadow libraries and those involved have been charged with criminal copyright infringement, wire fraud, and money laundering. But as the sites are taken down, mirrors appear.

In an authoritative peer-reviewed scientific journal in the field of medical sciences, a reference in the bibliography offers a hyperlink with a Uniform Resource Locator or URL. A URL is the address of a particular resource on the World Wide Web.

On clicking the URL, with a recognizable domain name, the startling response was — “not found.” Probing this conundrum deeper, revealed that the first author cited in the publication, who is presently an established academic at a recognized university, had no knowledge of the research or its findings.

Likewise, the sixth author in the citation was befuddled.  The citation contained in the bibliography of the publication was totally fabricated—an example of an artificial hallucination. Librarians and examinations syndicates are underscoring and highlighting the production of fake citations when using AI as a research assistant.

In New York, an advocate for thirty years provided the court with submissions that contained at least six fake judicial decisions, containing false quotes and bogus internal citations, according to the judge presiding over the matter in the Southern District of New York in an order.

Several of the purported cases that were cited in the pleadings did not appear to exist to either the judge or defence team.

It is now possible for human actors to come upon an artificial hallucination or a confabulation or delusion as a confident output of an AI system.

A hallucination is an output that is unfaithful to the provided source content. These responses cannot be justified by the training data used to build the AI platform. An AI hallucination is presently described as the pointless embedding of plausible random falsehoods.

AI hallucinations can result from insufficient training data in some cases, but it is also likely that some “incorrect” AI responses classified by humans as “hallucinations” may in fact be the “correct” answer that human reviewers are unable to favour or comprehend.

The architects of generative AI have erected a few guardrails to avoid the worst of these hallucinations. However, many in the field remain unsure about whether or not AI hallucinations are a solvable problem.