The Appen Ltd (ASX: APX) share price has had a difficult start to the year.
Since the start of 2022, the artificial intelligence data labelling services provider's shares are down 15% to $9.46.
This means the Appen share price is now down 58% over the last 12 months.
What's going on with the Appen share price?
The Appen share price has come under pressure in recent weeks for a number of reasons.
One of those is of course the broad weakness in the tech sector, which has been caused by concerns over the prospect of interest rate increases happening quicker than expected.
Another reason is the recent update from one of its biggest customers, Meta (Facebook), which revealed that the social media giant has experienced significantly weaker advertising demand and revenues than expected.
This doesn't bode well for Appen, as Facebook uses Appen's services to support its advertising operations. Appen's million-plus team of contractors help teach machines to predict which advertisements will resonate with which users.
But that may not be the only Meta blow that Appen has to deal with.
Will Meta disrupt Appen?
Also potentially weighing on the Appen share price has been a recent major development by Meta in relation to artificial intelligence and self-supervised learning.
Meta notes that self-supervised learning is "where machines learn by directly observing the environment rather than being explicitly taught through labeled images, text, audio, and other data sources." The latter is the type of service that Appen provides.
And while the social media giant highlights that self-supervised learning has powered many significant recent advances in artificial intelligence, it hasn't been overly successful and human interaction has continued to be necessary in most cases.
But that could be about to change thanks to Meta's data2vec, which is the first high-performance self-supervised algorithm that works across multiple modalities (text, images, speech, etc).
Meta explained: "We apply data2vec separately to speech, images and text and it outperformed the previous best single-purpose algorithms for computer vision and speech and it is competitive on NLP tasks. It also represents a new paradigm of holistic self-supervised learning, where new research improves multiple modalities rather than just one. It also does not rely on contrastive learning or reconstructing the input example. In addition to helping accelerate progress in AI, data2vec brings us closer to building machines that learn seamlessly about different aspects of the world around them. It will enable us to develop more adaptable AI, which we believe will be able to perform tasks beyond what today's systems can do."
"This paves the way for more general self-supervised learning and brings us closer to a world where AI might use videos, articles, and audio recordings to learn about complicated subjects, such as the game of soccer or different ways to bake bread. We also hope data2vec will bring us closer to a world where computers need very little labeled data in order to accomplish tasks," it added.
