MAROKO133 Eksklusif ai: AI Slop Now Invading Spotify’s Discover Weekly Lists Hari Ini

📌 MAROKO133 Breaking ai: AI Slop Now Invading Spotify’s Discover Weekly Lists Edis

Spotify is continuing to grapple with a tidal wave of AI slop that’s frustrating its users.

While the company recently promised to address the issue of AI impersonators and content farms that “push ‘slop’ into the ecosystem, and interfere with authentic artists working to build their careers,” the platform’s paying subscribers are increasingly fed up with being recommended slop.

A quick search on social media reveals that slop continues to find its way into users’ Discover Weekly, which are personalized playlists that refresh every Monday to serve them new music based on their listening habits.

“Discover Weekly is unusable now cause it is just full of AI slop,” one user complained on X-formerly-Twitter.

“Dear Spotify, please stop putting AI music in my Discover Weekly. Sincerely, everyone,” another wrote.

“I had no idea how bad it had gotten, since none of it was being shown to me,” one user wrote on the forum Hacker News. “Then a friend sent me his AI music on Spotify, I listened, and my recommendations were all AI for my usual genres suddenly.”

According to an August 2024 forum post on Spotify’s Community page, the issue has been around for well over a year now.

“It has gotten really bad lately,” a subscriber wrote in a December post. “This week’s Discover Weekly has four [AI-generated] songs in the first five entries. It makes me very sad that this garbage penetrates the recommended playlists of users.”

Many users threatened to jump ship and leave Spotify altogether as a result.

“Six songs out of 30 on my Discover Weekly playlist on Spotify were AI this week,” one user wrote in a Bluesky post. “Ridiculous that Spotify is pushing this crap on us. Looks like that’s it for Spotify.”

It’s a damning trend, with Spotify’s algorithm actively recommending slop to users who are paying monthly for a Premium subscription.

“Spotify has been recommending me so much AI music in my Discover Weekly that I can’t even rely on it for finding new songs anymore,” another Bluesky user lamented.

In the face of it all, Spotify has refused to implement a blanket ban on AI slop. In late September, the company announced a slew of new policies to protect artists against “spam, impersonation, and deception,” including a new “filter” that it says can detect common tactics used by spammers to game its royalties system.

The company also promised to establish “AI disclosures for music with industry-standard credits.”

However, Spotify fell short of forbidding AI tunes entirely, arguing that “music has always been shaped by technology” and that “at its best, AI is unlocking incredible new ways for artists to create music and for listeners to discover it.”

“This journey isn’t new to us,” the company wrote in its announcement at the time. “We’ve invested massively in fighting spam over the past decade. In fact, in the past 12 months alone, a period marked by the explosion of generative AI tools, we’ve removed over 75 million spammy tracks from Spotify.”

While browsing their Discover Weekly playlists, users are finding “songs with weird, all-caps artists with AI pictures in them.” Others have pointed out artists with “no text in bios” and “ten plus releases each in 2025 alone.”

Besides polluting Discover Weekly playlists, we’ve also come across AI-generated impersonations showing up in Spotify’s New Releases playlists, which are designed to automatically surface new music by known artists.

Earlier this year, a self-proclaimed “indie rock band” called The Velvet Sundown made headlines after accumulating millions of streams. The “band” was later revealed to be an “ongoing artistic provocation designed to challenge the boundaries of authorship, identity, and the future of music itself in the age of AI.”

Spotify has also been caught populating the profiles of long-dead artists with AI-generated songs imitating their style.

As reporter Kieran Press-Reynolds argued in a recent column for Pitchfork, the company likely isn’t particularly incentivized to remove AI tracks from its platform.

“Spotify won’t prohibit this music — not because it thinks it’s innovative or ushering in a new era of technological futurism (the platform has never cared about culture) but simply because it’s generating streams,” Press-Reynolds wrote. “If the company actually wants to be transparent and control spam, they need to go way harder.”

More on Spotify: Bon Iver Side Project’s Spotify Page Features an AI Slop Song

The post AI Slop Now Invading Spotify’s Discover Weekly Lists appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Update ai: New 'Markovian Thinking' technique unlocks a path

Researchers at Mila have proposed a new technique that makes large language models (LLMs) vastly more efficient when performing complex reasoning. Called Markovian Thinking, the approach allows LLMs to engage in lengthy reasoning without incurring the prohibitive computational costs that currently limit such tasks.

The team’s implementation, an environment named Delethink, structures the reasoning chain into fixed-size chunks, breaking the scaling problem that plagues very long LLM responses. Initial estimates show that for a 1.5B parameter model, this method can cut the costs of training by more than two-thirds compared to standard approaches.

The quadratic curse of long-chain reasoning

For an LLM to solve a complex problem, it often needs to generate a long series of intermediate “thinking” tokens, often referred to as chain-of-thought (CoT). In recent years, researchers have found that using reinforcement learning (RL) to train models to produce longer CoTs (sometimes referred to as LongCoT) has significantly improved their reasoning capabilities.

However, the standard method for this has a critical flaw: The AI's "state" (the prompt plus all the reasoning tokens it has generated thus far in its processing) grows with every new reasoning token. For modern transformer-based models, this means the computational cost explodes quadratically as the reasoning chain gets longer, making it prohibitively expensive to train models for very complex tasks.

Most current attempts to manage this cost focus on limiting how much thinking the model does, implicitly preferring shorter solutions or terminating the process early. While these methods offer some relief, the Mila researchers still operate within the LongCoT framework and are thus fundamentally bound by its quadratic nature.

Instead of trying to control the computational growth, Mila created an RL environment that avoids the quadratic problem altogether. As co-author Amirhossein Kazemnejad explained, the goal is to enable capabilities like multi-week reasoning and scientific discovery. "That regime (and the RL needed to enable such capabilities) is not supported by the current LongCoT paradigm, because of quadratic compute cost," he said.

Thinking in chunks with Delethink

The researchers' solution is a paradigm they call the "Markovian Thinker," where the model reasons while keeping the size of its reasoning context window constant. The core idea is to change the RL setup to separate "how long the model thinks" from "how much context it must process." If done correctly, a Markovian Thinker turns the quadratic growth problem into linear compute and fixed memory requirements for LLM reasoning.

The researchers put this paradigm into practice through Delethink, which forces the model to reason in a sequence of fixed-size chunks, such as 8,000 tokens at a time. Within each chunk, the model reasons as it normally would, using the classic attention mechanism. But when it reaches the limit of the chunk, the environment resets the context, creating a new prompt that includes the original query plus a short "carryover" from the previous chunk. For example, the carryover could be the last few tokens of the previous chunk of CoT or a summary of the most important results.

This rearrangement of the problem forces the model to learn how to embed a summary of its progress, or a "textual Markovian state," into this carryover to continue its reasoning in the next chunk. This addresses the common concern of whether the model can remember important details from earlier steps. 

According to Kazemnejad, the model learns what to remember. "With training… the model is forced to learn to carry forward the task-critical state," he explained. He added crucial clarification for practical use: The original input prompt is not modified, including the documents or contextual data added to it. “Our approach is aimed at the reasoning phase and does not modify the prompt," he said.

Delethink in action

To test their approach, the researchers trained R1-Distill-1.5B with Delethink on a dataset of competition-level math problems, then evaluated it against several benchmarks. The model was trained to reason for up to 24,000 tokens but with fixed 8,000-token chunks.

The researchers compared this to models trained with the standard LongCoT-RL method. Their findings indicate that the model trained with Delethink could reason up to 24,000 tokens, and matched or surpassed a LongCoT model trained with the same 24,000-token budget on math benchmarks. On other tasks like coding and PhD-level questions, Delethink also matched or slightly beat its LongCoT counterpart. “Overall, these results indicate that Delethink uses its thinking tokens as effectively as LongCoT-RL with reduced compute,” the researchers write.

The benefits become even more pronounced when scaling beyond the training budget. While models trained with LongCoT quickly plateaued at their training limits, the Delethink-trained model continued to improve its performance. For instance, some math problems were only solved after the model reasoned for up to 140,000 tokens, far beyond its 24,000-token training budget. This linear compute advantage is substantial for enterprise applications. The researchers estimate that training a model to an average thinking length of 96,000 tokens would require 27 H100-GPU-months with LongCoT, versus just 7 with Delethink.

This efficiency extends directly to inference, the primary operational cost for most enterprises. "Models trained in Markovian Thinking use the same inference style (delethink-tracing) during test time, which provides the same advantages of linear compute and constant memory after training," said Kazemnejad. He offered a practical example: An AI agent could "debug a large codebase and think for a long time… which of course reduces the cost significantly compared to the conventional LongCoT approach."

Interestingly, the researchers found that off-the-shelf reasoning models, even without any specific training, already exhibit some ability to think in a Markovian way. This finding has immediate practical implications for developers. "In practice, this means that — without Delethink-RL— these models can already run a delethink-tracing wrapper and perform competitively with LongCoT on our benchmarked tasks," Kazemnejad said.

Their experiments with larger models such as GPT-OSS 120B showed robust performance with Delethink across a range of complex tasks. This latent ability provides a strong starting point for RL training, helping explain why the method is so effective. “Together, these results suggest that Delethink is compatible and scales with state-of-the-art models,” the researchers conclude.

The success of Markovian Thinking shows it may be possible for "next-generation reasoning models to think for millions of tokens," the researchers note. This opens the door to fundamentally new AI capabilities, moving beyond current constraints.

"Markovian Thinking… opens the path for models that can 'think' for very long horizons, which we view as a necessary step toward eventual scientific discovery," Kazemnejad said. "Our approach removes a key bottleneck and can allow training for much longer horizon tasks, which enables next-gen capabilities.&quot…

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🔗 Sumber: venturebeat.com