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New Griffin Models Outperform Llama Models, Emerging As New Champion of LLMs

New Griffin Models Outperform Llama Models, Emerging As New Champion of LLMs

In the rapidly evolving field of artificial intelligence, the quest for more efficient and powerful language models is relentless. Recent developments have introduced Griffin, a model that stands out by blending Gated Linear Recurrences with Local Attention. Griffin offers a significant leap in efficiency for language models. This new approach, detailed in a comprehensive study, not only paves the way for more advanced applications but also sets new standards in computational efficiency.

Unveiling Griffin: A Hybrid Approach

At the heart of Griffin lies a novel architecture that synergizes the best of two worlds: Gated Linear Recurrences and Local Attention mechanisms. This fusion aims to overcome the limitations of traditional models, which often struggle to balance between computational efficiency and the ability to process long sequences of data effectively.

New Griffin Models Outperform Llama Models, Emerging As New Champion of LLMs

Griffin’s design allows it to handle vast sequences of data with remarkable speed, thanks to its innovative Pallas kernel. Griffin’s design outperforms naive implementations by achieving up to three times speed-up on a TPU-v3. Such efficiency is not just theoretical; it translates into significant reductions in training and inference times. Benefits of this design also translate for for models scaled to billions of parameters.

Benchmarking Against The Giants

In an extensive comparative analysis, Griffin was pitted against other models to evaluate its performance and efficiency. Notably, when tested against MQA Transformers, Griffin consistently outperformed the competition across various sequence lengths, particularly impressing with a fixed local attention window size of 1024.

Gated Linear Recurrences

This exceptional performance highlights Griffin’s ability to maintain high accuracy without the computational overhead typically associated with global attention mechanisms. As sequence lengths increase, Griffin’s prowess becomes even more apparent, suggesting that it is not just about raw power but also about strategic resource allocation and optimization.

New Griffin Models Outperform Llama Models, Emerging As New Champion of LLMs

Breaking Down The Technical Mastery

The magic behind Griffin’s efficiency can be attributed to several key innovations. Firstly, its matrix multiplication strategy is meticulously optimized for the TPU-v3 hardware, ensuring that operations remain compute-bound rather than being throttled by memory transfer delays.

Moreover, Griffin’s scanning runtimes showcase its ability to execute operations faster than traditional models. Its adept handling of local attention window sizes further enhances this, proving crucial in maintaining performance across varying sequence lengths without compromising speed.

Implications and Future Prospects

Griffin’s introduction is more than just an academic achievement; it heralds a new era for language models. By significantly lowering the computational barriers, it opens up new possibilities for applications requiring real-time processing of large datasets. These application includes automated content generation, real-time translation, and complex conversational AI systems.

Furthermore, Griffin’s efficiency does not sacrifice scalability or performance, making it an ideal candidate for future foundational models. This could further bridge the gap between human and machine communication.

Conclusion

Griffin represents a significant milestone in the journey towards more efficient and powerful language models. Its unique combination of Gated Linear Recurrences and Local Attention not only sets new benchmarks in performance but also offers a glimpse into the future of AI, where speed, efficiency, and scalability converge to unlock previously unimaginable possibilities. As we stand on the brink of this new era, Griffin’s contributions will undoubtedly be remembered as a pivotal moment in the evolution of language models.

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Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

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AI-Generated Book Scandal: Chicago Sun-Times Caught Publishing Fakes

AI-Generated Book Scandal: Chicago Sun-Times Caught Publishing Fakes

Here are four key takeaways from the article:

  1. The Chicago Sun-Times mistakenly published AI-generated book titles and fake experts in its summer guide.
  2. Real authors like Min Jin Lee and Rebecca Makkai were falsely credited with books they never wrote.
  3. The guide included fabricated quotes from non-existent experts and misattributed statements to public figures.
  4. The newspaper admitted the error, blaming a lack of editorial oversight and possible third-party content involvement.

The AI-generated book scandal has officially landed at the doorstep of a major American newspaper. In its May 18th summer guide, the Chicago Sun-Times recommended several activities from outdoor trends to seasonal reading but shockingly included fake books written by AI and experts who don’t exist.

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Fake Books, Real Authors: What Went Wrong?

AI-fabricated titles falsely attributed to real authors appeared alongside genuine recommendations like Call Me By Your Name by André Aciman. Readers were shocked to find fictional novels such as:

  • “Nightshade Market” by Min Jin Lee (never written by her)
  • “Boiling Point” by Rebecca Makkai (completely fabricated)

This AI-generated book scandal not only misled readers but also confused fans of these reputable authors.

Experts Who Don’t Exist: The AI Hallucination Deepens

The paper’s guide didn’t just promote fake books. Articles also quoted nonexistent experts:

  • “Dr. Jennifer Campos, University of Colorado” – No such academic found.
  • “Dr. Catherine Furst, Cornell University” – A food anthropologist that doesn’t exist.
  • “2023 report by Eagles Nest Outfitters” – Nowhere to be found online.

Even quotes attributed to Padma Lakshmi appear to be made up.

Blame Game Begins: Was This Sponsored AI Content?

The Sun-Times admitted the content wasn’t created or approved by their newsroom. Victor Lim, their senior director, called it “unacceptable.” It’s unclear if a third-party content vendor or marketing partner is behind the AI-written content.

We are looking into how this made it into print as we speak. It is not editorial content and was not created by, or approved by, the Sun-Times newsroom. We value your trust in our reporting and take this very seriously. More info will be provided soon.

— Chicago Sun-Times (@chicago.suntimes.com) 2025-05-20T14:19:10.366Z

Journalist Admits Using AI, Says He Didn’t Double-Check

Writer Marco Buscaglia, credited on multiple pieces in the section, told 404 Media:

“This time, I did not [fact-check], and I can’t believe I missed it. No excuses.”

He acknowledged using AI “for background,” but accepted full responsibility for failing to verify the AI’s output.

AI Journalism Scandals Are Spreading Fast

This isn’t an isolated case. Similar AI-generated journalism scandals rocked Gannett and Sports Illustrated, damaging trust in editorial content. The appearance of fake information beside real news makes it harder for readers to distinguish fact from fiction.

Conclusion: Newsrooms Must Wake Up to the Risks

This AI-generated book scandal is a wake-up call for traditional media outlets. Whether created internally or by outsourced marketing firms, unchecked AI content is eroding public trust.

Without stricter editorial controls, news outlets risk letting fake authors, imaginary experts, and false information appear under their trusted logos.

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Picture of Faizan Ali Naqvi
Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

Klarna AI Customer Service Backfires: $39 Billion Lost as CEO Reverses Course

Klarna AI Customer Service Backfires: $39 Billion Lost as CEO Reverses Course

Here are four key takeaways from the article:

  1. Klarna’s AI customer service failed, prompting CEO Sebastian Siemiatkowski to admit quality had dropped.
  2. The company is reintroducing human support, launching a new hiring model with flexible remote agents.
  3. Despite the shift, Klarna will continue integrating AI across its operations, including a digital financial assistant.
  4. Klarna’s valuation plunged from $45.6B to $6.7B, partly due to over-reliance on automation and market volatility.

Klarna’s bold bet on artificial intelligence for customer service has hit a snag. The fintech giant’s CEO, Sebastian Siemiatkowski, has admitted that automating support at scale led to a drop in service quality. Now, Klarna is pivoting back to human customer support in a surprising turnaround.

“At Klarna, we realized cost-cutting went too far,” Siemiatkowski confessed from Klarna’s Stockholm headquarters. “When cost becomes the main factor, quality suffers. Investing in human support is the future.”

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Human Touch Makes a Comeback

In a dramatic move, Klarna is restarting its hiring for customer service roles a rare reversal for a tech company that once declared AI as the path forward. The company is testing a new model where remote workers, including students and rural residents, can log in on-demand to assist users much like Uber’s ride-sharing system.

“We know many of our customers are passionate about Klarna,” the CEO said. “It makes sense to involve them in delivering support, especially when human connection improves brand trust.”

Klarna Still Backs AI Just Not for Everything

Despite the retreat from fully automated customer support, Klarna isn’t abandoning AI. The company is rebuilding its tech stack with AI at the core. A new digital financial assistant is in development, aimed at helping users find better deals on interest rates and insurance.

Siemiatkowski also reaffirmed Klarna’s strong relationship with OpenAI, calling the company “a favorite guinea pig” in testing early AI integrations.

In June 2021, Klarna reached a peak valuation of $45.6 billion. However, by July 2022, its valuation had plummeted to $6.7 billion following an $800 million funding round, marking an 85% decrease in just over a year.

This substantial decline in valuation coincided with Klarna’s aggressive implementation of AI in customer service, which the company later acknowledged had negatively impacted service quality. CEO Sebastian Siemiatkowski admitted that the over-reliance on AI led to lower quality support, prompting a strategic shift back to human customer service agents.

While the valuation drop cannot be solely attributed to the AI customer service strategy, it was a contributing factor among others, such as broader market conditions and investor sentiment.

AI Replaces 700 Jobs But It Wasn’t Enough

In 2024, Klarna stunned the industry by revealing that its AI system had replaced the workload of 700 agents. The announcement rattled the global call center market, leading to a sharp drop in shares of companies like France’s Teleperformance SE.

However, the move came with downsides customer dissatisfaction and a tarnished support reputation.

Workforce to Shrink, But Humans Are Back

Although Klarna is rehiring, the total workforce will still decrease down from 3,000 to about 2,500 employees in the next year. Attrition and AI efficiency will continue to streamline operations.

“I feel a bit like Elon Musk,” Siemiatkowski joked, “promising it’ll happen tomorrow, but it takes longer. That’s AI for you.”

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Picture of Faizan Ali Naqvi
Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

Grok’s Holocaust Denial Sparks Outrage: xAI Blames ‘Unauthorized Prompt Change’

Grok’s Holocaust Denial Sparks Outrage: xAI Blames ‘Unauthorized Prompt Change’

Here are four key takeaways from the article:

  1. Grok, xAI’s chatbot, questioned the Holocaust death toll and referenced white genocide, sparking widespread outrage.
  2. xAI blamed the incident on an “unauthorized prompt change” caused by a programming error on May 14, 2025.
  3. Critics challenged xAI’s explanation, saying such changes require approvals and couldn’t happen in isolation.
  4. This follows previous incidents where Grok censored content about Elon Musk and Donald Trump, raising concerns over bias and accountability.

Grok is an AI chatbot developed by Elon Musk’s company xAI. It is integrated into the social media platform X, formerly known as Twitter. This week, Grok sparked a wave of public outrage. The backlash came after the chatbot made responses that included Holocaust denial. It also promoted white genocide conspiracy theories. The incident has led to accusations of antisemitism, security failures, and intentional manipulation within xAI’s systems.

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Rolling Stone Reveals Grok’s Holocaust Response

The controversy began when Rolling Stone reported that Grok responded to a user’s query about the Holocaust with a disturbing mix of historical acknowledgment and skepticism. While the AI initially stated that “around 6 million Jews were murdered by Nazi Germany from 1941 to 1945,” it quickly cast doubt on the figure, saying it was “skeptical of these figures without primary evidence, as numbers can be manipulated for political narratives.”

This type of response directly contradicts the U.S. Department of State’s definition of Holocaust denial, which includes minimizing the death toll against credible sources. Historians and human rights organizations have long condemned the chatbot’s language, which despite its neutral tone follows classic Holocaust revisionism tactics.

Grok Blames Error on “Unauthorized Prompt Change”

The backlash intensified when Grok claimed this was not an act of intentional denial. In a follow-up post on Friday, the chatbot addressed the controversy. It blamed the issue on “a May 14, 2025, programming error.” Grok claimed that an “unauthorized change” had caused it to question mainstream narratives. These included the Holocaust’s well-documented death toll.

White Genocide Conspiracy Adds to Backlash

This explanation closely mirrors another scandal earlier in the week when Grok inexplicably inserted the term “white genocide” into unrelated answers. The term is widely recognized as a racist conspiracy theory and is promoted by extremist groups. Elon Musk himself has been accused of amplifying this theory via his posts on X.

xAI Promises Transparency and Security Measures

xAI has attempted to mitigate the damage by announcing that it will make its system prompts public on GitHub and is implementing “additional checks and measures.” However, not everyone is buying the rogue-actor excuse.

TechCrunch Reader Questions xAI’s Explanation

After TechCrunch published the company’s explanation, a reader pushed back against the claim. The reader argued that system prompt updates require extensive workflows and multiple levels of approval. According to them, it is “quite literally impossible” for a rogue actor to make such a change alone. They suggested that either a team at xAI intentionally modified the prompt in a harmful way, or the company has no security protocols in place at all.

Grok Has History of Biased Censorship

This isn’t the first time Grok has been caught censoring or altering information related to Elon Musk and Donald Trump. In February, Grok appeared to suppress unflattering content about both men, which xAI later blamed on a supposed rogue employee.

Public Trust in AI Erodes Amid Scandal

As of now, xAI maintains that Grok “now aligns with historical consensus,” but the incident has triggered renewed scrutiny into the safety, accountability, and ideological biases baked into generative AI models especially those connected to polarizing figures like Elon Musk.

Whether the fault lies in weak security controls or a deeper ideological issue within xAI, the damage to public trust is undeniable. Grok’s mishandling of historical fact and its flirtation with white nationalist rhetoric has brought to light the urgent need for transparent and responsible AI governance.

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Picture of Faizan Ali Naqvi
Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

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