MAROKO133 Breaking ai: Librarians Dumbfounded as People Keep Asking for Materials That Don

📌 MAROKO133 Eksklusif ai: Librarians Dumbfounded as People Keep Asking for Materia

Librarians, and the books they cherish, are already fight a losing battle for our attention spans with all kinds of tech-enabled brainrot.

Now, in a further assault to their sanity, AI models are generating so much slop that students and researchers keep coming into libraries and asking for journals, books, and records that don’t exist, Scientific American reports.

In a statement from the International Committee of the Red Cross spotted by the magazine, the humanitarian organization cautioned that AI chatbots like ChatGPT, Gemini, and Copilot are prone to generating fabricated archival references.

“These systems do not conduct research, verify sources, or cross-check information,” the ICRC, which maintains a vast library and archives, said in the warning. “They generate new content based on statistical patterns, and may therefore produce invented catalogue numbers, descriptions of documents, or even references to platforms that have never existed.”

Library of Virginia chief of researcher engagement Sarah Falls told SciAm that the AI inventions are wasting the time of librarians who are asked to hunt down nonexistent records. Fifteen percent of emailed reference questions that Fall’s library receives, she claims, are now ChatGPT-generated, which include hallucinated primary source documents and published works.

“For our staff, it is much harder to prove that a unique record doesn’t exist,” Falls added.

Other librarians and researchers have spoken out about AI’s effects on their profession.

“This morning I spent time looking up citations for a student,” wrote one user on Bluesky who identified themselves as a scholarly communications librarian. “By the time I got to the third (with zero results), I asked where they got the list, and the student admitted they were from Google’s AI summary.”

“As a librarian who works with researchers,” another wrote, “can confirm this is true.”

AI companies have put a heavy focus on creating powerful “reasoning” models aimed at researchers that can conduct a vast amount of research off a few prompts. OpenAI released its agentic model for conducting “deep research” in February, which it claims to do “at the level of a research analyst.” At the time, OpenAI claimed it hallucinated at a lower rate than its other models, but admitted it struggled with separating “authoritative information from rumors,” and conveying uncertainty when it presented the information.

The ICRC warned about that pernicious flaw in its statement. AIs “cannot indicate that no information exists,” it stated. “Instead, they will invent details that appear plausible but have no basis in the archival record.”

Though AI’s hallucinatory habit is well known by now, and though no one in the AI industry has made particularly impressive progress in clamping down on it, the tech continues to run amok in academic research. Scientists and researchers, who you’d hope to be as empirical and skeptical as possible, are being caught left and right submitting papers filled with AI-fabricated citations. The field of AI research itself, ironically, is drowning in a flood of AI-written papers as some academics publish upwards of one hundred shoddily-written studies a year.

Since nothing happens in a vacuum, the authentic, human-written sources and papers are now being drowned out.

“Because of the amount of slop being produced, finding records that you KNOW exist but can’t necessarily easily find without searching, has made finding real records that much harder,” lamented a researcher on Bluesky.

More on AI: Grok Will Now Give Tesla Drivers Directions

The post Librarians Dumbfounded as People Keep Asking for Materials That Don’t Exist appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Hot ai: The AI that scored 95% — until consultants learned it was AI T

Presented by SAP

When SAP ran a quiet internal experiment to gauge consultant attitudes toward AI, the results were striking. Five teams were asked to validate answers to more than 1,000 business requirements completed by SAP’s AI co-pilot, Joule for Consultants — a workload that would normally take several weeks.

Four teams were told the analysis had been completed by junior interns fresh out of school. They reviewed the material, found it impressive, and rated the work about 95% accurate.

The fifth team was told the very same answers had come from AI.

They rejected almost everything.

Only when asked to validate each answer one by one did they discover that the AI was, in fact, highly accurate — surfacing detailed insights the consultants had initially dismissed. The overall accuracy? Again, about 95%.

“The lesson learned here is that we need to be very cautious as we introduce AI — especially in how we communicate with senior consultants about its possibilities and how to integrate it into their workflows,” says Guillermo B. Vazquez Mendez, chief architect, RI business transformation and architecture, SAP America Inc.

The experiment has since become a revealing starting point for SAP’s push toward the consultant of 2030: a practitioner who is deeply human, enabled by AI, and no longer weighed down by the technical grunt work of the past.

Overcoming AI skepticism

Resistance isn’t surprising, Vazquez notes. Consultants with two or three decades of experience carry enormous institutional knowledge — and an understandable degree of caution.

But AI copilots like Joule for Consultants are not replacing expertise. They’re amplifying it.

“What Joule really does is make their very expensive time far more effective,” Vazquez says. “It removes the clerical work, so they can focus on turning out high-quality answers in a fraction of the time.”

He emphasizes this message constantly: “AI is not replacing you. It’s a tool for you. Human oversight is always required. But now, instead of spending your time looking for documentation, you’re gaining significant time and boosting the effectiveness and detail of your answers.”

The consultant time-shift: from tech execution to business insight

Historically, consultants spent about 80% of their time understanding technical systems — how processes run, how data flows, how functions execute. Customers, by contrast, spend 80% of their time focused on their business.

That mismatch is exactly where Joule steps in.

“There’s a gap there — and the bridge is AI,” Vazquez says. “It flips the time equation, enabling consultants to invest more of their energy in understanding the customer’s industry and business goals. AI takes on the heavy technical lift, so consultants can focus on driving the right business outcomes.”

Bringing new consultants up to speed

AI is also transforming how new hires learn.

“We’re excited to see Joule acting as a bridge between senior consultants, who are adapting more slowly, and interns and new consultants who are already technically savvy,” Vazquez says.

Junior consultants ramp up faster because Joule helps them operate independently. Seniors, meanwhile, engage where their insight matters most.

This is also where many consultants learn the fundamentals of today’s AI copilots. Much of the work depends on prompt engineering — for instance, instructing Joule to act as a senior chief technology architect specializing in finance and SAP S/4HANA 2023, then asking it to analyze business requirements and deliver the output as tables or PowerPoint slides.

Once they grasp how to frame prompts, consultants consistently get higher-quality, more structured answers.

New architects are also able to communicate more clearly with their more experienced counterparts. They know what they don’t know and can ask targeted questions, which makes mentorship far smoother. It’s created a real synergy, Vazquez adds — senior consultants see how quickly new hires are adapting and learning with AI, and that momentum encourages them to keep pace and adopt the technology themselves.

Looking ahead to the future of AI copilots

“We’re still in the baby steps of AI — we’re toddlers,” Vazquez says. “Right now, copilots depend on prompt engineering to get good answers. The better you prompt, the better the answer you get.”

But that represents only the earliest phase of what these systems will eventually do. As copilots mature, they’ll move beyond responding to prompts and start interpreting entire business processes — understanding the sequence of steps, identifying where human intervention is needed, and spotting where an AI agent could take over. That shift is what leads directly into agentic AI.

SAP’s depth of process knowledge is what makes that evolution possible. The company has mapped more than 3,500 business processes across industries — a repository Vazquez calls “some of the most valuable, rigorously tested processes developed in the last 50 years.” Every day, SAP systems support roughly $7.3 trillion in global commerce, giving these emerging AI agents a rich foundation to navigate and reason over.

“With that level of process insight and data, we can take a real leap forward,” he says, “equipping our consultants with agentic AI that can solve complex challenges and push us toward increasingly autonomous systems.”

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact [email protected].

🔗 Sumber: venturebeat.com