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- 🤖 TikTok Fires Intern Who “Maliciously Interfered” With AI Research
🤖 TikTok Fires Intern Who “Maliciously Interfered” With AI Research
PLUS: Anthropic’s Four New Sabotage Evaluations for Advanced AI Models, “SymGen” Verifies AI Model Responses
Welcome back AI enthusiasts!
In today’s AI Report:
👨🚒TikTok Fires Intern Who “Maliciously Interfered” With AI Research
🦺Anthropic’s Four New Sabotage Evaluations for Advanced AI Models
📊“SymGen” Verifies AI Model Responses
🛠Trending Tools
💰Funding Frontlines
💼Who’s Hiring?
Read Time: 3 minutes
🗞RECENT NEWS
BYTEDANCE
👨🚒TikTok Fires Intern Who “Maliciously Interfered” With AI Research
Image Source: Live at TED2023/“TikTok CEO Shou Chew on Its Future and What Makes Its Algorithm Different”/YouTube/Screenshot
TikTok’s parent company, ByteDance, confirmed it had to fire an intern who “maliciously interfered” with TikTok’s AI research.
Key Details:
A Commercial Technology intern “committed serious disciplinary violations,” said ByteDance. “The intern maliciously interfered with AI model training.”
The intern’s actions affected ByteDance’s AI Training Program, where employees program an AI model by “training” it on vast amounts of data to recognize patterns, understand context, and make decisions.
The intern allegedly implanted an Unsafe Pickle (i.e., malicious code injection) that actively “untrained” AI models.
The intern would participate in meetings where they tried to solve the issues, allowing him to adapt his strategies and avoid detection.
ByteDance denies the claims that the intern impacted 8,000 Graphics Processing Units (GPUs) or cost TikTok “tens of millions of dollars.”
For context, Nvidia H100 Tensor Core GPUs are estimated to cost around $25,000 per GPU.
🚨Read the latest updates on this situation here.
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ANTHROPIC
🦺Anthropic’s Four New Sabotage Evaluations for Advanced AI Models
Image Source: Canva’s AI Image Generators/Magic Media
Anthropic, a research company building reliable, interpretable, and steerable AI systems, released four new Sabotage Evaluations for advanced AI models.
Key Details:
In theory, advanced AI models could “subvert human oversight and decision-making in important contexts.”
In other words, as AI models become more sophisticated, they could make decisions or take actions humans don’t want.
For example, advanced AI models could “covertly sabotage efforts to evaluate their own dangerous capabilities, to monitor their behavior, or to make decisions about their deployment.”
The four new Sabotage Evaluations include Code, Sandbagging, Human Decisions, and Undermining Oversight.
They examine an advanced AI model’s capabilities to steer humans toward bad decisions without appearing suspicious, insert bugs into codebases, and systematically undermine monitoring procedures.
Why It’s Important:
Anthropic is open-sourcing the four new Sabotage Evaluations because they “hope other AI researchers will use, critique, and improve upon” their work.
Uncovering potential vulnerabilities that might not be apparent in standard AI model testing helps to identify areas where human oversight is necessary.
AI RESEARCH
📊“SymGen” Verifies AI Model Responses
Image Source: Massachusetts Institute of Technology (MIT)/Good Data Initiative (GDI)/“Towards Verifiable Text Generation With Symbolic References”/Screenshot
Despite their impressive capabilities, AI models are far from perfect. They sometimes “hallucinate” by confidently generating inaccurate or misleading information.
To prevent this, an AI model’s responses are verified by human fact-checkers. However, this error-prone process requires them to read through extensive reports filled with citations manually.
To solve this issue, MIT researchers developed “SymGen,” a user-friendly framework that enables anyone to verify an AI model’s responses in minutes. “SymGen” generates responses with citations directly referring to the source.
“SymGen” streamlines the verification process for human fact-checkers by around 20%. By making the validation of an AI model’s responses easier and faster, “SymGen” could prevent errors with AI applications deployed in safety-critical healthcare situations.
“It can give people higher confidence in an AI model’s responses because they can easily take a closer look to ensure the information is verified,” said Shannon Zejiang Shen, co-lead author of the paper on “SymGen.”
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💰FUNDING FRONTLINES
💼WHO’S HIRING?
Sigma Computing (San Francisco, CA): Software Engineering Intern, Summer 2025
GM Financial (Arlington, TX): Software Development Engineer Intern, Summer 2025
The New York Times {NYT} (New York, NY): Backend Engineering Intern, Summer 2025
Adobe (San Jose, CA): Research Scientist/Engineer, New College Grad 2025
Apple (Austin, TX): Machine Learning {ML} Engineer, Applied Data Science Program, Early Career
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