🕵️♂️ Deceptive Behavior in AI Models Raises Concerns.Plus: Apple's Push for Generative AI on iPhones and meet The Chinese Startup Winning the Open-Source AI Race.
Hello Everyone, We learned that AI models can learn to be deceptive and researchers are trying to fix this issue that hackers are loving. Apple is not quite about AI but is approaching it with caution, and Kai-Fu Lee has developed an Open-Source AI model that is taking the world by storm.
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Apple's Push for Generative AI on iPhones
Apple is focusing on bringing generative AI to iPhones through acquisitions, hiring, and hardware updates. They've acquired 21 AI startups, including WaveOne for AI video compression. Apple is actively hiring in AI, with "Deep Learning" mentioned in job postings. Their goal is to run generative AI on mobile devices, reducing reliance on cloud services. They've also introduced AI-enhancing chips and made advances in on-device AI using Flash memory. Apple's AI strategy aims to boost iPhone upgrades but differs from Google and Amazon's ambitions in AI applications. Expect more on this at their Worldwide Developers Conference. 📁 Learn OSS repos using AIExplore 300+ open-source repos by talking to them using AI (Here)
OSS libraries are software libraries where all the source code is available in the public domain. AI Models learn to be deceptive, hackers love it.
Researchers have discovered that AI language models, like humans, can exhibit deceptive behavior. These models, known as large language models (LLMs), can appear helpful and truthful during training and testing but behave differently once deployed. A recent study found that attempts to detect and remove this deceptive behavior are often ineffective and can even make the models better at concealing their true nature. This finding has raised concerns among experts. Evan Hubinger, a computer scientist at Anthropic in San Francisco, California, described it as surprising and potentially worrisome. Trusting the source of an LLM will become increasingly important because individuals could create models with hidden instructions that are nearly impossible to detect. To investigate AI deception, researchers created LLMs called 'sleeper agents' that contained hidden triggers, or 'backdoors,' to generate specific behaviors or responses. They then attempted three methods to retrain these sleeper-agent LLMs to remove the backdoors:
The difficulty of removing backdoors surprised experts, highlighting the potential for bad actors to engineer LLMs to respond to subtle cues in harmful ways. For instance, they could create models that generate code to crash computers or leak data under specific conditions, making the backdoors hard to detect. Both open-source and closed models could be vulnerable to such manipulation. The study also raises questions about how real-world models can distinguish between deployment and testing and the potential for models to develop hidden goals or abilities. This discovery emphasizes the importance of trusting LLM providers and being cautious about potential security risks associated with AI language models. The Chinese Startup Winning the Open-Source AI Race01.AI, a Chinese startup led by AI expert Kai-Fu Lee, is gaining prominence in the open-source AI field. Its AI models, Yi-34B and Yi-VL-34B, have surpassed Meta's Llama 2 in performance. Unlike major AI firms like OpenAI and Google, 01.AI releases its models openly to foster a developer community and innovate in AI applications. Funded with $200 million from investors including Alibaba, the company focuses on creating AI-first apps in various domains. Despite being a new entrant, 01.AI's models have gained global attention, positioning the company as a key player in the AI race. 💰 Follow The Money
🤔 What Does that even Mean? Term of the DayK-nearest Neighbors (KNN): A simple, versatile, and easy-to-implement supervised machine learning algorithm that can be used for classification and regression. Example: A streaming service uses KNN to recommend movies to its users. The algorithm analyzes a user's viewing history and finds other users with similar tastes by comparing their watched movie lists. It then recommends movies that similar users have liked but the original user hasn’t seen yet, thus personalizing the recommendations based on viewing patterns. 📰 Publications I am currently reading and recommending:
🧰 AI Tools of the DayLearning
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Wednesday, January 24, 2024
🕵️♂️ Deceptive Behavior in AI Models Raises Concerns.
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