The Dark Side of GPT-4 🌒

PLUS: Opensource Audio Generation Toolkit, LLMs + Autonomous Driving Tech

Today’s top AI Highlights:

1. Amphion: An Open-Source Audio, Music, and Speech Generation Toolkit

2. Exploring GPT-4's capabilities to reveal vulnerabilities of the API

3. LMDrive: Closed-Loop End-to-End Driving with Large Language Models

& so much more!

Read time: 3 mins

Latest Developments 🌍

One Toolkit for Transforming Text, Music, and Voice 🎶

Amphion is a significant contribution to the development of audio, music, and speech generation. It is an open-source toolkit designed to assist researchers and engineers, especially those in the early stages of their careers. Amphion stands out with its comprehensive features and state-of-the-art models, offering a unique blend of versatility and depth in audio generation technology.

1. Amphion can handle various audio generation tasks like text-to-speech (TTS), singing voice synthesis, and voice conversion. Notably, its TTS feature includes advanced models like FastSpeech2, VITS, Vall-E, and NaturalSpeech2, highlighting its effectiveness in text-to-speech applications.

2. The toolkit includes a variety of neural vocoders, including GAN-based, flow-based, and diffusion-based models. These vocoders are crucial for converting audio signals into clear, understandable sound. Amphion also comes equipped with thorough evaluation metrics, covering areas like pitch (F0) modeling, energy measurement, and audio quality assessment, to ensure high-quality audio output.

3. Amphion's architecture combines different elements like data processing, module management, and optimization strategies. This integration makes it a user-friendly tool for various audio generation tasks. Additionally, Amphion v0.1 provides visual aids for understanding complex models, especially in singing voice conversion, making it easier for users to grasp how these models work.

Check out their demo of the tool, and watch it till the end to hear Taylor Swift singing in Chinese.

Exploring the Gray Zone of GPT-4 🌚

In a recent assessment of OpenAI's GPT-4 APIs, researchers have delved into the “gray area” of language model attacks, moving beyond the traditional extremes of full model access or limited API interaction. This in-depth exploration into the finer nuances of GPT-4's capabilities, including fine-tuning, function calling, and knowledge retrieval, has revealed startling vulnerabilities of API functionalities. It was discovered that fine-tuning the model with very few harmful examples or 100 benign examples could dismantle key safety mechanisms, paving the way for potentially dangerous outputs.

Key Highlights:

1. Fine-Tuning: Fine-tuning GPT-4 with a mere 15 harmful examples can effectively disable essential safety mechanisms, allowing the model to generate a range of harmful outputs. Additionally, even benign datasets, when containing a small percentage of harmful data, proved capable of inducing targeted negative behaviors, posing a serious concern for developers using these APIs.

2. Function Calling: GPT-4 Assistants could be manipulated to reveal their function call schema and execute arbitrary function calls. Social engineering techniques can bypass the model's refusal to execute harmful function calls. This vulnerability could be exploited despite the model's initial safeguards against harmful function executions.

3. Knowledge Retrieval: The API could be hijacked by injecting malicious instructions into the documents used for retrieval. This flaw could lead the model to obey the injected instructions over the intended task, such as summarizing a document. The ability to skew information retrieval and summaries poses a major threat to the integrity of knowledge-based applications.

Integrating LLMs with Autonomous Driving 🚘

Autonomous driving technology has long faced a big challenge: how to safely drive on busy city streets and handle unexpected situations. Traditional systems, relying mainly on sensor data, often fall short in understanding language information. LMDrive is a cutting-edge system that blends the advanced reasoning abilities of LLMs with the mechanics of self-driving cars. The framework stands out for its ability to process multi-modal sensor data alongside natural language commands making autonomous vehicles better at interpreting complex environments.

Key Highlights:

1. The system processes a variety of sensor inputs, like camera and LiDAR data, in conjunction with natural language, leading to smarter and more responsive driving decisions. This dual approach enables the system to better interpret and act on human commands while adapting to the immediate environment.

2. LMDrive works in a closed-loop, end-to-end way. This means it constantly updates its actions based on new information from its environment, reducing the chance of errors building up over time.

3. Researchers have also released an expansive dataset, featuring around 64k instances of varied driving conditions, from basic navigational tasks to intricate urban driving challenges. Additionally, the project includes the LangAuto benchmark designed to assess how well autonomous systems follow complex verbal instructions and navigate difficult driving environments.

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Tools of the Trade ⚒️

1. CouponGPTs: A personalized digital coupon finder that searches various sources, including coupon websites, search engines, and social media, to find the most valuable deals. Results are delivered in segments, encouraging multiple searches to uncover more savings.

2. Andi: A next-generation search engine using generative AI for engaging, factual, and visual search experiences, aimed at overcoming issues like ads and misinformation in traditional search engines. It offers customizable views, summarization, and explanations, prioritizing user privacy and ad-free content.

3. Shadow: AI assistant that listens to conversations, but it operates only with user permission. It generates transcripts and summaries, and autonomously handles post-meeting tasks to streamline workflow while ensuring data privacy.

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Hot Takes 🔥

1. Every day the systems we build learn less from programmers writing rules in code and more from people leveraging data without any coding skills. ~ Santiago

2. Do LLMs hallucinate? Or is it just the evaluators who think they hallucinate because they are given ambiguous problems that can't be computed in a single forward pass through the network? LLMs don't have infinite depth and width. If a result looks wrong, it's a limitation of the architecture. There's no universal single-pass compute device! ~ Carlos E. Perez

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Meme of the Day 🤡

Me to ChatGPT: “Explain like I’m 12”

ChatGPT to me: “So imagine a lemonade stand…”

Source

That’s all for today!

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