AI for Embedded Systems | Embedded systems podcast, in Pyjama

Summary

In this engaging discussion, a group of five individuals explore the practical applications of AI in embedded systems. They delve into the current capabilities of AI for tasks like reading and interacting with data sheets, with mixed results. The conversation covers the challenges of relying on AI for coding assistance, the limitations of AI in understanding specific documentation, and the potential for AI to generate code and unit tests. The group also touches on the broader implications of AI in the software development process, highlighting both its benefits and the need for cautious adoption.

Takeaways

• 😀 The group discusses the use of AI in embedded systems and its current applications, focusing on large language models.
• 🔍 Wasim shares his experience using AI to interpret data sheets, noting the model's mixed success in providing relevant information.
• 📚 The conversation highlights the limitations of AI when dealing with poorly documented or proprietary hardware data sheets.
• 🤖 A member of the group explores using local AI models like llama 3 for tasks to maintain data privacy, especially for company-specific hardware.
• 🛠️ The group acknowledges AI's utility in writing blockware code such as HTML and CSS, but its less effective performance with more complex or custom software engineering code.
• 🔧 Some participants find AI-generated code suggestions distracting and sometimes inaccurate, leading to a preference for disabling certain AI features.
• 🔄 The discussion points out AI's tendency to 'hallucinate' or generate incorrect information, necessitating verification of its outputs.
• 🔑 The importance of understanding AI's limitations is emphasized, such as its inability to understand the context as deeply as a human expert.
• 🔑️‍🔒 Privacy and security are considered when deciding to use local AI models to avoid uploading sensitive data to the cloud.
• 📈 The group sees potential in AI for reducing research time and providing meaningful responses for common queries found on the internet.
• 🛑 The script concludes with a cliffhanger about whether AI will replace embedded engineers, suggesting that it's currently far from happening.

Q & A

What is the main topic of discussion in the video?

The main topic of discussion is the use of AI in embedded systems, focusing on how AI, particularly large language models, can be applied in this field.

How is Wasim using AI in his current work?

Wasim is exploring the use of AI to read and chat with datasheets, although he has faced challenges with the accuracy of the responses.

What is the general consensus about the reliability of AI-generated responses for technical documentation?

The consensus is that AI-generated responses can be hit or miss, often providing incorrect or incomplete information, which can be unreliable for technical documentation.

What alternative method is being explored for using AI with local PDFs?

An alternative method involves using a local LLM model like LLaMA 3 and converting PDFs to text and embeddings for querying, thus avoiding cloud-based solutions for proprietary data.

What are some challenges mentioned regarding the use of AI for writing code?

Challenges include AI generating incorrect code, creating distractions with irrelevant autocomplete suggestions, and sometimes hallucinating incorrect solutions.

What are the advantages of using AI for scripting languages mentioned in the discussion?

AI is found to be helpful in writing scripts like Python and JavaScript, as well as generating boilerplate code for HTML and CSS.

How do the speakers use AI for generating unit tests?

They use AI to generate basic unit tests by inferring from the code, which can help in testing all combinations of input data types and expected outputs.

What is one of the significant limitations of AI in coding, according to the discussion?

A significant limitation is AI's inability to handle custom or complex codebases effectively, often generating more noise than useful logic.

What are the speakers' thoughts on the future improvement of AI in coding?

They believe that as AI gets used more often and receives more feedback, its accuracy and usefulness in coding will improve over time.

What is a common problem with AI-generated technical solutions as highlighted in the video?

A common problem is AI's tendency to hallucinate solutions that seem plausible but are actually incorrect, leading to confusion and mistrust in its responses.

Why do some speakers prefer to run AI models locally rather than using cloud-based solutions?

They prefer local models to ensure the privacy and security of proprietary data, which might be at risk if uploaded to cloud-based AI services.

What is the perceived gap between AI's current capabilities and the potential to replace embedded engineers?

The perceived gap is significant, as AI currently lacks the ability to fully understand and implement complex hardware and software integration, which is critical in embedded engineering.