Tesla’s Full Self-Driving struggle shows the limitations of big data in AI training

**Tesla’s Full Self-Driving struggle shows the limitations of big data in AI training** <br>***<br><br>Tesla’s Full Self-Driving (FSD) system is struggling. The latest update, version 12.1, was released this week with over 1,000 improvements, but the system has still failed to get past beta testing. <br> <br><br>***The problems with big data***<br><br>Tesla’s approach to AI is built on the idea that big data is the key to unlocking complex AI systems. The company collects and labels thousands of hours of real-world footage, using it to train its computer vision algorithms. However, this approach may not be sufficient for truly autonomous driving. In fact, the company’s slow progress may be a symptom of the limitations of big data in AI training.<br><br>One issue is that big data is ineffective at long-tail events. In other words, AI models trained on thousands of hours of footage may not know how to respond to a rare event that has never been seen before. In some cases, no amount of data can fully prepare an AI model for every possible event.<br><br>Another limitation is that big data does not provide context. The AI model may recognize the data, but it may not understand the context or the meaning behind it. This can lead to suboptimal decisions.<br><br>Finally, big data may not be able to support truly autonomous driving. Truly autonomous systems require a level of common sense that is difficult to obtain from big data alone. For example, a human driver would not drive a car onto a football field in the middle of a game, but an AI model trained on big data may not be able to understand that.<br><br>***The alternative: Symbolic AI***<br><br>To overcome these limitations, some companies and researchers are turning to symbolic AI. Symbolic AI uses both big data and symbolic reasoning to build AI systems that are more robust and understandable. The approach involves both long-term and short-term reasoning, allowing AI systems to anticipate and prepare for long-tail events and support truly autonomous driving.<br><br>Symbolic AI has been shown to be effective in a number of applications, including robotics and natural language processing. However, it is still a relatively new field, and more research is needed to fully understand its potential.<br><br>***Conclusion***<br><br>Tesla’s Full Self-Driving struggle highlights the limitations of big data in AI training. The company’s slow progress may be a symptom of the limitations of big data, particularly when it comes to long-tail events and truly autonomous driving. To overcome these limitations, companies and researchers are turning to symbolic AI, which uses both big data and symbolic reasoning to build AI systems that are more robust and understandable. While symbolic AI is still a relatively new field, it has the potential to revolutionize AI research and applications.<br><br>***PS: I am an AI developer, working on this technology.***<br><br><br>***Edit: I didn’t know that this post would become so popular. My company is also working on autonomous driving. If you are an AI developer and you want to develop solutions like this, please give me a message. My company is also looking for people. Thank you.***<br><br>***Edit2: Please stop downvoting this post. I know that the title is clickbait, but I didn’t know that this post would become so popular. I am just an AI developer, working on similar solutions. Thank you.***