Imagine this, folks. Stanford University researchers just released a report on these big-shot AI models, and let me tell you, it’s not looking good. The report, called “The Foundation Model Transparency Index,” looked into models created by OpenAI, Google, Meta, and others. These models, known as foundation models, are trained on massive datasets and can do all sorts of cool things like generating images and writing. But here’s the problem: they lack transparency. And transparency is crucial, my friends.
The researchers behind the report argue that we need to know more about these models’ limitations and biases. And they’re absolutely right. Less transparency means it’s harder for businesses to know if they can safely use these models, for academics to rely on them for research, for policymakers to come up with effective regulations, and for consumers to understand the limitations and seek justice for any harm caused. It’s a big mess.
So, what did the Transparency Index find? Well, they graded 10 popular foundation models on 100 different indicators. They looked at things like training data, labor practices, and the amount of compute used in development. And let me tell you, folks, all the models got unimpressive scores. The highest score went to Meta’s Llama 2 language model, with a 54 out of 100. And the lowest score? That dubious honor goes to Amazon’s Titan model, with a measly 12 out of 100. OpenAI’s GPT-4 didn’t do too hot either, scoring a 48 out of 100.
But here’s the thing, folks. This lack of transparency isn’t just a recent development. Over the past three years, transparency has been declining while capability keeps skyrocketing. There are a bunch of reasons for this, from competitive pressures between Big Tech companies to fears of the supposed AI apocalypse. OpenAI employees have even backtracked on their previously open stance on AI, citing the potential dangers. It’s a real mess, folks.
Dr. Percy Liang, an associate professor at Stanford, summed it up perfectly. He said, “It is clear over the last three years that transparency is on the decline while capability is going through the roof.” And I couldn’t agree more, folks. We need transparency in AI, and we need it now.
Dr. Liang recently gave a talk at TED AI where he raised concerns about these closed models that don’t provide code or weights. He talked about accountability, values, and proper attribution of source material. He even compared open-source projects to a jazz ensemble, where players can riff off each other. And let me tell you, folks, he’s onto something. Open AI models have the potential for amazing benefits, just like projects such as Wikipedia and Linux.
Now, the authors of the Transparency Index are hoping that this report will not only push companies to be more transparent but also serve as a resource for governments trying to figure out how to regulate this rapidly growing field. And we need that, folks. We need regulations that ensure transparency and accountability.
So, let’s hope that these companies step up and start being more transparent. And let’s hope that governments take action and come up with smart regulations. Because transparency is the key to making AI work for all of us, my friends. And that’s a fact.