Eclectic-AI

You and your friend are training a neural network for classification. Both of you are using identical training data. The data has four classes with 40% examples of cat images, 10% images of dogs, and 25% each of horse and sheep images. Since the deadline for the project is nearing, both of you decide to run only a few epochs and get to report writing. At the same time, the two of you have a friendly wager of $10 going to the winner of the better model. At the end of training, you find out that your model, Net1, is making 30% recognition errors and the resulting distribution of assigned labels to the training data is 25% each for four classes. As luck would have it, your friend's model, Net2, is also yielding 30% error rate but the assigned labels in the training set are different with 40% cats, 10% dogs, 10% horse, and 40% sheep. Since the error rate by both models is identical, your friend declares a tie. You on the other hand are insisting that your model Net1 is slightly better

Large language models (LLMs) continue to fascinate us with their capabilities to answer our questions, generate presentations and essays for us and many other assorted tasks. These models are also good at generating code for user specified tasks. However, almost all of them do not run the code for us; they simply give us the code that we can copy and execute.   Recently, Google has given its large language model, Bard , the computational capabilities as well. Bard thus not only provides the code but also executes it while answering user's questions. I wanted to check this feature of Bard. Below is what happened when I asked Bard a question that involved some computation. Not only generating the code for entropy calculation and running it, Bard went on to explain entropy and its answer. Google characterizes computing by Bard in response to user questions as "writing code on the fly" method. The company says, "So far, we've seen this method improve the accuracy of