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ChatGPT Delivers Ideas and Answers on Demand, If You Know How To Ask

ChatGPT Delivers Ideas and Answers on Demand, If You Know How To Ask

Robyn Bresnahan and I chatted about this on CBC Ottawa Morning on 13-Dec-2022. Have a listen 👇

Machine learning (ML) and artificial intelligence (A.I.) have moved from sci-fi to buzzword to everyday tool. The latest models are making main stream headlines.

Creations from custom art generators like Dall-E 2, Midjourney, and Stable Diffusion have been trending on social media globally by capturing everyone’s imagination. Targeted solutions like custom AI profile avatars and marketing copy generators are finding audiences by creating unique solutions to very specific creative challenges.

Each of these solutions is a type of generative machine learning model. The latest to catch everyone’s attention? ChatGPT.

Machine Learning Models

At the heart of machine learning are models. A model is basically a computer program. But instead of the program delivering very direct results (e.g., click this button to load this webpage), these programs try to predict or infer answers to questions in a specific problem space.

A model is trained on a large amount of data. That training process allows the model (or program) to make connections and identify patterns between various data points. Those patterns are key to the success of the model.

Given new, similar data, the model uses those patterns to predict or infer a result.

Let’s say you train the model on pictures of all sorts of different pets. Dogs, cats, birds, fish, etc. This training is clearly labeled. So, the program looks at the first photo and it is told, “This is a picture of a dog.” Repeat that several hundred thousand or even millions of times.

That builds the pattern of “A dog usually has four legs, fur, a tail, and a long nose.” Another pattern of, “A fish is always in water, has fins, no arms or legs, gills, and a larger tail.”. And so on.

Now, when the program is run, the user gives the model a new picture it’s never seen before. Essentially asking, “What pets are in this photo?”

The model then uses the patterns it knows about to infer what’s in the picture, returning the result of “This is probably a cat and this is probably a dog.” The answer is probably because the model is using inference to basically guess what’s in the picture. It doesn’t actually know what’s in the picture, but based on other picture’s it’s been trained on, these are the most likely answers.

Generative Models

The latest wave of machine learning models work in reverse. Instead of giving them a picture and asking what’s in it, you provide a prompt and the model infers what that might look like or sound like.

Here, I used the prompt, “An odd couple pairing of a cat and a dog. Hayao Miyazaki style, digital painting”

Dall-E prompt, “An odd couple pairing of a cat and a dog. Hayao Miyazaki style, digital painting”

Based on what the model—in this case, Dall-E 2—knows about cats, dogs, and Hayao Miyazaki’s art style, this is what it came up with.

ChatGPT is a wrapper around the GPT-3 generative large language model. It does the same thing as Dall-E, but with text. You provide a prompt and it will deliver a writing sample to match. When I asked ChatGPT to describe itself, this is what it produced…

ChatGPT is a language model that’s been trained to chat with you. It’s based on GPT-3, which is a really advanced model. ChatGPT can generate responses that sound like they’re coming from a real person, so it’s great for things like chatbots or text-based dialogue systems. It’s not a specific product or tool, but more like a type of model that you can use for different applications.

The results can be quite impressive when they are accurate. Remember the model is designed to produce passable prose, not perfectly accurate text content.

ChatGPT takes things a step further and provides a discussion-like interface to it’s model. Using that interface, I explored the key concepts behind this technology. You can read the results of that below in, “A Quick Chat With ChatGPT

What Problems Does ChatGPT Solve Well?

ChatGPT isn’t the end-all, be-all of generative AI. It isn’t going to replace human creative or human effort. It can help accelerate your work though. Allie K. Miller puts together a great list of areas where ChatGPT excels in this Twitter thread 🧵👇

I agree with most of her key areas, specifically:

ChatGPT is a wonderful way to get past staring at a blank page! You can check it out at

A Quick Chat With ChatGPT

Mark: What is machine learning?

ChatGPT: Machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed. It is based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Machine learning algorithms use statistical techniques to find patterns in data and use them to make predictions or take actions. This can be used in a wide range of applications, such as image and speech recognition, natural language processing, and even in self-driving cars.

Mark: How do I make my own model?

ChatGPT: To make your own machine learning model, you will need to gather a large amount of data that is relevant to the problem you are trying to solve. This data will be used to train the model, which is the process of feeding the data into the algorithm and adjusting the parameters of the model to improve its accuracy. Once the model is trained, you can then use it to make predictions or take actions based on new data.

There are many different algorithms and techniques that can be used for training a machine learning model, and the choice of which to use will depend on the specific problem you are trying to solve. Some common algorithms include decision trees, support vector machines, and neural networks.

To create a machine learning model, you will need to have some knowledge of programming and statistics, as well as a basic understanding of the problem you are trying to solve. There are many tutorials and online courses available that can help you learn the basics of machine learning and get started with creating your own model.

Mark: What is the difference between a discriminative and a generative model?

ChatGPT: A discriminative model is a type of machine learning algorithm that is used to predict the value of a target variable (such as whether an email is spam or not) based on a set of input features (such as the sender, subject, and content of the email). It does this by directly modeling the relationship between the input features and the target variable, and using this model to make predictions on new data.

A generative model, on the other hand, is a type of algorithm that is used to learn the underlying distribution of the data, and can then generate new samples that are similar to the data it was trained on. This allows the model to capture the underlying patterns and structure of the data, and can be used to make predictions or take actions based on this learned information.

One key difference between discriminative and generative models is that a discriminative model focuses on predicting the target variable based on the input features, while a generative model is more concerned with modeling the underlying distribution of the data. This means that a generative model is often more flexible and can be used to solve a wider range of problems, but may require more data and computational resources to train.

Mark: What are the biggest technical challenges with generative models?

ChatGPT: There are several technical challenges associated with generative models, some of which include:

  1. Data availability and quality: Generative models require a large amount of high-quality data to learn the underlying distribution of the data and generate new samples that are similar to the training data. If the data is not sufficient or is of poor quality, the model may not be able to accurately capture the patterns and structure of the data, which can reduce its performance.
  2. Computational complexity: Generative models can be computationally intensive to train, especially for large datasets or complex data distributions. This can make them difficult to run on standard hardware, and may require specialized hardware or distributed computing systems to handle the large amounts of data and computational workload.
  3. Mode collapse: One of the main challenges with generative models is the phenomenon of mode collapse, where the model only learns to generate a subset of the data, rather than the entire distribution. This can result in poor performance and a lack of diversity in the generated samples.
  4. Evaluation and comparison: Generative models can be difficult to evaluate and compare, as they are often evaluated on their ability to generate realistic samples, rather than on a more traditional metric such as accuracy or error rate. This can make it challenging to determine which model is the best for a particular task or dataset.

Mark: How can i address bias in my models?

ChatGPT: Bias in machine learning models can arise from several sources, including the data used to train the model, the algorithm used to build the model, and the way in which the model is used in practice. To address bias in your models, there are several steps you can take, including:

  1. Collect and use a diverse and representative dataset: Ensuring that the data used to train your model is diverse and representative of the real-world population can help to reduce bias in the model. This means using a dataset that includes examples from a wide range of individuals, with varying characteristics such as age, gender, race, and socio-economic status.
  2. Use fairness metrics and algorithms: There are a number of fairness metrics and algorithms that can be used to measure and address bias in machine learning models. These can help to identify potential sources of bias in your model and provide suggestions for how to mitigate them.
  3. Regularly evaluate and test your model: It is important to regularly evaluate and test your model to ensure that it is performing well and not introducing any bias. This can be done by conducting tests on the model and comparing its performance on different subgroups within the population.
  4. Be transparent and accountable: Being transparent about the data and algorithms used to build your model, as well as the assumptions and limitations of the model, can help to promote accountability and trust in the model. This can include sharing information about the model’s performance and any measures taken to address bias.

Mark: What is the difference between training and inference?

ChatGPT: Training and inference are two distinct phases in the development and use of a machine learning model. Training is the process of building a machine learning model by feeding it a large amount of data and adjusting the model’s parameters to improve its performance.

This is typically done using a labeled dataset, where the input data and the corresponding target values are known. The goal of training is to find the model parameters that best fit the data and enable the model to make accurate predictions on new, unseen data.

Inference, on the other hand, is the process of using a trained model to make predictions or take actions on new data. This involves providing the model with input data and using the model to generate predictions or take actions based on that data. Inference can be performed using either the same data as was used for training, or new, unseen data.

The main difference between training and inference is that training involves building and adjusting the model, while inference involves using the trained model to make predictions or take actions on new data. Training is typically done offline, while inference is typically done in real-time.

Mark: What are some examples of problems that machine learning is ideally suited to solve?

ChatGPT: Machine learning is well-suited to a wide range of problems, including:

  1. Image and speech recognition: Machine learning algorithms can be trained to recognize objects, people, and spoken words in images and audio recordings. This can be used in applications such as face recognition for security systems, or speech-to-text for voice-controlled assistants.
  2. Natural language processing: Machine learning algorithms can be used to process and understand human language, which can be applied to tasks such as language translation, sentiment analysis, and automatic summarization of text.
  3. Fraud detection: Machine learning can be used to detect patterns and anomalies in large datasets, which can be useful for identifying fraudulent transactions in financial systems, or for detecting spam emails.
  4. Predictive maintenance: Machine learning can be used to predict when equipment or machinery is likely to fail, which can help to prevent disruptions and costly downtime.
  5. Recommendation systems: Machine learning can be used to personalize recommendations for products, music, or movies based on a user’s past behavior and preferences.

These are just a few examples of the many problems that machine learning can be used to solve. Machine learning algorithms are highly flexible and can be applied to a wide range of problems in many different fields.

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