Text generation: Generative AI can be used to generate text, such as news articles, blog posts, and product descriptions. This can be helpful for businesses that need to produce a lot of content on a regular basis.
Image generation: Generative AI can be used to generate images, such as product renders, marketing materials, and even art. This can be helpful for businesses that need to create visual content for their products or services.
Audio generation: Generative AI can be used to generate audio, such as music, podcasts, and audiobooks. This can be helpful for businesses that need to create audio content for their products or services.
Code generation: Generative AI can be used to generate code, such as software applications and web pages. This can be helpful for businesses that need to develop new software or websites.
In addition to these general applications, generative AI is also being used for a variety of more specialized applications, such as:
Drug discovery: Generative AI can be used to discover new drugs by generating new molecules that have the potential to be effective treatments for diseases.
Fraud detection: Generative AI can be used to detect fraudulent transactions by generating synthetic data that mimics real-world data.
Customer service: Generative AI can be used to create chatbots that can answer customer questions and resolve issues.
Education: Generative AI can be used to create personalized learning experiences for students by generating content that is tailored to their individual needs.
To see e2e view use generative AI slides. The session is wonderful and describe modeling architecture, applications, evaluation, trade off, ethical challenges. Slide also describe Foundation model and how to fine tune custom model
Evaluating generative AI models is a challenging task. There is no single metric that can accurately capture the quality of a generative model, as different models have different strengths and weaknesses. However, there are a number of metrics that can be used to assess different aspects of a generative model's performance.
Some of the most common metrics for evaluating generative AI models include:
Inception score: The inception score is a metric that measures the quality of generated images. It is calculated by comparing the distribution of activations of the last layer of an Inception v3 network on real and generated images.
Frechet Inception Distance: The Frechet Inception Distance is another metric for measuring the quality of generated images. It is calculated by comparing the distributions of features extracted by an Inception v3 network on real and generated images.
BLEU score: The BLEU score is a metric for measuring the similarity of generated text to a reference text. It is calculated by comparing the n-gram overlap between the generated text and the reference text.
Human evaluation: Human evaluation is the gold standard for evaluating generative AI models. It involves asking humans to rate the quality of generated outputs.
It is important to note that no single metric can accurately capture the quality of a generative AI model. The best way to evaluate a generative model is to use a combination of metrics and to consider the specific application of the model.
In addition to the metrics listed above, there are a number of other factors that can be considered when evaluating generative AI models, such as:
The diversity of generated outputs: A good generative model should be able to generate a wide variety of outputs.
The realism of generated outputs: A good generative model should be able to generate outputs that are indistinguishable from real data.
The efficiency of the model: A good generative model should be able to generate outputs quickly and efficiently.
Generative AI is a powerful tool that can be used to improve a wide range of business processes. However, there are a number of trade-offs and conflicts to consider when using generative AI in a business setting.
One of the biggest trade-offs is between the cost of training a generative AI model and the benefits it can provide. Generative AI models can be very expensive to train, and the cost can vary depending on the complexity of the model and the size of the dataset used to train it. However, the benefits of generative AI can also be significant, such as increased productivity, improved customer service, and new product development.
Another trade-off to consider is between the accuracy of generative AI models and their speed. Generative AI models can be very accurate, but they can also be very slow. This is because they need to process a large amount of data in order to generate outputs. In some cases, it may be necessary to sacrifice accuracy in order to get the results quickly.
Finally, it is important to consider the ethical implications of using generative AI in a business setting. Generative AI models can be used to generate fake news, create deepfakes, and even design new weapons. It is important to be aware of these risks and to take steps to mitigate them.
Here are some specific examples of trade-offs and conflicts to consider when using generative AI in a business setting:
Cost: The cost of training a generative AI model can be prohibitive for small businesses.
Accuracy: Generative AI models can be inaccurate, especially if they are not trained on a large enough dataset.
Speed: Generative AI models can be slow, which can be a problem for businesses that need to generate outputs quickly.
Ethics: Generative AI models can be used to create fake news, deepfakes, and even new weapons. It is important to be aware of these risks and to take steps to mitigate them.
Who owns the data that is used to train a generative AI model?
Who owns the output of a generative AI model?
What are the rights of the people whose data is used to train a generative AI model?
How can we ensure that generative AI models are used in a responsible and ethical way?
These are complex questions that do not have easy answers. However, it is important to start thinking about them now, as generative AI becomes more widely used.
it is important to start thinking about them now, as generative AI becomes more widely used.
Here are some additional details about each of these challenges:
Who owns the data that is used to train a generative AI model?
The answer to this question is not always clear. In some cases, the data may be owned by the company that collected it. In other cases, the data may be owned by the people who provided it. There is no one-size-fits-all answer, and it is important to consider the specific circumstances in each case.
Who owns the output of a generative AI model?
The answer to this question is also not always clear. In some cases, the output of a generative AI model may be considered to be the property of the company that created the model. In other cases, the output may be considered to be the property of the people who provided the data that was used to train the model. Again, there is no one-size-fits-all answer, and it is important to consider the specific circumstances in each case.
What are the rights of the people whose data is used to train a generative AI model?
People who provide their data to be used in a generative AI model have certain rights. These rights include the right to know how their data is being used, the right to have their data deleted, and the right to be compensated for the use of their data. It is important to respect these rights when using generative AI models.
How can we ensure that generative AI models are used in a responsible and ethical way?
There are a number of things that can be done to ensure that generative AI models are used in a responsible and ethical way. These include:
Developing clear guidelines for the use of generative AI models.
Educating people about the potential risks and benefits of generative AI models.
Creating mechanisms for people to report concerns about the use of generative AI models.
Enforcing laws and regulations that protect people's rights in relation to generative AI models.
By taking these steps, we can help to ensure that generative AI is used in a way that benefits society and does not harm individuals.