This excerpt is taken from the 100X.VC Startups Sector Outlook Report. Read it at www.100x.vc/research

Generative AI refers to a type of artificial intelligence that can generate new content or data. Generative AI systems work by using machine learning algorithms to learn the statistical patterns and characteristics of a given dataset, and then using this information to generate new data that is similar to the original dataset. Some of the opportunities provided by generative AI include:

• Data generation:

  Generative AI can be used to generate large amounts of synthetic data that can be used to train machine learning models. This can be particularly useful in cases where it is difficult to obtain real-world data, such as in the development of self-driving cars.

• Text generation:

  Generative AI can be used to generate natural language text that is difficult to distinguish from text written by humans. This can be used for tasks such as chatbots, content creation, and automatic translation.

• Image generation:

  Generative AI can be used to generate realistic images, such as photographs of people or objects that do not actually exist. This can be used for tasks such as creating virtual product mockups or generating training data for image recognition models.

• Music and video generation:

  Generative AI can be used to generate music and video content, such as music tracks and short video clips. This can be used for tasks such as creating personalised playlists or generating video content for social media platforms.    

• Personalization:

  Generative AI can be used to personalise content or experiences for individual users, such as personalised recommendations or customised marketing messages.

• Research and development:

  Generative AI can be used to explore the space of possible solutions to a particular problem, such as in drug discovery or material design.

• Variational Autoencoders

  have been used to generate synthetic images of handwritten digits, clothing, and even furniture.

• Evolutionary algorithms

  have been used to generate synthetic protein sequences for drug discovery.

These generative models are potentially valuable across a number of business functions, but marketing applications are perhaps the most common. We have already seen that these generative AI systems lead rapidly to a number of legal and ethical issues. “Deepfakes,” or images and videos that are created by AI and purport to be realistic but are not, have already arisen in media, entertainment, and politics. However, the creation of deepfakes required a considerable amount of computing skill. Now, however, almost anyone will be able to create them. No doubt that the development of such capabilities would have dramatic and unforeseen implications for content ownership and intellectual property protection, but they are also likely to revolutionise knowledge and creative work. Assuming that these AI models continue to progress as they have in the short time they have existed, we can hardly imagine all of the opportunities and implications that they may engender.

This excerpt is taken from the 100X.VC Startups Sector Outlook Report. Read it at www.100x.vc/research