Riding the AI tsunami: The next wave of generative intelligence

VIME makes the agent self-motivated; it actively seeks out surprising state-actions. We show that VIME can improve a range of policy search methods and makes significant progress on more realistic tasks with sparse rewards (e.g. scenarios in which the agent has to learn locomotion primitives without any guidance). Training involves tuning the model’s parameters for different use cases and then fine-tuning results on a given set of training data. For example, a call center might train a chatbot against the kinds of questions service agents get from various customer types and the responses that service agents give in return. An image-generating app, in distinction to text, might start with labels that describe content and style of images to train the model to generate new images.

• For example, it can turn text inputs into an image, turn an image into a song, or turn video into text.
• Rather than simply perceive and classify a photo of a cat, machine learning is now able to create an image or text description of a cat on demand.
• That said, the impact of generative AI on businesses, individuals and society as a whole hinges on how we address the risks it presents.
• One Google engineer was even fired after publicly declaring the company’s generative AI app, Language Models for Dialog Applications (LaMDA), was sentient.

As it grows in popularity, the technology has simultaneously triggered excitement and fear among individuals, businesses and government entities. A series of graphs show predicted compound annual growth rates from generative AI by 2040 in developed and emerging economies considering automation. This is based on the assumption that automated work hours are reintegrated in work at today’s productivity level. Two scenarios are shown for early and late adoption Yakov Livshits of automation, and each bar is broken into the effect of automation with and without generative AI. The addition of generative AI increases CAGR by 0.5 to 0.7 percentage points, on average, for early adopters, and 0.1 to 0.3 percentage points for late adopters. In the overall average for global growth, generative AI adds about 0.6 percentage points by 2040 for early adopters, while late adopters can expect an increase of 0.1 percentage points.

Concerns about generative AI

However, as you might imagine, the network has millions of parameters that we can tweak, and the goal is to find a setting of these parameters that makes samples generated from random codes look like the training data. Or to put it another way, we want the model distribution to match the true data distribution in the space of images. The field saw a resurgence in the wake of advances in neural networks and deep learning in 2010 that enabled the technology to automatically learn to parse existing text, classify image elements and transcribe audio. Researchers have been creating AI and other tools for programmatically generating content since the early days of AI.

What’s more, the models usually have random elements, which means they can produce a variety of outputs from one input request—making them seem even more lifelike. Building a generative AI model has for the most part been a major undertaking, to the extent that only a few well-resourced tech heavyweights have made an attempt. OpenAI, the company behind ChatGPT, former GPT models, and DALL-E, has billions in funding from boldface-name donors.

Video

Since the created text and images are not exactly like any previous content, the providers of these systems argue that they belong to their prompt creators. But they are clearly derivative of the previous text and images used to train the models. Needless to say, these technologies will provide substantial work for intellectual property attorneys in the coming years. 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.

We’re quite excited about generative models at OpenAI, and have just released four projects that advance the state of the art. For each of these …