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 contributions we are also releasing a technical report and source code. But in the long run, they hold the potential to automatically learn the natural features of a dataset, whether categories or dimensions or something else entirely. Vendors will integrate generative AI capabilities into their additional tools to streamline content generation workflows.

Is this the start of artificial general intelligence (AGI)?

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

DeepMind is a subsidiary of Alphabet, the parent company of Google, and Meta has released its Make-A-Video product based on generative AI. These companies employ some of the world’s best computer scientists and engineers. But there are some questions we can answer—like how generative AI models are built, Yakov Livshits what kinds of problems they are best suited to solve, and how they fit into the broader category of machine learning. Machine learning is the foundational component of AI and refers to the application of computer algorithms to data for the purposes of teaching a computer to perform a specific task.

Manufacturers can use generative AI to help optimize operations, maintenance, supply chains – even energy usage – for lower costs, higher productivity and greater sustainability. A generative AI model will learn from existing performance, maintenance and sensor data, forecasts, external factors and more, then provide recommended strategies for improvement. Gen AI is a big step forward, but traditional advanced analytics and machine learning continue to account for the lion’s share of task optimization, and they continue to find new applications in a wide variety of sectors.

For example, Google published a blog post to let the world know they have created two models to turn low-resolution images into high-resolution images. This learning methodology involves manually marked training information for supervised training and unmarked data for unsupervised training methods. Here, unmarked data is used to develop models that can predict more than the marked training by enhancing the data quality.

But once a generative model is trained, it can be “fine-tuned” for a particular content domain with much less data. This has led to specialized models of BERT — for biomedical content (BioBERT), legal content (Legal-BERT), and French text (CamemBERT) — and GPT-3 for a wide variety of specific purposes. Some examples of foundation models include LLMs, GANs, VAEs, and Multimodal, which power tools like ChatGPT, DALL-E, and more. ChatGPT draws data from GPT-3 and enables users to generate a story based on a prompt. Another foundation model Stable Diffusion enables users to generate realistic images based on text input [2].

Natural language processing (NLP) and chatbots can help public sector workers respond faster to citizen needs, such as improving emergency services to those in flood-prone areas or assisting underserved neighborhoods. As methods of analyzing unstructured text data evolved, the 1970s through the 1990s saw growth in semantic networks, ontologies, recurrent neural networks and more. From 2000 through 2015, language modeling and word embedders improved, and Google Translate emerged. Gen AI tools can already create most types of written, image, video, audio, and coded content.

Organizations undergoing digital and AI transformations would do well to keep an eye on gen AI, but not to the exclusion of other AI tools. Just because they’re not making headlines doesn’t mean they can’t be put to work to deliver increased productivity—and, ultimately, value. Overall, generative AI has the potential to significantly impact a wide range of industries and applications and is an important area of AI research and development. Another factor in the development of generative models is the architecture underneath.

The implications of generative AI are wide-ranging, providing new avenues for creativity and innovation. In design, generative AI can help create countless prototypes in minutes, reducing the time required for the ideation process. In the entertainment industry, it can help produce new music, write scripts, or even create deepfakes. Generative AI has the potential to revolutionize any field where creation and innovation are key. Radically rethinking how work gets done and helping people keep up with technology-driven change will be two of the most important factors in harnessing the potential of generative AI. It’s also critical that companies have a robust Responsible AI foundation in place to support safe, ethical use of this new technology.