IBM rolls out new generative AI features and models
For example, popular applications like ChatGPT, which draws from GPT-3, allow users to generate an essay based on a short text request. On the other hand, Stable Diffusion allows users to generate photorealistic images given a text input. Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks (code). Efficient exploration in high-dimensional and continuous spaces is presently an unsolved challenge in reinforcement learning. Without effective exploration methods our agents thrash around until they randomly stumble into rewarding situations. This is sufficient in many simple toy tasks but inadequate if we wish to apply these algorithms to complex settings with high-dimensional action spaces, as is common in robotics.
These images are examples of what our visual world looks like and we refer to these as “samples from the true data distribution”. We now construct our generative model which we would like to train to generate images like this from scratch. Concretely, a generative model in this case could be one large neural network that outputs images and we refer to these as “samples from the model”.
What is Generative AI?
The software uses complex machine learning models to predict the next word based on previous word sequences, or the next image based on words describing previous images. LLMs began at Google Brain in 2017, where they were initially used for translation of words while preserving context. Online communities such as Midjourney (which helped win the art competition), and open-source providers like HuggingFace, have also created generative models.
Microsoft’s first foray into chatbots in 2016, called Tay, for example, had to be turned off after it started spewing inflammatory rhetoric on Twitter. Transformer architecture has evolved rapidly since it was introduced, giving rise to LLMs such as GPT-3 and better pre-training techniques, such as Google’s BERT. In the company’s second fiscal quarter, IBM reported revenue that missed analyst expectations as the company suffered from a bigger-than-expected Yakov Livshits slowdown in its infrastructure business segment. Revenue contracted to $15.48 billion, down 0.4% year-over-year, just below the analyst consensus for Q2 sales of $15.58 billion. In the meantime, Tarun Chopra, IBM’s VP of product management for data and AI, filled in some of the blanks via an email interview. One of our core aspirations at OpenAI is to develop algorithms and techniques that endow computers with an understanding of our world.
How generative AI—like ChatGPT—is already transforming businesses
The firm’s conclusion was that it would still need professional developers for the foreseeable future, but the increased productivity might necessitate fewer of them. As with other types of generative AI tools, they found the better the prompt, the better the output code. Over the past few months, there has been a huge amount of hype and speculation about the implications Yakov Livshits of large language models (LLMs) such as OpenAI’s ChatGPT, Google’s Bard, Anthropic’s Claude, Meta’s LLaMA, and, most recently, GPT4. ChatGPT, in particular, reached 100 million users in two months, making it the fastest growing consumer application of all time. What exactly are the differences between generative AI, large language models, and foundation models?
The incredible depth and ease of ChatGPT have shown tremendous promise for the widespread adoption of generative AI. To be sure, it has also demonstrated some of the difficulties in rolling out this technology safely and responsibly. But these early implementation issues have inspired research into better tools for detecting AI-generated text, images and video. Industry and society will also build better tools for tracking the provenance of information to create more trustworthy AI.
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.
Some of the most well-known examples of transformers are GPT-3 and LaMDA. The discriminator is basically a binary classifier that returns probabilities — a number between 0 and 1. And vice versa, numbers closer to 1 show a higher likelihood of the prediction being real. Here are some of the key Gartner predictions considering generative AI. The next step is uploading model artifacts to GCS, like the model file or handler.
Besides, such things as responsible AI make it possible to avoid or completely reduce the drawbacks of innovations like generative AI. We can enhance images from old movies, upscaling them to 4k and beyond, generating more frames per second (e.g., 60 fps instead of 23), and adding color to black and white movies. Although some users note that on average Midjourney draws a little more expressively and Stable Diffusion follows the request more clearly at default settings. In healthcare, one example can be the transformation of an MRI image into a CT scan because some therapies require images of both modalities.
Generative AI enables users to quickly generate new content based on a variety of inputs. Inputs and outputs to these models can include text, images, sounds, animation, 3D models, or other types of data. 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. One such recent model is the DCGAN network from Radford et al. (shown below). This network takes as input 100 random numbers drawn from a uniform distribution (we refer to these as a code, or latent variables, in red) and outputs an image (in this case 64x64x3 images on the right, in green). As the code is changed incrementally, the generated images do too—this shows the model has learned features to describe how the world looks, rather than just memorizing some examples.
In this blog, we will show how you can streamline the deployment of a PyTorch Stable Diffusion model by leveraging Vertex AI. PyTorch is the framework used by Stability AI on Stable Diffusion v1.5. Vertex AI is a fully-managed machine learning platform with tools and infrastructure designed to help ML practitioners accelerate and scale ML in production with the benefit of open-source frameworks like PyTorch. A generative AI system is constructed by applying unsupervised or self-supervised machine learning to a data set. The capabilities of a generative AI system depend on the modality or type of the data set used.
In this paper, Rein Houthooft and colleagues propose VIME, a practical approach to exploration using uncertainty on generative models. 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).