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According to a Forbes article, large AI models are considered one of six AI trends to watch for in 2022. As large-scale AI models continue their superior performances across different domains, trends emerge, leading to distinguished and efficient AI applications that have never been seen in the industry. For example, Microsoft-owned GitHub and OpenAI partnered to launch Copilot recently. Copilot plays the role of an AI pair programmer, offering suggestions for code and entire functions in real-time. Such developments continue to make coding easier than before. Another example released by OpenAI, DALL-E 2, is a powerful tool that creates original and realistic images as well as art from only simple text.

Text-to-image generation is the hot algorithmic process right now, with OpenAI's Craiyon (formerly DALL-E mini) and Google's Imagen AIs unleashing tidal waves of wonderfully weird procedurally generated art synthesized from human and computer imaginations. On Tuesday, Meta revealed that it too has developed an AI image generation engine, one that it hopes will help to build immersive worlds in the Metaverse and create high digital art. A lot of work into creating an image based on just the phrase, "there's a horse in the hospital," when using a generation AI. First the phrase itself is fed through a transformer model, a neural network that parses the words of the sentence and develops a contextual understanding of their relationship to one another. Once it gets the gist of what the user is describing, the AI will synthesize a new image using a set of GANs (generative adversarial networks).

Tedeschi (see more here) created the first image using text describing a famous scene in Woody Allen's masterpiece, ' Manhattan'. The description goes as follows: 'A b/w scene of a man and a woman seated on a bench, in New York, Queensboro bridge in the background, foggy atmosphere.' Jumping back to AI, the artist asked DALL·E to work as a storyboard artist using the description selected above; to complete the toolkit, Tedeschi threw in some pictures of the Queensboro bridge to better represent the scene. The resulting images were created in 15 seconds. 'It is very interesting how the AI merges'archetypal' concepts (man, woman, seated, bench) with elements derived from the use of real-world descriptions: the sentence'Queensboro bridge' triggered – along with the generation of the bridge itself – realistic and specific urban details like trees, skyline,' he comments.

Forbes News, the world's leading voice, recently declared large AI models as one of six AI trends to watch for in 2022. As large-scale AI models continue their superior performances across different domains, trends emerge, leading to distinguished and efficient AI applications that have never been seen in the industry. For example, Microsoft-owned GitHub and OpenAI partnered to launch Copilot recently. Copilot plays the role of an AI pair programmer, offering suggestions for code and entire functions in real-time. Such developments continue to make coding easier than before.

AI21 Labs has raised $64 million in a funding round to help it compete against OpenAI and other NLP leaders. Competition in NLP (Natural Language Processing) is heating up. OpenAI is currently seen as the industry leader with its GPT-3 model but rivals are gaining traction. Investors see AI21 Labs as one of the most promising contenders. "We completed this round during a period of market uncertainty, which highlights the confidence our investors have in AI21's vision to change the way people consume and produce information," said Ori Goshen, Co-Founder and Co-CEO of AI21 Labs.

Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they're like optical illusions for machines. In this post we'll show how adversarial examples work across different mediums, and will discuss why securing systems against them can be difficult. At OpenAI, we think adversarial examples are a good aspect of security to work on because they represent a concrete problem in AI safety that can be addressed in the short term, and because fixing them is difficult enough that it requires a serious research effort. To get an idea of what adversarial examples look like, consider this demonstration from Explaining and Harnessing Adversarial Examples: starting with an image of a panda, the attacker adds a small perturbation that has been calculated to make the image be recognized as a gibbon with high confidence. The approach is quite robust; recent research has shown adversarial examples can be printed out on standard paper then photographed with a standard smartphone, and still fool systems.

New products often come with disclaimers, but in April the artificial intelligence company OpenAI issued an unusual warning when it announced a new service called DALL-E 2. The system can generate vivid and realistic photos, paintings, and illustrations in response to a line of text or an uploaded image. One part of OpenAI's release notes cautioned that "the model may increase the efficiency of performing some tasks like photo editing or production of stock photography, which could displace jobs of designers, photographers, models, editors, and artists." So far, that hasn't come to pass. People who have been granted early access to DALL-E have found that it elevates human creativity rather than making it obsolete. Benjamin Von Wong, an artist who creates installations and sculptures, says it has, in fact, increased his productivity. "DALL-E is a wonderful tool for someone like me who cannot draw," says Von Wong, who uses the tool to explore ideas that could later be built into physical works of art.

Unlike other, more famous large language models such as OpenAI's GPT-3 and Google's LaMDA, BLOOM (which stands for BigScience Large Open-science Open-access Multilingual Language Model) is designed to be as transparent as possible, with researchers sharing details about the data it was trained on, the challenges in its development, and the way they evaluated its performance. OpenAI and Google have not shared their code or made their models available to the public, and external researchers have very little understanding of how these models are trained. BLOOM was created over the last year by over 1,000 volunteer researchers in a project called BigScience, which was coordinated by AI startup Hugging Face using funding from the French government. It officially launched on July 12. The researchers hope developing an open-access LLM that performs as well as other leading models will lead to long-lasting changes in the culture of AI development and help democratize access to cutting-edge AI technology for researchers around the world.

Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to class imbalance. Synthetic data generation, i.e., oversampling, is a common remedy used to improve classifier performance. State-of-the-art linear interpolation approaches, such as LoRAS and ProWRAS can be used to generate synthetic samples from the convex space of the minority class to improve classifier performance in such cases. Deep generative networks are common deep learning approaches for synthetic sample generation, widely used for synthetic image generation. However, their scope on synthetic tabular data generation in the context of imbalanced classification is not adequately explored. In this article, we show that existing deep generative models perform poorly compared to linear interpolation based approaches for imbalanced classification problems on smaller tabular datasets. To overcome this, we propose a deep generative model, ConvGeN that combines the idea of convex space learning with deep generative models. ConvGeN learns the coefficients for the convex combinations of the minority class samples, such that the synthetic data is distinct enough from the majority class. Our benchmarking experiments demonstrate that our proposed model ConvGeN improves imbalanced classification on such small datasets, as compared to existing deep generative models, while being at-par with the existing linear interpolation approaches. Moreover, we discuss how our model can be used for synthetic tabular data generation in general, even outside the scope of data imbalance and thus, improves the overall applicability of convex space learning.