Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. For Dave Rogenmoser, CEO of AI content platform Jasper -- which raised $125 million in funding a week ago -- the sheer level of hype and scale of chatter around generative AI last week was unexpected. Jasper's announcement came just one day after Stability AI, which developed its text-to-image generator Stable Diffusion, announced its own massive $101 million raise. "I didn't know Stability was going to announce on Monday -- and then ours stacking on that definitely hyped up the whole market," he said. But Rogenmoser says that hype aside, generative AI -- which describes artificial intelligence using unsupervised learning algorithms to create new digital images, video, audio, text or code -- is no flash in the pan.

An image generator is a software that creates images from the prompt text that can be used many purposes such in Graphic designing, Book templates etc. Many companies will use Image generator for creating new designs for anything they want. Many people will also use image generators for more traditional purposes, such as creating memes and creating artworks. Image generators are very useful because they can create an unlimited number of images without the need to find models or real world images. Even sometimes images are purely fictions but it does look like it fictions. DALL-E 2: OpenAI DALL-E 2 is an AI model developed by OpenAI that has been trained to generate images from text .

What can an AI tell you about Black Friday deals? Not much, it turns out, but when we posed a few contrarian thoughts about the upcoming shopping bacchanalia, AI image generator Dall-E 2 (opens in new tab) returned some interesting images that got us thinking about what this Black Friday and Cyber Monday shopping season will be like for consumers. I think it's a safe assumption that this won't be a typical Black Friday and pre-Christmas shopping season. We face extraordinary upheaval around the world, and a deepening cost of living crisis that in many countries is threatening to turn into a full-blown recession. Yet, recent studies point to consumers being both more financially cautious and, yet, more deeply invested in Black Friday than in recent years. During Amazon's Prime Day 2 (its new attempt to own the shopping season by preempting Black Friday), US consumers spent, according to Numerator (opens in new tab), on average $46.68, which is down roughly $15 from Amazon's mid-year Prime Day.

We introduce the first work to explore web-scale diffusion models for robotics. DALL-E-Bot enables a robot to rearrange objects in a scene, by first inferring a text description of those objects, then generating an image representing a natural, human-like arrangement of those objects, and finally physically arranging the objects according to that image. The significance is that we achieve this zero-shot using DALL-E, without needing any further data collection or training. Encouraging real-world results with human studies show that this is an exciting direction for the future of web-scale robot learning algorithms. We also propose a list of recommendations to the text-to-image community, to align further developments of these models with applications to robotics.

AI-powered self-driving cars got kicked to the curb, with the shuttering of Argo AI. Big Tech had a tough time, with earnings wipeouts for Amazon, Microsoft and Google. And Meta's big bet on the metaverse has the company in free fall, at least for now. However, none of that negative-nellie news seems to stop the FOMO – the fear of missing out – at least in the world of AI. This time the hyped-up anxiety is not about autonomous cars (the "not" in the Hot or Not), but generative AI – a suddenly-sexy sector described with words like "miraculous," "transformative" and "a coming-out party" [subscription required].

Generative AI is undeniably having a moment. OpenAI's text-to-image creator DALL-E has been dazzling the public for months, while its standout rival, a newcomer dubbed Stability AI, just raked in a cool $101 million in funding for its Stable Diffusion system. Video and music generators are popping up as well, and some experts predict that synthetic media will soon make up the vast majority of digital content. But according to Will Manidis, the founder and CEO of AI-driven healthcare startup ScienceIO, generative AI is all flash, no substance -- and while it might be attracting VC cash now, most ventures will quickly fade into startup oblivion. "There are hundreds of millions of dollars being deployed towards glorified tech demos built on top of identical datasets," the founder wrote in a Tuesday Twitter thread, referring to these generative machine systems.

Today, stock image giant Shutterstock has announced an extended partnership with OpenAI, which will see the AI lab's text-to-image model DALL-E 2 directly integrated into Shutterstock "in the coming months." In addition, Shutterstock is launching a "Contributor Fund" that will reimburse creators when the company sells work to train text-to-image AI models. This follows widespread criticism from artists whose output has been scraped from the web without their consent to create these systems. Notably, Shutterstock is also banning the sale of AI-generated art on its site that is not made using its DALL-E integration.

If you've been closely following the progress of Open AI, the company run by Sam Altman whose neural nets can now write original text and create original pictures with astonishing ease and speed, you might just skip this piece. If, on the other hand, you've only been vaguely paying attention to the company's progress and the increasing traction that other so-called "generative" AI companies are suddenly gaining and want to better understand why, you might benefit from this interview with James Currier, a five-time founder and now venture investor who cofounded the firm NFX five years ago with several of his serial founder friends. Currier falls into the camp of people following the progress closely -- so closely that NFX has made numerous related investments in "generative tech" as he describes it, and it's garnering more of the team's attention every month. In fact, Currier doesn't think the buzz about this new wrinkle on AI isn't hype so much as a realization that the broader startup world is suddenly facing a very big opportunity for the first time in a long time. "Every 14 years," says Currier, "we get one of these Cambrian explosions. We had one around the internet in '94.

Latent representations of drugs and their targets produced by contemporary graph autoencoder-based models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target interactions. However, most existing approaches model the node's latent spaces in which node distributions are rigid and disjoint; these limitations hinder the methods from generating new links among pairs of nodes. In this paper, we present the effectiveness of variational graph autoencoders (VGAE) in modeling latent node representations on multimodal networks. Our approach can produce flexible latent spaces for each node type of the multimodal graph; the embeddings are used later for predicting links among node pairs under different edge types. To further enhance the models' performance, we suggest a new method that concatenates Morgan fingerprints, which capture the molecular structures of each drug, with their latent embeddings before preceding them to the decoding stage for link prediction. Our proposed model shows competitive results on two multimodal networks: (1) a multi-graph consisting of drug and protein nodes, and (2) a multi-graph consisting of drug and cell line nodes. Our source code is publicly available at https://github.com/HySonLab/drug-interactions.

A coordinate system is a foundation for every quantitative science, engineering, and medicine. Classical physics and statistics are based on the Cartesian coordinate system. The classical probability and hypothesis testing theory can only be applied to Euclidean data. However, modern data in the real world are from natural language processing, mathematical formulas, social networks, transportation and sensor networks, computer visions, automations, and biomedical measurements. The Euclidean assumption is not appropriate for non Euclidean data. This perspective addresses the urgent need to overcome those fundamental limitations and encourages extensions of classical probability theory and hypothesis testing , diffusion models and stochastic differential equations from Euclidean space to non Euclidean space. Artificial intelligence such as natural language processing, computer vision, graphical neural networks, manifold regression and inference theory, manifold learning, graph neural networks, compositional diffusion models for automatically compositional generations of concepts and demystifying machine learning systems, has been rapidly developed. Differential manifold theory is the mathematic foundations of deep learning and data science as well. We urgently need to shift the paradigm for data analysis from the classical Euclidean data analysis to both Euclidean and non Euclidean data analysis and develop more and more innovative methods for describing, estimating and inferring non Euclidean geometries of modern real datasets. A general framework for integrated analysis of both Euclidean and non Euclidean data, composite AI, decision intelligence and edge AI provide powerful innovative ideas and strategies for fundamentally advancing AI. We are expected to marry statistics with AI, develop a unified theory of modern statistics and drive next generation of AI and data science.