OpenAI is a non-profit research organization dedicated to developing and applying artificial intelligence (AI) for the benefit of humanity as a whole. Elon Musk and Sam Altman founded the company in 2015, headquartered in San Francisco, California. OpenAI was founded partly due to its founders' existential fears about the potential for a disaster caused by carelessness and misuse of general-purpose AI. The company focuses on fundamental advances in artificial intelligence and its capabilities. The company's two founders and other investors began with a $1 billion endowment. Elon Musk left the company in February 2018 due to potential conflicts with his work at Tesla, Nikola Tesla's electronics company.

After posting the following AI-generated images, I got private replies asking the same question: "Can you tell me how you made these?" So, here I will provide the background and "how to" of creating such AI portraits, but also describe the ethical considerations and the dangers we should address right now. Generative AI – as opposed to analytical artificial intelligence – can create novel content. It not only analyzes existing datasets but it generates whole new images, text, audio, videos, and code. As the ability to generate original images based on written text emerged, it became the hottest hype in tech. It all began with the release of DALL-E 2, an improved AI art program from OpenAI.

Among the AI-related technologies to have emerged in the past several years is generative AI--deep-learning algorithms that allow computers to generate original content, such as text, images, video, audio, and code. And demand for such content will likely jump in the coming years--Gartner predicts that by 2025, generative AI will account for 10% of all data created, compared with 1% in 2022. "Théâtre D'opéra Spatial" is an example of AI-generated content (AIGC), created with the Midjourney text-to-art generator program. Several other AI-driven art-generating programs have also emerged in 2022, capable of creating paintings from single-line text prompts. The diversity of technologies reflects a wide range of artistic styles and different user demands.

Generative AI models have made great strides in the past few years. Physics-inspired Diffusion Models have ascended to state-of-the-art performance in several domains, powering models like Stable Diffusion, DALL-E 2, and Imagen. Researchers from MIT have recently unveiled a new physics-inspired generative model, this time drawing inspiration from the field of electrodynamics. This new type of model - the Poisson Flow Generative Model (PFGM) - treats the data points as charged particles. By following the electric field generated by the data points, PFGMs can create entirely novel data. PFGMs constitute an exciting foundation for new avenues of research, especially given that they are 10-20 times faster than Diffusion Models on image generation tasks, with comparable performance. In this article, we'll take a high-level look at PFGM theory before learning how to train and sample with PFGMs. After that we'll take another look at the theory, this time perfoming a deep dive starting from first principles. Then we'll look at how PFGMs stack up to other models and other results before ending with some final words. Several families of generative models have evolved throughout the development of AI. Other approaches, like GANs, cannot explicitly calculate likelihoods, but can generate very high-quality samples.

Tools like DALL-E 2, Stable Diffusion and Midjourney, which generate images based on a few lines of text, briefly set social media ablaze this year. But Amazon's entry into the AI art world is a bit different. Create with Alexa lets children guide the creation of animated stories using a few kid-friendly prompts. Since Create with Alexa is visual storytelling, it's only available on Echo Show devices, not the company's audio-only speakers. Amazon says it works whether the device is in Amazon Kids mode or not.

"Without understanding the cause and effect of interactions within the world, no AI model, algorithm, technique, application, or technology is real and true", be it: Natural language generation converting structured data into the native language; Speech recognition converting human speech into a useful and understandable format by computers; Virtual agents, computer applications that interact with humans to answer their queries, from Google Assistant to the Watson; Biometrics, to identify individuals based on their biological characteristics or behaviors, with fingerprints and faces, hand veins, irises, or voices biometric modalities; Decision management systems for data conversion and interpretation into predictive models; Machine learning empowering machine to make sense from data sets without being actually programmed, to make informed decisions with data analytics and statistical models; Robotic process automation configuring a robot (software application) to interpret, communicate and analyze data; Peer-to-peer network connecting between different systems and computers for data sharing without the data transmitting via server; Deep learning platforms based on ANNs teaching computers and machines to learn by example just the way humans do; Generative AI (GANs, Transformers, Autoencoders) referring to unsupervised and semi-supervised machine learning algorithms that enable computers to use existing content like text, audio and video files, images, or code to create new possible content as completely original artifacts. It leverages AI and ML algorithms to generate artificial content such as text, images, audio and video content based on its training data to trick the user into believing the content is real, facing legal challenges concerning data privacy; Generative AI models with image generation algorithms generating photographs of human faces, objects and scenes, image-to-image conversion, text-to-image translation, film restoration, semantic-image-to-photo translation, face frontal view generation, photos to emojis, face aging, media and entertainment: deep fake technology; AI optimized hardware support artificial intelligence models, as #neuralnetworks, #deeplearning, and #computervision, including CPUs, GPUs, TPUs, OPUs to handle scalable workloads, special purpose built-in silicon for neural networks, neuromorphic chips, etc.; Real AI is NOT about representing computational models of intelligence, described as structures, models, and operational functions that can be programmed for problem-solving, inferences, language processing, etc. Real AI is about the computational models of reality and mentality, described as causal structures, models, and operational functions that can be programmed for problem-solving and inferences for a wide range of goals in a wide range of environments.

AI models trained on enormously large datasets, specifically large language or vision-language models, represent a phenomenon that has by far outgrown the notion of "being just a tool". Regardless of how and for what purpose such models are practically employed, they in themselves represent a valuable object of study. "In themselves" in this context includes not only all the culturally dependent patterns learned from the training data, but also the culturally dependent attempts to control, modify or erase culturally dependent patterns in the training data. Focusing on the comparative analysis of outputs from two very popular text-to-image synthesis models, DALL E 2 [1] and Stable Diffusion [2], this paper tries to tackle the pros and cons of striving towards culturally agnostic vs. culturally specific AI models. Implemented in one way or another, most commonly the "guiding principle" behind many existing text-to-image generators is the ground-breaking vision-language model CLIP and its emerging derivatives (e.g.

The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders. A standard IV is expected to be related to the treatment variable and independent of all other variables in the system. However, it is challenging to search for a standard IV from data directly due to the strict conditions. The conditional IV (CIV) method has been proposed to allow a variable to be an instrument conditioning on a set of variables, allowing a wider choice of possible IVs and enabling broader practical applications of the IV approach. Nevertheless, there is not a data-driven method to discover a CIV and its conditioning set directly from data. To fill this gap, in this paper, we propose to learn the representations of the information of a CIV and its conditioning set from data with latent confounders for average causal effect estimation. By taking advantage of deep generative models, we develop a novel data-driven approach for simultaneously learning the representation of a CIV from measured variables and generating the representation of its conditioning set given measured variables. Extensive experiments on synthetic and real-world datasets show that our method outperforms the existing IV methods.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. The metaverse skyrocketed into our collective awareness during the height of the pandemic, when people longed for better ways to connect with each other than video calls. Gaming's hot growth during the pandemic also pushed it forward. But the metaverse became so trendy that it now faces a backlash, and folks aren't talking about it as much. Yet technologies that will power the metaverse are speeding ahead.

When GPT-3 was first launched in 2020, users were surprised with the huge performance leap from its predecessor, GPT-2. It's been over two years since OpenAI has been discreet about GPT-4--only letting out dribs of information, remaining silent for most of the time. As people have been talking about this for months, several sources hint that it's already out. Hopefully, sometime from December to February, we might be able to see the new model. Release is planned for Dec-Feb.