The text-generating program ChatGPT, developed by artificial intelligence research company OpenAI, can write about many subjects in a variety of …

Will AI art eventually permanently replace human artists? Never, with the statistical data predictive ML/DL/AI, as image generators or art programs; for it is brainless and mindless to its depth, mimicking artistic intelligence by its not very intelligent developers. Some might insist that today's ML/LLM models could do the art, as painting and composing, like human artists, thus meeting the Creativity Turing Test (TT), as AI-Da, who is sold as'the world's first ultra-realistic robot artist and whose first exhibition of self-portraits was on display at the Design Museum. Again, the deepfake AI art engines like DALL-E 2, Midjourney and Imagen can take an arbitrary text description and create original artwork IF ONLY PROPERLY PROMPTED. Their SENSELESS images range from simple to dizzyingly complex, from concrete to abstract, from cartoonish to photorealistic, as Jason Allen's A.I.-generated work, "Théâtre D'opéra Spatial," which took first place in the digital category at the Colorado State Fair.

The tool was created by OpenAI, an artificial intelligence laboratory launched several years ago with funding from Elon Musk and others. The bot is powered by a "large language model," AI software that is trained to predict the next word in a sentence by analyzing massive amounts of internet text and finding patterns by trial and error. ChatGPT was also refined by humans to make its answers more conversational, and many have noted its ability to produce paragraphs that are often humorous or even philosophical.

OpenAI has released ChatGPT, a new dialogue language model (LM) based on the GPT-3.5 family series (trained on text and code) and similar to InstructGPT (aligned with reinforcement learning through human feedback). The company set up an online demo and people are losing their minds over it. In a nutshell, ChatGPT is a chatbot that can "answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests." This nicely encapsulates the reason why ChatGPT is so special: "admit", "challenge", and "reject" are unusual verbs to describe the behavior of an LM. However, it isn't an exaggeration in ChatGPT's case (countless examples that I'll share soon assert it).

The success this year of powerful new generative artificial intelligence models like Open AI's ChatGPT and Stability AI's Stable Diffusion, have laid the groundwork for a new era of AI tech set to explode even further in 2023. Google, though equipped with its own powerful (but definitely not sentient) LaMDA AI chatbot, says it doesn't plan rushing its models out to the public. Executives at the company clarified their comparatively cautious approach during an all-hand meeting according to CNBC where employees asked if they were potentially losing a complete-edge to less cautious upstarts. Unlike smaller startup AI firms with little to lose, CEO Sundar Pichai and AI division head Jeff Dean said mistakes made by Google in generative AI risk tainting users' reputation of the company on its other more trusted products. In other words, if Google released LaMDA to the public and it immediately started spewing out hateful misinformation, would users still view their Google search results with the same confidence?

After years in which artificial intelligence-generated content was known more for its comic absurdity--only occasionally drifting into disconcerting realism--2022 was the year that generative AI finally graduated into a full-fledged creative force. A host of realistic image generators led by research group OpenAI's Dall-E 2 made it easy for anyone to create lifelike visuals with a simple text prompt. Meanwhile, OpenAI's ChatGPT put a conversational interface on the organization's state-of-the-art text generation system, allowing users to simply instruct a machine what to write and receive a detailed and rhetorically sound--if not always factually correct--passage in seconds. These new systems, trained on datasets that span hundreds of millions of images and pages of text, respectively, have already led to widespread experimentation among brands, agencies, burgeoning startups and creative tool integrations. But experts say 2023 will be the year that brand marketers and agencies start to get serious about how synthetic content of this sort can actually be deployed to serve bottom lines and augment human creativity.

Our popular event StateoftheArt() is back again this coming January. This time around we're proud to announce that we will open with an exciting discussion with Dr. Sebastian Raschka on generative AI and LLMS and an open Q&A for a chance to to learn more about the future of AI and deep learning. Certificates will be provided to those who attend this section. Afterwards, we deep-dive into exciting ways deep learning is applied across a wide variety of industries with leaders from Home Depot, Momentive, & Twilio. Following that is "The Economy of the Future" where you can tune in to an economist and learn their perspective on AI/ML.

Agarwal asserts that Poly's generative AI is superior to most in terms of the quality of assets it produces.

We asked DALL-E 2 to generate'An oil painting of a tabby cat reading New Scientist magazine on the train' Artificial intelligence continued to make great strides in a variety of fields in 2022, but perhaps one of the biggest shocks was the emergence of AI models that can generate photorealistic images from a simple text description. "It was totally unexpected, I would say, at the end of 2021. Unexpectedly mind-blowing," says Thomas Wolf, co-founder of Hugging Face, a website that allows people to share AI code and data sets.

Type "Teddy bears working on new AI research on the moon in the 1980s" into any of the recently released text-to-image artificial intelligence image generators, and after just a few seconds the sophisticated software will produce an eerily pertinent image. Seemingly bound by only your imagination, this latest trend in synthetic media has delighted many, inspired others and struck fear in some. Google, research firm OpenAI and AI vendor Stability AI have each developed a text-to-image image generator powerful enough that some observers are questioning whether in the future people will be able to trust the photographic record. As a computer scientist who specializes in image forensics, I have been thinking a lot about this technology: what it is capable of, how each of the tools have been rolled out to the public, and what lessons can be learned as this technology continues its ballistic trajectory. Although their digital precursor dates back to 1997, the first synthetic images splashed onto the scene just five years ago.