An Autonomous AI artist just made $351,600 at Sotheby’s

Botto, an autonomous AI artist, has made waves by generating $351,600 in sales at Sotheby’s, establishing a brand new benchmark in AI art. Since its inception in 2021 by Mario Klingemann and ElevenYellow, Botto has achieved over $4 million in sales, creating artworks independently while being curated by a 15,000-member community, BottoDao.

The recent exhibition “Exorbitant Stage” showcased six NFT lots that surpassed expectations, highlighting Botto’s significant role in redefining artistic authorship. Despite challenges within the NFT market post-2022, Botto’s success signals a possible resurgence for AI-generated art and underscores the evolving relationship between human creativity and machine intelligence within the art world. Read more.

Google is developing AI technology, under the code name Project Jarvis, that may autonomously complete tasks like web browsing, research, and online shopping directly inside a browser. The technology, powered by Google’s upcoming Gemini language model, is anticipated to launch by December.

This agent-based AI aligns with efforts by Google and other corporations, equivalent to Microsoft-backed OpenAI and Anthropic, to enable models able to interacting with computers to perform routine tasks autonomously. Project Jarvis represents Google’s approach to expanding AI’s functional role from passive language processing to lively, browser-based task execution.

The event positions Google amongst leaders exploring computer-using agents, able to navigating and taking actions based on real-time information without user intervention. Google has yet to release a public comment regarding Project Jarvis or its potential market applications. Read more.

Meta’s NotebookLlama is taking a shot at Google’s NotebookLM podcast generator with an “open” alternative that turns text files—like PDFs or blogs—into conversational, podcast-style recordings. Powered by Meta’s Llama models, NotebookLlama adds a little bit of scripted flair and back-and-forth interruptions to simulate live dialogue before converting it into audio via open-source text-to-speech.

The output has a robotic edge, with occasional voice overlaps, which Meta’s team attributes to the present text-to-speech model limitations. Researchers suggest improvements are possible with dual-agent setups for more natural dialogue pacing. Like its counterparts, NotebookLlama hasn’t quite sidestepped the AI “hallucination” problem, so some details in its content should still lean fictional. While early, NotebookLlama is a curious mix of open-source AI and podcasting, putting Meta on the map in automated content creation—with some fine-tuning still in sight. Read more.

Apple has introduced Ferret-UI 2, an modern AI system designed to reinforce app interaction across a spread of devices, including iPhones, iPads, Android devices, web browsers, and Apple TV. The system achieved a formidable 89.73 in UI element recognition tests, outperforming its predecessor and even GPT-4o, which scored 77.73.

Slightly than counting on precise click coordinates, Ferret-UI 2 intelligently discerns user intent, pinpointing the proper buttons with ease. Its adaptive architecture balances image resolution and processing needs, achieving 68% accuracy on iPads and 71% on Androids when trained on iPhone data. Yet, it stumbles when transitioning between mobile and TV or web interfaces, grappling with layout discrepancies.

This innovation paves the way in which for voice assistants like Siri to tackle more complex tasks, navigating apps and the net through easy voice commands. Read more.

OpenAI has announced that it would not release a model generally known as Orion this 12 months, contradicting our recent reports about its product roadmap. An organization spokesperson confirmed, “We don’t have plans to release a model code-named Orion this 12 months,” while hinting at other forthcoming technologies. This statement is available in response to a report from The Vergewhich suggested that Orion would launch by December, with early access given to trusted partners, including Microsoft.

Speculation surrounds Orion being a successor to GPT-4o, potentially trained on synthetic data from OpenAI’s reasoning model, o1. OpenAI has indicated it would proceed developing each GPT models and reasoning models, each addressing distinct use cases. The anomaly in OpenAI’s announcement leaves room for alternative possibilities regarding its next major release. Read more.

Researchers at Yale University have unearthed a curious phenomenon: AI models thrive when trained on data that strikes the proper balance between order and chaos, dubbed the “fringe of chaos.” Utilizing elementary cellular automata (ECAs) of various complexity, the team found that models trained on Class IV ECAs—those who dance between order and chaos—excelled in tasks like reasoning and predicting chess moves.

In contrast, models fed overly simplistic patterns often learned to supply trivial solutions, missing out on sophisticated capabilities. This structured complexity appears crucial for knowledge transfer across tasks. The findings might also explain the prowess of huge language models like GPT-3 and GPT-4, where the varied training datasets could mimic the advantages seen with complex ECA patterns. More exploration is on the horizon, because the team plans to scale up their experiments with larger models and much more intricate systems. Read more.

A recent study from TU Darmstadt exposes the surprising shortfalls of cutting-edge vision language models (VLMs), including the much-lauded GPT-4o, when tackling Bongard problems—those deceptively easy visual puzzles that test our abstract reasoning. In a reasonably underwhelming performance, GPT-4o managed to resolve just 21 out of 100 puzzles, with models like Claude, Gemini, and LLaVA trailing even further behind.

This glaring gap highlights the disparity in visual intelligence between humans and AI. The study also raises vital questions on the efficacy of current AI evaluation benchmarks, suggesting they could inadequately reflect true reasoning capabilities. Researchers advocate for a reevaluation of assessment methods to higher capture the nuances of visual reasoning in AI systems. Read more.