InnovationWarrior.Com’s Post

How Stability AI’s Founder Tanked His Billion-Dollar Startup via InnovationWarrior.Com #ai #innovation #venture_capital #AI #consumertech #Daily_Cover #daily_cover #editors_pick #Editors_Pick #Innovation #premium #Premium_Content #premiumcontent #Venture_Capital

An insightful detailed overview of how the once famed Stable Diffusion product, the text to image Gen AI startup burned more than $100 million in around 1 year to the verge of bankruptcy and CEO resignation. #ai #genai #growth #startup https://lnkd.in/egWeN7rc

After nearly one year in the Bay Area, I am coming to the conclusion that we are very close to a fully automated AI Business Unicorn. Everyone is talking about a three person unicorn, then a one person unicorn like Sam Altman here https://lnkd.in/dsaAn6K5. But I am going one step further with SquaredAutomation and creating a fully AI automated unicorn in which everything is an AI from a CEO to the simplest dev. Here my article in medium : Title: The AI Revolution: Ushering in the Era of the Fully Automated Unicorn https://lnkd.in/eR-sNBQv

Quick thoughts on the platform shift that is AI and what it might mean for venture-scale outcomes

💡 Which companies are disrupting in the age of #AI? PitchBook data helped inform CNBC's 2024 Disruptor 50 list, which highlights private companies upending the classic definition of disruption as artificial intelligence leads new business models beyond the era of better, faster and cheaper innovation. Roughly two-thirds of the 50 companies on the list describe AI as “critical” to their businesses. How were these companies chosen? Quantitative metrics included company-submitted data on workforce size and diversity, scalability, and sales and user growth. Some of this information has been kept off the record and was used for scoring purposes only. CNBC also brought in data from a pair of outside partners: PitchBook, which provided data on fundraising, implied valuations and investor quality; and IBISWorld, whose database of industry reports we use to compare the companies based on the industries they are attempting to disrupt.  Learn more about the making of this list and who the disruptors are here: https://lnkd.in/dJ27NSZu

Finding the Perfect Investors for Your Startup with AI: The Ultimate Guide

[GPTZero offers a detection tool that helps identify whether a piece of content was AI generated.] It is getting crucial to identify whether content has been created using AI or not, recent research papers rely on body, face, and other organ patterns movements (eyes, hands, hair) to detect deepfakes that humans can't catch with their naked eyes, others tend to use blockchain to assign a token to any verified content taking into consideration the huge amount of data produced and mined every day plus the integrity of the issuers!. i believe that detecting the AI content in the future will be based on a deep dive into the backend of the produced resources, the file extensions generated by AI code (libraries and modules, matrix, and arrays), thus any verified video/photo/audio recorded and produced using different pyramid than the generative AI content, or pixel2pixcel discriminators trained to count the gaps that have not been filled once generated (this might overlap with the recent electronic warfare techniques). BUT building AI-detecting tools based on front-end only will not work shortly as the generative AI is getting more realistic fine-tuning new models every day, the detecting model that checks symmetric eyebrows, syncing lips or eye iris, etc., is just similar to the "find the difference between these two pictures/videos" IQ quiz!. Generative AI content could be flagged using a special zipper extension that no matter how much duplicated/replicated/generated AI realistic content (deepfakes and so on), the genuine/original content still has different underlined patterns. AI generative seeds(videos/photos/audio) produced from a pickle model are different from the same ones made by a camera/. If there are two videos of a world leader, one of them deep fake speaking a different language or different statements, a detection tool is built to check whether both files' structures might be more effective on the long run, or not bus ten!. #zip #unzip ;)

Huge congratulations to Scale AI for raising $1 billion in Series F funding, putting the company’s new valuation at $13.8 billion! As the data foundry for AI, Scale powers the data behind the end-to-end AI lifecycle, ensuring model builders and enterprises alike have the data they need to deploy AI confidently. This new funding will help accelerate the abundance of frontier data that will pave the road to Artificial General Intelligence. “Nearly every major large language model is built on top of our data foundry, so for us this is really a milestone…I think the entire industry expects that AI is only going to grow, the models are only gonna get bigger, the algorithms are only going to get more complex and, therefore, the requirements on data will continue growing…” - founder & CEO, Alexandr Wang Check out more in Fortune’s exclusive:

Summary: The article discusses tech mogul Vinod Khosla and his recent success as a prominent investor. Khosla's main focus is on AI and its potential impact on various industries. He also addresses concerns regarding AI regulation and competition with Chinese AI. Key takeaways: Khosla's recent $50 million investment in OpenAI has garnered attention and success. He is passionate about the potential for AI to disrupt and improve various industries. Khosla's focus on AI regulation and concerns about Chinese AI competition. Counter arguments: Some may argue that Khosla's intense focus on AI may overshadow other important areas of technology. Critics may question the sustainability of AI in certain industries and its potential negative effects on job displacement. #ai #artificialintelligence #tech #venturecapital #vc #startups