Joe Fuqua’s Post

Fascinating insights on the challenges surrounding AI and data usage! It's intriguing to see the limitations imposed on AI LLMs by websites. The evolving landscape raises important questions about data access and the future implications on legal matters. The potential benefits of granting LLMs access to knowledge are compelling, highlighting the need for a balance between accessibility and intellectual property rights. As companies navigate this terrain, the value of proprietary data sources is set to rise significantly, offering crucial insights for AI applications. While LLM models continue to advance, the true potential of AI lies in reshaping business operations and processes. The key hurdle remains the capability of companies to adapt and leverage AI effectively. Moreover, the global shortage of data centers and AI compute capacity presents a notable challenge, particularly in emerging markets worldwide. #AI #DataUsage #BusinessTransformation #EmergingTechnologies

It will be interesting to see how data access evolves in the AI era. The challenges of bringing the right data to AI models will reshape workflows and business processes

"The Data That Powers A.I. (AI) Is Disappearing Fast" This morning, I came across a very interesting read in the New York Times. The article highlights a significant shift in the landscape of AI model training. Over the past year, many of the most important web sources have restricted the use of their data, as revealed by a study from the Data Provenance Initiative, an MIT-led research group. The study examined 14,000 web domains included in three commonly used AI training datasets—C4, RefinedWeb, and Dolma—and discovered an "emerging crisis in consent." Publishers and online platforms are increasingly taking steps to prevent their data from being harvested. The researchers estimate that 5% of all data, and 25% of data from the highest-quality sources, has been restricted. These developments have profound implications. For new entrants, it raises questions about maintaining healthy competition. For AI developers, the cost of training models is likely to increase as accessible data dwindles. Moreover, this shift impacts regulatory and research governance, necessitating a balance between innovation and ethical data use. This growing data scarcity poses significant challenges for AI systems, which rely heavily on high-quality data to function effectively. As publishers and platforms tighten control over their content, AI companies face difficulties in maintaining the data flow necessary for model training and improvement. Smaller AI startups and academic researchers are particularly affected, as they may not afford to license data directly from publishers. As we navigate this evolving landscape, it is crucial to develop new tools and practices that respect data ownership while fostering AI advancements. The study underscores the importance of ethical considerations in data usage and the need for a sustainable approach to AI development. https://lnkd.in/dszZvnA7 #AI #MachineLearning #DataPrivacy #EthicalAI #Research #Technology #Innovation #DataScience #Regulation #Competition

It's getting harder to source A.I. training data. Right now, we're in a classic "tech is moving faster than governance" situation, with tensions emerging over AI companies using data without user consent. More websites and publishers are starting to restrict AI companies from using their content to train AI. There need to be proper frameworks for ethically sourcing quality data and empowering people with more control over their data. Take a look at this New York Times Article for more details. https://lnkd.in/eErRthg8 #AI #Data #DataOwnership #Privacy #DataGovernance #MachineLearning #AIEthics #Innovation #Tech #ArtificialIntelligence

📢 The Data That Powers A.I. Is Disappearing Fast! 🔍 Recent research from MIT's Data Provenance Initiative reveals: 📉 Key Findings: - 5% of data in major AI training sets is now restricted - 25% of high-quality data has become inaccessible - More websites are using the Robots Exclusion Protocol to block data scraping 💡 Impact on A.I. Development: - Challenges for generative AI tools like ChatGPT, Google's Gemini, and Claude - Smaller AI companies and academic researchers hit hardest 💼 Industry Response: - Reddit and StackOverflow now charging for data access - Some publishers, including The New York Times, taking legal action - AI companies exploring synthetic data as an alternative 🔗Looking Ahead: - Need for better tools to control data use - Balancing fair use with data creators' rights Read the full story on The New York Times for more insights #AI #DataPrivacy #TechNews #Innovation #ArtificialIntelligence #Research https://lnkd.in/gpCGqdYr

AI is only as good as the data used to inform it For years, it’s used enormous troves of text, images and videos pulled from the internet to train the models. Now, that data is drying up. Over the past year, many of the most important web sources used for training A.I. models have restricted the use of their data, according to a study published this week by the Data Provenance Initiative, an M.I.T.-led research group. The study, which looked at 14,000 web domains that are included in three commonly used A.I. training data sets with significantly less acccess. (for understandable and intellectual property reasons.) Does this mean AI will be getting dumber? What about healthcare research data? #AI #data #digitalhealth #healthCareInnovation

HAS AI PEAKED? My opinion: going out of fashion “New research from the Data Provenance Initiative has found a dramatic drop in content made available to the collections used to build artificial intelligence.” “The study also found that as much as 45 percent of the data in one set, C4, had been restricted by websites’ terms of service.” “IFor years, A.I. developers were able to gather data fairly easily. But the generative A.I. boom of the past few years has led to tensions with the owners of that data — many of whom have misgivings about being used as A.I. training fodder, or at least want to be paid for it.” “ …. there’s also a lesson here for big A.I. companies, who have treated the internet as an all-you-can-eat data buffet for years, without giving the owners of that data much of value in return.” #ai #artificialintelligence #dataprovenance #training https://lnkd.in/ev2-cNad

As #web domains go into full defense mode against #AI to prevent their #data from being harvested, will AI #training #models, and latecomers to the AI ecosystem, suffer? asks @MIT-led group. The Data That Powers A.I. Is Disappearing Fast https://lnkd.in/eErRthg8

Could the breakneck pace of AI deployment start hitting speed bumps? This article discusses that the amount of publicly available data used to train AI datasets could be drying up due to paywalls, litigation and licensing deals which may squeeze out smaller AI players, researchers and nonprofits. I don't see the FOMO of AI dying out anytime soon, but the reality of data access could create a reckoning of who succeeds in the space. From the article: "'Data is the main ingredient in today’s generative A.I. systems, which are fed billions of examples of text, images and videos. Much of that data is scraped from public websites by researchers and compiled in large data sets, which can be downloaded and freely used, or supplemented with data from other sources. Learning from that data is what allows generative A.I. tools like OpenAI’s ChatGPT, Google’s Gemini and Anthropic’s Claude to write, code and generate images and videos. The more high-quality data is fed into these models, the better their outputs generally are. For years, A.I. developers were able to gather data fairly easily. But the generative A.I. boom of the past few years has led to tensions with the owners of that data — many of whom have misgivings about being used as A.I. training fodder, or at least want to be paid for it. As the backlash has grown, some publishers have set up paywalls or changed their terms of service to limit the use of their data for A.I. training. Others have blocked the automated web crawlers used by companies like OpenAI, Anthropic and Google.'" #ai #openai #google #datasets #anthropic

14,000 web domains included in three commonly used A.I. training data sets have taken steps to prevent their data from being harvested. The researchers estimate that in the three data sets — called C4, RefinedWeb and Dolma — 5 percent of all data, and 25 percent of data from the highest-quality sources, has been restricted. #AI #Data