As the free online encyclopedia turns 25, it's facing political opposition, AI scraping, dwindling volunteers, and a public that may no longer believe in its ideals. In 2010, the FBI sent Wikipedia a letter that would be intimidating for any organization to receive. The missive demanded that the free online encyclopedia remove the FBI's logo from an entry about the agency, claiming that reproducing the emblem was illegal and punishable with fines, imprisonment, "or both." Rather than back down, a lawyer for the Wikimedia Foundation, which hosts Wikipedia, shot back a sharp refusal outlining how the FBI's interpretation of the relevant statute was incorrect and saying that Wikipedia was "prepared to argue our view in court." It worked--the FBI dropped the matter.

In the era of (multi-modal) large language models, most operational processes can be reformulated and reproduced using LLM agents. The LLM agents can perceive, control, and get feedback from the environment so as to accomplish the given tasks in an autonomous manner. Besides the environment-interaction property, the LLM agents can call various external tools to ease the task completion process. The tools can be regarded as a predefined operational process with private or real-time knowledge that does not exist in the parameters of LLMs. As a natural trend of development, the tools for calling are becoming autonomous agents, thus the full intelligent system turns out to be a LLM-based Multi-Agent System (LaMAS). Compared to the previous single-LLM-agent system, LaMAS has the advantages of i) dynamic task decomposition and organic specialization, ii) higher flexibility for system changing, iii) proprietary data preserving for each participating entity, and iv) feasibility of monetization for each entity. This paper discusses the technical and business landscapes of LaMAS. To support the ecosystem of LaMAS, we provide a preliminary version of such LaMAS protocol considering technical requirements, data privacy, and business incentives. As such, LaMAS would be a practical solution to achieve artificial collective intelligence in the near future.

Meta's blowout year continues after the company reported another stellar financial quarter on Wednesday. But shares fell in after-hours trading after the company missed Wall Street expectations for daily active users. Wall Street analysts had high expectations for the Instagram and WhatsApp parent company, projecting an 18% jump in sales year over year. The company reported 40.6bn in sales, a 19% increase year over year that outpaced investor expectations of 40.19bn. Meta, which saw a 25% jump in its share price over the past two months, reported 6.03 in earnings per share (EPS), surpassing Wall Street's expectations of an EPS of 5.29.

Google plans to launch a new chatbot search later this year. Google's biggest challenge with a chatbot search is shifting responsibility: Right now, Google provides links to answers provided by website publishers. With a chatbot search, it would provide direct and authoritative answers to questions. Google is already partially responsible for the answers to search queries today: by prioritizing search results, Google influences information retrieval. With zero-click search, Google self-consciously cites more or less appropriate snippets of web pages for search queries. But it will be with chatbot search, planned for 2023 according to the New York Times, that Google will have to take responsibility for the written word.

A knowledge market can be described as a type of market where there is a consistent supply of data to satisfy the demand for information and is responsible for the mapping of potential problem solvers with the entities which need these solutions. It is possible to define them as value-exchange systems in which the dynamic features of the creation and exchange of intellectual assets serve as the fundamental drivers of the frequency, nature, and outcomes of interactions among various stakeholders. Furthermore, the provision of financial backing for research is an essential component in the process of developing a knowledge market that is capable of enduring over time, and it is also an essential driver of the progression of scientific investigation. This paper underlines flaws associated with the conventional knowledge-based market, including but not limited to excessive financing concentration, ineffective information exchange, a lack of security, mapping of entities, etc. The authors present a decentralized framework for the knowledge marketplace incorporating technologies such as blockchain, active inference, zero-knowledge proof, etc. The proposed decentralized framework provides not only an efficient mapping mechanism to map entities in the marketplace but also a more secure and controlled way to share knowledge and services among various stakeholders.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

The data associated with machine learning can be extremely valuable, but, Kimberley Bayliss of Haseltine Lake Kempner writes in this co-edited article, before it can be monetized, there are some major issues to be resolved. One of the things I hear over and over again from inventors is that data is the most valuable asset in machine learning (ML). After all, an ML model is only as good as the quality and quantity of data on which it is trained. If data is really that valuable, the burning question is whether it can be successfully protected and monetized. Just as employees must be aware when they access a trade secret, and the responsibilities that come with it, employees must also be aware of their responsibilities when accessing and using company data.

During the projected period, the worldwide blockchain AI market is estimated to reach US$ 2.8 billion, growing at a CAGR of 22.9 percent. In 2021, the market is expected to be worth USD 285.7 million, and in 2022, it will be worth USD 358 million. The increased monetization of consumer data through the use of blockchain can be credited to the market's rise. Data monetization would make AI and advanced blockchain more accessible to growing businesses. In addition, continuous developments in AI, IoT, and big data technologies are important aspects that will shape the industry in the future.

The exchange of data between corporations is known as data monetization. It is the process of earning income or creating new revenue streams by utilizing data, which is estimated to support expansion of the global data monetization market. Direct data monetization as well as indirect data monetization is the two forms of data monetization. The sale of raw data is known as direct data monetization. Companies are making income directly from the sale of data in this scenario.

When Microsoft injected $1 billion in artificial intelligence project co-founded by Elon Musk called OpenAI, it changed everything for the project. It brought the AI moonshot into the limelight and made much of its research more concrete. It's hard to catch how quickly things moved, but in November, 2021 something incredible happend amid all the headlines at OpenAI. That's when Microsoft launched the Azure OpenAI Service (November, 2021) -- giving Azure customers the ability to utilize OpenAI's machine learning models. Unveiled at the company's recent Ignite event, the service is based on GPT-3, a language model developed by OpenAI, the company which Microsoft backed with $1bn in 2019.