Decentralized Intelligence Network (DIN) addresses the significant challenges of data sovereignty and AI utilization caused by the fragmentation and siloing of data across providers and institutions. This comprehensive framework overcomes access barriers to scalable data sources previously hindered by silos by leveraging: 1) personal data stores as a prerequisite for data sovereignty; 2) a scalable federated learning protocol implemented on a public blockchain for decentralized AI training, where data remains with participants and only model parameter updates are shared; and 3) a scalable, trustless rewards mechanism to incentivize participation and ensure fair reward distribution. This framework ensures that no entity can prevent or control access to training on data offered by participants or determine financial benefits, as these processes operate on a public blockchain with an immutable record and without a third party. It supports effective AI training, allowing participants to maintain control over their data, benefit financially, and contribute to a decentralized, scalable ecosystem that leverages collective AI to develop beneficial algorithms.

Koomey's law This law posits that the energy efficiency of computation doubles roughly every one-and-a-half years (see Figure 1–7). In other words, the energy necessary for the same amount of computation halves in that time span. To visualize the exponential impact this has, consider the face that a fully charged MacBook Air, when applying the energy efficiency of computation of 1992, would completely drain its battery in a mere 1.5 seconds. According to Koomey's law, the energy requirements for computation in embedded devices is shrinking to the point that harvesting the required energy from ambient sources like solar power and thermal energy should suffice to power the computation necessary in many applications. Metcalfe's law This law has nothing to do with chips, but all to do with connectivity. Formulated by Robert Metcalfe as he invented Ethernet, the law essentially states that the value of a network increases exponentially with regard to the number of its nodes (see Figure 1–8).

The tale is a broad allegory for the power of decentralization. If a game skill or item was an immutable blockchain token – what we would now call a non-fungible token (NFT) – a company like Blizzard Entertainment couldn't nerf, or weaken, your Siphon Life even if it wanted to. This suggests a further possibility: Because non-fungible tokens live on public blockchains, they can be read by any game's software. If Siphon Life was an NFT that lived on a public blockchain, there was a possible future in which you could use it not just in World of Warcraft but in Assassin's Creed or Uncharted or, who knows, Tetris.

Rather than letting players port weapons or powers between games, non-fungible tokens will more likely serve as building blocks for new games and virtual worlds. One of the most enduring legends in the cryptocurrency industry is that Vitalik Buterin started Ethereum because his warlock got nerfed. "I happily played World of Warcraft during 2007-2010," Vitalik wrote in one version of the story. "But one day Blizzard removed the damage component from my beloved warlock's Siphon Life spell. I cried myself to sleep, and on that day I realized what horrors centralized services can bring. I soon decided to quit."

In order to understand DAO blockchain clearly, you need to take a look at a practical scenario. Think of a situation in which a ship crashed on an unknown and desolate island with hundreds of survivors. Now, the survivors must work in coordination and cooperation with each other to survive on the island until help arrives. Therefore, the survivors must follow a specific set of rules for maintaining coordinated behavior to survive on the island. However, the survivors would definitely need rulers and people to enforce the rules, thereby leading to the principal-agent dilemma. Interestingly, the principal-agent dilemma provides an ideal explanation for different DAO examples and how they work. The people making decisions on behalf of other people are referred to as agents, and the others are referred to as the principal. Since decision-makers or the agents distribute risk associated with their actions among others, the risk for the principal increases profoundly.

With the technological advances in machine learning, effective ways are available to process the huge amount of data generated in real life. However, issues of privacy and scalability will constrain the development of machine learning. Federated learning (FL) can prevent privacy leakage by assigning training tasks to multiple clients, thus separating the central server from the local devices. However, FL still suffers from shortcomings such as single-point-failure and malicious data. The emergence of blockchain provides a secure and efficient solution for the deployment of FL. In this paper, we conduct a comprehensive survey of the literature on blockchained FL (BCFL). First, we investigate how blockchain can be applied to federal learning from the perspective of system composition. Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL. We also survey the applications of BCFL in reality. Finally, we discuss some challenges and future research directions.

Machine learning has recently enabled large advances in artificial intelligence, but these results can be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and maintain them. Published proposals to provide models and data for free for certain tasks include Microsoft Research's Decentralized and Collaborative AI on Blockchain. The framework allows participants to collaboratively build a dataset and use smart contracts to share a continuously updated model on a public blockchain. The initial proposal gave an overview of the framework omitting many details of the models used and the incentive mechanisms in real world scenarios. For example, the Self-Assessment incentive mechanism proposed in their work could have problems such as participants losing deposits and the model becoming inaccurate over time if the proper parameters are not set when the framework is configured. In this work, we evaluate the use of several models and configurations in order to propose best practices when using the Self-Assessment incentive mechanism so that models can remain accurate and well-intended participants that submit correct data have the chance to profit. We have analyzed simulations for each of three models: Perceptron, Nave Bayes, and a Nearest Centroid Classifier, with three different datasets: predicting a sport with user activity from Endomondo, sentiment analysis on movie reviews from IMDB, and determining if a news article is fake. We compare several factors for each dataset when models are hosted in smart contracts on a public blockchain: their accuracy over time, balances of a good and bad user, and transaction costs (or gas) for deploying, updating, collecting refunds, and collecting rewards.

Artificial Intelligence solutions will soon run on top of blockchains, increasing machine learning capability and even creating new financial products. Blockchain-AI convergence is inevitable because both deals with data and value. Blockchain enables secure storage and sharing of data or anything of value. AI can analyze and generate insights from data to generate value. We will consider two (out of many) areas where blockchain and AI can be combined.

Blockchain technology is all the rage at the moment. Firms across the globe are looking to leverage the upstart technology to simplify processes, improve accountability, and remove many a traditional intermediary. AITCA Platform, enters the space with a private blockchain offering aimed at providing a package of products harnessing blockchain, Artificial Intelligence, as well as IOT (Internet Of Things) with a focus on privacy and security. AITCA, which stands for Artificial Intelligence Technology Cryptocurrency Assets, is a free Private Blockchain Network. The network gives users access to blockchain, IoT, encrypted cloud storage, VoIP (Voice Over Internet Protocol), AI, Smartbots, as well as cyber security functionality all on a single platform.

Innovation is advancing quicker than ever. Organizations and people that don't stay aware of some of the significant-tech patterns take the risk of being abandoned. Understanding the key patterns will enable individuals and organizations to plan and embrace opportunities. Gartner recently has disclosed top technology trends for 2020. Considering Gartner's predictions and other predictions, let's review technology trends for 2020.