But it means the network gets choked by games like CryptoKitties. But if we want blockchain to live up to its biggest promises, we need a solution that can coordinate large ecosystems with diverse governance models, managing huge numbers of actions all happening at once. The good news is--a lot of that is just number crunching. And GPUs are very good at that. That's why Cardstack is creating Tally: a Layer 2 protocol that lets dApps harness GPU power to perform heavy-duty calculations.
That market cap has grown more than 20 times since last year, when the cryptocurrency boom began. With bitcoin's booming popularity comes problems. Speculation in bitcoin and other cryptocurrencies is rampant. Scams abound, and plenty of initial coin offerings (ICOs) have overpromised or underdelivered spectacularly. Through it all, the world has focused on how bitcoin and other cryptocurrencies could implode and go to zero--or make you rich--depending on who you ask.
The blockchain space is full of projects without a clear use case and clear value, but, as we'll see, there are also projects which are quite the opposite. Nebula-AI (NBAI) is creating a decentralized AI computing platform which will make AI DAPPS (which they call DAI Apps) a reality. The first product and demo that they released is Quant-AI: A cutting-edge trading price prediction tool. In this article, we'll dive into the specifics of this project and take a look at Quant AI and the broader concept of DAI Apps. Thanks to the use of GPUs and parallel computing for algorithms like machine learning (ML) AI has finally become widely viable.
Alan Turing would have been proud of our achievements in computer vision, speech, natural language processing and autonomous systems. However, there are still many challenges and we are still some distance from building machines that can pass the Turing test. In this paper, we discuss some of the biggest questions concerning intelligent machines and we attempt to answer them, as much as can be explained by modern AI. Turing choose to avoid answering this question directly, however, it is important to have a clear and concise meaning of thinking that incorporates lessons from neuroscience and Artificial Intelligence. We define thinking as "The process by which we evaluate features learned from past experiences in order to make decisions about new problems" In the context of human thinking, when you see a person and you are faced with the task of determining who the person is (The New Problem), a activity (The Process) begins in your brain that goes through the search space of all the people whose face you can remember (The Experience), you then begin to consider the nose, eyes, skin color, dressing, height, speech and any other observable treats (The Features), the process then attempts to match these features to a particular person based on people we have seen before, if no satisfactory match is found, the brain concludes that this is a stranger (The Decision).