TORONTO, ON / ACCESSWIRE / July 28, 2021 / DigiMax Global Inc. (the'Company' or'DigiMax') (CSE:DIGI)(OTC:DBKSF), a company that provides artificial intelligence ("AI") and cryptocurrency technology solutions, is pleased to announce that it has signed its first collaboration agreement to expand CryptoHawk services into Hong Kong and surrounding areas. CryptoHawk is an Artificial Intelligence driven, price-trend prediction tool that can be profitably used by any investor interested in trading Bitcoin or Ethereum. The tool is different as it uses AI and machine learning to capture profit from the volatility of crypto currencies, rather than incur the risk of buy-and-hold investments. As previously announced by the Company, in its first full month of operation in June 2021, CryptoHawk signals achieved a 1-month, long-short return on BTC of more than 25% compared to a buy-and-hold return for the same period of a loss of 10%. In both up and down markets, CryptoHawk has the potential to deliver subscribers much higher returns when trading.
We live in the age of artificial intelligence, machine learning and robots. Now, one company is combining all these next-generation technologies, and it's doing it on Cardano. Charles Hoskinson, the Cardano founder recently met Grace, a humanoid robot that will be integrated into Cardano in the near future. Grace was developed by SingularityNet, a decentralized AI company that has chosen to migrate to Cardano from Ethereum. Sophia is world-famous for being the world's first'lifelike' social humanoid robot, going as far as being granted citizenship in Saudi Arabia (she even sold an NFT for close to $700,000, so she is very pro-crypto).
Most top global companies have investments in Artificial Intelligence and the market for AI is expected to grow to $267 billion by 2027. Understanding AI and its applications is increasingly important. The University of Hong Kong (HKU) Business School MBA program offers a specialist AI module, which introduces students to the technology and its uses within various industries. With an understanding of cutting-edge tech topics like AI, HKU MBA students go on to land jobs in a variety of high-tech industries, many benefiting from Hong Kong's vibrant tech startup scene. HKU MBA alums, Aakriti Jain (class of 2020) and Geeseok Oh (class of 2019), used what they learned about AI from the MBA to secure exciting jobs in fintech.
Scientists have developed a device modelled on the human brain that can learn by association in the same way as Pavlov's dog. In the famous experiment, Russian physiologist Ivan Pavlov conditioned a dog to associate a bell with food. In order to replicate this way of learning, researchers from Northwestern University in the US and the University of Hong Kong developed so-called "synaptic transistors" capable of simultaneously processing and storing information in the same way as a brain. Instead of a bell and food, the researchers conditioned the circuit to associate light with pressure by pulsing an LED lightbulb and then immediately applying pressure with a finger press. The organic electrochemical material allowed the device to build memories and after five training cycles, the circuit associated light with pressure in such a way that light alone was able to trigger a signal for the pressure.
The sixth generation (6G) systems are generally recognized to be established on ubiquitous Artificial Intelligence (AI) and distributed ledger such as blockchain. However, the AI training demands tremendous computing resource, which is limited in most 6G devices. Meanwhile, miners in Proof-of-Work (PoW) based blockchains devote massive computing power to block mining, and are widely criticized for the waste of computation. To address this dilemma, we propose an Evolved-Proof-of-Work (E-PoW) consensus that can integrate the matrix computations, which are widely existed in AI training, into the process of brute-force searches in the block mining. Consequently, E-PoW can connect AI learning and block mining via the multiply used common computing resource. Experimental results show that E-PoW can salvage by up to 80 percent computing power from pure block mining for parallel AI training in 6G systems.
Blockchain & AI are the major architecture techs of our time. Its convergence is a key factor for the present & future of tech. These emerging & foundation technologies deal with data, value storage creation and lead the digital transformation of the 4IR. The history of Artificial Intelligence AI began in antiquity, with the power of imagination – myths, stories, rumours making artificial beings endowed with intelligence or consciousness by master craftsmen, magic. The History of Blockchain & Ledgers start when the first recorded ledgers systems were found in Mesopotamia, today's Iraq, 7000 years ago.
Bitcoin, as one of the most popular cryptocurrency, is recently attracting much attention of investors. Bitcoin price prediction task is consequently a rising academic topic for providing valuable insights and suggestions. Existing bitcoin prediction works mostly base on trivial feature engineering, that manually designs features or factors from multiple areas, including Bticoin Blockchain information, finance and social media sentiments. The feature engineering not only requires much human effort, but the effectiveness of the intuitively designed features can not be guaranteed. In this paper, we aim to mining the abundant patterns encoded in bitcoin transactions, and propose k-order transaction graph to reveal patterns under different scope. We propose the transaction graph based feature to automatically encode the patterns. A novel prediction method is proposed to accept the features and make price prediction, which can take advantage from particular patterns from different history period. The results of comparison experiments demonstrate that the proposed method outperforms the most recent state-of-art methods.
The temporal nature of modeling accounts as nodes and transactions as directed edges in a directed graph -- for a blockchain, enables us to understand the behavior (malicious or benign) of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platforms to operate in a secure manner. Motivated by this, we introduce temporal features such as burst and attractiveness on top of several already used graph properties such as the node degree and clustering coefficient. Using identified features, we train various Machine Learning (ML) algorithms and identify the algorithm that performs the best in detecting which accounts are malicious. We then study the behavior of the accounts over different temporal granularities of the dataset before assigning them malicious tags. For Ethereum blockchain, we identify that for the entire dataset - the ExtraTreesClassifier performs the best among supervised ML algorithms. On the other hand, using cosine similarity on top of the results provided by unsupervised ML algorithms such as K-Means on the entire dataset, we were able to detect 554 more suspicious accounts. Further, using behavior change analysis for accounts, we identify 814 unique suspicious accounts across different temporal granularities.
The technology practice will be headed by Etienne Luquet Farías and Israel Cedillo Lazcano, specialists in the field who will offer comprehensive and strategic legal advice in all issues relating to innovation, both in the private and public sectors. They will be responsible for the design and supervision of projects relating to the development of technology-focused startups, software and hardware IP, technology transfer, privacy policies and venture capital, as well as fintech, crypto assets, artificial intelligence and machine learning. The technology practice will assist clients in the determination of possible civil or criminal liabilities arising from the creation and use of algorithms, the assignment of rights, the drafting of codes of ethics and regulation through the use of technologies, among other needs. "Technology law involves a plurality of legal norms and technical issues, making it a particularly complex cross-disciplinary practice. Through the use of new technologies, legal problems can be solved in a new way, creating new opportunities," the firm said in a statement.
In addition, Banks likely constrained given higher capital preservation requirements 2020 will be challenging for FinTechs to navigate, but prosperous times remain ahead post crisis where Disruptive winners take it all and demand for AI, Tech and IoT companies that help financials transform to a digital and Data driven interaction will surge.