StormGain is a crypto trading platform for everyone. It's a convenient solution for those who want to profit from either the growth or decline of the cryptocurrency market and from long-term investments in crypto assets.

Johnson & Johnson (J&J) is an American multinational corporation founded in 1886 that develops medical devices, pharmaceuticals, and consumer packaged goods. Its common stock is a component of the Dow Jones Industrial Average and the company is ranked No. 36 on the 2021 Fortune 500 list of the largest United States corporations by total revenue.

Fractal has raised $360 million from TPG in a new financing round and entered the unicorn club as the Mumbai and San Francisco-headquartered AI startup, which counts Google and Wells Fargo among its customers, scales its offerings and begins preparation for an IPO. The new financing round valued the 21-year-old startup "at well north of $1 billion," said Srikanth Velamakanni, co-founder and group chief executive at Fractal, in an interview with TechCrunch. TPG invested in the startup through its TPG Capital Asia, its Asia-focused private equity platform. The new round, which brings the startup's all-time raise to about $685 million, involves some secondary share purchase as well. Fractal Analytics provides artificial intelligence and analytics solutions to scores of Fortune 100 firms.

Altada Technology Solutions, a provider of artificial intelligence (AI) solutions supporting improved data-driven decision making in the asset management community, is expanding on a global basis with the addition of a London office. Through 2021, the company has added offices in New York, San Francisco, Malta, Dublin and Barcelona. Altada was founded in 2018 in Cork, Ireland with a view to ensuring the ethical and responsible use of AI. Its financial services solutions cover investment and portfolio management, and are built on technology that allows firms to analyse key variables in a fast and accurate way, and provide sentiment and valuation analyses that help decision makers efficiently allocate investments. Altada's London office, its first in the UK, is a step in its business growth strategy.

"Artificial intelligence is to trading what fire was to the cavemen." That's how one industry player described the impact of a disruptive technology on a staid industry. In other (less creative) words, AI is a game changer for the stock market. While humans remain a big part of the trading equation, AI plays an increasingly significant role. According to a recent study by U.K. research firm Coalition, electronic trades account for almost 45 percent of revenues in cash equities trading.

Artificial intelligence will create so much wealth that every adult in the United States could be paid $13,500 per year from its windfall as soon as 10 years from now. So says Sam Altman, co-founder and president of San Francisco-headquartered, artificial intelligence-focused nonprofit OpenAI. "My work at OpenAI reminds me every day about the magnitude of the socioeconomic change that is coming sooner than most people believe," Altman, who posted Tuesday. "Software that can think and learn will do more and more of the work that people now do." Altman calls it an "AI revolution," and compares it in magnitude to the agricultural, industrial and computational technological revolutions.

By 2025, more than 75% of venture capital and early-stage investor executive reviews will be informed using AI and data analytics. In other words, AI might determine whether a company makes it to a human evaluation at all, deemphasizing the importance of pitch decks and financials. That's according to a new whitepaper by Gartner, which predicts that in the next four years, the AI- and data-science-equipped investor will become commonplace. Increased advanced analytics capabilities are shifting the early-stage venture investing strategy away from "gut feel" and qualitative decision-making to a "platform-based" quantitative process, according to Patrick Stakenas, senior research director at Gartner. Stakenas says data gathered from sources like LinkedIn, PitchBook, Crunchbase, and Owler, along with third-party data marketplaces, will be leveraged alongside diverse past and current investments.

Airbnb has raised the price of its shares ahead of its initial public offering this week, betting investors will pay more given its resiliency during the pandemic. In a government filing Monday, Airbnb said it expects to price its shares between $56 and $60 each, up from a range of $44 to $50 earlier this month. Airbnb is expected to issue a final share price late Wednesday ahead of its Thursday IPO on the Nasdaq Stock Market. The new price would let the San Francisco-based home sharing company raise up to $3.4 billion in the offering. That's more than double the $18 billion the company was valued at during a private fundraising round in the spring, when the pandemic shut down global travel and its prospects were uncertain.

AI Daily Roundup starts today! We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities in artificial intelligence, Machine Learning, Robotic Process Automation, Fintech and human-system interactions. We will cover the role of AI Daily Roundup and their application in various industries and daily lives. RingCentral, Inc. a leading provider of global enterprise cloud communications, collaboration, and contact center solutions, announced that the San Francisco Symphony is using RingCentral Video, part of its flagship product RingCentral Office, to connect with audiences virtually with fun and interactive entertainment.

Graph convolutional networks (GCNs) have been employed as a kind of significant tool on many graph-based applications recently. Inspired by convolutional neural networks (CNNs), GCNs generate the embeddings of nodes by aggregating the information of their neighbors layer by layer. However, the high computational and memory cost of GCNs due to the recursive neighborhood expansion across GCN layers makes it infeasible for training on large graphs. To tackle this issue, several sampling methods during the process of information aggregation have been proposed to train GCNs in a mini-batch Stochastic Gradient Descent (SGD) manner. Nevertheless, these sampling strategies sometimes bring concerns about insufficient information collection, which may hinder the learning performance in terms of accuracy and convergence. To tackle the dilemma between accuracy and efficiency, we propose to use aggregators with different granularities to gather neighborhood information in different layers. Then, a degree-based sampling strategy, which avoids the exponential complexity, is constructed for sampling a fixed number of nodes. Combining the above two mechanisms, the proposed model, named Mix-grained GCN (MG-GCN) achieves state-of-the-art performance in terms of accuracy, training speed, convergence speed, and memory cost through a comprehensive set of experiments on four commonly used benchmark datasets and a new Ethereum dataset.