Geedge Networks, a company with ties to the founder of China's mass censorship infrastructure, is selling its censorship and surveillance systems to at least four other countries in Asia and Africa. A leak of more than 100,000 documents shows that a little-known Chinese company has been quietly selling censorship systems seemingly modeled on the Great Firewall to governments around the world. Geedge Networks, a company founded in 2018 that counts the "father" of China's massive censorship infrastructure as one of its investors, styles itself as a network-monitoring provider, offering business-grade cybersecurity tools to "gain comprehensive visibility and minimize security risks" for its customers, the documents show. In fact, researchers found that it has been operating a sophisticated system that allows users to monitor online information, block certain websites and VPN tools, and spy on specific individuals. Researchers who reviewed the leaked material found that the company is able to package advanced surveillance capabilities into what amounts to a commercialized version of the Great Firewall--a wholesale solution with both hardware that can be installed in any telecom data center and software operated by local government officers.

Effective internet traffic prediction in smaller ISP networks is challenged by limited data availability. This paper explores this issue using transfer learning and data augmentation techniques with two LSTM-based models, LSTMSeq2Seq and LSTMSeq2SeqAtn, initially trained on a comprehensive dataset provided by Juniper Networks and subsequently applied to smaller datasets. The datasets represent real internet traffic telemetry, offering insights into diverse traffic patterns across different network domains. Our study revealed that while both models performed well in single-step predictions, multi-step forecasts were challenging, particularly in terms of long-term accuracy. In smaller datasets, LSTMSeq2Seq generally outperformed LSTMSeq2SeqAtn, indicating that higher model complexity does not necessarily translate to better performance. The models' effectiveness varied across different network domains, reflecting the influence of distinct traffic characteristics. To address data scarcity, Discrete Wavelet Transform was used for data augmentation, leading to significant improvements in model performance, especially in shorter-term forecasts. Our analysis showed that data augmentation is crucial in scenarios with limited data. Additionally, the study included an analysis of the models' variability and consistency, with attention mechanisms in LSTMSeq2SeqAtn providing better short-term forecasting consistency but greater variability in longer forecasts. The results highlight the benefits and limitations of different modeling approaches in traffic prediction. Overall, this research underscores the importance of transfer learning and data augmentation in enhancing the accuracy of traffic prediction models, particularly in smaller ISP networks with limited data availability.

By the end of 2023, nearly half of all internet traffic was bots, found a study by United States cybersecurity company Imperva. Bad bots reached their highest levels recorded by Imperva, making up 34 percent of internet traffic, while good bots made up the remaining 15 percent. This was partly due to the increasing popularity of artificial intelligence (AI) for generating text and images. According to Baydoun, the pro-Israeli bots they found mainly aim to sow doubt and confusion about a pro-Palestinian narrative rather than to make social media users trust them instead. Bot armies - thousands to millions of malicious bots - are used in large-scale disinformation campaigns to sway public opinion.

Internet traffic volume estimation has a significant impact on the business policies of the ISP (Internet Service Provider) industry and business successions. Forecasting the internet traffic demand helps to shed light on the future traffic trend, which is often helpful for ISPs decision-making in network planning activities and investments. Besides, the capability to understand future trend contributes to managing regular and long-term operations. This study aims to predict the network traffic volume demand using deep sequence methods that incorporate Empirical Mode Decomposition (EMD) based noise reduction, Empirical rule based outlier detection, and $K$-Nearest Neighbour (KNN) based outlier mitigation. In contrast to the former studies, the proposed model does not rely on a particular EMD decomposed component called Intrinsic Mode Function (IMF) for signal denoising. In our proposed traffic prediction model, we used an average of all IMFs components for signal denoising. Moreover, the abnormal data points are replaced by $K$ nearest data points average, and the value for $K$ has been optimized based on the KNN regressor prediction error measured in Root Mean Squared Error (RMSE). Finally, we selected the best time-lagged feature subset for our prediction model based on AutoRegressive Integrated Moving Average (ARIMA) and Akaike Information Criterion (AIC) value. Our experiments are conducted on real-world internet traffic datasets from industry, and the proposed method is compared with various traditional deep sequence baseline models. Our results show that the proposed EMD-KNN integrated prediction models outperform comparative models.

Helping enrich people's lives with our research, we've partnered with businesses across Alphabet to apply our technology towards improving the products and services used by billions of people every day. One of our key partners is YouTube, who are on a mission to give everyone a voice and show them the world. Working together with YouTube's product and engineering teams, we've helped optimise the decision-making processes that increase safety, decrease latency, and enhance the viewer, creator, and advertiser experience for all. With video surging during the COVID-19 pandemic, and the total amount of internet traffic expected to grow in the future, video compression is an increasingly important problem. Working together with YouTube, we explored the potential for our AI model, MuZero, to improve the VP9 codec, a coding format that helps compress and transmit video over the internet.

In 2016, we introduced AlphaGo, the first artificial intelligence program to defeat humans at the ancient game of Go. Its successors, AlphaZero and then MuZero, each represented a significant step forward in the pursuit of general-purpose algorithms, mastering a greater number of games with even less predefined knowledge. MuZero, for example, mastered Chess, Go, Shogi, and Atari without needing to be told the rules. But so far these agents have focused on solving games. Now, in pursuit of DeepMind's mission to solve intelligence, MuZero has taken a first step towards mastering a real-world task by optimising video on YouTube.

This is the web version of Eye on A.I., Fortune's weekly newsletter covering artificial intelligence and business. To get it delivered weekly to your in-box, sign up here. In January 2020, in a Fortune magazine cover story, I chronicled the corporate race for artificial general intelligence, a kind of human-like or even superhuman A.I. that is the staple of science fiction. The pursuit of AGI, as it's more commonly called, has led to many of the machine learning innovations that underpin the current A.I. boom. But that boom is centered around narrow A.I--software that can perform one, specific task well.

Amazon SageMaker Studio is a web-based fully integrated development environment (IDE) where you can perform end-to-end machine learning (ML) development to prepare data and build, train, and deploy models. Like other AWS services, Studio supports a rich set of security-related features that allow you to build highly secure and compliant environments. One of these fundamental security features allows you to launch Studio in your own Amazon Virtual Private Cloud (Amazon VPC). This allows you to control, monitor, and inspect network traffic within and outside your VPC using standard AWS networking and security capabilities. For more information, see Securing Amazon SageMaker Studio connectivity using a private VPC.

About 10 undisclosed companies in Europe, Canada and the U.S. are using Centricity's software platform in sectors such as grocery, nonfood retail, apparel and consumer electronics, said Chief Executive Michael Brackett, who founded the company in late 2019. Centricity employs about 50, up from less than 10 last April, and its planned fundraise could bring total venture-capital investment to $12.5 million. Startups such as Centricity, which build software and services aimed directly at large enterprise customers, have been capitalizing on the increased demand for their services during the coronavirus pandemic, as companies have been forced to accelerate their digital initiatives to remain competitive. Companies use Centricity's AI-based insights to help predict what customers will want to buy in around one to three months, depending on the client, so they can stock their shelves accordingly. Its technology can also be used by research and development divisions at companies interested in launching new products.

It's no secret that global Internet traffic has dramatically increased thanks to a pandemic forcing everyone to work and learn from home. It's difficult to find exact, comprehensive figures, but it's clear that as a result of COVID-19, Internet traffic is up generally by 30% to 50%. And video conferencing is likely a major culprit. After all, Zoom's revenues jumped by an unbelievable yearly 355% in the second quarter, to $663.5 million, after COVID-19 made the videoconferencing platform part of many people's daily routine. Zoom says it now has over 300 million daily users.