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Movements, leaders, victories and the continuing fight for equality.
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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
In August 1908, a white mob attacked Springfield, Illinois’s Black community, destroying homes and businesses and lynching two Black men. National outrage over the violence helped spur the movement that created the NAACP the following year.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
| Y.3181 | |
|---|---|
| Architectural framework for machine learning in future networks including IMT-2020 | |
 | |
| Status | In force |
| Year started | 2022 |
| Latest version | (07/22) July 2022 |
| Organization | ITU-T |
| Base standards | Y.3172, Y.3173, Y.3176 |
| Domain | machine learning, 5G |
| License | Freely available |
| Website | www |
Y.3181 is an ITU-T Recommendation specifying an Architectural framework for Machine Learning Sandbox in future networks (e.g. 5G, IMT-2020).[1] The standard describes the requirements and architecture for a machine learning sandbox (computer security) a in future networks including IMT-2020.
The integration of AI/ML has been identified as one of the key features of future networks. However, network operators have the challenge of maintaining the operational performance and associated key performance indicators during or after this integration. In addition, the introduction of Machine Learning (ML) techniques to fifth-generation (5G) networks may raise concerns regarding transparency, reliability, and availability of ML methods, techniques and data. Often, ML methods are seen as black boxes (especially for deep learning, the internal operation of the model is unknown because it is too complex or even hidden) that can learn complex patterns from training datasets.[2]
However, such datasets may be limited and/or too complex, thus questions arise regarding the accuracy of the output of the ML mechanism. In particular, reducing the generalization error is the main concern in applying any kind of Supervised Learning (SL) approach, which can be high even if the test error is kept low (this phenomenon is commonly known as overfitting).[3] Apart from SL methods, other branches of ML such as Unsupervised Learning (UL) and Reinforcement Learning (RL) deal with uncertainty in one way or another. Such uncertainty may entail the application of changes in the network leading to unacceptable performance.[4]
On the one hand, unsupervised learning aims to find patterns from data without any guidance (unlabelled data) and hence lacks validation. On the other hand, RL is based on the learning-by-experience paradigm. RL has been shown to be of great utility for single-agent approaches in controlled scenarios, however notable adverse effects can appear as a result of the competition raised by multiple systems sharing the same resources (e.g., while providing heterogeneous services using common network resources). Moreover, when multiple systems are competing for the same market of users, exploration may hurt a system's reputation in the near term, with adverse competitive effects.[5]
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In computer security, a sandbox is a security mechanism for separating running programs, usually in an effort to mitigate system failures and/or software vulnerabilities from spreading. The sandbox metaphor derives from the concept of a child's sandbox—a play area where children can build, destroy, and experiment without causing any real-world damage. It is often used to analyze untested or untrusted programs or code, possibly originating from unverified or untrusted third parties, suppliers, users or websites, without risking harm to the host machine or operating system. A sandbox typically provides a tightly controlled set of resources for guest programs to run in, such as storage and memory scratch space. Network access, the ability to inspect the host system, or read from input devices are usually disallowed or heavily restricted. In the sense of providing a highly controlled environment, sandboxing can be comparable to virtualization. Sandboxing is frequently used to test unverified programs that may contain a virus or other malicious code without allowing the software to harm the host device.
In information security, threat hunting is the process of proactively searching for threats against computer systems in order to protect them. This is in contrast to traditional threat management measures, such as firewalls, intrusion detection systems (IDS), malware sandbox (computer security) and SIEM systems, which typically involve an investigation of evidence-based data after there has been a warning of a potential threat. Threat analyst Lesley Carhart stated that there is no consensus amongst practitioners what threat hunting actually entails.
Y.3181 is an ITU-T Recommendation specifying an Architectural framework for Machine Learning Sandbox in future networks (e.g. 5G, IMT-2020). The standard describes the requirements and architecture for a machine learning sandbox (computer security) a in future networks including IMT-2020.
Computer security software or cybersecurity software is any computer program designed to influence information security. This is often taken in the context of defending computer systems or data, yet can incorporate programs designed specifically for subverting computer systems due to their significant overlap, and the adage that the best defense is a good offense. The defense of computers against intrusion and unauthorized use of resources is called computer security. Similarly, the defense of computer networks is called network security. The subversion of computers or their unauthorized use is referred to using the terms cyberwarfare, cybercrime, or security hacking (later shortened to hacking for further references in this article due to issues with hacker, hacker culture and differences in white/grey/black 'hat' color identification). The computer security software products industry was launched in the second half of the 1970s when computer firms and new IT startups chose alternative paths to offer commercial access control systems to organizational mainframe computer users. These developments were led by IBM's Resource Access Control Facility and SKK's Access Control Facility 2.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Mae Jemison, aboard Space Shuttle Endeavour in 1992.
