February 19, 2023
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
Despite progress on short-term applications, 2023 will not see error correction disappear. Far from it, the holy grail of quantum computing will continue to be building a machine capable of fault tolerance. 2023 may create software or hardware breakthroughs that will show how we’re closer than we think, but otherwise, this will continue to be something that is achieved far beyond 2023. Despite it being everything to some quantum companies and investors, the future corporate users of quantum computing will largely see it as too far off the time horizon to care much. The exception will be government and anyone else with a significant, long-term interest in cryptography. Despite those long time horizons, 2023 will define clearer blueprints and timelines for building fault-tolerant quantum computers for the future. Indeed, there is also an outside chance that next year will be the year when quantum rules out the possibility of short-term applications for good, and doubles down on the 7- to 10-year journey towards large-scale fault-tolerant systems.
The challenge is instead of trying to keep the proverbial IT lights on during the COVID-19 era, IT teams are now being asked to innovate to advance digital business transformation initiatives, said Orlandini. A full 87% of survey respondents cited modernizing critical applications as a key success driver. As a result, many organizations are embracing platform engineering to bring more structure to their DevOps processes, he noted. The challenge, however, is striking a balance between a more centralized approach to DevOps and maintaining the ability of developers to innovate, said Orlandini. The issue, of course, is that in addition to massive technical debt, the latest generation of applications are more distributed than ever. The survey found 91% of respondents now rely on multiple public cloud providers for different workloads, with 54% of data residing on a public cloud. However, the survey also found on-premises IT environments are still relevant, with 20% planning to repatriate select public cloud workloads to an on-premises model over the next 12 months.
Both government and private industries have been collecting and using facialimages for years. However, critics of facial recognition technology accuse it of racial, ethnic, gender and age-based biases, as it struggles to properly identify people of color and women. The algorithms in facial recognition tend to perpetuate discrimination in a technology meant to add security rather than adding risk. The updated NIST digital guidelines will directly address the struggles of facial recognition in particular, and biometrics overall. “The forthcoming draft will include biometric performance requirements designed to make sure there aren’t major discrepancies in the tech’s effectiveness across different demographic groups,” FCW reported. Rather than depend on digital photos for proof, NIST will add more options to prove identity. Lowering risk is as important to private industries as it is to federal agencies. Therefore, it would behoove enterprises to take steps to rethink their identity proofing.
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As a new computing paradigm in the cloud era, Serverless architecture is a naturally distributed architecture. Its working principle is slightly changed compared with traditional architectures. In the traditional architecture, developers need to purchase virtual machine services, initialize the running environment, and install the required software (such as database software and server software). After preparing the environment, they need to upload the developed business code and start the application. Then, users can access the target application through network requests. However, if the number of application requests is too large or too small, developers or O&M personnel need to scale the relevant resources according to the actual number of requests and add corresponding policies to the load balance and reverse proxy module to ensure the scaling operation takes effect timely. At the same time, when doing these operations, it is necessary to ensure online users will not be affected. Under the Serverless architecture, the entire application release process and the working principle will change to some extent.
It’s a programming model for writing big data processing pipelines which iportable and unified. Now what does it mean exactly: First let’s understand the use cases for big data processing pipelines. Batch processing: Batch processing is a data processing technique used in big data pipelines to analyze and process large volumes of data in batches or sets. In batch processing, data is collected over a period of time, and then the entire batch of data is processed together Stream processing : Processing data as it is generated. It is a data processing technique to process data in real-time as it is generated, rather than in batches. In stream processing, data is processed continuously, as it flows through the pipeline. ... Beam offers multi-language pipelines which is basically a pipeline that is constructed using one Beam SDK language and incorporates one or more transforms from another Beam SDK language. The transforms from the other SDK language are known as cross-language transforms.?
ChatGPT has also been useful within cybernetic defense, by being asked to create a Web Application Firewall (WAF) rule to detect a specific type of attack, in the threat hunting scenario, where it is possible that the tool creates a machine learning model in any language, such as python, so that the tool can analyze the network traffic of a .pcap file, where the network packets were captured and thereby identify possible malicious behavior, such as a network connection with a malicious IP address that is already known and may indicate that a device is compromised, indicate an unusual increase in attempts to access the network through brute force, among other possibilities. ... This is worrying to the point of schools in NYC City blocking access to ChatGPT due to concern about the negative impacts this can generate on the students’ learning process, since in most cases, depending on the question, the answer is already provided without any effort or without having to study.