Businesses are using artificial intelligence for many different tasks in the everyday work environment and beyond. While developers have yet to fully harness the power of artificial intelligence, its use cases continue to grow at an exponential rate. AI is the widely-encompassing branch of computer science concerned with the building of smart machines. These machines typically required human intelligence in the past. As an interdisciplinary science, artificial intelligence has many different approaches to its technology and practices.
And it is in this higher dimension that the Monte Carlo method particularly shines as compared to Riemann sum based approaches. We introduced the concept of Monte Carlo integration and illustrated how it differs from the conventional numerical integration methods. We also showed a simple set of Python codes to evaluate a one-dimensional function and assess the accuracy and speed of the techniques. The broader class of Monte Carlo simulation techniques is more exciting and is used in a ubiquitous manner in fields related to artificial intelligence, data science, and statistical modeling. For example, the famous Alpha Go program from DeepMind used a Monte Carlo search technique to be computationally efficient in the high-dimensional space of the game Go. Numerous such examples can be found in practice.
Data science is a field focused on extracting knowledge from data. Put into lay terms, obtaining detailed information applying scientific concepts to large sets of data used to inform high-level decision-making. Take the ongoing COVID-19 global pandemic for example: Government officials are analyzing data sets retrieved from a variety of sources, like contact tracing, infection, mortality rates, and location-based data to determine which areas are impacted and how to best adjust on-going support models to provide help where it is most needed while trying to curb infection rates. Big data, as it is often called, is the collective aggregation of large sets of data culled from multiple digital sources. These swaths of data tend to be rather large in size, variety (types of data), and velocity (the rate at which data is collected).
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Most people are familiar with the Internet of Things (IoT), which refers to smart objects in a connected network, as this diagram shows. A "smart" object has a sense of its environment, and it makes decisions (locally or together with peers and a cloud server), then puts those decisions into action. To be smart, the object must have a brain to carry intelligence. So far, the way to do this is to embed a computer in the object. For example, you can put a Cortex-M CPU with Bluetooth 5.1 in a chip smaller than 2x2mm and embed it into almost anything.
Alibaba has unveiled a new operating system for the cloud, with CTO Jeff Zhang likening it to upgrading a computer to use Windows. Speaking at the Chinese giant's virtual Apsara Conference 2020 on Thursday, Zhang said this new cloud, Alibaba Cloud 2.0, will allow operations to be more user-friendly and intuitive, enabling more organisations to migrate their workloads and run them without having to understand how to code. "In short, we want to make cloud technology accessible to everyone … just like water and electricity is," he said through a translator. The CTO said Alibaba Cloud 2.0 was an "all-in-one platform augmented with Aspara system and digital native operating system". "I believe that Alibaba Cloud 2.0 will change the way we run our workloads on the cloud and the way we implement applications. Zhang said the digital native operating system -- which he likened to LEGO blocks of the app development world -- is defining new organisations in new ways, and that it was also transforming software development methods, leaving process-based development methods behind. "The system will be intelligent, future-proof, mobile, and driven by big data, widening and deepening the cooperation between platforms and organisations.
Quickly shifting to remote work has enterprises looking to meet the ops needs of a suddenly distributed team, and there are open source options to get them there. The recent mad rush to scale to remote work may prove to be a key chapter in DevOps and AIOps evolution. This need for rapid, widescale change is creating a real conundrum concerning AIOps, DevOps, and ITSM, as organizations seek the best monitoring and incident response solution for their now distributed enterprises. The key question both the DevOps and IT service management (ITSM) communities need to answer is how quickly they can pivot and adapt to increasing demands for operational intelligence. Artificial intelligence for IT Operations (AIOps) brings together artificial intelligence (AI), analytics, and machine learning (ML) to automate the identification and remediation of IT operations issues.
San Francisco-based web experimentation company Optimizely is releasing a new version of its flagship platform that includes a new user interface, new data and analytics offerings, and integrations with AWS and Salesforce. Optimizely's core product is Web Experimentation, which enables non-technical staff to conduct A/B testing on the company's website using Optimizely's visual editor. Meantime, Optimizely's Full Stack product enables developers to experiment deeper into the tech stack to test things like search ranking algorithms or mobile app functionality. A/B testing has been a notable space of the software market as customers look to develop digital channels and improve experiences. Optimizely allows for quick A/B testing that can speed up software and code delivery.
Welcome to Signals & Threads, in-depth conversations about every layer of the tech stack, from Jane Street. Today, I'm going to have a conversation with Andrey Mokhov about build systems. Build systems are an important but I think poorly understood and often unloved part of programming. Developers often end up with only a hazy understanding of what's going on with their build system learning just enough to figure out what arcane invocation they need to get the damn thing working and then stop thinking about it at that point, and that's a shame because build systems matter a lot to our experience as developers. A lot of what underlies a good developer experience really comes out of the build system that you use and also there's a lot of beautiful ideas and structure inside of build systems. Sadly, a lot of that beauty is obscured by a complex thicket of messy systems of different kinds and a complicated ecosystem of different build systems for different purposes, and I'm hoping that ...