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Software and Hardware Requirements for Android Studio

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Software and Hardware Requirements for Android Studio Software and hardware requirements for android studio Continue Hot on the heels of Android 11 Developer Preview, Android Studio 3.6 is now available on a stable channel, which means that developers can start making confident use of it for their projects. This brings a number of useful features and updates, including the new Split View's Design Editor for faster design and preview of XML layouts. Another exciting new feature is the support of several displays in the Android emulator. Automatic detection of memory leakage meanwhile promises to make debugging much easier. You can check out the full array of features from the Android Developers blog, or get the highlights below. Split View and EditingPerhaps The most interesting new feature in Android Studio 3.6 is Split View for Design Editors. This allows you to see the XML code side by side along with the preview render. It's a small thing, but in fact it makes life a lot easier to view the effect that changes the code right away (and vice versa). The view you choose will also be saved on a case-by-case basis, which means you can easily download the preferred setup depending on the file you're editing. While we discuss design, we should also note the new color collector, making it much easier to select and fill color values without a set of values. It's available through the XML editor and design tools. Faster development When it comes to development, a few new changes should make life easier for Android Developers in Android Studio 3.6.View Binding is a particularly welcome inclusion that will offer a compilation of security time when linking to opinions. With this option, you'll create a binding class for each XML layout file in the module. This will actually replace the need for findViewByID: you can easily refer to any type of ID without risking zero pointer exceptions or class exceptions. This can prove very useful and reduce a lot of patterns. Other new updates include the release of the IntelliJ 2019.2 platform with better launch time and new tool services, as well as support for Kotlin for more Android NDK features. Updates to the Android Gradle plug-in include support for the Maven Publish Gradle plug-in. This allows you to create artifacts in the Apache Maven repository. Testing and debugging Android Emulator 29.2.12 makes it easier for developers to interact with the location of the emulation device. Google Maps is now built into the advanced control menu, making it easy to specify locations and create routes. Perhaps more appropriate is still the support of several virtual displays that will be useful for those designing for devices such as Samsung Galaxy Fold.Read also: Development for folding devices: What you need to know, memory detection detect activity and snippet instances that may have leaked. Build time has also improved for debugging builds thanks to the use of quality of life changesIt's only a small selection of updates available in Android Studio 3.6. You'll find plenty of other small updates as you use the new software too: including the resumed SDK downloads, which is perfect for those who don't always have an hour spare to download the latest Android image! Grab Android Studio 3.6 here. Of course, on the Canary Channel you can already get your hands on Android Studio 4.1. What do you think of these new features? What would you like to see come to Android Studio in the future? What is the future according to Google? It's a pretty exciting place. Artificial intelligence rules supreme. All the information in the world is available right at our fingertips, and of course Google is the company to provide it. Machine learning is at the heart of everything that Google doesGoogle may seem diversified in recent years, exploring everything from self-driving cars to smartphones. The truth is that machine learning is actually at the heart of everything it does. Google started out as a search engine, and has naturally expanded into machine learning and AI. In this way, Google can understand the questions you ask it and provide the appropriate answers, rather than just a list of search results with appropriate phrases. Search engine optimizers will be familiar with the algorithm 'RankBrain' that feeds this smart search. Google Assistant evolved from the same natural language processing, combined with voice recognition, which made it possible through machine learning. Similarly, initiatives such as Google Lens show us how machine learning can be used using computer vision to help us find what we face in the real world. In fact, AI-first is not a step away from search, but its natural development. But it goes much further. Why does Google need hardware for its vision to work, does something like Google Pixel fit into it all? The answer is simple: in order to make the most of artificial intelligence, which is ultimately a form of software, Google needs the right hardware to run it. Google wants to become a go-to solution for AI, just like it's a go-to search solution. This means that he wants to put a Google assistant in his pocket. Google Assistant faces competition from Apple, Microsoft, Amazon and even Samsung. Seeing as AI is likely to dominate the industry in the coming years, Google will have to fight to get ahead of this package. If you have a Google assistant in your pocket, why would you ask an echo point to set a timer or save a reminder? As our own Bogdan Petrovan suggested in his recent article, Google may not really care smartphones it sells. The key is to demonstrate to other OEM manufacturers how close integration with its services can help them satisfy customers and put pressure on companies to place the function front and center. Because the pixel and and The two exist as viable alternatives for consumers, OEMs must ensure their devices also offer Google Assistant to stay competitive. Google wants an assistant front and center on every smartphone, even iPhones! This means that it should have some impact on the direction of both hardware and software. This symbiotic connection works both ways. The hardware supports Google's vision of conquering AI, but AI also creates new hardware capabilities that might not exist otherwise. Google CEO Sundar Pichai said he doesn't just want Google hardware to use AI in the future, but wants AI to inspire future products that might not exist otherwise: Google Clips is a great example of this. Even Google's self- driving cars are an example of machine learning apps, relying on how they are on computer vision in order to identify hazards and respond accordingly. The role of cloudIt has become clear that Google has a very clear plan for the future and it revolves around machine learning and AI. The goal is the same as ever: Organize world information and make it universally accessible and useful. It has become apparent that AI and machine learning offer the best tools to achieve this. The equipment serves as a channel between user and machine learning, and encourages other OEMs to get on board, showing what is possible. To be clear, AI and machine learning are not the same: machine learning is just one aspect of AI that handles image recognition. As always, Gary explains the differences best. Virtual assistants such as Alexa, Siri and Google Assistant are currently working on the cloud. Your voice commands are saved, processed to some extent, and sent to the server for additional processing, so that the answer can be generated. This is necessary because most smartphones do not have the necessary power for the intensive algorithms on which machine learning relies, such as image recognition, necessary to understand voice commands or to recognize distinctive patterns in images. Instead, hard work is done on the cloud. To do this, Google uses an initiative called TensorFlow, a library of useful machine learning algorithms processed by the Cloud Tensor Processing Units (CTPUs) that work on their servers. The exciting part is that developers are free to use these offers through Google's cloud platform. Is there an idea that requires machine learning to work? Now you can make it a reality! This is another example of how Google is delving into the hardware to way into AI, but it also shows why the cloud is such a necessary part of his vision. The problem is that AI applications are somewhat limited by being unloaded this way. This not only creates an obvious speed bottleneck, but also introduces new security problems and requires a permanent Internet connection. If you have an idea of an idea requires machine learning to work - and now you can make it a reality! Fortunately, we are on the verge of the hardware that ai can offer on board thanks to new neural processing units (NPCs). Google Pixel 2 includes Pixel Visual Core - the company's first mobile chip, which unsurprisingly engages in machine learning. The chip is designed to help support hdr' function of the Pixel camera, which in itself is a function of machine learning. This is an advantage provided by Google by taking control of its hardware. In the future we might see this leading to more image and machine learning applications too. Other companies are also coming out with their own NPCs to better handle AI applications on the device. Phones do not need strictly these types of specialized chips to handle machine learning.
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