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What GitHub's Copilot tool reveals about AI's future in software development

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Software developers speak the language of computers. Conversant in commands and symbols, engineers rely on coding skills to craft applications. Tools that support developers are evolving, making the next generation of engineers more akin to train conductors who rely on algorithms to turn natural language cues into applications. With AI feedback, tools promise software applications that come together fast and easy. That's the gist of Copilot, a tool built by GitHub and OpenAI.


Amazon SageMaker tutorial and model

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This code pattern describes a way to gain insights by using Watson OpenScale and a SageMaker machine learning model. It explains how to create a logistic regression model using Amazon SageMaker with data from the UC Irvine machine learning database. The pattern uses Watson OpenScale to bind the machine learning model deployed in the AWS cloud, create a subscription, and perform payload and feedback logging. With Watson OpenScale, you can monitor model quality and log payloads, regardless of where the model is hosted. This code pattern uses the example of an Amazon Web Service (AWS) SageMaker model, which demonstrates the independent and open nature of Watson OpenScale.


Thinking, fast and slow: AI edition

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Francesca draws inspiration from Daniel Kahneman's division of human cognition into two different systems, which he names "System 1" and "System 2". System 1 is a fast kind of cognition that's generally emotional and based on instinct. It relies on simplified models of the world to deliver rapid responses to stimuli. Flinches, identifying objects at a distance, and reading and understanding a simple message from a friend are all routed to System 1. System 2 however is more deliberative: in Kahnema's model, it's responsible for complex reasoning. A class of problem might first be addressed by System 2 before migrating to System 1 once it becomes ingrained through repetition.


Face Recognition in Under 25 Lines of Python Code

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In this article, we will look at an amazingly simple way to get started with face recognition using Python and the OpenCV Open Library. OpenCV is the most popular computerized library. Originally written in C / C, it now provides Python binding. OpenCV uses machine learning algorithms to search faces within an image. Because the face is so hard, there is not a single simple test that will tell you whether it got a face or not.


How AI in Healthcare Is Changing the Industry

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AI in healthcare is something that is revolutionizing the industry and medical treatment that we as the patients receive. But AI, in general, is making inroads into virtually every field and aspect of society. Healthcare AI companies like NVIDIA healthcare and Google DeepMind Health are breaking new ground, with innovations that are helping to save lives. Let's dive into the world of AI so that you can have a better understanding of what it is all about and where it is going. AI stands for artificial intelligence.


Top 20 Data Science and Machine Learning Projects in Python (Part-II)

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I hope you all are enjoyed reading my earlier article Part - I 10/20, and I trust that would be useful for you. Let's discuss the rest of the project quickly. When you're dealing with NLP based problem statement, we must focus on "Text Data" preparation before you can start using it for any NLP algorithm. The foremost step is that text cleaning and processing is an important task in every machine learning project, even if we are working on the text-based task and making sense of textual data. So, when dealing with text, we must take extra causes for Text Classification, Text Summarization, understanding Tokenization, and Bag of Words preparation.


British court disagrees with Australia, rules that AIs cannot be patent inventors

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The UK and Australia may have made a historic pact last week, but one thing they can't agree on is whether AIs can be patent inventors. AIs are increasingly being used to come up with new ideas and there's an argument they should therefore be listed as the inventor by patent agencies. However, opponents say that patents are a statutory right and can only be granted to a person. US-based Dr Stephen Thaler, the founder of Imagination Engines, has been leading the fight to give credit to machines for their creations. Dr Thaler's AI device, DABUS, consists of neural networks and was used to invent an emergency warning light, a food container that improves grip and heat transfer, and more.


Machine Learning Cases in Business Industries

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Innovative technologies appear every day. Today the center of development is machine learning based on artificial intelligence. ML applications and programs will become an integral part of the optimization and success of companies. Already, these tools are helping to proactively detect equipment malfunctions, create personalized recommendations for customers, and find rational approaches to problem solving. Such programs cope with some tasks perfectly, while others still require the attention of people.


Why your next co-worker could be a bot

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Sponsored Throughout history, technology has continued to augment what people can do. We're perfectly capable of travelling from one place to another, or calculating in our heads, but cars and spreadsheets make us undeniably better at it. Technologies take care of the tasks that humans find boring, difficult or even impossible to do. They can free us to spend time where we bring the most value: creativity, problem-solving, compassion, and personal interaction. However, there's often resistance to disruptive technologies when they're first introduced.


3 ways to simplify the future of work for your company

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One of the great things about working in consulting is that much of my time is devoted to thinking about big, complex problems, and considerations around the future of work most certainly fit in that category. However, we as consultants also tend to overcomplicate some of these challenges, preferring grand, multi-year strategies to pragmatic advice at times. However, defining the future of work for your teams need not be a complex endeavor. SEE: Wellness at work: How to support your team's mental health (free PDF) (TechRepublic) The past several months have served as the ultimate laboratory for the future of work, forcing companies to test everything from remote working, to non-traditional schedules, to new and novel staffing arrangements. Now that we have a moment to catch our collective breath, it's worth considering how you approach the future of work in a more disciplined and diligent manner without turning it into an overly complex endeavor.