In the last years, artificial neural networks (ANN) have successfully been applied across a number of tasks, such as image classification, speech recognition and natural language understanding. Two main drivers, firstly a large amount of (labelled) data, and secondly growing compute resources, allowed to use technology that dates back to the 1960s and break records on many benchmarks. However, designing well-performing ANNs requires expert knowledge and experience. Neuroevolution aims at solving this difficult and often time-consuming process by applying evolutionary techniques. This article aims to provide an introduction to the re-emerging field of neuroevolution by supplying a definition, differentiating between other fields and areas and giving an overview by summarising a number of recent research papers. Inspired by biological neural networks, ANNs consist of many artificial neurons that are connected to each other. In a process, mostly referred to as training or learning, ANNs are optimised to solve specific, predefined tasks such as detecting faces. Learning is usually realised by employing a form of stochastic gradient descent (SGD) in order to change the network parameters in such a way that a future prediction for the same input is closer to the desired output. The architecture of a network, i.e. how the neurons are connected to each other, plays a very important role in whether or not an ANN can be trained to successfully learn a task. Over the years, human experts carefully designed complex architectures such as the VGGNet, AlexNet, GoogleNet, ResNet and many more to achieve and often surpass human-level performance on many different tasks.

History is filled with myths of "mechanical men" and "autonomous thinking" machines. Looking back, we're standing on the shoulders of giants in everything we do. The concept of artificial neural networks is introduced. The model of the Perceptron is invented. The LISP programming language is invented. Alan Turing proposes the Turing Test.

What glory (was with us?) Was'mid those forests (thee?) I can imagine you Proudly roar and say, 'I am the tiger'" The above text is part of an AI-generated rewriting of William Blake's classic poem The Tyger, as programmed by American writer Gwern Branwen using OpenAI's latest GPT-3 technology. Apart from the Pythonesque repetition of "thee", it unerringly evokes the spirit of mystic romanticism – and, crucially, it feels poetic. It should come as no surprise that machines are getting quite good at generating interesting strings of words. Fed on an ever-expanding diet of online content, their algorithms can continually learn how to improve their output. Numerous software services, such as Nichesss, CopyAI and Writesonic, are giving marketers access to these systems for copywriting purposes. They can create social media posts, blog outlines, product strategies and corporate slogans. But is any of it actually worth reading? Kim Darragon, a marketing expert and founder of the Kim Does Marketing consultancy, has tested out some of the software. She thinks that "AI-generated content can be pretty impressive.

Microsoft has officially introduced its first GPT-3-powered feature in a customer product, eight months after it exclusively licensed the sophisticated OpenAI language model. The tech giant has announced at the virtual Build developers conference that it's integrating GPT-3 in Power Apps, which even people with no coding experience can use to build business productivity apps. With the new features in place, Power Apps will be even easier to use -- in fact, it'll give users the power to code by using plain conversational language. GPT-3 is the largest language model ever trained and is capable of generating text so human-like, it could write believable fake news. Microsoft invested $1 billion in OpenAI back in 2019 and got access to the language tech for its own use and for its Azure cloud customers.

Microsoft has announced its first commercial use case for AI language model GPT-3, for which the company purchased an exclusive license last year. Developed by OpenAI, GPT-3 is capable of generating accurate passages of text based on only a few basic prompts. Soon after the model was released, one tester also found it could be taught to compose code with just a few tweaks, leading to speculation over how Microsoft might utilize the technology. At its Build 2021 event, Microsoft has revealed that GPT-3 will be put to work in combination with Power Fx, the company's low-code open source programming language. The pairing will allow developers to code applications using natural language inputs, expediting application development and helping devs pick up advanced concepts more quickly.

These last years we have seen the enormous progress made by AI algorithms. Computer vision has been dominated these last years by deep learning, showing excellent performances on very diverse tasks ranging from radiography analysis to biometric identification. We have also witnessed the emergence of smart assistants using voice recognition, helping us in our everyday life and in our homes. While these results are quite promising, one question remains: can we trust these AI models? There are several faces of trust that must be addressed, such as algorithm fairness, transparency and its respect of confidentiality.

Build Any souls wondering what Microsoft would do with its GPT-3 investment have been given an answer with a Power Fx update lightly seasoned with the AI tech. Microsoft gained exclusive rights to use OpenAI's GPT-3 in September last year, allowing it to embed the text-and-code-generating machine-learning model into its own products. Available in preview from next month, the technology was shown off at Microsoft's Build 2021 shindig today, and represents the latest attempt by the Windows giant to get folks from low code to no code and bring its Power platform closer to the masses. Looking initially like a jumped-up version of IntelliSense, the technology attempts to parse natural language entered by the user and generate the corresponding Excel-like language of Power Fx to perform the requested task. The idea is that you type in something like, "show me the readers who commented at the weekend," and it should generate the formulas to retrieve that information.

Microsoft today announced PyTorch Enterprise, a new Azure service that provides developers with additional support when using PyTorch on Azure. PyTorch is a Python-centric open-source machine learning framework with a focus on computer vision and natural language processing. It was originally developed by Facebook and is, at least to some degree, comparable to Google's popular TensorFlow framework. Frank X. Shaw, Microsoft's corporate VP for communications, described the new PyTorch Enterprise service as providing developers with "a more reliable production experience for organizations using PyTorch in their data sciences work." With PyTorch Enterprise, members of Microsoft's Premier and Unified support program will get benefits like prioritized requests, hands-on support and solutions for hotfixes, bugs and security patches, Shaw explained.

Microsoft is using the power of GPT-3's natural language artificial intelligence (AI) to help people who don't know how to code write their own software using Microsoft's PowerApps development platform, unveiled at Microsoft's Build developer conference. Redmond has hoped that PowerApps would become a powerful corollary to its Office suite, but the platform has languished a bit. Microsoft originally set up PowerApps in 2015 around a set of programming templates, pulling data from user-defined sources and then outputting results. Think of it like the next level of a traditional macro in Microsoft Office--it's a way for an average user to write a program to instruct Windows to perform a task, but with minimal or no knowledge of program coding. The problem is that even what Microsoft calls a "low code" or "no code" approach can be time-consuming and complex.

In September 2020, Microsoft purchased an exclusive license to the underlying technology behind GPT-3, an AI language tool built by OpenAI. Now, the Redmond, Washington-based tech giant has announced its first commercial use case for the program: an assistive feature in the company's PowerApps software that turns natural language into readymade code. The feature is limited in its scope and can only produce formulas in Microsoft Power Fx, a simple programming language derived from Microsoft Excel formulas that's used mainly for database queries. But it shows the huge potential for machine learning to help novice programmers by functioning as an autocomplete tool for code. There's a million-developer shortfall in the US alone," Charles Lamanna, CVP of Microsoft's Low Code Application Platform, tells The Verge. "So instead of making the world learn how to code, why don't we make development environments speak the language of a normal human?" Microsoft has been pursuing this vision for a while through Power Platform, its suite of "low code, no code" software aimed at enterprise customers. These programs run as web apps and help companies that can't hire experienced programmers tackle basic digital tasks like analytics, data visualization, and workflow automation. GPT-3's talents have found a home in PowerApps, a program in the suite used to create simple web and mobile apps. Lamanna demonstrates the software by opening up an example app built by Coca-Cola to keep track of its supplies of cola concentrate. Elements in the app like buttons can be dragged and dropped around the app as if the users were arranging a PowerPoint presentation. But creating the menus that let users run specific database queries (like, say, searching for all supplies that were delivered to a specific location at a specific time) requires basic coding in the form of Microsoft Power Fx formulas. "This is when it goes from no code to low code," says Lamanna. "You go from drag and drop, click click click, to writing formulas.