Originally published on Towards AI. An artificial neural network (ANN) is usually implemented with frameworks such as TensorFlow, Keras or PyTorch. Such frameworks are suitable for very complex ANNs. As a data scientist, however, it is essential to understand the basics. This article aims to help you understand how a neural network works.

"AI will be the best or worst thing to happen to humanity." How does this technology impact artists? Can #AiArt growing communities contribute, specifically to the living-now among us (and quite possibly struggling) artists whose works are fed to the algorithms without their consent? The endeavours that are complex, beautiful, dangerous, and challenging are more valuable in contrast to something common, simple and easy, naturally. To have that distinction removed or obfuscated in any way will undermine the core value structure of society as we know it. Again Hyper-Novelty comes to mind.

Speech Recognition Models are designed to recognize and transcribe spoken language into text. This technology is widely used in virtual assistants such as Amazon's Alexa, Google Assistant, and Apple's Siri. These models are trained on large datasets of audio recordings and use machine learning algorithms such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to make predictions. Google's Speech-to-Text, Amazon's Transcribe, and Microsoft's Azure Speech Services are some of the popular examples of Speech Recognition Models. Sound Event Detection Models, on the other hand, are designed to recognize specific sounds or events within an audio clip.

In Early February 2022, Sony's "first AI breakthrough", GT Sophy, made its appearance on the cover page of Nature magazine [2]. GT Sophy is a racing AI built to match with world-class level players in Gran Turismo Sport, the latest installation of the legendary game series on PlayStation 4. GT7 is famous for its extremely realistic simulation of real-life racing experience, which largely complicates the production of GT Sophy at the early stage. Every tiny decision that GT Sophy makes may change the result of the race entirely. Thus, there is little simplification can be done to the training process. Sony's AI team needs to take all possible factors, like drifting effects caused by the passage of nearby cars, to perform any estimation.

Remember Bing, Microsoft's (barely used) search engine? It looks like it may be getting a much-needed makeover. The Verge reports(Opens in a new window) that earlier this week, a "new Bing" interface using AI chatbot ChatGPT appeared and then swiftly vanished. It was previously reported(Opens in a new window) that Microsoft was interested in capitalizing on the tool's massive popularity and impressive intelligence, and it's possible that what users saw is an early version of that experience that went live by mistake. One of those users, Owen Yin, posted about his brief experience(Opens in a new window) with the "new Bing" on Medium.

Machine learning system design interviews are the most difficult to tackle of all technical interview questions. This book provides a reliable strategy and knowledge base for approaching a broad range of ML system design questions. It provides a step-by-step framework for tackling an ML system design question. It includes many real-world examples to illustrate the systematic approach, with detailed steps you can follow. This book is an essential resource for anyone interested in ML system design, whether they are beginners or experienced engineers.

Google is set to unveil how it plans to rival the wildly successful ChatGPT AI, possibly within days. The technology giant has scheduled a 40-minute event for Wednesday 8 February, when it will reveal how it is "reimagining how people search for, explore and interact with information". It's not clear whether the event will be AI-focused, but it comes days comes after Google's chief Sundar Pichai announced that the firm will make its chatbot technology available publicly in the coming weeks. Speaking on a call with investors in parent company Alphabet on Thursday, Mr Pichai said people will be able to "engage directly" with Google's conversational AI - starting with one called LaMDA, which has been in testing. Google has reportedly been fast-tracking its plans for so-called large language models since ChatGPT's launch. ChatGPT itself is one such model - an AI chatbot trained on a huge amount of text data, which

Marketing is an important aspect that should not be overlooked when it comes to getting more visibility for your business. Blog is the best way to create a recognizable brand in today's competitive landscape. Therefore, leveraging content marketing strategies can be a great way to establish your business and give you an edge over competitors. Publishing blog posts is a great way to increase the topical authority of your website and have Google better understand your business. It will result in a higher ranking on search engine results pages (SERPs), making it easier for potential customers to discover and convert to your products or services.

Unsupervised machine learning is a type of machine learning where the model is trained on a dataset without any labeled output. The goal of unsupervised learning is to uncover hidden patterns or relationships in the data. Unsupervised learning is useful when labeled data is not available or when the goal is to discover new relationships in the data. However, it can be more challenging to evaluate the results of unsupervised learning compared to supervised learning, as there is no clear metric to assess the performance of the model. In conclusion, unsupervised learning is a powerful tool for understanding and extracting information from complex and unlabeled data.

Kubernetes: This open-source system allows you to automate the deployment, scaling, and management of containerized applications. It can be particularly useful for managing machine learning workflows, as it allows you to easily scale up or down as needed. Docker: It is a tool designed to make it easier to create, deploy, and run applications by using containers. Containers allow you to package an application with all of the parts it needs, such as libraries and other dependencies, and ship it all out as one package. This makes it easier to run the application on any other machine because everything it needs is contained in the package.