From machine learning basics articles to interesting ML news and academic papers, this list has something for both beginners and those more well-versed in the field. The articles below should help strengthen your knowledge of basic AI concepts and keep you up to date on trending AI topics.
AI and Machine Learning Basics Articles
AI Terms Every Beginner Should Know - This article on Towards Data Science introduces and explains 14 abbreviations commonly used in the field of machine learning. From broad AI concepts to specific types of neural networks and specialized fields, this article contains concise explanations, helpful visual aids and links to more detailed explanations of each term.
The Difference Between AI, ML, and DL - Artificial intelligence and machine learning are terms often used interchangeably. However, there are some differences between them. This blog post contains a clear and thorough explanation of AI, ML, and DL (deep learning) and how to differentiate between them.
Annotation Services for Machine Learning - To create a high-performing ML model, you need to train it on high-quality data. Moreover, this data often needs to be annotated in order for the model to understand and learn from it. As a result, the rise of the machine learning industry has led to the birth of data annotation companies. This article explains the value data annotation companies provide, types of annotation services, and how to decrease costs when outsourcing such services.
What is Image Annotation? - Computer vision is one of the largest areas of research within machine learning. Tasked with creating machines that can see and interpret the world visually, computer vision models are trained on annotated image data. Therefore, the growth of computer vision technology has led to rampant demand for image annotation services. This article explains what image annotation is and five common image annotation methods.
What is Facial Recognition? - Within computer vision, face recognition is a field that is garnering a lot of attention and investment from numerous technology companies. With applications in security, surveillance, augmented reality, and smartphone apps, facial recognition has numerous profitable use cases. Furthermore, it has the potential to improve numerous workflows in businesses around the world. Using helpful diagrams and examples, this article provides a clear explanation of what facial recognition is and how it works.
Machine Learning News Articles
U.S. House Intelligence Committee Holds Hearing on Deepfakes and AI - Recently, deepfakes have been one of the hottest topics in AI. The unprecedented development of deepfake and synthetic audio technology led to a U.S. House of Representatives in June of 2019. The hearing was used to discuss the threats posed by deepfakes and other synthetic media technology.
Microsoft Launches AirSim on Unity - The market for autonomous vehicles continues to grow year over year, spearheaded by companies like Tesla, Uber, and Google. To help streamline autonomous vehicle training, Microsoft launched a virtual training environment called AirSim. This article explains what AirSim is, the benefits of using it, and how to access it.
Waymo Says ‘Completely Driverless’ Vehicles are Imminent - Owned by Alphabet Inc., Google’s parent company, Waymo seems to be ramping up their efforts to place autonomous vehicles on roads in Arizona. An email from Waymo to members of their “early rider” program was leaked on Reddit, describing what to expect in the coming months. This article includes the email in full with confirmation from a Waymo spokesperson.
Replika AI Launches Chatbot that Mimics Your Personality - Chatbots are one of the most common use cases of machine learning technology. Often used to automate customer service, almost everyone has had some experience talking to a chatbot. However, Replika is not a customer service chatbot. Rather, it can be a friend, mentor, or even a reflection of yourself. This article explains the birth of Replika and how it uses text conversations as training data to evolve over time and replicate your personality.
Google Creates a Search Engine for Datasets - Due to the growing demand for large open datasets, many students and data scientists have turned to sites like Github and Kaggle to find training data. To help make open data accessible around the world, Google launched a new search engine specifically for datasets. This article introduces Google Dataset Search and explains how to use it.
Bonus: AI Scholarly Articles and Papers
For those looking for more advanced machine learning articles, the following items are scholarly works in specialized fields of AI.
Don’t Classify, Translate - Automated product categorization is an important area of ML research, for some of the world’s largest ecommerce companies. On sites like Amazon and Rakuten, thousands of merchants add millions of products to the online catalogs every year. Automated product classification helps to minimize mistakes in product listings and provide a good user experience on such websites. In this paper, researchers propose a new product classification method based on machine translation.
Multilingual Twitter Sentiment Classification: The Role of Human Annotators - This paper explains an interesting study on the sentiment analysis of Twitter posts. Furthermore, the results of the paper show how the quality of human annotators can strongly affect the accuracy of the model.
Probabilistic Face Embeddings - With facial recognition systems, deterministic face embeddings fail to properly address noise in the input images. In this study, data scientists at Michigan State University propose probabilistic face embeddings, a method for creating face embeddings that takes noise and ambiguity into account.
Machine learning is growing at an exponential rate, with more and more companies introducing AI technology into their infrastructure. Hopefully, the articles above helped add to your foundation of AI knowledge.
Please follow me on Hacker Noon to keep up with all the latest machine learning news and guides.