If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Online conversations are ubiquitous in modern life, spanning industries from video games to telecommunications. This has led to an exponential growth in the amount of online conversation data, which has helped in the development of state-of-the-art natural language processing (NLP) systems like chatbots and natural language generation (NLG) models. Over time, various NLP techniques for text analysis have also evolved. This necessitates the requirement for a fully managed service that can be integrated into applications using API calls without the need for extensive machine learning (ML) expertise. AWS offers pre-trained AWS AI services like Amazon Comprehend, which can effectively handle NLP use cases involving classification, text summarization, entity recognition, and more to gather insights from text.
It was on a Friday evening, and all of a sudden, I was receiving many notifications from Twitter on my phone, within a very short time interval. It was unusual, to see many people starting to follow me within a couple of seconds or minutes. So it is when I checked that I saw the tweet from AWS which mentioned I had been nominated as a Machine Learning Hero. And then I saw myself as part of the list of the new AWS Heroes for that batch-The first Machine Learning Hero in Sub-Saharan Africa, and currently 01 out of about 34 in the world. What is common amongst Heroes of all categories is that everyone's story is different and no one can clearly tell how they actually ended up becoming a Hero.
Amazon SageMaker helps data scientists and inventors to prepare, make, train, and deploy high- quality machine learning models by bringing together a broad set of capabilities purpose- erected for machine learning. Amazon SageMaker make available a set of solutions for the most common use cases that may be deployed readily with just a few clicks to make it easier to grow started. Amazon SageMaker is a completely accomplished machine learning service. Data scientists and developers may speedily and easily build and train machine learning models with SageMaker. They can straight deploy them into a production-ready hosted environment.
This post is co-written with Sowmya Manusani, Sr. Staff Machine Learning Engineer at Zendesk Zendesk is a SaaS company that builds support, sales, and customer engagement software for everyone, with simplicity as the foundation. It thrives on making over 170,000 companies worldwide serve their hundreds of millions of customers efficiently. The Machine Learning team at Zendcaesk is responsible for enhancing Customer Experience teams to achieve their best. By combining the power of data and people, Zendesk delivers intelligent products that make their customers more productive by automating manual work. Zendesk has been building ML products since 2015, including Answer Bot, Satisfaction Prediction, Content Cues, Suggested Macros, and many more.
This article was published as a part of the Data Science Blogathon. Amazon Sagemaker is arguably the most powerful, feature-rich, and fully managed machine learning service developed by Amazon. From creating your own labeled datasets to deploying and monitoring the models on production, Sagemaker is equipped to do everything. It can also provide an integrated Jupyter notebook instance for easy access to your data for exploration and analysis, so you don't have to fiddle around with server configuration. Sagemaker supports bring-your-own-algorithms and frameworks, which offer flexible distributed training options that adjust to your specific workflows.
Automated Machine Learning (AutoML) is a concept that provides the means to utilise existing data and create models for non-Machine Learning experts. In addition to that, AutoML provides Machine Learning (ML) professionals ways to develop and use effective models without spending time on tasks such as data cleaning and preprocessing, feature engineering, model selection, hyperparameter tuning, etc. Before we move any further, it is important to note that AutoML is not some system that has been developed by a single entity. Several organisations have developed their own AutoML packages. These packages cover a broad area, and targets people at different skill levels.
The R programming language is one of the most commonly used languages in the scientific space, being one of the most commonly used languages for machine learning (probably second following python) and arguably the most popular language amongst mathematicians and statisticians. It is easy to get started with, free to use, with support for many scientific and visualisation libraries. While R can help you analyse your data, the more data you have the more compute power you require and the more impactful your analysis is, the more repeatability and reproducibility is required. Analysts and Data Scientists need to find ways to fulfil such requirements. In this post we briefly describe the main ways of running your R workloads on the cloud, making use of Amazon SageMaker, the end-to-end Machine Learning cloud offering of AWS.
Identifying paraphrased text has business value in many use cases. For example, by identifying sentence paraphrases, a text summarization system could remove redundant information. Another application is to identify plagiarized documents. In this post, we fine-tune a Hugging Face transformer on Amazon SageMaker to identify paraphrased sentence pairs in a few steps. A truly robust model can identify paraphrased text when the language used may be completely different, and also identify differences when the language used has high lexical overlap.