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) …
Artificial intelligence can "understand" and shape much of what happens in people's lives. AI apps like Amazon Alexa, Apple Siri, and Google Assistant answer questions and converse with people who call out their name. In navigation apps, they help people drive from one location to another. Models also can scan platforms for the use of fraudulent credit cards, and help diagnose cancer. Still, experts and advocates have voiced concerns about the long-term impact and implications of AI applications.
This article will dive you into the built-in string methods that are used in various text processing tasks in machine learning projects. String methods help to implement sequence operations with the help of these methods. Let's see all the string methods used in the string class of python. First, assign a string to a variable and that variable will be an instance or object of the string class. In this method, the string is return with a first letter capital.
As we know, Machine Learning is ubiquitous in our day to day lives. From product recommendations on Amazon, targeted advertising, and suggestions of what to watch, to funny Instagram filters. If something goes wrong with these, it probably won't ruin your life. Maybe you won't get that perfect selfie, or maybe companies will have to spend more on advertising. We need to be able to dissect our model, we will need to be able to understand and explain our model before it goes anywhere near a production system.
AI, machine learning, and natural language processing are beginning to play a much larger role in enterprise businesses, whether it is in customer service, customer relationship management, or even learning initiatives. In what ways is AI being incorporated into your DX strategy? Companies are investing in AI-based platforms in increasing numbers each year, and as a result, the AI worldwide software market revenue is expected to top $247 billion dollars, and the global AI market revenue is expected to be $327 billion this year, according to a report by Statista. This article will look at the ways AI is being used by enterprise businesses. One of the most obvious ways that AI is being used by brands is for "live" customer service interactions.
Xiaomi has released Mi Watch Revolve Active yesterday. The device comes with the addition of an oxygen sensor and Alexa support, but less premium materials make it 15% cheaper than the original. To remind, Mi Watch Revolve is essentially a rebranded version of Mi Watch Color that is meant for India and a few other countries. There are no significant differences between the two timepieces apart from the name. To make things even more confusing, Mi Watch Revolve Active seems to be a rebranded version of Mi Watch Color Sports Edition.
If you're reading this article, you probably know about Deep Learning Transformer models like BERT. They're revolutionizing the way we do Natural Language Processing (NLP). In case you don't know, we wrote about the history and impact of BERT and the Transformer architecture in a previous post. These models perform very well. And why does BERT perform so well in comparison to other Transformer models? Some might say that there's nothing special about BERT.
We all know that there are troves of data that exist online about us and our browsing, clicking, and spending habits. However, given all that information and the people that spend their lives on the internet, how do those who tailor the ads we see parse that information? As with many things these days, it's useful to have machines to help. RJ Talyor is the CEO and founder of Pattern89, an Indianapolis-based marketing firm using the power of artificial intelligence (AI) to help advertisers figure out what works and what doesn't when it comes to the ads we see everyday. I spoke with him about how AI is helping marketers figure out not only who to target, but what elements to include in those ads.
This post is to share with you the recent publication of the book: "Data Science for Economics and Finance: Methodologies and Applications", by Sergio Consoli, Diego Reforgiato Recupero, and Michaela Saisana. The use of data science and artificial intelligence for economics and finance is providing benefits for scientists, professionals and policy-makers by improving the available data analysis methodologies for economic forecasting and therefore making our societies better prepared for the challenges of tomorrow. This book is a good example of how combining expertise from the European Commission, universities in the U.S. and Europe, financial and economic institutions, and multilateral organizations, can bring forward a shared vision on the benefits of data science applied to economics and finance; from the research point of view to the evaluation of policies on the other hand. It showcases how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the economic and financial sectors. At the same time, the book is making an appeal for further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies.
You can see a complete working example in our Colab Notebook, and you can play with the trained models on HuggingFace. Since being first developed and released in the Attention Is All You Need paper Transformers have completely redefined the field of Natural Language Processing (NLP) setting the state-of-the-art on numerous tasks such as question answering, language generation, and named-entity recognition. Here we won't go into too much detail about what a Transformer is, but rather how to apply and train them to help achieve some task at hand. The main things to keep in mind conceptually about Transformers are that they are really good at dealing with sequential data (text, speech, etc.), they act as an encoder-decoder framework where data is mapped to some representational space by the encoder before then being mapped to the output by way of the decoder, and they scale incredibly well to parallel processing hardware (GPUs). Transformers in the field of Natural Language Processing have been trained on massive amounts of text data which allow them to understand both the syntax and semantics of a language very well.
Artificial Intelligence though having become a common term in today's time, not just to the technologically aware citizens of the world, but even among regular people has the potential to drive humanity forward in an exponential impact index that hasn't surfaced yet. The untapped potential of AI will take years and if not many more decades to come to fruition before its growth comes to a halt. In this article, we talk about Artificial Intelligence and its key elements and the services provided by Microsoft Azure to help innovators build AI Intelligent Systems. Artificial Intelligence (AI) is the branch of computer science with multiple inter-relations to various domains which refers to the creation of intelligence forms that imitate human capabilities and behavior. Artificial intelligence was first ever coined in 1955 and was envisioned for general artificial intelligence during the initial inception but later, progressed into domain-specific and task-based artificial intelligence.