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) …
Support for the development and promotion of Artificial Intelligence is born from the deepest roots of state power in China that defined, for the new qualitative leap of that society, Innovation as its axis and Science and Technology as its sustenance. Li Keqiang, the Prime Minister, summed it up by stating that "science and technology change the world, innovation forms the future". The AI is expressly included. Xi Jinping, the President of China, described it this way: "We need to build an innovative world economy to generate new drivers of growth. Innovation holds the key to fundamentally unleashing the growth potential.
I've been asked to recommend a good book on big data that's not Big Data (which I coauthored with Viktor Mayer-Schönberger, images below). It's a hard question for several reasons. First, though I'm usually brutally critical of my work, it's not a bad book. Yet it's also a difficult question because there are so many good books to chose from. The question becomes: which book is right for whom?
What if it's actually quite mundane? Machine learning jobs are supposed to be the big new thing in financial services. After all, Goldman Sachs has created an elite new AI team, J.P. Morgan's assigned ex-credit trader Samik Chandarana to develop machine learning strategies and has already unleashed LOXM, a new self-teaching trading algorithm, and UBS CEO Sergio Ermotti says 30% of banking jobs are due to dissolve because of this kind of automation in the next 10 years. It might seem therefore that you should be positioning yourself now to chase machine learning jobs (even though Bank of America CTO Catherine says you're already too late). But what if the finance jobs of the future are a lot less exciting than the finance jobs of the past?
There have been some remarkable developments lately in the world of artificial intelligence, from much publicized progress with self-driving cars to machines now composing Chopin imitations or just being really good at video games. Central to these advances are a number of tools around to help derive deep learning and other machine learning models, with Torch, Caffe, and Theano amongst those at the fore. However, since Google Brain went open source in November 2015 with their own framework, TensorFlow, we have seen the popularity of this software library skyrocket to be the most popular deep learning framework. Reasons include the wealth of support and documentation available, its production readiness, the ease of distributing calculations across a range of devices, and an excellent visualization tool: TensorBoard. Ultimately, TensorFlow manages to combine a comprehensive and flexible set of technical features with great ease of use.
In an era of great uncertainty and disruption for automotive manufacturers, Mercedes and its parent company Daimler are jumping in full throttle as leaders of the 4th Industrial Revolution. Not only are they designing new vehicles, but their services, influence in the transportation industry and factories are transforming to embrace the new opportunities and demands of their customers. Other companies should follow their lead to thrive in the new industrial revolution. What is the 4th Industrial Revolution? Often referred to as industry 4.0, the 4th Industrial Revolution is the shift to smart factories that use a combination of cyber-physical systems, the Internet of Things and the Internet of Systems to connect the entire production chain and make decisions on its own.
People like me working in the field of Natural Language Processing almost always come across the task of replacing words in a text. The reasons behind replacing the words may be different. Now, if the number of words to replace and the corpus of text is not huge i.e within thousands, then Regular Expressions have always been my solution. But as I started working on bigger and bigger datasets with tens of thousands of documents and sometimes millions, I noticed that performing the above tasks started taking days. In today's fast-moving world, this is not the amount of time one would want to invest in a very simple but important task.
A mere mention of Artificial Intelligence would have our heads spinning, thinking about the latest sci-fi movie, robots conspiring to overtake human existence, space travels and whatnot. However, outside the realms of science fiction, the technology has been majorly helping organizations sift through heavy data sets to discover anomalies and best practices. By taking into account enormous data points, organizations can not only envision the future with a certain degree of certainty but also be better prepared for the same. Supply chain is perhaps the most data-rich environment in the businesses of today. The chain supports an open flow of data from a diverse set of sources.
Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another. In this new laser-focused Ebook written in the friendly Machine Learning Mastery style that you're used to, finally cut through the math, research papers and patchwork descriptions about natural language processing. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects. Click to jump straight to the packages. We are awash with text, from books, papers, blogs, tweets, news, and increasingly text from spoken utterances.
China remains the manufacturing powerhouse of the world, but many of its leading players are facing challenges such as overbuilt capacity and weak demand. Revenue growth has slowed, and profitability has stagnated and in some cases declined. While China enjoys some advantages such as mature manufacturing bases, fiscal support, a large base of tech-savvy consumers and more platform players, it also has hurdles such as increasing labour and material costs. Plus, the piecemeal deployment and implementation of investments in digital technologies hinder the ability of Chinese businesses to innovate with connected and intelligent products. Recognising the challenges, in 2015 China launched "Made in China 2025" as part of a road map for the country's latest industrial modernisation.
While advancements in big data analytics have done a good job at helping marketers target mass markets and people of like interests, they fall short of understanding a person's unique interests and going that extra mile of treating people like individuals. While there is no argument that people have overlapping interests, there is a false assumption that just because a person falls into a certain category (i.e. A person will express hundreds of different interests that extend beyond any given category; this is what makes a person unique. Big data approaches don't treat people like individuals. Instead, they tend to bucket people into broad categories.