What Is Natural Language Processing?

What Is Natural Language Processing?
#AI #MachineLearning #ML #NLP #tech

  • The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers.
  • Computational linguistics also became known by the name of natural language process, or NLP, to reflect the more engineer-based or empirical approach of the statistical methods.
  • The statistical dominance of the field also often leads to NLP being described as Statistical Natural Language Processing, perhaps to distance it from the classical computational linguistics methods.
  • As machine learning practitioners interested in working with text data, we are concerned with the tools and methods from the field of Natural Language Processing.
  • In their text on applied natural language processing, the authors and contributors to the popular NLTK Python library for NLP describe the field broadly as using computers to work with natural language data.

Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software.
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AI Superstar Andrew Ng Is Democratizing Deep Learning With A New Online Course

#AI hero @AndrewYNg is #democratizing #deeplearning w/ new online course
in @FastCompany

  • That’s the vision of Andrew Ng, a founder of the Google Brain deep learning project, and former head of AI at Baidu–a position he left in March–who is today announcing a set of five interconnected online courses on the subject.
  • “Today, if you want to learn deep learning, there are lots of people searching online, reading [dozens of] research papers, reading blog posts, and watching YouTube videos,” Ng tells Fast Company.
  • As Ng sees it, getting to an AI-powered economy is going to take the work of much more than any one, or even several companies.
  • “I hope we can build an AI-powered future that provides everyone affordable healthcare, accessible education, inexpensive and convenient transportation, and a chance for meaningful work for every man and woman,” Ng says in his announcement, which is the first from his newly created company, deeplearning.ai.
  • Ng is aware that many people are still confused by AI, often getting bogged down in the different subspecialties, and lingo that can easily be misused.

The founder of Google Brain and former head of Baidu’s AI efforts wants to train a giant new workforce to help make “AI the new electricity.”

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Learn how AI is changing the application development landscape

Learn how #AI is changing the #application #development landscape. Get the report:

  • Is AI on your software development roadmap?
  • With technologies like advanced machine learning, deep learning, natural language processing, and business rules, AI is poised to disrupt both how developers build applications and the nature of those applications.
  • The risks—unrealistic expectations, integration with traditional applications, and more—can’t be ignored as your organization strives for the rewards of an accelerated development cycle and a new generation of self-learning applications.
  • Uncover this shifting digital landscape and how your business can take advantage of it in the Forrester Research report, “How AI Will Change Software Development And Applications.”
  • Fill out the form at right to read the free report.

Learn how AI is changing the application development landscape
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Microsoft Takes On Google and Deep Mind with AI Research Lab

Microsoft Takes On Google and Deep Mind with 
#AI Research Lab

  • Microsoft has announced the creation of its new research lab, Microsoft Research AI, which will focus on general-purpose artificial intelligence (AI) technology development and provide ethics oversight for its AI operations.
  • Microsoft has created a new research lab which will focus on general-purpose artificial intelligence (AI) technology development.
  • The lab will be called Microsoft Research AI, and will capitalize on the company’s existing AI expertise while pursuing new hires from related fields like cognitive psychology.
  • Located within Microsoft’s Redmond HQ, the lab will ultimately be home to a team of more than 100 AI scientists exploring learning, natural language processing, perception systems, and other areas.
  • This broader focus is in contrast with the more common and specific AI systems we see now, like those that perform facial recognition tasks.

Microsoft has announced the creation of its new research lab, Microsoft Research AI, which will focus on general-purpose artificial intelligence (AI) technology development and provide ethics oversight for its AI operations.
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Is China in the driver’s seat when it comes to AI?

Is #China in the driver’s seat when it comes to #AI?

  • In the final months of the Obama administration, the U.S. government published two separate reports noting that the U.S. is no longer the undisputed world leader in AI innovation and expressing concern about China’s emergence as a major player in the field.
  • The reports recommended increased expenditure on machine learning research and enhanced collaboration between the U.S. government and tech industry leaders to unlock the potential of AI.
  • But despite these efforts, 91 percent of the 1,268 tech founders, CEOs, investors, and developers surveyed at the international Collision tech conference in New Orleans in May 2017 believed that the U.S. government is “fatally under-prepared” for the impact of AI on the U.S. ecosystem.
  • Research firm CB Insights found that Chinese participation in funding rounds for American startups came close to $10 billion in 2016, while recent figures indicate that Chinese companies have invested in 51 U.S. AI companies, to the tune of $700 million.
  • But of further surprise was the 50 percent of all respondents who believed the U.S. would lose its dominant position in the tech world to China within just five years.

In the battle of technological innovation between East and West, artificial intelligence (AI) is on the front line. And China’s influence is growing.
Continue reading “Is China in the driver’s seat when it comes to AI?”

Learn how AI is changing the application development landscape

Learn how #AI is changing the #application #development landscape. Get the report:

  • Is AI on your software development roadmap?
  • With technologies like advanced machine learning, deep learning, natural language processing, and business rules, AI is poised to disrupt both how developers build applications and the nature of those applications.
  • The risks—unrealistic expectations, integration with traditional applications, and more—can’t be ignored as your organization strives for the rewards of an accelerated development cycle and a new generation of self-learning applications.
  • Uncover this shifting digital landscape and how your business can take advantage of it in the Forrester Research report, “How AI Will Change Software Development And Applications.”
  • Fill out the form at right to read the free report.

Learn how AI is changing the application development landscape
Continue reading “Learn how AI is changing the application development landscape”

AI could save government $41 billion, report says

#AI could save government $41 billion, report says 

 #fintech #insurtech

  • The 28-page report, titled AI-augmented Government, examines several case studies, provides a taxonomy of AI systems, and concludes that in the federal government alone, automation with “high investment” could free up as many as 1.2 billion hours of work and save up to $41.1 billion annually.
  • Through the use of rules-based systems, machine translation, computer vision, machine learning, robotics and natural language processing, the report notes the unusual but “tantalizing” paradigm presented by AI in which speed is increased, quality is improved, and cost is reduced — all in parallel.
  • William Eggers, a co-author of the report and executive director of the Deloitte Center for Government Insights, told StateScoop that automation technologies are now improving in performance at an exponential rate.
  • Report authors note that automation can be broken into four types:

    The lesson to learn from these different types of automation, Eggers said, is that adoption of automation is a two-stage process: First government should automate every task it can, and then it should search for ways to augment human tasks.

  • Government agencies looking to use automation for the first time will find an easy entrée in robotic process automation, Eggers said.

Technologies like computer vision, machine learning and natural language processing will transform government at all levels sooner than people think, researchers report.
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AI Is So Hot, Even Freelancers Are in Demand — ADTmag

#AI Is So Hot, Even Freelancers Are in Demand -- ADTmag

  • One might not normally equate AI development — which can involve complicated, cutting-edge technologies such as cognitive computing, machine learning, deep learning and so on in long-term initiatives — as something suitable for freelance projects, but news from freelancing firm Upwork indicates that’s the case.
  • Upwork, which matches employers with freelancers, published data from its Web site that showed AI was the second-fastest-growing in-demand skill in the first quarter of the year.
  • But, among development-related skills, AI is No. 1, followed by skills such as natural language processing, C++ development, Swift development and others, with the AI-related machine learning skill also making the top 20 list.
  • “With artificial intelligence (AI) at the forefront of the conversation around what the future of work holds, it’s no surprise it is the fastest-growing tech skill and the second fastest-growing skill overall,” Upwork said in a statement last week.
  • Perhaps surprisingly, that pervasiveness didn’t show up in Upwork’s previous listing of in-demand skills for the third quarter of 2016, in which AI didn’t even place in the top 20 (though machine learning was No. 1):

Artificial intelligence is such a hot topic in software development that companies are even seeking freelancers to help out, making it the No 1. sought-after tech skill in Upwork’s latest quarterly report.
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Which deep learning network is best for you?

#DeepLearning: Which deep learning network is best for you? #BigData

  • Caffe is a popular deep learning network for vision recognition.
  • Caffe 2 continues the strong support for vision type problems but adds in recurrent neural networks (RNN) and long short term memory (LSTM) networks for natural language processing, handwriting recognition, and time series forecasting.
  • MXNet supports deep learning architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) including Long Short-Term Memory (LTSM) networks.
  • However, with Facebook’s most recent announcement, it is changing course and making Caffe 2 its primary deep learning framework so it can deploy deep learning on mobile devices.
  • DL4J has a rich set of deep network architecture support: RBM, DBN, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), RNTN, and Long Short-Term Memory (LTSM) network.

Open source deep learning neural networks are coming of age. There are several frameworks that are providing advanced machine learning and artificial intelligence (A.I.) capabilities over proprietary solutions. How do you determine which open source framework is best for you?
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Practical Deep Learning For Coders—18 hours of lessons for free

Practical #DeepLearning For Coders—18 hours of lessons for free

  • After this course, I cannot ignore the new developments in deep learning—I will devote one third of my machine learning course to the subject.
  • I’m a CEO, not a coder, so the idea that I’d be able to create a GPU deep learning server in the cloud meant learning a lot of new things—but with all the help on the wiki and from the instructors and community on the forum I did it!
  • Sometimes I feared whether I would be able to solve any deep learning problems, as all the research papers I read were very mathy beyond reach of simple intuitive terms.
  • But Jeremy and Rachel (Course Professors) believe in the theory of ‘Simple is Powerful’, by virtue of which anyone who takes this course will be able to confidently understand the simple techniques behind the ‘magic’ Deep Learning.
  • The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.

fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!

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