Text to Speech | IBM Watson Developer Cloud Watson Text to Speech provides a REST API to synthesize speech audio from an input of plain text. Multiple voices, both male and female, are available across Brazilian Portuguese, English, French, German, Italian, Japanese, and Spanish. Once synthesized in real-time, the audio is streamed back to the client with minimal delay. The Text to Speech service now enables developers to control the pronunciation of specific words. Intended Use Anywhere there's a need to communicate using the spoken word, particularly assistance tools for the vision-impaired, reading-based education tools, or mobile applications. You Input: Brazilian Portuguese plain text English plain text French plain text German plain text Italian plain text Japanese plain text Spanish plain text Service output:
Real English ESL Videos & Lessons. Real English is a Registered Trademark of The Marzio School. G2 | Sensemaking – One Year Birthday Today. Cognitive Basics Emerging. Following a quiet two-plus year gestation period, G2 came to life January 28th, 2011 – one year ago today – when I formally announced its existence here: Sensemaking on Streams – My G2 Skunk Works Project: Privacy by Design (PbD). “This new technology, something that might be characterized as a “big data analytic sensemaking” engine, is designed to make sense of new observations as they happen, fast enough to do something about it, while the transaction is still happening.” Oh, the difference a year makes. Over the last twelve months G2 has evolved in many ways. Three of the more exciting characteristics to emerge are: Orders of Magnitude, appreciation of G2 now has the ability to consider proportions when determining the best way to count things … evolving counting methods in real-time, without external influence (humans). Geospatial and Temporal Reasoning, use of G2 is showing early promise in the area of geospatial and temporal reasoning. Confusion, awareness of Happy Birthday G2!
The Jeonju Hub Making Sense of What You Know Over the last few years, Jeff Jonas, chief scientist of the IBM Entity Analytics group and an IBM Fellow, led a development project at IBM code-named “G2”—technology to advance Sensemaking analytics. The system can make assertions based on data from real-time, real-world events. According to Jonas, “Sensemaking is designed to find the obvious.” InfoSphere* Sensemaking locates related observations that when viewed together point to something of interest—the evidence at hand making it obvious. Sensemaking over any real-world entity Contextualizing across diverse observations types Self-correcting false positives and false negatives Performing real-time tasks “This technology is designed to support diverse and ever-changing missions. The InfoSphere Sensemaking technology aims to help organizations manage information and provide better predictions in real time. < Return to main article
Starfall's Learn to Read with phonics IBM’s Jeff Jonas on Baking Data Privacy into Predictive Analytics Jeff Jonas of IBM Privacy by Design, an outlook toward software development developed in the 1990s, urges companies to bake privacy protection features into its analytic systems and processes from their conception. While many executives have supported the notion of anonymizing personal data when using it to gain insights into consumer behavior, few have come to personify the evolution of the practice as much as Jeff Jonas, an IBM Fellow and Chief Scientist of the IBM Entity Analytics Group. Jonas, who is founder of Systems Research & Development (SRD), which IBM acquired in 2005, is best known for his innovative “sense-making” technology, which allows organizations to gather and analyze data from a variety of sources in real time. A version of that technology, known as G2, is embedded into the latest version of IBM’s SPSS Modeler software. Data Informed: How did you get involved in the Privacy by Design movement? Related Stories Consumer data privacy and the importance of a social contract.
ESL Teacher Resources, Job Boards, and Worksheets Forget Humans vs. Machines: It’s a Humans + Machines Future Forget humans versus machines: humans plus machines is what will drive society forward. This was the central message conveyed by Dr. John Kelly, senior vice president of IBM Research, at the Augmenting Human Intelligence Cognitive Colloquium, which took place yesterday in San Francisco. Organized by IBM, the colloquium brought together machine learning leaders in industry and academia to discuss how artificial intelligence can augment human intelligence — by helping us make sense of the quintillion bytes of data generated each day. Dr. It’s not about machines gaining intelligence or taking over the world, said Kelly. “I think the key question is: What’s the price of not knowing?” Around 80% of data is unstructured, meaning that current computing systems can’t make sense of it. Yet analyzing unstructured data is far from a theoretical problem. IBM hopes to make sense of medical images with cognitive computing. Take medicine, for example. The problem lies in both hardware and software.
Workshop Resources Artificial Neural Networks for Beginners » Loren on the Art of MATLAB Deep Learning is a very hot topic these days especially in computer vision applications and you probably see it in the news and get curious. Now the question is, how do you get started with it? Today's guest blogger, Toshi Takeuchi, gives us a quick tutorial on artificial neural networks as a starting point for your study of deep learning. Contents MNIST Dataset Many of us tend to learn better with a concrete example. train.csv - training datatest.csv - test data for submission Load the training and test data into MATLAB, which I assume was downloaded into the current folder. tr = csvread('train.csv', 1, 0); sub = csvread('test.csv', 1, 0); The first column is the label that shows the correct digit for each sample in the dataset, and each row is a sample. figure colormap(gray) for i = 1:25 subplot(5,5,i) digit = reshape(tr(i, 2:end), [28,28])'; imagesc(digit) title(num2str(tr(i, 1))) end Data Preparation You will be using the nprtool pattern recognition app from Neural Network Toolbox.
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