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AWS launches 5 ML services, deep learning cam

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At the AWS Re:Invent conference in Las Vegas last week, Amazon Web Services announced five new machine learning services and a deep learning-enabled wireless video camera for developers. 

At the AWS Re:Invent conference in Las Vegas last week, Amazon Web Services announced five new machine learning services and a deep learning-enabled wireless video camera for developers.

Amazon SageMaker is a fully managed service for developers and data scientists to quickly build, train, deploy, and manage their own machine learning models. AWS also introduced AWS DeepLens, a deep learning-enabled wireless video camera that can run real-time computer vision models to give developers hands-on experience with machine learning. And, AWS announced four new application services that allow developers to build applications that emulate human-like cognition: Amazon Transcribe for converting speech to text; Amazon Translate for translating text between languages; Amazon Comprehend for understanding natural language; and, Amazon Rekognition Video, a new computer vision service for analyzing videos in batches and in real-time. To learn more about AWS’s machine learning services, visit: https://aws.amazon.com/machine-learning.com.

Amazon SageMaker and AWS DeepLens make machine learning accessible to all developers

Today, implementing machine learning is complex, involves a great deal of trial and error, and requires specialized skills. Developers and data scientists must first visualize, transform, and pre-process data to get it into a format that an algorithm can use to train a model. Even simple models can require massive amounts of compute power and a great deal of time to train, and companies may need to hire dedicated teams to manage training environments that span multiple GPU-enabled servers. All of the phases of training a model—from choosing and optimizing an algorithm, to tuning the millions of parameters that impact the model’s accuracy—involve a great deal of manual effort and guesswork. Then, deploying a trained model within an application requires a different set of specialized skills in application design and distributed systems. As data sets and variables grow, customers have to repeat this process again and again as models become outdated and need to be continuously retrained to learn and evolve from new information. All of this takes a lot of specialized expertise, access to massive amounts of compute power and storage, and a great deal of time. To date, machine learning has been out of reach for most developers.

Amazon SageMaker is a fully managed service that removes the heavy lifting and guesswork from each step of the machine learning process. Amazon SageMaker makes model building and training easier by providing pre-built development notebooks, popular machine learning algorithms optimized for petabyte-scale datasets, and automatic model tuning. Amazon SageMaker also dramatically simplifies and accelerates the training process, automatically provisioning and managing the infrastructure to both train models and run inference to make predictions using these models. AWS DeepLens was designed from the ground-up to help developers get hands-on experience in building, training, and deploying models by pairing a physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services to support learning and experimentation.

“Our original vision for AWS was to enable any individual in his or her dorm room or garage to have access to the same technology, tools, scale, and cost structure as the largest companies in the world. Our vision for machine learning is no different,” said Swami Sivasubramanian, VP of Machine Learning, AWS. “We want all developers to be able to use machine learning much more expansively and successfully, irrespective of their machine learning skill level. Amazon SageMaker removes a lot of the muck and complexity involved in machine learning to allow developers to easily get started and become competent in building, training, and deploying models.”

With Amazon SageMaker developers can:

  • Easily build machine learning models with performance-optimized algorithms: Amazon SageMaker is a fully managed machine learning notebook environment makes it easy for developers to explore and visualize data they have stored in Amazon Simple Storage Service (Amazon S3), and transform it using all of the popular libraries, frameworks, and interfaces. Amazon SageMaker includes ten of the most common deep learning algorithms (e.g. k-means clustering, factorization machines, linear regression, and principal component analysis), which AWS has optimized to run up to ten times faster than standard implementations. Developers simply choose an algorithm and specify their data source, and Amazon SageMaker installs and configures the underlying drivers and frameworks. Amazon SageMaker includes native integration with TensorFlow and Apache MXNet with additional framework support coming soon. Developers can also specify any framework and algorithm they choose by uploading them into a container on the Amazon EC2 Container Registry.
  • Fast, fully managed training: Amazon SageMaker makes training easy. Developers simply select the type and quantity of Amazon EC2 instances and specify the location of their data. Amazon SageMaker sets up the distributed compute cluster, performs the training, outputs the result to Amazon S3, and tears down the cluster when complete. Amazon SageMaker can automatically tune models with hyper-parameter optimization, adjusting thousands of different combinations of algorithm parameters to arrive at the most accurate predictions.
  • Deploy models into production with one click: Amazon SageMaker takes care of launching instances, deploying the model, and setting up a secure HTTPS end-point for the application to achieve high throughput and low latency predictions, as well as auto-scaling Amazon EC2 instances across multiple availability zones (AZs). It also provides native support for A/B testing. Once in production, Amazon SageMaker eliminates the heavy lifting involved in managing machine learning infrastructure, performing health checks, applying security patches, and conducting other routine maintenance.

With AWS DeepLens, developers can:

  • Get hands-on machine learning experience: AWS DeepLens is the first of its kind: a deep-learning enabled, fully programmable video camera, designed to put deep learning into the hands of any developer, literally. AWS DeepLens includes a HD video camera with on-board compute capable of running sophisticated deep learning computer vision models in real-time. The custom-designed hardware, capable of running over 100 billion deep learning operations per second, comes with sample projects, example code, and pre-trained models so even developers with no machine learning experience can run their first deep learning model in less than ten minutes. Developers can extend these tutorials to create their own custom, deep learning-powered projects with AWS Lambda functions. For example, AWS DeepLens could be programmed to recognize the numbers on a license plate and trigger a home automation system to open a garage door, or AWS DeepLens could recognize when the dog is on the couch and send a text to its owner.
  • Train models in the cloud and deploy them to AWS DeepLens: AWS DeepLens integrates with Amazon SageMaker so that developers can train their models in the cloud with Amazon SageMaker and then deploy them to AWS DeepLens with just a few clicks in the AWS Management Console. The camera runs the models, in-real time, on the device.

“We’ve deepened our relationship with AWS, adding them as an Official Technology Provider of the NFL and are excited to use Amazon SageMaker for our next-generation stats initiative,” said Michelle McKenna-Doyle, SVP and CIO, National Football League. “With Amazon SageMaker in our toolkit, our developers can stop worrying about the undifferentiated heavy lifting of machine learning, and start adding new visualizations, stats, and experiences that our fans will adore.”

As the world’s leading provider of high-resolution Earth imagery, data and analysis, DigitalGlobe works with enormous amounts of data every day. “DigitalGlobe is making it easier for people to find, access, and run compute against our 100PB image library which is stored in the AWS cloud in order to apply deep learning to satellite imagery,” said Dr. Walter Scott, Chief Technology Officer of Maxar Technologies and founder of DigitalGlobe. “We plan to use Amazon SageMaker to train models against petabytes of earth observation imagery datasets using hosted Jupyter notebooks, so DigitalGlobe’s Geospatial Big Data Platform (GBDX) users can just push a button, create a model, and deploy it all within one scalable distributed environment at scale.”

Hotels.com is a leading global lodging brand operating 90 localized websites in 41 languages, “At Hotels.com, we are always interested in ways to move faster, to leverage the latest technologies and stay innovative,” says Matt Fryer, VP and Chief Data Science Officer of Hotels.com and Expedia Affiliate Network. “With Amazon SageMaker, the distributed training, optimized algorithms, and built-in hyperparameter features should allow my team to quickly build more accurate models on our largest data sets, reducing the considerable time it takes us to move a model to production. It is simply an API call. Amazon SageMaker will significantly reduce the complexity of machine learning, enabling us to create a better experience for our customers, fast.”

Intuit recognizes the enormous value and power of machine learning to help its customers make better decisions and streamline their work, every day. “With Amazon SageMaker, we can accelerate our artificial intelligence initiatives at scale by building and deploying our algorithms on the platform,” says Ashok Srivastava, Chief Data Officer at Intuit. “We will create novel large-scale machine learning and AI algorithms and deploy them on this platform to solve complex problems that can power prosperity for our customers.”

Thomson Reuters is the world’s leading source of news and information for professional markets. “For over 25 years we have been developing advanced machine learning capabilities to mine, connect, enhance, organize and deliver information to our customers, successfully allowing them to simplify and derive more value from their work,” said Khalid Al-Kofahi, who leads Thomson Reuters center for AI and Cognitive Computing. “Working with Amazon SageMaker enabled us to design a natural language processing capability in the context of a question-answering application. Our solution required several iterations of deep learning configurations at scale using the capabilities of Amazon SageMaker.”

“Deep learning is something that our students find really inspiring. It seems like every week now it is leading to new breakthroughs in robotics, language, and biology. What I like about AWS DeepLens is that it seems likely to democratize access to experimenting with machine learning,” said Andrew Moore, Dean of the School of Computer Science at Carnegie Mellon University. “Campuses like ours are going to be really excited to bring AWS DeepLens into our classrooms and labs to help accelerate the process of getting students into real-world deep learning.”

New speech, language, and vision services allow app developers to easily build intelligent applications

For those developers who are not experts in machine learning, but are interested in using these technologies to build a new class of apps that exhibit human-like intelligence, Amazon Transcribe, Amazon Translate, Amazon Comprehend, and Amazon Rekognition video provide high-quality, high-accuracy machine learning services that are scalable and cost-effective.

“Today, customers are storing more data than ever before, using Amazon Simple Storage Service (Amazon S3) as their scalable, reliable, and secure data lake. These customers want to put this data to use for their organization and customers, and to do so they need easy-to-use tools and technologies to unlock the intelligence residing within this data,” said Swami Sivasubramanian, VP of Machine Learning, AWS. “We’re excited to deliver four new machine learning application services that will help developers immediately start creating a new generation of intelligent apps that can see, hear, speak, and interact with the world around them.”

  • Amazon Transcribe (available in preview) converts speech to text, allowing developers to turn audio files stored in Amazon S3 into accurate, fully punctuated text. Amazon Transcribe has been trained to handle even low fidelity audio, such as contact center recordings, with a high degree of accuracy. Amazon Transcribe can generate a time stamp for every word so that developers can precisely align the text with the source file. Today, Amazon Transcribe supports English and Spanish with more languages to follow. In the coming months, Amazon Transcribe will have the ability to recognize multiple speakers in an audio file, and will also allow developers to upload custom vocabulary for more accurate transcription for those words.
  • Amazon Translate (available in preview) uses state of the art neural machine translation techniques to provide highly accurate translation of text from one language to another. Amazon Translate can translate short or long-form text and supports translation between English and six other languages (Arabic, French, German, Portuguese, Simplified Chinese, and Spanish), with many more to come in 2018.
  • Amazon Comprehend (available today) can understand natural language text from documents, social network posts, articles, or any other textual data stored in AWS. Amazon Comprehend uses deep learning techniques to identify text entities (e.g. people, places, dates, organizations), the language the text is written in, the sentiment expressed in the text, and key phrases with concepts and adjectives, such as ‘beautiful,’ ‘warm,’ or ‘sunny.’ Amazon Comprehend has been trained on a wide range of datasets, including product descriptions and customer reviews from Amazon.com, to build best-in-class language models that extract key insights from text. It also has a topic modeling capability that helps applications extract common topics from a corpus of documents. Amazon Comprehend integrates with AWS Glue to enable end-to-end analytics of text data stored in Amazon S3, Amazon Redshift, Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, or other popular Amazon data sources.
  • Amazon Rekognition Video (available today) can track people, detect activities, and recognize objects, faces, celebrities, and inappropriate content in millions of videos stored in Amazon S3. It also provides real-time facial recognition across millions of faces for live stream videos. Amazon Rekognition Video’s easy-to-use API is powered by computer vision models that are trained to accurately detect thousands of objects and activities, and extract motion-based context from both live video streams and video content stored in Amazon S3. Amazon Rekognition Video can automatically tag specific sections of video with labels and locations (e.g. beach, sun, child), detect activities (e.g. running, jumping, swimming), detect, recognize, and analyze faces, and track multiple people, even if they are partially hidden from view in the video.

“At Isentia, we built our media intelligence software in a single language. To expand our capabilities and address the diverse language needs of our customers, we needed translation support to generate and deliver valuable insights from non-English media content. Having tried multiple machine translation services in the past, we are impressed with how easy it is to integrate Amazon Translate into our pipeline and its ability to scale to handle any volume we throw at it. The translations also came out more accurate and nuanced and met our high standards for clients,” says Andrea Walsh, CIO at Isentia.

“RingDNA is an end-to-end communications platform for sales teams. Hundreds of enterprise organizations use RingDNA to dramatically increase productivity, engage in smarter sales conversations, gain predictive sales insights, improve their win rate and coach reps to succeed faster than ever before. A critical component of RingDNA’s Conversation AI requires best of breed speech-to-text to deliver transcriptions of every phone call. RingDNA is excited about Amazon Transcribe since it provides high-quality speech recognition at scale, helping us to better transcribe every call to text,” said Howard Brown, CEO and Founder at RingDNA.

“The Post strives to give its nearly 100 million readers the best experience possible and relevant content recommendations are a key part of that mission,” said Dr. Sam Han (PhD), Director of Data Science at The Washington Post. “With Amazon Comprehend, we can leverage the continuously-trained NLP capabilities like Keyphrase and Topic APIs to potentially allow us to provide even better content personalization, SEO, and ad targeting capabilities.”

“Building intelligent applications to help customers drive their businesses is our entire focus,” said Manjunath Ganimasty, V.P. Software Development with Infor. “Amazon Comprehend allows us to analyze unstructured text within search, chat, and documents to understand intent and sentiment. This capability enables us to train our Coleman AI skillset, and also provide a truly focused and tailored search experience for our customers.”

“Natural language processing is hard. We’ve looked at everything from closed to open-source solutions to analyze and make sense of our data, but couldn’t find a practical solution that would allow us to stay agile, scalable, and cost effective. Amazon Comprehend provides a continuously-trained model allowing us to focus on our business and innovate in Supply Chain Management (SCM),” said Minh Chau, Head of Engineering at Elementum.

“The City of Orlando is excited to work with Amazon to pilot the latest in public safety software through a unique, first-of-its-kind public-private partnership,” said John Mina Police Chief., City of Orlando. “Through the pilot, Orlando will utilize Amazon’s Rekognition Video and Acuity technology in a way that will use existing City resources to provide real-time detection and notification of persons-of-interest, further increasing public safety and operational efficiency opportunities for the City of Orlando and other cities across the nation. ”

“The analytic features of Amazon Rekognition Video are impressive. They can, for example, help with search of historical and real time video for persons-of-interest, providing efficiencies and awareness by automating this typically human task,” Dan Law, Chief Data Scientist at Motorola.

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AppDate: DStv jumps on music bandwagon

In this week’s AppDate, SEAN BACHER highlights DStv’s JOOX, Cisco’s Security Connector, Diski Skills, Namola and Exhibid.

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DStv JOOX

DStv is now offering JOOX, a music streaming service owned by China’s Tencent, to DStv Premium, Compact Plus and Compact customers.

In addition to streaming local and international artists, JOOX allows one to switch to karaoke mode and learn the lyrics as well as create and share playlists. Users can add up to four friends or family to the service free of charge.

DStv Family, Access and EasyView customers can also log in to the free JOOX service directly through JOOX App, but will be unable to add additional friends and won’t be able to listen to add-free music.

Platform: Access the JOOX service directly from the services menu on DStv or download the JOOX app for an iOS or Android phone.

Expect to pay: A free download.

Stockists: Visit the store linked to your device.

 

Cisco Security Connector

With all the malware, viruses and trojans doing the rounds, it is difficult for users and enterprises to ensure that they don’t become targets. Cisco, in collaboration with Apple, has brought out its Cisco Security Connector to protect users. The app is designed to give enterprises and users overall visibility and control over their network activity on iOS devices. It does this by ensuring compliance of mobile users and their enterprise-owned iOS devices during incident investigations, by identifying what happened, who it affected, and the risk of the exposure. It also protects iPhone and iPad users from accessing malicious sites on the Internet, whether on the corporate network, public Wi-Fi, or cellular networks. In turn, it prevents any viruses from entering a company’s network.

Platform: iPhones and iPads running iOS 11.3 or later

Expect to pay: A free download

Stockists: Visit the Apple App Store for downloading instructions.

 

Diski Skills

The Goethe-Institut, in co-operation with augmented reality specialists Something Else Design Agency, has created a new card game which celebrates South African freestyle football culture, and brings it alive through augmented reality. Diski Skills is quick card game, set in a South African street football scenario, showing popular tricks such as the Shibobo, Tsamaya or Scara Turn. Each trick is rated in categories of attack, defence and swag – one wins the game by challenging an opponent strategically with the trick at hand. Through augmented reality, the cards come alive. Move a smartphone over a card and watch as the trick appears on the screen in a slow motion video. An educational value is added as players can study the tricks and learn more about the idea behind it.

 

The game will be launched on 27 October 2018 at the Goethe-Institut.

For more information visit: www.goethe.de

 

Namola

With  recent news of kidnappings on the rise, a lot more thought is going into keeping children safe. Would your child know what to do in an emergency? Have you actually asked them?

Namola, supported by Dialdirect Insurance, is a free mobile safety app. Namola’s simple interface makes it an ideal way for children to learn how to get help in an emergency. All they need to do is activate the app and push a button to get help that they need, even when their parents are not around.

Parents need to install the app on their child’s phone, hold down the request assistance button, program emergency numbers that will automatically be dialled when the emergency button is pushed, and teach their children how and when to use the app.

Platform: Android and iOS

Expect to pay: A free download.

Stockists: Visit the store linked to your device.

 

Exhibid

Exhibid could be thought of as Tinder, but for for art lovers. The interface looks very similar to the popular mobile dating app, in that users swipe left for a painting that doesn’t appeal to them, or swipe right for something they like. Once an art piece is liked by swiping right, one can start bidding or make an offer on it. The bid is automatically sent to the artist. Should he or she accept the offer, the buyer makes a payment through the app’s secure payment gateway and the two are put in contact to make arrangements for delivery.

Platform: Android and iOS

Expect to pay: A free download.

Stockists: Visit the store linked to your device.

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New kind of business school

At a recent meeting, ALLON RAIZ, founder and CEO of Raizcorp, realised that in order for today’s youth to become entrepreneurs, teachers, the curriculum and the parents need continually expose them to entrepreneurial thinking from a young age.

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Several years ago, I found myself in a meeting with my business partner and two of my staff members. In front of us was a client who was sharing some of the frustrations in his business. At the end of the meeting, my partner and I were extremely excited about the prospect of two massive opportunities we had both independently identified while listening to the client. My two staff members, on the other hand, completely missed them. This led me to wonder what it was in my own and my partner’s backgrounds that allowed us to so easily spot opportunities while my two staff members remained oblivious … I realised that the difference was that my partner and I both had an early exposure to entrepreneurship while they didn’t.

Not long afterwards, I was delivering a lecture about how Raizcorp grows and develops small businesses at Oxford University’s Said Business School in my role as their Entrepreneur-in-Residence. I mentioned the above incident and spoke about my intention of going into children’s education with a view to providing an entrepreneurial perspective.

One of the professors in attendance asked me if I’d ever heard of a piece of research by Henrich R Greve called Who wants to be an entrepreneur? The deviant roots of entrepreneurship. It’s a pretty unfortunate title but a fascinating piece of research nonetheless. It highlights how certain contexts in childhood result in a much a higher probability of becoming an entrepreneur. For example, kids who participate in solo sports such as tennis or athletics are more likely to become entrepreneurs than children who play team sports like soccer and cricket. Conversely, your mother’s participation in the parent-teacher association has a negative correlation to you becoming an entrepreneur. I spent the rest of the afternoon in the professor’s office discussing other research papers that unequivocally proved that context during your childhood has a massive influence on whether or not you will follow the entrepreneurial route.

Another member of the lecture audience was a double-PhD from the USA who was completing her MBA at Oxford. After the lecture, she approached me and volunteered to help build a framework to incorporate entrepreneurship in the school curriculum without interfering with the formal requirements of the CAPS curriculum.

She spent nine months in South Africa working with me to build out a practical framework. The next phase of the plan was to find the right school at which to embark upon this journey. In December 2015, Raizcorp purchased Radley Private School and we began our entrepreneurial education adventure in earnest in 2016.

At the centre of the Radley philosophy is that the school (the physical building), the teachers, the curriculum and the parents are the “marinade” in which the kids need to soak in order to be continuously exposed to entrepreneurial thinking from a young age. The aim was that if, in future, the kids found themselves sitting in a boardroom with me and my partner, they too would be able to identify the opportunities that we did.

A big shift this year has been the launch of our Entrepreneurial Educator Guide (EEG) programme where we have been training our Radley teachers (whom we call guides) to understand entrepreneurship, business language, business concepts, financial documents and the like. (The EEG training makes use of Raizcorp’s internationally accredited entrepreneurial learning and guiding methodologies.) We have also employed a full-time staff member to ensure that these concepts are imbedded into all lesson plans and classroom activities.

Through my network at Raizcorp, I have been pleasantly surprised by the massive support we’re receiving from prominent entrepreneurs and businesses who want to participate in our Radley Exposure programme, where we take our kids of all ages on visits to different types of businesses so they can understand the difference between retail, wholesale, manufacturing, logistics and so on. Prominent businesspeople have put up their hands to come to the school and tell their stories of hard work, resilience and perseverance. This ties in beautifully with the 17 entrepreneurial concepts that we are instilling into our Radley learners (such as opposite eyes, lateral thinking and opposable mind), while never compromising on our quality academic offering.

As parents, we’ve all heard the terrible statistics about the probability of our kids finding jobs in the future. At Radley, we’re working hard to ensure that our kids have a legitimate and lucrative alternative to finding traditional employment and that is to become an entrepreneur. Radley is all about producing job creators and not job seekers!

To enrol your child or find out more about the school, please visit www.radley.co.za.

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