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 As data sets and variables grow, customers have to repeat this process again and again as models become outdated and need to be continuously retrained to learn and evolve from new information.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.
Prepare your cam to capture the Blood Moon
On 27 July 2018, South Africans can witness a total lunar eclipse, as the earth’s shadow completely covers the moon.
Also known as a blood or red moon, a total lunar eclipse is the most dramatic of all lunar eclipses and presents an exciting photographic opportunity for any aspiring photographer or would-be astronomers.
“A lunar eclipse is a rare cosmic sight. For centuries these events have inspired wonder, interest and sometimes fear amongst observers. Of course, if you are lucky to be around when one occurs, you would want to capture it all on camera,” says Dana Eitzen, Corporate and Marketing Communications Executive at Canon South Africa.
Canon ambassador and acclaimed landscape photographer David Noton has provided his top tips to keep in mind when photographing this occasion. In South Africa, the eclipse will be visible from about 19h14 on Friday, 27 July until 01h28 on the Saturday morning. The lunar eclipse will see the light from the sun blocked by the earth as it passes in front of the moon. The moon will turn red because of an effect known as Rayleigh Scattering, where bands of green and violet light become filtered through the atmosphere.
A partial eclipse will begin at 20h24 when the moon will start to turn red. The total eclipse begins at about 21h30 when the moon is completely red. The eclipse reaches its maximum at 22h21 when the moon is closest to the centre of the shadow.
David Noton advises:
- Download the right apps to be in-the-know
The sun’s position in the sky at any given time of day varies massively with latitude and season. That is not the case with the moon as its passage through the heavens is governed by its complex elliptical orbit of the earth. That orbit results in monthly, rather than seasonal variations, as the moon moves through its lunar cycle. The result is big differences in the timing of its appearance and its trajectory through the sky. Luckily, we no longer need to rely on weight tables to consult the behaviour of the moon, we can simply download an app on to our phone. The Photographer’s Ephemeris is useful for giving moonrise and moonset times, bearings and phases; while the Photopills app gives comprehensive information on the position of the moon in our sky. Armed with these two apps, I’m planning to shoot the Blood Moon rising in Dorset, England. I’m aiming to capture the moon within the first fifteen minutes of moonrise so I can catch it low in the sky and juxtapose it against an object on the horizon line for scale – this could be as simple as a tree on a hill.
- Invest in a lens with optimal zoom
On the 27th July, one of the key challenges we’ll face is shooting the moon large in the frame so we can see every crater on the asteroid pockmarked surface. It’s a task normally reserved for astronomers with super powerful telescopes, but if you’ve got a long telephoto lens on a full frame DSLR with around 600 mm of focal length, it can be done, depending on the composition. I will be using the Canon EOS 5D Mark IV with an EF 200-400mm f/4L IS USM Ext. 1.4 x lens.
- Use a tripod to capture the intimate details
As you frame up your shot, one thing will become immediately apparent; lunar tracking is incredibly challenging as the moon moves through the sky surprisingly quickly. As you’ll be using a long lens for this shoot, it’s important to invest in a sturdy tripod to help capture the best possible image. Although it will be tempting to take the shot by hand, it’s important to remember that your subject is over 384,000km away from you and even with a high shutter speed, the slightest of movements will become exaggerated.
- Integrate the moon into your landscape
Whilst images of the moon large in the frame can be beautifully detailed, they are essentially astronomical in their appeal. Personally, I’m far more drawn to using the lunar allure as an element in my landscapes, or using the moonlight as a light source. The latter is difficult, as the amount of light the moon reflects is tiny, whilst the lunar surface is so bright by comparison. Up to now, night photography meant long, long exposures but with cameras such as the Canon EOS-1D X Mark II and the Canon EOS 5D Mark IV now capable of astonishing low light performance, a whole new nocturnal world of opportunities has been opened to photographers.
- Master the shutter speed for your subject
The most evocative and genuine use of the moon in landscape portraits results from situations when the light on the moon balances with the twilight in the surrounding sky. Such images have a subtle appeal, mood and believability. By definition, any scene incorporating a medium or wide-angle view is going to render the moon as a tiny pin prick of light, but its presence will still be felt. Our eyes naturally gravitate to it, however insignificant it may seem. Of course, the issue of shutter speed is always there; too slow an exposure and all we’ll see is an unsightly lunar streak, even with a wide-angle lens.
On a clear night, mastering the shutter speed of your camera is integral to capturing the moon – exposing at 1/250 sec @ f8 ISO 100 (depending on focal length) is what you’ll need to stop the motion from blurring and if you are to get the technique right, with the high quality of cameras such as the Canon EOS 5DS R, you might even be able to see the twelve cameras that were left up there by NASA in the 60’s!
How Africa can embrace AI
Currently, no African country is among the top 10 countries expected to benefit most from AI and automation. But, the continent has the potential to catch up with the rest of world if we act fast, says ZOAIB HOOSEN, Microsoft Managing Director.
To play catch up, we must take advantage of our best and most powerful resource – our human capital. According to a report by the World Economic Forum (WEF), more than 60 percent of the population in sub-Saharan Africa is under the age of 25.
These are the people who are poised to create a future where humans and AI can work together for the good of society. In fact, the most recent WEF Global Shapers survey found that almost 80 percent of youth believe technology like AI is creating jobs rather than destroying them.
Staying ahead of the trends to stay employed
AI developments are expected to impact existing jobs, as AI can replicate certain activities at greater speed and scale. In some areas, AI could learn faster than humans, if not yet as deeply.
According to Gartner, while AI will improve the productivity of many jobs and create millions more new positions, it could impact many others. The simpler and less creative the job, the earlier, a bot for example, could replace it.
It’s important to stay ahead of the trends and find opportunities to expand our knowledge and skills while learning how to work more closely and symbiotically with technology.
Another global study by Accenture, found that the adoption of AI will create several new job categories requiring important and yet surprising skills. These include trainers, who are tasked with teaching AI systems how to perform; explainers, who bridge the gap between technologist and business leader; and sustainers, who ensure that AI systems are operating as designed.
It’s clear that successfully integrating human intelligence with AI, so they co-exist in a two-way learning relationship, will become more critical than ever.
Combining STEM with the arts
Young people have a leg up on those already in the working world because they can easily develop the necessary skills for these new roles. It’s therefore essential that our education system constantly evolves to equip youth with the right skills and way of thinking to be successful in jobs that may not even exist yet.
As the division of tasks between man and machine changes, we must re-evaluate the type of knowledge and skills imparted to future generations.
For example, technical skills will be required to design and implement AI systems, but interpersonal skills, creativity and emotional intelligence will also become crucial in giving humans an advantage over machines.
“At one level, AI will require that even more people specialise in digital skills and data science. But skilling-up for an AI-powered world involves more than science, technology, engineering and math. As computers behave more like humans, the social sciences and humanities will become even more important. Languages, art, history, economics, ethics, philosophy, psychology and human development courses can teach critical, philosophical and ethics-based skills that will be instrumental in the development and management of AI solutions.” This is according to Microsoft president, Brad Smith, and EVP of AI and research, Harry Shum, who recently authored the book “The Future Computed”, which primarily deals with AI and its role in society.
Interestingly, institutions like Stanford University are already implementing this forward-thinking approach. The university offers a programme called CS+X, which integrates its computer science degree with humanities degrees, resulting in a Bachelor of Arts and Science qualification.
Revisiting laws and regulation
For this type of evolution to happen, the onus is on policy makers to revisit current laws and even bring in new regulations. Policy makers need to identify the groups most at risk of losing their jobs and create strategies to reintegrate them into the economy.
Simultaneously, though AI could be hugely beneficial in areas such as curbing poor access to healthcare and improving diagnoses for example, physicians may avoid using this technology for fear of malpractice. To avoid this, we need regulation that closes the gap between the pace of technological change and that of regulatory response. It will also become essential to develop a code of ethics for this new ecosystem.
Preparing for the future
With the recent convergence of a transformative set of technologies, economies are entering a period in which AI has the potential overcome physical limitations and open up new sources of value and growth.
To avoid missing out on this opportunity, policy makers and business leaders must prepare for, and work toward, a future with AI. We must do so not with the idea that AI is simply another productivity enhancer. Rather, we must see AI as the tool that can transform our thinking about how growth is created.
It comes down to a choice of our people and economies being part of the technological disruption, or being left behind.