Artificial intelligence (AI) is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence such as problem solving, learning and pattern recognition. Machine Learning (ML) and Deep Learning (DL) are both computer science fields that are derived from the discipline of Artificial Intelligence. Machine learning describes the ability of a machine to learn the things such as language, behavior, patterns and habits without having been programmed to do so, or preloaded with that knowledge. Deep learning is designed to imitate the way human brain works. It involves training the artificial neural networks to comprehend data based on certain criteria. Example of machine learning in practice is Image Recognition in which neural networks are able to recognize different data patterns to learn, what, for example, a fish looks like.

AI becomes smarter and learns faster with more data, and businesses worldwide are fueling their growth leveraging machine learning and deep learning solutions.

The ability to build and leverage applications that learn on their own is a powerful idea. The challenges related to building sophisticated AI systems center mostly around scale as the databases are massive, training can be computationally challenging and inferring predictions can be hard.

How Amazon rebuilt itself around AI

Amazon is no stranger to AI Services. It was one of the pioneers to use the technology to drive its product recommendations. Amazon AI is Amazon Web Services (AWS) deeply customizable machine learning and AI platform. It is designed to be scalable to match an enterprise’s specific needs. Amazon AI is structured in three layers at services level.

These are:

• Amazon Rekognition
• Amazon Polly
• Amazon Lex

These APIs are pre-trained and pre-tuned so that these can be used by the developers with little to no AI expertise.

At the platform level, AWS supports deep learning engines such as Apache MXNet, pyTorch, TensorFlow, Caffe/Caffe2 and others.

One layer lower are AI platforms which are:

Amazon Machine Learning: It contains a series of visualization tools to analyze data without the need to learn complex machine learning tools.

Amazon Elastic MapReduce (EMR): It’s a managed Hadoop framework which is designed to perform real-time analytics, log analysis, predictive analytics and other data operations.

Spark: It’s an Apache product that runs inside of Amazon AI as a distributed processing system for big data workloads.

Below that are the AI frameworks, all of which run as part of the Amazon Deep Learning. The AI framework level is a basic Linux environment with the required tools to do AI and machine learning work. It is also the most development intensive of the three frameworks.

What AWS has to offer in the realm of artificial intelligence?

Artificial Intelligence is a major component in enterprise cloud deployments and applications. Amazon AI is a collection of cloud-hosted tools and services that enable enterprises to use a variety of technologies such as:

• Chatbots
• Document sentiment analysis
• Natural language understanding (NLU)
• Machine and deep learning, and
• Image and video recognition

These services help the developers create advanced applications for the end users to allow them interact in a variety of ways. This collection of tools and services includes:

Machine Learning

Amazon Machine Learning (Amazon ML) provides visualization tools and wizards that guide the developers to create machine learning models without having to learn complex ML algorithms. You can easily obtain predictions for your application using simple APIs, without managing any infrastructure or implementing custom prediction generation code. The service uses powerful algorithms to create ML models by finding patterns in your existing data and uses these models to process new data and generate predictions for your application.

Deep Learning

It dives deeper on the data than machine learning using layered algorithms. It typically uses neural networks to sort through large amounts of structured data to train models for informed predictions. The technology leverages many of Amazon AI tools and services.

Amazon Lex

Speech recognition and natural language understanding (NLU) require sophisticated deep learning algorithms to be trained on massive amount of data and infrastructure. With this service, you can build conversational interfaces into any application using voice and text. It provides advanced deep learning functionalities of automatic search recognition (ASR) for converting speech to text, and natural language understanding to enable you to build applications with highly engaging user experience and lifelike conversational interactions. With its deep learning technologies, developers can easily build sophisticated, natural language conversational bots or Chatbots.

Amazon Polly

It’s a service that turns text into lifelike speech. It uses advanced deep learning technologies to synthesize speech and sounds like a human voice. It lets you create applications that talk. Polly includes 47 lifelike voices spread across 24 languages. Polly delivers consistently fast response time to support real-time, interactive conversation. Simply send the text you want converted into speech to the Polly API, and it will immediately return the audio stream to your application that you can play directly or store in a standard audio file format such as MP3.

Amazon Recognition

With this service, you can easily add image analysis to your applications, detect objects, scenes and faces in them, and also search and compare faces. The service uses deep neural network models to detect and label thousands of objects and scenes in your images. You can build powerful visual search and discovery into your applications. It provides two ways to feed the corresponding APIs:

Inlined Image: It’s a base 64 encoded representation of the input image, serialized as part of the API request.

Cloud Storage: It’s a simple reference to an image file hosted on Amazon S3.

Amazon SageMaker

Enables developers to create and train machine learning models. There are built-in algorithms for some of the most popular machine learning use cases. Jupiter notebooks help you visualize S3 data, or you can use AWS Glue to pull and translate data from AWS-hosted databases. A developer can train the model and deploy it into production. It has a modular structure so you can use any or all of its capabilities in your existing machine learning workflows.

Amazon Comprehend

It allows you to input large volumes of documents for textual analysis. It makes use of natural language processing for the analysis of text document and classifies entities into organized subsets, such as locations or people. The service leverages AI algorithms to rank keywords or phrases with a confidence score in relation to their importance in the document. It uses Neural Language Processing (NLP) to better understand and determine meaning within the text. The technology learns how to comprehend and process language as it was intended by the human being speaking or writing it.

AWS DeepLens

This programmable video camera can perform onboard analysis of the videos it captures. It comes with projects that help beginner-level and expert developers hone their machine learning skills. It allows you to get up and running with deep learning quickly and easily. The smart camera can recognize faces, objects, creatures and motions. It comes with recognition techniques that developers can build upon for their own projects. Object and action recognition can help seamlessly create something like demonstrations by developing an algorithm that learns to recognize how the two are paired for different outcomes.

Amazon Transcribe

This automatic speech recognition service enables you to submit audio files for transcription to text with timestamp for specific words in the original audio file. One can easily transcribe audio from common file formats, such as MP3 and WAV. It uses machine learning technologies to recognize the spoken word and transcribe it into text.

Amazon Translate

The service allows you to translate text from one language to another in real-time. This could be particularly helpful for the enterprises having worldwide audience to consume content. It uses machine learning to more naturally translate text from one language to another. It allows for multilingual web presence of your online properties where content can be seamlessly viewed in the chosen language of the viewer.

Alexa Voice Service (AVS)

The service leverages voice recognition and natural language understanding to provide a voice-controlled interface for the end users. It simplifies the development of voice-forward products by handling complex speech recognition and natural language understanding in the cloud. This way, the businesses can reduce their development costs and accelerate time to market.

Alexa Skills Kit (ASK)

The SDK allows you to build skills akin to voice apps for Alexa-enabled services. In includes APIs, webinars, code samples and documentation. Developers can create a variety of skill types, such as flash briefing, trivia, smart home, video skills and custom skills. There is a developer console for easy building of skills.

Flywheel – Amazon AI management strategy

Amazon calls its approach to AI as Flywheel which keeps AI innovation humming along and encourages energy and knowledge to spread to other areas of the company, just like a flywheel in engineering keeps the energy constant and spreads it to other areas of machine. Flywheel approach means the innovations around machine learning in one area of the company fuels the efforts of other team members. Essentially, what is created in one part of Amazon catalyzes AI and machine learning growth in other areas.

Amazon has come a long way since its early beginning in AI and machine learning. The company sells its machine learning approach through Amazon Web Services to clients like NASA and NFL.

AWS serves as a flexible, low-cost platform that can meet business requirements on the fly. The service is spurring innovation across various industry verticals, thanks to its ease of access from the cloud.

Who should use Amazon AI?

The platform is highly useful for the businesses that want to get into machine learning without having to make a large investment for hosting or analysis. The businesses that are already using AWS AI can enhance their big data processing capabilities without having to worry about splitting the systems between different providers.

Machine learning adding value to predictive analytics

Machine learning helps to improve the efficiency and effectiveness of business processes.

Data analysts are increasingly using machine learning techniques for predictive analytics as they tend to outperform statistical techniques for prediction problems. Potential applications of AI for predictions in business operations are broad and deep. Machine learning produces more accurate results when the behavior you want to predict is rare and the data sets have large number of varying features. ML algorithms tend to scale well with large volumes of data.

With cheap storage solutions, businesses looking to make better use of data can keep the data they used to discard. Machine learning helps the data analysts identify patterns in this data too.

Breadth of functionality

Businesses benefit from the continual evolution, innovation and iteration as they stand to access the newest features and enhancements instantly. They need not upgrade, deploy or mitigate.

Elasticity

Enterprises often overprovision to ensure that they had enough capacity to efficiently handle their business operations at the peak levels of demand. Implementation of artificial intelligence capabilities will allow them to provision the amount of resources that they actually need knowing that they can easily scale up or down as per the demand. This not only helps in reducing costs but also enhances an organization’s ability to meet the demands of its customers instantly.

How to get started with Amazon AI?

Anyone with AWS account can begin using the service after making payment. It’s charged on per-use basis, so costs should be negligible for basic use. The three Amazon AI services (Amazon Lex, Amazon Polly, Amazon Rekognition) can be used for free for a year, provided you perform only certain number of requests each month. After that, the services are still relatively cheap. Other components, like the frameworks and platforms are charged based on hours used.

AI is the new electricity

Not all organizations have access to the data, computing power and resources required for AI. For businesses without such resources, Amazon Web Services provides artificial intelligence services that enable them to take advantage of the benefits of AI without having to create their own training data sets or develop algorithms. AWS AI serves as a low cost, flexible platform that can meet business requirements on the fly. It provides the platform and resources to the businesses to get up and running quickly at low cost.

AWS lets the customers quickly spin up resources as and when they need them, deploying hundreds or even thousands of servers within minutes. They can quickly develop and roll out new applications