Computers are learning to think, read, and write. They’re also picking up human sensory function, with the ability to see and hear arguably to touch, taste, and smell, though those have been of a lesser focus. Machine intelligence technologies cut across a vast array of problem types from classification and clustering to natural language processing and computer vision and methods from support vector machines to deep belief networks. All of these technologies are reflected on this landscape.
A true machine intelligence system is one that can learn on its own. We’re talking about neural networks from the likes of Google’s DeepMind, which can make connections and reach meanings without relying on pre-defined behavioral algorithms. True A.I. can improve on past iterations, getting smarter and more aware, allowing it to enhance its capabilities and its knowledge.
Machine Learning Intelligence Technology or Services by companies which are rolling in the market are :
TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
Wit.ai makes it easy for developers to build applications and devices that you can talk or text to. Our vision is to empower developers with an open and extensible natural language platform. Wit.ai learns human language from every interaction, and leverages the community: what’s learned is shared across developers.
The basic idea of integration of Wit.ai to your mobile app is via API like node, python, ruby. There are many recipes out there for you. API training is done around Stories (domain-specific use cases), where the engine learns conversation flow from examples of user input with bot response.
This is a simple android app connected with Wit.ai, it takes images as input and describes what is in it in natural language. You can build an app that can basically talk to you. Great!
Here’s another example of Wit.ai.
IBM Watson Watson can understand all forms of data, interact naturally with people, and learn and reason, at scale. Watson can analyze and interpret all of your data, including unstructured text, images, audio and video. Provide personalized recommendations by understanding a user’s personality, tone, and emotion. you can create chat bots that can engage in dialog. you can utilize machine learning to grow the subject matter expertise in your apps and systems. Watson is available as a set of open APIs and SaaS products. The power of Watson through APIs that allow you to build cognition into your apps and products, whether it’s a web or native app, or even robotics.Take a look at “10 IBM Watson-Powered Apps That Are Changing Our World??? and this one too “5 unusual things you can do with IBM’s Watson???.
Google Cloud Machine Learning Platform
Google Cloud ML Platform provides modern machine learning services, with pre-trained models and a service to generate your own tailored models.Major Google applications use Cloud Machine Learning, including Photos , the Google app , Translate, Allo, and Inbox .
Google itself uses this service to develop their smart applications. Usage of their service is massive you can do a lot of things in your applications with the services they provide, and their documentation is mind blowing.
Amazon AI is Rekognition, is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, faces; recognize celebrities; and identify inappropriate content in images. You can also search and compare faces. Rekognition’s API enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications.
Amazon Rekognition is based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists to analyze billions of images daily for Prime Photos. Amazon Rekognition uses deep neural network models to detect and label thousands of objects and scenes in your images, and we are continually adding new labels and facial recognition features to the service.
Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments.
Rekognition identifies thousands of objects such as vehicles, pets, or furniture, and provides a confidence score. Rekognition also detects scenes within an image, such as a sunset or beach. This makes it easy for you to add features that search, filter, and curate large image libraries.
It has other capabilities like Facial Analysis as an example it can locate faces within images and analyze face attributes, such as whether or not the face is smiling or the eyes are open, Face Comparison like measures the likelihood that faces in two images are of the same person, and Facial Recognition. There are many use cases for this service like Searchable Image Library, Face-Based User Verification, Sentiment Analysis.
Another service under Amazon AI is Polly a text-to-speech service that uses a lot of machine learning smarts under the hood.
Amazon Polly is a service that turns text into lifelike speech. Polly lets you create applications that talk, enabling you to build entirely new categories of speech-enabled products.
The third and probably most important new service is called Lex. It is essentially the technology that fuels Amazon’s own Alexa service. It allows you to build conversational applications that can feature multi-step conversations.
You can use Amazon Lex to build chatbots and mobile applications that support engaging, lifelike interactions.It has many interesting use cases like Informational Bots, Application Bots.
Trends of intelligent apps are going on, time to catch up.