Smartest of the smart: 12 AI tools to make software intelligent

Source: Artificial Intelligence on Medium

Smartest of the smart: 12 AI tools to make software intelligent

How to pick a proper AI library for your software

Look around, and some mind-blowing things are happening in the world. Cars driving across the streets on autopilot mode, some girl named “Alexa” can manage all the electrical stuff in your house and tell your kid a bedtime story.

According to recent research, artificial intelligence will overcome human intelligence multiple times by 2029. Today, smartphones use AI integrations to make quality photos or provide virtual assistant functions. So, this synthetic brain that hides in depth of processor can change your software for the better.

However, today there are different fields of artificial intelligence. If you’re interested in this topic, you’ve definitely heard such concepts as deep learning, data science, and so on.

Since this intro drew your attention, let’s go through the list of all tools developers use to integrate AI in their projects. Also, we will shed some light on the difference between AI and machine learning and data science.

Machine learning frameworks and libraries

Firstly, we’ll talk about tools for machine learning. Some of them are worth mentioning.

Azure Machine Learning Studio

This is a machine learning solution developed by Microsoft that allows you to build, test, and deploy predictive analytics on your data. Moreover, it’s a cloud-based solution, so all of the operations are processed on the cloud. With the help of Azure ML, engineers can design features for improving customer service, establishing forecasts, predicting equipment malfunctions, etc.

Advantages:

  • Convenience. Cloud-based processing, access via a browser, and a set of features for collaboration make Azure ML studio a helpful, easy-to-access solution.
  • Variety of supported algorithms. Azure ML offers a ton of well-known algorithms that can be configured easily. You don’t even need hands-on experience with data science or algorithmic theory. All you need is to know when to use these algorithms.
  • Documentation by Microsoft standards. If you have ever had to work with .NET documentation, then you realize how detailed Microsoft documentation is. Azure ML provides documentation from quick starts and tutorials to deploying and managing the Machine Learning solutions for business.

IBM Watson

This one is a self-sufficient system that is able to learn, understand, and predict. IBM Watson can be applied to various fields of science and tech due to its broad functionality. Some of the use cases are: forecasting the outcome of political and sports events, predicting natural disasters based on monitoring cyclones and atmosphere conditions, etc.

IBM Watson provides API to third-party applications and services. Based on this API platform, developers can implement speech to text features, machine learning, and data science, track the outcome from AI across its lifecycle. Also, using Watson’s Assistant developers can build conversational interfaces into their applications. Watson contains around 50 APIs available at the moment.

Advantages:

  • Improved security. IBM Watson can help your company improve the safety of its digital information and processes. Cybersecurity specialists use machine learning to detect vulnerabilities in their systems with high accuracy.
  • Gathering Employee Intelligence. This ML software can also help to determine employee behavior patterns and estimate their mood to improve work conditions, team cooperation, and personal performance.

So, IBM Watson is meant to create software that simplifies work and makes life more comfortable.

PyBrain

This library provides versatility for algorithm implementation by eliminating the use of various libraries. It brings the algorithm to the fore, not the particularities of its realization. PyBrain gets a top tier position by providing a combination of the laconic syntax of Python and a wide variety of algorithms for machine intelligence.

Advantages:

  • Flexible environment for implementation of algorithm implementation. It provides an all-in-one solution, so you don’t have to think about additional libraries while working on your algorithm.
  • Grace lies in simplicity. The usage of algorithms has been greatly simplified with PyBrain. Various complicated algorithms and methods can be implemented quickly.
  • Open-source. PyBrain is an open-source AI library so that every developer can contribute to it.

Scikit-Learn

One of the branches of machine learning is data mining. So, it’s time to introduce a simple and efficient tool for data scientists. Scikit-Learn is based on several popular Python packages, particularly — NumPy, SciPy, and matplotlib. This library handles data processing, classification, and clustering tasks. Thus, it’s a perfect solution for data visualization, analysis, anomaly detection, and so on.

Advantages:

  • Scikit-Learn delivers a lot of highly useful utilities for data splitting, computing statistics, processing matrix operations.
  • Scikit-Learn has good documentation and a well-balanced and easy to use API.
  • NumPy integration.

TensorFlow

It is an efficient data flow-oriented machine learning library created by Google and later made open source in 2015. Basically, TensorFlow is a low-level tool for operations with complicated math, and its audience is researchers who know how to build learning architectures and turn them into operational software. In other words, it’s a programming system where you represent computations as graphs.

Advantages:

  • It is a modular software. Meaning some parts of it can be standalone.
  • Simple constructing methods. You can easily visualize every part of the graph.
  • CPU and GPU. It can be used on both CPU and GPU.
  • Open-source. TensorFlow is customizable and open source.

Torch

Torch is the library for scientific calculations that provides APIs for machine learning, based on compiled LuaJIT. The library is divided into modules which are responsible for different stages of neural network processes. Some of them are responsible for neural network structure, others for optimizing learning processes, and so on. Some features can be added by installing extra modules.

Advantages:

  • In Torch, everything is compiled, so depending on the code, you may have performance advantage compared to other machine learning libraries.
  • Torch creates complicated ML networks using containers mechanism.
  • This library contains already trained modules.
  • Torch provides dynamic computational graphs, so the network can change behavior while executed. It’s helpful in case of debugging and for constructing sophisticated models, allowing PyTorch’s expressions to be automatically differentiated.

Deep learning frameworks and libs

Deep Learning is a subfield of machine learning based on artificial neural networks with representation learning. This type of learning is profound and more complicated. Thus, let’s come through several tools that simplify deep learning.

Caffe

Caffe is an open-source framework written in C++, but at the same time, it’s distributed for commercial use. It also allows writing algorithms in Python. Caffe specializes in language and image recognition, machine vision, multimedia. It’s well optimized for speed and scalability, so the framework provides solutions for academic projects and business applications with the use of artificial intelligence.

Advantages:

  • Pre-trained models for building app prototypes.
  • Works well with other frameworks.
  • The open-source software that allows you to control integrations and modify the code for your needs.
  • High performance, provided by C++, makes Caffe one of the leaders among artificial intelligence frameworks for deep learning.

CNTK

The Microsoft Cognitive Toolkit (CNTK) is an open-source deep-learning tool for commercial distribution. It represents neural networks as a chain of computational steps on a directed graph. This toolkit is used in speech recognition cases, for example, Windows Cortana, Skype Translator, etc. It also can be used for translation software and image recognition. It is developed in C++ language.

Advantages:

  • CNTK is time-efficient. It can train systems faster than a lot of deep learning software.
  • Can achieve state-of-the-art performance on enterprise systems and benchmark tasks.
  • CNTK supports a variety of tasks such as speech, image, and text recognition, etc.
  • CNTK handles different models of networks — convolutional, feed-forward, recurrent neural networks, and their combinations.

DeepLearning4j

DeepLearning4j is written in Java and is compatible with every Java Virtual Machine language such as Kotlin, Scala, or Clojure. Primary computations are written in C, C++, and Cuda. This library was developed for business applications. DL4j takes advantage of distributed computing due to Apache Spark and Hadoop frameworks.

Advantages:

  • High performance and processing a large amount of data while using multi-GPU.
  • Libraries are completely open-sourced and maintained by the developers and the community.
  • DL4j is flexible and lets you combine different types of deep learning networks.

Keras

Keras is a high-level API for deep learning. It’s written in Python, and the most significant benefit is that you can run Keras on top of all the latest deep learning libraries or frameworks, such as Theano, TensorFlow, Torch, etc. Besides, it was developed for performing fast experiments. Keras provides an ability to get a result from an idea in the shortest terms possible.

Because of TensorFlow’s popularity, Keras has strong ties to this library. A lot of scientists prefer using Keras to navigate across TensorFlow. That reduces the chances of building a model that outputs the wrong conclusion.

Advantages:

  • Easy-to-use due to leaving some of the low-level API details behind.
  • Keras is based on Python, that’s why integrates well with the massive Python data science ecosystem.
  • Detailed, well-organized documentation.
  • Open-source code.
  • Ability to conduct experiments with the advent of the idea.

Swift AI

It is a library created for Swift language to use on Mac computers only. Swift AI is mostly used for designing neural networks together with deep learning algorithms. The field of Swift AI is considered to be written speech recognition.

Advantages:

  • Designed for Apple hardware and uses all of its powers.
  • Swift is fast. LLVM compiler that stands behind Swift is effective and highly optimized, so Swift is close to C at the performance.
  • Signal processing implementation.
  • Swift lays close to the hardware, so it’s easier to inspect code as, for example, with Python.

Theano

Theano is a Python library for scientific computing. It simplifies the creation of neural networks and provides flexibility in model architecture. Another important factor is the availability of different Python libraries, specifically scientific computing and visualization libraries. The best known of them is NumPy, SciPY, and ScikitLearn.

However, these benefits don’t even influence overall performance. The point is, critical parts of Theano are either executed as native code modules or transformed into C code dynamically. That’s how it’s possible to achieve C-kind performance while using Python.

Advantages:

  • Low-level entrance threshold for deep learning due to the simplicity of neural network creation.
  • Performance at the level of C language applications while having all the benefits of Python language.
  • Working with multi-layer perceptrons, recurrent neural networks, autoencoders, etc.

Future outlook on AI capabilities

We’ve walked through dozen of tools that can significantly change your app by adding artificial intelligence into it. But mobile applications is the drop in the bucket compared to the possible impact on all the spheres of our lives.

Besides data structuring, text, and image recognition, today, AI is used for a lot of different tasks.

Doctors use AI as minor assistance in their everyday routine, such as heart rate analysis, FAQ software for patients, and so on. Also, car manufacturers use AI for improving transmission quality and developing additional features like adaptive cruise control and autopilot.

Machine learning is applied in banking to estimate money loss and recognize unreliable customers. Moreover, some applications help users to optimize their expenses based on their spending patterns.

Less popular but still very interesting is the contribution of AI in the field of music. Based on the set of music given to AI and with the help of algorithms, the machine can produce unique composition never heard before. One of the examples is AIVA, the AI-based software that can compose music of different genres, such as pop, rock, Chinese folk music, etc.

To sum up, AI is getting better with the development of technologies. It’s already better than humans in some fields, and when the time comes, AI will change the world beyond recognition. As for now, every person who intends to develop an application can integrate AI inside. But to handle this task the right way, you should assign this task to the team of professionals.

Author’s bio

Vitaly Kuprenko is a technical writer at Cleveroad. It’s an Android and iOS app development company in Ukraine. He enjoys telling about tech innovations and digital ways to boost businesses.

  • Working with multi-layer perceptrons, recurrent neural networks, autoencoders, etc.

Future outlook on AI capabilities

We’ve walked through dozen of tools that can significantly change your app by adding artificial intelligence into it. But mobile applications is the drop in the bucket compared to the possible impact on all the spheres of our lives.

Besides data structuring, text, and image recognition, today, AI is used for a lot of different tasks.

Doctors use AI as minor assistance in their everyday routine, such as heart rate analysis, FAQ software for patients, and so on. Also, car manufacturers use AI for improving transmission quality and developing additional features like adaptive cruise control and autopilot.

Machine learning is applied in banking to estimate money loss and recognize unreliable customers. Moreover, some applications help users to optimize their expenses based on their spending patterns.

Less popular but still very interesting is the contribution of AI in the field of music. Based on the set of music given to AI and with the help of algorithms, the machine can produce unique composition never heard before. One of the examples is AIVA, the AI-based software that can compose music of different genres such as pop, rock, Chinese folk music, etc.

To sum up, AI is getting better with the development of technologies. It’s already better than humans in some fields, and when the time comes, AI will change the world beyond recognition. As for now, every person who intends to develop an application can integrate AI inside. But to handle this task the right way, you should assign this task to the team of professionals.

Author’s bio

Vitaly Kuprenko is a technical writer at Cleveroad. It’s an Android and iOS app development company in Ukraine. He enjoys telling about tech innovations and digital ways to boost businesses.