Artificial intelligence is no longer a fantasy but a reality that we are now living with. There is a lot to learn about it but this article aims to define it and look at some of the tools used in its development.

What is Artificial Intelligence?

AI can be simply described as the simulation of human intelligence processes by computer systems. Applications of AI contain natural language processing (NPL), speech recognition, machine vision as well as expert systems.

How does AI work?

AI entails a foundation of specialized software and hardware that will be used for writing and training machine learning algorithms. No one programming language can be described to be synonymous with AI, but a few including Java, R and Python are popular.

AI systems work by ingesting large amounts of labeled training data, analyzing the data for patterns and co-relations and using the patterns established to make future predictions. AI programming mainly focuses on three skills namely:

• Learning
• Reasoning
• Self-correction

Learning processes

In the learning aspect, focus is mainly put on acquiring data and creating rules on how the data will be turned into actionable information. These rules are referred to as algorithms and are meant to provide computing devices with instructions on how to complete specific tasks.

Reasoning processes

This aspect focuses on choosing the right algorithm to reach a certain desired outcome.

Self-correction processes

This aspect’s main focus is on continually fine tuning algorithms so that they continue providing desired results as more accurately as possible.

Advantages and disadvantages of AI

Let’s look at some of the advantages and disadvantages of AI.

Advantages

• Good at detail oriented jobs
• Reduce time for tasks that require high amounts of data
• Results delivered are consistent
• Virtual AI powered agents are always available

Disadvantages

• It is expensive
• The technical expertise required is deep
• There is a limited number of qualified individuals to develop AI tools
• The computer systems only know what they have been shown
• Lacks the ability to generalize from one task to the other

Artificial intelligence tools

Let’s now look at the numerous AI automation tools that are available in the industry today.

Scikit Learn

This is among the most widely used libraries in machine learning. Cross validation, feature extraction and supervised learning algorithm among others make it the go-to library for most developers.

Tensor flow

This is a machine learning framework by Google. It is also a Python friendly open-source library. It’s one of the most sort after AI development tools that facilitate numerical computations hence making future predictions more accurate and much easier.

It gives developers the freedom to concentrate on the logic part of the application rather than on the algorithms. It takes care of everything on the backend. The Tensor board allows developers to construct neural networks and even create graphical visualizations. It can be run conveniently on cloud, android and iOS devices.

PyTorch

PyTorch is also built on Python. It is almost similar to TensorFlow based on the nature of projects chosen. However, for faster development it is the better choice.

CNTK

This is Microsoft Cognitive Toolkit that focuses on creating deep learning neural networks. It’s built on similar lines with TensorFlow but is a little more complex to deploy. It has a broader range of APIs including Python, Java, C, and C++.

Caffe

This is the best application in industrial disposition and academic research projects. It’s an open-source AI tool with a Python interface and was developed by the University of California. It has an enormous processing power of up to 60 million images per day.

Apache MXNet

This AI tool is what Amazon has adopted as its deep learning framework on AWS. It is not directly owned by any major corporation. It can run smoothly on multiple machines and GPUs and supports a range of APIs including Python, Scala, R, JavaScript, Perl, Go and Julia.

Keras

Keras is an extremely user friendly tool that is built on TensorFlow. It’s the go-to tool when one is need of fast prototyping. It runs seamless on both GPU and CPU. It facilitates completion of experiments from the start to the end with little or no delay.

OpenNN

This is another open-source library that is often used to stimulate neural making it another important component of deep learning research. It’s written in the C++ language. It offers a platform for developers seeking to upgrade to advanced analytics.

AutoML

This is another AI tool that is currently available. It automates processes that are repetitive and tedious in nature like modeling, hence helping data scientists to concentrate on handling other problems at hand. It has made machine learning easier for anybody as even someone without much ML experience can easily navigate it.

H2O

This is an open-source business oriented AI tool. It is written in Java and its interfaces are for Python, R, Java, Scala, Coffeescript and JSON. This tool can be used in risk analysis, predictive modeling, insurance analytics and healthcare.

Types of AI

There are four types of AI namely:

Reactive AI

This one uses algorithms to give the best output based on a set of inputs. An example is a chess playing AI. It reacts to give the best possible strategy to win the game. They are however, very static and will give the same output in any other identical input.

Limited memory AI

This one can use past experience or update itself based on new data. The updating is nevertheless limited thus its name. The length of the memory is also relatively short.

Theory-of-mind AI

They have extensive ability to learn and even retain past experiences. They are fully adaptive. Examples include advanced chat bots that can even pass the Turing Test, by fooling a person to believe they are chatting with a human being.

Self-aware AI

This is where the AI is self-aware of its existence. It’s still in the world of science fiction and has not become a reality. Some experts however, are of the opinion that AI will never reach here.

Conclusion

Businesses that have been able to adopt AI have experienced growth within a short time thanks to the efficiency AI brings to the table. This AI era can be compared to the digital era that saw us move from the paper based way of doing things. This like AI is going to do, revolutionize our way doing things. At the center of this transformation are a number of AI tools which companies ought to compare and identify the one suitable for them based on their needs.

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