Artificial Intelligence 101

// Artificial Intelligence 101​

What is AI?

When we talk about artificial intelligence (AI), what we are referring to is the simulation of human intelligence in machines. More specifically, these are machines that have been programmed to think like humans and mimic their actions. In fact, the concept of artificial intelligence can also be applied to any machine that shows traits associated with a human mind, with such traits including learning and problem-solving.

The goal of AI is to be able to ‘think’ rationally and act in a way that maximises the chances of achieving a particular objective. The overarching aim of artificial intelligence is for machines to be able to learn, reason, and ultimately perceive the world around so as to achieve a particular goal.

Machine learning is another term often brought up when discussing artificial intelligence. It is a subset of AI and refers to the concept of computer programs automatically learning and adapting from new data, without human assistance. This type of automated learning is enabled through deep learning techniques, which involves the processing of a massive amount of unstructured data, including images, videos, and text.

// Artificial Intelligence 101​

Types of AI

There are two forms of artificial intelligence which are weak and strong variations.

Weak AI refers to a system whose objective is to accomplish one specific job. A simple example of this would be Apple’s Siri, where you ask the assistant a question and it replies with a pre-programmed answer.

Conversely, strong AI systems work to accomplish objectives that are much more human-like in their complexity. They are designed to handle situations that involve problem solving without human assistance. An example of a strong AI system would be a self-driving car.

Big Data Analytics

// Big Data Analytics

What is Big Data?

When we are discussing big data, we are referring to information that is organised in large and diverse sets that grow at an ever-increasing rate. The different aspects of big data are summed up in what is known as the “three v’s”. These refer to the volume of information, the velocity or speed that it is created and collected, and the variety or scope of the data points that are being covered. The origin of big data is often data mining with the data itself arriving in a variety of formats.

// Big Data Analytics

Two Types of Big Data

There are two categories of big data, which are the structured and unstructured varieties. Structured data is composed of information that is already managed by an organization in databases and spreadsheets. Oftentimes it is very numeric in nature. On the other hand, unstructured data refers to information that is not organised and cannot be categorized as part of a predetermined model or format.

// Big Data Analytics

Collection and Storage

Big data can be collected from a variety of sources. These include social media platforms, personal electronics and apps, via questionnaires, product purchases, and electronic check-ins. In fact, thanks to sensors and other inputs in smart devices, data can now be gathered across a much broader spectrum of circumstances and situations than what was previously thought possible.

In terms of storage, big data is often found in computer databases, with software designed specifically to handle such large and complex data sets used to analyse the stored data. Oftentimes, SaaS companies can be found specialising in the management of this form of complex data.

// Big Data Analytics

Big Data and its Relationship with AI

There is a strong relationship between big data and artificial intelligence. AI becomes better the more it is given data, and big data can be analysed by artificial intelligence better than any human could ever dream of.

The relationship between the two can be thought of as a cycle:

1

Data is given to the AI

2

AI becomes smarter

3

Human interaction is now required less

4

AI feeds itself new data

There are also several ways AI can work when working with big data, including:

Detecting anomalies

Determine the probability of future outcomes

Recognise patterns

Machine Learning

// Machine Learning

What is Machine Learning?

Big data can be collected from a variety of sources. These include social media platforms, personal electronics and apps, via questionnaires, product purchases, and electronic check-ins. In fact, thanks to sensors and other inputs in smart devices, data can now be gathered across a much broader spectrum of circumstances and situations than what was previously thought possible.

In terms of storage, big data is often found in computer databases, with software designed specifically to handle such large and complex data sets used to analyse the stored data. Oftentimes, SaaS companies can be found specialising in the management of this form of complex data.

// Machine Learning

How Does it Work?

Machine learning works by building high-quality algorithms to locate features and patterns in large quantities of data. By doing this, the algorithms can make decisions as well as prediction based on new data.

Applied Intelligence

Applied intelligence refers to the combination of analytics, artificial intelligence, and automation. Through this combination, high-functioning systems are able to greatly surpass human intelligence in a specific area. Applied intelligence has great potential for helping organisations find new and innovative ways to satisfy customers. This is because it can help determine actionable insights from data to help make better decisions. In effect, applied intelligence can help organizations make decisions based on what will happen in the future determined by real-time data, rather than going by the analysis of past data.

Deep Learning

Deep learning is a subset of machine learning and a function of artificial intelligence, which imitates how the human brain works in terms of processing information and creating patterns that are used to make decisions. Its applications range from use in detecting objects, recognizing speech, translating languages, and making decisions.

As a subset of machine learning,deep learning uses hierarchical levels of artificial neural networks to undertake the process of machine learning. The artificial neural networks are designed to be similar to the human brain, and they feature neuron nodes connected in a way that resembles a web. Compared to traditional programs that build analysis with data in a linear way, the hierarchical function of deep learning systems allow machines to process data in a more nonlinear approach.

Responsible AI

// Responsible AI

What is Responsible AI?

Responsible AI refers to an organisational-specific governance framework documenting the way the challenges of artificial intelligence are being addressed. It looks at this from both an ethical and legal perspective and attempts to resolve any ambiguity for who is responsible if something goes wrong.

// Responsible AI

Core Principles of Responsible AI

Artificial intelligence and the supporting machine learning models should be comprehensive, explainable, ethical and efficient.

It should be comprehensive in the sense that it has been clearly defined in relation to its testing and governance criteria, as this is an important step in preventing hacking and other security issues.

Certain aspects of the AI also need to be easily explainable. This includes the purpose of the AI, as well as the process used in its rationalization and decision making. This should be able to be understood by a typical, non-technical user.

Ethics also come to play in the design of AI. There should be a process in place to seek out and eliminate any biases in the machine learning models.

Finally, AI should be efficient. It needs to be able to continually run and rapidly respond to any operational environment changes.

Predictive Analytics

Predictive analytics uses data, statistical algorithms and machine learning techniques to identify the probability of future outcomes occurring based on historical data. The objective is to move past simply knowing what has happened and actually providing an assessment of what will occur in the future.

Any industry can benefit from using predictive analytics to reduce risks, optimize operations and increase revenue. Just some of these industries include banking and financial services; retail; oil, gas and utilities; governments and the public sector; health insurance; and the public sector.

Computer Vision

Computer vision is an area of study that is focused on giving computers the ability to see. It is a multidisciplinary field and could be considered a subfield of artificial intelligence and machine learning.

The objective of computer vision is to understand the content of digital images.This normally involves developing methods of reproducing a human-like capability to see.

Understanding the content of digital images may involve extracting a description from the image, which can be an object, a text description, a three-dimensional model, and more.

Natural Language Processing

Natural Language Processing (NLP) is a subset of artificial intelligence focusing on the way machines understand human language. The objective is to build systems that can understand text and perform tasks such as translation, checking grammar, or classifying the conversation topic.

Some popular examples of NLP include virtual assistants like Apple’s Siri, as well as chatbots found on many websites.

Intelligent Automation

Intelligent automation combines artificial intelligence, machine learning, and process automation to design smart business processes and workflows that think, learn, and adapt independently. Through the application of intelligent automation within enterprise operations, companies can increase efficiencies and gain new capabilities beyond what is humanly possible.

Face the Future with Artificial Intelligence

There are many more advancements and applications to be discovered with Artificial Intelligence, and it will soon be a critical component in gaining a competitive advantage in business. Make sure you and your business are not left behind.

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