Machine Learning and AI for your businesses

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Briefly, artificial intelligence (AI) computing, which incorporates machine learning, attempts to emulate how human beings solve problems and make decisions. In other words, it is the science and engineering that underpins the creation of intelligent machines and intelligent computer programs.

Briefly, artificial intelligence (AI) computing, which incorporates machine learning, attempts to emulate how human beings solve problems and make decisions. In other words, it is the science and engineering that underpins the creation of intelligent machines and intelligent computer programs.

Alan Turing was a leading AI pioneer who posed the question “Can machines think?” and developed the famous Turing Test, which has underpinned much of AI philosophy. Since then, AI development has followed a bumpy path with periods of rapid growth followed by stagnant periods of disillusionment. Currently, AI is big news and has generated many myths, inflated expectations, and even paranoia. For example, Elon Musk, whose driverless car business model  relies on AI, famously said, “AI is a fundamental risk to the existence of human civilisation.”

However, as Erik Larson points out in his book “The Myth of Artificial Intelligence,” AI is great at solving specific problems, as demonstrated by AlphaGo’s 2016 defeat of the Go World Champion and AI’s discovery of drugs to combat covid-19. However, no algorithm exists or is likely to exist for artificial general intelligence (AGI) or what Nick Bostrom describes in his book of the same name, Superintelligence. In other words, there is nothing to fear. Consequently, AGI is, for the foreseeable future, confined to science fiction. Shame in some ways – we all loved Ex Machina.

But enough of that – from a business perspective, we should focus on understanding the basics of AI and applying them to solving problems in the real world we inhabit today. To that purpose, we will next look at how machine learning and deep learning fit into AIs landscape.                                                                                                                                                                                                                                                                                                                                                                                        

What is Machine Learning?

You will undoubtedly have heard of machine learning and deep learning, both of which fall under the umbrella of AI, with deep learning being a spin-off of machine learning. Both learn, but they do so in different ways.

Cutting what could be a long story short, machine learning concerns learning supervised by human beings and usually uses structured or semi-structured data sets. However, deep learning can automatically manage unstructured data such as images and text and learn unsupervised.

Machine Learning Technology…

This is where neural networks enter the picture. Neural networks are our first stab at simulating the human brain in software and hardware. They work amazingly well, though they tend to make occasional elementary errors. And to add another dimension to the mystery, we do not fully understand how they work.

A neural network consists of up to millions of densely interconnected processing nodes, usually organised in layers. Data moves through than in a forward direction. For example, a node might connect to several nodes in the layer below it from which it receives data and several nodes in the layer above it to which it sends data.

The node processes the incoming data items, and if the result of the calculation exceeds a certain threshold, it passes the result to all its outgoing connections. The process is analogous in neuroscience to a neuron firing.

Neural nets are trained (or in deep learning train themselves) using massive datasets (big data) fed to the bottom layer. The data passes through the multiple layers which modify it continually until it emerges at the output layer. While training, weights apportioned to incoming data and thresholds are adjusted until the output provides the correct answer.

For instance, you might input an image of a cat. Initially, the output might be traffic lights or a dog. You then adjust until the output recognises the image as a cat. Nobody knows precisely what is happening in the body of the network, but we keep on training it until it consistently provides correct answers.

Business Applications of AI…

AI is highly capable of dealing with many business-critical tasks. These are just some examples of its capabilities  that currently fuel massive business growth:

  • Automatic speech recognition or natural language processing (NLP) – considerable advances in the field have been made in recent years. The technology underpins applications such as Amazon’s Alexa, chatbots and virtual assistants, text and sentiment analysis, machine translation,  and more.
  • Recommendation engines have grown into a huge business and are used by many streaming services such as Netflix, marketplaces such as Amazon, department stores, and social media. In addition, recommendation engines are appropriate to any situation where a human decides, and data is available on their previous behaviour.
  • Computer vision and image recognition have wide-ranging applications, including medicine for detecting and diagnosing disease, quality control in factories, autonomous vehicles, robotics, scene reconstruction, security, and crime detection.
  • High-frequency trading – Ai is transforming financial services globally. AI can make fast trading decisions and predict financial markets and capitalise on trading opportunities. When applied to high-frequency training, it is a game-changer and can potentially deliver huge profits.

A recent survey conducted by IBM on how businesses leverage AI solutions produced the following results.

How is artificial intelligence (including machine learning or deep learning) used in your business?
Business operation/functionPercentage of businesses applying it
IT operations53%
Digital data security43%
Data management and classification40%
Marketing analysis38%
Sales forecasting36%
Optimisation of customer experience35%
Fraud detection34%
Robotic process automation33%
Logistics optimisation33%
Workforce utilisation and32%
Production and inventory forecasting32%
Supply chain analysis and management31%
Network optimisation31%
Risk and compliance30%
Intelligence & surveillance analysis30%
Social network and customer communication analysis27%
Human resources optimisation27%
Financial management and planning26%
Anomaly detection25%
Predicting mechanical failures/preventive maintenance23%
Product development23%
Product recommendations or offers21%
Physical security21%
Contact center optimisation20%
Recommendation engine20%
Contract or legal document analysis and workflow13%

Data source: AI in the enterprise – from research conducted in 2021 by IBM Market Development & Insights

What is apparent from the wide range of areas that leverage AI is that there are few business areas where it is failing to make an impact. AI plays a vital role in areas ranging from automation of routine processes to complex decision-making.

Finally…

We will finish with three interesting statistics:

  • By 2030 analysts expect AI to contribute $15.7 trillion to the global economy
  • 37% of organisations and businesses currently use AI, and many more intend to do so
  • Although AI will replace 85 million jobs, it will create 97 million new ones

Is your organisation already employing AI and machine learning? If not, then now is the time to look at its potential seriously. AI’s transformative power is enormous, though we must take issue with Elon Musk – its threat to the existence of human civilisation is overstated.

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