Why is machine learning important?

Machine learning enables computers to automatically learn and improve from experience as opposed to being specifically programmed. As a result, it’s possible to create highly accurate and efficient models that can solve complex problems and make predictions based on new and unseen data. 

Advances in computing power, data storage and algorithm development have made machine learning more accessible and scalable, making it an essential tool for a wide range of applications across fields such as finance, healthcare and transportation. 

Where machine learning is used today 

You are likely interacting with some form or another of machine learning tech in your daily life, perhaps without even realising it. Applications currently using the tech include:

• Image recognition in social media apps
• Personalised recommendations in e-commerce websites
• Fraud detection in financial transactions
• Speech recognition in virtual assistants, such as Siri and Alexa
• Medical diagnosis and treatment recommendation systems
• Self-driving cars and autonomous vehicles
• Predictive maintenance in industrial settings
• Natural language processing for translation and sentiment analysis
• Credit scoring and risk assessment in lending and insurance 
• Energy load forecasting for efficient energy management.

What are the main types of machine learning?

There are three main varieties of machine learning: supervised, unsupervised and reinforcement learning. Let’s take a look at each.

• Supervised learning

With this approach, the algorithm is trained on a labelled dataset, where we already know the correct output. The algorithm then learns to map input variables to the correct output and can then make predictions on new, unseen data based on this learning. Supervised learning examples include image classification, speech recognition and regression analysis. 

• Unsupervised learning

Unlabelled datasets are used in unsupervised learning, so the algorithm needs to work to find patterns or structure within the data on its own. Tasks such as anomaly detection, clustering and dimensionality reduction are examples of this method.

• Reinforcement learning

With reinforcement, an agent is used to interact with an environment to learn how to make decisions that maximise a reward. The agent then receives feedback in the form of a reward signal and then uses this feedback to adjust its behaviour over time. This kind of learning is often used in game playing, robotics and other dynamic decision making tasks. 

What’s required to create quality machine learning systems?

Several key elements are required to create good learning systems. We’ve covered some of the top contenders to get you started. 

High-quality data

It’s crucial that the data used to train a machine learning model is of decent quality. Data ideally should be clean, representative and relevant to the problem of focus.

Appropriate algorithms

Choosing the right learning algorithm for a given problem is important. Each algorithm has its own strengths and weaknesses and the accurate choice will depend on the nature of the data and the problem being solved. 

Feature engineering

The process of selecting, transforming and scaling the input variables to a machine learning model is feature engineering. Quality feature engineering can greatly improve the accuracy of a model. 

Robust evaluation

It’s important to evaluate a machine learning model on a separate, unbiased dataset to ensure that it is not overfitting to the training data.

Iterative development

The development of good machine learning systems very often involves an iterative process of experimentation and refinement, where different algorithms, parameters and features are tested and improved over time. 

Domain knowledge

A deep understanding of the problem domain and the data is often critical to developing effective machine learning systems. This can help guide feature engineering algorithm selection and evaluation.

Adequate computing resources

Machine learning often involves processing large amounts of data, and may require substantial computing resources such as powerful GPUs and cloud-based computing services.

What is bootstrapping machine learning?

A technique of resampling data to improve the stability and accuracy of a model is known as bootstrapping. It involves randomly sampling the data with replacements to create multiple data sets, training a model on each dataset, and aggregating the results to get a more robust estimate of model performance. 

What are some popular machine learning methods?

There are a wide range of learning methods to choose from, with the best method being dependent upon the specific problem being solved and the nature of the data. Here are some of the most popular:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forests
• Naive Bayes
• Support Vector Machines (SVMs)
• K-Nearest Neighbours (KNN)
• Neural Networks (including deep learning)
• Gradient Boosting 
• Clustering (such as K-Means or Hierarchical clustering)

What is AlphaGo?

A computer program developed by a British artificial intelligence company, DeepMind, which was acquired by Google in 2015, AlphaGo is a deep learning neural network. It uses a combination of supervised learning and reinforcement learning to play the board game ‘Go’. In 2016, AlphaGo defeated Lee Sedol, one of the world’s top-ranked Go players, in a five-game match, making the first time an AI program had defeated a human professional Go player. This was considered a significant milestone in the field of AI and demonstrated the power of machine learning for complex decision making tasks.

What is the dark web?

Part of the internet’s digital landscape is not indexed by search engines. Known as the dark web, it is often accessed by those who lead or participate in illegal activities, including the sale of prohibited firearms, drugs, stolen data and hacking services. Due to the anonymity of the dark web, law enforcement agencies find it challenging to monitor and prosecute. 

While the dark web has a reputation for illegal activities, it’s important to acknowledge that not all content found there is illegal. There are legitimate purposes the dark web can serve, particularly the identity and privacy protection of reporters in repressive regimes, activists and whistleblowers. 

In this article we delve into different aspects of the dark web, including:

• Characteristics 
• Types of threats
• Dark vs deep vs surface web
• Pros and cons
• Example of the dark web
• Is it illegal in Australia to access the dark web?
• How do you get on the dark web?

Characteristics of the dark web

Core characteristics of the dark web include no webpage indexing, the use of virtual traffic tunnels, and no access via traditional browsers, all aiding in keeping content off the radar.

• No webpage indexing

Not indexed by standard search engines, the dark web uses a system that enables users to access content and sites that are invisible to anyone using what’s known as the ‘surface web’.

• Virtual traffic tunnels

Anyone who wants to access the dark web needs specialist software, such as Tor, which generates virtual tunnels for their internet traffic. These tunnels make it incredibly difficult for anyone to track user activity, or figure out their location. This anonymity acts like an invisibility cloak for criminals while they go about their illegal business.

• Inaccessible by traditional browsers

Standard browsers such as Safari, Chrome or Firefox won’t allow you to access the dark web. Those wanting to access the ‘dark side’ need specialised browsers such as ‘Tor Browser’ or ‘Tails’, both of which will give users access to hidden services and websites on the dark web.

Types of threats on the dark web

Similar to using the surface web, there are unsurprisingly a number of threats you may encounter if using the dark web. Let’s take a look at a few common contenders.

• Malicious software

Rife with malware and viruses that can infect and compromise a user’s device, malware is often spread through phishing emails or malicious links. 

• Government monitoring

In an attempt to identify illegal online activity, government and law enforcement agencies still monitor the network. Monitoring approaches could include tracking user activity and attempts to de-anonymise Tor traffic.

• Scams

False marketplaces and phishing emails are just some of the scams you could encounter on the dark web. Other common scams include Ponzi schemes and ‘investment opportunities’ that try to trick users into sharing their login details. Much like the surface web, theft of personal information and money are target areas.

What’s the difference between the Dark, deep and surface web?

You may have heard the terms dark, deep and surface web bandied about. They are essentially the three layers of the internet. 

Deep web

Also referred to as the hidden or invisible web, the deep web is not indexed, so it cannot be accessed by standard browsers. It’s unknown how big the deep web is, but it’s believed to be larger than the surface web. The deep web includes password-protected websites, content behind paywalls and subscription based services.

Dark web

Like the deep web, the dark web is also not indexed and cannot be accessed by regular browsers like Chrome and Safari. The difference between deep and dark is that the dark web uses encryption software to create even more security and is often completely inaccessible to the average internet user.

Surface web

This is the section of the internet that the majority of us engage with on a daily basis, but is speculated to only represent 4% of the total internet. Also referred to as the ‘visible web’ it’s the section that can be accessed and indexed by popular, standard search engines such as Google, Bing or Yahoo. The surface web consists of websites, web pages, and online content that are publicly available. 

Pros and cons of the dark web

The primary advantage of the dark web is anonymity. It is also the main disadvantage.

Those living in countries with repressive regimes can use this area of the internet to communicate without being monitored or arrested. The same benefit is available to whistleblowers, activists and journalists, enabling their privacy to be protected and the ability to release details that may otherwise be too dangerous to reveal. 

 It depends on who you speak to when it comes to perceived disadvantages of the dark web. The lack of regulation means users are more vulnerable to scams, fraud and hacking attempts, while it also lends itself to being used by criminals taking part in illegal activities.

Example of the dark web 

One of the most famous examples of the dark web is the Silk Road marketplace. An area where users could purchase illegal goods anonymously, the site was shut down by the FBI in 2013. Many copycat sites have since appeared to pick up where Silk Road left off. 

Is it illegal to go on the dark web in Australia?

While it isn’t illegal to access the dark web in Australia, or most other parts of the world, it is important to be mindful that much of the activity taking place is illegal. Accessing illegal content or taking part in any criminal activity is punishable by law, regardless of where the user is located geographically.

Free open-source software such as Tor is used to protect online privacy and enable anonymous communication. While used by some for legal activity, there are certainly illegal events taking place within the network too. As mentioned, users need to be aware of the risks involved before venturing in.