Wondering what some of the biggest AI concepts and techniques are?
Emerging as one of the most transformative technologies, AI is revolutionising industries and reshaping the way we interact with machines.
But behind the scenes, there exists a plethora of concepts and techniques driving the success of these intelligent systems.
As an AI-enablement professional services business, we know all about the fundamentals of AI. Cloud, data engineering, machine learning – we do it all.
That’s why we’re unravelling some of the most popular key concepts and techniques that power this remarkable technology.
Machine Learning (ML) forms the backbone of AI systems.
It’s essentially what enables machines to learn from data, improve their performance over time and give us the incredible capabilities that we have today.
Think of the relevant recommendations that come to you on Netflix or Youtube. Or the voice searches that you do with Alexa or Siri.
ML is the magic behind it all.
So what is an ML algorithm and what is an ML model? While frequently used interchangeably, each of these terms have different functions.
Simply put: ML algorithms are the computational procedure used to learn patterns from data. ML models are the result of that learning process and can be used for prediction or action.
is a mathematical or computational procedure used to learn patterns from data and make predictions or take actions based on that learning. It serves as the brain of the ML process, responsible for extracting insights from a given dataset.
ML algorithms are designed to solve specific types of problems, such as classification, regression, clustering, or recommendation.
is the output generated by an ML algorithm after it has learned patterns from the data. It represents the acquired knowledge and serves as a function or a mapping that can be used to make predictions or take actions on new, unseen data.
The model uses the learned patterns and parameters (of the algorithm) that allow it to generalise and make accurate predictions on new instances.
Look at it this way:
An ML algorithm is like a coach who carefully keeps notes on the performance and characteristics of each student to determine their respective positions on the field.
The ML model would be similar to a playbook developed by the coach. The playbook contains strategies and tactics based on the analysed player characteristics, enabling the coach to predict the position of a new student joining the team.
Taking inspiration from the structure and functioning of the human brain, neural networks have been a driving force behind recent advancements in AI.
Deep Learning, a subset of ML, utilises neural networks with multiple hidden layers to extract intricate patterns and representations from complex data.
They are essentially designed to recognise patterns and relationships in data, enabling machines to learn and make predictions.
Yup. Just like a brain.
Imagine a vast interconnected web of artificial neurons. Each neuron receives inputs, processes them, and produces an output. These outputs then become inputs for other neurons, forming a network of interconnected information flow.
It’s kind of like a team of detectives solving a complex crime case.
Each detective represents a neuron in the network. They receive pieces of evidence and information, process them, and provide their findings to other detectives. This allows them to build on one large, connected map of relationships in the data.
And the world of business is starting to love them. According to Levity.ai, the use of artificial neural networks (ANNs) in business has grown significantly in recent years, with a 270% increase in adoption.
Here are some practical examples of what neural networks are capable of:
You can train neural networks to recognise images of cats and dogs. The network takes an image as input, and through its interconnected layers of neurons, it analyses various features like shapes, patterns, and colours. Eventually, the network reaches a conclusion, determining whether the image contains a cat or a dog.
In sentiment analysis, a neural network can classify text as positive, negative, or neutral based on the underlying sentiment. By processing textual inputs word by word, the network learns to understand the context, identifying sentiment cues and providing a sentiment prediction.
Neural networks play a crucial role in autonomous driving systems. They process sensor data from cameras, LiDAR, and radar, recognising objects such as pedestrians, vehicles, and traffic signs. By analysing these patterns, the neural network guides the vehicle’s decision-making, allowing it to navigate the road safely.
Natural Language Processing (NLP):
NLP allows machines to understand and process human language.
Yes, you read that right.
Why would anyone want to do that? To facilitate and optimise communication between humans and computers.
Imagine NLP as a translator that helps bridge the gap between human language and computer understanding.
It allows machines to process and derive meaning from text or spoken words, just like a skilled interpreter helps people from different language backgrounds communicate effectively.
And by 2025, the global NLP market revenue is expected to reach £12,26 billion in hardware, £71,07 billion in software, and £14,42 billion in services.
Some of the most common use cases for this incredible technology are:
Voice assistants like Siri, Alexa, and Google Assistant use NLP to comprehend and respond to spoken commands or queries. They convert spoken words into text, analyse the text using NLP techniques, and generate appropriate responses or perform actions.
NLP enables machine translation systems to automatically translate text from one language to another. These systems analyse the structure and meaning of sentences, taking into account grammar, vocabulary, and context, to generate accurate translations. Popular examples include Google Translate and Microsoft Translator.
Chatbots and Virtual Assistants:
Chatbots and virtual assistants use NLP to understand and respond to users in a conversational way. By applying NLP techniques, they can extract intentions from user messages, provide relevant information, and carry out specific tasks, such as booking appointments or answering frequently asked questions.
Computer vision is a form of technology that enables computers to see, interpret, and understand visual information from images or videos.
It involves extracting meaningful insights and making sense of the visual world, similar to how our eyes and brain work together to process and comprehend visual stimuli.
Think of computer vision as a set of eyes for machines, allowing them to perceive and analyse the visual world around them, just like our eyes enable us to understand our surroundings.
To better grasp computer vision, let’s imagine it as a visual cortex for machines.
The visual cortex in our brain processes and interprets visual information, allowing us to recognize objects, detect motion, and make sense of what we see.
Similarly, computer vision provides machines with the ability to “see” and understand the visual content.
Computer vision enables machines to detect and identify objects within images or videos. For example, self-driving cars utilise computer vision algorithms to detect pedestrians, traffic signs, and other vehicles on the road. This information is then used to make decisions and navigate safely.
Facial recognition is a prominent application of computer vision technology. It allows machines to analyse and identify individuals based on their facial features. This has applications in security systems, access control, and even photo organisation, where software can automatically tag people in images.
Computer vision algorithms can classify images into different categories or classes. For instance, a computer vision model trained on a dataset of animal images can accurately classify new images into categories like cats, dogs, or birds. This has applications in content moderation, object recognition, and image search engines.
Computer vision plays a vital role in medical imaging, aiding in the analysis of X-rays, MRIs, and other medical scans. By detecting anomalies, segmenting organs, or identifying specific conditions, computer vision assists healthcare professionals in diagnosing diseases and planning treatments.
Computer vision equips machines with the ability to “see” and understand the visual world, similar to how our eyes and visual cortex process and comprehend visual stimuli.
By enabling machines to interpret visual information, computer vision expands the possibilities for automation, safety, and improved decision-making in various domains.
Reinforcement Learning (RL):
Reinforcement Learning (RL) is a machine learning approach that enables an agent to learn through trial and error in dynamic environments.
It is inspired by the way humans and animals learn by interacting with the world, receiving feedback in the form of rewards or penalties. RL focuses on training agents to make optimal decisions over time to maximise cumulative rewards.
Imagine reinforcement learning as a process similar to training a pet or teaching a child.
Just as a pet learns to perform tricks or a child learns to navigate the world through positive reinforcement, RL trains machines to make intelligent decisions through a reward-based learning process.
To better understand RL, let’s compare it to training a dog. You want your dog to learn a new trick, say “sit.” You start by giving the command, and when the dog sits, you reward it with a treat or praise.
If the dog doesn’t respond correctly, you withhold the reward. Over time, the dog learns to associate sitting with positive reinforcement and starts sitting more often.
According to Maruti Techlabs, RL is capable of doing the following:
Space management optimisation in warehouses:
RL algorithms can be built to optimise space utilisation in warehouses. These algorithms reduce transit time for stocking and retrieving products, leading to better space utilisation and improved warehouse operations.
RL techniques, such as Q-learning, can be leveraged to optimise pricing strategies based on supply and demand. RL algorithms help businesses adjust prices to maximise revenue from products.
Customer delivery optimisation:
RL can be used to optimise customer delivery processes. By using multi-agent systems and reinforcement learning, businesses can reduce fleet costs, improve execution time, and meet customer demands effectively.
RL algorithms are proving their worth in eCommerce by allowing merchants to learn and analyse customer behaviours. This enables retailers to tailor communications, promotions, and shopping experiences to capture customer loyalty.
Financial investment decisions:
Reinforcement learning can be used for evaluating trading strategies and optimising financial objectives. It has been applied in trading systems for single trading security or trading portfolios.
Generative models are a class of ML models that learn the underlying patterns and distribution of a given dataset.
These models are capable of creating new data samples that resemble a given training set. They enable the creation of new, realistic samples that resemble the original data.
Think of generative models as artistic painters who study a collection of artwork and then create their own unique pieces inspired by that collection.
To better understand generative models, let’s imagine them as artists who observe a gallery filled with paintings. They carefully study the styles, colours, and shapes of the artwork and then use that knowledge to create their own original paintings that capture the essence of the gallery.
And Gartner predicts that by 2025, generative AI will be producing 10% of all data (currently less than 1%) with 20% of all test data for consumer-facing use cases.
Additionally, by 2025, generative AI will be used by 50% of drug discovery and development initiatives, and by 2027, 30% of manufacturers will use generative AI to enhance their product development effectiveness.
So what are these generative models capable of?
By learning the patterns and structure of a dataset of images, generative models can generate new, realistic images related to the original ones.
It’s the technology behind the incredible imagery coming out of AI art tools like Dall-e and Midjourney. And the applications extend to generating realistic faces, creating artwork, or even producing synthetic data for training purposes.
Generative models can learn the patterns and relationships within a collection of text data, such as books or articles, and can generate coherent and contextually relevant sentences or paragraphs.
We’ve seen these models in action with tools like ChatGPT and Notion AI.
By learning the patterns and structures of any music, generative models can create new musical compositions.
There are numerous tools online like SoundRaw or Soundful that allow you to use these models to create your own AI-generated music. Whether background music, assisting composers, or even creating unique soundtracks for movies or games, the uses are vast.
Generative models can improve existing datasets by creating synthetic samples that expand the richness of the data.
This helps in training ML models with limited data and improves their generalisation capabilities. For instance, in computer vision tasks, generative models can generate new images with different backgrounds, lighting conditions, or object placements to augment the training dataset.
Equip Yourself With More AI News, Insights and Content
AI is a multifaceted field, drawing upon various concepts and techniques to build intelligent systems capable of perceiving, reasoning, and learning.
By understanding these core concepts, we gain insight into the inner workings of AI systems and appreciate their vast potential to transform industries and enhance our daily lives.
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