AI Blog Series

The Technologies that are enabling AI and Machine Learning

Introduction The possibilities of artificial intelligence (AI) and machine learning (ML) are seemingly endless. Every day, new developments are pushing the boundaries of what can be achieved in the world of AI and ML. From automated cars to facial recognition systems, it’s clear that AI and ML can change the way we live our lives.

But what technologies are enabling AI and ML? From deep learning to natural language processing (NLP), there are a myriad of technologies that are driving the AI revolution. In this blog post, we’ll explore the technologies that are powering this advancement in AI and ML so that you can better understand what’s driving the AI and ML revolution
Deep Learning Deep learning is a type of machine learning that uses a set of algorithms that take input data, identify patterns during the learning process, and learn to carry out tasks based on this data. This type of learning is typically done in an artificial neural network (ANN), which is essentially a circuit board that resembles the human brain in its structures and connections. With deep learning, machines can learn complex tasks without being explicitly instructed.

The most common types of deep learning neural networks are convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). These neural networks are used in a multitude of applications, including facial recognition, natural language processing, computer vision, robotics, and more.
Natural Language Processing Natural language processing (NLP) is a type of AI technology that enables machines to comprehend, analyse, and interpret human language. NLP can be used to understand and respond to a spoken or written statement, generate new natural language from structured data, and summarize large amounts of text. Furthermore, it can offer context-based interpretations and can even recognize sarcasm.

NLP has been instrumental in the development of conversational AI, helping machines interact more like humans. This type of technology is increasingly being used in many industries that rely heavily on customer service, such as banking, healthcare, and customer engagement. NLP can also be used for translation, sentiment analysis, and smart search engines, among other applications.
Reinforcement Learning Reinforcement learning is a type of machine learning that is commonly used in robotics and artificial intelligence. This type of learning focuses on learning by trial and error and rewards and punishments. Through reinforcement learning, machines can take an action in any given situation and can learn what behaviours lead to a favourable outcome.

Reinforcement learning has enabled machines to learn to play complex games like chess, complete sophisticated tasks as autonomous robots, and conduct medical diagnosis. This type of learning helps machines make better decisions by learning from their past experiences.
Unsupervised Learning Unsupervised learning is the type of machine learning that uses input data without labels to discover unknown patterns in data. With unsupervised learning, machines can recognize patterns in data without having to be explicitly instructed. This type of learning is often used in anomaly detection, cluster identification, and market segmentation.

Unsupervised learning can be used to identify correlations between different data sets, allowing machines to improve their accuracy and performance. This type of learning is also useful in tasks like facial recognition, natural language processing, and gene expression analysis.
Data One key factor enabling the proliferation of AI and ML is the availability of data. With increased sources of large-scale data available, machine learning algorithms can now be more accurately trained and deployed. From government-maintained datasets that are used to train facial recognition algorithms, to consumer-level health tracking devices which can provide anonymous, aggregated data that can be used to train ML models, data is becoming increasingly available.
Compute In addition to data, the availability of sophisticated computing power has also enabled AI and ML advancements. CPUs (Central Processing Units) used to drive computers and power devices have become smaller and more powerful over time. GPUs (Graphics Processing Units) and ASICs (application-specific integrated circuits) are now the key components in supercomputers that are used to crunch data in order to train AI and ML models. Amazon's EC2 instance and Google's Cloud ML Engine are examples of powerful cloud-based compute services that provide access to otherwise complex and costly computing resources for AI and ML development.
Software Frameworks The advent of software frameworks and libraries for AI and ML development is another key factor enabling the industry. Key frameworks, such as TensorFlow, Keras, PyTorch, and Caffe, simplify the development of AI and ML projects. These frameworks provide the structure and abstracted processes that developers require to accelerate the development of their models. As a result, the development of relatively sophisticated AI and ML models can now be achieved by developers in a relatively short amount of time.
Development Platforms Finally, the availability of consumer-level development platforms is enabling developers of all levels to get started in AI and ML development. Companies such as NVIDIA, Google, and Apple have created consumer level development boards and platforms that allow aspiring AI and ML developers to get started on their development journey. These platforms provide powerful GPU and AI processing capabilities, while also simplifying the development of AI and ML models. This accessibility has enabled beginners to get a kick-start on their development journey and to enable organizations to quickly build up their AI capabilities.
Conclusion The combined result of the key technology advancements mentioned above has resulted in an AI revolution. AI and ML models are increasingly prevalent, from autonomous driving technologies, to facial recognition software and chatbots. As these technologies continue to be developed and polished, the possibilities for AI and ML continue to grow.
profile Tim Davies
5 min read