With the importance of data to business operations, machine learning (ML) and artificial intelligence (AI) technologies have seen increasing levels of adoption by organisations in recent years. The utilisation of machine learning techniques allows AI systems to independently analyse data and recognise underlying patterns.
Specialists can leverage these insights to make better-informed decisions to drive success, significantly improving business intelligence (BI) and customer experiences. As the amount of data generated by businesses and consumers continue to grow, the demand for ML and AI technologies is increasing.
Organisations recognise that early adoption of ML and AI processes are likely to give them a competitive advantage in their respective markets. This has quickly increased demand for skilled professionals able to facilitate the integration and maintenance of these complex systems.
Examples of popular technologies which use ML and AI:
Machine learning algorithms identify patterns and relationships, make predictions, and automate decision-making processes. Meanwhile, AI enables businesses to perform tasks such as reasoning, analysis, and decision-making in popular products such as:
Digital assistants: By now, we are all familiar with Apple’s Siri and Amazon’s Alexa devices. These assistants use AI and ML for speech recognition, language interpretation and in their personalised responses to questions. We are also seeing similar processes being used in new AI-based language models such as ChatGPT.
Image and video recognition: The use of AI and ML algorithms to recognise and categorise digital media is used in security surveillance, medical imaging and software like Google Lens. This involves the creation of a neural network which processes individual image pixels.
Self-driving cars: Autonomous cars use computer vision (CV), high-resolution cameras and lidars which monitor a car’s immediate surroundings. Combined with machine learning and a multi-layered neural network, this allows for cars to analyse images with great accuracy and drive themselves in complex environments.
Cybersecurity: Applications designed for security utilise AI and ML to identify patterns which imply suspicious or unusual activity. This is proven to be far more effective than human or traditional software solutions.
The demand for skilled AI and ML professionals is increasing, with an almost 75% increase in job opportunities in the past four years. With how the market is developing, ML and AI jobs are poised to continue to increase in demand and provide high levels of compensation for decades to come.
In order to position yourself for success in this field, it is essential to have a solid foundation in computer science; including a proficient understanding of programming languages such as Python, Java, or C++. Additionally, a familiarity of data analytic processes and statistics is essential as AI and ML rely heavily on data.
What are some industry challenges?
While AI and ML are experiencing radical growth, it does experience its challenges. One is data quality. Incomplete or biased data can lead to inaccurate predictions and biased algorithms, compromising the integrity of their outputs. Tackling this is a complex and ongoing challenge.
There is also the question of transparency. Considering the complexity of ML algorithms, especially with deep learning neural networks, it is typically very difficult to understand the inner workings of the algorithm after its trained. This creates issues around the reliability of conclusions due to the perceived lack of transparency.
What are some important ML algorithms and techniques and skills in today’s market?
Deep learning: Machine learning is defined as the use and development of computer systems able to use algorithms and statistical models to learn and adapt without specific instruction.
Deep learning is a more elaborate process of machine learning, utilising an artificial neural network inspired by the human brain. This uses multiple layers of processing to extract progressively higher-level features from data.
Low level features refer to simple and observable data attributes which may be directly extracted without much analysis, such as pixel values in an image. High level features are complex or abstract data attributes such as patterns or relationships.
Natural Language Processing (NLP): The development of algorithms and models which analyse, understand and generate human language.
This technology uses several techniques including statistic models, machine learning and deep learning. Virtual assistants and speech recognition software are examples of applications which use NLP.
Big data analysis: Data sets too large and complex for traditional data processing software are called big data. This includes data from various sources in different sizes, some data sets being several zettabytes large. This sort of data often relates to human behaviour and interaction and is valuable to business intelligence.
The industries already using AI and ML extensively include healthcare, marketing, financial services and retail, however new industries are integrating AI and ML into their processes every day. This is driven by the general forward progression of technology along with the increasing availability of big data.
Learn more about the tech boom here.
Review your AI and Machine Learning skills
If you want to either jump into AI and ML or expand your current skills, there are several online courses, boot camps, and tutorials available to help build up your technical knowledge. Joining online communities and attending webinars/events is also a good way to stay up to date with ML and AI news and trends. Events like the AI summit and MLconf are very prominent in the tech space, which showcase the latest research and best practices in the evolving sector.
Work with us today to begin your tech journey!
The adoption of machine learning and AI technologies has become increasingly important for organisations as data continues to play a crucial role in business operations. With the increasing demand for skilled AI and ML professionals, individuals with technical knowledge in computer science and data analytics have a great opportunity to develop their careers.
Contact Senovo IT to enhance your IT career journey today.