As artificial intelligence (AI) and machine learning (ML) dominate headlines and reshape industries, they are not just buzzwords – they’re revolutionising the way we work.
With 2024 drawing to a close, Cambridge Advance Online, the University of Cambridge’s online short course provider, taps into the expertise of AI and data science academic lead, Dr Russell Hunter, to uncover the top ML trends business leaders need to know as they navigate this rapidly evolving landscape.
This comes as an increased number of UK businesses integrate AI into daily operations and more UK professionals explore careers within ML:
- IBM’s latest global AI adoption index found that 42% of enterprise-scale companies claim to be actively deploying AI in their business – the same amount that were still exploring its use the year prior.
- Latest Google Trends data indicates a 30% uplift in searches for machine learning jobs MoM while queries such as “how to become a machine learning engineer” and “machine learning engineer jobs” experience a 300% increase in interest over the last five years.
- Questions around how to get into the industry are commonly being asked alongside the term “machine learning”, including, “What are the basic requirements to learn machine learning?” and “Should I go into AI or ML?”.
Dr Russell Hunter works within the Department of Engineering at the University of Cambridge and leads Cambridge Advance Online’s Leveraging Big Data for Business Intelligence course.
“These advanced systems are transforming industries by accelerating the speed and precision of decision-making, driving greater efficiency and enhancing customer experiences.”
ML Ops
ML operationalisation management – or ML Ops for short – focuses on the deployment, monitoring and governance of ML models in production. “In the early phases of our innovation work in this space, there was a worry about drift in performance, managing multiple variations of models, and retraining new data without affecting the business”, Dr Hunter recounts.
“This is the kind of thing that ML Ops can help solve as it integrates best practices from a well-established practice in DevOps to ensure the reliable and scalable operation of ML systems.” The standardisation and streamlining of ML workflows through ML Ops have become essential as businesses scale their AI capabilities. This trend has solidified its place in the industry, enabling faster deployment and maintenance of ML models.
Autonomous decision-making
These advanced systems are transforming industries by accelerating the speed and precision of decision-making, driving greater efficiency and enhancing customer experiences. By automating manual processes, ML technologies can increase businesses’ abilities to analyse vast amounts of data quickly while uncovering patterns and making informed decisions.
Dr Hunter explains how autonomous systems can be applied to industries such as healthcare, “Sophisticated multimodal AI can analyse genetic data and patient histories to recommend personalised treatment plans. This leads to more effective and individualised health care. Similarly, by leveraging data from electronic health records, these systems can predict patient outcomes or complications, which allows for proactive intervention.”
Quantum machine learning
“As AI continues to grow and move forward, the computational resources needed to grow exponentially too”, notes Dr Hunter. This pioneering area is attracting significant research and investment, particularly in high-stakes industries like finance and pharmaceuticals and big names such as IBM and Google.
Dr Hunter continues, “Quantum AI has the potential to allow more accurate and complete models as they are not constrained by classical computing. This is more speculative for the future, but it’s an exciting frontier and has the potential to solve problems beyond the reach of classical algorithms.”
Edge AI
Another cutting-edge development, Edge AI brings an immediate processing capability which is crucial for applications in autonomous vehicles, industrial automation and healthcare monitoring, where time-sensitive tasks require prompt responses. According to Dr Hunter, this is achieved by processing data locally on the device, reducing latency, enabling real-time decision-making and minimising the amount of data that needs to be transmitted to central servers.
By processing sensitive information locally, this also enhances privacy and security, reducing the risk of data breaches during transmission. However, Dr Hunter does point out that “challenges such as hardware limitations, integration complexity, and the need for efficient management and maintenance of numerous edge devices curtail the full effectiveness of edge AI.”
Augmented workforces
While there are concerns that AI will replace humans in the workplace, Dr Hunter believes that the latest AI developments can augment rather than undermine human contributions, “The augmented workforce trend leverages AI to assist rather than replace human workers, transforming job roles and boosting productivity across various sectors.
“This collaboration between humans and AI combines the strengths of both, allowing AI to handle repetitive, data-intensive tasks while humans focus on strategic, creative and interpersonal activities that require emotional intelligence and critical thinking. Rather than eliminating jobs, AI reshapes them, leading to the creation of new roles that require managing, programming and collaborating with AI systems.”
It is crucial to keep an eye on these developments as a business leader, to ensure your organisation is fully equipped to gain an edge by leveraging AI and ML.
For an indepth look at these insights and additional trends, read Dr Hunter’s machine learning trend analysis on the CAO blog.