Machine learning is not new, but since its inception in the late 1950s, when the term was coined by IBM scientist Arthur Samuel, the technology has come a long way. Early milestones, such as computers defeating researchers in a game of checkers in the 1960s, highlighted its potential, but today, advanced artificial intelligence (AI) models tackle far more complex tasks, influencing our daily lives through applications including Internet searches and personalized recommendations.

As businesses across various sectors seek to integrate machine learning into their operations, AI is expected to play an increasingly pivotal role, in industries as diverse as healthcare, finance, transportation, and manufacturing; driving efficiencies and enabling innovative solutions—and therefore becoming an increasingly attractive investment opportunity.

Machine learning and big data: Uncovering key insights

Machine learning technically refers to the branch of artificial intelligence focusing on the development of algorithms which can learn to make decisions without human intervention. Over time, an AI’s accuracy progressively improves as it learns from data inputs. To simplify the creation and deployment of these powerful machine learning tools, developers often utilize pre-existing frameworks, which provide a standardized structure and set of tools, helping them build and deploy machine learning models more efficiently and effectively.

The last decade has witnessed a proliferation of alternative data sources. As the volume of big data at our disposal continues to grow, the market for data science is expected to do the same. Machine learning has become a vital component of this rapidly expanding field, employing statistical methods to uncover key insights that, in turn, can inform increasingly complex decision-making. Advancements in technology, specifically natural language processing, computer vision, and reinforcement learning, have facilitated the development of a plethora of innovative products and services.

Advantages and applications

Machine learning can provide a competitive edge for firms across various industries, as firms harness its power for analysis. Automated data handling is usually much faster than manual processes, and machine learning tools also have the ability to improve themselves over time, reducing the likelihood of errors and leading to cost efficiency.

For instance, in the financial sector, machine learning algorithms have been successfully applied to fraud detection, where they can quickly analyze vast amounts of transaction data to identify suspicious patterns, saving businesses both time and money. In the healthcare industry, machine learning models have been used to analyze medical images, resulting in improved diagnostic accuracy, and reduced human errors, ultimately leading to better patient outcomes. They are also increasingly being used in predictive healthcare, identifying biomarkers for diseases, and even eavesdropping on emergency calls, listening for signs of cardiac distress.

AI for investors

In 2018, International Data Corporation valued the worldwide AI market at around $28 billion. Today, that figure stands at around $120 billion, with the sector forecasted to continue to grow significantly—although estimates vary.

International Data Corporation has projected that global spending on AI could reach roughly $100 billion annually by 2025, with an annual growth rate of 40%. Estimates from Precedence Research suggest the global AI market will reach an astonishing half a trillion dollars by 2024, growing to over $1.5 trillion by 2030.

One way or another, investing in companies with exposure to AI and machine learning could be incredibly rewarding.

Investment in AI start-ups

Between 2020 and 2022, global investment in AI startups increased by $5 billion, with machine learning and chatbot companies (in other words, those focused on human-machine interaction) among the most recent top-funded ventures.

Some notable start-ups in the AI and machine learning space include OpenAI, the developer of the now-famous AI language model ChatGPT, UiPath, a company specializing in business automation, and DataRobot, an enterprise AI platform that automates the creation and deployment of machine learning models—or what they call ‘value-driven AI.’ The success of start-ups like these showcase the innovation and potential of machine learning, both as a tool and as an investment opportunity.

Implications for private equity

Private equity firms themselves increasingly use machine learning to evaluate buy-out opportunities and assess potential investments. Many firms may use machine learning algorithms to analyze large datasets from target companies, identifying patterns and trends that would be difficult or time-consuming for humans to discover.

To take one example, Blackstone has a dedicated data science team that uses machine learning in its operations. John Gray, the company’s COO, has said that “Data science empowers us to make better investment decisions…It is an integral part of Blackstone.” Hone Capital is another firm embracing AI and found that their machine learning models are able to recommend seed-stage companies with a similar rate of success (40%) to that achieved by the human investment team. By combining the two techniques, their hit rate is 3.5 times the industry average.

This all suggests that augmenting human expertise with AI technology will become increasingly common when it comes to investing.

A rapidly evolving investment landscape

With companies worldwide dedicating resources to the development of machine learning models, the sector is poised for massive growth. As technology improves, machine learning will continue to impact the way businesses operate across various industries.

Investors should be aware of some challenges associated with investing in AI and machine learning. As with many new technologies, the rapid pace of change may render some solutions obsolete quickly, while regulatory concerns may arise as governments attempt to control the use and development of AI. Additionally, ethical considerations, such as privacy and the potential for biased decision-making, should be considered when evaluating investment opportunities in this space.

However, even with these caveats, and despite being an emerging sector, machine learning offers fantastic potential for long-term investors who are comfortable with price volatility. As with any investment, it is essential to conduct thorough research, but those willing to navigate the challenges and uncertainties in this rapidly evolving landscape may see substantial returns on their investments.