Artificial Intelligence

What is Artificial Intelligence?

Artificial intelligence, or AI, is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities. Essentially, it is the science of making machines that can think and process information like humans.

AI is a very broad field, and it’s constantly evolving. There are many different approaches to creating AI, but its roots are in the use of data and algorithms. By analysing large amounts of data, AI systems can learn to identify patterns and make predictions. This is what allows them to perform many of the tasks that we traditionally associate with human intelligence.

Investors have been fixated on AI since the middle of 2022 thanks to its potential to revolutionize entire industries such as healthcare, finance, manufacturing and customer service, AI can automate tasks, improve efficiency, and generate new insights. This potential for disruption makes AI companies attractive investments into the biggest transformative technology since the internet. It has exited the world of science fiction and is being adopted rapidly.

The purpose of this note is to explain AI, where and why we, at Quartet, have exposure.

The Nuts and Bolts

Chip Manufacturers – The foundation of AI lies in computer hardware. Chip manufacturers like Nvidia, Intel, and ASML produce specialized processors called GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units). These chips are optimized for handling the complex mathematical calculations required for machine learning algorithms, the workhorses of AI.

Data Centres – These massive warehouses house countless servers packed with GPUs and TPUs. They provide the processing power needed to train machine learning models on enormous datasets. Data centres are specially designed to handle the immense heat generated by these powerful processors and ensure a constant flow of electricity to keep them running. Complex cooling systems are crucial to maintain optimal operating temperatures for the sensitive hardware.

Ethernet Cables and Network Infrastructure – The data centres wouldn’t function without a robust network. High-speed ethernet cables and fibre optic connections form the information superhighways which carry the vast amounts of data needed to train and operate AI models. This data can include text, images, videos, and sensor readings, depending on the specific application.

Cloud Computing – Not everyone has access to their own data centre. Cloud computing platforms like Google Cloud AI, Amazon Web Services (AWS) SageMaker, and Microsoft Azure offer access to powerful computing resources and pre-built AI tools. This allows companies and researchers to leverage AI without the massive upfront investment in hardware infrastructure. Protecting sensitive data and models from cyberattacks is paramount. Robust security measures are essential throughout the AI infrastructure.

Machine Learning – This is the core of AI. Machine learning algorithms are trained on massive datasets. These algorithms learn to identify patterns and relationships within the data, allowing them to make predictions or classifications on new, unseen data. There are various machine learning techniques, each suited for different tasks like image recognition, natural language processing, and recommendation systems.

Inference – Once a machine learning model is trained, it’s deployed for real-world use. This is called an inference. The model takes new, unseen data as an input and makes predictions or classifications based on what it learned during training. Inference can happen on various devices; from smartphones running facial recognition to self-driving cars using computer vision for navigation.

Applications and Future of the Workforce

Now we have our inference, what does this mean for the working world and the labour force currently employed? We like to think of AI as a tool, as with all tools they require direction from a human. Before computers became machines, people were employed as “human computers” to perform complex mathematical calculations. Unlike many feared, the invention of the computer increased the potential labour force.

Looking at the positives, AI can now process and understand human language with greater accuracy. This is enabling advancements in machine translation, chatbots, sentiment analysis, and voice assistants like Siri and Alexa. It is playing an increasingly important role in healthcare and is used for medical diagnosis, drug discovery, personalized medicine, and robot-assisted surgery. Its ability to analyse vast amounts of scientific data, accelerating research in fields like genomics, materials science, and climate modelling has meant progress is being made in leaps and bounds ahead of previous expectations.

There are however several obstacles to overcome before computers begin to think independently. The human language is full of subtleties, sarcasm, and context-dependent meaning. AI can struggle to grasp these nuances and may misinterpret the intent behind communication. In the same breath, many human decisions involve ethical considerations, moral dilemmas, and weighing potential consequences. While AI can be programmed to follow certain ethical guidelines, it can’t truly understand the complexities of human morality or make subjective judgments based on empathy or compassion.

Robots are becoming increasingly sophisticated, but they still lack the fine motor skills and adaptability of humans. Performing delicate tasks in unstructured environments or using common tools in unforeseen ways is yet to be tackled.

How to invest in AI?

The potential of AI for industry and business is enormous. For investors, it’s a good time to look beyond the headlines and find out what opportunities the world of AI holds. As AI transforms economies and industries, the scope of opportunities and risks will broaden, leading to increased disparity between winners and losers. We believe there are three areas to consider if investing in the space:

First Order Beneficiaries: “Enablers” The initial beneficiaries of AI are “enablers,” which include companies that build the necessary AI infrastructure. This group comprises semiconductor and semiconductor equipment manufacturers. This demand is primarily met by a select few companies, like Nvidia, ASML and Taiwan Semiconductor, who are capable of designing and manufacturing high-powered chips.

Emerging Opportunities: “Data and Security” As AI technologies advance, the range of opportunities will extend. Complex AI models rely heavily on the quality of the data they are trained on. Effective AI models require large quantities of well-organized, accurate, and secure data. Companies that can utilise data to boost operational efficiency, gain deeper customer insights, deliver more personalized experiences, and make well-informed, data-driven decisions will have a competitive edge.

Future Beneficiaries: “Applications” In the future, the “applications” layer comprising software companies and firms across various sectors that leverage AI to enhance their products and services. As AI becomes more specialised and tailored, businesses will increasingly adopt new technologies. Software companies that facilitate this transition by offering superior customer experiences and helping businesses maximize the value of AI technologies will be particularly well-positioned for success.

Conclusion

The rapid rise and transformative potential of generative AI and machine learning position these technologies to become hallmarks of the modern economy and financial markets. Identifying winners and losers and effectively leveraging AI is challenging, especially in the early stages of a new technology’s life cycle. There are numerous unknowns, including geopolitical risks, the potential misuse of AI models, complex regulatory issues, uncertain timelines for AI adoption, and unproven monetization models. The full macroeconomic impact of AI remains difficult to predict, requiring careful consideration and proactive policy measures to address potential negative consequences.

As in 2000, companies with strong business models, clear paths to profitability, and a focus on long-term growth are better positioned to survive market exuberance. We are wary of inflated valuations where they are not earned. The AI industry is still evolving, and these companies face competition and the challenge of turning their potential into profit. This is a complex issue with no easy answers, as it requires balancing innovation with control, and ensuring safety without stifling progress.

Risk Warning

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