Most investors already have exposure to AI, as many Australian public companies are either using AI or are actively looking to invest in or grow their business by incorporating AI. Deloitte’s survey reveals that 94% of business leaders believe AI will be crucial for success in the next five years. Goldman Sachs predicts AI could boost SP500 profits by over 30% in the next decade. Google has incorporated AI into their platforms, such as search engine, reverse image search, and productivity apps, and Microsoft is backing the popular chatbot ChatGPT, which indisputably shows the tremendous potential AI has in transforming lives.
Despite the hype, if you’re an investor interested in investing in AI or anything AI-related, here we have curated important information to help you understand what you’re putting your money in before parting ways with any cash.
How companies use AI
AI is pervasive in various domains. For instance, social media platforms use AI to power editing tools and boost user-generated content, as well as to analyse user behaviour and identify the type of content that users spend the most time on. In other domains, law enforcement agencies employ facial recognition software for investigations, and even some healthcare companies have plans to incorporate AI in nursing practices to ensure real-time service and better delivery of patient care.
Popular AI terms that investors should be aware of
Artificial Intelligence (AI): Development of computer systems that can perform tasks that typically require human intelligence. It involves the creation of algorithms and models that enable machines to learn, reason, and make decisions.
Example: AI is applicable in almost all areas and industries, such as smart home devices, autonomous vehicles, healthcare, finance, customer service and sales, and voice virtual assistants.
Machine Learning: A subset of AI that involves the development of algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed.
Example: The machine learning algorithm could differentiate images of cats and dogs from a large dataset of labelled images, by analysing the features present in these images, such as shapes, textures, and colours, and learn the patterns that distinguish cats from dogs. It would then use this learned knowledge to classify new, unseen images as either a cat or a dog.
Deep Learning: A subset of machine learning about teaching computers to learn and understand complex things by building artificial neural networks with many layers.
Example: Speech recognition technology. Deep learning algorithms can be trained to understand and transcribe spoken language accurately as commonly used in applications such as virtual assistants like Siri or Alexa.
Natural Language Processing (NLP): The branch of AI focused on enabling computers to understand, interpret, and generate human language, enabling applications such as language translation, sentiment analysis, and chatbots.
Example: Sentiment analysis. NLP algorithms can be used to understand and analyse the sentiment or emotion expressed in written text, such as social media posts, customer reviews, or news articles to determine whether each review is positive, negative, or neutral.
Computer Vision: A field within AI that aims to enable computers to understand and interpret visual information, enabling tasks such as object recognition, image classification, and autonomous driving.
Example: Image classification. Computer vision algorithms can be trained to classify objects or scenes in images based on their visual characteristics, such as automatically identifying different types of fruits in images. This is useful in agricultural settings, inventory management in supermarkets, or even in mobile apps that can recognise and provide information about different fruits simply by taking a picture.
Robotics: The integration of AI and physical machines, enabling them to perceive their environment, make decisions, and interact with the physical world. Robotic systems have applications in areas such as manufacturing, healthcare, and transportation.
Example: Autonomous mobile robot that navigates and performs tasks in an indoor environment, such as a stock-picking robot for heavy objects operating in a warehouse.
Data Analytics: The process of examining large datasets to uncover patterns, insights, and trends. AI techniques are often used in data analytics to extract meaningful information and support data-driven decision-making.
Example: Analysing customer purchase behaviour, pattern, and interaction in a retail company
Generative AI: Generative AI is a subset of AI that focuses on creativity and imagination. AI works by using complex algorithms and neural networks to understand patterns and relationships within the data it has been trained on. It then uses this understanding to generate new content that is similar in style or structure.
Example: Generative AI can be used to create a painting in the style of Van Gogh or compose a piece of music inspired by Mozart.
Predictive Analytics: The use of AI and statistical techniques to analyse historical data and make predictions about future outcomes. It helps investors identify trends, patterns, and potential investment opportunities.
Example: Predictive analytics is a credit card company using customer data to predict the likelihood of a customer defaulting on their payments through various data points such as customer income, credit score, payment history, and demographic information. They want to assess the creditworthiness of their customers and identify potential default risks.
Algorithmic Trading: The use of AI algorithms to automate the process of buying and selling financial instruments in the stock market. AI-powered trading systems can analyse vast amounts of data and execute trades based on predefined rules and strategies.
Example: A program that automatically scans the market, identifies trading opportunities based on the defined indicators, and executes trades accordingly. For example, if the stock’s price crosses above its 50-day moving average and the RSI indicator indicates oversold conditions, the algorithm may generate a buy signal. It can be advantageous to eliminate human emotion and execute trades with speed and precision.
Some things to look out when considering investment in an AI company:
Technology and expertise: Assess the company’s technical abilities and expertise in AI. Look for evidence of strong research and development (R&D) efforts and progress or a track record of innovative AI solutions.
Use cases and market potential: Evaluate the company’s use cases and the potential market demography for their AI products or services. Consider the specific problems they aim to solve and the size of the target market. Look for clear and compelling value propositions.
Intellectual property and data assets: Consider the company’s intellectual property portfolio and data assets. Evaluate the strength of their patents, proprietary algorithms, or unique datasets that provide a competitive advantage.
Market trends and adoption: Stay informed about the latest trends and developments in the AI industry. See how the company could incorporate or adopt this into their offerings to gauge their potential growth opportunities.
Risk assessment: Conduct a thorough risk assessment, considering factors such as technological risks, market volatility, regulatory changes, and competition and weigh them against potential rewards of investing in a modern, rapid-changing industry like AI.
Clara is a Business Reporter for The Sentiment.
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