Breaking Boundaries - Exploring The New Frontier Of AI


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Let's start with the basics - What is artificial intelligence?

Artificial intelligence (AI) can be thought of as the ability of machines to mimic human intelligence. AI involves the creation of algorithms and computer programs that can learn, reason, and solve problems based on the data they are exposed to. The concept has been around for decades, and basic AI techniques have been adopted extensively in business practices, particularly by large enterprises.

What is the difference between artificial intelligence, machine learning and deep learning?

AI is the branch of data science that encompasses everything, from the most basic algorithms to complex neural network models. For example, at its simplest level, think of a car manufacturing plant with robots that install and assemble vehicle parts based on a pre-programmed system. These robots have been engineered to perform relatively simple functions that automate processes we used to undertake manually. Machine learning (ML) is when algorithms make predictions based on a historical dataset and training instructions — let's use the robotics example above. The next step from basic AI to ML would involve teaching the robots to learn more efficient processes using predictive modeling rather than solely relying on preset instructions. And at the advanced end of the spectrum, deep learning (DL) models can teach themselves by analyzing large datasets without the benefit of any human training in the first place. An example is a Convolutional Neural Network (CNNS), commonly used in image processing and recognition systems. The key here is the immense computing power required for deep learning techniques.

Why is there so much 'hype' around AI at the moment?

In one word — chatGPT! AI, particularly ML models, have been around for some time, operating silently in the shadows. These systems are, in many ways, wholly intertwined in many of our daily lives as digital interactions become ever more frequent. Think of every time Netflix recommends a movie/TV show, a shopping suggestion from Amazon, the next video recommended by YouTube, TikTok, or Instagram — these are all machine learning systems predicting ideas from your preferences and habits (the dataset is you and the collective activity of every other subscriber).

However, chatGPT's launch late last year gave a sense of tangibility to the concept of artificial intelligence — the moment a machine could converse with us, almost like speaking to another human. At its core, chatGPT uses existing ML concepts and DL in language processing, producing human-like text. Yet, this feels like a moment of radical change, similar to the momentous iPhone launch in 2007. Apple effectively combined a telephone, internet browsing, and mp3 player, all existing technologies, into one product. It's important to note that we are still in the early stages of this technological revolution. The AppStore did not exist until a year after the iPhone launch, and it took many cycles for the product and applications to mature. Our continuous 'hype' cycle for anything new means that we tend to overestimate change in the near term and underestimate the change in the long term.

Where are we in the AI adoption timeline?

It is tempting to think that the Skynet system that is out to eliminate humans in the Terminator franchise is around the corner — our imaginations sometimes get the best of us. Artificial general intelligence (AGI) or real-world intelligence is still unresolved. Humans can find answers to problems that are yet to be solved, however, machines are not there yet. It is why we have yet to see the launch of fully autonomous driving systems despite Elon Musk's repeated promises. We are still in the early stages of this paradigm shift, akin to the late 90s for the internet era. The winners of that era, Google, Amazon, and Facebook, among others, were yet to emerge in many ways.

How do we assess the investment potential in this new frontier for technology?

Across the various fundamental equity strategies at TD Asset Management Inc. (TDAM), we participate in this secular theme in multiple ways. Following the picks and shovels approach, investing in companies that sell the tools that are a must for AI adoption. For example, advanced chip manufacturers are prime candidates due to the complexity of calculations involved and the ever-increasing size of datasets. Other options may include software companies that develop AI tools or companies that integrate AI into hardware, like wearable devices. Our Portfolio Managers and Analysts thoroughly analyze our portfolio holdings to determine how well companies compete in this space. Thinking through such concepts is an essential part of our function, as is investing through cycles of innovation and disruption. Continuing with that theme, aspiring investors should look beyond the hype and invest in quality companies with a technological edge and a track record of rapid innovation.

For more information about TDAM products, visit the ETF Resource Centre or Mutual Fund Resource Centre.

The information contained herein has been provided by TD Asset Management Inc. and is for information purposes only. The information has been drawn from sources believed to be reliable. The information does not provide financial, legal, tax or investment advice. Particular investment, tax, or trading strategies should be evaluated relative to each individual's objectives and risk tolerance.

Certain statements in this document may contain forward-looking statements (“FLS”) that are predictive in nature and may include words such as “expects”, “anticipates”, “intends”, “believes”, “estimates” and similar forward-looking expressions or negative versions thereof. FLS are based on current expectations and projections about future general economic, political and relevant market factors, such as interest and foreign exchange rates, equity and capital markets, the general business environment, assuming no changes to tax or other laws or government regulation or catastrophic events. Expectations and projections about future events are inherently subject to risks and uncertainties, which may be unforeseeable. Such expectations and projections may be incorrect in the future. FLS are not guarantees of future performance. Actual events could differ materially from those expressed or implied in any FLS. A number of important factors including those factors set out above can contribute to these digressions. You should avoid placing any reliance on FLS.

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