AI是数据科学的一个分支，涵盖了从最基本的算法到复杂的神经网络模型的所有内容。 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.

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.

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.

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.

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本文档中的部分陈述可能包含预测性的前瞻性陈述（“FLS”），其中包含“预计”、“预期”、“打算”、“认为”、“估计”和类似的前瞻性表述或其否定形式。前瞻性陈述基于当前对未来普遍的经济、政治、相关市场因素（例如利率和汇率、股票和资本市场）以及普遍经营环境的预计和预测，并假定不发生税法或其他法律或政府管制方面的任何变动或灾难事件。对于未来事件的预计和预测本身受无法预见的风险和不确定性的影响。此等预计和预测可能在未来并不准确。前瞻性陈述并非对未来表现的保证。实际发生的事件可能与前瞻性陈述明示或暗示的事件存在实质差异。包括上文所述各项因素在内的多个重要因素均可能造成这种背离。 You should avoid placing any reliance on FLS..

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