What is AI?
For a quick overview of AI, let’s hand it over to ChatGPT, the ever-popular generative AI chatbot.
Explain artificial intelligence in a few sentences.
Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks typically requiring human intelligence. It involves creating algorithms and models that enable machines to learn from data, recognize patterns, make decisions, and solve problems.
AI encompasses various subfields, including machine learning, natural language processing, computer vision, and robotics, and has applications in areas such as healthcare, finance, transportation, and entertainment.
Its goal is to replicate or augment human intelligence to improve efficiency, accuracy, and overall capabilities in various domains.
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Examples of AI in Everyday Life
Total Size of Global
AI Industry in 2022
$137 Billion
Total Size of AI Industry in 2030 (est.)
$1.81 Trillion
of companies are already using AI
35%
of companies are exploring AI for future use
42%
Jobs could be created by AI and automation by 2025
97 Million
Jobs could be replaced by AI and automation by 2025
85 Million
of mobile users already use AI-powered voice assistants
97%
Annual Cost to Run ChatGPT (est.)
$1 Billion
AI by the Numbers
Sources: International Data Corporation, CNBC, TechJury, IBM, World Economic Forum
Machine Learning
Deep Learning
Natural Language Processing
Digital Assistants
Maps and Navigation
Computer Vision
Fuzzy Logic
Expert Systems
Robotics
Deep Learning uses neural networks that mimic the structure of the brain to interpret input data and make predictions about the nature of that data. The “deep” part comes from the multiple layers of processing that the data undergo simultaneously, so that something as complex as a picture can be processed like a human would and understand what it’s looking at.
Machine Learning deals with computers “learning” and making decisions by themselves, based on data, without being specifically programmed to do so. These algorithms can continually improve their performance over time as they process more data and see the outcomes of their predictions / decisions.
Natural Language Processing allows computers to understand text or spoken words like humans. This is the technology that enables chatbots and language translators to comprehend and process input data.
Computer Vision deals with the tracking, interpretation and recognition of images and video. This is the technology that makes facial recognition and self-driving cars possible.
Fuzzy Logic is a system for dealing with uncertainty, which computers generally don’t handle well–after all, everything is either a 1 or a 0, a yes or a no. By measuring the degree to which something may be true or false (or some of both) in smaller increments, it handles real-world situations like a person would, given incomplete information to navigate a task.
Expert Systems are software programs that specialize in one specific task, kind of like a human expert. They rely on a human-programmed knowledge base and if-then rules to solve complex problems across fields like medicine, finance and cybersecurity.
Robotics is the branch that deals with robots, which are machines built to perform complex or laborious tasks automatically.
Many AI-powered products, from simple to advanced, combine one or more of these branches.
Roomba
ChatGPT
Self-Driving Cars
◾ Robotics
◾ Fuzzy Logic
◾ Computer Vision
◾ Fuzzy Logic
◾ Machine Learning
AI Stocks
The Infrastructure: Hardware
As AI becomes more advanced, it requires ever-greater amounts of hardware processing power and network bandwidth to operate. These companies are racing to fulfill those needs.
Advanced Micro Devices (AMD)
AMD is the strong #2 in the AI chip space. They’re racing to bring new AI-specific hardware to market, including the ultra-powerful MI300X chip that will start shipping later in 2023 to data center customers like Microsoft and Amazon.
Nvidia (NVDA)
Right now, Nvidia is the clear leader in AI semiconductors, with an estimated 80% market share of AI chips. Their A100 GPU chips power the majority of the enormous data centers required to train machine learning models. Even at nearly $10,000 each, these chips are being bought as fast as they can be produced.
Arista Networks (ANET)
As computing power demands for AI skyrocket, so do the network bandwidth demands between the data centers and consumers. Arista is a leader in networking hardware and a major supplier to data centers run by Meta Platforms and Microsoft.
HubSpot (HUBS), CrowdStrike (CRWD), Splunk (SPLK), Datadog (DDOG)
The Architects: Software
Executing multiple tasks––some that would have otherwise required human intelligence––has become much simpler thanks to the expansion of AI software. These companies are on the leading edge of AI software development, and in many cases, their software helps other companies integrate AI into their businesses.
Palo Alto Networks (PANW)
This company utilizes AI machine learning to identify cybersecurity and network threats for enterprise customers in real time, acting as a force multiplier for in-house IT teams.
Palantir (PLTR)
Palantir develops and sells access to cloud-based software platforms like Gotham and Foundry that use machine learning and AI to gather enormous amounts of data, then analyze them and model outcomes. Its main customers are U.S. government agencies (the CIA was an early investor), although its commercial customers are growing as the AI boom unfolds.
C3.ai (AI)
C3.ai is a SaaS (software-as-
a-service) company that who helps
its enterprise customers (largely in
energy and defense) adopt large-scale AI operations. For companies looking to harness the power of AI without developing their own, its plug-and-play solutions are a big accelerator.
ASML Holding (ASML), Applied Materials (AMAT), Broadcom (AVGO), Taiwan Semiconductor (TSM)
◾ Machine Learning
◾ Deep Learning
◾ Natural Language Processing
The Drivers: Big Techs Bringing AI to Consumers
Where are you likely to see AI in your everyday life? These companies are making big bets on integrating AI into their products to make them smarter, faster and–ultimately–to do cool stuff.
Microsoft (MSFT)
Microsoft has gone “all in” on AI, including investing over $10 billion in OpenAI, the company responsible for ChatGPT and DALL•E. Microsoft plans on integrating OpenAI’s technology into their Office suite and Bing search engine, firing fresh shots at Google on both fronts.
Alphabet (GOOGL)
Not to be outdone by ChatGPT, Alphabet is developing their own generative AI large language model (LLM) codenamed Bard. While the initial rollout was widely viewed as rushed and underwhelming, Google plans on powering forward and integrating this proprietary AI in many of its products, including search, maps, cloud and Android.
Tesla (TSLA)
In some ways, AI is the foundation of Tesla going forward, as their Full Self-Driving (FSD) technology ultimately relies on AI to work toward their goal of Level 5 Full Driving Automation–something that has never been done. After all, Elon Musk himself said that Tesla’s market cap is tied to its ability to solve autonomous driving. Without that, it’s just a car company with a 2% market share.
Want AI exposure without all the single-stock risk? Thematic ETFs are an easy, relatively-inexpensive way to bet on the trend without picking favorites.
AI ETFs
Make sure to check the holdings of ETFs before you invest–there may be heavy concentration in a stock you want to stay away from, for example. You can do a Google search for “[ticker name] holdings” and it’s usually the first result.
TIP
AIQ
Global X Artificial Intelligence & Technology ETF
3 Biggest holdings:
NVIDIA, Meta, Tesla
ARKQ
ARK Autonomous Tech.
& Robotics ETF
3 Biggest holdings:
Tesla, UiPath, Kratos Defense
THNQ
ROBO Global Artificial
Intelligence ETF
3 Biggest holdings:
Palo Alto Networks, NVIDIA, Alteryx
Biggest holdings data as of 6/20/23.
Before buying an AI Stock, ask yourself:
What is the company’s competitive advantage in their field–do they have a moat?
Are they offering a NEW product or service, or just iterating on an existing one that can be easily copied?
Always look for strong fundamentals like sales growth, earnings growth and profit margins to determine which stocks are worthy additions to your portfolio.
And always use stock charts to time your buying and selling. Fundamentals will tell you which stocks to buy, and charts will tell you when to buy.
Great! 😊
SILLY HUMAN, YOUR MEAT BRAIN CANNOT COMPREHEND ME
Chatbots
Smart Home
Health
Recommendation Algorithms
Cybersecurity
Facial Recognition
Apple Watch, Hospital Monitoring, Medical Research
Netflix, YouTube, Amazon
Spam Filtering, Virus Monitoring
Apple Face ID, Airport Security
Siri, Alexa
Waze, Google Maps
ChatGPT, Customer Service
Roomba, Nest
7 Branches of AI
More watchlist names:
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How to Invest in
Artificial Intelligence, or AI, has the possibility to usher in a technological revolution on par with the internet, the personal computer or even the internal combustion engine.
Forms of AI have existed for decades, and yet we’re only just beginning to scratch the surface of the world-altering potential AI offers.
For investors, this raises the question: How can you get in on the ground floor of this AI revolution?
More watchlist names:
Shopify (SHOP), Apple (AAPL), Adobe (ADBE), Meta Platforms (META), Amazon (AMZN)
More watchlist names:
Is this company profitable? If not, are they on a pathway to profitability with strong sales growth?
Right now, Nvidia is the clear
leader in AI semiconductors, with an
estimated 80% market share of AI chips.
Their A100 GPU chips power the majority
of the enormous data centers required to train machine learning models. Even at nearly $10,000 each, these chips are being bought as fast as they can be produced.
Artificial Intelligence, or AI, has the possibility to usher in a technological revolution on par with the internet, the personal computer or even the internal
combustion engine.
Forms of AI have existed for decades, and yet
we’re only just beginning to scratch the surface
of the world-altering potential AI offers.
For investors, this raises the question: How
can you get in on the ground floor of this
AI revolution?
Fuzzy Logic is a system for dealing with uncertainty, which computers generally don’t handle well–after all, everything is either a 1 or a 0, a yes or a no. By measuring the degree to which something may be true or false (or some of both) in smaller increments, it handles real-world situations like a person would, given incomplete information
to navigate a task.
Machine Learning deals
with computers “learning” and making decisions by themselves, based on data, without being specifically programmed to do so. These algorithms can continually improve their performance over time as they process more data and see the outcomes of their predictions / decisions.
Deep Learning uses
neural networks that mimic the structure of the brain to interpret
input data and make predictions about the nature of that data. The “deep” part comes from the multiple layers of processing that the data undergo simultaneously, so that something as complex as a picture can be processed like a human
would and understand what
it’s looking at.
Right now, Nvidia is the clear leader in AI semiconductors, with an estimated 80% market share of AI chips. Their A100 GPU chips power the majority of the enormous data centers required to train machine learning models. Even at nearly $10,000 each, these chips are being bought as fast as they can be produced.
C3.ai is a SaaS (software-as-a-service) company that who helps its enterprise customers (largely in energy and defense) adopt large-scale AI operations. For companies looking to harness the power of AI without developing their own, its plug-and-play solutions are a big accelerator.
ARKQ
ARK Autonomous Tech.
& Robotics ETF
3 Biggest holdings:
Tesla, UI Path, Kratos Defense
Artificial Intelligence, or AI, has the possibility to usher in a technological revolution on par with the internet, the personal computer or even the internal combustion engine.
Forms of AI have existed for decades, and yet we’re only just beginning to scratch the surface of the world-altering
potential AI offers.
For investors, this raises the question: How can you get
in on the ground floor of this AI revolution?
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