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It’s been a shorter span of time than usual between our Monthly Forums, with April’s occurring at the beginning of the month, earlier than we historically held the event. We had a lot to go through, but taking center stage was some important commentary on artificial intelligence. 

We discussed implementing an AI stock screen driven by feedback from our premium Twitter audience. Recognizing that AI is a trending topic, the aim is to sift through the hype to identify companies genuinely leveraging AI in ways that could positively impact their long-term growth and financials.

This approach isn’t new to GeoInvesting; having previously created a highly successful infrastructure screen.

The AI screen aims to include companies in our coverage universe with direct or indirect ties to AI, acknowledging that many firms traditionally not associated with AI are now being redefined or retooling their offerings to incorporate AI, analytics, and data management solutions. This includes a diverse array of companies involved in technologies behind AI, data analytics, power needs for AI applications, and storage solutions.

Our goal with this screen is to unearth companies that are not merely riding the AI hype wave but are making substantive contributions to their fields with AI technologies, potentially transforming their market positioning and financial outcomes.

Because we have been intensely using the tools that we have at our disposal, we amassed a dictionary of terms that will help us add the right stocks to this new screen. It’s actually been helpful in not only finding microcap companies, but also seems to work for larger companies embarking on AI initiatives.

Data Center Terms

  1. Cloud Computing: Delivery of computing services over the internet, including servers, storage, databases, networking, software, analytics, and intelligence.
  2. Edge Computing: A distributed computing model that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth.
  3. Colocation/Colo: The practice of renting space for your servers and other computing hardware at a third-party provider’s data center.
  4. Hyperscale Data Centers: Massive facilities designed to support robust and scalable applications. Companies like Amazon, Google, and Microsoft operate these to support cloud services.
  5. Modular Data Centers: A portable method of deploying data center capacity, consisting of pre-engineered modules and components to offer scalable data center capability with a quick deployment time.
  6. Green Data Centers: Facilities designed for maximum energy efficiency and minimum environmental impact, using green technologies.
  7. Infrastructure as a Service (IaaS): Online services that provide high-level APIs used to dereference various low-level details of underlying network infrastructure like physical computing resources, location, data partitioning, scaling, security, backup, etc.
  8. Software as a Service (SaaS): A software distribution model in which a cloud provider hosts applications and makes them available to end-users over the internet.
  9. Virtualization: The creation of a virtual version of something, such as virtual computer hardware platforms, storage devices, and computer network resources.

AI Terms

  1. Machine Learning (ML): A subset of AI that includes algorithms that improve automatically through experience.
  2. Deep Learning: A subset of ML based on artificial neural networks with representation learning. It is crucial for data science, including semantics, data mining, and pattern recognition.
  3. Natural Language Processing (NLP): The ability of a computer program to understand human language as it is spoken and written.
  4. Computer Vision: A field of AI that trains computers to interpret and understand the visual world.
  5. Robotics Process Automation (RPA): The use of software with AI and machine learning capabilities to handle high-volume, repeatable tasks that previously required humans to perform.
  6. Autonomous Vehicles: Vehicles capable of sensing their environment and moving safely with little or no human input, leveraging AI for navigation and control.
  7. AI Ethics and Governance: The consideration of the moral implications and responsibilities of AI, including fairness, transparency, and accountability.
  8. AI Chips: Specialized silicon chips designed to efficiently process AI tasks, such as those used for neural network computation and machine learning tasks.
  9. Data Analytics and Big Data: The process of examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.
  10. Quantum Computing: An area of computing focused on developing computer technology based on the principles of quantum theory, which can significantly impact AI’s processing capabilities.

Below is the full clip breaking down the plans, as well as some sub-clips extracted from the clip that address a few of the stocks that are now on the screen.

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