Over the past year, I have worked at a technology company focused on the field of AI (artificial intelligence). The only constant in AI is its rapid pace of evolution. Chat GPT, which was most consumers’ introduction to AI, simply I was one year oldand since then, the feverish pace at which AI has evolved, with on-set drama and all, has been incredible.
But what about the basics? What do you need to know as a business person or casual AI enthusiast to keep up to date with what you need to know? I’ve compiled an introduction aimed at guiding you through the fundamentals of AI, so you can be familiar and knowledgeable about the fundamentals. In the next parts of this series, I’ll cover practical AI use cases and companies you should follow.
A previous Forbes contributor wrote a similar guide 6 years ago. However, given the dramatic progress made since then, an updated version seemed not only appropriate, but necessary.
Glossary of terms
I wanted to first start with a set of key terms that you need to know in order to understand what artificial intelligence is, including AI itself. Of course, many define these terms differently, so I recommend doing your own research and delving deeper into the topic. But to get started, I recommend you know the following:
- Artificial Intelligence (AI): The general concept that machines are capable of performing tasks in a way that we would consider intelligent or human.
- Machine learning: A subset of AI focused on algorithms that can learn from data, identify patterns, and make decisions with minimal human intervention. You may hear terms here like “reinforcement learning” where humans actually improve algorithms.
- Deep learning: A specialized machine learning technique based on artificial neural networks with multiple layers capable of processing huge data sets and powering innovations such as computer vision and natural language processing. Frankly, this area of AI is extremely technical and way over my head.
- Neural networks: Computer systems, modeled on the neural networks of the human nervous system, that can gradually learn and become more accurate in tasks such as recognizing objects in images.
- Natural Language Processing (NLP): The branch of AI aims to enable computer systems to understand, interpret, communicate and manipulate human languages. For example, if you are an experienced user of Chat GPT, you are regularly exposed to it.
- Computer Vision: The field of AI aims to enable computers and systems to identify, analyze, classify and understand digital images and videos. For those using Google image search, this is a very practical use case for computer vision.
- Artificial General Intelligence (AGI): A hypothetical AI that demonstrates human-level intelligence and abilities in a variety of cognitive tasks. It’s a hot topic in the world of AI: will machines eventually become smarter than us? There is no clear definition of when AGI is “achieved.”
What are the core technologies that power AI?
So you want to create an AI company and you don’t know the different areas that need support? Or are you looking for investment ideas that potential companies could target? There are different areas of AI that present investment opportunities, pathways to employment, or avenues for further training and understanding.
- Data: You can’t have AI without large amounts of data. This is a trendsetter, and a hot topic, given Sarah Silverman’s opinion. Recent trial against Open AI and Meta for allegedly using copyrighted material for their models. Companies operating large data sets: Meta, Google, Microsoft, Amazon, Tesla, Waymo
- Computing power: Sure, Nvidia is the darling here lately, but you need a lot of computing power to process the data. Due to the chip shortage, companies are scrambling to get enough computing resources to process data. Chipmakers providing hardware for pattern training: Nvidia, Intel, AMD, Qualcomm Cloud infrastructure companies: AWS, Microsoft Azure, Google Cloud
- Algorithms: Advanced algorithms, like those developed by DeepMind, Open AI and Anthropic, provide the basic logic and step-by-step calculations for AI systems to learn. For example, various types of machine learning algorithms allow AI models to improve their performance in tasks through exposure to large, quality data sets. Algorithm innovators: DeepMind, Anthropic, OpenAI, Cohere
- Modeling: Engineers test and refine machine learning models, such as neural networks, to accurately power capabilities like computer vision and natural language understanding. For example, the task of correctly labeling images. Main modeling frameworks: TensorFlow (Google), PyTorch (Meta), MXNet (Amazon)
- Application interface: This is where you are probably most exposed. Applications like Chat GPT allow you to interact with GPT-4 and produce results. Consumer technology leaders building AI applications: Meta, Apple, Amazon, Microsoft, Open AI, Anthropic, MidJourney, Runway ML
Finally, large language models (LLMs) like GPT-4 have become a key part of conversational AI. LLMs provide the underlying language capabilities leveraged by chatbots and voice assistants.
Further reading
If you really want to understand how AI works, consider exploring these detailed technical documents. These selections not only provide excellent entry points, but are also widely recognized as essential contributions to the field. Please note, however, that this list is not exhaustive:
For a high-level, frequently updated perspective on AI, I recommend Rowan Cheung’s newsletter The summary.
I will be adding to this series frequently, so stay tuned.