Artificial intelligence (AI) training is the process of teaching an AI system to perceive, interpret and learn from data. That way, the AI will later be capable of inferencing—making decisions based on information it’s provided.
This type of training requires 3 important components: a well-designed AI model; large amounts of high-quality and accurately annotated data; and a powerful computing platform.
Properly trained, an AI’s potential is nearly limitless. For example, AI models can help anticipate our wants and needs, autonomously navigate big cities, and produce scientific breakthroughs.
It’s already happening. You experience the power of well-trained AI when you use Netflix’s recommendation engine to help decide which TV show or movie you want to watch next.
Or you can ride with AI in downtown Phoenix, Ariz. It’s home to the robotaxi service operated by Waymo, the autonomous-vehicle developer owned by Google’s parent company, Alphabet.
And let’s not forget ChatGPT, the current belle of the AI ball. This year has seen its fair share of fascination and fawning over this new generative AI, which can hold a remarkably human conversation and regurgitate every shred of information the internet offers—regardless of its accuracy.
AI can also be used for nefarious purposes, such as creating weapons, methods of cybercrime and tools that some nation states use to surveil and control their citizens. As is true for most technologies, it’s the humans who wield AI who get to decide whether it’s used for good or evil.
3 steps to train AI
AI training is technically demanding. But years of research aided by the latest technology are helping even novice developers harness the power of original AI models to create new software like indie video games.
The process of training enterprise-level AI, on the other hand, is incredibly difficult. Data scientists may spend years creating a single new AI model and training it to perform complex tasks such as autonomous navigation, speech recognition and language translation.
Assuming you have the programming background, technology and financing to train your desired type of AI, the 3-step process is straightforward:
Step 1: Training. The AI model is fed massive amounts of data, then asked to make decisions based on the information. Data scientists analyze these decisions and make adjustments based on the AI output’s accuracy.
Step 2: Validation. Trainers validate their assumptions based on how the AI performs when given a new data set. The questions they ask include: Does the AI perform as expected? Does the AI need to account for additional variables? Does the AI suffer from overfitting, a problem that occurs when a machine learning model memorizes data rather than learning from it?
Step 3: Testing. The AI is given a novel dataset without the tags and targets initially used to help it learn. If the AI can make accurate decisions, it passes the test. If not, it’s back to step 1.
Future of AI Training
New AI training theories are coming online quickly. As the market heats up and AI continues to find its way out of the laboratory and onto our computing devices, Big Tech is working feverishly to make the most of the latest gold rush.
One new AI training technique coming to prominence is known as Reinforcement Learning (RL). Rather than teaching an AI model using a static dataset, RL trains the AI as though it were a puppy, rewarding the system for a job well done.
Instead of offering doggie treats, however, RL gives the AI a line of code known as a “reward function.” This is a dynamic and powerful training method that some AI experts believe will lead to scientific breakthroughs.
Advances in AI training, high-performance computing and data science will continue to make our sci-fi dreams a reality. For example, one AI can now teach other AI models. One day, this could make AI training just another autonomous process.
Will the next era of AI bring about the altruism of Star Trek or the evil of The Matrix? One thing’s likely: We won’t have to wait long to find out.