Developers typically train a range of different models to provide for the needs of different services yet recognize that compute is expensive, but more importantly, accuracy is vital for inference and agent applications, particularly when dealing with commercial, industrial, military and scientific applications. This trade-off requirement has led to many research paths to attempt to lower the cost of training compute to maintain data integrity and accuracy through various “drop-out” and “over-fitting prevention” strategies, among others. 

Training Complexity

Training can be computationally expensive, especially for complex models like deep neural networks, which often involve millions (at this point trillions?) of parameters. It requires powerful hardware, such as GPUs or TPUs and large datasets to improve the model’s accuracy. Additionally, issues like overfitting, where the model performs well on the training data but poorly on new data, need to be managed through techniques like regularization and cross-validation.

Training decisions and computational methods, using these advanced techniques do perform better, but continue to have significant burdens downstream when deploying inference and into the world, facing the real challenges of operational complexity, configuration conflicts, underperforming requirements and unpredictable events for which data had never previously existed or the training inadequate. 

As training models get larger, compute gets more powerful and applications of AI inference expand dramatically, the movement has been to develop targeted agents to lower the cost of inference and tailor training to more narrow tasks that can be more efficiently resourced and controlled. Directionally, these innovations are very important and very effective.

Yet, the training trade-off space is determined by the expected needs of the application, the choice of the best neural network architecture and the quality and breadth of the training data itself. Once deployed, Config would characterize inference to be statically configured, lacking as other complex systems do, the ability to observe how well its design goals are being managed by the system resources in real time and having the means to change its configuration solutions to meet those needs.

Improving Inference

Everywhere in this website, Config’s Self-Aware™ and AdaptiveAI™ compute technologies are discussed, both commercially and technically, describing the important breakthrough in combining control theory to manage dynamics and AI to manage complexity, linked together with computational methods to make systems dynamically responsive to goals, under software control.

We have applied these advanced technologies to inference and can now train randomly and then drop out in a structured manner at inference time, responsive to goals. This is very important, permitting training models to be larger, while inference and agents can be tailored using goals to perform differently, as needed, without re-training. Not only does this open the door on “train once, apply anywhere” methodologies, but it also helps future-proof inference for changing system and user requirements over time and permits exposing users to having more UI/UX knobs to manage inference or agent behaviors for the particulars of their own requirements. 

In this context, we consider AI as just another set of complex systems, with the added problem that there is no historic concept of “configuration parameters” to be set. (That is, until now. Out of necessity to apply our computational methods to manipulate AI with goals, we’ve developed innovative techniques to make AI configurable.)

Whether managing latency to first token, power consumption, memory allocation, the creation of diverse supplier provided SLAs, or any other goals, Self-Aware™ and AdaptiveAI™ capabilities are a powerful addition to inference and agent development and deployments.

Improving Training

As complex and demanding as training is, companies always have full control over the training process, unlike the challenges of probabilistic outcomes and the constrained reach of classical inference and agent deployments. 

The inclusion of Self-Aware and AdaptiveAI computing capabilities into training practices has significant benefits:

• Lowering the cost of training by being able to use goals to produce a wide range of service levels from a single model.
• Using goals to adapt old models to new hardware.
• Using goals to adapt new models to old hardware.
• Collecting new data streams from observations of how goals can further influence drop-out and sparsity strategies.