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The challenges of scaling AI
AI can open up numerous opportunities and benefits for any organization, but scaling it is often challenging. Developers can face several obstacles while trying to scale their algorithms. However, a few specific roadblocks tend to come up more often than others. Understanding these challenges and their causes can help developers navigate solutions smoothly with their own AI projects.
One of the most common scaling challenges AI developers face is accurate live vs. test performance. The model may perform great in testing, but fall short when given a live, real-world challenge. For example, it might handle a small sample of data with ease, but when it is given a full-size dataset, it breaks down, or takes far longer than expected to process the information.
Industry experts have pointed out that this situation is often due to a lack of preparation before scaling up the model. Developers need to scale an AI's processing and storage capabilities to handle a large, complex dataset with the same speed and effectiveness as in training. The AI is trying to organize significantly more information, so it needs additional virtual space to work in.
Of course, it is often ideal to train a model using smaller chunks of data. Developers should be aware that the model must be adjusted before scaling up, or live performance will fall short of training benchmarks.
Sometimes an AI doesn’t scale well because of the people working on it rather than the tech itself. This particular obstacle may be more challenging to recognize and resolve, but it is possible. AI is still an emerging technology, so many organizations struggle to find the right people for their projects. The unique combination of skills needed to build a scalable, functional model is simply difficult to find.
Usually, an organization will hire a group of people and try to build a team that has most of the necessary skills. These employees will eventually become adept at AI development, but the learning curve can be a long one, leading to delays. Hiring the help of a skilled AI developer can help jumpstart things and lead to faster, more effective scaling.
AI models take an extensive amount of time to develop. Unfortunately, lengthy development time can make scaling a challenge, simply because pivoting isn't possible financially or logistically.
Most developers create a specialist AI that is very good at a specific task. These models can be highly effective but, unfortunately, have to be based largely on predictions. Developers have to use proof-of-concept research to predict whether or not their product will be useful for real-world applications. Unfortunately, this prediction doesn’t always pan out. An AI might be extremely good at what it is intended to do but will not work on a large scale.
Failing to consider potential large-scale impact realistically can lead to thousands or millions of dollars spent on a project that fails to offer a return on investment. This can be difficult to address, though certain strategies can help. Experts recommend utilizing a variety of input datatypes, such as audio or video, to better prepare an AI for learning new tasks quickly. Additionally, automating the machine learning process can streamline a model so it can adapt to new applications in less time.
It is particularly common on small- and medium-sized projects for an AI to fail to scale due to a lack of necessary technical infrastructure. This may be connected to unrealistic test performance and developmental issues but can also occur independently. Technical infrastructure can be lacking in a few areas, but this scalability challenge is typically easy to identify.
For example, an AI may not have the necessary processing power to perform a given task. The best way to resolve this will depend on individual circumstances. However, two potential solutions are: increasing physical server space, or switching to cloud computing.
Similarly, an AI that isn’t given enough storage space for the amount of data it needs to process will get congested and lose efficiency. Data volume can be a serious ongoing challenge, so storage needs to be assessed regularly and expanded if necessary. A developer may have selected the wrong type of database for the model, resulting in inefficient storage.
Even if an AI is given adequate processing power and data storage capacity, it may not deliver results at scale due to a lack of training information. Models can better comprehend various input data if more is included in training. Something that returns strange output when scaled up could mean the AI has not learned enough to scale effectively.
AI has faced extensive criticism over recent years due to the issue of data bias. It has become more popular and widely used and tested like never before. Unfortunately, this has revealed some serious scaling issues that have forced developers to test the behavior of their model in- depth.
For example, Amazon’s infamous hiring AI was shut down after staff members discovered it discriminated against candidates with the word “woman” or “women’s” in their resumes. This was because it was learning from past data in which managers' approval was weighted towards men, and, learning from this data, the same mistake was repeated.
Luckily, researchers and developers within the AI industry are working on solutions to black-box issues. One leading strategy is explainable AI. These models are designed to let developers see exactly how they make decisions. This allows training data flaws and inaccuracies to be identified and corrected before the model is launched or scaled. Similarly, some organizations like HUMAN Protocol are providing the infrastructure to allow companies to employ workers to label AI training data for them, which will help new models learn more accurately and efficiently.
Developers put hundreds of hours of hard work and dedication into creating innovative AI models, so it can be frustrating when something fails to scale. Luckily, many developers share the same struggles. The industry has developed solutions for these common problems that may be exactly what a developer needs to get their AI project back on track. As technology advances further over the years ahead, the sector will learn to navigate scaling struggles with ease by innovating the development and testing process for tomorrow’s cutting-edge products.
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The HUMAN Protocol Foundation makes no representation, warranty, or undertaking, express or implied, as to the accuracy, reliability, completeness, or reasonableness of the information contained here. Any assumptions, opinions, and estimations expressed constitute the HUMAN Protocol Foundation’s judgment as of the time of publishing and are subject to change without notice. Any projection contained within the information presented here is based on a number of assumptions, and there can be no guarantee that any projected outcomes will be achieved.