key factors for successful AI
Although artificial intelligence (AI) is still in its infancy, the technology is already helping companies to reduce costs, boost sales, increase efficiency, and improve customer service.
There is no business too small to benefit from AI and machine learning: AI-powered tools can help with functions as complex as piloting autonomous vehicles, and as basic as setting maintenance schedules for factory floors. The challenge for businesses is accessing the high-quality data needed to drive these tools
Time to launch
The faster you can train and launch your model, the higher the chance of it attaining the number one position. However, collecting, annotating and validating the data required to train a model to pilot a vehicle, for example, can take months. Reducing this time will have a major impact on a company’s bottom line. In the race to be the best, fast access to data is crucial.
However, preparing data for training models is a time-consuming (and often mind-numbing) task. According to a survey conducted by Forbes, data scientists spend 19% of their time collecting data and 60% of their time cleaning and organizing, meaning they spend around 80% of their time preparing and managing data for training. A staggering 76% of data scientists view data preparation as the least enjoyable part of their job, specifically because of its tedium and time demands.
Although fast training is key to gaining the competitive edge, data scientists still need to put in the time to ensure the datasets used to train the models are relevant and high-quality.
Volume of training data
Ask any machine learning engineer and they’ll tell you there isn’t enough data to train machines. In fact, you can never have too much data. (Unless, of course, you are worried about embedding your system–as is the case for mobile developers–or you are worried about latency in search engines or call center’s).
Generally speaking, the larger the set of balanced data (quality is key), the more accurate the model, and the faster you can launch to the marketplace. AI is data-hungry, and its appetite is never satisfied.
Precisely annotated data is to AI models as high-quality ingredients are to a fine meal. With strong datasets as a base, AI “chefs” can confidently focus on their craft. Without it, they’re trying to make French Onion Soup with no butter and a bag of rotten onions. Things can only end badly.
Could off-the-shelf data be the answer?
On-demand datasets, already annotated and validated, could be a cost-effective way for companies to launch their AI initiatives faster.
Off-the-shelf data is perfectly suited for building baseline models, which some data scientists believe vital to building effective products. Off-the-shelf domain corpora, for example, can also be used to adapt an already good model into something much better. Finally, off-the-shelf data can be used to simply add volume to the training data you already have, resulting in an improved model that can understand more languages.