In the race to be the best, these 2 elements will prove crucial
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.
The ultimate goal of AI is to help business become more profitable. However, there are two key factors to gaining the competitive advantage in AI: the time it takes to get your product to market and the size and quality of the training dataset used to train the model.
Factor 1: 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.
Factor 2: 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 centers).
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.
But it’s important to remember that although strong algorithms require lots of data, they don’t want just any old data. As Joao Freitas, the CTO of DefinedCrowd explains:
“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.”
The key to accurate, efficient and successful AI models is large, comprehensive and high-quality datasets. However, this can be costly and time-consuming to obtain.
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, for example.
For companies looking for a robust and dynamic library of instantly accessible datasets, available in multiple languages and domains, an online marketplace like DefinedData could be the answer.
DefinedData provides customers with on-demand access to high-quality data available in multiple languages, domains, recording types and pricing options. The initial offering includes scripted recordings in English, Italian, Portuguese, German, French and Dutch in the domains of healthcare, entertainment, hospitality, automotive and generic.
By May 2021, the library is expected to grow to include over 25,000 hours of both scripted and spontaneous recordings in the above languages as well as in Spanish, Hindi and Japanese. Additional domains will include banking, insurance, telecom, retail and IVR.
Customers are also able to choose between a one-time purchase or an annual subscription that provides access to existing and new datasets as they become available.
“Machine learning teams building AI models have always faced one particularly pressing problem, and that is continuous access to highly accurate data. When large enterprise tech firms want to launch their AI initiatives into the market quickly, they simply don’t have the time to collect and validate the data required to do so. DefinedData aims to solve this problem by providing customers with access to an extensive library of speech datasets that will rapidly accelerate their AI programs,” said DefinedCrowd’s VP of Engineering, Andrew Michalik.
“However, although strong algorithms require lots of data, they also require accurate and high-quality data,” said Michalik. “After all, the success of AI models depends on the quality of data used to fuel them. For those firms considering off-the-shelf data, they can rest assured that quality is at the core of everything we do at DefinedCrowd.”
The data trade-off
Like everything in life, determining whether off-the-shelf data or custom-built data is the best for your project is based on trade-offs. Ultimately, the best data set is one which fits your budget, and which meets the project’s data needs and time constraints.
Essentially, companies have three choices: they can build datasets, buy datasets, or do a little bit of both. For those looking to quickly expand their offering into the marketplace with high-quality and instantly accessible data, DefinedData may be the perfect solution.