TRENDS
12 understand language context . The Cortical . io platform trains the models by mimicking biology — the way the human brain learns , processes and understands language .
The training can be achieved with as few as a hundred documents and is as simple as having a person click through them to categorize bits of text . The process is anywhere from 50 to 1,000 times more energyefficient and less expensive to implement than traditional models , according to company cofounder and CEO Francisco Webber .
This solution works well for specific use cases because not all the processes that employ NLP need the broad capabilities of deep learning . New companies like Cortical . io are entering the market to challenge that status quo — and provide niche solutions that require a fraction of the computing power .
“ These small startups are saying that it doesn ’ t make sense to always use that sort of sledgehammer approach of deep learning ,” says Alan Pelz-Sharpe , founder of Deep Analysis , an analyst firm focused on emerging technologies . “ By taking a very narrow approach , they ’ re able to train on a much smaller data set , be up and running much more quickly and , frankly , be much more accurate .”
SPEED AND ACCURACY Take the example of a global accounting firm that needed to help customers disclose their leases . Most customers had thousands of lease agreements to comb through , and those agreements didn ’ t contain standard language that could be used effectively in keyword search . Cortical . io ’ s contract intelligence solution allowed the firm to train a model by annotating about 50 documents and then to automatically extract and classify data from the rest of the leases — reducing project completion time by about 80 %.
Webber , who developed the technology , says those are typical results for the company ’ s contract intelligence solution . “ And you don ’ t need a large team of support clerks — only two or three people monitoring the engine so it works properly ,” he says .
Speed and cost savings are not the only advantages . In general terms , contract interpretation is a good use case for AI . Research suggests that machine-based analysis is 98 % accurate , compared to 92 % for humans , according to World Commerce & Contracting , a nonprofit association that promotes commercial practice standards .
In the case of Cortical . io , accuracy and consistent quality result from the model training . For example , some employees may have less experience or produce lower quality work when fatigued at the end of the day . AI solves these problems when the customer ’ s most experienced subject matter experts train the model . And it doesn ’ t require an in-house AI expert , so anyone within the company can take the pre-trained model and train it further by adding new classifiers for their use case .
The Cortical . io platform eliminates another challenge inherent with traditional ML approaches — what the industry calls a “ black box ” or transparency problem .
“ These complex [ ML ] models are making the decisions once they ’ re up and running , but it ’ s not possible to know how they ’ re making them ,” explains Pelz-Sharpe . “ So you have to trust the technology enormously because it can never explain itself to you .”
In contrast , the Cortical . io platform provides