Drupal is a free and open source content management platform used for all sorts of websites, from personal blogs to NASA’s. During his opening keynote at DrupalCon Seattle 2019, Drupal Founder Dries Buytaert shared that 1 in 30 websites today are built on Drupal’s back-end framework.
Each year, the nonprofit Drupal Association hosts DrupalCon North America—an annual gathering of developers, UX designers, content managers, and more who use Drupal each day. I was pleased to speak at this year’s conference as part of the “Builder” track on a subject that influences all: artificial intelligence (AI) and the datasets used to train these products.
Today’s software teams integrate AI into a wider range of products than ever before. If you use cloud collaboration software such as Slack or Google Drive, you use the AI that’s built these products as well.
But AI’s benefits come with a warning: If the datasets used to train AI algorithms aren’t large or diverse enough, they risk perpetuating bias that will affect end users.
This isn’t an abstract concept: Judges in more than 12 U.S. states have used a machine learning (ML) algorithm called COMPAS to predict defendants’ likelihood of recidivism.
COMPAS’ results impact factors like the lengths of prison sentences and whether defendants are released on parole—even though the algorithm has incorrectly predicted that black defendants are more likely to recommit crimes.
Still think machine bias can’t hurt you? Consider what would happen if a speech recognition API system isn’t trained on a dataset that includes a wide range of accents and inflections. If that API is part of an autonomous car—and that car can’t recognize voice commands from a wide range of users—the end results could be deadly.
Now, the good news: Although machine bias is an unavoidable problem, it is not unmanageable. If you’re part of a product team tasked with building datasets used to train ML algorithms, you can start taking several steps to reduce the risk of bias.
Learn how by watching my DrupalCon presentation below. Then, let us know your thoughts in the comments and read more about how AI will impact small businesses across industries.
Quick quiz: Your most experienced technician is about to retire. How do you replace someone with 30+ years of experience without sacrificing the service quality your business is known for?
Replacing technicians is not easy: It takes time to train new employees, and you don’t want your customer service to suffer in the interim. According to a poll conducted by The Service Council, 70% of organizations are concerned about their aging field service workforce.
Note: This document, while intended to inform our clients about the current data privacy and security challenges experienced by IT companies in the global marketplace, is in no way intended to provide legal advice or to endorse a specific course of action. For advice on your specific situation, consult your legal counsel.
In the year since GDPR took effect, regulators have given millions of reasons to take it seriously: $57 million to be exact, the amount Google was fined back in January. And though the world’s largest internet company was the first to be fined, small businesses have faced the most difficulties.
GDPR‘s biggest impact, however, has been its role in fundamentally altering the conversation about data privacy.
Most small- or midsize-business owners admit they aren’t great at data analytics. They are simply passionate individuals, working hard to build the business of their dreams.
But in today’s age of big data, data analysis is important to build and expand your business. As your business grows, the volume, velocity, and variety of the data you handle will also increase. This makes data analysis a more complex and challenging task since it requires more knowledge of coding and statistics.
This is where business intelligence (BI) chatbots or BI bots can come to your rescue. BI bots understand data queries in spoken languages such as English and provide results. Natural language processing (NLP) and chatbots are emerging technologies that will make data analytics accessible to everyone.
Field service managers have it tough: They need to meet customer expectations, harmonize the work of field technicians with office processes, and, most importantly, turn a profit for the company. Not surprisingly, 73% of field service organizations say that they’re still struggling to achieve revenue growth.
Increasingly, field service organizations are turning to software to help them grow their business. But how exactly can field service management (FSM) software address the many challenges field managers face?