What is Conversational AI?
AI Can Now Tell Your Boss What Skills You Lack—and How You Can Get Them
Here's the conundrum with corporate online learning: there are so many classes available from sites like Coursera, edX, and Udacity that companies don't know what content to offer their employees. And once companies do choose a learning program, it's tough for them to figure out what skills their employees pick up and to what degree they've mastered them. They need an objective metric to evaluate proficiency.
A new AI-powered tool developed by Coursera aims to be that metric. The feature, which the Bay Area startup announced today, lets companies that subscribe to its training programs see which of their employees are earning top scores in Coursera classes; how their employees' skills measure up to their competitors'; and what courses would help fill any knowledge gaps. Companies will be able to access the tool, which uses machine learning to derive insights, in the online dashboard of their Coursera profiles later this year.
The new feature is just one example of the ways online-learning providers are using AI to match learners with courses, assess their ability, and tweak class content in response to feedback. Coursera has a data-science team that does everything from "collecting and storing data in a warehouse to interpreting information for making internal decisions to building algorithms that feed back into the site," according to Emily Glassberg Sands, who leads the group. Udacity's AI research team, which it established in 2017, analyzes student sentiment to see how lessons can be improved and computes whether learners like the changes. Udacity has also used AI-based chatbots to help students find relevant courses and answer common questions during the enrollment process. EdX says it experiments with AI to increase how well people learn and teach.
By addressing some of the most pernicious challenges in online learning, these AI features could inspire more people—and companies—to sign up for such training. Increasing the effectiveness of educational programs and measuring their impact are the top two priorities for companies that pay to train their staff, according to a 2017 survey of US-based businesses by Training magazine.
The market has plenty of room to grow; Coursera's business program, which targets companies, currently has 1,400 customers. (Overall, Coursera has 31 million learners, which makes it the world's largest online-learning provider.) "The Achilles' heel of the corporate learning industry is that no one knows how to demonstrate the return on investment," notes Leah Belsky, who heads the Coursera for Business program. "Companies know their people need to learn new skills to stay competitive, but they have a hard time communicating what the value of that learning is."
Coursera, too, wanted to be able to quantify the benefits of its classes. So a year and a half ago, its data-science team began developing machine-learning algorithms to map the 40,000 skills taught on Coursera's platform. First, the team used natural language processing (NLP) to determine how often instructors mentioned particular concepts during their classes. That information helped it identify which classes imparted which skills. Coursera can get some of that data by simply asking its instructors. But it also uses NLP because educators often think of themselves as teaching theoretical concepts while learners want to know what specific tools and technologies they will master, says Glassberg Sands.
Companies that subscribe to Coursera will be able to see how they rank compared to their competitors in specific skills. Coursera
Most recently, the team incorporated a psychometrics methodology called item response theory (IRT) into some of its machine-learning models to gauge learners' abilities based on how they performed on Coursera quizzes and assignments. The approach allowed the team to measure proficiency in a given skill area across learners who were answering different questions of varying difficulty, says Glassberg Sands. That's important because a more advanced learner will, on average, take harder courses on Coursera and attempt harder questions than a beginner. The IRT model accounts for that by figuring out what skill a particular quiz question is assessing and the relative difficulty of that quiz question before evaluating a learner's proficiency. "What you get is a percentile for your employees in each skill area, relative to whatever comparison group you choose, whether that's all the companies on Coursera or just companies in your industry or your country or companies of a similar size," says Glassberg Sands.
Most online-learning providers let their corporate customers see which of their employees are enrolled in classes, how they're progressing, and the feedback they're providing on their experiences. Coursera says machine learning enables it to examine how its millions of learners are performing across its platform and provide predictive insights, such as a given learner's skill level, along with more standard student metrics. In theory, these machine-learning models should also get smarter as learners and instructors create more data that can be fed back into the system.
Coursera expects the skills-benchmarking information, which will be updated daily, to be useful to learning-and-development specialists, HR professionals, and hiring managers, who previously would have had to guess what skills other companies were acquiring and which of their employees were most expert in, say, a particular programming language. But it's also bound to rankle some Coursera learners who don't want their bosses making employment decisions based on their online-learning performance.
Glassberg Sands and Belsky contend that the technology will benefit individuals in other ways, such as bringing attention to skilled people who might otherwise be overlooked. Software giant Adobe, which has been testing the new Coursera feature for the past month, agrees. "We probably have some experts who are located in remote offices where we may not be able to identify their level of talent easily," says Justin Mass, who leads Adobe's digital-learning program. "These features give us a true sense of what our employees know and are good at, which should help us assess talent more intelligently."
What Is NLP? Natural Language Processing Explained
Natural language processing definitionNatural language processing (NLP) is the branch of artificial intelligence (AI) that deals with training computers to understand, process, and generate language. Search engines, machine translation services, and voice assistants are all
While the term originally referred to a system's ability to read, it's since become a colloquialism for all computational linguistics. Subcategories include natural language generation (NLG) — a computer's ability to create communication of its own — and natural language understanding (NLU) — the ability to understand slang, mispronunciations, misspellings, and other variants in language.
The introduction of transformer models in the 2017 paper "Attention Is All You Need" by Google researchers revolutionized NLP, leading to the creation of generative AI models such as Bidirectional Encoder Representations from Transformer (BERT) and subsequent DistilBERT — a smaller, faster, and more efficient BERT — Generative Pre-trained Transformer (GPT), and Google Bard.
How natural language processing worksNLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning. Phrases, sentences, and sometimes entire books are fed into ML engines where they're processed using grammatical rules, people's real-life linguistic habits, and the like. An NLP algorithm uses this data to find patterns and extrapolate what comes next. For example, a translation algorithm that recognizes that, in French, "I'm going to the park" is "Je vais au parc" will learn to predict that "I'm going to the store" also begins with "Je vais au." All the algorithm then needs is the word for "store" to complete the translation task.
NLP applicationsMachine translation is a powerful NLP application, but search is the most used. Every time you look something up in Google or Bing, you're helping to train the system. When you click on a search result, the system interprets it as confirmation that the results it has found are correct and uses this information to improve search results in the future.
Chatbots work the same way. They integrate with Slack, Microsoft Messenger, and other chat programs where they read the language you use, then turn on when you type in a trigger phrase. Voice assistants such as Siri and Alexa also kick into gear when they hear phrases like "Hey, Alexa." That's why critics say these programs are always listening; if they weren't, they'd never know when you need them. Unless you turn an app on manually, NLP programs must operate in the background, waiting for that phrase.
Transformer models take applications such as language translation and chatbots to a new level. Innovations such as the self-attention mechanism and multi-head attention enable these models to better weigh the importance of various parts of the input, and to process those parts in parallel rather than sequentially.
Rajeswaran V, senior director at Capgemini, notes that Open AI's GPT-3 model has mastered language without using any labeled data. By relying on morphology — the study of words, how they are formed, and their relationship to other words in the same language — GPT-3 can perform language translation much better than existing state-of-the-art models, he says.
NLP systems that rely on transformer models are especially strong at NLG.
Natural language processing examplesData comes in many forms, but the largest untapped pool of data consists of text — and unstructured text in particular. Patents, product specifications, academic publications, market research, news, not to mention social media feeds, all have text as a primary component and the volume of text is constantly growing. Apply the technology to voice and the pool gets even larger. Here are three examples of how organizations are putting the technology to work:
Whether you're building a chatbot, voice assistant, predictive text application, or other application with NLP at its core, you'll need tools to help you do it. According to Technology Evaluation Centers, the most popular software includes:
There's a wide variety of resources available for learning to create and maintain NLP applications, many of which are free. They include:
Here are some of the most popular job titles related to NLP and the average salary (in US$) for each position, according to data from PayScale.
Become Fluent In Machine Learning With This 6-course Bundle On Sale
TL;DR: As of Jan. 21, The Ultimate Deep Learning & NLP Certification Bundle (worth $1,200) is on sale for just $29.99, which is over 97% off.
If you want to advance your career or learn a new skill, the least intimidating way to do it is to be able to learn on your own time and at your own pace. With the six-course Deep Learning and NLP Certification Bundle, you can do both and get ready to pass your NLP test in the process.
This course bundle has over 300 lessons on everything you need to know about machine learning. Start by learning how to bridge the gap between basic CNN and modern architectures, and even learn advanced applications of CNNs. From there, build on your skills with advanced NLP courses. You'll not only gain the fundamental building blocks of Deep NLP, but you'll also gain a higher level of thinking by building systems using all of the components you've absorbed so far. You'll be able to build applications for problems like text classification, machine translation, and even stock predictions. From there, courses will cover matrix factorization algorithms, building models that classify images, sentiment analysis for problem-solving, and much more.
Mashable Trend Report
Each course is taught by an expert, like the Lazy Programmer — a data scientist, full-stack software engineer, and big data engineer. He has taught thousands of students about data science, statistics, machine learning, and more at schools like Columbia University, NYU, Humber College, and The New School. As both a front and back-end programmer, he knows all the ropes and what jobs will require of you once you jump into the field.
The six-course Deep Learning and NLP Certification Bundle is valued at $1,200, since you can learn everything at your own pace and courses never expire. But for a limited time, you can save 97% and start learning on your couch for just $29.99.
Prices subject to change.
Opens in a new window
Credit: Lazy Programmer
Comments
Post a Comment