LAS VEGAS — Among fresh tech trends in HR, one that may garner the most interest is a new layer of software — which superstar analyst Josh Bersin called an employee experience platform — that will fit between core HR and talent management tools.
Bersin said he expects employee experience to become the next-generation employee portal — in other words, the go-to application for modern workers who need HR-based information. Vendors are lining up to address the need, he added.
“There is going to be a holy war for [what] system your employees use first,” said Bersin, an independent analyst who founded Bersin by Deloitte. Although his quote served as hyperbole, it nonetheless stuck with attendees here at the 2018 HR Technology Conference & Exposition.
“He hit home,” said Rita Reslow, senior director of global benefits at HR software vendor Kronos, based in Lowell, Mass. “We have all these systems, and we keep buying more.” But she wondered aloud when one product would tie her systems together for employees.
No vendor has achieved a true employee experience platform, Bersin told a room packed with 900 or so attendees at the conference on Tuesday. However, ServiceNow, PeopleDoc — which Ultimate Software acquired in July — and possibly IBM appear to have a head start, he added.
Tech trends in HR point to team successes
Josh Bersinindependent analyst
Bersin, who plans to release an extensive report about 2019 tech trends in HR, said software development within the industry reflects a shift in management that steers away from employee engagement and company culture in favor of increased team performance.
Unless a recession hits, “I think the focus of the tech market for the next couple of years … is on performance, productivity and agility,” he said.
The shift to productivity will require future technology to simplify work life, said Cliff Howe, manager of enterprise applications at Cox Enterprises, a communications and media company in Atlanta. “Our employees are being inundated,” Howe said. “We don’t want to hit our employees with too much [technology].”
Shop around for vendors that focus on your company’s particular market. For example, if your organization exhibits a compliance-based culture, find a vendor that mirrors that approach.
Evaluate the “personality of the vendor,” he said. As an example, determine if the vendor’s reps listen to your decision-makers and help them. If the answer is no, it may be time to drop that vendor from consideration.
AI auditing, real-time payrolls needed in future
In other upcoming tech trends in HR, Bersin pegged AI as a quickly growing field that smart HR departments will learn how to monitor and audit in the future. That notion was on the minds of many at the HR Technology Conference, for which TechTarget — the publisher of SearchHRSoftware — is a media partner.
AI innovation has increased rapidly in the last two years. Today, even small HR software vendors with three to five engineers can use technology from Google or IBM, combine it with open source options and scale a new product on the cloud quickly, Bersin said. HR professionals will need to adjust their skills in order to better understand why AI software makes its decisions, which is an area not fully grasped yet, he added.
Howe agreed AI has grown beyond wish-list status. “AI will be a requirement, rather than a shiny object,” he said.
Bersin also noted that software will need to reflect a possible switch to a continuous payroll model — perhaps as often as daily. Younger workers, some of whom might not have bank accounts, have increased their demands to be compensated in real time, and this request is not just for the gig economy, he said.
Today, fresh out of the Microsoft Research Montreal lab, comes an open-source project called TextWorld. TextWorld is an extensible Python framework for generating text-based games. Reinforcement learning researchers can use TextWorld to train and test AI agents in skills such as language understanding, affordance extraction, memory and planning, exploration and more. Researchers can study these in the context of generalization and transfer learning. TextWorld further runs existing text-based games, like the legendary Zork, for evaluating how well AI agents perform in complex, human-designed settings.
Text-based games – also known as interactive fiction or adventure games – are games in which the play environment and the player’s interactions with it are represented solely or primarily via text. As players moves through the game world, they observe textual descriptions of their surroundings (typically divided into discrete ‘rooms’), what objects are nearby, and any other pertinent information. Players issue text commands to an interpreter to manipulate objects, other characters in the game, or themselves. After each command, the game usually provides some feedback to inform players how that command altered the game environment, if at all. A typical text-based game poses a series of puzzles to solve, treasures to collect, and locations to reach. Goals and waypoints may be specified explicitly or may have to be inferred from cues.
Figure 2 – An example game from TextWorld with a house-based theme.
Text-based games couple the freedom to explore a defined space with the restrictions of a parser and game world designed to respond positively to a relatively small set of textual commands. An agent that can competently navigate a text-based game needs to be able to not only generate coherent textual commands but must also generate the right commands in the right order, with little to no mistakes in between. Text-based games encourage experimentation and successful playthroughs involve multiple game losses and in-game “deaths.” Close observation and creative interpretation of the text the game provides and a generous supply of common sense are also integral to winning text-based games. The relatively simple obstacles present in a TextWorld game serve as an introduction to the basic real-life challenges posed by text-based games. In TextWorld, an agent needs to learn how to observe, experiment, fail and learn from failure.
TextWorld has two main components: a game generator and a game engine. The game generator converts high-level game specifications, such as number of rooms, number of objects, game length, and winning conditions, into an executable game source code in the Inform 7 language. The game engine is a simple inference machine that ensures that each step of the generated game is valid by using simple algorithms such as one-step forward and backward chaining.
Figure 3 – An overview of the TextWorld architecture.
“One reason I’m excited about TextWorld is the way it combines reinforcement learning with natural language,” said Geoff Gordon, Principal Research Manager at Microsoft Research Montreal “These two technologies are both really important, but they don’t fit together that well yet. TextWorld will push researchers to make them work in combination.” Gordon pointed out that reinforcement learning has had a number of high-profile successes recently (like Go or Ms. Pac-Man), but in all of these cases the agent has fairly simple observations and actions (for example, screen images and joystick positions in Ms. Pac-Man). In TextWorld, the agent has to both read and produce natural language, which has an entirely different and, in many cases, more complicated structure.
“I’m excited to see how researchers deal with this added complexity, said Gordon.”
Microsoft Research Montreal specializes in start-of-the art research in machine reading comprehension, dialogue, reinforcement learning, and FATE (Fairness, Accountability, Transparency, and Ethics in AI). The lab was founded in 2015 as Maluuba and acquired by Microsoft in 2017. For more information, check out Microsoft Research Montreal.
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A fresh wave of artificial intelligence rolling through Microsoft’s language translation technologies is bringing more accurate speech recognition to more of the world’s languages and higher quality machine-powered translations to all 60 languages supported by Microsoft’s translation technologies.
The advances were announced at Microsoft Tech Summit Sydney in Australia on November 16.
“We’ve got a complex machine, and we’re innovating on all fronts,” said Olivier Fontana, the director of product strategy for Microsoft Translator, a platform for text and speech translation services. As the wave spreads, he added, these machine translation tools are allowing more people to grow businesses, build relationships and experience different cultures.
Microsoft’s research labs around the world are also building on top of these technologies to help people learn how to speak new languages, including a language learning application for non-native speakers of Chinese that also was announced at this week’s tech summit.
The new Microsoft Translator advances build on last year’s switch to deep neural network-powered machine translations, which offer more fluent, human-sounding translations than the predecessor technology known as statistical machine translation.
Both methods involve training algorithms using professionally translated documents, so the system can learn how words and phrases in one language are represented in another language. The statistical method, however, is limited to translating a word within the local context of a few surrounding words, which can lead to clunky and stilted translations.
Neural networks are inspired by people’s theories about how the pattern-recognition process works in the brains of multilingual humans, leading to more natural-sounding translations.
Microsoft recently switched 10 more languages to neural network-based models for machine translation, for a total of 21. The neural network-powered translations show between 6 percent and 43 percent improvement in accuracy depending on language pairs, according to an automated evaluation metric for machine translation known as the bilingual evaluation understudy, or BLEU, score.
“Over the last year, we have been rolling out to more languages, we have been making the models more complex and deeper, so we have much better quality,” said Arul Menezes, general manager of the Microsoft AI and Research machine translation team. He added that the neural network-powered translations for Hindi and Chinese, two of the world’s most popular languages, are available by default to all developers using Microsoft’s translation services.
Steps of the translation process
For a machine, the process of translating from one language to the next is broken down into several steps; each step has a stake in the quality of the translation. In the case of translating what a person speaks in one language, the first step is speech recognition, which is the process of converting spoken words into text.
All languages supported by Microsoft speech translation technologies now use a type of AI called long short-term memory for speech recognition, which together with additional data have led to an up to 29 percent increase in quality over deep neural network models for conversational speech.
“When you do speech translation, you first do speech recognition and then you do translation,” explained Menezes. “So, if you have an error in speech recognition, then that effect is going to be amplified at the next step because if you misrecognize a word, then the translation is going to be incomprehensible.”
The second step of machine translation converts the text from one language to the next, which Microsoft does with neural network-based models for 21 languages. The improvement in quality of translations is apparent even when only one of the languages is supported by a neural network-based model due to an approach that translates both languages through English.
Consider, for example, a person who wants to translate from Dutch to Catalan. Dutch is newly supported by neural networks; engineers are still working on the neural network support infrastructure for Catalan. End users will notice an improvement in the Dutch to Catalan translation using this hybrid approach because half of it is better, noted Menezes.
In the final step of speech translation, the translated text is synthesized into voice via text-to-speech synthesis technology. Here, too, speech and language researchers are making advances that produce more accurate and human-sounding synthetic voices. These improvements translate to higher quality experiences across Microsoft’s existing translation services as well as open the door to new language learning features.
For example, if you really want to learn to speak a foreign language, everyone knows that practice is essential. The challenge is to find someone with the time, patience and skill to help you practice pronunciation, vocabulary and grammar.
For people learning Chinese, Microsoft is aiming to fill that void with a new smartphone app that can act as an always available, artificially intelligent language-learning assistant. The free Learn Chinese app is launching soon on Apple’s iOS platform.
The app aims to solve a problem that is familiar to any langue learner who has spent countless hours in crowded classrooms listening to teachers, watching language-learning videos at home or flipping through stacks of flashcards to master vocabulary and grammar — only to feel woefully underprepared for real-world conversations with native speakers.
“You think you know Chinese, but if you meet a Chinese person and you want to speak Chinese, there is no way you can do it if you have not practiced,” explained Yan Xia, a senior development lead at Microsoft Research Asia in Beijing. “Our application addresses this issue by leveraging our speech technology.”
The application is akin to a teacher’s assistant, noted Frank Soong, principal researcher and research manager of the Beijing lab’s speech group, which developed the machine-learning models that power Learn Chinese as well as Xiaoying, a chatbot for learning English that the lab deployed in 2016 on the WeChat platform in China.
“Our application isn’t a replacement for good human teachers,” said Soong. “But it can assist by being available any time an individual has the desire or the time to practice.”
The language learning technology relies on a suite of AI tools such as deep neural networks that have been tuned by Soong’s group to recognize what the language learners are trying to say and evaluate the speakers’ pronunciation, rhythm and tone. They are based on a comparison with models trained on data from native speakers as well as the lab’s state-of-the art text-to-speech synthesis technology.
When individuals use the app, they get feedback in the form of scores, along with highlighted words that need improvement and links to sample audio to hear the proper pronunciation. “The app will work with you as a language learning partner,” said Xia. “It will respond to you and give you feedback based on what you are saying.”
Reaching more places
The Learn Chinese application and Microsoft’s core language translation services are powered by machine intelligence running in the cloud. This allows people the flexibility and convenience to access these services anywhere they have an internet connection, such as a bus stop, restaurant or conference center.
For clients with highly sensitive translation needs or who require translation services where internet connections are unavailable, Microsoft is now offering neural network powered translations for its on-premise servers. The development, Fontana noted, is one more example of how “the AI wave is advancing and reaching more and more places and more and more languages.”
John Roach writes about Microsoft research and innovation. Follow him on Twitter.
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