Beam search and exhaustive search are two extreme ends of text decoding algorithms with respect to the search depth. Beam search is limited in both search width and depth, whereas exhaustive search is a global search that has no such limitations. Surprisingly, beam search is not only computationally cheaper but also performs better than exhaustive search despite its higher search error. Plenty of research has investigated a range of beam widths, from small to large, and reported that a beam width that is neither too large nor too small is desirable. However, in terms of search depth, only the two extreme ends, beam search and exhaustive search are studied intensively. In this paper, we examine a range of search depths between the two extremes to discover the desirable search depth. To this end, we introduce Lookahead Beam Search (LBS), a multi-step lookahead search that optimizes the objective considering a fixed number of future steps. Beam search and exhaustive search are special cases of LBS where the lookahead depth is set to $0$ and $\infty$, respectively. We empirically evaluate the performance of LBS and find that it outperforms beam search overall on machine translation tasks. The result suggests there is room for improvement in beam search by searching deeper. Inspired by the analysis, we propose Lookbehind Heuristic Beam Search, a computationally feasible search algorithm that heuristically simulates LBS with 1-step lookahead. The empirical results show that the proposed method outperforms vanilla beam search on machine translation and text summarization tasks.

This is the third in a series on AI transforming the workplace. As the founding partner of Future Workplace, an HR advisory and research firm, Jeanne Meister spends much of her professional time thinking about artificial intelligence, HR and how the future will shake out. Currently, that's a future that is being rapidly reshaped by the pandemic. As more employers look to AI as part of the solution to the myriad challenges that will arise post-pandemic, Meister, while a strong proponent of AI-based solutions, says organizations must safeguard data, taking steps to avoid potential bias and a lack of transparency. "Employee awareness about privacy and how much they are willing to blithely share is intensifying," she says, "and must be seriously factored into any post-pandemic AI use."

LAS VEGAS--Artificial intelligence (AI) has pervaded most categories of HR technology, and those it hasn't touched are likely to see its impact soon. But questions and myths persist around the use of AI, issues that a panel of HR, talent acquisition and learning leaders explored in a session at the recent HR Technology Conference & Exposition. Jeanne Meister, founding partner of Future Workplace, an HR advisory and research firm in New York City, moderated the panel. "The conversation in HR has moved fully toward how AI and humans will best work together," Meister said. The conversation no longer is only about how AI will displace jobs." One slide in the presentation highlighted this quote from a Harvard Business Review article: "Over the next decade, AI won't replace managers.

It looks like Americans aren't too excited about working with artificial intelligence. According to a new survey from Oracle and Future Workplace, only 22 percent of American workers are "excited about AI." About 60 percent of Indian workers and 56 percent of Chinese workers are excited about working with AI. Only 8 percent of French workers are excited about it. The researchers asked over 8,000 people in 10 different countries how they feel about AI in the workplace.

I challenge you to talk about the future of work without talking about artificial intelligence (AI) – Impossible, right? It's indisputable that AI and related technologies will play a significant role in shaping the way we work and how businesses will need to adapt moving forward. But what does an artificially intelligent future at work really look like? We know that AI--and its machine learning subset--is great for traditional HR and recruiting tasks like mining thousands of resumes to find the best fit candidates for a given job. But to dive deeper into AI at work, I recently chatted with Jeanne Meister, future of work expert, founder of HR advisory firm Future Workplace and author of The 2020 Workplace and The Future Workplace Experience.

Employers are using artificial intelligence (AI) in recruiting chatbots, in video interviews to assess job candidates' body language or word choices, or to extract themes from engagement survey responses. But how companies are using AI to benefit the employee experience, support compliance efforts and ease administrative workloads is not as well-known. "So much of the buzz about AI has been for its'sexy' uses around sourcing and screening in recruiting, but there are a growing number of other applications of value to HR to be aware of," said Jeanne Meister, founding partner of Future Workplace, an HR advisory and research firm in New York City. One example of AI's expanding utility: using it to audit employees' expense reports, to ensure they comply with company policy and avoid wasteful spending. AppZen in Sunnyvale, Calif., uses AI to read and extract information from receipts to catch duplicates, out-of-policy spending, incorrect amounts or suspicious merchants.

I challenge you to talk about the future of work without talking about artificial intelligence (AI) – Impossible, right? It's indisputable that AI and related technologies will play a significant role in shaping the way we work and how businesses will need to adapt moving forward. But what does an artificially intelligent future at work really look like? We know that AI--and its machine learning subset--is great for traditional HR and recruiting tasks like mining thousands of resumes to find the best fit candidates for a given job. But to dive deeper into AI at work, I recently chatted with Jeanne Meister, future of work expert, founder of HR advisory firm Future Workplace and author of The 2020 Workplace and The Future Workplace Experience.

If you're in HR, you've undoubtedly heard two buzzwords as much or more than any others in the past few years: AI and employee experience. They're topics we often link to automation -- after all, the employee experience is a lot better when you're freed from tedious, repetitive processes. But AI is actually improving the employee experience in other ways that are often overlooked. We spoke with two leading AI experts about a few ways AI is making the employee experience better than ever. Let's consider employee experience for a moment.

In the following report from Fast Company magazine we can find some answers and reflections. Workplace culture is being influenced by disparate factors in significant ways. Demographic shifts, diversity and inclusion initiatives, talent shortages, automation, evolving technology, and an onslaught of data are converging to create both immediate and long-term changes. PwC chief people officer Mike Fenlon likens the changes to the cyberpunk writer William Gibson's popular quote, "The future is already here. It's just not evenly distributed," he says.

That's what 17% of workers who quit a new job within the first six months of hiring say, according to a BambooHR survey. Another 23% might have stayed if managers had communicated better, while 21% blamed poor training. These ex-employees aren't outliers; more than two-thirds of all workers surveyed report leaving a promising new position within half a year. In today's brutally competitive talent ecosystem, a hasty exit after a months-long search is a human resources manager's worst nightmare--and many are asking if artificial intelligence (AI) tools can prevent it. The hope isn't simply for AI to ensure smarter, faster hiring, but to improve the entire lifecycle of the employee experience, from job application to exit interview.