We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. IMTLab treats the whole interactive translation process as a task-oriented dialogue with a human-in-the-loop setting, in which human interventions can be explicitly incorporated to produce high-quality, error-free translations. To this end, a general communication interface is designed to support the flexible IMT architectures and user policies. Based on the proposed design, we construct a simulated and real interactive environment to achieve end-to-end evaluation and leverage the framework to systematically evaluate previous IMT systems. Our simulated and manual experiments show that the prefix-constrained decoding approach still gains the lowest editing cost in the end-to-end evaluation, while BiTIIMT achieves comparable editing cost with a better interactive experience.

Creating a successful AI-based solution is different from regular software development because how it will operate in a real-world situation isn't easily predicted. Development is an interactive process that requires extensive testing to ensure it will work well with a company's existing workflow and deliver a worthwhile return on its investment. As such, effective implementation requires an iterative approach that usually takes more time, effort, and money than traditional software efforts. Artificial intelligence is the key that can unlock a new generation of automation and productivity for businesses. But the development of AI can also help drive companies' sustainability efforts -- something increasingly important to today's workers.

From a broad perspective as a software engineer a big part of my work consists in aligning expectations between stakeholders and creating code that transforms and forwards data. During the last 15 years I have developed software and feel fascinated by this part of computer sciences. Curiosity and interest in other topics from the same field; data sciences and machine learning, made me decide to start a simple hands-on project in order to learn and practice in a typical experiment oriented approach. After two months I had created a software different from the others I had developed so far, it "learned" from data and after that it could make "predictions", and correct ones. The software result is an International Bank Account Number (or IBAN) validator.

The BDN Opinion section operates independently and does not set newsroom policies or contribute to reporting or editing articles elsewhere in the newspaper or on bangordailynews.com. Keith E. Sonderling is a commissioner on the U.S. Equal Employment Opportunity Commission.The views here are the author's own and should not be attributed to the EEOC or any other member of the commission. With 86 percent of major U.S. corporations predicting that artificial intelligence will become a "mainstream technology" at their company this year, management-by-algorithm is no longer the stuff of science fiction. AI has already transformed the way workers are recruited, hired, trained, evaluated and even fired. One recent study found that 83 percent of human resources leaders rely in some form on technology in employment decision-making.