For ASR, this milestone was first claimed in a 2016 research paper by Microsoft (Xiong et al., 2016) reporting that for the first time, they have achieved human parity in word error rate1 (WER) on the Switchboard benchmark (5.8% WER) while also achieving 11% WER on the CallHome benchmark, which is known to be more challenging to transcribe. In addition, the reported decoding speed was only 1.38 real time, which is in the realm of usability for some commercial systems. This announcement was highly publicized even in mainstream media outlets2. A follow-up paper in 2017 claimed further improvement to 5.1% WER on Switchboard but with no report on decoding speed (Xiong et al., 2018). Also in 2017, Google announced a 4.9% WER (on some undisclosed benchmark) at its annual I/O developer conference3.

Azure AI is driving innovation and improving experiences for employees, users, and customers in a variety of ways, from increasing workday productivity to promoting inclusion and accessibility. The success of Azure AI--featuring Azure Cognitive Services, Azure Machine Learning, and Azure OpenAI Service--is built on a foundation of Microsoft Research, a wide range of Azure products that have been tested at scale within Microsoft apps, and Azure customers who use these services for the benefit of their end users. As 2023 begins, we are excited to highlight 10 use cases where Azure AI is utilized within Microsoft and beyond. Speech transcription and captioning in Microsoft Teams is powered by Azure Cognitive Services for Speech. Microsoft achieved human parity in conversational speech recognition when it reached an error rate of 5.9 percent.

We had a lot of developments with multiple tops and turns. The sheer number and quality of the multiple papers and outcomes released in the ML space were amazing. We had innovations in GPU, newer models, lots of research into different fields, and some ground-breaking discoveries. The Machine Learning industry continued to grow by leaps and bounds. Here are some interesting stats.

Last week was very interesting for Machine Learning. There were a lot of events with potentially long-lasting consequences for Machine Learning. This article will go over some of them so that you're more informed about some important aspects of Machine Learning and the discussions surrounding them. These events might seem disjointed, but they provide different sides to a very important discussion in machine learning. GLUE (General Language Understanding Evaluation) and SuperGLUE are benchmarks for Natural Language Processing.

Imagine sitting in a circle with a few people where each of you knows only two languages -- one shared with the person on your left, and one shared with the person on your right. If you say something to the person on your right and ask them to pass on the message, it might very well be that, after being passed along all the languages, it comes out sounding very different from the original message. This might seem like a very weird game of Telephone to you, but in the same way that whispering impairs your ability to hear the message, so translation works as an imperfect communication channel. When you try to translate a message into a different language, you can change its intended meaning without being aware of it. Oftentimes messages are subjective, ambiguous, or, in some cases, even impossible to represent without any loss of information. But why is translation such a challenge? And in being so, can we ever achieve such a thing as a perfect translation?

Microsoft has reached a milestone in text-to-speech synthesis with a production system that uses deep neural networks to make the voices of computers nearly indistinguishable from recordings of people. With the human-like natural prosody and clear articulation of words, Neural TTS has significantly reduced listening fatigue when you interact with AI systems. Our team demonstrated our neural-network powered text-to-speech capability at the Microsoft Ignite conference in Orlando, Florida, this week. The capability is currently available in preview through Azure Cognitive Services Speech Services. Neural text-to-speech can be used to make interactions with chatbots and virtual assistants more natural and engaging, convert digital texts such as e-books into audiobooks and enhance in-car navigation systems.

A team of Microsoft researchers said Wednesday that they believe they have created the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person. Researchers in the company's Asia and U.S. labs said that their system achieved human parity on a commonly used test set of news stories, called newstest2017, which was developed by a group of industry and academic partners and released at a research conference called WMT17 last fall. To ensure the results were both accurate and on par with what people would have done, the team hired external bilingual human evaluators, who compared Microsoft's results to two independently produced human reference translations. Xuedong Huang, a technical fellow in charge of Microsoft's speech, natural language and machine translation efforts, called it a major milestone in one of the most challenging natural language processing tasks. "Hitting human parity in a machine translation task is a dream that all of us have had," Huang said.

The ethics of artificial intelligence will be critical to the success of AI going forward, a Microsoft leader and a keynote speaker at the AI Day event in Auckland next week says. Steve Guggenheimer, corporate vice president of Microsoft's AI Business, says that given AI has the potential to reshape not just industries and governments, but society as a whole. "Working on the ethics of the use of AI, from the beginning, in key areas like transparency, accountability, privacy and bias will be crucial to the success of AI going forward. "There is a strong focus on the ethical implications of the AI systems that are being built and deployed." The European Commission's group on ethics in science and new technologies recently warned that existing efforts to develop solutions to the ethical, societal and legal challenges AI presents are a'patchwork of disparate initiatives'. It added that uncoordinated, unbalanced approaches in the regulation of AI risked ethics shopping, resulting in the relocation of AI development and use to regions with lower ethical standards. AI Day on March 28 is being organised by NewZealand.AI and the AI Forum NZ, which is part of the NZTech Alliance, bringing together 14 national tech communities, more than 500 organisations and more than 100,000 employees to help create a more prosperous New Zealand underpinned by technology. Guggenheimer says one important element around the adoption of AI is the focus on having AI help to amplify human capabilities and allow them to do more versus simply replacing people and functions. "As AI is adopted by various organisations we are starting to see a few trends occurring.

A team of Microsoft researchers said March 14 that they believe they have created the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person. Researchers in the company's Asia and US labs said that their system achieved human parity on a commonly used test set of news stories, called newstest2017, which was developed by a group of industry and academic partners and released at a research conference called WMT17 last year. To ensure the results were both accurate and on par with what people would have done, the team hired external bilingual human evaluators, who compared Microsoft's results to two independently produced human reference translations. Xuedong Huang (pix, above), a technical fellow in charge of Microsoft's speech, natural language and machine translation efforts, called it a major milestone in one of the most challenging natural language processing tasks. "Hitting human parity in a machine translation task is a dream that all of us have had," Huang said.

Translation was traditionally considered a job in which the magic human touch would always ultimately trump a machine. That may no longer be the case, as a Microsoft AI translator just nailed one of the hardest challenges: translating Chinese into English with accuracy comparable to that of a bilingual person. Chinese is so difficult a language that it takes years for a non-native speaker to just about manage the 3,000 characters needed to read a newspaper. Previous attempts at automatic translation have amused the world, with gems such as "hand grenade" to indicate a fire extinguisher or a mysterious "whatever" dish on a restaurant menu. "For alphabetic languages, there's what they call a virtuous loop between the writing, speaking and listening -- those three categories constitute one composite skill," linguist David Moser told the Los Angeles Times.