People use AI for a wide range of speech recognition and understanding tasks, from enabling smart speakers to developing tools for people who are hard of hearing or who have speech impairments. But oftentimes these speech understanding systems don't work well in the everyday situations when we need them most: Where multiple people are speaking simultaneously or when there's lots of background noise. Even sophisticated noise-suppression techniques are often no match for, say, the sound of the ocean during a family beach trip or the background chatter of a bustling street market. One reason why people can understand speech better than AI in these instances is that we use not just our ears but also our eyes. We might see someone's mouth moving and intuitively know the voice we're hearing must be coming from her, for example.

Speech recognition is an important cog in Big Tech's AI machinery. But, despite its ubiquity, speech recognition is still a work in progress. Today, Facebook is heralding a major breakthrough in the way it trains these systems to learn new languages. The company says it has developed a method of building speech recognition tools that don't require transcribed data. The time consuming task involves humans listening to and transcribing hours of audio, a monotonous process that has to be repeated for each language. Whereas Facebook's "unsupervised" system learns purely from speech audio and unpaired text to give it a better sense of what human communication sounds like.

Does Google Assistant always say your name wrong, or maybe the names of people you know? You can soon teach the digital assistant how to pronounce them correctly. Google announced an update rolling out soon to Assistant, available on smartphones and Google Home speakers, that will allow users to teach it how to properly pronounce your name or those in your contacts. Google said the feature will initially be available in English but will roll out to offer more languages soon. "Names matter, and it's frustrating when you're trying to send a text or make a call and Google Assistant mispronounces or simply doesn't recognize a contact," said Yury Pinsky, director of product management at Google, in a blog post published Wednesday.

AutoML, the application of machine learning to create new automation tools, is branching out to new use cases, making itself useful for particularly tedious data science tasks when training speech recognition models. Among the latest attempts at automating the data science workflow is an AutoML tool from Deepgram, offering what the speech recognition vendor claims is a new model training framework for machine transcription. The startup's investors include Nvidia GPU Ventures and In-Q-Tel, the venture arm of the U.S. intelligence community. Deepgram's flagship platform scans audio data to train a speech recognition tool. Its deep learning tool uses a hybrid convolutional/recurrent neural network approach, training models via GPU accelerators.

Artificial Intelligence will undoubtedly reshape the business, making our lives easier and more sufficient. AI is seen as an indispensable tool for supporting humans in every aspects of life. In future, it will be the driving force for Industrial revolution mainly driven by data, networks and computing power. "The two fundamental pillars of digital transformation for any organization- "Speed" and "Customer Centric Innovation" which are on the top of CXOs' minds. Every enterprise is dealing with two basic questions, "How fast can you innovate?" and "Can you innovate fast enough?" That said we see two broad technology trends answering the aforementioned questions emerging across the board, "Cloud Native" and "AI". On one hand, enterprises who are in the experimentation and migration stages of cloud adoption have realized that the benefits of cloud goes well beyond apex optimization to acceleration of contextual innovation. And on the other hand, we see widespread adoption of NLP and cognitive computing to provide augmented/assistive intelligence and personalized experiences to customers. With Millennial in focus for most enterprises, delivering personalization has become important now more than ever. CXOs expect AI and specifically deep learning to pave the path to achieve such targeted personalization" said Anup Nair, VP and CTO, Mphasis Digital.