Natural Language


How artificial intelligence and machine learning produced robots we can talk to

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You've likely talked to a robot already without even knowing it. And you might have even heard the term "chatbot" in the news. But what is a chatbot? Essentially, a chatbot is just a robot chat that imitates human conversations through voice commands, text chats, or both. It's a virtual conversation in which one party is an online talking robot.


Technologies of the future, but where are AI and ML headed to?

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Today, when we look around, the technological advances in recent years have been immense. We can see driverless cars, hands-free devices that can turn on the lights, and robots working in factories, which prove that intelligent machines are possible. In the last four years in the Indian startup ecosystem, the terms that were used (overused rather) more than funding, valuation, and exit were artificial intelligence (AI) and machine learning (ML). We also saw investors readily putting in their money in startups that remotely used or claimed to use these emerging technologies. From deeptech, ecommerce, fintech, and conversational chatbots to mobility, foodtech, and healthcare, AI and ML have transformed most industry sectors today.


Traveloka: Using Data to Build a Universal Search Engine Lionbridge AI

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Traveloka is an online travel company that provides a one-stop platform for a range of ticketing services, including flights, accommodation, and attractions. As one of Southeast Asia's "unicorn" startups valued at over $1 billion, Traveloka is constantly searching for ways to improve their user experience. As part of this initiative, Traveloka has invested heavily in a number of artificial intelligence and machine learning projects. With an expanding list of 19 core product offerings, improving search capabilities was key to their continued growth. To do this, Traveloka built a search function to make it easy for users to browse the full range of products from a single search bar.


A.I.-powered voice transcription app Otter raises $10M, including from new strategic investor NTT DOCOMO – TechCrunch

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Otter.ai, an A.I.-powered transcription app and note-takers' best friend, has received a strategic investment from Japan's leading mobile operator and new Otter partner, NTT DOCOMO Inc. The two companies are teaming up to support Otter's expansion into the Japanese market where DOCOMO will be integrating Otter with its own A.I.-based translation service subsidiary, Mirai Translation, in order to provide accurate English transcripts which are then translated into Japanese. The investment was made by DOCOMO's wholly-owned subsidiary, NTT DOCOMO Ventures, Inc., but the size was undisclosed. However, the new round was $10 million in total, we're told. To date, Otter has raised $23 million in funding from NTT DOCOMO Ventures, Fusion Fund, GGV Capital, DFJ Dragon Fund, Duke University Innovation Fund, Harris Barton Asset Management, Slow Ventures, Horizons Ventures, and others.


Latest AI Chatbot Software Development Trends

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Software companies with tight-knit agile and strong release management practices have a significant competitive advantage. To realize this advantage, an organization must first optimize its release management process and identify the most appropriate platform and release management tools. In 2016, we heard the slow and steady drumbeat of chatbot Softwares and other AI-infused solutions that are focused AI Chatbot Software Development Company in India that including, predictive analytics and cloud offerings. In 2017, they will be fully well-established in companies across a wide range of industries and different latest trends will be introduced this year. This year it introduces more advanced techniques compared to last year.


Designing better voice assistants

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In the first article of our conversational AI series, we explored how the proliferation of voice assistants and messaging platforms are giving way to a new era of user interfaces (see the sidebar, "A five-part series on conversational AI"). Whether it's in the car, a phone, or a smart home device, nearly 112 million US consumers rely on their voice assistants at least once a month--and that number continues to grow.1 These can range from the mundane, such as misinterpreting a request for ordering a roll of paper towel, to the more troubling error of providing a harmful health recommendation (or conversely, providing an accurate, but difficult to interpret recommendation).2 Despite the uptick in adoption of voice-enabled virtual assistants, designing effective products is a nontrivial endeavor. Virtual assistants often deal with multiple, sometimes complex scenarios that require understanding a range of queries to which users expect a quick, accurate, and easily interpretable response.


Human beings are unable to connect with artificial intelligence: Pranav Mistry - ETtech

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Neon, the artificial human prototype conceptualized by computer scientist and inventor Pranav Mistry, created waves recently. The President and CEO of Samsung's STAR Labs told ET in an exclusive interview that he created Neon because human beings are unable to connect with artificial intelligence (AI) assistants such as Apple's Siri. The Palanpur (Gujarat)-born Mistry, considered one of the best innovative minds in the world right now, said Neon will be a companion to the elderly and to those who are lonely and could even work as fashion models or news anchors. The 38-year-old also spoke about the dangers posed by AI,echoing Google parent Alphabet Inc's chief Sundar Pichai who recently called upon governments to regulate AI. Edited Excerpts: When you started thinking about Neon, what was the problem you were trying to solve?


How artificial intelligence provided early warnings of the Wuhan virus

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During the kind of virus outbreak that China and other nations are now contending with, time is of the essence. The earlier the warning, the better the chance to contain the contagion. One problem, though, is that governments are sometimes reticent to share information. Such was the case in 2002 and 2003, when Chinese authorities were accused of covering up the SARS epidemic that eventually claimed over 740 lives around the world. With the current outbreak, involving a coronavirus that originated in Wuhan and has so far taken over 40 lives, the Chinese government is being more transparent, as Germany's health minister noted to Bloomberg yesterday on the sidelines of the World Economic Forum in Davos.


Streamlining Tech Support with Natural Language Processing

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Any company that makes and sells consumer or business products faces technical support challenges. With even the best-made products, some customers will inevitably have questions or encounter issues that cause them to turn to a help desk for support. For the tech support organization, the goal is always to streamline and accelerate the support experience, so customers get their questions answered and their issues resolved quickly and painlessly. To achieve this goal, the support organization needs tools that enable its agents to find the right information in an efficient manner. At Dell Technologies, these tools now include a homegrown machine-learning system that leverages natural language processing capabilities.


An AI Epidemiologist Sent the First Warnings of the Wuhan Virus

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On January 9, the World Health Organization notified the public of a flu-like outbreak in China: a cluster of pneumonia cases had been reported in Wuhan, possibly from vendors' exposure to live animals at the Huanan Seafood Market. The US Centers for Disease Control and Prevention had gotten the word out a few days earlier, on January 6. But a Canadian health monitoring platform had beaten them both to the punch, sending word of the outbreak to its customers on December 31. BlueDot uses an AI-driven algorithm that scours foreign-language news reports, animal and plant disease networks, and official proclamations to give its clients advance warning to avoid danger zones like Wuhan. Speed matters during an outbreak, and tight-lipped Chinese officials do not have a good track record of sharing information about diseases, air pollution, or natural disasters.