Google Blog: The new conversational Search experience we’re thankful for

Google Blog: The new conversational Search experience we’re thankful for. “This year, Google Search rolled out new ways to get you to the information you want, using context from your recent activity. Thanks to our newest language understanding capabilities, it’s now easier for you to get to a more specific, on-topic search, navigate a topic you’re interested in and find additional information relevant to that topic. Let’s check out how this improved understanding can help around this time of year.”

Complete Tutorial On Txtai: An AI-Powered Search Engine (Analytics India)

Analytics India: Complete Tutorial On Txtai: An AI-Powered Search Engine. “Searching is the most basic functionality that is seen in almost all applications. But it can be challenging when you have a large amount of data or documents and you need faster results. This is where natural language processing can be useful to us. With the development of new models in NLP, quicker computation and more accurate results are possible. One such development is a library called txtai. This enables a smarter way to apply natural language processing on search bars. In this article, we will see the different applications of the txtai and implement them in Python.”

Bing Blogs: Bing Releases Intelligent Question-Answering Feature to 100+ Languages

Bing Blogs: Bing Releases Intelligent Question-Answering Feature to 100+ Languages . “Recently, Bing expanded its intelligent question-answering feature to more than 100 languages, making AI and Bing itself more inclusive and accessible. What is amazing is this is achieved by using a language agnostic approach. In other words, the AI model generating the intelligent question-answering in Urdu is the same one generating the intelligent question-answering in Romanian.”

MIT Technology Review: OpenAI is giving Microsoft exclusive access to its GPT-3 language model

MIT Technology Review: OpenAI is giving Microsoft exclusive access to its GPT-3 language model. “The companies say OpenAI will continue to offer its public-facing API, which allows chosen users to send text to GPT-3 or OpenAI’s other models and receive its output. Only Microsoft, however, will have access to GPT-3’s underlying code, allowing it to embed, repurpose, and modify the model as it pleases.”

Harvard Business Review: The Next Big Breakthrough in AI Will Be Around Language

Harvard Business Review: The Next Big Breakthrough in AI Will Be Around Language. “The 2010s produced breakthroughs in vision-enabled technologies, from accurate image searches on the web to computer vision systems for medical image analysis or for detecting defective parts in manufacturing and assembly, as we described extensively in our book and research. GPT3, developed by OpenAI, indicates that the 2020s will be about major advances in language-based AI tasks.”

South China Morning Post: Baidu creates ‘world’s largest’ Chinese natural language processing database

South China Morning Post: Baidu creates ‘world’s largest’ Chinese natural language processing database. “Chinese search engine giant Baidu has launched what it says is the world’s largest Chinese natural language processing (NLP) database, among several other artificial intelligence (AI) products, as it seeks to diversify its revenue sources. NLP is a branch of AI involved in making computers understand the way humans naturally talk and type online, turning such information into structured data for further analysis.”

MIT Technology Review: The field of natural language processing is chasing the wrong goal

MIT Technology Review: The field of natural language processing is chasing the wrong goal. “What has the world really gained if a massive neural network achieves SOTA on some benchmark by a point or two? It’s not as though anyone cares about answering these questions for their own sake; winning the leaderboard is an academic exercise that may not make real-world tools any better. Indeed, many apparent improvements emerge not from general comprehension abilities, but from models’ extraordinary skill at exploiting spurious patterns in the data. Do recent ‘advances’ really translate into helping people solve problems?”

Neowin: New wiki project – Abstract Wikipedia – will boost content across languages

Neowin: New wiki project – Abstract Wikipedia – will boost content across languages. “The project was first proposed in a 22-page paper by Denny Vrandečić, founder of Wikidata, earlier this year. He had floated a new idea that would allow contributors to create content using abstract notation which could then be translated to different natural languages, balancing out content more evenly, no matter the language you speak.” My head would absolutely not wrap around this until I saw a page of examples.

World Health Organization: WHO launches new search feature for questions on COVID-19

World Health Organization: WHO launches new search feature for questions on COVID-19. “WHO’s COVID-19 webpage now features an enhanced natural language processing search bar, which understands questions posed in everyday language and more accurately delivers answers to those queries. Unlike traditional index-based search that delivers links, when someone enters a question about COVID-19 into the search bar on WHO’s COVID-19 page, the new search finds the most accurate information related to that question from WHO’s website. Yext, the technology company that developed this search function, provides WHO’s web team with regular feedback on the questions that visitors are asking so that WHO can adapt information on the web to meet the demand.”

Google AI Blog: An NLU-Powered Tool to Explore COVID-19 Scientific Literature

Google AI Blog: An NLU-Powered Tool to Explore COVID-19 Scientific Literature. Traditional search engines can be excellent resources for finding real-time information on general COVID-19 questions like ‘How many COVID-19 cases are there in the United States?’, but can struggle with understanding the meaning behind research-driven queries. Furthermore, searching through the existing corpus of COVID-19 scientific literature with traditional keyword-based approaches can make it difficult to pinpoint relevant evidence for complex queries. To help address this problem, we are launching the COVID-19 Research Explorer, a semantic search interface on top of the COVID-19 Open Research Dataset (CORD-19), which includes more than 50,000 journal articles and preprints. We have designed the tool with the goal of helping scientists and researchers efficiently pore through articles for answers or evidence to COVID-19-related questions.”

Quantum Stat: 100s of datasets for machine learning developers (and counting)

From last month, but I just learned about it today. Quantum Stat: 100s of datasets for machine learning developers (and counting). “With the advent of deep learning and the necessity for more and diverse data, researchers are constantly hunting for the most up-to-date datasets that can help train their ML model. Currently, NLP data seems to be scattered across several 3rd party libraries, Reddit, or in the research arms of big tech. And while these mediums are useful, there doesn’t seem to be a central hub for housing NLP data that can be easily reached and searched by the ML engineer. As a result, we’ve created the ‘Big Bad NLP Database,’ the world’s largest data library in natural language processing:”

The Next Web: MIT researchers developed a text-based system that tricks Google’s AI

The Next Web: MIT researchers developed a text-based system that tricks Google’s AI. “Now, researchers at Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT, have developed a new system called TextFooler that can trick AI models that use natural language processing (NLP) — like the ones used by Siri and Alexa. This is important to catch spam or respond to offensive language.”