use case


How AI is transforming the Smart Cities IoT? [Tutorial] Packt Hub

#artificialintelligence

According to techopedia, a smart city is a city that utilizes information and communication technologies so that it enhances the quality and performance of urban services (such as energy and transportation) so that there's a reduction in resource consumption, wastage, and overall costs. In this article, we will look at components of a smart city and its AI-powered- IoT use cases, how AI helps with the adaption of IoT in Smart cities, and an example of AI-powered-IoT solution. Hence, a smart city would be a city that not only possesses ICT but also employs technology in a way that positively impacts the inhabitants. This article is an excerpt taken from the book'Hands-On Artificial Intelligence for IoT' written by Amita Kapoor. The book explores building smarter systems by combining artificial intelligence and the Internet of Things--two of the most talked about topics today.


Customer service is poised for an AI revolution

ZDNet

These are the questions your firm should ask before going down the route of edge analytics and processing. If you're anything like me (or millions of other everyday consumers), you may be surprised to contact customer service only to be prompted with a litany of questions about who you are and what you're issue is. If I dial a service line, I've been conditioned to expect a friendly voice -- real or not -- that recognizes my phone number and asks if I'm calling about a recent transaction. What's more, I expect a similar experience if I connect over a myriad other digital touchpoints. Such is the level of sophistication we as customers now hold as standard, and the impacts on customer service are nothing short of transformational.


France is tapping into AI's potential for humanity

#artificialintelligence

Antoine Bruel, head of growth at Braincities and Céline Pluijm, key account manager at Wiidii share their thoughts on why France is fast-becoming a leader in establishing'AI for humanity', fresh from Hello Tomorrow… Artificial Intelligence (AI) is everywhere. Across industry verticals, it's being used to enable businesses and organisations to work smarter and faster than ever before. From automating repetitive transactions and manual tasks to powering customer support platforms, AI is transforming the way we work, live and interact with the world. According to PwC research, AI is estimated to provide $15.7 trillion in economic growth by 2030, creating opportunities for innovation on a global scale. AI, however, is as much a source of fascination as it is a cause for concern.


Machine Learning and RPA in Action: Email Management

#artificialintelligence

We recently announced the strategic alliance between Jidoka and BigML, where we explained the integration of RPA with other technologies such as Machine Learning. With this integration, Jidoka can provide Machine Learning capabilities in their RPA process automation platform. To explain the advantages and possibilities offered by this integration, today we present a practical example of the application of both technologies, Jidoka's RPA and BigML's Machine Learning: the automation of an e-mail classification process, a use case that will be presented by Jidoka's CEO, Víctor Ayllón, at the #MLSEV, our first Machine Learning School in Seville, which will be held on March 7-8 in Seville (Spain). Imagine for a moment that you are responsible for the customer service department of a large company. You and your team receive on a daily basis a very large number of customer emails that are addressed to different departments of the company.


Machine learning is becoming a strategic perimeter for GDPR compliance - SiliconANGLE

#artificialintelligence

Privacy advocates have placed an unfair stigma on machine learning. Despite what you may have heard through the mass media, ML is not some fiendish tool for invading people's privacy. Regardless, now that European Union's General Data Protection Regulation has taken effect, there's an even stronger scrutiny of ML applications in target marketing, customer engagement, experience optimization and other use cases that touch personally identifiable information, or PII. But in fact, ML is becoming a key element in how organizations manage compliance with GDPR and other privacy mandates. The core of ML's role in GDPR compliance is in its use as a tool for discovering, organizing, curating and controlling enterprise PII assets across complex, distributed application environments.


PolyAI scores $12M Series A to put its 'conversational AI agents' in contact centres

#artificialintelligence

PolyAI, a London startup founded by experts in the field of "conversational AI" -- including CEO Nikola Mrkšić, who was previously the first engineer at Apple-acquired VocalIQ -- has raised $12 million in Series A funding to deploy its tech in customer support contact centres. The round was led by Point72 Ventures, with participation from Sands Capital Ventures, Amadeus Capital Partners, Passion Capital and Entrepreneur First (EF). PolyAI's founders are graduates of EF, although they didn't meet during the company building program but already knew each other from their time at Cambridge's Dialog Systems Group, part of the Machine Intelligence Lab at the University of Cambridge. "We started PolyAI in 2017, straight after submitting our PhD theses," Mrkšić tells me. "At Cambridge, we developed state-of-the-art conversational technology, and starting a company was the best way to get this tech used in the real world. We brought many of our Cambridge colleagues with us and started building the commercial version of our conversational platform."


6 Ways to Utilize Machine Learning with Amazon Web Services and Talend

#artificialintelligence

The world has become a global village and interactions between people from different parts of the world are increasing day-by-day. Language was one of the major roadblocks in enabling free communication between people all over the world. The natural language processing services of Amazon like Amazon Comprehend and Amazon Translate help us to understand the dominant language any given text text, translate it and perform the sentiment analysis for the incoming textual information. Talend integrates these Amazon AI services to convert end to end applications like real-time sentimental analysis dashboard and multilingual customer care system. A quick example is the sentimental analysis dashboard as shown below. Talend is integrated with Amazon's Comprehend service to identify the customer sentiments in real time and to send the sentimental analysis details to downstream system dashboards. Another example which showcases Talend's integration capabilities with Amazon Comprehend and Amazon Translate services is the creation of a multi-lingual customer care system. The incoming messages are analyzed to understand the dominant language used in it and the text is translated from non-supported languages to supported language automatically. The two Talend KB articles I would recommend getting a detailed overview and hands-on experience about Talend's integration with above two Amazon services are as shown below.


AI in Healthcare: Enormous Opportunities - CIOL

#artificialintelligence

Artificial intelligence is transforming various industries and the impact of AI in Healthcare to be truly life changing. Already, it has been used to detect diseases and is helping to make better health decisions. According to research, investment in AI in healthcare expected to reach $6.6 billion by 2021. And according to Accenture, "The top AI applications may result in annual savings of $150 billion by 2026". Discussing on the same line, Arush Sogani, Director, Sysnet Global Technologies said, "Healthcare is a process-oriented industry that offers an enormous opportunity to use AI to drive improvements, help meet unmet demand, and automate repetitive tasks. This is seen across R&D, patient care, medical imaging, and management tasks".


Some of the Latest Trends in Artificial Intelligence - Nanalyze

#artificialintelligence

We're into our second year of publishing a "Global AI Race" series of articles on artificial intelligence startups from around the world and it continues to pose a challenge. We use an objective measure of "total funding taken in so far" and that excludes any firms that choose not to disclose funding or are bootstrapped. We search for various categorizations like "artificial intelligence" or "deep learning" and that means we'll miss any firms that haven't chosen those categories in their Crunchbase profile. But the ones we worry about the most are those firms that we might include in one of our "top AI startups" lists that don't actually do AI. It's a huge problem, and one that was highlighted recently by a European venture capital firm, MMC Ventures, that surveyed 2,830 startups in Europe that were classified as being AI companies and found out that 44% of these companies were incorrectly classified as being "AI startups."


H2O.ai Advances Leading Data Science and Machine Learning Platforms

#artificialintelligence

H2O WORLD SAN FRANCISCO – H2O.ai, the open source leader in AI and ML, today announced new and innovative capabilities for its data science and machine learning platforms, H2O, AutoML and H2O Driverless AI, to address the critical scalability and performance needs of all organizations. As part of these new capabilities, and to further the company's mission to democratize AI, H2O.ai has added several new algorithms that address common use cases that customers need today. In addition, H2O Driverless AI is a winner of InfoWorld's 2019 Technology of the Year for the second year in a row. The award honors and recognizes the best in software development, cloud computing, big data analytics, and machine learning tools. This year's judging panel recognized H2O Driverless AI for outpacing all other vendors with "automated simplicity" of its algorithms that do the heavy lifting of feature engineering, model selection, training and optimization – enabling even non-AI experts to uncover hidden patterns using both supervised and unsupervised machine learning.