How Artificial Intelligence is Transforming CRM Operations?

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To address client issues, effective information management and Artificial Intelligence together have proven to be an excellent amalgamation for organizations. Rather than just delivering products, understanding user behavior is vital for any business to survive and thrive. Any communication with clients can be taken in as a huge data source. It can be used for analyzing their behavior, tackling issues, changing process approach and operations and thus transforming user journey. However, integrating AI with CRM has been the most revolutionary step so far.


Harry Kazianis: Trump wise to avoid a devastating war with Iran in wake of attack on Saudi Arabia

FOX News

There's an old saying that wars are easy to get into but hard to get out of. President Trump understands this, which is why he wisely resisted the temptation to launch a military strike against Iran after that nation launched a missile and drone attack last week against Saudi Arabian oil facilities. When he was running for president, Trump promised the American people he would not jump into endless conflicts in the greater Middle East, where thousands of members of the U.S. military have been killed and wounded in wars in Iraq and Afghanistan. Fighting began in 2001 in Afghanistan and 2003 in Iraq and still continues in both countries. U.S. forces have also fought on a smaller scale in Syria to strike at terrorist targets.


AI will be the biggest disruptor in our lifetime: Amitabh Kant, CEO, NITI Aayog - Microsoft News Center India

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By 2021, digital transformation will add an estimated USD 154 billion to India's GDP, and increase the growth rate by 1 percent annually, according to an IDC study commissioned by Microsoft. The study also predicts that approximately 60 percent of India's GDP will be derived from digital products or services by 2021. With the government's vision of becoming a USD 5 trillion economy by 2024, Amitabh Kant, CEO, NITI Aayog believes technologies like Artificial Intelligence (AI) will propel India to achieve that target and even go beyond. "Our ambition should not just be to become a USD 5 trillion economy. Instead, we should aim to become a USD 10 trillion economy in the long run, growing at 9-10 percent year after year for three decades or more, to be able to lift our young population above the poverty line. All of this is not possible without using a large amount of data, AI and Machine Learning (ML) and bringing disruption in a vast range of areas," Kant said during a fireside chat with Anant Maheshwari, President Microsoft India at the Digital Governance Tech Summit 2019 in New Delhi.


Ticket Triaging with Natural Language Processing

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Natural Language Processing (NLP) is a massive space within artificial intelligence (AI), which enterprises are integrating into their existing platforms more each day. As petabytes of textual data become available each day, companies can leverage NLP to retrieve deeper insights. Aspects such as entities, sentiment, emotion, and keywords can be extracted from textual data and enterprises can leverage this information to pivot, understand customer sentiment, and improve internal efficiency. Watson Natural Language Understanding (NLU) and Watson Natural Language Classifier (NLC) are cutting-edge NLP technologies that provide deep insight into textual data. Watson NLU provides insight such as entities, emotion, keywords, sentiment, and categories, while Watson NLC allows users to train a classification model in under 15 minutes and classify text.


MAGICS Lab University of San Francisco

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San Francisco is known as a hub of tech innovation, making USF an ideal place to study computer and data science. The location gives students the opportunity to connect professionally with companies everyone knows: Google, Twitter, Facebook – the list goes on. But what opportunities does USF offer students to participate in peer reviewed scholarship, a place where current students and faculty can connect over tech R&D on campus? As of Fall 2018, the answer comes in the form of the weekly MAGICS Lab meetings, a way to gain valuable mentorship and learn about emerging technologies, a place where undergraduate, graduate students, and faculty all have the opportunity to learn, research, and publish together. This group welcomes all skill-levels, from novice to seasoned researchers alike.



How to quickly solve machine learning forecasting problems using Pandas and BigQuery Google Cloud Blog

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In the rest of this blog, we'll use an example to provide more detail into how to build a forecasting model using the above workflow. Machine learning is all about running experiments. The faster you can run experiments, the more quickly you can get feedback, and thus the faster you can get to a Minimum Viable Model (MVM). Let's build a model to forecast the median housing price week-by-week for New York City. We spun up a Deep Learning VM on Cloud AI Platform and loaded our data from nyc.gov into BigQuery.



Automated Machine Learning for Professionals - Updated

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Summary: As the Automated Machine Learning (AML) movement got underway a few years back there was an early branch between proprietary platforms and open source platforms. Since they continue to require fluency in Python or R we label them "professional". As the Automated Machine Learning (AML) movement got underway a few years back there was an early branch between proprietary platforms and open source platforms. Today, the primary difference between these is that the proprietary entries are largely code-free so that citizen data scientists / business analysts can use them in addition to data scientists. The open source versions are still reliant on your ability to code, or at least to copy code.


Can Data Analytics Make Dangerous Intersections Safer?

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Bellevue, Wash., located in the Seattle metro area, is undergoing a citywide review of near-miss incidents involving pedestrians, cyclists and other cars. Using images from its closed circuit video network, as well as high-level analytics and machine learning, the city wants to understand which streets and intersections are the most dangerous, and how they might be made safer. Bellevue is partnering with the group Together for Safer Roads (TSR), which represents a coalition of private-sector companies, including Brisk Synergies, to conduct a comprehensive near-miss study from August to September where roughly half of the city's network of 80 public video cameras will be used to gather some 34,000 hours of footage representing about 21 terabytes of data. The data will be processed by Brisk using artificial intelligence and machine learning to gain insights into "near-miss" incidents. "This is the first network-wide traffic safety monitoring assessment of its kind," said Franz Loewenherz, principal transportation planner for Bellevue.