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.


How big data and AI help online retailers compete in the digital era

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As brick-and-mortar retailers continue to struggle against online competitors, some are seeking out services that leverage big data and personalization to increase e-commerce sales. "During the rise of big data, it was said that data was the new oil," Brian Solis, principal analyst at Altimeter, told TechRepublic. "In an era of AI and machine learning however, personalized data is the new competitive advantage and will only become standard CX on the horizon." Indeed, 72% of retailers reported that AI will be a "competitive necessity" in the next five years, according to a recent Oxford Economics survey. One such tech option for retailers looking to fight off the competition is uSizy, a recommendation technology for fashion apparel and footwear businesses, which unveiled its latest product, uSizy Smart Business, on Wednesday.


Beyond the Nash Equilibrium: DeepMind's Clever Strategy to Solve Asymmetric Games

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Game theory is one of the most relevant aspects in modern multi-agent artificial intelligent(AI) systems. To some extent, the recent evolution of AI has triggered a renaissance in the field of game theory fostering innovation across all sorts of new areas. One of those areas is the field of asymmetric games that describe settings in which different players can follow different strategies. Last year, Alphabet's subsidiary DeepMind published a super innovative way to tackle asymmetric game problems. DeepMind's breakthrough can have profound implications in modern multi-agent, AI systems that are often modeled as asymmetric games.