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Manceps

Bringing AI & ML to a City Near You

According to the UN, 6.79 billion people will live in cities that have more than ten-million inhabitants. This staggering number represents a huge call to action for city planners to begin laying the infrastructure necessary to accommodate such rising populations. As cities grow, they will have to deal with challenges related to traffic congestion, limited resources, environmental degradation, among many others.
 
Fortunately, these systemic challenges can be more easily addressed by adopting a wide range of connected, internet-enabled technologies. Many of these technologies are powered by artificial intelligence, bringing forth automated control and management of complex city infrastructure.

WHAT'S DRIVING THE URGENCY TO ADOPT AI?

Traffic jams • Poor air quality • Lack of parking spaces • Violence against police • Expanding carbon footprints • Limited resources and person-hours • Urban migration • Economic competition
FREE RESOURCE: DISCUSSION QUESTIONS FOR AI READINESS
READ THE GUIDE

WHEN CITIES DEPLOY AI:

Commuters reduce their time in traffic by thousands of hours.
Drivers find parking spaces instantly.
Waste managers receive notifications when bins across town are getting full.
Law enforcement employs evidence-based data-driven strategies to keep people safe.
Street lights fine-tune themselves to use less power.
Cities use automation and big data analytics to ensure local governments run efficiently and with less bias.

APPLICATIONS

Citizen Sentiment Analysis

Natural Language Processing makes it possible for city governments to automatically aggregate the feedback of its citizens and transform those insights into action. For example, in the case of communicating with residents, instead of manually reading through discussion groups, social media posts, and other sources of feedback, decision-makers can use AI to automatically collect, curate, and summarize these sentiments. The ability to extract complaints via social media is particularly powerful as it’s far more likely for residents to complain on Twitter than phone their mayor.

Fantastic Customer Service

Artificial Intelligence brings scalability and uniformity to the ways cities provide customer service to their communities. For example, many governments across the United States have started to deploy their own chatbots to respond to residents’ questions. By making it easier for people to get help, government offices are free to shift customer-service resources to other community projects.
 
AI is the master of processing routine requests and detecting abnormalities. Governments can exploit these capabilities to disseminate information, deal with citizen requests, or detect wasteful spending or fraud

Security and Policing

Historical and geographical data can be put to work to predict where crimes are likely to take place. Such “pre-crime” initiatives have seen impressive results across big cities like LA, Chicago, and London. For these cities, increasing the police presence in a certain area is enough to reduce crime. By deploying models that process this kind of data, we can expect the cities of the future will be significantly safer, with police not only able to reduce the crime rate but also reduce their risk of harm.

Urban Planning

Many cities are turning to urban planning technologies to help them not only make more informed city planning designs but also design neighborhoods from the ground, up. Integration is a big key to success here. AI-powered tools can integrate a variety of in-house datasets including geography, regulations, existing developments with the latest research into the ways that city planning can affect citizen health, wealth, and happiness. Using machine learning and computational design, these tools can generate trillions of data-driven layouts and permutations.

Traffic Prevention

Most major cities have a vast network of live video feeds to track traffic as it flows around town. This footage can be incredibly detailed and useful; however, most municipalities are not putting it to work. By processing this footage using machine learning, city managers can not only respond to major traffic events automatically but also prevent them. When powered by AI, processing video means cities can have less congestion, better visibility on forthcoming disasters, and alerts for available street parking or expired meters.

TAKE THE NEXT STEP.

SPEAK WITH AN EXPERT. 

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DevFest West Coast 2020

Watch videos of some of the world's top AI experts discuss everything from Tensorflow Extended to Kubernetes to AutoML to Coral.

Video: Machine Learning Engineering with Tensorflow Extended

In this talk, Hannes is providing insights into Machine Learning Engineering with TensorFlow Extended (TFX). He introduces how TFX for machine learning pipeline tasks and how to orchestrate entire ML pipelines with TFX. The audience learns how to run ML production pipelines with Kubeflow Pipelines, and therefore, free the data scientist's time from maintaining production machine learning models.

Video: How to Build a Reproducible ML Pipeline

Solving a data science problem usually requires multiple steps. These steps can include extracting and transforming data, training a model, and deploying the model into production. In this session, we'll discuss how to specify those steps with Python into an ML pipeline. We'll show how to create a Kubeflow Pipeline, a component of the Kubeflow open-source project. The audience will learn about how to integrate TensorFlow Extended components into the pipeline, and how to deploy the pipeline to the hosted Cloud AI Pipelines environment on Google Cloud. The key takeaway is how to improve reuse and reproducibility of the machine learning process.

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Headquartered in the heart of Portland, Oregon, our satellite offices span North America, Europe, the Middle East, and Africa.

(503) 922-1164

Our address is
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Portland, OR 97209

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