The document discusses how machine learning can help architect Internet of Things (IoT) systems for widespread consumer adoption. It describes three examples of using machine learning with IoT data: (1) identifying patterns of risky drivers to adjust insurance premiums, (2) predicting short-term driving behavior to improve road safety, and (3) using long-term driving history with recurrent neural networks to provide customized nudging to change driver behavior over time. The document argues that machine learning can create value from IoT data and benefit consumers by making systems safer, lowering costs, and incentivizing good behaviors.
Michael Aston - The future of knowledge - Transversal customer summit 2016Transversal Ltd
Michael Aston, CTO Transversal, gives us some insight into the future of knowledge. Will A.I. take over and destroy us all? or will it make our lives even easier? And where does Transversal fit into the future of knowledge.
Credit card fraud detection using python machine learningSandeep Garg
This document provides an overview of machine learning tools, technologies, and the data preparation process. It discusses collecting and selecting relevant data, data visualization, labeling data for supervised learning, and transforming raw data into a tidy format. The document also covers various data preprocessing techniques, including data cleaning, formatting, handling missing values and outliers, smoothing, aggregation, generalization, and data reduction methods. The goal of these preprocessing steps is to prepare raw data into a structured format suitable for machine learning modeling.
This document provides an overview of Ravelin, an online fraud detection and prevention platform. It discusses how Ravelin uses a hybrid approach of human-generated rules and machine learning to identify fraud. Traditional methods use order-centric views while Ravelin recognizes that people, not cards, commit fraud. The document also emphasizes the importance of measuring precision and recall, using tools that increase productivity like BigQuery, and monitoring models in production to identify issues.
Twitter Sentiment Analysis in 10 Minutes using Machine LearningSkyl.ai
About the webinar:
Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook.
This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue!, Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more.
What you will learn
- How businesses are leveraging sentiment analysis to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: How to build a twitter sentiment analysis model
Intelie's Overview - How much could your company lose in a matter of minutes?Intelie
This corporate presentation by INTELIE introduces their real-time data analysis platform and services. It summarizes that INTELIE offers the LIVE platform for real-time operational visibility and intelligent alerts, as well as consulting services to help clients define key operational metrics to monitor. The presentation outlines INTELIE's architecture and methodology for real-time data analysis and its benefits for reducing response times to critical business events.
This document discusses how data science will impact the future of finance and accounting functions. It outlines that data science technologies will automate many routine tasks, but will also require more technical and analytical skills. The lines between finance, accounting, and data science will blur as these fields converge. Examples of how data science could be applied include automatically generating presentations and earnings call text from financial data, and allowing natural language queries of financial performance. The document argues that traditional accounting focuses too much on averages and aggregates, limiting analysis, and that data science techniques can provide more detailed insights by analyzing raw transactional data at scale.
Webinar: Analytics with NoSQL: Why, for What, and When?MongoDB
This document discusses analytics with NoSQL databases. It begins by defining different types of analytics like alerting, getting insights, and transforming data. It then discusses challenges like having lots of data in many formats from different sources. It provides examples of real-time analytics like credit card fraud detection and collaborative filtering. It argues that MongoDB is useful for analytics because it allows for horizontal scalability, flexibility to add new data, and high performance for ingesting and serving operational analytics. Specific use cases discussed include retail price optimization, smart grid analytics, mobile analytics, and financial customer insights. It concludes that analytics now require integrating real-time context and that MongoDB can help process data where it lands more flexibly.
Watson Analytics is a self-service analytics tool that allows non-technical users to access, analyze, and visualize their data without needing data scientists or IT resources. It uses natural language processing to help users ask questions of their data and provides guided predictive analytics to spark new insights. The tool delivers answers with confidence ratings and enables users to author dashboards and stories to communicate their findings to others.
Right now in institutions around the world, some of the greatest minds in computer science and statistics are coming up with amazing new algorithms and mathematically beautiful solutions. However it's entirely possible that the solutions they conceive will be impracticable in industry. The reason is simple; "the best answer is useless if it arrives too late to do anything with it". The key principle here is the compromise between 'accuracy' and 'latency'. In this talk I will describe examples where this holds true, and how I am using real-time machine learning models to solve challenges in eCommerce, Financial Services and Media companies.
http://tumra.com/blog/real-time-machine-learning-at-industrial-scale
Machine Learning & IT Service Intelligence for the Enterprise: The Future is ...Precisely
Enterprises with mainframes and Cloud/server architectures face unique issues and challenges and if your enterprise delivers a service whose operation spans mainframe and distributed and/or Cloud infrastructures (e.g. a mobile banking/customer app), this webinar is for you.
See how you can gain unique business and service-relevant context using your own machine data, including that from your z/OS mainframe. Implicitly learn patterns, eliminate costly false alerts, identify anomalies, and baseline normal operations by employing advanced analytics driven by machine learning. You’ll also see and learn about:
• Accelerating root-cause analysis and getting ahead of customer-impacting outages and slow-downs for your service
• “Glass Table” view for clickable visualization of the entire service-relevant infrastructure
• Machine Learning in IT Service Intelligence
• The Machine Learning Toolkit available today
This document provides an overview of machine learning and AI services available on Microsoft Azure. It discusses Azure Cognitive Services for computer vision, speech, language, and decision capabilities. It also covers Azure Machine Learning for building ML models using popular frameworks. Additional Azure services and tools mentioned include Databricks, ML VMs, Visual Studio Code, and hardware accelerators. Specific cognitive services like Face API, Custom Vision, Text Analytics, and Anomaly Detector/Metrics Advisor are described in more detail. Case studies and demos are referenced to illustrate real-world applications.
Emotion recognition - a new design paradigmDamien Malley
Using smartphones to recognize facial expressions, designers at TurboTax are identifying new challenges, tools and practices to build experiences that learn and adapt to emotion in real time.
MondoBrain and Synpulse offer a comprehensive big data and smart analytics solution powered by artificial intelligence to help businesses make data-driven decisions. The solution empowers users to understand, explain, predict, and monitor critical business issues without needing expensive data scientists or technical solutions. Past customers in the insurance industry have used the solution to improve pricing and underwriting, identify fraud, increase profitable growth and customer retention, and reduce losses.
How AI-Powered Search Drives Employee ExperienceLucidworks
This document discusses how AI-powered search can drive employee experience. It begins by defining digital transformation according to several sources and noting that skills involving hands-on problem solving will be less susceptible to automation. It then discusses how search is evolving from intranets and portals to being more connected. The rest of the document focuses on how an AI-powered search solution can help with exploration of information, integration of different data sources, and curation of search results through personalization and recommendations. It maintains search is crucial for digital transformation and improving employee engagement and productivity.
Gary Hope - Machine Learning: It's Not as Hard as you ThinkSaratoga
Gary Hope is currently the Data Platform Technical Specialist within Microsoft South Africa having previously worked for several large organisations including American Express and Siemens Business Solutions.
Slides from talks presented at Mammoth BI in Cape Town on 17 November 2014.
Visit www.mammothbi.co.za for details on the event. Follow @MammothBI on twitter.
Machine Learning: What Assurance Professionals Need to Know Andrew Clark
Machine learning has evolved past an esoteric technique worked on by academics and research institutes into a viable technology being deployed at many companies. Machine learning has been significantly changing the competitive landscape of business models worldwide, contributing to the demise of established business, such as Blockbuster, to creating entirely new businesses, such as algorithmic advertising. This presentation strives to address the questions of what assurance professionals need to know about this technology and how to provide assurance around machine learning implementations and its unique risks.
Recently, AI has spread its impacts on various fields and finance is not an exception. With over 15 years working in banking industry, Mr Le Cong Binh brought to VFS not only the theories but also the practical use case that reflected the best how AI was affecting this industry.
The Other 99% of a Data Science ProjectEugene Mandel
Slides from my talk at Open Data Science Conference 2016.
Algorithms and models are an important (and cool) part of data science. This talk is about all the other steps that it takes to deploy a data science project that makes a product slightly smarter. Stuff that you hear from practitioners, but is not covered well enough in books.
The Green Team was commissioned by Optika Solutions to create a 3D representation of discrete-event time series forecast output and results. They have completed initial documentation and are working on an innovative 3D visualization. An initial prototype was created to load Optika's dataset and visualize the data simply with Leap Motion gesture control, to demonstrate the feasibility of the project.
IoT-Daten: Mehr und schneller ist nicht automatisch besser.
Über optimale Sampling-Strategien, wie man rechnen kann, ob IoT sich rechnet, und warum es nicht immer Deep Learning und Real-Time-Analytics sein muss. (Folien Deutsch/Englisch)
Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16Boris Adryan
Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.
The document discusses methodologies for data science and the Internet of Things (IoT). It begins by noting that there is currently no single agreed upon methodology for solving data science problems for IoT (IoT analytics). It then poses some initial questions on whether a distinct IoT data science methodology is needed, and if IoT problems warrant a specific approach. While IoT analytics problems are typical data science problems, the document notes there are some unique considerations for IoT, such as the use of hardware, high data volumes, and streaming data.
Data Science for Internet of Things with Ajit JaokarJessica Willis
The document discusses methodologies for data science and the Internet of Things (IoT). It begins by noting that there is currently no single agreed upon methodology for solving data science problems for IoT (IoT analytics). It then poses some initial questions on whether a distinct IoT data science methodology is needed, and if IoT problems warrant a specific approach. While IoT data science problems are similar to general data science problems, the document notes there are some unique considerations for IoT, such as the use of hardware, high data volumes, and streaming data.
This document discusses the Internet of Things (IoT) market and key technologies. It attempts to map the IoT market across three axes: open vs closed ecosystems, instrumenting machines vs the physical world, and autonomous devices vs collaborative ecosystems. Some of the big players in IoT like Google, Microsoft, Amazon, GE and Cisco are mentioned. Key IoT components discussed include radios/communication, real-time analytics platforms, sensors, data collection, and actuators. Security, scaleability, and open standards are identified as important technologies for IoT. The document also briefly discusses what is happening in Norway's IoT market and opportunities in health, oil/gas, transportation, and other sectors.
Webinar: Machine Learning para MicrocontroladoresEmbarcados
Neste webinar, serão apresentados conceitos sobre inteligência artificial, assim como ferramentas disponíveis para o desenvolvimento integradas ao MPLAB X e ao Harmony 3 e demonstração de um sistema de detecção de anomalia utilizando um microcontrolador da família ATSAMD21 (ARM Cortex M0+).
This document discusses using LSTM neural networks for time series prediction. It provides examples of using LSTM to predict traffic times based on historical minute-by-minute traffic data and evaluating predictions against actual times. The document also discusses data preparation steps like feature engineering and dimensionality reduction needed before using LSTM on different types of time series data like text, images, or numerical values.
Internet of Things (IoT) - in the cloud or rather on-premises?Guido Schmutz
You want to implement a Big Data or Internet of Things (IoT) solution and like to know if it should be implemented in the cloud or on-premises. You are interested in the cloud offerings of vendors and what benefits they provide and if a similar solution would not be possible on-premises.
This presentation deals with this and other questions. Starting from a vendor-independent reference architecture and corresponding design patterns, different cloud solutions from various vendors are compared and rated. Additionally, it will be shown how such solution could be implemented on-premises and how a hybrid IoT solution could look like.
Streaming Analytics: It's Not the Same GameNumenta
This document discusses streaming analytics and how traditional machine learning algorithms are not well-suited for streaming data. It introduces Hierarchical Temporal Memory (HTM) as a new approach inspired by neuroscience that can handle streaming data, continuous learning, and temporal modeling. HTM uses sparse distributed representations and models sequences to make predictions and detect anomalies. The document provides examples of how HTM can be applied to problems like anomaly detection in server metrics, human behavior, geospatial tracking, social media streams, and stock prices. HTM algorithms are domain-independent and use the same codebase and parameters across different problem types.
Smart Traffic System using Machine LearningIRJET Journal
This document proposes a smart traffic system using machine learning to automatically manage traffic. Video cameras would capture live traffic footage which is processed using YOLO and AlexNet machine learning models to detect vehicles and determine traffic density in real-time. The system would then dynamically adjust traffic light timings based on the current traffic conditions to efficiently clear congestion and prioritize emergency vehicles by opening routes for ambulances. This smart traffic control approach aims to develop more efficient traffic management without human intervention to address issues like congestion from improper light timings or festivals that increase traffic volumes.
Industry of Things World - Berlin 19-09-16Boris Adryan
Dr. Boris Adryan gave a talk on the impact of IoT analytics on development budgets. He discussed that IoT data problems are often not as complex as perceived and do not necessarily require "big data" solutions or specialists. Basic data storage and processing can often be done cost-effectively using standard tools. True challenges lie in extracting useful insights, which may require specialized machine learning approaches. Not all analytics need to be real-time. The appropriate solution depends on the use case and desired insights.
The document discusses recent trends in information technology including virtual and augmented reality, cloud computing, 5G wireless, the Internet of Things (IoT), and big data analytics. It provides an agenda for the session covering these topics and case studies applying these technologies. Examples of how IoT is enabling industrial automation and transportation are presented. The growth of big data and opportunities it provides are also summarized. The document concludes with a discussion of how information technology is developing through artificial intelligence, machine learning, smart devices, data, and social media.
A late upload. This slide was presented on Aug 31, 2019, when I delivered a talk for AIoT seminar in University of Lambung Mangkurat, Banjarbaru. It's part of Republic of IoT 2019 event.
This document discusses the Internet of Things (IoT) and Tata Consultancy Services' (TCS) Connected Universe Platform (TCUP) for addressing IoT. It provides an overview of TCS, the requirements and challenges of IoT platforms, and the key components of TCUP including its architecture, analytics capabilities, and applications. TCUP aims to provide visibility into sensor data, insights through analytics, and control through a scalable and affordable platform to bridge the gap between sensors and applications. The document highlights several research publications and applications developed using TCUP.
Artificial Intelligence in practice - Gerbert Kaandorp - Codemotion Amsterdam...Codemotion
In this talk Gerbert will give an overview of Artificial Intelligence, outline the current state of the art in research and explain what it takes to actually do an AI project. Using practical cases and tools he will give you insight in the phases of an AI project and explain some of the problems you might encounter along the way and how you might be able to solve them.
Concepts, use cases and principles to build big data systems (1)Trieu Nguyen
1) Introduction to the key Big Data concepts
1.1 The Origins of Big Data
1.2 What is Big Data ?
1.3 Why is Big Data So Important ?
1.4 How Is Big Data Used In Practice ?
2) Introduction to the key principles of Big Data Systems
2.1 How to design Data Pipeline in 6 steps
2.2 Using Lambda Architecture for big data processing
3) Practical case study : Chat bot with Video Recommendation Engine
4) FAQ for student
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-khronos
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Mark Bünger, Vice President of Research at Lux Research, delivers the presentation "Imaging + AI: Opportunities Inside the Car and Beyond" at the December 2016 Embedded Vision Alliance Member Meeting. Bünger presents his firm’s perspective on how embedded vision will upend the automotive industry.
This document discusses streaming data processing and the adoption of scalable frameworks and platforms for handling streaming or near real-time analysis and processing over the next few years. These platforms will be driven by the needs of large-scale location-aware mobile, social and sensor applications, similar to how Hadoop emerged from large-scale web applications. The document also references forecasts of over 50 billion intelligent devices by 2015 and 275 exabytes of data per day being sent across the internet by 2020, indicating challenges around data of extreme size and the need for rapid processing.
Similar to Architecting IoT with Machine Learning (20)
In this talk, I speak about how the growth strategy for every market segment (innovators, early adopters, Early Majority, Late Majority) is different. And how to grow at each stage.
Machine Learning: For the people, By the people, Of the peopleRudradeb Mitra
In this talk, I show how Machine Learning is going to change the energy sector and make solar energy more accessible. I also give the example from the banking sector in Vietnam on how Machine Learning can help unbankable people get loans. I conclude by saying that my firm conviction is that Machine Learning has the ability to help those who have been left behind in the previous technological revolution.
This is a talk given to bankers at CCX Forum where I share how Machine Learning products can be built for retail banking sector, what are the challenges and how can they be overcome.
The document discusses how predictive analytics using neural networks, such as recurrent neural networks and long short term memory cells, can be applied to problems in industrial IoT, giving examples of how these techniques could be used to predict risky drivers from sensor data and to predict future customer purchases from shopping history data. It also outlines potential future directions for predictive analytics, such as using reinforcement learning approaches like Q-learning to develop intelligent agents.
Predictive Analytics using Neural NetworksRudradeb Mitra
In this presentation I explain how Neural Networks can be used to do predictive analytics. I take the use case of predicting user buying behavior and explain how word2vec and LSTM network can be used for that.
Predictive analytics can be used to disrupt product development in two key ways:
1. By analyzing past user behavior and orders, predictive models like neural networks and recurrent neural networks can predict future user behavior and needs and adapt products accordingly. This was demonstrated through a case study of order data from Instacart.
2. By analyzing attributes of users like driving behavior from a driver app and friends' networks, unsupervised neural networks can cluster users and infer new features for different groups, like incentives or gamification for improving driver safety. This was shown through a road trip tracking app case study.
3. The future of predictive analytics includes using self-organizing maps to predict bugs based on code dependencies and regions
I give an overview of current state of natural language analysis using machine learning algorithms. #naturallanguage
#machinelearning #artificianintelligence
This document discusses ethical issues related to artificial intelligence. It notes that nearly half of those polled oppose giving robots emotions or personalities. It also discusses using machine learning for credit scores, the lack of understanding of deep neural networks, reinforcement learning challenges like safe exploration and gaming reward functions. The document calls for ethically aligned design of AI through accountability, transparency, embedding human values, and allowing control over digital identities. However, it acknowledges that current guidelines are not possible given technology limitations.
LeadMagnet IQ Review: Unlock the Secret to Effortless Traffic and Leads.pdfSelfMade bd
Imagine being able to generate high-quality traffic and leads effortlessly. Sounds like a dream, right? Well, it’s not. It’s called LeadMagnet IQ, and it’s here to revolutionize your marketing efforts.
(Note: Download the paper about this software. After that, click on [Click for Instant Access] inside the paper, and it will take you to the sales page of the product.)
Redefining Cybersecurity with AI CapabilitiesPriyanka Aash
In this comprehensive overview of Cisco's latest innovations in cybersecurity, the focus is squarely on resilience and adaptation in the face of evolving threats. The discussion covers the imperative of tackling Mal information, the increasing sophistication of insider attacks, and the expanding attack surfaces in a hybrid work environment. Emphasizing a shift towards integrated platforms over fragmented tools, Cisco introduces its Security Cloud, designed to provide end-to-end visibility and robust protection across user interactions, cloud environments, and breaches. AI emerges as a pivotal tool, from enhancing user experiences to predicting and defending against cyber threats. The blog underscores Cisco's commitment to simplifying security stacks while ensuring efficacy and economic feasibility, making a compelling case for their platform approach in safeguarding digital landscapes.
Welcome to Cyberbiosecurity. Because regular cybersecurity wasn't complicated...Snarky Security
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While the fusion of technology and biology offers immense benefits, it also necessitates a careful consideration of the ethical, security, and associated social implications. But let's be honest, in the grand scheme of things, what's a little risk compared to potential scientific achievements? After all, progress in biotechnology waits for no one, and we're just along for the ride in this thrilling, slightly terrifying, adventure.
So, as we continue to navigate this complex landscape, let's not forget the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. After all, what could possibly go wrong?
-------------------------
This document provides a comprehensive analysis of the security implications biological data use. The analysis explores various aspects of biological data security, including the vulnerabilities associated with data access, the potential for misuse by state and non-state actors, and the implications for national and transnational security. Key aspects considered include the impact of technological advancements on data security, the role of international policies in data governance, and the strategies for mitigating risks associated with unauthorized data access.
This view offers valuable insights for security professionals, policymakers, and industry leaders across various sectors, highlighting the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. The analysis serves as a crucial resource for understanding the complex dynamics at the intersection of biotechnology and security, providing actionable recommendations to enhance biosecurity in an digital and interconnected world.
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The power of Snowflake analytics enables CRM systems to improve operational efficiency, while gaining deeper insights into closed/won opportunities.
In this webinar, learn how infusing Snowflake into your CRM can quickly provide analysis for sales wins by region, product, customer segmentation, customer lifecycle—and more!
Using prebuilt connectors, we’ll show how workflows using Snowflake, Salesforce, and Zendesk tickets can significantly impact future sales.
The History of Embeddings & Multimodal EmbeddingsZilliz
Frank Liu will walk through the history of embeddings and how we got to the cool embedding models used today. He'll end with a demo on how multimodal RAG is used.
This PDF delves into the aspects of information security from a forensic perspective, focusing on privacy leaks. It provides insights into the methods and tools used in forensic investigations to uncover and mitigate privacy breaches in mobile and cloud environments.
Intel Unveils Core Ultra 200V Lunar chip .pdfTech Guru
Intel has made a significant breakthrough in the world of processors with the introduction of its Core Ultra 200V mobile processor series, codenamed Lunar Lake. This innovative processor marks a fundamental shift in the way Intel creates processors, with a high degree of aggregation, including memory-on-package (MoP). The Core Ultra 300 MX series is designed to power thin-and-light devices that are capable of handling the latest AI applications, including Microsoft's Copilot+ experiences.
"Hands-on development experience using wasm Blazor", Furdak Vladyslav.pptxFwdays
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What conclusions we made and what mistakes we committed
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Smart mobility refers to the integration of advanced technologies and innovative solutions to create efficient, sustainable, and interconnected transportation systems. It encompasses various aspects of transportation, including public transit, shared mobility services, intelligent transportation systems, electric vehicles, and connected infrastructure. Smart mobility aims to improve the overall mobility experience by leveraging data, connectivity, and automation to enhance safety, reduce congestion, optimize transportation networks, and minimize environmental impacts.
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12. What are the problems?
• Viewing past data has almost no value
• Most IoT startups ended up just collecting big data
• Very few killer applications which creates x5-x10 value
13. Hidden cost of IoT
Relational Data Time series 'Big' Data
(activity, time)(name, address)
14. Relational legacy
data
Big data (eg time-series data)
Database
eg. Postgres
Database
eg. Mongo,
Casandra
Application logic (to split and combine data into
relational / non-relational part)
User Query
SQL query Time series
data query
Database architecture of IoT
@copyright: Rudradeb Mitra
28. IoT Architecture with Machine Learning
Database
Gamification
Social Engagement
Add a switch to turn off/onView Trip
Predictive +
Analytics
Machine Learning
Algo
@copyright: Rudradeb Mitra
29. What are the values?
• Find risky drivers and adjust their premiums so that majoity pay less
• Predict short and long term future to make roads safer by changing
behaviors
32. Patterns of risky drivers - Clustering
Picture taken from: http://www.ai-junkie.com/ann/som/som1.html
Find patterns in data
33. Self Organizing Maps (Clustering)
0.1
0.3
- Euclidean Distance between
training vector and weights
- The best match node is selected
- Adjust weights of neighboring nodes
to match this weight
0.55
0.15
0.31
0.49
Training
Data
0.6
0.9
0.8
@copyright: Rudradeb Mitra
34. Picture taken from http://www.ai-junkie.com/ann/som/som1.html
Most risky drivers
Most safe drivers
For the driving example
35. And how is that helpful?
• Premiums can be adjusted according to driving behavior and thus
majority will end up paying less
36. 2. Can we go further and predict short term future?
37. word2vec- Predicting next word
word2vec
- Does not understand words or
grammar
How
Feeling
You
Are
Today
Input words
embeddings
Predicted next word
Hidden
Layer
Output word
embedding
39. Example
word xi
N= 2
0
0
0
1
0 0.3 0 0.7 0 ... 0
0.1
0.77
0.39
.
.
0 0.29 0 0.55 0 ...0.3
0.5
0
Wi =
Wo =
Word embedding for Xi = X. Wi . Wo
Word embedding Xi = [0.33 ... 0.64 ]
0
0
0
1
X =
40. word2vec - 2d space
- Output matrix to 2
dimension using Principal
component Analysis
41. Picture taken from https://www.lucypark.kr/courses/2015-ba/text-mining.html
Germany - France + Paris
= Berlin
42. Prediction for drivers
score acceleration on Friday
score braking on Friday
word2vec
- Using the semantic association
between orders
(Product ID)
(Predicted score on
Sunday)
score braking on Saturday
score speed on Saturday
43. And how is that helpful?
• Advance short term warnings making roads safer and saving lifes
54. What can we achieve?
• Machine Learning can predict your future consumption (short term,
long term).
• If I know how much I will consume, I can save money and save
electricity!