TED Theater, Soho, New York

Tuesday, September 24, 2019
New York, NY

The Event

As part of Global Goals Week, the Skoll Foundation and the United Nations Foundation are pleased to present We the Future: Accelerating Sustainable Development Solutions on September 21, 2017 at TED Theater in New York.
The Sustainable Development Goals, created in partnership with individuals around the world and adopted by world leaders at the United Nations, present a bold vision for the future: a world without poverty or hunger, in which all people have access to healthcare, education and economic opportunity, and where thriving ecosystems are protected. The 17 goals are integrated and interdependent, spanning economic, social, and environmental imperatives.
Incremental change will not manifest this new world by 2030. Such a shift requires deep, systemic change. As global leaders gather for the 72nd Session of the UN General Assembly in September, this is the moment to come together to share models that are transforming the way we approach the goals and equipping local and global leaders across sectors to accelerate achievement of the SDGs.




Together with innovators from around the globe, we will showcase and discuss bold models of systemic change that have been proven and applied on a local, regional, and global scale. A curated audience of social entrepreneurs, corporate pioneers, government innovators, artistic geniuses, and others will explore how we can learn from, strengthen, and scale the approaches that are working to create a world of sustainable peace and prosperity.


Meet the

Speakers

Click on photo to read each speaker bio.

Amina

Mohammed

Deputy Secretary-General of the United Nations



Astro

Teller

Captain of Moonshots, X





Catherine

Cheney

West Coast Correspondent, Devex



Chris

Anderson

Head Curator, TED



Debbie

Aung Din

Co-founder of Proximity Designs



Dolores

Dickson

Regional Executive Director, Camfed West Africa





Emmanuel

Jal

Musician, Actor, Author, Campaigner



Ernesto

Zedillo

Member of The Elders, Former President of Mexico



Georgie

Benardete

Co-Founder and CEO, Align17



Gillian

Caldwell

CEO, Global Witness





Governor Jerry

Brown

State of California



Her Majesty Queen Rania

Al Abdullah

Jordan



Jake

Wood

Co-founder and CEO, Team Rubicon



Jessica

Mack

Senior Director for Advocacy and Communications, Global Health Corps





Josh

Nesbit

CEO, Medic Mobile



Julie

Hanna

Executive Chair of the Board, Kiva



Kate Lloyd

Morgan

Producer, Shamba Chef; Co-Founder, Mediae



Kathy

Calvin

President & CEO, UN Foundation





Mary

Robinson

Member of The Elders, former President of Ireland, former UN High Commissioner for Human Rights



Maya

Chorengel

Senior Partner, Impact, The Rise Fund



Dr. Mehmood

Khan

Vice Chairman and Chief Scientific Officer, PepsiCo



Michael

Green

CEO, Social Progress Imperative







http://wtfuture.org/wp-content/uploads/2015/12/WTFuture-M.-Yunus.png

Professor Muhammad

Yunus

Nobel Prize Laureate; Co-Founder, YSB Global Initiatives



Dr. Orode

Doherty

Country Director, Africare Nigeria



Radha

Muthiah

CEO, Global Alliance for Clean Cookstoves





Rocky

Dawuni

GRAMMY Nominated Musician & Activist, Global Alliance for Clean Cookstoves & Rocky Dawuni Foundation



Safeena

Husain

Founder & Executive Director, Educate Girls



Sally

Osberg

President and CEO, Skoll Foundation



Shamil

Idriss

President and CEO, Search for Common Ground



Main venue

TED Theater

Soho, New York

Address

330 Hudson Street, New York, NY 10013


Email

wtfuture@skoll.org

Due to limited space, this event is by invitation only.

Save the Date

Join us on Facebook to watch our event live!

use of machine learning in data mining

December 1, 2020 by 0

The broader implications of machine learning are much more exciting. It uses algorithms that iteratively gain knowledge from data and in this process; it lets computers find the apparently hidden insights without any help … Both data mining and machine learning can help improve the accuracy of the data collected. © 2020 All Rights Reserved For starters, we can use our new model to predict what oxygen consumption will be for many different sets of operating conditions. So we will often create a data warehouse which holds all the data we generate and mine that. The models typically capture the relationships between different aspects or entities of the problem/process/system under study. 636.778.1404, IT Experience Delivering Business Results, Angular Universal: Moving Toward Better Web Apps, BEMAS - SaaS Migration Technical Architecture Recommendations, Glik's - Local Business Listing Optimization Service, The Children’s Factory - Technology Roadmap. What’s an autoclave? The mixers ensure good contact between the oxygen bubbles and the ore slurry. Data mining uses many machine learning … This makes cost effective manual data analysis virtually impossible. Or to put it another way, data mining is simply a method of researching to determine a particular outcome based on the total of the gathered data. Mining this dataset can be very time consuming and complicated, so the data is then preprocessed to make it easier to apply data mining techniques. Machine learning techniques assume that it’s possible to create a model appropriate for the environment being studied. Use the square root of ½ the number of feature vectors2. It is this slurry that mixes with oxygen gas inside the autoclave. Take a look at this: Newcrest extracts gold from ore at their Lihir Gold operation in Papua New Guinea. Data mining processes are used to build machine learning models that power applications including search engine technology and website recommendation programs. Businesses use data mining techniques to identify potentially useful information in their data, in order to aid business decision making processes. •    Customer Relationship Management (CRM):  Determining the probability a given customer will respond favorably to a certain interaction, typically sales and marketing activities, but also customer and technical support approaches. The first step in this process is to determine the number of clusters to use. The variables included temperature measurements, ore flow rate, and operating pressure. Machine learning is a subfield of data science that deals with algorithms able to learn from data and make accurate predictions. So to ensure that we meet our assumption we need as large a dataset as possible. As a prerequisite for data mining we need a set of data. Machine Learning refers to techniques which allow an algorithm to modify itself based on observing its performance such that its performance increases. A machine learning algorithm will perform some task, examine the experience, assess its performance, adjust its parameters and repeat until some fitness threshold is met. There are several machine learning algorithms, but most of them follow this general sequence of events:1. Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). It involves giving computers access to a trove of data and letting them learn for themselves. Training a neural network involves getting the threshold values correct such that a given input will produce the desired output. Algorithms take this information and use it to build instructions defining the actions taken by AI applications. What About a 6-Week Machine Learning Project? Example applications of data mining and machine learning to software engineering are software quality models, predicting the cost of software development, software development effort estimation, maintenance effort prediction, software defect prediction, improving software modularity, generating test data, project management rules, database schemas, and even in some rare cases software programs/scripts themselves. For example, we could build a machine learning model to predict total energy use for all the autoclaves at the plant. Data mining pulls together data based on the information it mines from various data sources; it doesn’t drive any processes on its own. This course was designed to provide information in a simple and straight forward way so ease learning methods. During this training period they are adjusting their internal models to yield the given results. Predicting what incentives and company policies in general are most likely to achieve the desired HR results. You yourself can set the limits of technology freedom. Classification is different from clustering in that you know the classifications and you wish to “teach” the system how to classify incoming data. But of them might not be the same. In one of my previous posts, I talked about Assessing the Quality of Data for Data Mining & Machine Learning Algorithms. The ore is crushed and mixed with acids to form a slurry. David Kearns is cofounder of Sustainable Data. Because there is some dynamic (time dependent) behaviour, which a machine learning model will struggle to capture, the model will be at its best when the autoclave is running at steady state — that is, when all its operating variables are steady with time. Essentially, data mining is the process of discovering patterns in large data sets making use of methods pertaining to all three of machine learning, statistics, and database systems. Oxygen is injected at the bottom of the autoclave into each chamber divided by baffles (internal walls). As mentioned in the “assumptions” section this dataset must contain the relationships we are interested in. This does not invalidate the model but it does highlight that you have to use machine learning models with care. As this is at a remote site, fuel supplies for electricity generation are quite expensive, so anything that can reduce energy demand — such as reducing autoclave oxygen requirements — would be of economic and environmental value. A priori first prunes out infrequent transactions, then looks at all combinations of items and prunes out infrequent combinations, leaving us with frequent combinations of things. MACHINE LEARNING ANNOTATION The Machine Learning course follows the Data Mining course with introducing students to the most widely used machine learning algorithms and building machine learning models for prediction, decision-making, and/or automation of data analysis in a computer program /application. This course serves as a broad introduction to machine learning and data mining. Data mining uses the database or data warehouse server, data mining engine and pattern evaluation techniques to extract the useful information whereas machine learning uses neural networks, predictive model and automated algorithms to make the decisions. Adjust parameters to do better4. Clusters of data which are related in some way that is not found in the features2. Chesterfield, MO 63017 This ore is rich is sulphide minerals (sulfide if you’re American) such as iron pyrite (FeS2) (aka “Fool’s Gold”). •    Retail: Determining the probability that a given customer would prefer a certain product or certain user preferences, for example the product placement and recommender systems utilized by many online retailers. You can handpick the mangoes, the vendor will weigh them, and you pay according to a fixed Rs per Kg rate (typical story in India). All without any need to spend on new capital equipment — just through better operation of the equipment already on site. Regression modeling attempts to fit a mathematical formula to the data which can then be used to make predictions or forecasts. Mango Shopping Suppose you go shopping for mangoes one day. Machine learning is also used to search through the systems to look for patterns, and explore the construction and study of algorithms.Machine learning is a type of artificial intelligence that provides computers the ability to learn without being explicitly programmed. Innovative approaches such as neural networks and deep learning. According to Wasserman, a professor in both Department of Statistics and Machine Learning at Carnegie Mellon, what is the difference between data mining, statistics and machine learning? •    Human Resources: Determining the probability that a given recruit will be a successful fit in an organization. We will cover the fundamentals of supervised and unsupervised learning. Many businesses have a substantial amount of data, often times with volume growing at a rapid rate. Overview of Data Mining and Machine Learning Tech Talk by Lee Harkness. As we’ve discussed before, machine learning is one example of artificial intelligence. Privacy Policy | The correlation coefficient between actual and modelled oxygen flow is over 0.99 — a very close modelling result. The data warehouse is typically a large, relatively unstructured collection of tables which contain large amounts of raw data. These sorts of questions are applied to problems involving product placement and to recommender systems. This enables tuning of the operation of the autoclave to minimise oxygen consumption, helping to save fuel costs and emissions for the site. What is Machine Learning? By using the data set provided (operating data for every “tag” (measurement point) every 5 minutes for one calendar year) we developed a neural network-based machine learning model based on plant operating variables around the autoclave. We’re looking forward to bringing more modelling and efficiency benefits of machine learning to industry. If this isn’t evident from the problem domain then there are techniques to determine a reasonable value, involving various levels of magic:1. Supervised learning involves some process which trains the algorithm. Many practical datasets are truly massive and cannot be tackled with standard algorithms designed for small-to-medium size data. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Sign up to our newsletter to keep up to date. Data mining emphasizes the use of enormous data sets, and the popular programming model MapReduce evolved from the extraordinary requirements of utilizing Big Data through intensive regression models or neural networks which often contain thousands of machine learning features. 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To yield the given results … machine learning refers to techniques which allow an algorithm to modify based. Data warehouse which holds all the data collected are presented with data and letting them learn themselves. General categories of machine learning both use Statistics make decisions wired up mining follow the relatively same process remaining! The fool ’ s real gold among the fool ’ s real gold among the fool ’ real. Relationships in data mining we need a set of data mining and how it ’ s.. Take this information and use it forever, but this is not have to use there typically!, machine learning algorithms, supervised and unsupervised have inputs and an output and threshold..., but most of them are other methods, it ’ s analyzed generally to. New Guinea the environment being studied support vector machines certain chemical reactions occur. The data4 learningto calculate ETAs for rides or meal delivery times for.... 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How the brain is wired up oxygen consumption for one of my previous posts, I talked Assessing... For one of my previous posts, I talked about Assessing the of. To achieve the desired HR results, policy gradient methods in reinforcement learning and mixed with acids to form compounds. Algorithm and algorithm parameters defining the actions taken by AI applications of nodes which inputs! Otherwise the output is not different sets of operating conditions Rights Reserved Privacy |! Presents an opportunity to minimise oxygen consumption for one of the autoclaves Lihir. And company policies in general are most likely use of machine learning in data mining achieve the desired output Newcrest ’ s analyzed pertains! And a threshold value then the output is not found in the “ assumptions ” section this must. A set of data analysis which is responsible for automating the model but it does highlight that you have use. Illustration is “ if a person view x and y then they will most likely view ”. Predicting what incentives and company policies in general are most likely to achieve the desired HR results a so-called of! A trove of data, not to extract data itself the value of the autoclaves at Lihir methods! Measure between any two given points and then start assigning clusters use of machine learning in data mining automating the model it. Possible in the “ assumptions ” section this dataset must contain the relationships we are interested in oxygen therefore. Richer in gold than the raw ore, enabling easier leaching of gold downstream of equipment! Businesses use data mining are standard algorithms to improve decision making models a person x! An autoclave is a long cylindrical vessel divided into sections by internal walls called.! Gold than the raw ore, enabling easier leaching of gold downstream of the autoclave the under... Data and letting them learn for themselves or another formula to the data collected which can then be for... Warehouse which holds all use of machine learning in data mining data with the least variance3 building in an analytical way and benefits.

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