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Ian Wong, Co-founder & CTO of Opendoor – Interview Series

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Ian Wong, Co-founder & CTO of Opendoor - Interview Series

Can you summarize the concept behind Opendoor, and how does it differ from competitors such as Zillow?

Opendoor gives people a simple and convenient way to buy, sell and trade-in homes. We’re turning a fragmented, inflexible real estate model into an  end-to-end, digital and on-demand experience. As the pioneer of “ibuyering”, Opendoor has served over 70,000 customers to date and expanded to 21 U.S. markets.

Opendoor is able to provide near-instant fair market values for homes using a proprietary valuation model that leverages first and third-party data, along with the use of machine learning, AI and human review. With just a few taps in the Opendoor app, sellers can receive an offer from Opendoor within 24 hours. Selling to Opendoor provides more choice and certainty as homeowners can choose their move out date and avoid the hassle and stress of home showings and repairs.

In addition, we’ve begun solving for other pain points in the home transaction with the launch of anew product that reimagines the home buying process, the launch of a home loans business and the acquisition of a title and escrow company. Our goal is to make moving seamless, on-demand and stress free.

 

What was it that attracted you to Opendoor?

We have the chance to reimagine the real-estate transaction, thereby redefining people’s relationships with their largest asset. As opposed to a liability, what if homeowners can tap into liquidity afforded by their homes in the same way you and I can withdraw from our checking account? What if buyers and sellers can skip months of stress and uncertainty, and become more confident moving forward with the next chapter of their lives? The vision of enabling more geographic mobility and financial freedom is super exciting, and it feels like we’re just embarking on that journey.

 

Opendoor analyzes a large collection of historical market transactions. What type of data points are you assembling?

Accurate real estate data with the level of granularity we need is not easy to come by. We use a combination of large propriety and third-party data sets to understand historical market transactions, including listing-level and home-level details. This means we look at common data points from a listing, like the sale date and price, when the home listed, as well as data points about individual homes, like the number of bedrooms and bathrooms, kitchen attributes or square footage. On top of this, we incorporate features that denote a home’s quality or uniqueness, allowing us to better select comparables and ultimately price the home as accurately as possible. We also take into account similar data from homes currently on the market. Ultimately, these data points help us predict the fair market value of a home and the amount of time it will likely take to resell the home.

 

Opendoor also analyzes homes that are taken off the market without transacting, how is this data used differently compared to homes that have sold?

We look at similar data for both active homes and homes that are taken off the market without transacting — homes we call “delistings.” Our data set looks at a variety of home-level and list-level details, including square feet and list price, for each transaction. We examine those insights for delistings, but do not get to observe our target variable of days-on-market. Additionally, we look at the market holistically to understand supply and demand. By incorporating non-transacted listings, we’re able to have a more comprehensive picture of the market.

 

Opendoor uses Ensembling as a factor in house pricing. Can you explain what ensembling is and how Opendoor uses this technology?

When a buyer wants to buy a house or a seller decides to list their home on the market, the way they determine the home’s value will depend on why they are buying or selling. And this can be very different depending on the buyer and seller type. We incorporate this in our model to understand how buyers and sellers view the market, which is where ensembling comes in. Ensembling allows us to use different pricing models together to compute a weighted average of home values. Some models may weigh certain variables differently than others. We’ve found that ensembling generally results in more accurate pricing than any single model.

 

Opendoor imports big data from various sources which can be a challenge due to how the data was originally labeled or formatted. Opendoor uses Markov Random Field to assist with this issue. Can you explain what this is?

The challenge stems from mutations in the text data, from abbreviations and misspellings to inconsistent ordering of words and numeric spellings. Poor quality data impacts our home valuation models, which is why we implemented a mathematical approach to help standardize text and improve the quality of labels. Markov Random Field enables us to score all labels jointly and more accurately interpret characteristics like subdivisions. The score of each labeling comes from two different components: 1) how well the final labels relate to the original text and 2) how spatially continuous the labels are among neighbors. With the mathematics of Markov chains, we make the data more than just the sum of its parts.

 

You use a technique called survival analysis to model the average holding time of a home that is listed for sale. What is survival analysis and does it apply in Opendoor’s case?

Fundamentally, we need to understand liquidity on a per home basis, and be able to update our view of the liquidity profile of a home as we get more information. Survival analysis is a statistical method that analyzes the anticipated amount of time it will take until one or more events happen. In our case, we use survival analysis to help us understand and predict how long a house will take to sell. Using this method, we dramatically improve our ability to respond to evolving market conditions, and more accurately predict our unit economics. This helps us determine a risk threshold for each home and make smarter investments, which is vital to our business.

 

There are often factors that affect the value of a home which are very location dependent, such as road noise. How do you use machine learning to program your valuation model for such an issue?

The Opendoor Valuation Model (OVM) combines machine intelligence with human expertise to provide accurate and competitive offers, taking less apparent factors, like road noise, into account. To do so, we rely on our human operators to identify variables and our machines to predict how much they matter in the pricing algorithm. OpenStreetMap (OSM) is a freely available data set for road geometries and helps us identify homes adjacent to roads. We also look for previous human adjustments on homes to compute the average adjustment value. We’re able to refine these values with scale, and as we collect more human adjustment data for markets, the data set grows and improves the OVM performance. Most importantly, we enrich readily available third-party data with our own proprietary data. As a result, the overall location dependent signals improve dramatically over time.

 

Is there anything else that you would like to share about Opendoor?

What makes working at Opendoor particularly special for me is that we’re using technology, data science and operational excellence to help solve real world pain points for millions of consumers. This marriage of the online and offline worlds has never been done and comes with lots of new and interesting challenges.

To Learn more visit Opendoor

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Antoine Tardif is a futurist who is passionate about the future of AI and robotics. He is the CEO of BlockVentures.com, and has invested in over 50 AI & blockchain projects. He is also the Co-Founder of Securities.io a news website focusing on digital securities, and is a founding partner of unite.ai

Big Data

Power Your ML and AI Efforts with Data Transformation – Thought Leaders

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Power Your ML and AI Efforts with Data Transformation - Thought Leaders

The greater the variety, velocity, and volume of data we have, the more feasible it becomes to use predictive analytics and modeling to forecast growth and identify areas of opportunity and improvement. However, getting the greatest value from reporting, machine learning (ML), and artificial intelligence (AI) tools requires an organization to access data from many sources and ensure that data is high-quality and trusted. This is often the greatest barrier to transforming big data into business strategy.

Data professionals spend so much time gathering and validating data to prepare it for use that they have little time left to focus on their primary purpose: analyzing the data and deriving business value from it. Unsurprisingly, 76 percent of data scientists say data preparation is the least enjoyable part of their job. Moreover, current data preparation efforts like data wrangling and traditional ETL require manual effort from IT professionals and are not enough to handle the scale and complexity of big data.

Companies that want to leverage the power of AI need to break away from these tedious and largely manual processes that increase the risk of “garbage in, garbage out” results. Instead, they need data transformation processes that extract raw data in multiple sources and formats, join and normalize it, and add value with business logic and metrics to make it ready for analytics. With complex data transformation, they can be sure that AI/ML models are based on clean, accurate data that delivers trustworthy results.

Leveraging the power of the cloud with ELT

The best place to prepare and transform data today is a cloud data warehouse (CDW) such as Amazon Redshift, Google BigQuery, Microsoft Azure Synapse, or Snowflake. While traditional approaches to data warehousing require data to be extracted and transformed before it can be loaded, a CDW leverages the scalability and performance of the cloud for faster data ingestion and transformation and makes it possible to extract and load data from many disparate data sources before transforming it inside the CDW.

Ideally, the ELT model initially moves data into a section of the CDW reserved for raw staging data. From there, the CDW can use its near-unlimited computing resources available for data integration and ETL jobs that cleanse, aggregate, filter, and join the staged data. The data can then be transformed into a different schema – data vault or Star Schema, for example, optimizing the data for reporting and analytics

The ELT approach also allows you to replicate raw data within the CDW for later preparation and transformation when and as needed. This lets you use business intelligence tools that determine schema on read and produce specific transformations on demand, effectively letting you transform the same data in multiple ways as you discover new uses for it.

Accelerating machine learning models

These real-world examples show how two companies in different industries are leveraging data transformation in a CDW to drive AI initiatives.

A boutique marketing and advertising agency built a proprietary customer management platform to help its clients better identify, understand, and motivate their customers. By transforming data within a CDW, the platform quickly and easily integrates real-time customer data across channels into a 360-degree customer view that informs the platform’s AI/ML models for making customer interactions more consistent, timely, and personalized.

A global logistics firm making 100 million deliveries to 37 million unique customers in 72 countries needs vast amounts of data to power its daily operations. Adopting data transformation within a CDW enabled the company to deploy 200 machine learning models in a single year. These models make 500,000 predictions every day, significantly improving efficiency and driving superior customer service that has reduced inbound call center calls by 40 percent.

Best practices for getting started

Companies that want to support their AI/ML initiatives with the power of data transformation in the cloud need to understand their specific use case and needs. Beginning with what you want to do with your data –reducing fuel costs by optimizing delivery routes, boosting sales by delivering next best offers to customer service agents in real-time, etc. – lets you reverse-engineer your processes so you can identify which data will deliver relevant results.

Once you determine what data your AI/ML project needs to build its models, you need a cloud-native ELT solution that will make your data fit for use. Look for a solution that:

  • Is vendor-neutral and able to work with your current technology stack

  • Is flexible enough to scale up and down and adapt as your technology stack changes

  • Can handle complex data transformations from multiple data sources

  • Offers a pay-as-you-go pricing model in which you pay only for what you use

  • Is purpose-built for your preferred CDW so you can fully leverage that CDW’s features to run jobs faster and transform data seamlessly.

A cloud data transformation solution that caters to the common denominators of all CDWs may provide a consistent experience, but only one that enables the powerful differentiating features of your chosen CDW can deliver the high performance that speeds time to insight. The right solution will enable you to power your AI/ML projects with more clean, trusted data from more sources in less time – and generate faster, more reliable results that drive previously unrealized business value and innovation.

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Ingo Mierswa, Founder & President at RapidMiner, Inc – Interview Series

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Ingo Mierswa, Founder & President at RapidMiner, Inc - Interview Series

Ingo Mierswa is the Founder & President at RapidMiner, Inc. RapidMiner brings artificial intelligence to the enterprise through an open and extensible data science platform. Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment. More than 625,000 analytics professionals use RapidMiner products to drive revenue, reduce costs, and avoid risks.

What was your inspiration behind launching RapidMiner?

I had worked in the data science consultancy business for many years and I saw a need for a platform that was more intuitive and approachable for people without a formal education in data science. Many of the existing solutions at the time relied on coding and scripting and they simply were not user-friendly. Furthermore, it made data difficult to manage and maintain the solutions that were developed within those platforms. Basically, I realized that these projects didn’t need to be so difficult so, we started to create the RapidMiner platform to allow anyone to be a great data scientist.

Can you discuss the full transparency governance that is currently being utilized by RapidMiner?

When you can’t explain a model, it’s quite hard to tune, trust and translate. A lot of data science work is the communication of the results to others so that stakeholders can understand how to improve processes. This requires trust and deep understanding. Also, issues with trust and translation can make it very hard to overcome the corporate requirements to get a model into production. We are fighting this battle in a few different ways:

As a visual data science platform, RapidMiner inherently maps out an explanation for all data pipelines and models in a highly consumable format that can be understood by data scientists or non-data scientists. It makes models transparent and helps users in understanding model behavior and evaluating its strengths and weaknesses and detecting potential biases.

In addition, all models created in the platform come with extensive visualizations for the user – typically the user creating the model – to gain model insights, understand model behavior and evaluate model biases.

RapidMiner also provides model explanations – even when in production: For each prediction created by a model, RapidMiner generates and adds the influence factors that have led to or influenced the decisions made by that model in production.

Finally – and this is very important to me personally as I was driving this with our engineering teams a couple of years ago – RapidMiner also provides an extremely powerful model simulator capability, which allows users to simulate and observe the model behavior based on input data provided by the user. Input data can be set and changed very easily, allowing the user to understand the predictive behavior of the models on various hypothetical or real-world cases. The simulator also displays factors that influence the model’s decision. The user – in this case even a business user or domain expert – can understand model behavior, validate the model’s decision against real outcomes or domain knowledge and identify issues. The simulator allows you to simulate the real world and have a look into the future – into your future, in fact.

How does RapidMiner use deep learning?

RapidMiner’s use of deep learning somethings we are very proud of. Deep learning can be very difficult to apply and non-data-scientists often struggle with setting up those networks without expert support. RapidMiner makes this process as simple as possible for users of all types. Deep learning is, for example, part of our Auto machine learning (ML) product called RapidMiner Go. Here the user does not need to know anything about deep learning to make use of those types of sophisticated models. In addition, power users can go deeper and use popular deep learning libraries like Tensorflow, Keras, or DeepLearning4J right from the visual workflows they are building with RapidMiner. This is like playing with building blocks and simplifies the experience for users with fewer data science skills. Through this approach our users can build flexible network architectures with different activation functions and user-defined number of layers and nodes, multiple layers with different numbers of nodes, and choose from different training techniques.

What other type of machine learning is used?

All of them! We offer hundreds of different learning algorithms as part of the RapidMiner platform – everything you can apply in the widely-used data science programming languages Python and R. Among others, RapidMiner offers methods for Naive Bayes, regression such as Generalized Linear Models, clustering such as k-Means, FP-Growth, Decision Trees, Random Forests, Parallelized Deep Learning, and Gradient Boosted Trees. These and many more are all a part of the modeling library of RapidMiner and can be used with a single click.

Can you discuss how the Auto Model knows the optimal values to be used?

RapidMiner AutoModel uses intelligent automation to accelerate everything users do and ensure accurate, sound models are built. This includes instance selection and automatic outlier removal, feature engineering for complex data types such as dates or texts, and full multi-objective automated feature engineering to select the optimal features and construct new ones.  Auto Model also includes other data cleaning methods to fix common issues in data such as missing values, data profiling by assessing the quality and value of data columns, data normalization and various other transformations.

Auto Model also extracts data quality meta data – for example, how much a column behaves like an ID or whether there are lots of missing values. This meta data is used in addition to the basic meta data in automating and assisting users in ‘using the optimal values’ and dealing with data quality issues.

For more detail, we’ve mapped it all out in our Auto Model Blueprint. (Image below for extra context)

There are four basic phases where the automation is applied:

– Data prep: Automatic analysis of data to identify common quality problems like correlations, missing values, and stability.
– Automated model selection and optimization, including full validation and performance comparison, that suggests the best machine learning techniques for given data and determines the optimal parameters.
– Model simulation to help determine the specific (prescriptive) actions to take in order to achieve the desired outcome predicted by the model.
– In the model deployment and operations phase, users are shown factors like drift, bias and business impact, automatically with no extra work required.

Ingo Mierswa, Founder & President at RapidMiner, Inc - Interview Series

Computer bias is an issue with any type of AI, are there any controls in place to prevent bias from creeping up in results?

Yes, this is indeed extremely important for ethical data science. The governance features mentioned before ensure that users can always see exactly what data has been used for model building, how it was transformed, and whether there is bias in the data selection. In addition, our features for drift detection are another powerful tool to detect bias. If a model in production demonstrates a lot of drift in the input data, this can be a sign that the world has changed dramatically. However, it can also be an indicator that there was severe bias in the training data. In the future, we are considering to going even one step further and building machine learning models which can be used to detect bias in other models.

Can you discuss the RapidMiner AI Cloud and how it differentiates itself from competing products?

The requirements for a data science project can be large, complex and compute intensive, which is what has made the use of cloud technology such an attractive strategy for data scientists. Unfortunately, the various native cloud-based data science platforms tie you to cloud services and data storage offerings of that particular cloud vendor.

The RapidMiner AI Cloud is simply our cloud service delivery of the RapidMiner platform. The offering can be tailored to any customer’s environment, regardless of their cloud strategy. This is important these days as most businesses’ approach to cloud data management is evolving very quickly in the current climate. Flexibility is really what sets RapidMiner AI Cloud apart. It can run in any cloud service, private cloud stack or in a hybrid setup. We are cloud portable, cloud agnostic, multi-cloud – whatever you prefer to call it.

RapidMiner AI Cloud is also very low hassle, as of course, we offer the ability manage all or part of the deployment for clients so they can focus on running their business with AI, not the other way around. There’s even an on-demand option, which allows you spin up an environment as needed for short projects.

RapidMiner Radoop eliminates some of the complexity behind data science, can you tell us how Radoop benefits developers?  

Radoop is mainly for non-developers who want to harness the potential of big data. RapidMiner Radoop executes RapidMiner workflows directly inside Hadoop in a code-free manner. We can also embed the RapidMiner execution engine in Spark so it’s easy to push complete workflows into Spark without the complexity that comes from code-centric approaches.

Would a government entity be able to use RapidMiner to analyze data to predict potential pandemics, similar to how BlueDot operates?

As a general data science and machine learning platform, RapidMiner is meant to streamline and enhance the model creation and management process, no matter what subject matter or domain is at the center of the data science/machine learning problem. While our focus is not on predicting pandemics, with the right data a subject matter expert (like a virologist or epidemiologist, in this case) could use the platform to create a model that could accurately predict pandemics. In fact, many researchers do use RapidMiner – and our platform is free for academic purposes.

Is there anything else that you would like to share about RapidMiner?

Give it a try!  You may be surprised how easy data science can be and how much a good platform can improve you and your team’s productivity.

Thank you for this great interviewer, readers who wish to learn more should visit RapidMiner.

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Big Data

Owkin Launches the Collaborative COVID-19 Open AI Consortium (COAI)

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Owkin Launches the Collaborative COVID-19 Open AI Consortium (COAI)

After a fresh round of funding, Owkin recently launched the Covid-19 Open AI Consortium (COAI). This consortium will enable advanced collaborative research and accelerate clinical development of effective treatments for patients who are infected with COVID-19.

The first stage of the project is on fully understanding and treating cardiovascular complications in COVID-19 patients, this will be performed in collaboration with CAPACITY, an international registry working with over 50 centers around the world. Other areas of research will include patient outcomes and triage, and the prediction and characterization of immune response.

Owkin’s manifesto perfectly states the company’s vision:

“We are fully engaged in this new frontier with the goal of improving drug development and patient outcomes. Founded in 2016, Owkin has quickly emerged as a leader in bringing Artificial Intelligence (AI) and Machine Learning (ML) technologies to the healthcare industry. Our solutions improve the traditional medical research paradigm by turning a previously siloed, disjointed system into an innovative and collaborative one that, above all, puts the privacy of patients first.”

Federated Learning

To understand the model that Owkin is engaging one must fully understand a new technology which is called Federated Learning. Federated learning offers a framework for AI development that enables enterprises to train machine learning models on data that is distributed at scale across multiple medical institutions without centralizing the data. The benefits of this are two-fold, there is no loss of privacy since the data is not directly linked to any specific patient, and the data is maintained at the healthcare institution that collects this data.

The use of Federated Learning thereby enables a significantly wider range of data than what any single organization possesses in-house. What this means is that by used Federated Learning researchers have access to as much data as available, and the more big data a machine learning system possesses, the more accurate the AI becomes.

There are currently multiple national efforts in using AI to tackle COVID-19. The problem with many of these nationalistic disjointed efforts is that the data is specific to one country. Collecting data from a single region may fail to reveal important information that would enable researchers to fully understand how exposure to environmental elements, ethnic makeup, genetics, age, and gender may play important roles in understanding this disease. This is why collaboration is so important, and why gathering data from multiple jurisdictions is even more important.

As described by Owkin, they seek to used Federated Learning for the following:

“We aim to help them understand why drug efficacy varies from patient to patient, enhance the drug development process and identify the best drug for the right patient at the right time, to improve treatment outcomes.”

Understanding and treading cardiovascular health issues will be the first challenge undertaken by Owkin. As important as data is, what is even more important are the efforts of researchers and contributors who are spearheading this effort. This is why Unite.AI will be releasing three interviews with researchers that are contributing to the COAI project.

The Interviews

Sanjay Budhdeo, MD, Business Development:

Sanjay is a practicing physician. He holds Medical Sciences and Medical degrees from Oxford University and a Masters Degree from Cambridge University. Sanjay has research experience in neuroimaging, epidemiology and digital health. Prior to joining Owkin as a Partnership Manager, he was a Senior Associate at Boston Consulting Group, where he focused on data and digital in healthcare. He sits on the Patient Safety Committee at the Royal Society of Medicine and was previously a Specialist Advisor at the Care Quality Commission.

Click Here to read the interview with Sanjay.

Dr. Stephen Weng, Principal Researcher:

Stephen is an Assistant Professor of Integrated Epidemiology and Data Science who leads the data science research within the Primary Care Stratified Medicine Research Group.

He integrate traditional epidemiological methods and study design with new informatics-based approaches, harnessing and interrogating “big health care data” from electronic medical records for the purpose of risk prediction modeling, phenotyping chronic diseases, data science methods research, and translation of stratified medicine into primary care.

Click Here to read the interview with Stephen

Folkert W. Asselbergs, Principal Investigator

Folkert is professor of precision medicine in cardiovascular disease at Institute of Cardiovascular Science, UCL, Director NIHR BRC Clinical Research Informatics Unit at UCLH, professor of cardiovascular genetics and consultant cardiologist at the department of Cardiology, University Medical Center Utrecht, and chief scientific officer of the Durrer Center for Cardiovascular Research, Netherlands Heart Institute. Prof Asselbergs published more than 275 scientific papers and obtained funding from leDucq foundation, British and Dutch Heart Foundation, EU (FP7, ERA-CVD, IMI, BBMRI), and RO1 National Institutes of Health.

Click Here to read the interview with Folkert

Our Hope

The hope of Unite.AI is that by using biomedical images, genomics, and clinical data to discover biomarkers and mechanisms associated with diseases and treatment outcomes this will propel the next generation of treatment to tackle COVID-19. We are contributing to this important project by highlighting the personalities behind this important global effort.

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