Anthony is recognized as a thought leader and primary innovator of products, solutions, and technologies for the intelligent capture, RPA, BPM, BI and mobile markets.
ABBYY is an innovator and leader in artificial intelligence (Al) technology including machine learning and natural language processing that helps organizations better understand and drive context and outcomes from their data. The company sets a goal to grow and strengthen its leadership positions by satisfying the ever-increasing demand for AI-enabled products and solutions.
ABBYY has been developing semantic and AI technologies for many years. Thousands of organizations from over 200 countries and regions have chosen ABBYY solutions that transform documents into business value by capturing information in any format. These solutions help organizations of diverse industries boost revenue, improve processes, mitigate risk, and drive competitive advantage.
What got you initially interested in AI?
I first became interested in AI in the 90s. In my role, we were utilizing support vector machines, neural networks, and machine learning engines to create extraction and classification models. At the time, it wasn’t called AI. However, we were leveraging AI to address problems surrounding data and document-driven processes, problems like effectively and accurately extracting, classifying and digitizing data from documents. From very early on in my career, I’ve known that AI can play a key role in transforming unstructured content into actionable information. Now, AI is no longer seen as a futuristic technology but an essential part of our daily lives – both within the enterprise and as consumers. It has become prolific. At ABBYY, we are leveraging AI to help solve some of today’s most pressing challenges. AI and related technologies, including machine learning, natural language processing, neural networks and OCR, help power our solutions that enable businesses to obtain a better understanding of their processes and the content the fuels them.
You’re currently the Chief Innovation Officer at ABBYY. What are some of the responsibilities of this position?
In my role as Chief Innovation Officer for ABBYY, I’m responsible for our overall vision, strategy, and direction relative to various AI initiatives that leverage machine learning, robotic process automation (RPA), natural language processing and text analytics to identify process and data insights that improve business outcomes.
As CIO, I’m responsible for overseeing the direction of our product innovations as well as identifying outside technologies that are a fit to integrate into our portfolio. I initiated the discussions that lead to acquisition of TimelinePI, now ABBYY Timeline, the only end-to-end Process Intelligence platform in the market. Our new offering enables ABBYY to provide an even more robust and dynamic solution for optimizing the processes a business runs on and the data within those processes. We provide enterprises across diverse industries with solutions to accelerate digital transformation initiatives and unlock new opportunities for providing value to their customers.
I also guide the strategic priorities for the Research & Development and Product Innovation teams. My vision for success with regards to our innovations is guided by the following tenants:
- Simplification: make everything we do as easy as possible to deploy, consume and maintain.
- Cloud: leverage the growing demand for our capabilities within a cloud-based SaaS model.
- Artificial Intelligence: build on our legacy expertise in linguistics and machine learning to ensure we take a leadership role as it relates to content analytics, automation and the application of machine learning within the process automation market.
- Mobility: ensure we have best-of-breed on device and zero footprint mobile capture capabilities.
ABBYY uses AI technologies to solve document-related problems for enterprises using intelligent capture. Could you walk us through the different machine learning technologies that are used for these applications?
ABBYY leverages several AI enabling technologies to solve document-related and process-related challenges for businesses. More specifically, we work with computer vision, neural networks, machine learning, natural language processing and cognitive skills. We utilize these technologies in the following ways:
Computer Vision: utilized to extract, analyze, and understand information from images, including scanned documents.
Neural Networks: leveraged within our capture solutions to strengthen the accuracy of our classification and extraction technology. We also utilize advanced neural network techniques within our OCR offerings to enhance the accuracy and tolerance of our OCR technology.
Machine Learning: enables software to “learn” and improve, which increases accuracy and performance. In a workflow involving capturing documents and then processing with RPA, machine learning can learn from several variations of documents.
Natural Language Processing: enables software to read, interpret, and create actionable and structured data around unstructured content, such as completely unstructured document such as contracts, emails and other free-form communications.
Cognitive Skill: the ability to carry out a given task with determined results within a specific amount of time and cost. Examples within our products including extracting data and classifying a document.
ABBYY Digital Intelligence solutions help organizations accelerate their digital transformation. How do you define Digital Intelligence, how does it leverage RPA, and how do you go about introducing this to clients?
Digital Intelligence means gaining the valuable, yet often hard to attain, insight into an organization’s operation that enables true business transformation. With access to real-time data about exactly how their processes are currently working and the content that fuels them, Digital Intelligence empowers businesses to make tremendous impact where it matters most: customer experience, competitive advantage, visibility, and compliance.
We are educating our clients as to how Digital Intelligence can accelerate their digital transformation projects by addressing the challenges they have with unstructured and semi-structured data that is locked in documents such as invoices, claims, bills of lading, medical forms, etc. Customers focused on implementing automation projects can leverage Content Intelligence solutions to extract, classify, and validate documents to generate valuable and actionable business insights from their data.
Another component of Digital Intelligence is helping customers solve their process-related challenges. Specifically in relation to using RPA, there is often a lack of visibility of the full end-to-end process and consequently there is a failure to consider the human workflow steps in the process and the documents on which they work. By understanding the full process with Process Intelligence, they can make better decisions on what to automate, how to measure it and how to monitor the entire process in production.
We introduce this concept to clients via the specific solutions that make up our Digital Intelligence platform. Content Intelligence enables RPA digital workers to turn unstructured content into meaningful information. Process Intelligence provides complete visibility into processes and how they are performing in real time.
What are the different types of unstructured data that you can currently work with?
We transform virtually any type of unstructured content, from simple forms to complex and free-form documents. Invoices, mortgage applications, onboarding documents, claim forms, receipts, and waybills are common use cases among our customers. Many organizations utilize our Content Intelligence solutions, such as FlexiCapture, to transform their accounts payable operations, enabling companies to reduce the amount of time and costs associated with tedious and repetitive administrative tasks while also freeing up valuable personnel resources to focus on high-value, mission critical responsibilities.
Which type of enterprises best benefit from the solutions offered by ABBYY?
Enterprises of all sizes, industries, and geographic markets can benefit from ABBYY’s Digital Intelligence solutions. In particular, organizations that are very process-oriented and document driven see substantial benefits from our platform. Businesses within the insurance, banking and financial services, logistics, and healthcare sectors experience notable transformation from our solutions.
For financial service institutions, extracting and processing content effectively can enhance application and onboarding operations, and also enable mobile capabilities, which is becoming increasingly important to remain competitive. With Content Intelligence, banks are able to easily capture documents submitted by the customer – including utility bills, pay stubs, W-2 forms – on virtually any device.
In the insurance industry, Digital Intelligence can significantly improve claims processes by identifying, extracting, and classifying data from claim documents then turning this data into information that feeds into other systems, such as RPA.
Digital Intelligence is a cross-industry solution. It enables enterprises of all compositions to improve their processes and generate value from their data, helping businesses increase operational efficiencies and enhance overall profit margins.
Can you give some examples of how clients would benefit from the Digital Intelligence solutions that are offered by ABBYY?
Several recent examples come to mind relating to transforming accounts payable and claims. A billion-dollar manufacturer and distributor of medical supplies was experiencing double-digit sales growth year-over-year. It used ABBYY solutions with RPA to automate its 2,000/day invoices and achieved significant results in productivity and cost efficiencies. Likewise, and insurance company digitized its 150,000+ annual claims processing. From claim setup to invoice clarity it achieved more than 5,000 hours of productivity benefits.
Another example is with a multi-billion global logistics company that had a highly manual invoice processing challenge. It had dozens of people processing hundreds of thousands of invoices from 124 different vendors annually. When it first considered RPA for its numerous finance activities, it shied away from invoice processing because of the complexity of semi-structured documents. It used our solutions to extract, classify and validate invoice data, which included machine learning for ongoing training of invoices. If there was data that could not be matched, invoices went to a staff member for verification, but the points that needed to be checked were clearly highlighted to minimize effort. The invoices were then processed in the ERP system using RPA software bots. As a result, its accounts payables are now completely automated and is able to processes thousands of invoices at a fraction of the time with significantly less errors.
What are some of the other interesting machine learning powered applications that are offered by ABBYY?
Machine learning is at the heart of our Content Intelligence solutions. ML fuels how we train our classification and extraction technology. We utilize this technology in our FlexiCapture solution to acquire, process, and validate data from documents – even complex or free form ones – and then feed this data into business applications including BPM and RPA. Leveraging machine learning, we are able to transform content-centric processes in a truly advanced way.
Is there anything else that you would like to share about ABBYY?
It goes without saying that these are uncertain and unprecedented times. ABBYY is fully committed to helping businesses navigate these challenging circumstances. It is more important than ever that businesses have what it takes to make timely, intelligent decisions. There is a lot of data coming in and it can be overwhelming. We are committed to making sure organizations are equipped with the technologies they need to deliver outcomes and help customers.
I really enjoyed learning about your work, for anyone who wishes to learn more please visit ABBYY.
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.
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.
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.
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.”
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.
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.
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.
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.
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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- Power Your ML and AI Efforts with Data Transformation – Thought Leaders