Hilary Mason, general manager for machine learning at Cloudera, discussed AI in the real world in her keynote the recent Open FinTech Forum.

We are living in the future – it is just unevenly distributed with “an outstanding amount of hype and this anthropomorphization of what [AI] technology can actually provide for us,” observed Hilary Mason, general manager for machine learning at Cloudera, who led a keynote on “AI in the Real World: Today and Tomorrow,” at the recent Open FinTech Forum.

AI has existed as an academic field of research since the mid-1950s, and if the forum had been held 10 years ago, we would have been talking about big data, she said. But, today, we have machine learning and feedback loops that allow systems continue to improve with the introduction of more data.

Machine learning provides a set of techniques that fall under the broad umbrella of data science. AI has returned, from a terminology perspective, Mason said, because of the rise of deep learning, a subset of machine learning techniques based around neural networks that has provided not just more efficient capabilities but the ability to do things we couldn’t do at all five years ago.

Imagine the future

All of this “creates a technical foundation on which we can start to imagine the future,’’ she said. Her favorite machine learning application is Google Maps. Google is getting real-time data from people’s smartphones, then it is integrating that data with public data sets, so the app can make predictions based on historical data, she noted.

Getting this right, however, is really hard. Mason shared an anecdote about how her name is a “machine learning-edge case.” She shares her name with a British actress who passed away around 2005 after a very successful career.

Late in her career, the actress played the role of a ugly witch, and a search engine from 2009 combined photos with text results. At the time, Mason was working as a professor, and her bio was paired with the actress’s picture in that role. “Here she is, the ugly hag… and the implication here is obvious,’’ Mason said. “This named entity disambiguation problem is still a problem for us in machine learning in every domain.”

This example illustrates that “this technology has a tremendous amount of potential to make our lives more efficient, to build new products. But it also has limitations, and when we have conferences like this, we tend to talk about the potential, but not about the limitations, and not about where things tend to go a bit wrong.”

Machine learning in FinTech

Large companies operating complex businesses have a huge amount of human and technical expertise on where the ROI in machine learning would be, she said. That’s because they also have huge amounts of data, generally created as a result of operating those businesses for some time. Mason’s rule of thumb when she works with companies, is to find some clear ROI on a cost savings or process improvement using machine learning.

“Lots of people, in FinTech especially, want to start in security, anti-money laundering, and fraud detection. These are really fruitful areas because a small percentage improvement is very high impact.”

Other areas where machine learning can be useful is in understanding your customers, churn analysis and marketing techniques, all of which are pretty easy to get started in, she said.

“But if you only think about the ROI in the terms of cost reduction, you put a boundary on the amount of potential your use of AI will have. Think also about new revenue opportunities, new growth opportunities that can come out of the same technologies. That’s where the real potential is.”

Getting started

The first thing to do, she said is to “drink coffee, have ideas.” Mason said she visits lots of companies and when she sees their list of projects, they’re always good ideas. “I get very worried, because you are missing out on a huge amount of opportunity that would likely look like bad ideas on the surface.”

It’s important to “validate against robust criteria” and create a broad sweep of ideas. Then, go through and validate capabilities. Some of the questions to ask include: is there research activity relevant to what you’re doing? Is there work in one domain you can transfer to another domain? Has somebody done something in another industry that you can use or in an academic context that you can use?

Organizations also need to figure out whether systems are becoming commoditized in open source; meaning “you have a robust software and infrastructure you can build on without having to own and create it yourself.” Then, the organization must figure out if data is available — either within the company or available to purchase.

Then it’s time to “progressively explore the risky capabilities. That means have a phased investment plan,’’ Mason explained. In machine learning, this is done in three phases, starting with validation and exploration: Does the data exist? Can you build a very simple model in a week?

“At each [phase], you have a cost gate to make sure you’re not investing in things that aren’t ready and to make sure that your people are happy, making progress, and not going down little rabbit holes that are technically interesting, but ultimately not tied to the application.”

That said, Mason said predicting the future is of course, very hard, so people write reports on different technologies that are designed to be six months to two years ahead of what they would put in production.

Looking ahead

As progress is made in the development of AI, machine learning and deep learning, there are still things we need to keep in mind, Mason said. “One of the biggest topics in our field right now is how we incorporate ethics, how we comply with expectations of privacy in the practice of data science.”

She gave a plug to a short, free ebook called “Data Driven: Creating a Data Culture,” that she co-authored with DJ Patil, who worked as chief data scientist for President Barack Obama. Their goal, she said, is “to try and get folks who are practicing out in the world of machine learning and data science to think about their tools [and] for them to practice ethics in the context of their work.”

Mason ended her presentation on an optimistic note, observing that “AI will find its way into many fundamental processes of the businesses that we all run. So when I say, ‘Let’s make it boring,’ I actually think that’s what makes it more exciting.’”

You can watch the complete presentation below:


Blockchain has benefits all the way along the supply chain, said Sally Eaves, of Forbes Technology Council, speaking at Open FinTech Forum.

Blockchain and its ability to “embed trust” can help elevate trust, which right now, is low, according to Sally Eaves, a chief technology officer and strategic advisor to the Forbes Technology Council, speaking at The Linux Foundation’s Open FinTech Forum in New York City.

People’s trust in business, media, government and non-government organizations (NGOs) is at a 17-year low, and businesses are suffering as a result, Eaves said.

Additionally, Eaves said, 87 percent of millennials believe business success should be measured in more than just financial performance. People want jobs with real meaning and purpose, she added.

To provide further context, Eaves noted the following urgent global challenges:

  • 1.5 billion people cannot prove their identity (which has massive implications in not just banking but education as well)
  • 2 billion people worldwide do not have a bank account or access to a financial institution
  • Identity fraud is estimated to cost the UK millions of euros annually.

Blockchain for good

Blockchain has benefits all the way along the supply chain, she said. “Supply chains that are non-transparent, ethical or sustainable are prevalent, especially in developing nations, alongside high levels of illegal trade with no traceability or accountability.” Eaves is focusing on the transparency of blockchains and their integration with other technologies.

More needs to be done in the areas of AI development, blockchain, and cybersecurity with teams that are truly diverse and inclusive, she stressed. People also need to look at interoperability and changing the current approach of “siloed thinking.”  Eaves said she doesn’t believe just in using advanced technologies but in repurposing older technology as well. “I’m all about sustainable, environmental, and economic impact.”

Mining costs, scalability, and performance are other considerations for blockchain, and some of the newer “flavors” of blockchain that Eaves is working on are “dealing with that head on,” she noted.

A particular project she mentioned is the Sustainable Asset Exchange, which addresses ethical mineral supply, ethical diamonds, food, and bamboo as a replacement for plastic, and how that can be traded fairly. Blockchain technology can be used as well as RFID and other technologies at every stage of the supply chain, she said.

All of this is geared at what Eaves called “a triple bottom line,’’ focused on sustainable development in economic, social, and environmental benefits and in bringing them together. “It doesn’t have to be an either or,” she said. “Sometimes, if we talk about one thing or another, we never look enough at how we can integrate them. And that’s what I passionately believe in.”

Headway is being made. Eaves cited research from Stanford University Business School that shows two-thirds of 193 early blockchain projects are expected to start demonstrating impact and tangible benefits in the next six months.

A three-way opportunity for change

Eaves went on to discuss opportunities from technological convergence. Another project she mentioned she is working on is in precision medicine, using blockchain security alongside machine learning to delve into pattern recognition to improve population disease management.

She predicted a rise in blockchain as a service; opportunities from science and technology pairings, such as genomics and blockchain; and opportunities to apply blockchain for social impact and to contribute to Sustainable Development Goals (SDGs).

Blockchain and evolving sustainable business models

The last segment of Eaves’s talk focused on using bamboo as a sustainable replacement for plastic in manufacturing bicycles, as well as in 3D printing and building modular homes.

“We need to make the application of advanced technology accessible to all and make it feel like this is something that is valuable and relevant to our everyday lives – not just something for the few,’’ she said. Her goal is to use blockchain to create sustainable business models that combine profit and purpose and are real-world and relevant to everyone.

We need to have a “cross-fertilization of ideas” from different aspects of the economy and addressing non-advanced technology issues, Eaves said. “You can’t have the best ideas in the world and most advanced forms of technology if we haven’t got the basic infrastructure right,’’ Eaves said. Otherwise, “we won’t get to that point of acceleration.”

You can watch the entire presentation below:

Learn more about blockchain at the upcoming Hyperledger Global Forum. Sign up to receive updates:

Enterprise open source adoption has its own set of challenges, but it becomes easier if you have a clear plan to follow. At Open FinTech Forum, Ibrahim Haddad provides guidelines based on proven practices.

2018 marks the year that open source disrupts yet another industry, and this time it’s financial services. The first-ever Open FinTech Forum, happening October 10-11 in New York City, focuses on the intersection of financial services and open source. It promises to provide attendees with guidance on building internal open source programs along with an in-depth look at cutting-edge technologies being deployed in the financial sector, such as AI, blockchain/distributed ledger, and Kubernetes.

Several factors make Open FinTech Forum special, but the in-depth sessions on day 1 especially stand out. The first day offers five technical tutorials, as well as four working discussions covering open source in an enterprise environment, setting up an open source program office, ensuring license compliance, and best practices for contributing to open source projects.

Enterprise open source adoption has its own set of challenges, but it becomes easier if you have a clear plan to follow. At Open FinTech, I’ll present a tutorial session called “Using Open Source: An Enterprise Guide,” which provides a detailed discussion on how to use open source. We’ll start by answering the question, “Why Open Source,” then discuss how to build an internal supporting infrastructure and look at some lessons learned from over two decades of enterprise open source experience. This session — run under the Chatham House Rule — offers a workshop-style environment that is a mix of presentation and discussion triggered by audience questions. The workshop is divided into five sections, explored below.

Why Open Source?

This question may seem trivial but it’s a very important consideration that even the most open source mature companies revisit regularly. In this part of the workshop, we’ll examine seven key reasons why enterprises should engage with open source software, regardless of industry and focus, and how they can gain incredible value from such engagements.

The Importance of Open Source Strategy

Going through the exercise of establishing an open source strategy is a great way to figure out your company’s current position and its future goals with respect to open source. These strategy discussions will usually evolve around goals you’d like to achieve, along with why and how you’d like to achieve them. In this part of the tutorial, we discuss the many questions to consider when determining your open source strategy and tie that to your product and services strategy for a path to a better ROI.

Implementing an Open Source infrastructure

Once you have identified your company’s open source strategy, you need to build infrastructure to support your open source efforts and investments. That infrastructure should act as a enabler for your efforts in using open source, complying with license, contributing to projects, and leading initiatives. In the workshop, I’ll present these various elements that together form an incredible enabling environment for your open source efforts.

Recommended Practices (17 of them)

When IBM pledged to spend $1 billion on Linux R&D back in 2000, it was a major milestone. IBM was a pioneer in the enterprise open source world, and the company had to learn a lot about working with open source software and the various communities. Other companies have since followed suit, and many more are now entering open source as it becomes the new normal of software development.  The question is: How can you minimize the enterprise learning curve on your own open source journey? We’ve got you covered. In this talk, we’ll explore 17 lessons learned from nearly two decades of enterprise experience with open source software.


Beyond implementing these best practices, open source adoption requires a cultural shift from traditional software development practices to a more open and collaborative mindset. Internal company dynamics need to be favorable to open source efforts. As an open source leader inside your organization, you will face several challenges in terms of funding resources, justifying ROI, getting upstream focus, etc. These challenges often require a major shift in mindset and a lot of education up the chain. We will explore various considerations relating to culture, processes, tools, continuity, and education to ensure you are on track to open source success in your organization.

We hope to see you at Open FinTech Forum for an informative and high-value event.

Sign up to receive updates on Open FinTech Forum:

Join 500+ CIOs, senior technologists, and IT decision makers at Open FinTech Forum, October 10-11 in New York.

The Schedule is Now Live for Open FinTech Forum!

Join 500+ CIOs, senior technologists, and IT decision makers at Open FinTech Forum to learn the best strategies for building internal open source programs and how to leverage cutting-edge open source technologies for the financial services industry, including AI, Blockchain, Kubernetes, Cloud Native and more, to drive efficiencies and flexibility.

Featured Sessions Include:

  • Build Intelligent Applications with Azure Cognitive Service and CNTK – Bhakthi Liyanage, Bank of America
  • Smart Money Bets on Open Source Adoption in AI/ML Fintech Applications – Laila Paszti, GTC Law Group P.C.
  • Adapting Kubernetes for Machine Learning Workflows – Ania Musial & Keith Laban, Bloomberg
  • Real-World Kubernetes Use Cases in Financial Services: Lessons learned from Capital One, BlackRock and Bloomberg – Jeffrey Odom, Capital One; Michael Francis, BlackRock; Kevin Fleming, Bloomberg; Paris Pittman, Google; and Ron Miller, TechCrunch
  • Distributed Ledger Technology Deployments & Use Cases in Financial Services – Hanna Zubko, IntellectEU; Jesse Chenard, MonetaGo; Umar Farooq, JP Morgan; Julio Faura, Santander Bank; and Robert Hackett, Fortune
  • Enterprise Blockchain Adoption – Trends and Predictions – Saurabh Gupta, HfS Research
  • Why Two Sigma Contributes to Open Source – Julia Meinwald, Two Sigma
  • Three Cs to an Open Source Program Office – Justin Rackliffe, Fidelity Investments

Sign up to receive updates on Open FinTech Forum:

Secure your spot now.


Linux Foundation members and LF project members receive a 20% discount on registration pricing. FinTech CIOs and senior technologists may receive a 50% discount on registration fees.

Email for discount codes.

Keynotes announced for Open FinTech Forum, coming up October 10-11 in New York.

Announcing the initial lineup of financial services leaders keynoting at Open FinTech Forum!

Keynote Speakers Include:

  • Brian Behlendorf, Executive Director, Hyperledger
  • Sally Eaves, Chief Technology Officer, Strategic Adviser and Member of the Forbes Technology Council
  • Yuri Litvinovich, Senior Cloud Engineer, Scotiabank
  • Hilary Mason, General Manager of Machine Learning, Cloudera
  • Rob Palatnick, Managing Director and Chief Technology Architect, DTCC
  • Bob Sutor, Vice President for IBM Q Strategy and Ecosystem, IBM Research

Focusing on the intersection of financial services and open source, Open FinTech Forum will provide CIOs and senior technologists guidance on building internal open source programs and an in-depth look at cutting-edge open source technologies including AI, blockchain/distributed ledger and Cloud Native/Kubernetes that can be leveraged to drive efficiencies and flexibility.

The full event agenda will be announced on August 23.

Sign up to receive updates on Open FinTech Forum:

Secure your spot now.


Linux Foundation members and LF project members receive a 20% discount on registration pricing. FinTech CIOs and senior technologists may receive a 50% discount on registration fees.

Email for discount codes.