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Jupyter Con - New York 23-25 Aug









View Natalino Busa's profile on LinkedIn





Principal Data Scientist, Director for Data Science, AI, Big Data Technologies. O’Reilly author on distributed computing and machine learning.



Natalino leads the definition, design and implementation of data-driven financial and telecom applications. He has previously served as Enterprise Data Architect at ING in the Netherlands, focusing on fraud prevention/detection, SoC, cybersecurity, customer experience, and core banking processes.


​Prior to that, he had worked as senior researcher at Philips Research Laboratories in the Netherlands, on the topics of system-on-a-chip architectures, distributed computing and compilers. All-round Technology Manager, Product Developer, and Innovator with 15+ years track record in research, development and management of distributed architectures, scalable services and data-driven applications.

Wednesday, January 11, 2017

AI Q&A: Natalino Busa

In preparation to my next talk at the Global Artificial Intelligence(AI) Conference on January 19th, January 20th, & January 21st 2017, I have written down a few thoughts on AI and how it could possibly contribute to the enterprise and in particular to Financial and Telecom services. See you guys live in California, Santa Clara!

PS. I have 5 complimentary tickets for the AI enthusiasts among you! 
Send me a message via linkedin for the promo code. Cheers - Natalino

Natalino, tell us about yourself and your background.
I have worked in industrial research at Philips, where I spent several years researching and developing on-chip distributed computing algorithms, especially applied to video, image and audio processing. When I moved on in my career path, I held on my analytical background, and it turned out to be a wise move. When Big Data, Machine Learning and modern AI have emerged as part of the digital transformation in the past decade, I was happy to apply distributed computing and machine learning techniques on large datasets to solve enterprise challenges and provide innovative solutions for banking and telecom applications.

What have you been working on recently?
I have worked on streaming computing, in particular using the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). Also I have been quite busy with anomaly detection techniques for the financial market, in particular detecting fraud and cyber security attacks in retail banking. I have spent quite some time lately in understanding how dimensionality reduction, manifolds and topological analysis can be applied to bank transactions in order to extract patterns and efficiently cluster data.

Tell me about the right tool you used recently to solve financial customer problem?
I have been using a number of mixed Machine Learning and AI techniques. For example, I have been using DBSCAN clustering and stacked auto-encoders for features extraction, I have also done some data exploration and visualization using TDA and T-SNE. Tensorflow, Keras and Scikit-Learn are great tools to analyze financial datasets and identify payment clusters.

Where are we now today in terms of the state of artificial intelligence, and where do you think we’ll go over the next five years?
Artificial Intelligence today works extremely well under two conditions: the availability of large amount of data/examples, and labeled data (supervised learning). Given the current state of the art in AI, these are the typical conditions to train/learn AI systems, and in particular deep neural networks. I think that this alone will provide a tremendous boost in areas where data can be accumulated easily. However, when the data is scarse, the current deep learning and AI methods are less effective. I do expect more abstraction in the the next 5 years, as we have learned AI to read, write, speak, and listen, the next goal, in my opinion should be learn AI to perform abstract thinking.

There is a negative perception around AI and even some leading technology folks have come out against it or saying that it’s actually potentially harmful to society. Where are you coming down on those discussions? How do you explain this in a way that maybe has a more positive beneficial impact for society?
The negative perception of AI usually rests on two interpretations. Let's discuss them one by one. The first one is "It's harmful because machines will replace people". Actually, this is true, but this is hardly news. For centuries, in our progress we have created machines to reduce labour and manual work and so far this has only provided more work, albeit different sort of works. AI is not very different in this respect than what has happened during the industrial revolution with steam engines and factories. The second negative interpretation of AI is "It's harmful because machine will autonomously take decisions about and for people." I think that this second danger is indeed real and we need to make sure that AI systems are ethically and legally compliant. I believe that AI should be part of monitor and control mechanisms.  We have to make sure that AI is not seen as a free-running system but rather as very powerful data analysis tool that we can use in order to accelerate progress, for instance in science and healthcare. Personally I see AI as a sort of a "data lens" that we can put on when we want to better understand the patterns in our datasets.

When you’re hiring, what types of people are you hiring? The job market for traditional programmers, engineers is  very difficult to get into AI space. Are you hiring from that talent pool or is that a different talent pool? In terms of talent, how do you go about ensuring you get the best AI people at your company?
A good understanding of Machine Learning and Data Science is fundamental to step into the wider AI space. In particular, considering that AI and Deep Learning are very much hyped, it's extremely important to be to critical on the methods used, rather than blindly applying one ML algorithm after the other. Notions such as over/under-fitting, (cross) validation, cost optimization must be well understood, especially if the candidate comes from an engineering background. Practical experience is also paramount as AI, Machine Learning and Deep Learning require coding and hands-on swiftness. So a good mix of theory and practice will get you there, provided sufficient study and practice.

Will progress in AI and robotics take away the majority of jobs currently done by humans? Which jobs are most at risk?
Definitely some of the jobs we have today might disappear in the future. Some attempts are already happening: think for instance of the race for "chatbots". Secretarial, call centers, and clerk jobs could be replaced by machines. Ironically, data scientists, in particular data analysts who collect data and provide reporting and aggregated results on data could also be be replaced by AI systems. In general, all tasks where an AI is able to extract and learn the "recipe" can be potentially done by machines.

What can AI systems do now?
AI systems today are extremely good at recognize patterns and learn from examples. They can work extremely well at detecting anomalies, recommend items and actions based on past history. The latest AI models are quite good at extracting semantic and meaning from organic data such as videos, images, text and speech. This AI system are currently used to categorize content, in many applications ranging from security to forensics to predictive maintenance to social networks and personalized marketing.

When will AI systems become more intelligent than people?
One day for sure. 
When this singularity will happen, I can't really tell, but not in our lifetimes in my opinion.

Which AI scientists do you admire the most?
I love the roots of the field, in particular Yoshua Bengio and Peter Norvig for the foundational work they have done in the past 30 years. From the new school my favs are Richard Socher and Nando de Freitas.
You’ve already hired Y number of  people approximately. What would be your pitch to folks out there to join your Organization? Why does your organization matter in the world?
Data is the new electricity which is powering the world. And AI, ML and Data Science tools are the turbines and the engines which are conveying this electricity and transforming it in the most amazing Financial and Telecom Applications. The slogan? "AI for Finance: Help the customer, Protect the customer"

Is AI going to change Financial Services?
Yes I do think so. Most financial services currently are very basic, in the way they interact with customers. AI will provide new ways to better understand the need and the behavior of individual and provide relevant, personalized and helpful hints for everyone to better manage their financial life.

Is Deep Learning directly applicable to finance?
Yes, many use cases already have accumulated large amounts of data to work on and the outcomes/labels are known (aka supervised learning). For these use cases Deep Learning can be applied straight away. However there are still some scenario where the data is not that abundant and where the deep learning techniques need to operate in an unsupervised way. We need more results from the scientific community on that front.

What are some of the best takeaways that the attendees can have from your "AI and Big Data in Commerce"  talk?
- There is definitely tons of data which is currently unused. - AI can be effectively apply to existing hard problems such as better regulatory compliance, risk management, fraud and cyber security defence. - AI works well in less "sexy" domains such as Financial Services. - Tensorflow, Keras, T-SNE and Scikit-Learn are great tools to build and train AI applications for financial services.

What are the top 5 AI Use cases in enterprises?

  • Personalized recommenders, 
  • Cybersecurity, 
  • Personalized Marketing,
  • Operational Excellence, 
  • Predictive Services
Which company do you think is winning the global AI race?The one with the largest group of AI scientist/engineers, and the biggest collection of data.

Any closing remarks?
Looking forward to see you guys live in Santa Clara, California at the Global Artificial Intelligence(AI) Conference on January 19th, January 20th, & January 21st 2017