As you know, last week, we hosted an open Q&A event with our very own Dr. Alessandro Negro – the Chief Scientist at GraphAware and author of the book Graph-Powered Machine Learning. In this short blog post, we’d like to share some of the highlights from the event for those of you who missed it.
The event opened with an introduction from GraphAware CEO, Michal Bachman, who shared his excitement over the perfect timing of the book. To quote, Michal said, more and more people are starting to realize that graphs and machine learning are “a natural fit, a match made in heaven.”
Alessandro also highlighted the perfect timing of the book being published, bringing value for people who are currently using graphs and starting to realize they want to do something more. “As if we could see the future.”
Graph-Powered Machine Learning should definitely be on the to-read list of every data scientist as it talks about new possibilities and opportunities to solve problems in the field. However, the book is also a good read for industry newbies. For instance, the first three chapters provide a broader understanding of the field, and the simple introductory sections in each part of the book introduce the reader to the main problems of recommendation, fraud detection, and natural language processing. The book can also serve as a bible of graph-powered machine learning, referring, and pointing you to many other articles and books that can provide more insight and information on certain topics.
Here are some more event highlights:
Top issues machine learning can help with
One thing that machine learning can do better than humans is processing vast amounts of data faster. Humans, on the other hand, are better at making accurate decisions. Thus, machines and machine learning can be beneficial in dealing with issues that require large amounts of data to be processed fast. The key to solving projects that we could not solve effectively before is the collaboration of machines and humans. This brings us to one of the core beliefs we hold at GraphAware, and share with Alessandro – AI should be understood as augmented rather than artificial intelligence. In other words, machines and humans have the potential to accomplish more complex tasks when working together. We want to help make this happen faster by drawing attention to the human at the center of this collaboration. We believe human abstracting capabilities will be the power of AI in the future.
Scalability = the key challenge to fast adoption of graph-powered machine learning
The key obstacle to the fast adoption of graph-powered machine learning is the dimension of the data many organizations nowadays have. The large amounts of data we have, and keep collecting, translates to the issues of processing it, managing the source of knowledge, finding the right hardware, software, and algorithms to use to process this amount of data. Scalability is thus the next big challenge that will need to be dealt with in the future.
The story behind the cover
The author is a proud Italian from Apulia, who wanted to honor his roots and origin. That is why the cover features a Tarantella dancer – Tarantella is a traditional folk dance of the Apulia region that is still a living tradition today. You can read more about this in the first pages of the book. (Special thanks to Manning, for allowing him to do this as they usually choose the covers of the books they publish themselves.)
Alessandro’s favorite machine learning project
When Alessandro started with machine learning, he wrote the first library on top of Neo4j that provided recommendations. This library, and the approaches used in it, are still applicable now. Yet, recommendations are one of the most needed (across various contexts and industries), and the most challenging machine learning projects. That is why – partly due to the sentiment and the challenging nature and complexity of recommendations – they are still Alessandro’s personal favorite.
The second book
And finally, we need to talk about the second book. This book will be a continuation of Graph-Powered Machine Learning, and will focus on Knowledge Graphs and knowledge graph algorithms. Our whole research team is writing the book, so you’re really in for a treat. The MEAP version should be available by Manning by the beginning of next year, meaning you will most likely be able to read the first chapters in January or February.
Additional resources:
[VIDEO] Watch the recording of the event
[BLOG] Read the overview of the first chapter of Graph-Powered Machine Learning
