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Alexander Hudek, Co-Founder & CTO of Kira Systems – Interview Series

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Alex Hudek is the Co-Founder & CTO of Kira Systems. He holds  Ph.D and M.Math degrees in Computer Science from the University of Waterloo, and a B.Sc. from the University of Toronto in Physics and Computer Science.

His past research in the field of bioinformatics focused on finding similarities between DNA sequences. He has also worked in the areas of proof systems and database query compilation.

When did you initially become interested in machine learning and AI?

I’ve always been interested in computer science. In undergrad I took courses in algorithms for planning and logic, machine learning and AI, numerical computing, and other topics. My interest in machine learning grew more specifically during my PhD at the University of Waterloo. There, I used machine learning methods to study DNA. Afterwards, I dove more deeply into formal logics as part of my postdoctoral research. Logic and reasoning is in some ways the “other side” of the coin in approaches to AI and I felt it important to know more about it.

Some of your past research in the field of bioinformatics focused on finding similarities between DNA sequences. Could you discuss some of this work?

The main body of my thesis involved building a more realistic model DNA mutation using Hidden Markov Models. I used this more complex model in a new algorithm designed to find regions of DNA that share common ancestry with other species. In particular, this new algorithm can find much more weakly related sequence regions than previous algorithms for the task.

Before my PhD, I worked in a research lab that was part of the human genome project. One of the most notable projects I helped complete was the first complete draft of human chromosome 7.

 

What was the initial inspiration behind launching Kira?

The idea for Kira came from my co-founder, Noah Waisberg. He had spent hours in his career as a lawyer doing the sort of work we’ve now built AI to do. It was an interesting idea to me because it involved natural language and the problem was well scoped, and I could see the business potential. There is something alluring about building AI that can understand human language because language is so closely related to human cognition.

 

Can you describe what Contract Analysis Software is and how it benefits legal professionals?

Kira uses supervised machine learning, meaning an experienced lawyer feeds provisions from real contracts into a system designed to learn from those examples. The system studies this data, learns what language is relevant, and builds probabilistic provision models. The models are then tested against a set of annotated agreements that the system is unfamiliar with in order to determine its readiness. This highly accurate machine learning technology can identify and analyze virtually any provision in any contract, resulting in customer-reported time savings of 20-90%. This increased productivity helps Law Firms by increasing their Realization Rates, gives them more opportunity to grow their revenue and preserve their existing clients. For corporations, it drives better productivity in-house reducing the amount of external legal spend required.

 

Natural Language Processing (NLP) is difficult for most companies, could you discuss some of the additional challenges that are faced when it comes to processing legal terminology and other nuances that are unique to the legal profession?

For many people legal language can seem very foreign, but it turns out that from a machine learning perspective it’s not actually that different. There are a few more unique things; capitalization is more important and sentences can be much longer than normal, but overall we haven’t needed significantly different NLP approaches than in other domains.

One aspect that is significantly different is the need for data privacy and customization. Legal professionals are required to keep client data confidential, and using it in a machine learning product that pools or shares training data is at odds with those requirements. In fact, even keeping training data is often not possible as they have obligations to delete client data after a project concludes. Thus, being able to train models without vendors in the loop becomes critical, as do machine learning techniques that make it hard or impossible to recover any part of the training data by inspecting learned models. Techniques that allow you to take an existing model and update it with new training data without retraining from scratch are also a must have.

On the customization front, there is a need for clients to be able to build their own models. This is because for more complex legal concepts there can be reasonable disagreement among professionals, and firms often want to tune or build models to match their own unique positions.

 

Could you describe how deep learning is used to categorize data within Kira software?

We don’t use much deep learning in our product, though our internal research team does spend a lot of time evaluating and exploring deep learning solutions. So far, on the sorts of problems we face, deep learning techniques are only matching non-deep learning approaches, or at best getting a very small increase. Given the huge computation overhead of deep learning methodologies, as well as challenges in keeping training data private, they haven’t been compelling enough to adopt so far.

 That said, we do find deep learning approaches to be very compelling and we think they have a potential to become big in NLP one day. To that end, we continually evaluate and explore deep learning NLP approaches so that we can be ready to adopt when the advantages start outweighing the disadvantages.

 

What are some of the built-in provision models that Kira offers?

Currently Kira can identify and extract over 1,000 built-in provisions, clauses, and data points (smart fields). They relate to a multitude of different topics, from M&A Due Diligence—which Kira was originally conceived to assist with—to Brexit; to Real Estate. The smart fields are built by our team of subject matter experts that include experienced lawyers and accountants. With our machine learning technology, Kira’s standards require virtually every smart field to achieve a minimum of 90% recall, meaning our software will find 90% or more of the provision, clause or data point you’re specifically looking for within your contracts or documents, reducing risks and errors in the contract review process. In addition, an unlimited number of custom fields can be created/taught by a firm to automatically identify and extract relevant insights using our Quick Study tool.

 

The legal world is often known for being slow to adopt new technology. Do you find that there’s an education hurdle when it comes to educating law firms?

Lawyers really like to know how things work, so education is important. It’s no harder to teach lawyers about machine learning and AI then other professionals, but it is definitely required to have training materials ready. Many of the adoption hurdles are social too; people often ask about best practices in adapting their internal processes to use AI, or are interested in how they can use AI to change their business offerings in a way that gives them advantages beyond just efficiency improvements.

Compared to when we started Kira Systems in 2011, law firms today are far more savvy about AI and technology. Many have innovation teams who are tasked with investigating new technology and encouraging adoption of new solutions.

 

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

Academic literature and open source machine learning libraries were instrumental in helping us bootstrap the company. We believe that open information and software is a huge boon to the world. In light of that, I’m especially happy that our research team publishes the results of many of our research efforts in academic journals and conferences. Aside from demonstrating that we push the boundaries of the state of the art, this allows us to give back to the communities that helped us get started, and that we continue to get a ton of value from. You can find our papers at https://kirasystems.com/science/.

To learn more visit Kira Systems.

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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 the Co-Founder of Securities.io a news website focusing on digital securities, and is a founding partner of unite.AI. He is also a member of the Forbes Technology Council.