Tag: AI

AI Scientist

Location: Hybrid

Blooma is a web-based platform that uses Artificial Intelligence (AI) and Machine Learning (ML) to automate the underwriting process and allows CRE professionals to evaluate (and close) more deals in less time. We’re looking for an AI Scientist to help automate information retrieval and forecasting for commercial real estate lenders. In this role, you’ll apply Natural Language Processing (NLP) and Machine Learning technologies to assist in financial processes such as data extraction from unstructured documents, as well as forecasting outcomes for complex financial deals.

You should be interested in building a product from scratch and excited about working with private data. A strong background in Data Science/CS/AI or software engineering/quantitative field with additional specialization in machine learning is required.

Responsibilities 
  • Build and deploy NLP/ML models for data extraction from multiple unstructured and semi-structured documents
  • Process large quantities of complex data and forecast financial outcomes of long-term deals
  • Collaborate with Product and Engineering teams to discuss and design new products, features and enhancements
Required Education & Experience
  • M.S. or Ph D in Computer Science (or relevant field) with a focus on NLP or Machine Learning
  • 5+ years of NLP/ML experience (e.g., Entity Extraction, Topic Modeling, Text Classification)
  • Proven experience in building industry strength systems
  • Proven experience in processing large sets of data
  • Experience in Java is mandatory
  • Experience in the financial industry is a plus

    Resume

    Cover Letter

    Industry Adaptation and Privacy in the New Era of AI

    Philomena Lamoureaux, Head of AI

    How data security is changing everything.

    In a collaboration between BCG and MIT from 2018 (Ransbotham et al., 2018), researchers found that when it comes to artificial intelligence, companies and organizations can be classified into 4 groups: 18% Pioneers, 33% Innovators, 16% Experimenters and 34% Passives.

    Pioneers are enterprises with an extensive understanding of AI tools and concepts, and embrace AI in significant ways. Innovators have a good understanding, but still display little actual application of AI in their business. Experimenters are using AI for their business, but without seeking an in-depth understanding of the AI methods. Finally, Passives lack both in-depth understanding and application of AI technology.

    Interestingly, all four groups agree that AI will change their business model in the next few years. This means that sooner rather than later, AI applications will penetrate the entire corporate landscape. This will be important as AI becomes a pillar of competitiveness, allowing companies to get things done faster and more accurately and to reduce time spent on less desirable work.

    “Private AI technology allows a company to use data to train highly performing models without exposing or sharing confidential data.”

    In the past, laws around data privacy have hampered the evolution of AI applications in many industries which deal with private data, such as sensitive or personal information. Typical examples are the healthcare and financial sectors. In recent years however, emerging technologies have enabled the secure use of private data to build and use AI. These private AI technologies are accelerating the quantity and nature of novel AI applications, and transforming the AI landscape. Thus, Pioneers, Innovators, and Experimenters in the financial sector have started to use AI on their private data.

    Private AI technology allows a company to use data to train highly performing models without exposing or sharing confidential data. Although the field of private AI is very cutting edge, there are a few notable methods already, including differential privacy, homomorphic encryption, federated learning, and data anonymization. For example, federated learning is an advanced, state-of-the-art method used by Google and the healthcare industry (full disclosure: Blooma uses it too). This method involves making use of client data, without the client having to share their data with anyone.

    In a typical set up, a model is deployed to those client locations and trained on the data there. After training, the model is sent back to a central location where it is merged with the models of other clients. After this merge, you end up with a model which has learned from data across all clients without sharing the data in one central place. Federated learning, if done right, ensures that there is no exchange between each individual client’s data. Thus, federated learning allows a company to learn from confidential data while keeping the data secure and private.

    Meanwhile, the introduction of AI into the corporate landscape has led to changes in legislation which continue to develop. The General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) are two statutes that have recently come into play in the attempt to promote and regulate data privacy and security in the European Union and California, respectively. Additionally, Brazil passed the General Data Protection Law that came into effect in February 2020. According to Richard Koch, Managing Editor of the GDPR, EU, these legislations that have begun to emerge have some common principals, including the importance of defining personal data and certain fundamental rights relating to data subjects (2018). Private AI allows us to obey those laws while capitalizing on private data. Whether you consider yourself or your business closer to the Pioneer or the Passive end of things, your data is already a part of this changing landscape, and security will continue to be the driving factor in the future evolution of private AI technology. Despite the many differences between the statutes mentioned above, we can only assume this is just the beginning of the movement toward an increase in legislation to govern data privacy.


    Philomena Lamoureux
    Philomena Lamoureux

    Philomena Lamoureux, Head of AI at Blooma, is a published PhD specialized in ML, NLP and privacy-preserving AI solutions.

    Your Computer Gives You an A for Effort Today

    Tal Almog, Chief Operating Officer

    Using AI to help you make decisions, not make decisions for you.

    I demonstrate our product to many people, so I get to hear a lot of feedback and questions. One of the most potentially confusing aspects of what we do is using artificial intelligence and machine learning to generate a “score” for a certain commercial real estate property or proposed deal. It’s sometimes hard to explain what this really means, so I’ll try again here.

    People sometimes think of the scores that are awarded an Olympic gymnast after a routine – we’ve all wondered about what can sometimes appear to be an arbitrary system of judging something. But we have to remember that this kind of interpretive scoring of a performance is not what is happening in the world of artificial intelligence.

    When we engage with a CRE lender, our AI technology learns their own specific ideal lending profile. Over 3,000 data points are summarized into a “score” for them. The real value is in the automated collection and analysis, of course, and the score itself that is generated at the end is really just an interpretation. A computer can keep track of 3,000 data points and see the result in the data, but a person needs it to be streamlined and summarized. The artificial intelligence doesn’t start out with any idea of what is a good or bad deal, it simply compares all of the data to the lending profile that was preset by the user. By learning what kinds of deals you like, in other words, it can tell you how good a new deal should look to you. Our different customers might score the same transaction in wildly different ways, and perhaps they should.  

    “You may think you know all of the factors that determine whether something fits your ideal profile, but sometimes the AI gets to know them even better than you do.”

    Often times, our customers want the score explained to them. This makes sense – if your child came home with a C on their report card, you might ask them why it is a C rather than a B (or a D). The score itself doesn’t tell the whole story. This is one reason we use the type of machine learning that we do, so that we can go back and explain every data point that led to a conclusion. In other words, we can tell you exactly why your child got a C instead of an A. Some deep learning models (neural networks) don’t really allow for that kind of going back and parsing out exactly where each piece of data contributing to the whole originated. This would obviously not work in regulated environments such as banks where an audit trail is always necessary.  

    Finally, there’s another advantage to teaching AI what you like and then letting it score opportunities for you. You may think you know all of the factors that determine whether something fits your ideal profile, but sometimes the AI gets to know them even better than you do. Data doesn’t lie, and I have definitely seen examples of an AI system being able to “teach” users that there may be certain patterns that they are looking for (or avoiding) that they weren’t even aware of. For example, AI might look at a large number of previous loans and be able to find patterns in the data that led to late payments or even loan default, that you might have not been aware of. Now you can simply use this insight to update your lending profile. It can be disconcerting to learn that the way you’ve been articulating your ideal profile might not be as accurate as you thought. But those of us who are open to learning something new can benefit from this greatly.

    What Does A Valuable AI System Mean To Me?

    Shy Blick, Chief Technology Officer

    Engaging your neural network and decision signature in time for breakfast.

    You are standing in line at your favorite deli and you see that they recently changed the entire menu. There is a long line of people behind you. You feel a slight increase in your stress level as you tell yourself that you have about 20-30 seconds to decide what to order, to be considerate to those waiting behind you.

    Your analytical mind immediately strikes out a few items for being too expensive or not in line with your diet, but you are still left with quite a few items to choose from. In the context of your life, this decision is far from “existential” and therefore not very important. You use your gut/intuition/your fast brain (See Daniel Kahneman’s great book Thinking, Fast and Slow) and decide.

    Who knows why you might order one thing one day and something else the next. You may not even be sure yourself. Much of your day is spent making those non “fight or flight” decisions, at work, at home, and whenever you’re interacting with the world.

    “In reality, to me, a valuable AI system is one that is able to ‘record’ and digitally store a portion of the experience of a specific human, so as to be able to mimic their decision-making process digitally.”

    Without you being aware of it, these sets of small decisions can be made into a “signature” that is unique to you. When asked about you, people may say things like “well, that person is conservative in their choices” or “when buying clothing I should be asking that person, they have the best fashion sense,” etc. This “signature” defines you as a person as much as your physical features, knowledge or abilities do.

    How do you make decisions? The neural network in your brain uses its vast experience, having been exposed to years of life where events continuously impact it. The sum of your senses “teach” your brain to avoid certain things and crave others. The same process takes place in your professional life. Your accumulated experience means that you know how to approach a problem and determine a course of action without a lot of hesitation and without having to spend too much time thinking about it.

    This accumulated knowledge is priceless. We all have these “hidden gems” of knowledge that are unique to us.

    In the movies, artificial intelligence is often a self-aware, auto-evolving entity that craves knowledge and is able to harvest data and make sense of it by itself. Often with a British accent, for some reason. In reality, to me, a valuable AI system is one that is able to “record” and digitally store a portion of the experience of a specific human, so as to be able to mimic their decision-making process digitally.

    In other words, what if we could have the ability to forecast what Albert Einstein would do given a choice of approaches to solve a problem? What if we could consult Andy Warhol on what color to choose for a school art project? Or ask Sun Tzu how to strategize our next Fortnite game?

    A valuable AI system is able to focus on a certain “vertical” of a human’s experience. By using a “fly on the wall” paradigm along with a feedback loop, it should be able, in time, to have the ability to mimic that person’s decision-making process.

    In the not so far future you will walk into your office craving breakfast, and your favorite deli’s delivery drone will meet you at the entrance. It will have one of those new menu items it chose for you based on your unique “digital” decision signature. While you enjoy the delicious item you will probably say to yourself “wow, how did they know exactly what I wanted?”

    1010 S Coast Highway 101, Suite 105
    Encinitas, CA 92024

    888-521-2479
    info@blooma.ai

    We're SOC 2 (Type 1 & 2) Compliant!

    Interested in a demo or have more questions?

    Contact us

    © 2021 Blooma, Inc.