Episode 120: Your AI Insurance Appraiser Has Arrived with Tractable CEO, Alex Dalyac


This post is by David Poole from Georgian Partners

The process for making a car insurance claim is slow and cumbersome. Waiting as an appraiser with a clipboard goes over the damage, wondering how their biases and human fallibility might affect the outcome. That painstaking process includes data collection though – and it turns out that dataset is the perfect training ground for AI.

Alex Dalyac is our guest on this episode of the Georgian Impact Podcast. He’s the Co-founder and CEO of Tractable AI. Until recently humans had the edge over AI when it comes to image classification tasks – but the scales have now tipped in the computer’s favor. Tractable is leveraging that fact to revolutionize the way damage from automotive accidents and natural disasters is assessed.

You’ll hear about:

  • How accident and disaster damage appraisals could be 10x faster using AI.
  • Why Tractable chose to pivot their AI’s strengths from plastic pipes to the insurance industry.
  • How Alex and his team convinced competing insurance companies to pool their data – and how they keep that data safe.
  • The challenges of selling in such a consolidated industry.
  • Tractable’s approach to improving trust and transparency.

Listen to every episode:
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Who is Alex Daylac?

Alex Dalyac is the Co-founder and CEO of Tractable AI , an artificial intelligence company specialized in visual tasks for traditional industries. The company’s current focus is insurance and automotive, where its AI predicts the cost to repair a vehicle based on photos of the damage. Its products are used by leading insurers in Europe and North America. Tractable was spun off from Alex’s research at Imperial College London, where he led the Computing department’s first industrial application of deep learning. Prior to research, Alex was at hedge fund Quant.

The post Episode 120: Your AI Insurance Appraiser Has Arrived with Tractable CEO, Alex Dalyac appeared first on Georgian Partners.

Episode 116: Level Up with Machine Learning


This post is by Jon Prial from Georgian Partners

Machine learning isn’t just for the Googles and Facebooks of the world. But how can startups (or even, growth equity investment firms 😃🤓) do data science right?

Our guest on this episode of the Georgian Impact Podcast is Ji Chao Zhang. He says that data scientist may be the most important job of the 21st century – but also the least understood. Luckily, Ji Chao understands it better than most – he’s the Director of Software Engineering here at Georgian Partners, and he and his team have consulted with scores of companies around our thesis areas including Applied Artificial Intelligence.

You’ll hear about:


Listen to every episode:
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Who is Ji Chao Zhang?

Ji Chao Zhang is Georgian Partners’ Director of Software Engineering and a member of the Georgian Impact team. In that role he leads our internal software engineering efforts and supports portfolio engagements.

Prior to joining Georgian Partners, Ji Chao was a Software Development Engineer at Amazon, where he worked on the design and development of the data platform, business analytics and machine learning systems to support supply chain optimization and fulfillment.

Ji Chao holds a Master of Computer Science in computer software engineering Continue reading Episode 116: Level Up with Machine Learning

Building Effective Machine Learning Teams


This post is by David Poole from Georgian Partners

The post Building Effective Machine Learning Teams appeared first on Georgian Partners.

A Practical Guide to Improving User Retention


This post is by Evan Lewis from Georgian Partners

A Practical Guide to
Improving Product Retention

In both of the startups I have been involved with, I learned the hard way that there is a massive difference between customer retention and user retention. Particularly in B2B SaaS, startups can scramble to retain customers even if their user adoption isn’t high. You can ask customers to trust you and promise that things will get better, but if you don’t address the underlying issue of user retention, you will inevitably see customer churn that will stunt your growth.

In my last role as a product manager, one of the things I struggled with was figuring out which metrics were most important to measure. We knew that user retention was a bit of a blind spot, so we started there and set out to see the light.

We began by looking at the number of users logging in during a given month as an indication of retention. Seems logical, right?

Well, that number was skewed drastically because many customers had Single Sign-On for our app. Our system indicated they had “logged in”, even if they hadn’t actually used the product or gotten any value from it.

Next, we thought to look at the number of users that were performing the core action in a given month. Surely, that would be a good indication of retention!

Wrong. Just because someone performs an action once in a month doesn’t mean they Continue reading A Practical Guide to Improving User Retention

Episode 115: Ghost Work: the Hidden Workers of AI


This post is by Jon Prial from Georgian Partners

The Gig economy. We know what that means. Outsourcing jobs. We know what that means. Working remotely. We know what that means. But what happens when all three are combined?

Our guest on this episode of the Georgian Impact Podcast, Mary Gray, co-authored Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass and if you are growing your Machine Learning and AI investments Mary’s got some thoughts on the bad and the good behind what type of workers and workforce you might be needing to be able to proudly talk to your customers about the company you strive to be.

You’ll hear about: 

  • The manual work behind many AI projects
  • A new model for employment relations
  • How micro-employment platforms guarantee the skills you need
  • How to do good by your contract employees

 

Listen to every episode:
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Who is Mary Gray?

Mary Gray is Senior Principal Researcher at Microsoft Research and a fellow at the E.J. Safra Center for Ethics Fellow and Berkman Klein Center for Internet and Society Faculty Affiliate at Harvard University.

Using the tools of anthropology and critical media studies, Mary looks at how material conditions and everyday uses of technologies transform people’s lives.

Her most recent book, “Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass,” co-authored with computer scientist Siddharth Suri, explores the lives of people paid to train artificial intelligence and, Continue reading Episode 115: Ghost Work: the Hidden Workers of AI

Acquiring Top ML Talent


This post is by Kathryn Christie from Georgian Partners

Acquiring Top
ML Talent

Describing the battle for talent in the ML space as competitive doesn’t do it justice. Impossible might come closer. Salaries are huge, and talent is scarce; it’s a job-seekers market. Anyone who has experience and talent is fielding multiple offers with eye-watering salaries.

Because of this, a lot of people are understandably trying to break into ML. Previously, this work was only for PhDs; nowadays online courses make it easier to get qualified, and all major universities offer data science master’s programs. Unfortunately, the quality of these junior candidates is highly variable, and most recruiters can’t tell who is the real deal. A candidate can have all the right buzzwords on their resume but lack the business knowledge and expertise that’s needed to succeed.

Since job titles are not standardized, it’s important to dig into what applicants have actually been doing. Inflated titles are all too common, and it takes time to sift through resumes and screen candidates.

There are, though, candidates who find the call of a startup environment appealing. They want to have a big impact, determine the product vision and see their work in production. The game is to unearth these gems.

You can follow the tips in this blog to attract talented individuals to your organization.

 

There are, candidates who find the call of a startup environment appealing. They want to have a big impact, determine the product vision and see their work in production. Continue reading Acquiring Top ML Talent

Building ML Teams Resources


This post is by David Poole from Georgian Partners

Resources to Build
an ML Strategy
and Team

We’ve pulled together twelve top resources to help you set the vision, prioritize opportunities, and hire a remarkable team. To get started with AI, there are critical steps that must be taken to ensure success.

Understanding the feasibility of the opportunity comes first, and having the right team is next. If you are considering AI for your business, these resources are an excellent place to start.

Strategy Resources

1. Georgian Partners Principles of Applied AI

This white paper introduces our ten principles of applied AI and lays out a detailed framework for getting started. The paper shows how you can avoid common mistakes and provides you with a method of measuring your progress. You’ll gain a better understanding of what business AI problems can solve and what it would take for you to implement a solution.

2. Andrew Ng’s AI Transformation Playbook

Andrew Ng’s AI Transformation Playbook delivers insights gleaned from his days leading the Google Brain team as well as the Baidu AI Group. By following the Playbook, Ng says that any AI-focused buisiness can gain the same kind of momentum and success he details here. The Playbook has a strong focus on building an in-house AI team as well as how companies can leverage it to their best advantage.

3. AI Canvas Presentation

The AI Canvas is a methodology that helps you build business cases for AI projects. It will prove your concept and Continue reading Building ML Teams Resources

The Metrics that Matter for Growth Stage Startups in 2020


This post is by Evan Kerr from Georgian Partners

Early lessons on SaaS metrics

Early in my career in growth equity, I was (willingly) pulled into the rabbit hole of SaaS metrics.

As strange as it might sound, the change of pace was actually refreshing to me, as SaaS metrics felt relatively straightforward at the time. No complex DCF or LBO models and only a handful of inputs to worry about? As a former investment banker, I considered this a win.

I was also excited when it dawned on me that SaaS metrics don’t change much… as in almost never. David Skok published his widely-read SaaS Metrics 2.0 framework over a decade ago in 2008. That article remains highly relevant to this day.

As I developed a deeper understanding of SaaS metrics, I read many blogs and frameworks on the web. If you’ve done any Googling in this realm, you’ve probably reached the same conclusion I did: everything that could possibly be said already has been. So, once I had found my favorite articles and formed my perspective, I stopped pushing myself to learn more on the subject. In other words, I thought I had it covered.

In hindsight: boy, was I wrong.

Fast-forward a few years, I can tell you that SaaS metrics are anything but straightforward. There are important variances in the way that each company calculates and perceives them. Board members often disagree on which metrics are most important and provide mixed guidance to entrepreneurs. And even if you get the calculations right, knowing what Continue reading The Metrics that Matter for Growth Stage Startups in 2020

Constrained Optimization: How to Do More with Less


This post is by Chang Liu from Georgian Partners

What is Constrained Optimization?

Constrained optimization is a field of study in applied mathematics. You can use the tools it provides wherever you need to allocate scarce resources efficiently in complex, dynamic and uncertain situations. Think of it as the mathematical solution to the question: how do you do more with less?

For instance, have you ever wondered how you would make the best product recommendation to an individual customer? You might have 10,000 products and 10,000 customers. On top of that, each customer has their own preferences and you have limited inventory for each product. This is the type of problem where constrained optimization shines.

In these types of problems, there are three components:

  1. Variables: which product gets recommended to which customer?
  2. Constraints: each customer has their own preferences and each product has limited inventory.
  3. An objective: how to maximize total customer satisfaction.

Depending on the variables, constraints and objectives, there are different methods, with commercial and open-source solutions available for each. They’re are often categorized under linear programming (LP), quadratic programming (QP), mixed integer programming (MIP), constraint programming (CP) and others.

Constrained Optimization vs. Machine Learning

Constrained optimization is not machine learning. Here’s how the two differ:

  • Constrained optimization helps with making decisions while machine learning helps with making inferences
  • Constrained optimization does not learn from data
  • Constrained optimization does not depend on the amount of data, but on the availability of information

How Constrained Optimization Helps in a SaaS Company

Everyone knows the value of data Continue reading Constrained Optimization: How to Do More with Less

Episode 114: What Makes a Successful AI Project?

In this week’s podcast Jon Prial is joined by Tara Khazaei, Chief Data Scientist, National AI Team, Customer Success Unit at Microsoft. Jon and Tara talk about how domain knowledge, as well as statistical intuition, make for more successful outcomes in machine learning projects. They discuss performance through the lens of projects Tara and her team have led at Microsoft. For instance, you’ll hear about projects the Microsoft team led to improve customer service at a bank, to improve bus time departure estimates and to predict opiate overdoses. Through their conversation, you’ll hear what makes a successful AI project.

In this episode you’ll hear:

Episode 112: Designing for Humans and Machines

Human-centered design has helped to make truly great products, by designing the product so that the user has a great experience and perceives value. Now, to be successful, we have to design products that consider intelligent machines as users and optimize the interactions they have with human users.

In this episode of the Georgian Impact Podcast, Jon Prial and Lindsay Ellerby, Senior Designer at Normative, discuss the concept of centaur design – designing solutions for a world of AI-human hybrid systems.

You’ll hear about: 

  • What EQ looks like in machines
  • How to think about the user in an age of intelligent machines
  • How to create positive human-machine interactions
  • What centaur design is and how it will impact the future of design

 

Listen to every episode:
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Who is Lindsay Ellerby?

Lindsay Ellerby is a designer who excels Continue reading Episode 112: Designing for Humans and Machines

What IoT Means for the Future of Technology Businesses

What IoT Means for the Future of Technology Businesses

What is IoT?

IoT (Internet of Things) refers to the expansion of internet connectivity and digital capabilities into objects — from thermostats to glucometers to industrial equipment to sensors for autonomous vehicles. Effectively, IoT merges the physical and digital worlds, with human-to-object and object-to-object connections that can power ambient intelligence.

Affordable sensors and devices, advances in networking and the growing ubiquity of devices such as mobile phones and wearables have set the stage for IoT to grow. IoT businesses show hardware and software beginning to merge in domains such as smart home applications, a trend that is likely to continue.

How IoT Produces Value

IoT produces value by enabling new digital and physical experiences and reducing friction in existing ones. Here are some of the key drivers:

IoT-Value-Infographic_V1-Mobile

Operationalizing Responsible AI

I recently spoke on a panel at the TWIML AI Platforms Conference discussing how to operationalize responsible AI with Rachel Thomas (Center for Applied Data Ethics),  Guillaume Saint-Jacques (LinkedIn) and Khari Johnson (VentureBeat). It was a great discussion and we touched several different topics. These included:

  • Creating a company vision and values around responsible AI
  • The biggest challenges and techniques to address them
  • Why team diversity is so important

Creating a culture of trust

We started our conversation by discussing the steps that every organization should take to lay the groundwork for the responsible development of machine learning and AI systems.

At Georgian Partners, we believe companies should begin by creating a clear vision where trust is your guiding light. This means developing a business model that seeks to optimize both the value of your offering and the comfort level of customers or end-users of your Continue reading Operationalizing Responsible AI

How to Build a Mature Machine Learning Organization

How to Build a Mature Machine Learning Organization

The Georgian Impact team adds value to our portfolio companies by accelerating their adoption of major trends such as applied AI. We regularly assess how we can be most effective and have the biggest impact through our work, whether it’s advisory work, applied research engagements or the development of software products.

We’ve spent a lot of time thinking about the paths that software companies take towards maturity in artificial intelligence and machine learning (AI/ML). As a result, we created an AI/ML maturity framework for companies to plot their own maturity and identify activities to progress to the next stage.

Why Did We Create The Framework?

We use this maturity framework at Georgian to identify how we can help our companies adopt AI. Working with them, we help to set the strategy and identify the highest value

OpportunityMatrix_V1
Principles of Applied AI_300-393
image-iphone-mockup

Continue reading How to Build a Mature Machine Learning Organization

Episode 109: Who’s Who in your Data with Jeff Jonas

A lot of effort goes into identifying who we are almost from the very moment we’re born. Birth certificates, passports, fingerprints, now facial recognition. So why does it still feel like companies don’t know us? Why do they send the same offer several times? Shouldn’t it be easy for CRMs to get it right? Well, it can be if you cleanse your data using entity resolution software.

In this episode of the Georgian Impact Podcast, Jon Prial talks with Jeff Jonas, CEO of Senzing. Jeff has worked in the area of entity resolution for almost 35 years. Over that time he’s worked with casinos, government agencies and enterprises.

You’ll hear about: 

  • Why entity resolution is a harder problem than you’d imagine
  • How it can be used to detect financial and voter fraud and identify terrorists
  • Why privacy by design is core to Senzing’s thinking

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