How To Create An AI (Artificial Intelligence) Model

Tom Taulli Tom Taulli
July 20, 2020 AI & Machine Learning

Lemonade is one of this year’s hottest IPOs and a key reason for this is the company’s heavy investments in AI (Artificial Intelligence). The company has used this technology to develop bots to handle the purchase of policies and the managing of claims. 

Then how does a company like this create AI models? What is the process? Well, as should be no surprise, it is complex and susceptible to failure. 

But then again, there are some key principles to keep in mind. So let’s take a look:

Selection: There are hundreds of algorithms to choose from. In some cases, the best approach is to use several (this is known as ensemble modelling). 

“Selecting the right model starts with gaining a thorough understanding of what the organization wishes to achieve,” said Shadi Sifain, who is the senior manager of data science and predictive analytics at Paychex. “Selecting the right model often also involves balancing a number of requirements including model performance, accuracy, interpretability, and compute power among other factors,”

It’s important to realize that you need the right kind of data for certain models. If anything, this is one of the biggest challenges in the AI development process. “On average, the data preparation process takes 2X or in some cases 3X longer that just the design of the machine learning algorithm,” said Valeria Sadovykh, who is the Emerging Technology Global Delivery Lead at PwC Labs.

So in the early phases of a project, you need to get a good sense of the data. “Conduct an exploratory analysis,” said Dan Simion, who is the VP of AI & Analytics at Capgemini North America. “Visualize the data in 2-dimensions and 3-dimensions, then run simple, descriptive statistics to understand the data more effectively. Next, check for anomalies and missing data. Then clean the data to get a better picture of the sample size.”

But there is no perfect model, as there will always be trade-offs.

“There is an old theorem in the machine learning and pattern recognition community called the No Free Lunch Theorem, which states that there is no single model that is best on all tasks,” said Dr. Jason Corso, who is a Professor of Electrical Engineering and Computer Science at the University of Michigan and the co-founder and CEO of Voxel51. “So, understanding the relationships between the assumptions a model makes and the assumptions a task makes is key.”

Training: Once you have an algorithm – or a set of them – you want to perform tests against the dataset. The best practice is to divide the dataset into at least two parts. About 70% to 80% is for testing and tuning of the model. The remaining will then be used for validation. Through this process, there will be a look at the accuracy rates.

The good news is that there are many AI platforms that can help streamline the process. There are open source offerings, such as TensorFlow, PyTorch, KNIME, Anaconda and Keras, as well as proprietary applications like Alteryx, Databricks, DataRobot, MathWorks and SAS. And of course, there are rich AI systems from Amazon, Microsoft and Google. 

“The key is to look for open source tools which allow for easy and quick experimentation,” said Monica Livingston, who is the Director of AI Sales at Intel. “If you prefer to purchase 3rd party solutions, there are many ISVs offering AI-based solutions for tasks like image recognition, chat bots, defect detection and so on.”

Feature Engineering: This is the process of finding the variables that are the best predictors for a model. This is where the expertise of a data scientist is essential. But there is also often a need to have domain experts help out. 

“To perform feature engineering, the practitioner building the model is required to have a good understanding of the problem at hand—such as having a preconceived notion of possible effective predictors even before discovering them through the data,” said Jason Cottrell, who is the CEO of Myplanet. “For example, in the case of predicting defaults for loan applicants, an effective predictor could be monthly income flow from the applicant.”

But finding the right features can be nearly impossible in some situations. This could be the case with computer vision, such as when used with autonomous vehicles. Yet using sophisticated deep learning can be a solution. 

“These days, neural networks are used to learn features, as they are better at understanding statistics than humans,” said Eric Yeh, who is a computer scientist at the Artificial Intelligence Center at SRI International. “However, they are not necessarily a panacea and might develop features that were not intended as well. The famous example is the image classifier which was developed to detect tanks and jeeps. Instead, it learned to detect night and day since all jeep photos were taken in the day and all tank photos were taken in the museum at night.”

  • Experfy Insights

    Top articles, research, podcasts, webinars and more delivered to you monthly.

  • Tom Taulli

    Tags
    AI ModelCreateKey Principles
    Leave a Comment
    Next Post
    How to Boost Sales in Your Company

    How to Boost Sales in Your Company

    Leave a Reply Cancel reply

    Your email address will not be published. Required fields are marked *

    More in AI & Machine Learning
    AI & Machine Learning,Future of Work
    AI’s Role in the Future of Work

    Artificial intelligence is shaping the future of work around the world in virtually every field. The role AI will play in employment in the years ahead is dynamic and collaborative. Rather than eliminating jobs altogether, AI will augment the capabilities and resources of employees and businesses, allowing them to do more with less. In more

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    How Can AI Help Improve Legal Services Delivery?

    Everybody is discussing Artificial Intelligence (AI) and machine learning, and some legal professionals are already leveraging these technological capabilities.  AI is not the future expectation; it is the present reality.  Aside from law, AI is widely used in various fields such as transportation and manufacturing, education, employment, defense, health care, business intelligence, robotics, and so

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    5 AI Applications Changing the Energy Industry

    The energy industry faces some significant challenges, but AI applications could help. Increasing demand, population expansion, and climate change necessitate creative solutions that could fundamentally alter how businesses generate and utilize electricity. Industry researchers looking for ways to solve these problems have turned to data and new data-processing technology. Artificial intelligence, in particular — and

    3 MINUTES READ Continue Reading »

    About Us

    Incubated in Harvard Innovation Lab, Experfy specializes in pipelining and deploying the world's best AI and engineering talent at breakneck speed, with exceptional focus on quality and compliance. Enterprises and governments also leverage our award-winning SaaS platform to build their own customized future of work solutions such as talent clouds.

    Join Us At

    Contact Us

    1700 West Park Drive, Suite 190
    Westborough, MA 01581

    Email: support@experfy.com

    Toll Free: (844) EXPERFY or
    (844) 397-3739

    © 2023, Experfy Inc. All rights reserved.