There are lots of great text classification packages out there that can help you solve your business problem. For example, deciding on the sentiment of a sentence. However, there is a complication. Many tasks are custom, and therefore there is no pre-trained off-the-shelf model for you to use out-of-the-box. Making a custom text classification model is very challenging for beginners. The differences between the custom machine learning packages are often highly nuanced. As such, significant experience is required to choose between the different possible tools. Furthermore, technical experience is required to implement the solution of choice for your dataset. …
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I recently saw a post on LinkedIn from MIT professor Max Tegmark about a new ML library his lab released. I decided to try it out. The paper is AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity, submitted June 18th, 2020. The first author is Silviu-Marian Udrescu, who was generous enough to hop on a call with me and explain the backstory of this new machine learning library. The library, called AI Feynman 2.0, helps to fit regression…
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1. Abstract: Why AI for Internal Audit and Risk Management?
3. Contemporary Internal Audit Challenges
4. AuditMap.ai: A Platform for Audit Enhancement
5. Limitations and the Way Forward
Internal audit tasks within large organizations are slowed by the volume of documentation. Slow audit response time, sampling-based audit planning, and reliance on keyword searches are all indicators that automation is required to accelerate internal audit tasks. Audit quality also suffers when relevant gaps or risks are not disclosed to stakeholders in a timely manner. This work outlines a workflow automation solution called AuditMap.ai. The…
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3. Dataset and Data Preparation
4.1 Biased Model: A Model with Gender and Name Origin Biases
4.2 Replacing Names in Training Data and at Inference Time
4.3 Augmenting Training Data with Additional Gender Information
This article took a long time to prepare. Thank you to Professor Miodrag Bolic from the University of Ottawa for reviewing this article and providing valuable feedback.
Small business classification means looking at a business name and putting a label onto it. This is an important task within many applications where you want…
Here at Lemay.ai, we know it can be stressful to launch an artificial intelligence initiative. That’s why we’re here to help! We’re a leading AI consulting firm that has the expertise in deep learning, software, and cloud computing to help your business soar. We take a holistic approach, analyzing your full technical infrastructure to identify solutions. At the end of our interaction, you’ll be left with cutting edge AI solutions that will help you stand out amongst the competition, and be a more data-driven organization.
In recognition of our efforts, we’ve been named a leading AI developer by Clutch, a…
Close your eyes, and think in your head about a doll. Now image that same doll can talk. Watch the doll talking. What you just did there in your head — the thing you can see talking — is the merger of the idea of a doll with the idea of talking. As it turns out, we can use artificial intelligence to add concepts together using algebra, and that’s what this article is about. I like to call it “an algebra of ideas”, because we are using basic math to add concepts together, forming compound concepts.
The research findings were interesting. For example:
“73% of People Don’t Trust AI Voice Technology Such as Google Duplex to Make Simple Calls, Though Trust May Build as Usage Increases”
I have some more thoughts on this in the B2C space that I want to put out there. Here we go.
At Starbucks, you pay extra because they are “high end”, and do things like learn…
Science is messy. I don’t think people outside the science field appreciate the ratio of failures to successes. In my work, I very often fail to develop an idea to completion. Sometimes the model doesn’t work. Sometimes the idea is just wrong. Sometimes the idea needs to change. Artificial intelligence is more about experimentation and iteration than it is building up strong and clear solutions from paper to production.
The great tragedy of Science — the slaying of a beautiful hypothesis by an ugly fact. — T.H. Huxley
In this article I’m going to show you a simple way to reason about the predictions made by an image classification neural network model. I have provided you with the code to recreate my findings, but you don’t need to read the code to understand the article (hopefully).
The idea you should get from this article is that you want to have some understanding of what a neural model “sees” or “where it is looking” so that you can believe the predictions it makes with more confidence.
Google’s definition of a retainer is “a fee paid in advance to someone, especially an attorney, in order to secure or keep their services when required.” In this article you will get some insight into how AI consultants can think about adding retainer clients versus engaging in the more standard corporate engagements of defined scope and duration.
There are already some great articles out there on how to select your hourly rate as a consultant, and I’ll try not to replicate that here. Instead, I’m going to hone in on how to maneuver the initial conversations at the lead and…