Reducing AI Bias with Rejection Option-based Classification

Recently, I started writing a series of posts exploring bias in AI and different ways to mitigate it in a workflow in greater detail. In my last two blogs, I covered reweighing as a mitigation technique within the pre-processing stage of modeling, and adversarial debiasing during the in-processing (model training) stage of the machine learning workflow.
The third stage in the machine learning (ML) pipeline where we can intervene to reduce bias is called post-processing. Post-processing algorithms are mitigation steps that can be applied to the model predictions. On Fairness and Calibration [1], Equality of Opportunity in Supervised Learning [2] and Decision Theory for Discrimination-aware Classification [3] are among the different post-processing bias mitigation techniques proposed from academic literature.

#artificial-intelligence #machine-learning #data-science

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Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

This Week in AI - Issue #22 | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.Have fun!

Research Papers

• Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
• Slowing Down the Weight Norm Increase in Momentum-based Optimizers
• The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
• AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
• Coherent Reconstruction of Multiple Humans from a Single Image
• Variational Bayesian Monte Carlo with Noisy Likelihoods
• GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning
• Are we done with ImageNet?
• How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds
• Background Modeling via Uncertainty Estimation for Weakly-supervised Action Localization
• The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
• Image Deconvolution via Noise-Tolerant Self-Supervised Inversion
• Learning a Unified Sample Weighting Network for Object Detection

Articles

• Deepfake Detection Challenge Results: An open initiative to advance AI
• Slightly Unnerving AI Produces Human Faces Out of Totally Pixelated Photos
• NeoML Released as TensorFlow Alternative
• Google Meet takes on Zoom with AI-powered noise cancellation
• Open Compound Domain Adaptation
• Dense pose for animal classes with transfer learning
• 10 Ways Enterprises Are Getting Results From AI Strategies
• An understanding of AI’s limitations is starting to sink in
• Why we switched from Flask to FastAPI for production machine learning
• Simple Transformers
• The key differences between rule-based AI and machine learning
• Deep learning-based surrogate models outperform simulators and could hasten scientific discoveries
• HANDS-ON GUIDE TO OBJECT DETECTION USING YOLO
• Artificial neural networks that mimic the flexibility and computing power of the brain
• MIT researchers train AI to predict how humans paint works of art
• What Do Machine Learning Engineers Do Anyway?
• AI Is Amazing But Complicated, And We Don’t Necessarily Need To Plunge In Headfirst
• Deep learning networks track up to 100 animals simultaneously
• Is artificial intelligence changing art?

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

Amsterdam And Helsinki Launch Open AI Registers

Amsterdam and Helsinki both launched an Open AI Register at the Next Generation Internet Summit. According to sources, these two cities are the first in the world that are aiming to be open and transparent about the use of algorithms and AI in the cities.

Currently, in the beta version, Algorithm Register is an overview of the artificial intelligence systems and algorithms used by the City of Amsterdam. The register is an effort to show where the cities are currently making use of AI and how the algorithms work.

Jan Vapaavuori, Mayor of Helsinki stated, “Helsinki aims to be the city in the world that best capitalises on digitalisation. Digitalisation is strongly associated with the utilisation of artificial intelligence. With the help of artificial intelligence, we can give people in the city better services available anywhere and at any time. In the front rank with the City of Amsterdam, we are proud to tell everyone openly what we use Artificial Intelligence for.”

#news #ai register #amsterdam ai #helsinki ai #open ai register #ai

Fact-Based AI In A Nutshell

Using Fact-Based Modelling to Kickstart One-Shot Learning

An earlier article, Fact-Based AI — Improving on a Knowledge Graph, I provided a vision for Fact-Based Modelling’s future in AI while providing background knowledge to digest. Here we get straight to the facts.

It has become apparent within AI research that Machine Learning (ML), Deep Learning (DL), and Neural Networks, in general, are not mechanisms that lend themselves readily to “one-shot learning”.

Neural Networks, in general, must be trained on large sets of training data resulting in a mechanism which provides a probabilistic result based on live data that approximates the training data set.

Train a suitable neural network on a set of images of a panda, then provide a new image of a panda and the neural network will give a probability of what it believes the new image to be, and hopefully that prediction is ‘a panda’.

The less training data provided the neural network, no matter how optimised it is in its inner structure, the less chance that it will provide a favourable result. That is, you cannot reliably provide a suitable neural network with the picture of just one panda and expect it to recognise another and different picture of a panda. In general, it cannot learn a concept, or set of rules, in one shot.

This is heavily contrasted with human learning, in which a person can readily grasp and apply knowledge learned, having only been introduced to the problem space once. This is true over a vast domain of problem spaces.

One-shot learning is a recognised problem for Machine Learning and Deep Learning, and experts in the field are working on ways to overcome that problem. It seems unlikely to me that ML/DL strategies will get to Artificial General Intelligence (AGI) without some form of one-shot learning.

A problem within ML/DL approaches is that data, by way of accumulated weightings, is passed through a network of nodes and where little logic is applied to the data at each node, but rather statistics-based math is applied to the values of the accumulated weightings received by the node. The result of the applied statistical math over input data provides output data graded by probabilities, providing a probabilistic logic over the input data.

#fact-based-modelling #fact-based-ai #artificial-intelligence #machine-learning #ai