Introduction

AI/ML use-cases are pervasive. The enterprise use-cases can be broadly categorized based on the three core technical capabilities enabling them: Predictive Analytics, Computer Vision (CV) and Natural Language Processing (NLP). The Enterprise AI story has so far been focused on the Cloud. The general perception is that it takes a large amount of data and powerful machines, e.g., Graphical Processing Units (GPUs), to run AI applications.

Edge AI aims to bring all the goodness of AI to the device. The idea is to bring the processing as close as possible to the devices generating the data.

In its simplest form…

Introduction

With the current saturation setting into Deep Learning (DL) methods, there is quite a bit of expectation that Reinforcement (RL) will be the next big thing in AI.

Given that RL based approaches can basically be applied to any optimization problem, its enterprise adoption is picking up fast.

RL refers to a branch of Artificial Intelligence (AI), which is able to achieve complex goals by maximizing a reward function in real-time. The reward function works similar to incentivizing a child with candy and spankings, such that the algorithm is penalized when it takes a wrong decision and rewarded when it…

Debmalya Biswas, Imad Aad, Gian Paolo Perrucci

Abstract. The ever increasing popularity of apps stems from their ability to provide highly customized services to the user. The flip side is that in order to provide such services, apps need access to very sensitive private information about the user. This leads to malicious apps that collect personal user information in the background and exploit it in various ways. Studies have shown that current app vetting processes which are mainly restricted to install time verification mechanisms are incapable of detecting and preventing such attacks. We argue that the missing fundamental aspect here…

Federated learning [1], also known as Collaborative Learning, or Privacy preserving Machine Learning, enables multiple entities who do not trust each other (fully), to collaborate in training a Machine Learning (ML) model on their combined dataset; without actually sharing data — addressing critical issues such as privacy, access rights and access to heterogeneous confidential data.

This is in contrast to traditional (centralized) ML techniques where local datasets (belonging to different entities) need to be first brought to a common location before model training. …

This is an extended article accompanying the presentation on “Open Source Enterprise AI/ML Governance” at Linux Foundation’s Open Compliance Summit, Dec 2020 (link) (pptx)

Open Source Software Enterprise Adoption Trends

The best AI/ML software today from model development (scikit-learn, TensorFlow, PyTorch) to deployment (Kubeflow, Spark) is Open Source. According to Gartner, “more than 90% of companies rely on Open Source Software”. The below snapshot should give you an idea of the pervasiveness of Open Source Software (OSS) in the Enterprise.

Abstract. With the growing adoption of Open Source based AI/ML systems in enterprises, there is a need to ensure that AI/ML applications are responsibly trained and deployed. This effort is complicated by different governmental organizations and regulatory bodies releasing their own guidelines and policies with little to no agreement on the definition of terms, e.g. there are 20+ definitions of ‘fairness’. In this article, we will provide an overview explaining the key components of this ecosystem: Data, Models, Software, Ethics and Vendor Management. We will outline the relevant regulations such that Compliance/Legal teams are better prepared to establish a comprehensive…

Making Sense of Big Data

Abstract. We present a Reinforcement Learning (RL) based approach to implement Recommender Systems. The results are based on a real-life Wellness app that is able to provide personalized health / activity related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features, e.g. user sentiment, and scenarios where the RL reward needs to computed based on multiple and unreliable feedback channels (e.g., sensors, wearables). …

Abstract. Heating, Ventilation and Air Conditioning (HVAC) units are responsible for maintaining the temperature and humidity settings in a building. Studies have shown that HVAC accounts for almost 50% energy consumption in a building and 10% of global electricity usage. HVAC optimization thus has the potential to contribute significantly towards our sustainability goals, reducing energy consumption and CO2 emissions. In this work, we explore ways to optimize the HVAC controls in factories. Unfortunately, this is a complex problem as it requires computing an optimal state considering multiple variable factors, e.g. the occupancy, manufacturing schedule, temperature requirements of operating machines, air…