How To Make Autonomous Cars Trustworthy — IEEE Standards Association (07 Apr 2021)

Lately, reinforcement learning has been a source of controversy as to whether reward is enough to take appropriate “intelligent” decisions. Reinforcement learning (RL), which does not require historical and/or labelled data, when compared to deep learning, is based on the reward paradigm where the agent (self-driving vehicle) is rewarded as it navigates through its environment. The reward can be computed by measuring the quality (value) of the overall performance of its navigation in its environment. The aim is to get the agent (the vehicle) to act in its environment so as to maximize its reward while also considering the long…


Mandelbrot fractal set. Benoit Mandelbrot is a pioneer of fractal geometry — Hunting the Hidden Dimension — PBS

Both ML and AI involve feature extraction procedures, either explicitly or implicitly, such as shape or texture features extraction. However, such features often exhibit complex and fractal behavior that can be a challenge to capture or extract using Euclidean geometry.

Fractals everywhere — Hunting the Hidden Dimension (clip)—PBS NOVA

Addressing such complexity using feature engineering based on fractal geometry can help tremendously to better leverage ML and explain AI. This was based on an early work that I have conducted with colleagues using Matlab and Neural Network with fractals to capture features of gene expression morphogenesis. …


R programming language

R is a functional programming and scripting language written in a functional style to help to understand the intent and to ensure that the implementation corresponds to that very intent. R is becoming an essential tool for research, finance and analytics-driven companies such as Google, Facebook, LinkedIn, IBM, Microsoft and Amazon. There is a growing importance of R based on survey conducted by IEEE. Goggle used R to demonstrate the value of massively parallel computational infrastructure for forecasting based on the MapReduce paradigm. …


In addition to energy savings, IoT applications can help in water savings of up to 230 billion cubic meters of water, mostly in agriculture sector, by the year 2030. This is based on a recent report on “Sustainability in new and emerging technologies”. [1]

IoTs’ contribution in saving water and managing water scarcity will be enormous, in addition to their contribution in real-time maintenance. The world’s largest dams may have a water storage capacity of 100 billion cubic meters or more, such as the the Daniel-Johnson dam in Quebec. The reservoir surface area of the dam covers nearly 2000 km2…


Climate Adaptation Summit 2021 took place on 25 January this year (2021), to accelerate the implementation of the 2015 Paris Agreement on Climate Change. The summit started, in the opening sessions, with the urgency to accelerate the implementation of the Paris Agreement. Economy is the Environment and Education (People). Time is limited to catch up and to keep the world under the 2 degrees Celsius, a pledge that was made five years ago.


Mathematics of Big Data (Isaac Newton Institute)

Big Data is growing unprecedentedly so rapidly that is now bypassing concepts and we have to catch up to have better insights. As large data sets are growing unprecedentedly massively, thus the name “Big Data”, development and elaboration of mathematical conceptual/theoretical frameworks will be required to catch up to turn these large data sets into actionable insights.

“The trouble is, we don’t have a unified, conceptual framework for addressing questions of data complexity…Big data without a “big theory” to go with it loses much of its usefulness, potentially generating new unintended consequences.” Geoffrey West (2013)

Data sets are revealing more…


Edge and fog analytics are on the rise as a result of an unprecedented increase in data capture and data flow from the Internet of Things devices. These IoT devices are spreading rapidly with the expansion of cloud-to-edge new range of machine learning and artificial intelligence (ML/AI) chips that are built-in to accommodate high-performance computing (HPC) for processing streaming data. [1] ML and AI at the edges include deep learning (DL) and reinforcing learning (RL) to process data flow and carry out analytics on real-time. …


Credit : The Simplilearn Edge: Big Data and Analytics (2018)

Big Data, as its name implies, involves enormous amount of data sets. The name “Big Data” first appeared in academic publications in the 1990s. As of 2008 it has been widely used and continued to do so with the spread of cloud infrastructure, machine learning and artificial intelligence. [1]

Today, there is even a more “rush to compute unlike anything we’ve ever seen before”, wrote Matt Day[2]. Big Data is increasingly sought-for to detect hidden patterns in data since the presence of patterns in any data is an indication for possibility for prediction and for discovery. …


“Historical perspective differs from history in that the object of historical perceptive is to sharpen one’s vision of the present not the past”. Barbara S. Lawrence

Introduction

Studies of historical perspective of ML can help to further sharpen its present for the future. According to Barbara Lawrence: “Historical perspective expands research horizons by encouraging study of the relative stability of phenomena, providing alternative explanations for phenomena, and aiding problem formulation and research design.” [1]


IT Chronicles (2019)

Big data has grown tremendously rapidly, in recent years, to an unprecedented position leading to data to attract more attention and to be used, in many different ways, than it did with table or structured data. However, it is also creating unprecedentedly new challenges including epistemic challenges.

Research on Big data identified challenges that are not only technological but also epistemological challenges related for instance to the establishment of theoretical and conceptual frameworks to scale inferences and machine learning algorithms. Sampling paradigm has been also changed under Big data as more data doesn’t inherently remove sampling bias, the volume may…

OperAI - Operational Artificial Intelligence

OperAI develops IoTs with Math and AI Embedded Solutions to speed up and streamline operational processes at the edges of the cloud.

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