Newsletter ~ Issue #109

Artificial Intelligence News


Editors Choice

AI washing: The sooner you know the better

In this world of technology do you feel the constant buzzing of the word “Artificial Intelligence”? From toothbrushes to self-driving cars, everything comes with the Artificial Intelligence (AI) tag. Is everything truly AI or is…

Why is AI so smart and yet so dumb?

At the most basic level, the reason for Moravec’s Paradox is simple: We don’t know how to program general intelligence (yet). We’re already good at getting AI to do specific things, but most toddler level…

Artificial Intelligence and Life in 2030

An oldie, but still relevant. Published by Stanford University as part of its 100 year study. The good: current AI research trends by select domains + general life in cities. The bad: it misses out…

Latest News

Tableau absorption by Salesforce may mark the end of an era

Last year’s acquisition of Tableau by Salesforce was a milestone for the end of an era and the inception of a new one, which will make data analysts of many who had never thought to…

Machine learning/ AI for Research – use it or refuse it?

To start, try getting a clear idea of what a machine learning solution would have to achieve. Then go through the flowchart.

Elon Musk’s Neuralink: A Fitbit for the brain

All you need to know about Neuralink implant. Why Elon Musk calls it a Fitbit for the brain?


AI + Growth Marketing = Smart Marketing: Lean AI

There are many exciting ways you can apply the power of AI and ML to streamline marketing processes across the entire customer marketing funnel to help growth teams work smarter by automating.

Your Machine Learning Engineers don’t need PhDs or Masters Degrees

Asking Machine Learning/ AI hires to have fancy degrees is outdated. Here’s why. Should your machine learning hire have a PhD? Do you need a PhD to work in ML?

Why all Data Scientists should understand behavioral economics

Understanding behavioral economics can help data scientists create better, more effective machine learning models.


Time Series Analysis, Modeling & Validation

This article is the fourth in the series on the time-series data. In this article we will work through a time series forecasting project from end-to-end, from importing the dataset, analyzing and transforming the time…

Content-Based Recommenders using Natural Language Processing

In this blog, we talk about the core to Persoana-ization. We will discuss Content-based Recomender Systems, what are they, the different types, and how these systems work. We will also create a content-based recommender system…

Tokenization techniques in NLP: How a sequence of text can be split into meaningful units?

What is Tokenization? Why it is important? and; How tokenization is achieved? We will go through various Tokenization techniques, the issues identified with those techniques and how these problems…


Web Services (Part 1) – Create and deploy

Azure Machine Learning Studio (classic) publishes models as web services that can easily be consumed by custom apps or BI tools such as Excel.

Smart Buildings through Artificial Intelligence

On the outside, every building is made of bricks and mortar, but with advanced technology, we can create smart buildings. The global smart building market that was valued at USD 7.0 billion in 2014 is expected to reach 36 billion US dollars by 2020. Let us look at ways AI can transform buildings into “Smart Buildings”.

17 strategies for dealing with Data, Big Data, and Even Bigger Data

Python is the most popular language for scientific and numerical computing. Pandas is the most popular for cleaning code and exploratory data analysis. Using pandas with Python allows you to handle much more data.


‘Must-Read’ AI papers suggested by Experts – Pt 2

To be honest, I don’t believe in singling out any one paper as being more important than the rest, since I think all papers build on each other, and we should acknowledge science as a collaborative effort.

Top 4 Python libraries for interpreted Machine Learning

Because of the buzz around artificial intelligence bias, organizations are increasingly in need of an explanation of both the predictions of the models being created and how they work. Fortunately, there are a growing number of libraries that the Python programming language offers to solve this problem.

What books are we reading on AI and Machine Learning in 2020?

Whether you are a seasoned professional in this industry or just starting to dip your toes in, there is always more to learn about AI and machine learning.

Stay ahead of the curve. Share your ideas and feedback.