Newsletter ~ Issue #107

Artificial Intelligence News


Editors Choice

The shifting sands of “A.I.”

The public debate around A.I. is consequential for funding, research, regulation, and the extent of its malign misuse. Our discourse is failing because we collectively flaunt several definitions of the term.

The Future Society, UNESCO, and others partner on pandemic decision-making platform

An alliance aiming to build an AI-powered pandemic decisioning tool for policymakers, health care leaders, and scientists.

The Data Mining process

To this day, simply dumping a pile of data into even the most advanced machine is unlikely to give you back anything meaningful, let alone produce the outcome that you desire. Intelligent systems still need people to ask the right questions, set goals, and evaluate the performance.

Latest News

Artificial Intelligence to be the future for making hiring decisions

Businesses have started to use Artificial Intelligence in their recruiting process to help them recruit efficiently, especially during the post-pandemic crises. Let’s look at what that means.

Neuralink will share progress on linking human brains with AI next month

Elon Musk’s startup Neuralink says it will share progress next month on the company’s mission to link human brains with AI.

Deep Learning vs. Machine Learning — What’s the difference?

Everyone who seeks to better understand the field of artificial intelligence should begin by understanding the terms and its differences. The good news: It’s not as difficult.


Bad Data is ruining your performance

Your CRM has messy data. Sales teams are getting irrelevant prospects. Marketing is making embarrassing mistakes. Reports keep giving incorrect insights. You introduce more processes. You invest. More marketing budget allocated. More resources are added. Sounds all too familiar? You’re not alone.

Zapata CEO Christopher Savoie: The QC and ML business use case is ‘a when, not an if’

The story of quantum computing hardware companies is well known. But as tech giants Amazon and Microsoft push the quantum computing conversation to the cloud, we’re also seeing quantum computing software companies emerge. One such company, Zapata, is building an enterprise software platform for quantum computing.

AI at the edge is enabling the push toward defect-free factories

If there’s one goal every manufacturer in the multi-trillion-dollar industrial segment shares, it’s operating a factory free from production defects.


4 pillars of data visualization

Distribution – Relationship – Composition – Comparison. An important concept in statistics and data science is distribution. Below is a representation of students’ heights distribution in a swimming class. 4 pillars of data visualization.

Eliminating bias from Machine Learning is a dangerous idea

Elon Musk’s startup Neuralink says it will share progress next month on the company’s mission to link human brains with AI.

Deep comprehensive Correlation Mining

Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabeled data through Deep comprehensive Correlation Mining.


Machines that can understand human speech: The conversational pattern of AI

The real-world discriminatory outcomes of data science’s tinkering have led to a movement advocating for the elimination of bias from machine learning and so-called ‘responsible AI’.

Google’s AI tool lets users trigger mobile app actions with natural language instructions

Google is investigating ways AI might be used to ground natural language instructions to smartphone app actions.

Finding the needle in a haystack on a quantum computer

Grover’s quantum search algorithm finds the target element from a list of unordered elements in O(√N) time.


Insights: The Productivity Step

Artificial Intelligence and Disruptive Change. Understanding the changing work environment and Insights into the Productivity Step.

6 important Python libraries for Data Science and ML

Enhance data science skills and jump on a career with Just into Data Tutorials + Applications. 6 important Python libraries for machine learning and data science.

Sktime: a unified Python Library for Time Series Machine Learning

The “sklearn” for time series forecasting, classification, and regression. Solving data science problems with time series data in Python is challenging.

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