Homepage of Armando Vieira

Data Scientist, Entrepreneur and Speaker

I am an experienced Data Scientist, Speaker, Entrepreneur and AI Innovation Advocate.

I founded Lidinwise, a consultant company, to help companies take advantage of the opportunities of Machine Learning to leverage their data and create competitive data driven business. I work with big organizations, and Startups as well, as a strategic advisor and hands-on consultant.

I obtained a PhD in Physics and pioneered research in Deep Neural Networks back in 2000, with several publications in peer-review journals. I have extensive experience in machine learning and AI in international teams as lead data scientist and head of data science. 

More recently, I focus my efforts in helping organizations leverage the capabilities of machine learning and AI to become data driven and efficient. 

In my last book, Business Applications of Deep Learning, I discuss how AI will disrupt almost every business, from Banking to Medicine and the future of data driven organisations. 

I’m also interested in societal aspects and implications of AI and ethical consequences of decisions taken automatically by cognitive algorithms.
Pioneer in Deep Neural Networks with this publication in 2003 (cited in the recent review on Deep Learning by Jurgen Schmidhuber) and a more recent one on Applicationbs of Deep Learning in Biomedicine and Prediction of online user behaviour on an ecommerce site.

My book “Deep Neural Networks: Overview and Business Applications“. A summary in a format of book chapter is available here.

Screenshot 2018-03-04 07.55.32

My TEDx talk 13 March (minute 11):

My new book chapter “Digital Markets Unleashed“, By Springer, August 2017 – on applications of Deep Learning and AI in Banks.

More on my Linkedin profile or Github account.

Armando Vieira

Check my latest work based on conditional Generative Adversarial Neural networks (cGAN) applied to facade generation.

My video presentation on “AI, from a long Winter to a blossoming Spring“.

My presentation at EARL conference, London 2015:

Portfolio of Customers


Symbols and meaning

I just come across a recent publication of Nate Soares by the Miri institute https://intelligence.org/files/RealisticWorldModels.pdf concerning a theoretical framework on artifical cognitive agents building models of the world. The author refers to the Solomonof induction problem...

Knowledge Representation and Visualisation in Graphs using Convolutional Neural Networks

My newest paper is “Knowledge Representation and Visualisation in Graphs using Convolutional Neural Networks”. Here is the abstract: “Knowledge Graphs (KG) constitute a flexible representation of complex relationships between entities particularly useful for...

Fresh perspective on the symbol grounding problem

A striking feature of the human brain is the ability to associate abstract concepts with the sensory input signals, such as visual and audio. As a result of this multimodal association, a concept can be located and translated from a representation of one modality (visual) to...

Neural Networks for symbolic deductive reasoning?

The goal of Artificial General Intelligence (AGI) is to create computer systems with human-like intelligence. AGI systems should be able to reason and learn from experience by interacting with the environment in a mostly unsupervised way. To build intelligent machines it is...

My perspective on Tensorflow

Tensorflow is a framework for deep learning recently released by Google. It is based on a set of Theano libraries (heavily relying on tensors – thus the name of the project). But it’s much more than Theano. Together with the symbolic manipulation of variables, much in...

Digging for gold: how machine learning can disrupt the IT talent acquisition market

Talent acquisition and retention is the single most determinant factor of success in IT companies. It’s also the most cumbersome, unreliable and stupid process I ever experienced. Being on both sides of the fence, I got astonished by the lack of preparation of candidates applying...

A new dating algorithm (part 2/4)

Knowing me knowing you Topic modeling is essentially a statistical technique to uncover the hidden subjects, or topics, that occur in a collection of documents.  The method is completely unsupervised and the learning is performed based on subtle co-occurrences of words in a...

Going Deeper in Online Dating: a new matching algorithm

Artificial Intelligence (AI) populates our imagination. Instead of fear it why not embrace it to solve one of the oldest, hardest and most important problem of humanity: the search of a soul mate. It’s a big and messy problem. But it’s also a very interesting one that, since...

Seasonality effects in cars sales using machine learning

Seasonality effects on second hand cars sales from Armando Vieira