resource centre

To understand more of the Fourth Industrial Revolution, I suggest the book “Shaping the Fourth Industrial Revolution” by Founder and Executive Chairman of World Economic Forum, Klaus Schwab

Kai Fu Lee, who wrote the bestseller “ A.I. Superpowers” is among the many who share this view. See the interview with him as a reference: https://fortune.com/2019/01/10/automation-replace-jobs/

What is AI?

This definition was introduced by Max Tegmark in the Book Life 3.0

This IBM article is one of many that covers the difference between the three types of AI: https://www.ibm.com/blogs/systems/ai-machine-learning-and-deep-learning-whats-the-difference

If you are looking for a more technical approach to Deep Learning, the I suggest you read the book Deep Learning (MIT Press Essential Knowledge series) by John D. Kelleher

You can find the full definition at this Wikipedia page: https://en.wikipedia.org/wiki/Intuition

This research from the three Stanford professors Andrea Stevenson Won, Jeremy N. Bailenson, Joris H. Janssen shows as an example of why Deep Learning is superior to IT technologies in the most complex area, non-verbal communication. https://vhil.stanford.edu/mm/2014/won-ieeeac-nonverbal-predicts-learning.pdf

Carlos Perez describes the concept of Deep Learning and intuition well in his book “Artificial Intuition: The Improbable Deep Learning Revolution.”

The concept is described on this Wikipedia page: https://en.wikipedia.org/wiki/DIKW_pyramid

Superintelligence by Nick Bostrom is one of the books that work with this perspective.

You can read more on criteria for joining Mensa on Wikipedia: https://en.wikipedia.org/wiki/Mensa_International or on their website https://www.us.mensa.org/learn/about/

Kevin Kelly is a futurist and best-selling author. He explains the concept well at this TED talk. https://www.ted.com/talks/kevin_kelly_how_ai_can_bring_on_a_second_industrial_revolution/transcript

The three concepts of intelligence is explained by Max Tegmark in the book Life 3.0

This Wikipedia page explains the concept in more detail and has links to more literature on the subject. https://en.wikipedia.org/wiki/Artificial_general_intelligence

This Forbes article elaborates on what could drive us towards Artificial General Intelligence, and what could inhibit us from reaching this stage, https://www.forbes.com/sites/cognitiveworld/2019/06/10/how-far-are-we-from-achieving-artificial-general-intelligence/

Vincent C. Müller is a Professor of Philosophy from Eindhoven University of Technology, and Nick Bostrom from the University of Oxford made an interesting expert survey on when we can expect to reach Artificial Super Intelligence. It is called “Future Progress in Artificial Intelligence: A Survey of Expert Opinion”, and you can download it as PDF here: https://philpapers.org/archive/MLLFPI

https://www.bbc.com/future/article/20181129-the-ai-transforming-the-way-aircraft-are-built

The page shows how the part was designed https://www.autodesk.com/customer-stories/airbus

If you are a tech expert and what to know how to work with Image Recognition, then I suggest you read this Programming Computer Vision with Python by Jan Erik Solem

In my view, the best book on Text Mining is by Charu C. Aggarwal. It is written for developers and is called “Machine Learning for Text.”

InNatural Language Processing is a topic that you must have a technical background to understand. I don’t know now any non-technical books on the matter. So, the best tech book, in my opinion, is “Introduction to Natural Language Processing (Adaptive Computation and Machine Learning series)” by the Georgia Tech professor Jacob Eisenstein.

Natural Language Processing is also a topic that is only approached in the literature from a tech perspective. I find the best book to be “Handbook of Natural Language Processing” by Nitin Indurkhya.

The book on Recommendation Engines that I can recommend the most is a part of The MIT Press Essential Knowledge series. It is simply called “Recommendation Engines”, and is written by Michael Schrage

Predictive Analytics is so widely used that there is now a book available in the “For Dummies series”. It is called “Predictive Analytics For Dummies”, and is a great place to start. It is written by Anasse Bari.

Prescriptive Analytics A Complete Guide is, in my view, the best book to read if you need an introduction to the topic. It is written by Gerardus Blokdyk.

https://petapixel.com/2014/08/28/flip-ikea-catalog-75-photography-see-cgi/

Competitive Strategy: Techniques for Analyzing Industries and Competitors by Michael E. Porter

Blue Ocean Strategy, Expanded Edition: How to Create Uncontested Market Space and Make the Competition Irrelevant by W. Chan Kim

“Generative Design (Form + Technique)” by Asterios Agkathidis shows 11 different projects that have been made with Generative Design software. The book is a great place to start if you want to understand the topic in more detail.

The Future of How Things Get Made: Harnessing the Power of Artificial Intelligence for the Next Frontier of Design

https://cs.stanford.edu/people/esteva/nature/

You can learn more on this concept in the book “The Lean Start-up” by Eric Rice.

https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet

You can download the executive summary of the McKinsey report “JOBS LOST, JOBS GAINED: WORKFORCE TRANSITIONS IN A TIME OF AUTOMATION” here

The book “DROPSHIPPING: Discover how to create a Standard e-Commerce on Shopify Amazon and eBay” let you in on the concept in more detail. It is written by Christopher Miller.

One of the better solutions on the market is Albert. You can read more on https://albert.ai/

A Google search on “House of Cards created with data” generates over 900 million results. So the Internet has got the story covered. You can get an easy overview of the one from The Next Web: https://thenextweb.com/insider/2016/03/20/data-inspires-creativity/

https://www.forbes.com/sites/goncalodevasconcelos/2015/03/04/the-third-industrial-revolution-internet-energy-and-a-new-financial-system/#6ff9da41271a

https://www.ted.com/talks/kevin_kelly_how_ai_can_bring_on_a_second_industrial_revolution?referrer=playlist-what_happens_when_the_robots_take_our_jobs

https://www.visualcapitalist.com/millions-lines-of-code/

The US National Highway Traffic Safety Administration estimates that 92% to 96% of all traffic accidents was caused by human errors. The data can be found here. https://www.digitaltrends.com/cars/2016-nhtsa-fatality-report/. The global number of leather traffic accidents was estimated to be 1.3 million, according to the WHO. Read more here: https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries

Annemieke Verboon from the University of Helsinki, Department of Philosophy, has published an excellent article on Porphyry that you can download for free at academia.edu. It is called “The Medieval Tree of Porphyry: An Organic Structure of Logic, in A. Worm and P. Salonis (eds.), The Tree. Symbol, Allegory and Structural Device in Medieval Art and Thought, International Medieval Research” This is the link to the article:

The scientific term is called Cognitive mapping, and this article from Nature explains why humans are different form chimpanzees on that parameter. https://www.nature.com/scitable/knowledge/library/primate-cognition-59751723/