Nowadays, we are witnessing several paradigm shifts in mobility systems and services. Cities are decarbonizing the transportation sector and are moving from car-centric mobility to multimodal mobility; from restricted mobility in two-dimensional streets to 3D mobility; from rigid schedule mobility to mobility on demand and on an as-needed basis and from fragmented unconnected mobility to seamless integrated mobility. Mobility companies move from manufacturing and trade economy to service economy or servitization such as Mobility-as-a-Service (MaaS) and from the unsustainable “number of vehicles sold”’-based revenue model to vehicle miles traveled (VMT)-based, infonomics-based data and customer experience monetization and passenger economy-based revenue…

Photo by richard thomposn on Unsplash

What is Intelligence?

The great philosopher Socrates (470/469BC–399BC) said “I know that I am intelligent, because I know that I know nothing”. Albert Einstein (1879–1955) saw imagination as the true sign of intelligence not knowledge. While Stephen Hawking (1942–2018) sees intelligence as the ability to adapt to change. Many theories have been coined to explain biological intelligence such as Charles Spearman (1863–1945) theory, Theory of Multiple Intelligences by Howard Earl Gardner (1943- ) and Triarchic Theory of Intelligence by Robert Sternberg (1949- ). According to Spearman’s theory, intelligence is defined as the ability to obtain and use knowledge in a productive way. Theory…

Source: Alaa Khamis

AI and Global Economy

AI will have a major impact on the global economy. It is estimated that the market for predictive analytics software will amount to more than $6.5 billion worldwide in 2019, advanced driver assistance systems will have a global market volume of $18 billion in 2019, compared to $5.6 billion in 2012, global voice recognition market in enterprise, consumer, and healthcare is to reach $113 billion in 2017, compared to $53 billion in 2012 and natural language processing market is anticipated to become worth more than $9.9 billion in 2018 compared to $3.8 billion is 2013. These forecasts and the tremendous…

There is a need for a systematic procedure for data collection, machine learning (ML) model development, model evaluation and model deployment. Fig. 1 illustrates a 7-step procedure to develop and deploy data-driven machine learning models.

Fig. 1 7-steps ML Procedure | Credit: Alaa Khamis

These steps are explain in the following sub-section.

Step-1: Problem Characterization

First step in creating successful ML models is to understand the problem at hand, characterize it and elicitate all the required knowledge from a domain expert to help in collecting the relevant the data and understanding the target requirements. The importance of the problem and its challenging aspects need to be understood. Different relevant…

Credit: Alaa Khamis

Data and Insights

Insights are the new gold not the data as data is worth very little unless this data is turned into critical actionable insights. These insights can be used to support decision making and can help in refining the design and manufacturing processes. Machine learning algorithms can be used to accomplish different applied data mining tasks. These tasks can be descriptive analytics, predictive analytics, diagnostic analytics, and prescriptive analytics. Descriptive data analytics provides insight into the past and the present while predictive analytics forecasts the future. …

Alaa Khamis

Senior AI Expert at General Motors Canada | Former Professor of AI and Robotics

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