Introduction on being productive with artificial intelligence
This article aims at collecting some insights on how machine learning applications are improving our working productivity by focusing on the distinction between artificial intelligence (AI) and Intelligence Augmentation (IA)
A recent article published on Venture Beat by the title “How machine learning influences your productivity” (see sources below) sheds light on this important distinction that is often misunderstood.
Intelligence Augmentation vs Artificial Intelligence
Intelligence augmentation, or intelligence amplification is an idea that goes back to the 50s. It’s mainly about using information technology to improve the way humans take decisions. So, a very simple example would be in a chess game a computer could suggest the best move to a human player by analyzing the opponent’s moves and trying to predict the underlying strategy.
A well developed usage of IA is the so called Augmented Learning. In augmented learning systems adapt to the learner’s ability.
The debate dates back to an article by IBM in May 2016 when they answered to a request put forward by the White House on the risks and advantages of artificial intelligence.
A more sophisticated distinction can be found in Ransdell (2002)
“IA research is computer programming which aims at providing a computational basis for augmenting or increasing the effectiveness of human thinking by assisting it, as distinct from attempting to replace it by a machine simulation.”
This could be part of an interesting philosophical discussion that goes back to Alan Turing and recently re-stated by Ransdell (2002) who mainly calls on the work of Skagestad in trying to understand the relationship between IA and Peirce‘s view on thought.
Intelligence augmentation applications
Applications can be anywhere there is data, and the machine can help understand correlations and thus take better decisions.
A company called Cognitive Scale, for example, received the total sum of $ 25 million from some of the most significant IT companies worldwide, namely Microsoft, IBM, Intel.
Their actual industries in which they operate are:
- Financial services
So for example, in healthcare, augmented machine intelligence can improve both the customer experience and the care manager work.
It is no news that IBM bought Promontory Capital to start testing their Watson Finance application (see news here), this means that the machine will not start investing by its own (this would be real artificial intelligence) but it will augment the financial analyst intelligence by providing them with data and unseen relationship between these data.
Altify created the world’s first “Augmented Intelligence Sales Platform”: Altify Max.
The platform claims the following features:
Altify Max monitors what is happening in the opportunity, the account, the pipeline and the forecast, assesses the impact and notifies the salesperson with the prescribed best next action to take. Altify Max has 30 years of deep sales knowledge and insights built in to the software, and customers can extend and customize the knowledge with their own insights. This real-time coaching solution makes sales teams exponentially more effective for greater sales results.
Other applications regard the very creation of websites, such as CMS systems, to improve customer journey mapping, recommendation engines, customer insights and so on.
Another interesting and growing field of application for IA is in task and project management. This goes back to already 2005, where some scholars (see Ahmed et al. 2005) tried to develop a fuzzy logic model to predict the effort needed for IT project development.
In a more practical environment for everyday use. Trevor.ai is a new application claiming it can connect tasks to calendars and schedule effort according to predictive models
It will suggest what and when to work on, connecting automatically tasks to calendar.
We’ve just scratched the surface of what IA is bringing to our everyday’s life. Of course this will be a transition phase before full fledged artificial intelligence applications will be part of our working and lifestyle.
Ahmed, M. A., Saliu, M. O., & AlGhamdi, J. (2005). Adaptive fuzzy logic-based framework for software development effort prediction. Information and Software Technology, 47(1), 31-48.
Ransdell, J. (2002). The relevance of Peircean semiotic to computational intelligence augmentation.