As Founder and CEO of Mobilewalla, I am responsible for setting the business and technical strategy of the company and for providing the leadership for us to achieve our revenue goals.
In its most basic sense, AI is using machines (i.e. automation) to perform decision-making tasks that were traditionally performed by humans.
From a technology perspective, AI is a set of capabilities, like algorithms and data, that when used together creates an automated decision-making system.
I disagree with this statement because it takes too narrow a view of broader human endeavors—poetry, art, music, cinema—all that moves human beings are profound as well.
In the area of human technology interaction, then yes, I would agree that AI is very profound.
I am a computer scientist by training. I majored in computer science in graduate school and then worked in computer science departments at large universities. My PhD research was in the area of large-scale data management, which has been my focus.
I have always worked on, and enjoyed, solving complex problems with data. One such problem is AI. As it turns out, much of the complex data manipulation that I innovated is extremely useful for AI.
The net is that I have always worked in an area that was essential to driving AI.
"Nothing in AI is ever 100%, and in many examples the output of AI needs to be interpreted and acted on by humans."
An implementation of AI in real life that I find to be very useful and effective is image recognition. Lots of things we do daily are based on image recognition. For example, fingerprint-based identification on your phone.
Image recognition is not exact. Things never match 100% (like your thumbprint). The matching is all predictive and AI-driven, and the algorithms determine if the match is close enough.
What I find most exciting about AI is the ability to scale mundane tasks that take humans a lot of time and effort.
Today, AI is being applied by organizations in many different areas. These application areas have different error-tolerances. AI is probabilistic and predictive and is better suited to situations that can tolerate more ambiguity (like marketing), not life threatening situations where there is no room for error (like medical decisions).
Nothing in AI is ever 100%, and in many examples the output of AI needs to be interpreted and acted on by humans.
In summary, my biggest worry about AI are the secondary effects of applying it to areas that have low tolerance for uncertainty.
That it will remove humans from decision-making.
Certainty. In the end, AI can only offer options with likelihood, choices with weights. AI outputs solutions from things you can model where you are making a choice.
A human can conceptualize abstract things, AI cannot.
Self-driving vehicles.
"Marketers need to have clear goals for an AI implementation and a good use case that is a fit for this technology. They need to make sure they have enough of the right data necessary to build an effective ML model and they need to make sure they have the right people and technology resources."
So many business processes incorporate AI. It is hard to go into a business and be a manager without understanding the processes you are managing. I would specifically create a course on applications of AI in business. Not a computer science course, but a case study-oriented course that brings in AI-driven examples from different industry segments and gives a business leader the knowledge and understanding they need to effectively use AI to solve their business problems.
Marketers need to have clear goals for an AI implementation and a good use case that is a fit for this technology. They need to make sure they have enough of the right data necessary to build an effective ML model and they need to make sure they have the right people and technology resources.
Access to the breadth and depth of data necessary to power AI. AI is very data intensive and most organizations' internal data will not scale to meet the needs of AI. It is important for marketers to understand the internal data they have access to and to look externally at data sources that can enrich what they currently have and allow them to expand their AI efforts.
I don’t see AI fully automating and eliminating marketing jobs. There is just too much nuance in effective marketing to rely on everything being run by machines.
Marketers need to ensure the use of data with permissible purpose and consent.
They should also be relying less on data sampling and try to get larger, anonymized, population-level data sets.
Move from viewing a consumer as an individual and instead as a part of well-organized groups (like fitness lovers, gamers, frequent diners, travelers, etc.).
AI can help with granular, highly accurate and nuanced, multi-dimensional segmentation which still allows a high degree of personalization, while simultaneously abstracting away consumer identity to preserve privacy.
"I don’t see AI fully automating and eliminating marketing jobs. There is just too much nuance in effective marketing to rely on everything being run by machines."
Ex Machina.
I, Robot.
Mobilewalla's whitepapers, our upcoming AI Academy course and our recent webinars.