The Flaw in Tech Companies’ AI Strategy
There is a lot of talk about artificial intelligence; sadly, not a lot of substance. We are in such early days of AI that I prefer to talk about what is happening in Machine Learning since that is the current stage of the AI future we are in. We are currently trying to teach computers to see, hear, learn, and more. Right now, that is the focus of every major effort that will someday be considered AI. When I think about how tech companies will progress in this area, I think about it from the standpoint of what data they have access to. In reality, data is the foundation of machine learning and thus, the foundation for the future of AI. I fully expect many companies to turn data into intelligence and use what they have collected to teach machines. There may very well be a plethora of specialized artificial intelligence engines for things like commerce, banking, oil and gas, astrology, science, etc., but the real question in my mind is who is in the best position to develop a specialized AI assistant tuned to me.
While several of the tech companies I’m going to mention may not be focused on personal AI, I’m going to make some points within the lens of the goal of personal AI vs. a general purpose AI. The question is, who is developing Tony Stark’s version of Jarvis for individual consumers? The ultimate computing assistant designed to learn, adapt, and augment all of our weaknesses as humans and bring new levels of computational capabilities to the forefront for its user.
With the assumption that Facebook, Amazon, Google, Microsoft, and Apple are trying to build highly personalized agents, I want to look at the flaws and challenges each of them face in the present day.
Facebook no doubt wants to be the personal assistant at all levels for consumers. However, like all the companies I’m going to mention, they have a data problem. This problem does not mean they don’t have a lot of data — quite the contrary. Facebook has a tremendous amount of data. However, they have a lot of the wrong kind of data to deliver a highly personalized artificial assistant for every area of your life.
Facebook’s dilemma is they see only the person the consumer wants them to see. The data shared on Facebook by a user is often not the full picture of that person. It is often a facade or a highly curated version of one’s self. You present yourself on Facebook the way you want to be perceived and do not share all the deep personal issues, preferences, problems or truly intimate aspects of your life. Facebook sees and is learning about the facade and not the true person behind the highly curated image presented on Facebook.
We share with Facebook only what we want others to see and that means Facebook is only seeing part of us and not the whole picture. Certainly not the kind of data that helps create a truly personalized AI agent.
I remain convinced Amazon is one of the more serious players in the AI field and potentially in a strong position to compete for the job of being my personal assistant. Amazon’s challenge is it is commonly a shared service. More often than not, people share an Amazon Prime account or an Amazon account in general across their family. So. Amazon sees a great deal of my family’s commerce data. However, it has no idea if it is me or my wife or my kids who are making the transaction. It’s so often blatantly clarified for me as I’m surfing Facebook or some other site that is an Amazon affiliate and I see all the personal hygiene and cosmetic ads for items my wife has searched for on Amazon. Nothing like killing time on Facebook and seeing ads for Snail and Bee facial masks presented to me in every way possible.
While Amazon, with their Alexa assistant, is competing for the AI agent in my life, it has no idea how to distinguish me from other people who share my Amazon account thus making it very hard for Amazon to build a personalized agent just for me when it observes and learns from the vast data set of my shopping experience but does not know what I’m shopping for versus what my family is shopping for. The shared dynamic of the data Amazon is getting makes it hard for them to truly compete for the personal AI. However, it does put them in a good position to compete more for the family or group AI than the individual.
Google is an interesting one. Billions of people use Google’s search engine every day, but the key question remains, how much can you learn about a person from their search query? You can certainly get a glimpse into the context and interest at any given time by someone who is running a query and, if you keep building a profile of that person from their searches then, over time, it is certainly possible to get a surface level understanding. But I’m not sure you can know a person intimately from their searches.
No doubt, Google is building a knowledge profile of its users on more than just their search queries as you use more of Google’s services. Places you go if you use Maps. Conversations you have if you use their messaging apps and email, etc. No doubt, the more Google services you use, the more Google can know and learn about you. The challenge is that, for many consumers, they do not fully and extensively use all of Google’s services. So Google is also seeing only a partial portrait of a person and not the entirety which is necessary to develop a truly personal and intimate AI agent.
Microsoft is in an interesting position because they, like Google and Apple, own an operating system hundreds of millions of people use on a daily basis for hours on end. However, I would argue the position Microsoft is in is to learn about your work self, not so much your personal self. Because they are only relevant, from an OS and machine learning standpoint on the desktop and laptop, then they are stuck learning mostly and, in many cases only, about your work self. Indeed, this is incredibly valuable in itself and Microsoft is in a position to develop an AI designed to help you be productive and get more work done in an efficient manner. The challenge for Microsoft is to be able to learn more about the personal side of one’s life when all they will see and learn from is the work side.
Lastly, we turn to Apple. On paper, Apple is in one of the best positions to develop an agent like Siri to fully know all the intimate dynamics of those who use Apple devices. Unlike Google, it is more common for consumers to use the full suite of Apple’s services from Maps, to email, to cloud storage and sync, to music, to photos, etc. However, Apple’s stance to champion consumer privacy has put them in a position to willingly and purposely collect less data rather than more.
If data is the backbone of creating a useful AI agent designed to know you and help you in all circumstances of your life, then the more it knows about you the better. Apple seems to want to grab as little data as possible, with the added dynamic of anonymizing that data so they don’t truly know it’s you, in order to err on the side of privacy.
I have no problem with these goals but I am worried Apple’s stance puts them in a compromised position to truly get the data they need to make better products and services.
In each of these cases, all the main tech companies have flaws in their grand AI strategy. Now, we certainly have many years until AI becomes a reality but the way I’m analyzing the potential winners and losers today is on the basis of the data they have on their customers in order to build a true personal assistant that adds value at every corner of your life. While many companies are well positioned, there remain significant holes in their strategy.