When people break into banks, they don’t tend to deposit money. Which explains why JPMorgan Chase and Wells Fargo spend billions of dollars each year on artificial intelligence, physical security and cybersecurity to prevent financial and identity theft.
When Cambridge Analytica compromised 87 million Facebook users’ data, they deposited millions of dollars into Facebook’s bank account. Which may explain why Facebooks’ massive, world-class team of Artificial Intelligence professionals in Building 20 were not working to prevent bad actors from purchasing ads and stimulating users emotions with sensational material (a.k.a. click-bait).
Cambridge Analytica claims they have 5,000 data points on American voters. They leveraged Facebook’s artificial intelligence powered capability that predicts how each of their two billion users will behave, think and purchase. Yes, if you are a Facebook user, they may be able to predict your future behavior better than you. Advertisers have found this insight to be so valuable that they helped drive Facebooks net income to $15 Billion in 2017.
What is Artificial Intelligence?
Artificial Intelligence is the machine’s (computer system) ability to perform tasks that normally require human intelligence and the capability (machine learning) to improve its performance without humans explaining how to do it.
The artificial intelligence buzz, or internet click bait, has promoted fear that artificial intelligence may become the supreme ruler of mankind and that it will one day eliminate your job. Word travels fast when Elon Musk states AI is more dangerous than nuclear weapons and when McKinsey predicts that 73 million jobs in the U.S. will be eliminated by 2030. There are few views, shares and likes for another McKinsey study which predicted AI will drive as much as $5.8 trillion of new economic value across nineteen industries after they analyzed four hundred AI use cases.
Most AI Resources Focus on What Humans Can’t Do
The popular stories that go viral include AI beating champions of chess, Jeopardy and Go, yet quietly Facebook’s AI experts tune their machine learning models to predict how each of their 2 billion users will think, act and buy. We are inundated with AI-powered autonomous vehicles stories that forecast the elimination of millions of driving jobs, while Goggle’s AI team behind the scene are tuning their machine learning models to you, your context and what you my buy online. We are no longer surprised by stories of how AI is able to detect cancer on medical images as well as radiologists, yet we don’t hear how Bridgewater Associates’ AI team is effectively turning their AI frameworks to predict financial markets for their investors of over $150B.
We mostly hear about the small fraction of AI investment going to what humans can do, such as play games, drive cars and diagnose cancer. We hear very little about the objectives of most AI investments, which is to get machines to perform that haven’t ever been effectively done by humans. Even if humans can predict the human behavior of a single person from Cambridge Analytica’s 5,000 data points, could humans effectively do that with 87 million users? Could humans effectively make sense of billions of data points processed by Google’s and Bridgewater’s machine learning models that predict what people want and the financial markets?
AI Investment is Going to Managing Complexity
Complexity is the unpredictability of many impacting elements (people and realities) operating, interacting and reacting in both certain and uncertain ways. To achieve a complex challenge, we must manage the uncertainty of complexity, yet we’re not taught that in school. Complexity involves many certain and uncertain elements that interact, and get in the way of our health, relationships, career goals, transforming organizations or finding cancer cures. Yet, we are taught to use certain known elements and formulas to find answers, which doesn’t help with complexity. While proven formulas and calculations may work in school, they fail to address the complexity of predicting human behavior, what people want and the financial markets.
This explains why Facebook, Google and Bridgewater abandoned their predictive algorithms and replaced them with Artificial Intelligence fed by machine learning models. Facebook CEO Mark Zuckerberg sees AI as the only way to address many complex challenges.
Zuckerberg referred to AI technology more than 30 times during ten hours of questioning from congressional lawmakers Tuesday and Wednesday, saying that it would one day be smart, sophisticated and eagle-eyed enough to fight against a vast variety of platform-spoiling misbehavior, including fake news, hate speech, discriminatory ads and terrorist propaganda. (Washington Post)
Mark Zuckerberg cited AI as the solution for many of the most complex challenges.
Moderating hate speech? AI will fix it. Terrorist content and recruitment? AI again. Fake accounts? AI. Russian misinformation? AI. Racially discriminatory ads? AI. Security? AI. (The Verge)
How Does Facebook Manage Complexity with AI?
The AI technology of Facebook, Google and Bridgewater have operated in the shadows behind protective proprietary walls while driving massive shareholder value. For Facebook, the shadows began to fade when stories emerged about bad actors using Facebook during Brexit and Presidential elections. Then came the Cambridge Analytica story, which resulted in Facebook losing $35B in shareholder value. Then details of the activities inside Facebook’s Building 20 started to emerge.
Facebook uses an artificial intelligence-powered prediction engine called “FBLearner Flow” that self-improves by inputs from its several machine-learning models. The machine learning models process billions of data points from its two billion users’ activity to predict thousands of things including: users in a photograph, what people want in their data feed and what is likely to be spam. Inside documents state that the AI system can predict your behaviors.
Rather than focus on replacing humans, Facebook uses AI to augment their human engineers. “We’re able to do things that we have not able to do before,” he says Facebook Chief technology officer Mike Schroepfer.
Humans can’t cost-effectively interpret massive data sets and make sense of thousands of certain and uncertain elements, that interact and react in unpredictable ways. Software developers can’t create computer algorithms for each Facebook user nor can they develop algorithms that can interpret how thousands of certain and uncertain elements, will interact and react. Artificial Intelligence, fed with machine learning models, are beginning to do what humans or computer algorithms can’t do to. They interpret what we want in our Facebook feeds, what we are searching for with Google and what the financial markets will do.
Lesson From Facebook
- It’s What Humans Can’t Do – AI is not simply cost-effective automation that replaces what humans can do, it is a way to address complexity in way that wasn’t possible before AI.
- Algorithms Can’t Do It – Predictive algorithms are limited and become less effective as the number of data elements grow. Facebook, Google and Bridgewater moved to machine learning models that can improve their performance without humans programming how to do it.
- You’re Missing Data Elements – You will need data elements that you don’t have today. Facebook and Google capture data outside their applications to help their machine learning models. If you need more on this, asks members of Congress who were briefed by Mark Zuckerberg on internet cookies.
- You Need More Data – Machine Learning requires massive amount of data to be effective. Most AI professionals work for companies that have massive data sets like Amazon, Netflix, and Apple.
- You Better Start Now – Machine Learning Models take many years to develop. They need to go through many iterations to get the data elements, their associates and weighting right to be effective. It is not an algorithm that can be written, it could take 3 to 5 years of tuning machine learning models before they may be effective.
- You Need to Know All The Elements That Impact Your Mission – When Facebook, Google, and Bridgewater didn’t predict human behavior or financial markets correctly, they found they were missing elements that impact human behavior or financial markets. You need to invest time and resources upfront to identify all the impacting elements or your AI system could drive poor business decisions.
- You Are Never Done. When managing complexity, you need substantial resources to continue working to address change and the unpredictable elements. The machine learning models will need to be adjusted based on the thousands of changing realities and new insight.
- Protect Your Data. It costs almost nothing to share digital data. It can cost $35B in shareholder value if your data ends up in the hands of organizations that use the data like Cambridge Analytica.
- Add Digital Product Miss-use to Your Risk Mitigation Program. Don’t wait until your company is trending on Twitter before using AI to address mis-use of your digital systems like Facebook.
- Artificial Intelligence Professionals Are Very Expensive. If you want to hire AI resources, you will need to compete with Google, Facebook, Apple, Amazon and Uber. Top AI talent are commanding one million dollar salaries even at non-profits.