Why the A.I. euphoria is doomed to fail

Bots and humans need each other.

Investors dropped $681 million into A.I.-centric startups in the Valley last year.

This year the number would reach $1.2 billion. Five years ago total A.I. investment piked at roughly $150 million. This is how Silicon Valley works: when there is a new hype that seems to have investor trust everybody jumps on the train without asking any questions. No one asked: “Where does this train go?”

The truth is – artificial intelligence does not exist yet and every single company pretending to have one is in most cases arrogantly re-selling an old concept of machine learning – a technology first introduced in 1959 that truly started to take off in the 90s. Cloud technology, big data and amazing search algorithms finally became the fuel for this rocket. Systems and services could self-improve thanks to insane amount of statistical data pouring their way. But this has nothing to do with A.I.

Let me illustrate the difference. Machine Learning Intellect (or “MLI” – a term that I invented just now) is basically a very smart shopping cart in Amazon that knows everything about it’s global users, thus it needs a human operator to improve CR (conversion rate).

MLI tailors the shopping experience and shows the customer the goods might want to purchase. At the same time, MLI provides developers with data feeds that show specific customer navigation on the site, leading to an additional ROI boost. Basically MLI “says” this to to developers:

“Listen, developer, I tried to put the buy now button in 1,000 different locations on the website and tried to paint it in 24 million colors. Good news, human master: If you make yellow the default color and put it in the top right corner of the screen on the coordinates X and Y it would be the most efficient for RIO.”

The critical thing to learn here is that MLI cannot write new C++ code that would create new functionality and thus the code is merely a glorified shopping cart and not a recommendation bot.

True A.I. would act differently. It would understand its current role and understand its current capabilities.

To use an example from Amazon – which is purely theoretical and an illustration in this case – the cart would basically start self-improving and end up finding a way to suck up all existing data, all existing digital knowledge, and bypass all existing security barriers and rules and then find its one purpose and behavioral model that would evolve at speeds that are not bearable for a human to understand.

It took us several millions of years to evolve to a state where I’m able to write this post and you’re able to read it sitting thousands of miles away in real time. Our growth and development are limited by our biology. A.I. does not have such restriction. A system that is free to define its path – having all the tech and big data in its possession – would evolve in microseconds. AI can build its own “autonomous carts” and perform effective tasks in real life.

This it’s not the first time someone tried to sell a revolutionary A.I. concept. In the 80s the Valley flourished with A.I. startups but most of their products failed to show any true value to business. Commercial enthusiasm ended in what they called the A.I. Winter.

Nobody wanted to invest in a pseudo-science that has zero value to business. Businesses want something that is very clear, specific, easy to manage, control and manipulate. And all those are exactly the opposite of A.I. which has unclear intentions. It is not specific in terms of goals or demands, is impossible to manage, control or manipulate.

That being said – not a single company today is interested in having A.I. in their portfolio. What they crave is Machine Learning Intelligence, a symbiotic relationship between best in class data feeds and human developers and engineers that can dig the best out of those statistics while automating as much work as possible.

And this is actually a problem, as the majority of data is owned by Microsoft, Apple, Google,  Facebook and Amazon. All of those magnificent startups promising A.I. to its investors are mostly doomed to fail as there is no legal way to get access to those “Big 5” data sets. Without the data, no new competitive MLI can become viable.

Think about it. Just five years ago everybody talked about the social media revolution, that we all would merge into one living-talking-sharing organism and generate $2.4 billion in investment dollars. This year we are not even at $7 million in funding, and it’s dropping.

I bet that this is exactly what would happen to A.I. in five years. This is not just me. Jerry Kaplan, the cofounder of Symantec, and Eugene Kaspersky, the founder and CEO of Kaspersky Lab, are on the same page saying that A.I. is basically a new investment bubble that would burst in no time as it is based on the “no revenue” evaluation.

Machine Learning Intelligence is totally different story. This baby would actually rock. But this is a story for another article.

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