Rafael Garrido Rivas | Managing Partner | everis USA

Some Thoughts About the Cognitive Revolution We Live In

The thesis and final project for my Bachelor of Science in industrial engineering was about a speech recognition system (which actually only understood numbers) that I programmed in C++ using neural networks and Hidden Markov Models. As surprising as it may sound, it was actually pretty limited in scope. I trained it using a data set collected from my entire college dorm population, which consisted of approximately 150 students that patiently stopped by my room in their spare time to say a few numbers to my system. The training processing time was kind of exasperating… it took all night to train the system by running Linux on my Compaq 486. Eventually, the system became very accurate in recognizing numbers no matter who said them. At about the same time, Deep Blue beat Garry Kasparov, the chess world champion…. for the first time! The next 3 games, Garry won… the system was not actually “intelligent,” in the same way a calculator is not intelligent, it just followed rules.

Not even in my best dreams, could I have imagined that some 20 years later the same machine learning principles I used in my numbers recognizer system would be at the epicenter of the incredible cognitive revolution we are immersed in today.

The pace of technological change is unprecedented. After looking at the Tensorflow website this past weekend and browsing through all of the readily available assets that the community has created and shared so far, it seems to me that what took several months to build at the time, could easily be done in less than a week today, and could be deployed with a much better performance even on mobile devices, because where else would we want to use it these days? Our civilization seems to have strongly embraced the African proverb of — “If you want to go fast, go alone. If you want to go far, go together” — and that is great news for this endeavor.

For those of us who are inclined to see the glass half full, AI-powered productivity growth is a promise to lift the global economy, by enabling companies to innovate and reach underserved markets more effectively with existing products, and by allowing for the creation of new products and services over the longer term.

To understand what we currently refer to as AI, its capabilities, as well as to separate common myth from reality, it is essential to know that the machine learning type that has been dubbed “deep learning,” is still based on artificial neural networks which are rooted on how the human brain works. Researchers train neural networks by adjusting the activation functions and their respective weights to get the desired outputs. With a single network layer (as I had in my number recognizer) you can only identify simple patterns, but with multiple layers you can find patterns within patterns. Current neural network systems are typically composed of twenty to thirty layers. This heightened level of abstraction is the main reason for significant improvements in machine learning and AI. There are all sorts of AI variations (supervised, unsupervised, reinforced learning, transfer learning, etc), but I won’t get into that here.

Machine learning relies on a bottom-up, data-based approach to recognize patterns. It utilizes the same approach that my children used to learn four different languages, one of immersion rather than grammar memorization. Simply said, instead of coding the

logic to distinguish data, you simply show the system the data and tell it what is and what is not, such that the computer builds the appropriate program.

Today, there are several factors contributing to this AI-boom:

This poses infinite opportunities that could only potentially be limited by our narrow imagination. The challenge is therefore not a computational one, but rather one of evolutionary change in the way we think and work. There is no longer a Moore’s Law wall to hit. Any repetitive task for which there is large amounts of data to train the machines, can and will be automated.

So… this is it? Not yet… What does the future have in store for us? Experts say that all of today’s booming AI use cases revolve around applications of what they call “narrow AI,” in which machine-learning techniques are being developed to solve very specific problems. The major challenge is to develop AI that can tackle general problems in much the same way that humans can, but such “general artificial intelligence,” seems to be decades away.

In the meantime, all industries are leveraging this “narrow AI” and its data to gain new insights and create advances in their field. The internet is inundated with use cases for almost every industry, and below are a few examples that are useful to understand how AI is embedded in our daily lives without us even being aware of it:

The dream of any marketer is gradually becoming true. AI allows to go from segmentation to micro segmentation by making sense of a lot of company and social data and presenting pricing and promotion options that companies had never seen before. Companies can know their customers better and be very precise about what to offer to them, when to offer it and through what channels (where). Today, retailers routinely use these “next best product to buy” algorithms, and similar use cases will flourish in other industries to enrich customer journeys.

What does this mean to your industry or business? What are the specific opportunities? What’s the value? What are the use cases? While no solution will typically be a “silver bullet” that makes a genuinely transformative impact on its own, each of them will make your model more difficult to copy, and make it better than your competitor’s.

By now, I may have spooked some readers, since the societal implications are apparent. If machines can read X-ray or MRI images as well as or better than radiologists who have years of training and experience, do we need the radiologists?

According to McKinsey, about half of current work activities (not jobs) can technically be automated, and while less than 5 percent of jobs have the potential for full automation, almost 30 percent of tasks in 60 percent of occupations could be computerized.

Even white collar jobs are under the threat of automation. In reality, the distinction is not between manual and cognitive skills or blue-collar vs white-collar work, but rather whether a job has large elements of repetition, and massive amounts of data are available or can be collected to train the algorithms. Jobs that are routinely, repetitive, and predictable can be done by machines better, faster, and cheaper, and will probably be done by machines, sooner or later. In and of itself, this is neither good nor bad, it is a fact.

In my humble opinion, AI can make us more human. Nobody likes doing repetitive tasks. In the same way calculators allowed people to devote their time to more meaningful and complex jobs or ATMs shifted the role of bank tellers from one of simply dispensing cash to one of customer service, AI will not replace human judgment, but will be a major complementary asset that will free humans to focus on tasks that really need them. Companies will be able to serve underserved populations, and focus their expert’s time on the jobs that really require their expertise.

Automation will spur growth in the need for critical thinking, creativity, complex information processing, and social and emotional skills such as communication and empathy. The so called “high touch professions” will probably be on the rise.

While some jobs will be eliminated, others will change or will be created, and hopefully the change will be for the better. A trend everyone seems to agree with is that the pace of change will be proportionate to the wages at least in the United States..

The core competency we need in the future is the ability and desire to learn. AI will maybe help in telling us what we need to learn.

Conclusively, the consultant inside of me suggests a couple of guidelines for the best ways to leverage AI inside organizations:

Think of what business you are in, or you can be in. Be the blurrier of your industry’s boundaries. Don’t define your competitors narrowly. Define them broadly or others will do that for you.

As companies across sectors are increasingly harnessing AI’s power in their operations, think of how you can best leverage your assets, staying true to your DNA, to embrace this cognitive revolution.

Whether you have a lab, a venture, an outpost in Silicon Valley, incubators or accelerators around the world, do internal or external hackathons, or invest along with other VCs to get a peek into what’s next, or all of the previous combined, make sure that you leverage them all to jump onto the AI wagon sooner than later. This journey will take understanding, courage, conviction, and enthusiasm.

Exponential intelligence for exponential companies

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