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Tech, Strategy, and other interesting things by Roberto Zoia

Strategy and Technology

Amazon, cashier-less stores, and the race for AI supremacy

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Amazon just announced that it is licensing Just Walk Out, its cashier-less convenience store technology, to other convenient stores and retailers. Just Walk Out is a technology that relies heavily on deep learning (AI), sensor fusion, and visual recognition.

Opening brick and mortar stores is not only capital-intensive, but has low net margins. If Amazon had chosen to continue growing this business lines this way, like it has with its 26 Amazon Go stores, it would also have had to operate the stores. On the other hand, licensing its technology to third parties make sense because it allows faster penetration and higher margins on lower revenues. Also, it converts former competitors into potential customers.

But there is more. As Enrique Dans writes, _the new element that defines competitiveness today is data. In deep learning, there’s no data like more data1. In the race for AI supremacy, the company who has more data to train its deep learning algorithms has the lead.

The stores that implement Just Walk Out will generate a significant amount of data for Amazon. Even if Amazon has said that it “only” will collect the data needed for generating the customer’s receipt, this included detailed data from sensors, videocameras, and other information needed to run the system. The volume of information is significant.

For Amazon, Just Walk Out is not just the natural growth of one of its business lines. It’s about building a flywheel where the operation of the stores that use Amazon’s technology will generate the data that will be used to train the system itself, in benefit of Amazon and its clients. This will give Amazon a significant head start over its competitors in this field.

What about Waymo, Google’s self-driving car project? Wouldn’t it be better for Waymo if instead of trying to build its own car fleet, it would license its technology to car makers?

(This article is also available in Spanish.)

(Featured image photo by SounderBruce – Own work, CC BY-SA 4.0.)

  1. cfr Kai-Fu Lee, AI Superpowers: China, Silicon Valley, and the New World Order. Kai-Fu Lee is the founder of Sinovation Ventures, founder director of Microsoft Research Asia, and past-president of Google China. ↩

La Pirámide Invertida de la Invención

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Todo el mundo tiene ideas. Pocos las ponen en práctica. Kevin Kelly, en su libro What Technology Wants, citando a Jay Walker:

“Puede haber decenas de miles de personas que conciben la posibilidad de la misma invención al mismo tiempo. Pero menos de uno de cada diez se imagina cómo se podría implementar. De estos, solo uno de cada diez llega a pensar los detalles prácticos y soluciones específicas. De este último grupo, solo uno de cada diez logrará un diseño funcional. Y finalmente, solo uno de todos esos milles que tuvieron la idea lograrán que la idea arraige en la cultura. En nuestro laboratorio nos involucramos en todas esas etapas, en las proporciones esperadas.”

En otras palabras, a nivel conceptual la simultaneidad de una idea es generalizada e inevitable; tu brillante idea la tendrán muchos. Pero conforme bajamos, habrá menos personas en cada etapa. Y en el momento en que estés tratando de llevar tu idea al mercado, probablemente seas el único porque estás en la cúspide de la pirámide de personas que tuvieron inicialmente la misma idea.

Cualquier persona razonable que vea esta pirámide entenderá que la probabilidad de que alguien logre la adopción de la lámpara eléctrica es 100%, mientras que la probabilidad de que fuera Edison el que la inventara es uno en 10,000.

The Inverted Pyramid of Invention

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Everybody has ideas, few put them into action.

“There might be tens of thousands of people who conceive the possibility of the same invention at the same time. But less than one in ten of them imagines how it might be done. Of these who see how to do it, only one in ten will actually think through the practical details and specific solutions. Of these only one in ten will actually get the design to work for very long. And finally, usually only one of all those many thousands with the idea will get the invention to stick in the culture. At our lab we engage in all these levels of discovery, in the expected proportions.” In other words, in the conceptual stage, simultaneity is ubiquitous and inevitable; your brilliant ideas will have lots of coparents. But there’s less coparentage at each reducing stage. When you are trying to bring an idea to market, you may be alone, but by then you are a mere pinnacle of a large pyramid of others who all had the same idea.

Any reasonable person would look at that pyramid and say the likelihood of someone getting a lightbulb to stick is 100 percent, although the likelihood of Edison’s being the inventor is, well, one in 10,000.

— Kevin Kelly quoting Jay Walker in What Technology Wants.

La disrupción de la Inteligencia Artificial y la necesidad de aprender a aprender

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En una de las sesiones de la reunión anual del World Economic Forum de este año (2020) en Davos, Yuval Harari habló de los peligros de la disrupción tecnológica. Harari es autor, entre otros, del libro Sapiens. De animales a dioses: Una breve historia de la humanidad.

Uno de los efectos de tecnologías disruptivas como la Inteligencia Artificial (AI, por sus siglas en inglés) es la desaparición de determinados trabajos, que serán automatizados por la tecnología. Para Harari, la pregunta no es si las nuevas tecnologías impulsarán la aparición de otros nuevos trabajos —lo harán— sino si seremos capaces de aprender las habilidades que estos trabajos requieren.

Considerando que la inteligencia artificial está lejos aún de alcanzar todo su potencial –no ha hecho más que empezar– podemos esperar una cascada de disrupciones cada vez mayores. Aquellos que no puedan mantener el paso quedarán rezagados. “(…) en el pasado, los humanos tenían que luchar contra la explotación. En el siglo XXI, la gran batalla estará en no convertirse en irrelevante.”. Ser irrelevante, económica y políticamente hablando, puede ser más duro que ser explotado, comenta Harari.

No pude evitar conectar las advertencias de Harari a lo que Carl Newport llama habilidades claves para destacar en la nueva economía: la habilidad de dominar rápidamente temas complejos; y, la la habilidad de producir a nivel élite, tanto en calidad como en velocidad.

Las dos habilidades pueden aprenderse, pero no en un periodo corto de tiempo. Lo mejor es empezar hoy.

Este artículo también se publicó en inglés con el título The Disruption of AI and the Need to Gain New Skills.

The Disruption of AI and the Need to Gain New Skills

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Yuval Harari is best known for his book Sapiens: A Brief History of Humankind. At the World Economic Forum Annual Meeting in Davos this year (2020), he focused his talk on the dangers of technological disruption.

Disruptive technologies like Artificial Intelligence (AI) will certainly eliminate jobs. For Harari the question is not whether new jobs will be created —they will be— but whether people will be able to learn new skills fast enough to adapt to these jobs. But it doesn’t stop with the immediate generation affected by those changes. Considering that AI is nowhere near its full potential, automation will be a cascade of ever bigger disruptions. People will need to reinvent themselves again and again. Those who can’t keep the pace will fall behind. “(…) in the past human had to struggle against exploitation, in the twenty-first century the really big struggle will be against irrelevance.”

I couldn’t help but connect Harari’s warnings to what Carl Newport’s describes as the abilities to thrive in the new economy: The ability to quickly master hard things, and the ability to produce at an elite level, in terms of both quality and speed.

Both abilities can be learned, but not in a short period of time. So better start today.

This article has also been published in Spanish with the title La disrupción de la Inteligencia Artificial y la necesidad de aprender a aprender.