La inteligencia artificial como Big Challenge

Omar Sultan Al-Ulama Un Ministro de Inteligencia Artificial de 27 años

Pensar lo impensable es el gran desafío del siglo XXI. Diseñar lo inesperado, movernos en aguas turbulentas, desandar caminos trillados, sacarnos del lodo de nuestras confusiones e incertidumbre tirándonos de nuestros propios cabellos (bootstrapping) al mejor estilo Baron de Munchausen es precisamente lo que debemos instalar como competencias antidisciplinarias en los no-alumnos del tercer Milenio.

Como bien exige Joi Ito (en Whiplash) de lo que se trata es de vivir a la altura de la antidisciplinariedad (u omnidisciplinariedad) en todo momento. Mas allá de toda fragmentación disciplinaria, evadiendo los obstáculos epistemológicos propios del reduccionismo, captando con mirada holística (atravesada a su vez por una capacidad de resolver los macro- problemas) de largo plazo y poderosa resolución, entender lo que vendrá -no como continuación del pasado sin sorpresas- sino como su alteración y superación.

Definir esos macroproblemas está en el espíritu de los tiempos como lo hacen aquí las Naciones Unidas

What Are The World’s Biggest Problems?

1. Poverty
2. Hunger
3. Water
4. Education
5. Inequality

y lo resumen en sus grandes desafios

___________

Partnership con las máquinas

Curiosamente en este detallado listado que incluye al final 17. Partnership para conseguir los objetivos le falta, para nuestro gusto un 18, a saber un partnership con las máquinas, ya que de no lograrlo a tiempo y en forma corremos el riesgo, no necesariamente de extinguir la especie humana absorbidos por ellas, pero si de perder un escalón evolutivo que haría imposible su continuidad, ya que sin las inteligencias sintéticas no será posible resolver los 16 anteriores.

Para hacer este recorrido necesitamos un andamio conceptual, que orientado por ka siguiente pregunta batesoniana:

¿Cuál es el pattern que conecta al diseño con el hambre, al arte con la indigencia, a la ciencia con la sustentabilidad, a la superinteligencias con la supervivencia humana?

Las preguntas que no podemos dejar de formularnos son enormes y generalmente están muy mal tratadas:

¿Los robots se adueñarán todos nuestros trabajos?
¿Alguna vez un robot será tan inteligente como un chico de 3 años?
¿Será posible controlar a las IA?
¿Es posible que una inteligencia inferior cree a una superior?
¿Es Internet una superinteligencia?
¿Hay mejores médicos (robóticos) que Dr House?
¿Hay vida posible sin Waze (agenets inteligentes)?
¿Que pensamos acerca de las máquinas que piensan?
¿Porque las IA salvarán a la humanidad al colonizar el espacio?

¿Siguen vigentes las 3 leyes robóticas de Asimo formulas en Runaround/Circulo Vicioso (1942))

1 Un robot no hará daño a un ser humano o, por inacción, permitir que un ser humano sufra daño.
2 Un robot debe obedecer las órdenes dadas por los seres humanos, excepto si estas órdenes entrasen en conflicto con la 1ª Ley.
3 Un robot debe proteger su propia existencia en la medida en que esta protección no entre en conflicto con la 1ª o la 2ª Ley.1

Venimos trabajando hace muchos años en estas cuestiones, y nuestras preocupaciones/propuestas decantaron en un evento realizado en el DF el 16 de Febrero de 2017 titulado VOR: Superinteligencias.

Aquí una síntesis Inteligencias humanas vs sintéticas de los temas tratados que serán el eje del taller de hoy

Aquí una presentación que sintetiza el estado de la cuestiòn

REFERENCIAS

Las referencias están divididas en links, libros, películas, programas de investigación, charlas TED.

Un lugar especial corresponde al backstage del armado de la conferencia VOR que llevó cerca de un año y que implicó una investigación, entrevistas, visitas, lecturas, experiencias y diseños intrincados sumamente sugerentes.

1 LIBROS

Domingos, Pedro The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Algorithms increasingly run our lives. They find books, movies, jobs, and dates for us, manage our investments, and discover new drugs. More and more, these algorithms work by learning from the trails of data we leave in our newly digital world. Like curious children, they observe us, imitate, and experiment. And in the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask.

Machine learning is the automation of discovery—the scientific method on steroids—that enables intelligent robots and computers to program themselves. He charts a course through machine learning’s five major schools of thought, showing how they turn ideas from neuroscience, evolution, psychology, physics, and statistics into algorithms ready to serve you

Nick Bostrom Superintelligences: Paths, Dangers, Strategies

As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence. But we have one advantage: we get to make the first move. Will it be possible to construct a seed Artificial Intelligence, to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation? What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us?

McAfee, by Andrew & Brynjolfsson, Erik Machine, Platform, Crowd: Harnessing Our Digital Future (2017)

We live in strange times. A machine plays the strategy game Go better than any human; upstarts like Apple and Google destroy industry stalwarts such as Nokia; ideas from the crowd are repeatedly more innovative than corporate research labs. MIT’s Andrew McAfee and Erik Brynjolfsson know what it takes to master this digital-powered shift: we must rethink the integration of minds and machines, of products and platforms, and of the core and the crowd. In all three cases, the balance now favors the second element of the pair, with massive implications for how we run our companies and live our lives.

Brian Christian & Tom Griffiths Algorithms to Live By: The Computer Science of Human Decisions (2017)

What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of the new and familiar is the most fulfilling? These may seem like uniquely human quandaries, but they are not. Computers, like us, confront limited space and time, so computer scientists have been grappling with similar problems for decades. And the solutions they’ve found have much to teach us. In a dazzlingly interdisciplinary work, Brian Christian and Tom Griffiths show how algorithms developed for computers also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one’s inbox to peering into the future, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.

John Brockman (ed) What to Think About Machines That Think: Today’s Leading Thinkers on the Age of Machine Intelligence (2015)

Stephen Hawking recently made headlines by noting, “The development of full artificial intelligence could spell the end of the human race.” Others, conversely, have trumpeted a new age of “superintelligence” in which smart devices will exponentially extend human capacities. No longer just a matter of science-fiction fantasy (2001, Blade Runner, The Terminator, Her, etc.), it is time to seriously consider the reality of intelligent technology, many forms of which are already being integrated into our daily lives. In that spirit, John Brockman, publisher of Edge. org (“the world’s smartest website” – The Guardian), asked the world’s most influential scientists, philosophers, and artists one of today’s most consequential questions: What do you think about machines that think?

2 PELICULAS

2001

Blade Runner

Ex-Machina

Minority Report

3 PROGRAMAS INVESTIGACION

IA Now Institute NYU

Vector Institute for Artifical Intelligence, Toronto

Mit Media Lab

Time well spent

4. CHARLAS TED

Nick Bostrom What happens when our computers get smarter than we are? #TED2015

Artificial intelligence is getting smarter by leaps and bounds — within this century, research suggests, a computer AI could be as “smart” as a human being. And then, says Nick Bostrom, it will overtake us: “Machine intelligence is the last invention that humanity will ever need to make.” A philosopher and technologist, Bostrom asks us to think hard about the world we’re building right now, driven by thinking machines. Will our smart machines help to preserve humanity and our values — or will they have values of their own?

Fei Fei Li How we are teaching computers to understand pictures

When a very young child looks at a picture, she can identify simple elements: “cat,” “book,” “chair.” Now, computers are getting smart enough to do that too. What’s next? In a thrilling talk, computer vision expert Fei-Fei Li describes the state of the art — including the database of 15 million photos her team built to “teach” a computer to understand pictures — and the key insights yet to come.

Jeremy Howard: The wonderful and terrifying implications of computers that can learn

What happens when we teach a computer how to learn? Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. (One deep learning tool, after watching hours of YouTube, taught itself the concept of “cats.”) Get caught up on a field that will change the way the computers around you behave … sooner than you probably think.

Kevin Slavin: How algorithms shape our world

Kevin Slavin argues that we’re living in a world designed for — and increasingly controlled by — algorithms. In this riveting talk from TEDGlobal, he shows how these complex computer programs determine: espionage tactics, stock prices, movie scripts, and architecture. And he warns that we are writing code we can’t understand, with implications we can’t control.

Patrick Lin The ethical dilemma of self-driving cars –

As artificial intelligence advances, it may very well use the programmable ethics settings found in self-driving cars as a platform to build upon

If we determined today that favoring “quantity of lives” is the sole rule to follow for self-driving cars, for example, a much more developed, Skynet-esque AI of the future might calculate that citizens of industrialized countries are making the world uninhabitable for a majority of people and their many generations of offspring. Ethically, that AI could justify eradicating a large swath of the population so that an even larger percent can live.