6. Deep mind, inteligencias que aprenden y «no vemos que no vemos», se complejiza la ecuación

The real challenge here is not designing and programming robots to beat us at things, but instead to work with us. The idea of us all being replaced by robots is disturbing to most people, and would require a total redesign of society without precedent. We are moving toward a society where robots will play a bigger role than ever, there’s no doubt about that: the issue here is how it happens and what comes later. Those are questions that will take time to answer. What kinds of society we live in, wealth distribution, the role of humans, the development of society, these are the key questions. But the simple truth is that technological development cannot be stopped. Enrique Dans Now a machine can beat a human at Go, what next?

Version en castellano: Reflexiones s/ machine learning Go y lo que viene después

1. Contando historias, mystery boxes, mcguffins and engagement skills

JJ Abrams TED Talk Clip (2007) – The Mystery Box and Technology

Charla completa en

The mystery box

el McGuffin de Hitchcock

The director and producer Alfred Hitchcock popularized the term «MacGuffin» («a plot device that motivates the characters and advances the story») and the technique, with his 1935 film The 39 Steps, an early example of the concept.[4][5] Hitchcock explained the term «MacGuffin» in a 1939 lecture at Columbia University:

It might be a Scottish name, taken from a story about two men on a train. One man says, «What’s that package up there in the baggage rack?» And the other answers, «Oh, that’s a MacGuffin». The first one asks, «What’s a MacGuffin?» «Well,» the other man says, «it’s an apparatus for trapping lions in the Scottish Highlands.» The first man says, «But there are no lions in the Scottish Highlands,» and the other one answers, «Well then, that’s no MacGuffin!» So you see that a MacGuffin is actually nothing at all.

Teasers and involvements: del pitch convencional al nano-pitch y a los 6 pitches de Daniel Pink en To sell is human: 1. Question pitch, 2.e-mail pitch; 3. Rhyming pitch; 4. the Twitter pitch; 5. the subject-line pitch; 6. the Pixar pitch

Pink and Carine Gallo agree that the purpose of these elevator pitch alternatives is not to say something so profound that it flips a switch in your listeners’ minds and they adopt your idea on the spot. The pitch is an opportunity to engage, collaborate, and participate in the development of your idea. Above all, don’t just think of your pitch as a traditional 30-second sales promo you would use in an elevator ride. Think creatively and use one or several of the six alternatives to stand apart from your competition.

2 Nuevas formas de pensar/investigar

“Deep neural networks” (or deep minds) learn in a way that is closer to how our brains learn.

After just four hours of game play, a deep neural net developed by Google’s DeepMind has managed to come up with a Space Invaders strategy so optimal that it was better than any person’s strategy.

DeepMind: inside Google’s super-brain

DeepMind has been combining two promising areas of research -a deep neural network and a reinforcement-learning algorithm – in a really fundamental way. We’re interested in algorithms that can use their learning from one domain and apply that knowledge to a new domain.»

Artificial intelligence tends to get a bad rap in popular culture: as cyborg assassins in Terminator, or operating systems, like Samantha in Her, that lure us into unwitting love.

most processes can be understood, including creativity.» An AI making an entertaining movie? «I’m thinking more on a basic level — putting disparate things together to make a new hypothesis. A novel or film is many decades away, though with music, a more limited domain, there are already passable projects that hint at what’s possible.»

2.1 Evolving our way to artificial intelligence

https://theconversation.com/evolving-our-way-to-artificial-intelligence-54100

Mastering the game of Go with deep neural networks and tree sear

The 21 smartest AI scientists working at Google DeepMind

2.2 Beyond today’s crowdsourced science to tomorrow’s citizen science cyborgs

Citizen science cyborgs

Zooniverse

get people across the globe to donate some part of their cognitive surplus, pool it with others’ and apply it to scientific research.

rather than trawling through mountains of data by themselves, they will teach computers how to analyze data.

analyzing images, Image Recognition

galaxies,

Experience a privileged glimpse of the distant universe as observed by the SDSS and the CTIO, and tested through state-of-the-art simulations.

climate records

Welcome to Old Weather
Help scientists recover Arctic and worldwide weather observations made by United States ships since the mid-19th century by transcribing ships’ logs. These transcriptions will contribute to climate model projections and will improve our knowledge of past environmental conditions. Historians will use your work to track past ship movements and tell the stories of the people on board.

or snapshots from the Serengeti

Welcome to Snapshot Serengeti

Hundreds of camera traps in Serengeti National Park, Tanzania, are providing a powerful new window into the dynamics of Africa’s most elusive wildlife species. We need your help to classify all the different animals caught in millions of camera trap images.

2.3 Mas alla de la cultura de los promedios

When U.S. air force discovered the flaw of averages
In the early 1950s, a young lieutenant realized the fatal flaw in the cockpit design of U.S. air force jets. Todd Rose explains in an excerpt from his book, The End of Average.

3. Futuro del trabajo

3.1 Irrelevancia trabajo

Worldwide, 13% of Employees Are Engaged at Work
by Steve Crabtree
http://www.gallup.com/poll/165269/worldwide-employees-engaged-work.aspx

3.2 Automatización

Frank Pasquale on The Future of the Professions: How Technology Will Transform the Work of Human Experts

Automating the Professions: Utopian Pipe Dream or Dystopian Nightmare?

¿Y si los robots cobran un sueldo?

los Estados Robóticos de América.

Médicos, abogados, ejecutivos de empresas e incluso los columnistas de tecnología del The New York Times habrán visto raleadas sus filas, reemplazados por algoritmos atractivos que lo saben todo.

¿Cómo será el futuro si los humanos quedan al margen de las tareas cotidianas y profesionales? Los especialistas y emprendedores analizan las alternativas para enfrentar las transformaciones que experimentará la sociedad en las próximas décadas

Médicos, abogados, ejecutivos de empresas e incluso los columnistas de tecnología del The New York Times habrán visto raleadas sus filas, reemplazados por algoritmos atractivos que lo saben todo.

How GM Beat Tesla to the First True Mass-Market Electric Car

3.3 No vemos que no vemos

Public Predictions for the Future of Workforce Automation

A majority of Americans predict that within 50 years, robots and computers will do much of the work currently done by humans – but few workers expect their own jobs or professions to experience substantial impacts