Category: Education


“Es el momento de hacer que nuestros niños sean más inteligentes que la inteligencia artificial”

By Hugo Angel,

Danqing Wang Computer ABC

ENTREVISTA | Noriko Arai, directora del Todai Robot Project »

Noriko Arai quiere revolucionar el sistema educativo para que los humanos no pierdan la batalla laboral contra los robots

Noriko Arai durante su charla TED en Vancouver.

Noriko Arai durante su charla TED en Vancouver. Bret Hartman / TED


Una vez al año, medio millón de estudiantes japoneses realizan el examen de acceso a la universidad, ocho pruebas tipo test. Menos del 3% lo harán suficientemente bien como para hacer la segunda parte, un examen escrito diseñado especialmente para el acceso a la Universidad de Tokio (Todai), la más prestigiosa de Japón. Noriko Arai, de 54 años, directora del Centro de Investigación para el Conocimiento en la Comunidad del Instituto Nacional de Informática y del Todai Robot Project, está trabajando en un robot que pueda aprobar todos estos exámenes para aprender las posibilidades y las limitaciones de la inteligencia artificial.

En 2013, tras dos años de Proyecto, el robot Todai sacó una nota suficientemente buena para ser admitido en 472 de 581 universidades privadas. En 2016, su nota estuvo entre el 20% de las mejores en los exámenes tipo test, y en entre el 1% de los mejores en uno de los dos exámenes de matemáticas. Además, fue capaz de escribir una redacción sobre el comercio marítimo del siglo XVII mejor que la mayoría de los estudiantes. “Tomó información del libro de texto y de Wikipedia y la combinó sin entender ni pizca”, explicó Arai durante su reciente charla TED en Vancouver. “Ni Watson, ni Siri, ni Todai Robot pueden leer. La inteligencia artificial no puede entender, solo hace como que entiende”.

Más que contenta por su robot, Arai quedó alarmada por los resultados. “¿Cómo es posible que esta máquina no inteligente lo hiciera mejor que nuestros niños?”, se preguntaba. Preocupada por el futuro laboral de las nuevas generaciones, realizó un experimento con estudiantes y descubrió que un tercio de ellos fallaron preguntas sencillas porque no leen bien, un problema que, piensa, existe en todo el mundo. “Nosotros, los humanos, podemos comprender el significado de las cosas, algo que no puede hacer la inteligencia artificial. Pero la mayoría de los estudiantes reciben conocimiento sin comprender el significado, y eso no es conocimiento, es memorización, y la inteligencia artificial puede hacer lo mismo. Debemos crear un nuevo sistema educativo”.

Pregunta: ¿Por qué decidió una matemática como usted meterse en el mundo de los robots?
Respuesta: La inteligencia artificial consiste en intentar escribir el pensamiento en lenguaje matemático. No hay otra forma para que la inteligencia artificial sea inteligente. Como matemática creo que el pensamiento no puede escribirse en el lenguaje matemático. Descartes dijo lo mismo. Mi primera impresión fue que la inteligencia artificial es imposible. Utiliza probabilidad y estadística sumada a la lógica. En el siglo XX se usaba solo la lógica, y por supuesto que no todo puede ser escrito con lógica, como los sentimientos, por ejemplo. Ahora están usando estadística, imitando el pasado para ver cómo actuar cuando encontremos cosas nuevas.

P. No le gusta cuando la gente dice que la inteligencia artificial podría conquistar el mundo…
R. Estoy harta de esa imagen, por eso decidí crear un robot muy inteligente, utilizando lo último en investigación para ver sus limitaciones. Watson de IBM y Google Car, por ejemplo, tienden a mostrar solo las cosas buenas. Nosotros queremos mostrarlo todo. También lo que no es capaz de hacer.

P. Al intentar mejorar la inteligencia artificial, usted vio que había que mejorar la educación.

R. Sabía que mi robot era ininteligente, cargado de conocimientos que no sabe cómo usar correctamente porque no entiende el significado. Quedé estupefacta al ver que este robot que no es inteligente escribió una redacción mejor que la mayoría de los estudiantes. Así que decidí investigar lo que estaba ocurriendo en el mundo humano. Estaría más contenta si hubiera descubierto que la inteligencia artificial adelantó a los estudiantes porque es mejor en memorizar y computar, pero ese no era el caso. El robot no comprende el significado, pero tampoco la mayoría de los estudiantes.

P. ¿Cree usted que el problema es que dependemos tanto de Siri y Google para resolver nuestras dudas que por eso no procesamos la información bien?
R. Estamos analizando el porqué. Algo que podemos ver es que antes todo el mundo leía el periódico, incluso la gente pobre. Pero ahora la mayoría de las parejas jóvenes no leen el diario porque lo tienen en su teléfono. No compran libros porque la mayoría de las historias están en blogs. No tienen calendario o hasta reloj en casa porque lo tienen en el teléfono. Los niños se crían sin números ni letras en su ambiente. Y también tienden a tener conversaciones en mensajes de texto muy cortos. Tienen menos oportunidades de leer, creo.

P. Parte del proyecto Todai es ver qué tipo de trabajos la inteligencia artificial podría quitarle a los humanos.
R. En Japón, antes, todo el mundo era clase media, no había gente muy rica, ni gente muy pobre. Pero cuando la inteligencia artificial llega a una sociedad se lleva muchos trabajos, incluidos los puestos de banqueros o analistas. Quienes pierden su trabajo por culpa de la inteligencia artificial puede que no encuentren otro en mucho tiempo. Quizás haya trabajos como corregir los errores cometidos por la inteligencia artificial, trabajos muy duros y más insignificantes que nunca, como en Tiempos Modernos de Chaplin. Alguien con talento, creativo, inteligente, determinado, bueno en la lectura y la escritura, tendrá más oportunidades que nunca porque incluso si nació en un pueblo, mientras tenga acceso a Internet dispondrá de mucha información para aprender gratuitamente y llegar a hacerse millonario. Es mucho más fácil comenzar un negocio que en el siglo XX. Pero alguien que no tiene ese tipo de inteligencia, probablemente se quede atrapado entre las multitudes. Lo que pasa es que todos tienen derecho a voto, y, en ese sentido, somos todos iguales. Si cada vez hay más y más gente que se siente atrapada y solo la gente inteligente gana dinero, y los utiliza para ganar más dinero, pensarán mal de la sociedad, odiarán a la sociedad, y las consecuencias las sufriremos todos, en todo el mundo.

P. ¿Cuál piensa que es la solución?
R. Ahora es el momento de hacer que nuestros niños sean más inteligentes que la inteligencia artificial. He inaugurado el Instituto de Investigación de la Ciencia para la Educación este mes para investigar cuántos estudiantes tienen malos hábitos de lectura y escritura, y por qué, y ver cómo podemos ayudarles a modificar esos hábitos para que puedan adelantar al robot usando su poderío humano. Me gustaría que estuviéramos como en Japón de los años setenta, cuando todo el mundo era de clase media, todos nos ayudábamos y no necesitábamos más dinero del que somos capaces de gastar en nuestra vida. Todo el mundo debería estar bien educado, saber leer y escribir, pero no solo el significado literal. Todos deberíamos aprender con profundidad, leer con profundidad para poder mantener nuestro trabajo.

ORIGINAL: El País
Por Isaac Hernández Isaac Hernández. Vancouver
6 JUN 2017

Silicon Valley Then and Now: To Invent the Future, You Must Understand the Past

By admin,

William Shockley’s employees toast him for his Nobel Prize, 1956. Photo courtesy Computer History Museum.
You can’t really understand what is going on now without understanding what came before.
Steve Jobs is explaining why, as a young man, he spent so much time with the Silicon Valley entrepreneurs a generation older, men like Robert Noyce, Andy Grove, and Regis McKenna.
It’s a beautiful Saturday morning in May, 2003, and I’m sitting next to Jobs on his living room sofa, interviewing him for a book I’m writing. I ask him to tell me more about why he wanted, as he put it, “to smell that second wonderful era of the valley, the semiconductor companies leading into the computer.” Why, I want to know, is it not enough to stand on the shoulders of giants? Why does he want to pick their brains?
It’s like that Schopenhauer quote about the conjurer,” he says. When I look blank, he tells me to wait and then dashes upstairs. He comes down a minute later holding a book and reading aloud:
Steve Jobs and Robert Noyce.
Courtesy Leslie Berlin.
He who lives to see two or three generations is like a man who sits some time in the conjurer’s booth at a fair, and witnesses the performance twice or thrice in succession. The tricks were meant to be seen only once, and when they are no longer a novelty and cease to deceive, their effect is gone.
History, Jobs understood, gave him a chance to see — and see through — the conjurer’s tricks before they happened to him, so he would know how to handle them.
Flash forward eleven years. It’s 2014, and I am going to see Robert W. Taylor. In 1966, Taylor convinced the Department of Defense to build the ARPANET that eventually formed the core of the Internet. He went on to run the famous Xerox PARC Computer Science Lab that developed the first modern personal computer. For a finishing touch, he led one of the teams at DEC behind the world’s first blazingly fast search engine — three years before Google was founded.
Visiting Taylor is like driving into a Silicon Valley time machine. You zip past the venture capital firms on Sand Hill Road, over the 280 freeway, and down a twisty two-lane street that is nearly impassable on weekends, thanks to the packs of lycra-clad cyclists on multi-thousand-dollar bikes raising their cardio thresholds along the steep climbs. A sharp turn and you enter what seems to be another world, wooded and cool, the coastal redwoods dense along the hills. Cell phone signals fade in and out in this part of Woodside, far above Buck’s Restaurant where power deals are negotiated over early-morning cups of coffee. GPS tries valiantly to ascertain a location — and then gives up.
When I get to Taylor’s home on a hill overlooking the Valley, he tells me about another visitor who recently took that drive, apparently driven by the same curiosity that Steve Jobs had: Mark Zuckerberg, along with some colleagues at the company he founded, Facebook.
Zuckerberg must have heard about me in some historical sense,” Taylor recalls in his Texas drawl. “He wanted to see what I was all about, I guess.
 
To invent the future, you must understand the past.

I am a historian, and my subject matter is Silicon Valley. So I’m not surprised that Jobs and Zuckerberg both understood that the Valley’s past matters today and that the lessons of history can take innovation further. When I talk to other founders and participants in the area, they also want to hear what happened before. Their questions usually boil down to two:

  1. Why did Silicon Valley happen in the first place, and 
  2. why has it remained at the epicenter of the global tech economy for so long?
I think I can answer those questions.

First, a definition of terms. When I use the term “Silicon Valley,” I am referring quite specifically to the narrow stretch of the San Francisco Peninsula that is sandwiched between the bay to the east and the Coastal Range to the west. (Yes, Silicon Valley is a physical valley — there are hills on the far side of the bay.) Silicon Valley has traditionally comprised 

  • Santa Clara County and 
  • the southern tip of San Mateo County. In the past few years, 
  • parts of Alameda County and 
  • the city of San Francisco 

can also legitimately be considered satellites of Silicon Valley, or perhaps part of “Greater Silicon Valley.

The name “Silicon Valley,” incidentally, was popularized in 1971 by a hard-drinking, story-chasing, gossip-mongering journalist named Don Hoefler, who wrote for a trade rag called Electronic News. Before, the region was called the Valley of the Hearts Delight,” renowned for its apricot, plum, cherry and almond orchards.
This was down-home farming, three generations of tranquility, beauty, health, and productivity based on family farms of small acreage but bountiful production,” reminisced Wallace Stegner, the famed Western writer. To see what the Valley looked like then, watch the first few minutes of this wonderful 1948 promotional video for the “Valley of the Heart’s Delight.”
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Three historical forces — technical, cultural, and financial — created Silicon Valley.
 
Technology
On the technical side, in some sense the Valley got lucky. In 1955, one of the inventors of the transistor, William Shockley, moved back to Palo Alto, where he had spent some of his childhood. Shockley was also a brilliant physicist — he would share the Nobel Prize in 1956 — an outstanding teacher, and a terrible entrepreneur and boss. Because he was a brilliant scientist and inventor, Shockley was able to recruit some of the brightest young researchers in the country — Shockley called them “hot minds” — to come work for him 3,000 miles from the research-intensive businesses and laboratories that lined the Eastern Seaboard from Boston to Bell Labs in New Jersey. Because Shockley was an outstanding teacher, he got these young scientists, all but one of whom had never built transistors, to the point that they not only understood the tiny devices but began innovating in the field of semiconductor electronics on their own.
And because Shockley was a terrible boss — the sort of boss who posted salaries and subjected his employees to lie-detector tests — many who came to work for him could not wait to get away and work for someone else. That someone else, it turned out, would be themselves. The move by eight of Shockley’s employees to launch their own semiconductor operation called Fairchild Semiconductor in 1957 marked the first significant modern startup company in Silicon Valley. After Fairchild Semiconductor blew apart in the late-1960s, employees launched dozens of new companies (including Intel, National and AMD) that are collectively called the Fairchildren.
The Fairchild 8: Gordon Moore, Sheldon Roberts, Eugene Kleiner, Robert Noyce, Victor Grinich, Julius Blank, Jean Hoerni, and Jay Last. Photo courtesy Wayne Miller/Magnum Photos.
Equally important for the Valley’s future was the technology that Shockley taught his employees to build: the transistor. Nearly everything that we associate with the modern technology revolution and Silicon Valley can be traced back to the tiny, tiny transistor.
 
Think of the transistor as the grain of sand at the core of the Silicon Valley pearl. The next layer of the pearl appeared when people strung together transistors, along with other discrete electronic components like resistors and capacitors, to make an entire electronic circuit on a single slice of silicon. This new device was called a microchip. Then someone came up with a specialized microchip that could be programmed: the microprocessor. The first pocket calculators were built around these microprocessors. Then someone figured out that it was possible to combine a microprocessor with other components and a screen — that was a computer. People wrote code for those computers to serve as operating systems and software on top of those systems. At some point people began connecting these computers to each other: networking. Then people realized it should be possible to “virtualize” these computers and store their contents off-site in a “cloud,” and it was also possible to search across the information stored in multiple computers. Then the networked computer was shrunk — keeping the key components of screen, keyboard, and pointing device (today a finger) — to build tablets and palm-sized machines called smart phones. Then people began writing apps for those mobile devices … .
You get the picture. These changes all kept pace to the metronomic tick-tock of Moore’s Law.
The skills learned through building and commercializing one layer of the pearl underpinned and supported the development of the next layer or developments in related industries. Apple, for instance, is a company that people often speak of as sui generis, but Apple Computer’s early key employees had worked at Intel, Atari, or Hewlett-Packard. Apple’s venture capital backers had either backed Fairchild or Intel or worked there. The famous Macintosh, with its user-friendly aspect, graphical-user interface, overlapping windows, and mouse was inspired by a 1979 visit Steve Jobs and a group of engineers paid to XEROX PARC, located in the Stanford Research Park. In other words, Apple was the product of its Silicon Valley environment and technological roots.
Culture
This brings us to the second force behind the birth of Silicon Valley: culture. When Shockley, his transistor and his recruits arrived in 1955, the valley was still largely agricultural, and the small local industry had a distinctly high-tech (or as they would have said then, “space age”) focus. The largest employer was defense contractor Lockheed. IBM was about to open a small research facility. Hewlett-Packard, one of the few homegrown tech companies in Silicon Valley before the 1950s, was more than a decade old.
Stanford, meanwhile, was actively trying to build up its physics and engineering departments. Professor (and Provost from 1955 to 1965) Frederick Terman worried about a “brain drain” of Stanford graduates to the East Coast, where jobs were plentiful. So he worked with President J.E. Wallace Sterling to create what Terman called “a community of technical scholars” in which the links between industry and academia were fluid. This meant that as the new transistor-cum-microchip companies began to grow, technically knowledgeable engineers were already there.
Woz and Jobs.
Photo courtesy Computer History Museum.
These trends only accelerated as the population exploded. Between 1950 and 1970, the population of Santa Clara County tripled, from roughly 300,000 residents to more than 1 million. It was as if a new person moved into Santa Clara County every 15 minutes for 20 years. The newcomers were, overall, younger and better educated than the people already in the area. The Valley changed from a community of aging farmers with high school diplomas to one filled with 20-something PhDs.
All these new people pouring into what had been an agricultural region meant that it was possible to create a business environment around the needs of new companies coming up, rather than adapting an existing business culture to accommodate the new industries. In what would become a self-perpetuating cycle, everything from specialized law firms, recruiting operations and prototyping facilities; to liberal stock option plans; to zoning laws; to community college course offerings developed to support a tech-based business infrastructure.
Historian Richard White says that the modern American West was “born modern” because the population followed, rather than preceded, connections to national and international markets. Silicon Valley was bornpost-modern, with those connections not only in place but so taken for granted that people were comfortable experimenting with new types of business structures and approaches strikingly different from the traditional East Coast business practices with roots nearly two centuries old.
From the beginning, Silicon Valley entrepreneurs saw themselves in direct opposition to their East Coast counterparts. The westerners saw themselves as cowboys and pioneers, working on a “new frontier” where people dared greatly and failure was not shameful but just the quickest way to learn a hard lesson. In the 1970s, with the influence of the counterculture’s epicenter at the corner of Haight and Ashbury, only an easy drive up the freeway, Silicon Valley companies also became famous for their laid-back, dressed-down culture, and for their products, such as video games and personal computers, that brought advanced technology to “the rest of us.
 
Money

The third key component driving the birth of Silicon Valley, along with the right technology seed falling into a particularly rich and receptive cultural soil, was money. Again, timing was crucial. Silicon Valley was kick-started by federal dollars. Whether it was

  • the Department of Defense buying 100% of the earliest microchips, 
  • Hewlett-Packard and Lockheed selling products to military customers, or 
  • federal research money pouring into Stanford, 

Silicon Valley was the beneficiary of Cold War fears that translated to the Department of Defense being willing to spend almost anything on advanced electronics and electronic systems. The government, in effect, served as the Valley’s first venture capitalist.

The first significant wave of venture capital firms hit Silicon Valley in the 1970s. Both Sequoia Capital and Kleiner Perkins Caufield and Byers were founded by Fairchild alumni in 1972. Between them, these venture firms would go on to fund Amazon, Apple, Cisco, Dropbox, Electronic Arts, Facebook, Genentech, Google, Instagram, Intuit, and LinkedIn — and that is just the first half of the alphabet.
This model of one generation succeeding and then turning around to offer the next generation of entrepreneurs financial support and managerial expertise is one of the most important and under-recognized secrets to Silicon Valley’s ongoing success. Robert Noyce called it “re-stocking the stream I fished from.” Steve Jobs, in his remarkable 2005 commencement address at Stanford, used the analogy of a baton being passed from one runner to another in an ongoing relay across time.
 
So that’s how Silicon Valley emerged. Why has it endured?

After all, if modern Silicon Valley was born in the 1950s, the region is now in its seventh decade. For roughly two-thirds of that time, Valley watchers have predicted its imminent demise, usually with an allusion to Detroit.

  • First, the oil shocks and energy crises of the 1970s were going to shut down the fabs (specialized factories) that build microchips. 
  • In the 1980s, Japanese competition was the concern. 
  • The bursting of the dot-com bubble
  • the rise of formidable tech regions in other parts of the world
  • the Internet and mobile technologies that make it possible to work from anywhere: 

all have been heard as Silicon Valley’s death knell.

The Valley of Heart’s Delight, pre-technology. OSU Special Collections.
The Valley economy is notorious for its cyclicity, but it has indeed endured. Here we are in 2015, a year in which more patents, more IPOs, and a larger share of venture capital and angel investments have come from the Valley than ever before. As a recent report from Joint Venture Silicon Valley (***) put it, “We’ve extended a four-year streak of job growth, we are among the highest income regions in the country, and we have the biggest share of the nation’s high-growth, high-wage sectors.” Would-be entrepreneurs continue to move to the Valley from all over the world. Even companies that are not started in Silicon Valley move there (witness Facebook).
Why? What is behind Silicon Valley’s staying power? The answer is that many of the factors that launched Silicon Valley in the 1950s continue to underpin its strength today even as the Valley economy has proven quite adaptable.
Technology
The Valley still glides in the long wake of the transistor, both in terms of technology and in terms of the infrastructure to support companies that rely on semiconductor technology. Remember the pearl. At the same time, when new industries not related directly to semiconductors have sprung up in the Valley — industries like biotechnology — they have taken advantage of the infrastructure and support structure already in place.
Money
Venture capital has remained the dominant source of funding for young companies in Silicon Valley. In 2014, some $14.5 billion in venture capital was invested in the Valley, accounting for 43 percent of all venture capital investments in the country. More than half of Silicon Valley venture capital went to software investments, and the rise of software, too, helps to explain the recent migration of many tech companies to San Francisco. (San Francisco, it should be noted, accounted for nearly half of the $14.5 billion figure.) Building microchips or computers or specialized production equipment — things that used to happen in Silicon Valley — requires many people, huge fabrication operations and access to specialized chemicals and treatment facilities, often on large swaths of land. Building software requires none of these things; in fact, software engineers need little more than a computer and some server space in the cloud to do their jobs. It is thus easy for software companies to locate in cities like San Francisco, where many young techies want to live.
Culture
The Valley continues to be a magnet for young, educated people. The flood of intranational immigrants to Silicon Valley from other parts of the country in the second half of the twentieth century has become, in the twenty-first century, a flood of international immigrants from all over the world. It is impossible to overstate the importance of immigrants to the region and to the modern tech industry. Nearly 37 percent of the people in Silicon Valley today were born outside of the United States — of these, more than 60 percent were born in Asia and 20 percent in Mexico. Half of Silicon Valley households speak a language other than English in the home. Sixty-five percent of the people with Bachelors degrees working in science and engineering in the valley were born in another country. Let me say that again: 2/3 of people in working in sci-tech Valley industries who have completed their college education are foreign born. (Nearly half the college graduates working in all industries in the valley are foreign-born.)
Here’s another way to look at it: From 1995 to 2005, more than half of all Silicon Valley startups had at least one founder who was born outside the United States.[13] Their businesses — companies like Google and eBay — have created American jobs and billions of dollars in American market capitalization.
Silicon Valley, now, as in the past, is built and sustained by immigrants.
Gordon Moore and Robert Noyce at Intel in 1970. Photo courtesy Intel.
Stanford also remains at the center of the action. By one estimate, from 2012, companies formed by Stanford entrepreneurs generate world revenues of $2.7 trillion annually and have created 5.4 million jobs since the 1930s. This figure includes companies whose primary business is not tech: companies like Nike, Gap, and Trader Joe’s. But even if you just look at Silicon Valley companies that came out of Stanford, the list is impressive, including Cisco, Google, HP, IDEO, Instagram, MIPS, Netscape, NVIDIA, Silicon Graphics, Snapchat, Sun, Varian, VMware, and Yahoo. Indeed, some critics have complained that Stanford has become overly focused on student entrepreneurship in recent years — an allegation that I disagree with but is neatly encapsulated in a 2012 New Yorker article that called the university “Get Rich U.”
 
Change
The above represent important continuities, but change has also been vital to the region’s longevity. Silicon Valley has been re-inventing itself for decades, a trend that is evident with a quick look at the emerging or leading technologies in the area:
• 1940s: instrumentation
• 1950s/60s: microchips
• 1970s: biotech, consumer electronics using chips (PC, video game, etc)
• 1980s: software, networking
• 1990s: web, search
• 2000s: cloud, mobile, social networking
The overriding sense of what it means to be in Silicon Valley — the lionization of risk-taking, the David-versus-Goliath stories, the persistent belief that failure teaches important business lessons even when the data show otherwise — has not changed, but over the past few years, a new trope has appeared alongside the Western metaphors of Gold Rushes and Wild Wests: Disruption.
“Disruption” is the notion, roughly based on ideas first proposed by Joseph Schumpeter in 1942, that a little company can come in and — usually with technology — completely remake an industry that seemed established and largely impervious to change. So: Uber is disrupting the taxi industry. Airbnb is disrupting the hotel industry. The disruption story is, in its essentials, the same as the Western tale: a new approach comes out of nowhere to change the establishment world for the better. You can hear the same themes of adventure, anti-establishment thinking, opportunity and risk-taking. It’s the same song, with different lyrics.
The shift to the new language may reflect the key role that immigrants play in today’s Silicon Valley. Many educated, working adults in the region arrived with no cultural background that promoted cowboys or pioneers. These immigrants did not even travel west to get to Silicon Valley. They came east, or north. It will be interesting to see how long the Western metaphor survives this cultural shift. I’m betting that it’s on its way out.
Something else new has been happening in Silicon Valley culture in the past decade. The anti-establishment little guys have become the establishment big guys. Apple settled an anti-trust case. You are hearing about Silicon Valley companies like Facebook or Google collecting massive amounts of data on American citizens, some of which has ended up in the hands of the NSA. What happens when Silicon Valley companies start looking like the Big Brother from the famous 1984 Apple Macintosh commercial?
A Brief Feint at the Future
I opened these musings by defining Silicon Valley as a physical location. I’m often asked how or whether place will continue to matter in the age of mobile technologies, the Internet and connections that will only get faster. In other words, is region an outdated concept?
I believe that physical location will continue to be relevant when it comes to technological innovation. Proximity matters. Creativity cannot be scheduled for the particular half-hour block of time that everyone has free to teleconference. Important work can be done remotely, but the kinds of conversations that lead to real breakthroughs often happen serendipitously. People run into each other down the hall, or in a coffee shop, or at a religious service, or at the gym, or on the sidelines of a kid’s soccer game.
It is precisely because place will continue to matter that the biggest threats to Silicon Valley’s future have local and national parameters. Silicon Valley’s innovation economy depends on its being able to attract the brightest minds in the world; they act as a constant innovation “refresh” button. If Silicon Valley loses its allure for those people —

  • if the quality of public schools declines so that their children cannot receive good educations, 
  • if housing prices remain so astronomical that fewer than half of first-time buyers can afford the median-priced home, or 
  • if immigration policy makes it difficult for high-skilled immigrants who want to stay here to do so — 

the Valley’s status, and that of the United States economy, will be threatened. Also worrisome: ever-expanding gaps between the highest and lowest earners in Silicon Valley; stagnant wages for low- and middle-skilled workers; and the persistent reality that as a group, men in Silicon Valley earn more than women at the same level of educational attainment. Moreover, today in Silicon Valley, the lowest-earning racial/ethnic group earns 70 percent less than the highest earning group, according to the Joint Venture report. The stark reality, with apologies to George Orwell, is that even in the Valley’s vaunted egalitarian culture, some people are more equal than others.

Another threat is the continuing decline in federal support for basic research. Venture capital is important for developing products into companies, but the federal government still funds the great majority of basic research in this country. Silicon Valley is highly dependent on that basic research — “No Basic Research, No iPhone” is my favorite title from a recently released report on research and development in the United States. Today, the US occupies tenth place among OECD nations in overall R&D investment. That is investment as a percentage of GDP — somewhere between 2.5 and 3 percent. This represents a 13 percent drop below where we were ten years ago (again as a percentage of GDP). China is projected to outspend the United States in R&D within the next ten years, both in absolute terms and as a fraction of economic development.
People around the world have tried to reproduce Silicon Valley. No one has succeeded.
And no one will succeed because no place else — including Silicon Valley itself in its 2015 incarnation — could ever reproduce the unique concoction of academic research, technology, countercultural ideals and a California-specific type of Gold Rush reputation that attracts people with a high tolerance for risk and very little to lose. Partially through the passage of time, partially through deliberate effort by some entrepreneurs who tried to “give back” and others who tried to make a buck, this culture has become self-perpetuating.
The drive to build another Silicon Valley may be doomed to fail, but that is not necessarily bad news for regional planners elsewhere. The high-tech economy is not a zero-sum game. The twenty-first century global technology economy is large and complex enough for multiple regions to thrive for decades to come — including Silicon Valley, if the threats it faces are taken seriously.
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Robert Reich: The Nightmarish Future for American Jobs and Incomes Is Here

By admin,

Even knowledge-based jobs will disappear as wealth gets more concentrated at the top in the next 10 years.
Photo Credit: via YouTube
What will happen to American jobs, incomes, and wealth a decade from now?
Predictions are hazardous but survivable. In 1991, in my book The Work of Nations, I separated almost all work into three categories, and then predicted what would happen to each of them.
The first category I called “routine production services,” which entailed the kind of repetitive tasks performed by the old foot soldiers of American capitalism through most of the twentieth century — done over and over, on an assembly line or in an office.
I estimated that such work then constituted about one-quarter of all jobs in the United States, but would decline steadily as such jobs were replaced by
  • new labor-saving technologies and
  • by workers in developing nations eager to do them for far lower wages.

I also assumed the pay of remaining routine production workers in America would drop, for similar reasons.

I was not far wrong.
The second category I called “in-person services.This work had to be provided personally because the “human touch” was essential to it. It included retail sales workers, hotel and restaurant workers, nursing-home aides, realtors, childcare workers, home health-care aides, flight attendants, physical therapists, and security guards, among many others.
In 1990, by my estimate, such workers accounted for about 30 percent of all jobs in America, and I predicted their numbers would grow because — given that their services were delivered in person — neither advancing technologies nor foreign-based workers would be able to replace them.
I also predicted their pay would drop. They would be competing with
  • a large number of former routine production workers, who could only find jobs in the “in-person” sector.
  • They would also be competing with labor-saving machinery such as automated tellers, computerized cashiers, automatic car washes, robotized vending machines, and self-service gas pumps —
  • as well as “personal computers linked to television screensthrough which “tomorrow’s consumers will be able to buy furniture, appliances, and all sorts of electronic toys from their living rooms — examining the merchandise from all angles, selecting whatever color, size, special features, and price seem most appealing, and then transmitting the order instantly to warehouses from which the selections will be shipped directly to their homes. 
  • So, too, with financial transactions, airline and hotel reservations, rental car agreements, and similar contracts, which will be executed between consumers in their homes and computer banks somewhere else on the globe.”

Here again, my predictions were not far off. But I didn’t foresee how quickly advanced technologies would begin to make inroads even on in-person services. Ten years from now I expect Amazon will have wiped out many of today’s retail jobs, and Google‘s self-driving car will eliminate many bus drivers, truck drivers, sanitation workers, and even Uber drivers.

The third job category I named “symbolic-analytic services.” Here I included all the problem-solving, problem-identifying, and strategic thinking that go into the manipulation of symbols—data, words, oral and visual representations.
I estimated in 1990 that symbolic analysts accounted for 20 percent of all American jobs, and expected their share to continue to grow, as would their incomes, because the demand for people to do these jobs would continue to outrun the supply of people capable of doing them. This widening disconnect between symbolic-analytic jobs and the other two major categories of work would, I predicted, be the major force driving widening inequality.
Again, I wasn’t far off. But I didn’t anticipate how quickly or how wide the divide would become, or how great a toll inequality and economic insecurity would take. I would never have expected, for example, that the life expectancy of an American white woman without a high school degree would decrease by five years between 1990 and 2008.
We are now faced not just with labor-replacing technologies but with knowledge-replacing technologies. The combination of
  • advanced sensors,
  • voice recognition,
  • artificial intelligence,
  • big data,
  • text-mining, and
  • pattern-recognition algorithms,

is generating smart robots capable of quickly learning human actions, and even learning from one another. A revolution in life sciences is also underway, allowing drugs to be tailored to a patient’s particular condition and genome.

If the current trend continues, many more symbolic analysts will be replaced in coming years. The two largest professionally intensive sectors of the United States — health care and education — will be particularly affected because of increasing pressures to hold down costs and, at the same time, the increasing accessibility of expert machines.
We are on the verge of a wave of mobile health applications, for example, measuring everything from calories to blood pressure, along with software programs capable of performing the same functions as costly medical devices and diagnostic software that can tell you what it all means and what to do about it.
Schools and universities will likewise be reorganized around smart machines (although faculties will scream all the way). Many teachers and university professors are already on the way to being replaced by software — so-called “MOOCs” (Massive Open Online Courses) and interactive online textbooks — along with adjuncts that guide student learning.
As a result, income and wealth will become even more concentrated than they are today. Those who create or invest in blockbuster ideas will earn unprecedented sums and returns. The corollary is they will have enormous political power. But most people will not share in the monetary gains, and their political power will disappear. The middle class’s share of the total economic pie will continue to shrink, while the share going to the very top will continue to grow.
But the current trend is not preordained to last, and only the most rigid technological determinist would assume this to be our inevitable fate. We can — indeed, I believe we must — ignite a political movement to reorganize the economy for the benefit of the many, rather than for the lavish lifestyles of a precious few and their heirs. (I have more to say on this in my upcoming book, Saving Capitalism: For the Many, Not the Few, out at the end of September.)
Robert B. Reich has served in three national administrations, most recently as secretary of labor under President Bill Clinton. He also served on President Obama’s transition advisory board. His latest book is “Aftershock: The Next Economy and America’s Future.” His homepage is www.robertreich.org.
May 7, 2015
ROBERT B. REICH, Chancellor’s Professor of Public Policy at the University of California at Berkeley and Senior Fellow at the Blum Center for Developing Economies, was Secretary of Labor in the Clinton administration. Time Magazine named him one of the ten most effective cabinet secretaries of the twentieth century. He has written thirteen books, including the best sellers “Aftershock” and “The Work of Nations.” His latest, “Beyond Outrage,” is now out in paperback. He is also a founding editor of the American Prospect magazine and chairman of Common Cause. His new film, “Inequality for All,” is now available on Netflix, iTunes, DVD, and On Demand.

AI Won’t End the World, But It Might Take Your Job

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Andrew Ng. Ariel Zambelich/WIRED

There’s been a lot of fear about the future of artificial intelligence.

Stephen Hawking
and Elon Musk worry that AI-powered computers might one day become uncontrollable super-intelligent demons. So does Bill Gates.

But Baidu chief scientist Andrew Ng—one of the world’s best-known AI researchers and a guy who’s building out what is likely one of the world’s largest applied AI projects—says we really ought to worry more about robot truck drivers than the Terminator.

In fact, he’s irritated by the discussion about scientists somehow building an apocalyptic super-intelligence. “I think it’s a distraction from the conversation about…serious issues,” Ng said at an AI conference in San Francisco last week.

Ng isn’t alone in thinking this way. A select group of AI luminaries met recently at a closed door retreat in Puerto Rico to discuss ethics and AI. WIRED interviewed some of them, and the consensus was that there are short-term and long-term AI issues to worry about. But it’s the long-term questions getting all the press.
Artificial intelligence is likely to start having an important effect on society over the next five to 10 years, according to Murray Shanahan, a professor of cognitive robotics with Imperial College, Professor of Cognitive Robotics. “It’s hard to predict exactly what’s going on,” he told WIRED a few weeks ago, “but we can be pretty sure that these technologies are going to impact and society quite a bit.

The way Ng sees it, it took the US about 200 years to switch from an agricultural economy where 90 percent of the country worked on farms, to our current economy, where the number is closer to 2 percent. The AI switchover promises to come must faster, and that could make it a bigger problem.

That’s an idea echoed in two MIT academics, Erik Brynjolfsson and Andrew McAfee, who argue that we’re entering a “second machine age,” where the accelerating rate of change brought on by digital technologies could leave millions of medium-and-low skilled workers behind.

Some AI technologies, such as the self-driving car, could be extremely disruptive, but over a much shorter period of time than the industrial revolution. There are three million truck drivers in the US, according to the American Trucking Association. What happens if self-driving vehicles put them all out of a job in a matter of years?

With recent advances in perception, the range of things that machines can do is getting a boost. Computers are better at understanding what we say and analyzing data in a way that used to be the exclusive domain of humans.

Last month, Audi’s self-driving car took WIRED’s Alex Davies for a 500 mile ride. In Cupertino, California’s Aloft Hotel a robot butler can deliver you a toothbrush. Paralegals are now finding their work performed by data-sifting computers. And just last year, Google told us about a group of workers who were doing mundane image recognition work for the search giant—jobs like figuring out the difference between telephone numbers and street addresses on building walls. Google figured out how to do this by machine, and so they’ve now moved onto other things.

Ng, who also co-founded the online learning company Coursera, says that if AI really starts taking jobs, retraining all of those workers could present a major challenge. When it comes to retraining workers, he said, “our education system has historically found it very difficult.

ORIGINAL: Wired

By Robert McMillan
02.02.15

Artificial Intelligence Planning Course at Coursera by U of Edimurgh

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ORIGINAL: Coursera

The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications.


About the Course

The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications. It will allow you to:

  • Understand different planning problems
  • Have the basic know how to design and implement AI planning systems
  • Know how to use AI planning technology for projects in different application domains
  • Have the ability to make use of AI planning literature

Planning is a fundamental part of intelligent systems. In this course, for example, you will learn the basic algorithms that are used in robots to deliberate over a course of actions to take. Simpler, reactive robots don’t need this, but if a robot is to act intelligently, this type of reasoning about actions is vital.

Course Syllabus

Week 1: Introduction and Planning in Context
Week 2: State-Space Search: Heuristic Search and STRIPS
Week 3: Plan-Space Search and HTN Planning
One week catch up break
Week 4: Graphplan and Advanced Heuristics

Week 5: Plan Execution and ApplicationsM

Exam week

Recommended Background

The MOOC is based on a Masters level course at the University of Edinburgh but is designed to be accessible at several levels of engagement from an “Awareness Level”, through the core “Foundation Level” requiring a basic knowledge of logic and mathematical reasoning, to a more involved “Performance Level” requiring programming and other assignments.

Suggested Readings

The course follows a text book, but this is not required for the course:
Automated Planning: Theory & Practice (The Morgan Kaufmann Series in Artificial Intelligence) by M. Ghallab, D. Nau, and P. Traverso (Elsevier, ISBN 1-55860-856-7) 2004.

Course Format

Five weeks of study comprising 10 hours of video lecture material and special features videos. Quizzes and assessments throughout the course will assist in learning. Some weeks will involve recommended readings. Discussion on the course forum and via other social media will be encouraged. A mid-course catch up break week and a final week for exams and completion of assignments allows for flexibility in study.

You can engage with the course at a number of levels to suit your interests and the time you have available:

  • Awareness Level – gives an overview of the topic, along with introductory videos and application related features. This level is likely to require 2-3 hours of study per week.
  • Foundation Level – is the core taught material on the course and gives a grounding in AI planning technology and algorithms. This level is likely to require 5-6 hours of study per week of study.
  • Performance Level – is for those interested in carrying out additional programming assignments and engaging in creative challenges to understand the subject more deeply. This level is likely to require 8 hours or more of study per week.

FAQ

  • Will I get a certificate after completing this class?Students who complete the class will be offered a Statement of Accomplishment signed by the instructors.
  • Do I earn University of Edinburgh credits upon completion of this class?The Statement of Accomplishment is not part of a formal qualification from the University. However, it may be useful to demonstrate prior learning and interest in your subject to a higher education institution or potential employer.
  • What resources will I need for this class?Nothing is required, but if you want to try out implementing some of the algorithms described in the lectures you’ll need access to a programming environment. No specific programming language is required. Also, you may want to download existing planners and try those out. This may require you to compile them first.
  • Can I contact the course lecturers directly?You will appreciate that such direct contact would be difficult to manage. You are encouraged to use the course social network and discussion forum to raise questions and seek inputs. The tutors will participate in the forums, and will seek to answer frequently asked questions, in some cases by adding to the course FAQ area.
  • What Twitter hash tag should I use?Use the hash tag #aiplan for tweets about the course.
  • How come this is free?We are passionate about open on-line collaboration and education. Our taught AI planning course at Edinburgh has always published its course materials, readings and resources on-line for anyone to view. Our own on-campus students can access these materials at times when the course is not available if it is relevant to their interests and projects. We want to make the materials available in a more accessible form that can reach a broader audience who might be interested in AI planning technology. This achieves our primary objective of getting such technology into productive use. Another benefit for us is that more people get to know about courses in AI in the School of Informatics at the University of Edinburgh, or get interested in studying or collaborating with us.
  • When will the course run again?It is likely that the 2015 session will be the final time this course runs as a Coursera MOOC, but we intend to leave the course wiki open for further study and use across course instances.

  Category: AI, Education, MOOC
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5 Robots Booking It to a Classroom Near You

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IMAGE: ANDY BAKER/GETTY IMAGES

Robots are the new kids in school.
The technological creations are taking on serious roles in the classroom. With the accelerating rate of robotic technology, school administrators all over the world are plotting how to implement them in education, from elementary through high school.
In South Korea, robots are replacing English teachers entirely, entrusted with leading and teaching entire classrooms. In Alaska, some robots are replacing the need for teachers to physically be present at all.
Robotics 101 is now in session. Here are five ways robots are being introduced into schools.
1. Nao Robot as math teacher
IMAGE: WIKIPEDIA
In Harlem school PS 76, a Nao robot created in France, nicknamed Projo helps students improve their math skills. It’s small, about the size of a stuffed animal, and sits by a computer to assist students working on math and science problems online.
Sandra Okita, a teacher at the school, told The Wall Street Journal the robot gauges how students interact with non-human teachers. The students have taken to the humanoid robotic peer, who can speak and react, saying it’s helpful and gives the right amount of hints to help them get their work done.
2. Aiding children with autism
The Nao Robot also helps improve social interaction and communication for children with autism. The robots were introduced in a classroom in Birmingham, England in 2012, to play with children in elementary school. Though the children were intimidated at first, they’ve taken to the robotic friend, according to The Telegraph.
3. VGo robot for ill children

Sick students will never have to miss class again if the VGo robot catches on. Created by VGo Communications, the rolling robot has a webcam and can be controlled and operated remotely via computer. About 30 students with special needs nationwide have been using the $6,000 robot to attend classes.
For example, a 12-year-old Texas student with leukemia kept up with classmates by using a VGo robot. With a price tag of about $6,000, the robots aren’t easily accessible, but they’re a promising sign of what’s to come.

4. Robots over teachers
In the South Korean town of Masan, robots are starting to replace teachers entirely. The government started using the robots to teach students English in 2010. The robots operate under supervision, but the plan is to have them lead a room exclusively in a few years, as robot technology develops.
5. Virtual teachers


IMAGE: FLICKR, SEAN MACENTEE
South Korea isn’t the only place getting virtual teachers. A school in Kodiak, Alaska has started using telepresence robots to beam teachers into the classroom. The tall, rolling robots have iPads attached to the top, which teachers will use to video chat with students.
The Kodiak Island Borough School District‘s superintendent, Stewart McDonald, told The Washington Times he was inspired to do this because of the show The Big Bang Theory, which stars a similar robot. Each robot costs about $2,000; the school bought 12 total in early 2014.

Joi Ito: Want to innovate? Become a “now-ist”

By admin,

Remember before the internet?” asks Joi Ito. “Remember when people used
to try to predict the future?
” In this engaging talk, the head of the
MIT Media Lab skips the future predictions and instead shares a new approach to creating in the moment: building quickly and improving constantly, without waiting for permission or for proof that you have the right idea
. This kind of bottom-up innovation is seen in the most fascinating, futuristic projects emerging today, and it starts, he says, with being open and alert to what’s going on around you right now.
Don’t be a futurist, he suggests: be a now-ist.

Preparing Your Students for the Challenges of Tomorrow

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ORIGINAL: Edutopia
August 20, 2014

Right now, you have students. Eventually, those students will become the citizens — employers, employees, professionals, educators, and caretakers of our planet in 21st century. Beyond mastery of standards, what can you do to help prepare them? What can you promote to be sure they are equipped with the skill sets they will need to take on challenges and opportunities that we can’t yet even imagine?

Following are six tips to guide you in preparing your students for what they’re likely to face in the years and decades to come.

1. Teach Collaboration as a Value and Skill Set Students of today need new skills for the coming century that will make them ready to collaborate with others on a global level. Whatever they do, we can expect their work to include finding creative solutions to emerging challenges.
2. Evaluate Information Accuracy 
New information is being discovered and disseminated at a phenomenal rate. It is predicted that 50 percent of the facts students are memorizing today will no longer be accurate or complete in the near future. Students need to know
  • how to find accurate information, and
  • how to use critical analysis for
  • assessing the veracity or bias and
  • the current or potential uses of new information.
These are the executive functions that they need to develop and practice in the home and at school today, because without them, students will be unprepared to find, analyze, and use the information of tomorrow.
3. Teach Tolerance 
In order for collaboration to happen within a global community, job applicants of the future will be evaluated by their ability for communication with, openness to, and tolerance for unfamiliar cultures and ideas. To foster these critical skills, today’s students will need open discussions and experiences that can help them learn about and feel comfortable communicating with people of other cultures.
4. Help Students Learn Through Their Strengths 
Children are born with brains that want to learn. They’re also born with different strengths — and they grow best through those strengths. One size does not fit all in assessment and instruction. The current testing system and the curriculum that it has spawned leave behind the majority of students who might not be doing their best with the linear, sequential instruction required for this kind of testing. Look ahead on the curriculum map and help promote each student’s interest in the topic beforehand. Use clever “front-loading” techniques that will pique their curiosity.
5. Use Learning Beyond the Classroom
New “learning” does not become permanent memory unless there is repeated stimulation of the new memory circuits in the brain pathways
. This is the “practice makes permanent” aspect of neuroplasticity where neural networks that are the most stimulated develop more dendrites, synapses, and thicker myelin for more efficient information transmission. These stronger networks are less susceptible to pruning, and they become long-term memory holders. Students need to use what they learn repeatedly and in different, personally meaningful ways for short-term memory to become permanent knowledge that can be retrieved and used in the future. Help your students make memories permanent by providing opportunities for them to “transfer” school learning to real-life situations.
6. Teach Students to Use Their Brain Owner’s Manual
The most important manual that you can share with your students is the owner’s manual to their own brains. When they understand how their brains take in and store information (PDF, 139KB), they hold the keys to successfully operating the most powerful tool they’ll ever own. When your students understand that, through neuroplasticity, they can change their own brains and intelligence, together you can build their resilience and willingness to persevere through the challenges that they will undoubtedly face in the future.How are you preparing your students to thrive in the world they’ll inhabit as adults?

How a 574-year-old school is preparing for a world without classrooms

By admin,

ORIGINAL: QZ
January 16, 2014
What people think of when they think of Eton. Reuters/ Eddie Keogh
What does a nearly six-century-old private school that charges $54,000 a year in tuition do when confronted with start-ups with bright ideas about how education should work? If you’re Eton College, alma mater to much of the British establishment including the serving prime minister and the mayor of London, you work with them.
Along with Oxford University’s Said Business School, Eton Collegea high school for boys between the ages of 13 and 18—has partnered with Emerge Venture Lab, a London-based accelerator, to support educational technology, or “edtech,” start-ups. The first cohort of six start-ups will start the program in London with a party on Jan. 17. Eton will not provide financial support or take equity (though it does not rule that out), but its teachers will help guide the start-ups in the program and may also try the new products with their students. “It’s quite easy for the technology to dominate and the way we’re interested in getting involved is to make sure there is a pedagogical influence as well,” Eton’s Serena Hedley-Dent told Quartz.
Behind this ancient institution’s embrace of the modern is an intense awareness that things are about to change, and dramatically. “Nobody knows for sure where this business is going to be in five to ten years’ time. It would be very foolish of us to bury our heads in the sand and assume that education in schools will carry on in the traditional way,” says Percy Harrison, head of information technology at Eton and executive director of the newly-formed Eton Online Ventures.
Even the oldest institutions must serve modern needs. Eton College
Two trends are driving the change.

  1. The first is that old culprit—mobile devices such as tablets. If students are used to interacting with information on tablets at home, it seems strange to them to not use one at school. But schools and universities need to figure out how best to use them.
  2. The second trend is more structural: While education is often seen as the process of teaching children facts and skills, another important aspect is learning to grow up and dealing with life’s ups and downs. 

Harrison says that is the most important aspect of what Eton does, but one that existing education technology companies have not focused on. That is the area the school is interested in promoting.

Eton would not reveal the names or nature of the start-ups the program has accepted in advance of a formal announcement on Jan. 17, but Harrison says the products he is most interested in are those that help people work in groups. “The MOOC [online courses] world for example can be a rather lonely one. And if you get stuck how do you get unstuck? So it’s those kinds of features we’re really looking for,” he says.
Education technology is still very much in its infancy, as anybody who has experienced some of the widely-used platforms such as Blackboard can tell you. But it is a rising sector. Mendeley, a London start-up for academic collaboration, was bought last year for £65 million ($100 million) by science and academic publisher Reed Elsevier. Other areas of focus are learning analytics, which give educators the tools to monitor student performance and, the idea is, predict which ones are lagging behind or likely to fail. The way education technology is advancing, however, suggests no reason to believe that the social aspects of education and the skill-acquiring aspects of education need to be in the same place. That is why, says Harrison, “it’s important to us that if technology is going to play a bigger part, both in the classroom or indeed in replacing the classroom, [that it] drives people towards a good teaching style rather than a bad one.

  Category: Education, History, MOOC
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The Global Search for Education: Education and Jobs

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The Future of Employment study makes clear that what matters most today is what you can do with what you know, rather than how much you know.

– Dr. Tony Wagner

What does today’s technology mean for tomorrow’s jobs and how can we better structure our education system to ensure that the future working population can prosper in the labor market?

A large range of 20th century jobs are endangered by the machine age. A recent Oxford Martin School study by Dr. Carl Benedikt Frey (Oxford Martin School) and Dr. Michael A. Osborne (Department of Engineering Science, University of Oxford) found that 47% of current US jobs are at risk of automation within the next twenty years. Further, despite recent job growth in the service industry sector, occupations within this industry are highly susceptible. Frey and Osborne assessed the degree to which 702 specific jobs are vulnerable to computerization, distinguishing these occupations into categories of high, medium and low risk.

Job Automation May Threaten Half of U.S. Workforce (Bloomberg)

Mobile robots and ‘smart’ computers — that learn on the job — make it likely that occupations employing about half of today’s U.S. workers could be possible to automate in the next decade or two, according to an Oxford University study that estimated the probability of computerization of more than 700 occupations. Published March 12, 2014

Sources: University of Oxford, Carl Benedikt Frey and Michael A. Osborne
GRAPHIC: AKI ITO / BLOOMBERG NEWS & DAVE MERRILL / BLOOMBERG VISUAL DATA

One of the main ways governments have helped people during previous waves of technological progress is through education system reform. What should government be doing now to make the changes that are necessary?To discuss these issues further, I am joined today in The Global Search for Education by Dr. Carl Frey and Dr. Michael Osborne, authors of The Future of Employment: How Susceptible are Jobs to Computerization, and Dr. Tony Wagner, Expert in Residence at Harvard University‘s Innovation Lab. Tony will be leading a presentation on Education for Innovation at last week’s OPPI Festival in Helsinki, Finland.

I can only recommend that young people continue to gain the kind of cognitive and creative skills that give them a competitive edge over machines.” Dr. Michael A. Osborne

Gentlemen, could you please summarize what you believe were your most important findings in your study?

Michael: We found that a substantial fraction (47%) of current US employment is at risk of automation within the next twenty years. While some of these occupations are in categories previously thought unthreatened by automation, such as logistics and services, we expect automation to continue to predominately threaten low-skilled workers. In fact, we found a strong negative relationship between the average degree of education within an occupation and its susceptibility to computerization. In a similar way, this was true for the average wage: the better-paid jobs, featuring largely better-educated workers, are unlikely to be automated in the near future. Quantitatively, we found that if only a quarter of people within an occupation have a bachelor’s degree or better, the occupation would likely have a fifty-fifty chance of being automatable within the foreseeable future. If half of workers within an occupation have at least a bachelor’s degree, its probability of automation is close to zero. It seems clear that education is a crucial issue in considering future jobs.

Can you speak a little about the range of 20th century jobs that are endangered by the machine age?  

Michael: We firstly expect that existing trends of automation in production will continue: robots, with ever improving sensors and manipulators, will continue to replace factory workers. We further predict that  

many sales jobs are vulnerable: online shopping and self-checkouts will only continue to become more popular at the expense of human salespeople and cashiers. In fact,  

telemarketers were rated as one of the most computerizable occupations; to the dismay, no doubt, of anyone who is sick of speaking to robots on the phone. Perhaps more surprisingly,

we expect transportation and clerical jobs to be at risk from new technologies. Autonomous vehicles threaten many logistics occupations, such as drivers of forklifts or mine vehicles, while

big data analytics place occupations reliant on storing or accessing information at risk, such as tax preparers.

As evidence for the latter: we’re already seeing paralegal jobs replaced by algorithms, so this is not an unreasonable prediction.

We finally suspect that many jobs in the service sector will be increasingly at risk, with the growth of service robotics and sophisticated algorithms.

As examples, court reporters may have their jobs threatened by transcription software, and

electronics repair jobs are already being affected by the declining costs of increasingly complex electronic items. This is particularly alarming given the recently high fraction of workers undertaking service work.

Nonetheless, many other service sector jobs are likely to remain unautomatable; as an example, human housekeepers are still much better at their jobs than robots.

Carl: To expand a bit on that, what we are saying is that service occupations that do not require much creative and social intelligence are likely to be automated. Some personal service jobs, however, do require especially some social intelligence. These, we think, will not be automated.

In the short run, the government could support employment by stimulating the demand for personal services. In the long run, I do not believe there is much of a substitute for training workers to work with computers.” – Dr. Carl Benedikt Frey

Please discuss some of the characteristics of occupations not at risk of computerization. 

Michael: These jobs involve tasks at which machines are relatively poor: tasks involving creativity or social intelligence. As examples, I think

  • recreational therapists,
  • mental health counselors and
  • primary school teachers are relatively safe for the foreseeable future.
  • Many people may also be surprised to learn that occupations requiring work in very cluttered environments are also relatively safe. For example, the perceptual capacity of a human housekeeper, able to distinguish unwanted dirt from a pot plant, is unlikely to be matched by a robot cleaner for many decades.

Tony, why does this evolutionary phase require more revolutionary changes in education versus the gradual changes we have seen in previous generations?

The Future of Employment study makes clear that what matters most today is what you can do with what you know, rather than how much you know. Many recent college graduates find themselves unemployed or underemployed because they lack the skills needed in an increasingly innovation-driven economy. With academic content knowledge having become a commodity that’s available on every internet-connected device, the ability to 

  • initiate, 
  • discern, 
  • persevere, 
  • collaborate, and 
  • to solve problems creatively 

are the qualities most in demand today and will be increasingly important in the future. The problem is that our education system was designed, primarily, to teach the three R’s and to transmit content knowledge. We need to create schools that coach students for skill and will, in addition to teaching content. If we don’t make this transition quickly, a growing number of our youth will be unemployable at the same time that employers complain that they cannot find new hires that have the skills they need.

We need to create schools that coach students for skill and will, in addition to teaching content. If we don’t make this transition quickly, a growing number of our youth will be unemployable at the same time that employers complain that they cannot find new hires who have the skills they need.” – Dr. Tony Wagner

What recommendations would you make to governments about retraining workers who are now or will be unemployed as a result of this evolution? 

Tony: I wish I had an intelligent answer to this important question, but I’m a “recovering” high school English teacher, not an economist. My hunch is that it will take a generation to better prepare young people for the new economy. Meanwhile, perhaps we’ll need to put people to work repairing our crumbling infrastructure, helping out in preschools and assisted living homes, and so on. There is a lot to be done to make our country a better and more humane place to live. The question is: are we willing to pay people to do this work?

Carl: In the short run, the government could support employment by stimulating the demand for personal services. In the long run, I do not believe there is much of a substitute for training workers to work with computers.

If you were speaking to a group of high school students today, what fields and disciplines would you encourage them to explore to ensure success in the job market?

 Tony: First, I would encourage them to pursue their real interests. Curiosity and intrinsic interest trump mere academic achievement today. Secondly, I’d suggest they consider designing an interdisciplinary major in college around a problem of interest to them. Innovation increasingly happens at the intersections of academic disciplines, not within them.

Michael: One thing that came out very clearly from our analysis was the continuing importance of education. In particular, we found a strong negative trend between an occupation’s average level of education and its probability of computerization. As such, I can only recommend that young people continue to gain the kind of cognitive and creative skills that give them a competitive edge over machines. In particular, and I may be biased, but occupations revolving around creative uses of data are likely to be resistant to automation for some time. Further, people skills: the ability to negotiate, or persuade, are likely to become increasingly important for human work, due to their resistance to automation. Finally, manual work in unstructured environments is probably a fairly safe bet: gardeners are unlikely to have to worry about their jobs for a good long while.

 

C. M. Rubin, Dr. Tony Wagner, Dr. Carl Benedikt Frey, Dr. Michael A. Osborne

Photos are courtesy of the Oxford Martin School and Tony Wagner.

For more information on the Oxford Martin School Study:

http://www.futuretech.ox.ac.uk/sites/futuretech.ox.ac.uk/files/The_Future_of_Employment_OMS_Working_Paper_1.pdf

For more information on Education for Innovation at the OPPI Festival: http://oppifestival.com/

In The Global Search for Education, join me and globally renowned thought leaders including Sir Michael Barber (UK), Dr. Michael Block (U.S.), Dr. Leon Botstein (U.S.), Professor Clay Christensen (U.S.), Dr. Linda Darling-Hammond (U.S.), Dr. Madhav Chavan (India), Professor Michael Fullan (Canada), Professor Howard Gardner (U.S.), Professor Andy Hargreaves (U.S.), Professor Yvonne Hellman (The Netherlands), Professor Kristin Helstad (Norway), Jean Hendrickson (U.S.), Professor Rose Hipkins (New Zealand), Professor Cornelia Hoogland (Canada), Honourable Jeff Johnson (Canada), Mme. Chantal Kaufmann (Belgium), Dr. Eija Kauppinen (Finland), State Secretary Tapio Kosunen (Finland), Professor Dominique Lafontaine (Belgium), Professor Hugh Lauder (UK), Professor Ben Levin (Canada), Lord Ken Macdonald (UK), Professor Barry McGaw (Australia), Shiv Nadar (India), Professor R. Natarajan (India), Dr. Pak Tee Ng (Singapore), Dr. Denise Pope (US), Sridhar Rajagopalan (India), Dr. Diane Ravitch (U.S.), Richard Wilson Riley (U.S.), Sir Ken Robinson (UK), Professor Pasi Sahlberg (Finland), Professor Manabu Sato (Japan), Andreas Schleicher (PISA, OECD), Dr. Anthony Seldon (UK), Dr. David Shaffer (U.S.), Dr. Kirsten Sivesind (Norway), Chancellor Stephen Spahn (U.S.), Yves Theze (Lycee Francais U.S.), Professor Charles Ungerleider (Canada), Professor Tony Wagner (U.S.), Sir David Watson (UK), Professor Dylan Wiliam (UK), Dr. Mark Wormald (UK), Professor Theo Wubbels (The Netherlands), Professor Michael Young (UK), and Professor Minxuan Zhang (China) as they explore the big picture education questions that all nations face today.

The Global Search for Education Community PageC. M. Rubin is the author of two widely read online series for which she received a 2011 Upton Sinclair award, “The Global Search for Education” and “How Will We Read?” She is also the author of three bestselling books, including The Real Alice in Wonderland, and is the publisher of CMRubinWorld.

Follow C. M. Rubin on Twitter: www.twitter.com/@cmrubinworld

TAGS: Education Technology Future of Employment Carl Benedikt Frey
Michael A. Osborne Automation of Production Tony Wagner The Global
Search for Education Computerizable Occupations Job Automation Oxford
Martin School 20th Century Job Market C. M. Rubin Education for
Innovation OPPI Festival Education Reform Occupation Computerization

The 11-Minute Guide To All 8 Intelligences

By admin,

ORIGINAL: Edudemic
October 25, 2013The theory of Multiple Intelligences was originally proposed by Howard Gardner some 30 years ago and is gaining increasing recognition and impact. In particular, the theory has deep implications on how to structure teaching, education and assessment, independently on whether we are talking about traditional schools, MOOC or corporate eLearning. In recent times, Howard Gardner himself has turned much of his attention to the impact of the multiple intelligence theory on education and individual development, helping to accelerate this development.

It is important to stress that the Multiple Intelligence theory here discussed should not be confused with theories on different types of personality or different learning styles. The eight intelligences defined by Gardner exists simultaneously in each individual and they cooperate to create an individual profile, with strengths and weaknesses, which evolves over time. This way of looking at the skills of individuals calls for an educational approach involving differentiation and multiplicity in the teaching strategy, but without falling into the kind of rigidity and stereotypes that may be associated with some theories on types of personalities and learning styles.

The video below explains the basic concept of the theory of Multiple Intelligences, its impact on educational strategies, and provide some concepts that would allow education to reach more learners and develop the learner’s skill in a more comprehensive manner.

The video consists of three sections.

  • It starts introducing Gardner and the main problem of education versus different individual skills.
  • Then it turns to present the eight intelligences as proposed by Howard Gardner, including a suggested learning strategy for each of these intelligences.
  • Then in the third and final part it presents the concepts of personalization and pluralization, defined in respect of the multiple intelligences theory, as the guiding principle for a more including and developing educational strategy.

Regardless of what type of student, their age, level, or subject you’re teaching, awareness and understanding of the theory of multiple intelligences will help reach more and deeper in the teaching effort. Even for people not directly involved in education, this theory will help in getting a more nuanced and deeper picture of human skills and personalities.

 Founder of SlideTalk (www.slidetalk.net), a company focused on converting PowerPoint presentation into engaging talking videos. Share your powerpoint presentations, eLearning content, business presentations and tutorials as engaging talking videos, by using high-quality and multilingual text-to-speech technology, with no need for expensive and time-consuming voice recordings.

IBM reveals its top five innovation predictions for the next five years

By admin,

ORIGINAL: Venture Beat, IBM
December 16, 2013 10:30 PM

IBM. IBM director of education transformation Chalapathy Neti.
IBM revealed its predictions for five big innovations that will change our lives within five years.
IBM. Bernie Meyerson, vice president of innovation at IBM.
The IBM 5 in 5 is the eighth year in a row that IBM has made predictions about technology, and this year’s prognostications are sure to get people talking. We discussed them with Bernie Meyerson, vice president of innovation at IBM, and he told us that the goal of the predictions is to better marshal the company’s resources in order to make them come true.
We try to get a sense of where the world is going because that focuses where we put our efforts,” Meyerson said. “The harder part is nailing down what you want to focus on. Unless you stick your neck out and say this is where the world is going, it’s hard to you can turn around and say you will get there first. These are seminal shifts. We want to be there, enabling them.
In a nutshell, IBM says:
  • The classroom will learn you.
  • Buying local will beat online.
  • Doctors will use your DNA to keep you well.
  • A digital guardian will protect you online.
  • The city will help you live in it.
Meyerson said that this year’s ideas are based on the fact that everything will learn. Machines will learn about us, reason, and engage in a much more natural and personalized way. The innovations are being enabled by cloud computing, big data analytics, and adaptive learning technologies. IBM believes the technologies will be developed with the appropriate safeguards for privacy and security, but each of these predictions raises privacy and security issues.
As computers get smarter and more compact, they will be built into more devices that help us do things when we need them done. IBM believes that these breakthroughs in computing will amplify our human abilities. The company came up with the predictions by querying its 220,000 technical people in a bottoms-up fashion and tapping the leadership of its vast research labs in a top-down effort.
Here’s some more detailed description and analysis on the predictions.

IBM. In five years, the classroom will learn you.
1). The classroom will learn you. Globally, two out of three adults haven’t gotten the equivalent of a high school education. But IBM believes the classrooms of the future will give educators the tools to learn about every student, providing them with a tailored curriculum from kindergarten to high school.

Your teacher spends time getting to know you every year,” Meyerson said. “What if they already knew everything about how you learn?

In the next five years, IBM believes teachers will use “longitudinal data” such as test scores, attendance, student behavior on electronic learning platforms — and not just the results of aptitude tests. Sophisticated analytics delivered over the cloud will help teachers make decisions about which students are at risk, their roadblocks, and the way to help them. IBM is working on a research project with the Gwinnett County Public Schools, the 14th largest school district in the U.S. in New York State with 170,000 students. The goal is to increase the district’s graduation rate. And after a $10 billion investment in analytics, IBM believes it can harness big data to help students out.

“You’ll be able to pick up problems like dyslexia instantly,” Meyerson said. “If a child has extraordinary abilities, they can be recognized. With 30 kids in a class, a teacher cannot do it themselves. This doesn’t replace them. It allows them to be far more effective. Right now, the experience in a big box store doesn’t resemble this, but it will get there.”

IBM. In years, buying local will beat online as you get online data at your fingertips in the store.

2. Buying local will beat online. Online sales topped $1 trillion worldwide last year, while many physical retailers have gone out of business as they fail to compete on price with the likes of Amazon.com. But innovations for physical stores will make buying local turn out better. Retailers will use the immediacy of the store and proximity to customers to create experiences that can’t be replicated by online-only retail. The innovations will bring the power of the web right to where the shopper can touch it. Retailers could rely on artificial intelligence akin to IBM’s Watson, which played Jeopardy better than many human competitors. The web can make sales associates smarter, while augmented reality can deliver more information to the store shelves. With these technologies, stores will be able to anticipate what a shopper most wants and needs. And they won’t have to wait two days for shipping.

The store will ask if you would like to see a certain camera and have a salesperson meet you in a certain aisle where it is located,” Meyerson said. “The ability to do this painlessly, without the normal hassle of trying to find help, is very powerful.

This technology will get so good that online retailers are likely to set up retail showrooms to help their own sales.

It has been physical against online,” Meyerson said. “But in this case, it is combining them. What that enables you to do is that mom-and-pop stores can offer the same services as the big online retailers. The tech they have to serve you is as good as anything in online shopping. It is an interesting evolution but it is coming.

IBM. In five years, doctors will routinely use your DNA to keep you well.
3. Doctors will use your DNA to keep you well. Global cancer rates are expected to jump by 75 percent by 2030. IBM wants computers to help doctors understand how to a tumor affects a patient down to their DNA. They could then figure out what medications will best work against the cancer, and fulfill it with a personalized cancer treatment plan. The hope is that genomic insights will reduce the time it takes to find a treatment down from weeks to minutes.

The ability to correlate a person’s DNA against the results of treatment with a certain protocol could be a huge breakthrough,” Meyerson said. “You’ll be able to look at your DNA and find out” if there are magic bullet treatments that will address your particular ailment.




IBM. In five years, a digital guardian will protect you online.

4. A digital guardian will protect you online. We have multiple passwords, identifications, and devices than ever before. But security across them is highly fragmented. In 2012, there were 12 million victims of identity fraud in the U.S. In five years, IBM envisions a digital guardian that will become trained to focus on the people and items it is entrusted with. This smart guardian will sort through contextual, situational and historical data to verify a person’s identity on different devices. The guardian can learn about a user and make an inference about behavior that is out of the norm and may be the result of someone stealing that person’s identity. With 360 degrees of data about someone, it will be much harder to steal an identity.

In this case, you don’t look for the signature of an attack,” Meyerson said. “It looks at your behavior with a device and spots something anomalous. It screams when there is something out of the norm.

IBM. In five years, the city will help you live in it.
5. The city will help you live in it. IBM says that, by 2030, the towns and cities of the developing world will make up 80 percent of urban humanity and by 2050, seven out of every 10 people will be a city dweller. To deal with that growth, the only way cities can manage is to have automation, where smarter cities can understand in real-time how billions of events occur as computers learn to understand what people need, what they like, what they do, and how they move from place to place.

IBM predicts that cities will digest information freely provided by citizens to place resources where they are needed. Mobile devices and social engagement will help citizens strike up a conversation with their city leaders. Such a concept is already in motion in Brazil, where IBM researchers are working with a crowdsourcing tool that allows users to report accessibility problems, via their mobile phones, to help people with disabilities better navigate urban streets.

Of course, as in the upcoming video game Watch Dogs from Ubisoft, a bad guy could hack into the city and use its monitoring systems in nefarious ways. But Meyerson said, “I’d rather have the city linked. Then I can protect it. You have an agent that looks over the city. If some wise guy wants to make the sewage pumps run backwards, the system will shut that down.
The advantage of the ultra-connected city is that feedback is instantaneous and the city government can be much more responsive.
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