lunes, 28 de marzo de 2016

40 Years Later: Apple 3.0 (Jean-Louis Gassée)

immense @gassee 40 Years Later: Apple 3.0 | Monday Note

Simplifying, but without distorting the key concept, humankind needed a more flexible means of expression than hieroglyphs, mere pictures on a cave wall, and invented alphabets and numerals, symbols that have no intrinsic meaning. Combined into sentences, phrases, and formulae, these symbols gave us tremendous power to think, persuade, seduce, and calculate. The same set of symbols could be used in sacred texts, Elizabethan poetry, Marcus Aurelius’ meditations, Wall Street pitches, and General Relativity. 
But our invention was too much for our central nervous system: We had trouble memorizing long strings of symbols; few people could do long division in their head, let alone extract cubic roots. 
Luckily, we are the Homo Faber, the tool-making species, and thus began a long procession of computing, storage, and communication devices, from the abacus to electro-mechanical devices and on to big, expensive computers called mainframes. Electronics moved from tubes to transistors to integrated circuits, propelled by our unquenchable thirst for symbol manipulation. In the early 70s, 8-bit microprocessorsappeared and the personal computer revolution started.  
I saw the Apple ][ for what it represented: A machine that extended the reach of your mind and your body, a device that powered five key activities: Think, Organize, Communicate, Learn, and Play. 
Apple 1.0 was a turbulent period: The rise of the Apple ][, its loss to the IBM PC and Microsoft; the hope and trouble with the Macintosh… 
Apple 2.0 began in late 1996 when Jobs managed what turned out to be a reverse acquisition of Apple… 
We’re now in the Apple 3.0 era, under Tim Cook’s leadership… 
 
Apple won’t become boring with age. The company is just as exciting — and occasionally as unexpected — as they were 40 years ago. …

jueves, 24 de marzo de 2016

PSED: La investigación debe estar abierta #science20

En algunas conversaciones sobre "innovar"  he dicho abiertamente que si se investiga con dinero público el resultado debe ser público y que así las empresas pueden innovar alrededor de esa investigación.

Esto supondría que las universidades (como "investigador público") deberían de dejar de limitar la innovación con patentes y de rentabilizar la investigación con licencias. Evidentemente si hay que pagar, es más fácil para las grandes compañías hacerlo y que así puedan innovar alrededor de esos objetos de propiedad industrial, reforzando aún más su posición en el mercado, dificultando la competencia.

Por supuesto es un cambio de paradigma muy grande que supone dar la vuelta a "todo lo establecido". Y esa ambición es justamente el primer limitante para que nadie se sume a la idea cuando ésta la comento en algunos foros…

Lo explica muy bien Fernanda Peset en la presentación de maredata (desde el min. 1:04 a 1:15 ó a 2:00). Maredata es un proyecto, que junto a otros como Eexcess, favorece los datos abiertos (#opendata), la ciencia 2.0 y desde una perspectiva más amplia, la web 3.0 (la web semántica), el cloud-computing y el big-data a través de la interoperabilidad.

Lo que no acabo de entender es cómo va a encajar esta apertura del conocimiento de las universidades con la política actual de sus OTRIs, enfocada al 100% a retorno de la "inversión" mediante el licenciamiento de las patentes…


Pensamientos
Sin
Elaborar
Demasiado

Company culture

En esta esta entrada sobre Netflix. ya hace un tiempo reflexionaba sobre la importancia de una fuerte (y, mucho mejor, escrita) cultura de empresa.

Viene esto al caso al llamar Alvaro González nuestra atención al respecto del "handbook" de GitLab.

Eso me ha recordado la presentación, sobre esto mismo –la cultura–, de Google.

Me recuerda también esto a una entrada sobre la ideal manera de ser, que empezó pensando en emprendedores y acabó concluyendo en una serie de valores y prácticas que creo serían sanas en general ;-)
Confianza, resiliencia, empatía/psicología, paciencia, esfuerzo y mindfulness

Creo que voy a abrir una página en el blog para ir enlazando estos documentos

lunes, 21 de marzo de 2016

De la teoría a la práctica en tu vida

Algo en su interior les impide o les hace desconocer cómo ponerlo en práctica. Ese algo se llamo miedo y suele estar conectado con la siguiente lista de emociones (según el maestro Rafael Bisquerra):
  • En un primer nivel la galaxia emocional que alimenta el miedo está formada por el pavor, el pánico, el horror, el terror, el temor y el susto.
  • En un segundo nivel, la galaxia emocional del miedo contiene vulnerabilidad, recelo, desasosiego y espanto.
  • El miedo está conectado también con otras galaxias emocionales: Ira, asco y ansiedad; e indirectamente con la tristeza y la emoción social de la vergüenza, que es el orgullo herido.

De la teoría a la práctica en tu vida | iniciativa vorpalina

capítulo I de esa belleza de libro que es el Hagakure, escrito hace ahora trescientos años (1716), el maestro Yamamoto Tsunetomo comparte lo siguiente:

Un maestro de espada dijo en su vejez:
En nuestra vida atravesamos varios niveles en el estudio. 
En el nivel inferior, la persona estudia sin obtener resultados, y tiene la impresión de que él es torpe y los demás también. El que está en este nivel no sirve para nada. 
En el nivel medio, sigue siendo inútil, pero es consciente de sus carencias y también es capaz de advertir las carencias de los demás.
En el nivel más elevado, se enorgullece de su propia habilidad, le agradan las alabanzas de los demás y lamenta la falta de habilidad de sus compañeros. Un hombre así tiene valía. El hombre que está en el nivel más alto tiene aspecto de no saber nada.
En general, estos son los niveles. Pero existe un nivel trascendente que es el más excelente de todos. En él, la persona es consciente de que el Camino que sigue es interminable, y no considera nunca que ha llegado su final. Conoce bien sus carencias y no llega a pensar nunca, en su vida, que ha conseguido superarlas. Pero ello no le impide avanzar. No tiene pensamientos orgullosos; contempla el Camino en toda su extensión con humildad. Se cuenta que el maestro Yagyu dijo una vez: “Yo no conozco el modo de vencer a los demás, sino el de vencerme a mí mismo”
Avanza diariamente a lo largo de tu vida adquiriendo más habilidad que el día anterior, más habilidad que hoy. El proceso es interminable.”

domingo, 20 de marzo de 2016

When Revenue Isn’t The Answer

 Interesantísimo artículo. He visto lo que cuenta…

When Revenue Isn’t The Answer — Medium

You became so focused on closing deals and winning customers that you missed finding real product/ market fit. Only after you raised your Series A did you realize that velocity does not equal repeatability when it comes to enterprise sales, and that the latter means far more than the former. Suddenly you’re not scaling as fast as you’d expected and modeled.

Even for seasoned entrepreneurs, this initial taste of success can be intoxicating. The product works, and a few clients are signed up. Money — for the first time — is flowing in and not just out, and a decent sales pipeline seems ample evidence of product/market fit. That’s the good news. At the same time, you begin to feel the weight of competition both real and imagined. Existing investors are pressuring you to think about the next round. Now, you’re sure, is the moment to move swiftly forward.
But here’s the catch: Closed deals and sales velocity are not exclusive measures of product/ market fit. Maybe, among your first customers, there are wide variations in the core use cases for the product. Maybe your team is struggling with lengthy sales cycles.

How do you know if you’ve achieved real, meaningful, and differentiated product/ market fit? Here are a few key questions to test your thesis:
1. Do you know, at a granular level, which potential customers you should target?
2. Who are the influencers? Who are the buyers? Where does their budget come from?
3. What marketing channels should you use to target those customers?
4. Do you have a simple and — most importantly — single marketing message?
5. Do you know what your sales process is, and is it an easy process?
6. Do you have a clearly defined product roadmap that’s aligned to your target market?
7. Can you hire junior sales reps, ramp them quickly, and have them close deals with consistent results?

It’s a cruel irony that while early customers and revenue often feel like the lifeblood of your business, these things might actually be killing you. It’s not ultimately about selling to customers; it’s about fundamentally understanding what core functionality causes customers to buy.

domingo, 6 de marzo de 2016

On where Artificial Intelligence went wrong

Interesting… although quite complex (for me) and long… Some qutesAlgunas citas de:
Noam Chomsky on Where Artificial Intelligence Went Wrong - The Atlantic

"Behaviorist principles of associations could not explain the richness of linguistic knowledge, our endlessly creative use of it, or how quickly children acquire it with only minimal and imperfect exposure to language presented by their environment.

According to Marr, a complex biological system can be understood at three distinct levels. The first level ("computational level") describes the input and output to the system, which define the task the system is performing. In the case of the visual system, the input might be the image projected on our retina and the output might our brain's identification of the objects present in the image we had observed. The second level ("algorithmic level") describes the procedure by which an input is converted to an output, i.e. how the image on our retina can be processed to achieve the task described by the computational level. Finally, the third level ("implementation level") describes how our own biological hardware of cells implements the procedure described by the algorithmic level.

As written today, the history of cognitive science is a story of the unequivocal triumph of an essentially Chomskyian approach over Skinner's behaviorist paradigm -- an achievement commonly referred to as the "cognitive revolution," though Chomsky himself rejects this term. While this may be a relatively accurate depiction in cognitive science and psychology, behaviorist thinking is far from dead in related disciplines. Behaviorist experimental paradigms and associationist explanations for animal behavior are used routinely by neuroscientists who aim to study the neurobiology of behavior in laboratory animals such as rodents, where the systematic three-level framework advocated by Marr is not applied

Noam Chomsky, speaking in the symposium, wasn't so enthused. Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field's heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the "new AI" -- focused on using statistical learning techniques to better mine and predict data -- is unlikely to yield general principles about the nature of intelligent beings or about cognition.

High-throughput sequencing, a technique by which millions of DNA molecules can be read quickly and cheaply, turned the sequencing of a genome from a decade-long expensive venture to an affordable, commonplace laboratory procedure. … The great geneticist and Nobel-prize winning biologist Sydney Brenner once defined the field as "low input, high throughput, no output science." Do we rely on powerful computing and statistical approaches to tease apart signal from noise, or do we look for the more basic principles that underlie the system and explain its essence? The urge to gather more data is irresistible, though it's not always clear what theoretical framework these data might fit into. These debates raise an old and general question in the philosophy of science: What makes a satisfying scientific theory or explanation, and how ought success be defined for science?

And how the linear thinking and processes could be, finally changed by quantum computing (as far as I seemed to understand…)

sábado, 5 de marzo de 2016

Cómo estar en desacuerdo

On How to Disagree | The Book of Life

Disagreement is especially pressing now, because of certain large societal forces that have been building for the last couple of centuries.
1. Politics
The developed world is now democratic. We’ve long been moving away from habits of deference and from hierarchies in which most people don’t feel it’s their role to have much of a view about a lot of things.
2. Relationships of Equality
To ask any couple what they disagree about is one of the more fascinating and consoling exercises: we realise we’re not alone.
3. Technology
Technology has made disagreement more vivid. We are very readily brought into contact with other people’s abrasive attitudes – which, until recently – we could never have encountered.


HELPFUL MOVES

  • DON’T RUSH OVER DISAGREEMENTS
  • DISAGREEMENT IS NORMAL
  • DON’T IMPORT ENERGY FROM ELSEWHERE INTO YOUR DISAGREEMENT – Energy gets imported in at least five distinctive ways:
  1. Anger is redirected and concentrated
  2. We are arguing with the past
  3. Frustrated sexual desire gets channelled into disagreement
  4. We are trying to compensate for a feeling of humiliation
  5. We long for clarity in an ambiguous world



  • TRAUMA: AUDIT OF THE BIOGRAPHY OF IDEAS
  • UNFORTUNATE INTRODUCTIONS TO IDEAS
  • SURRENDER THE PLEASURES OF SELF-RIGHTEOUSNESS
  • THE USES OF ART IN SOLVING DISAGREEMENT
  • UNDERSTAND THE ROLE OF TECHNOLOGY IN CREATING ENEMIES


  1. The fear of not being heard at all
  2. The sense of being anonymous
  3. It’s not a person on the other end



  • SNOBBERY
  • HUMOUR
  • THE ROLE OF SEDUCTION AND REASSURANCE
  • STRATEGIC PESSIMISM - The pessimist therefore shifts the focus away from an attempt to convert to an attempt to endure and manage disagreement. This involves two responses:
  1. Human Rigths
  2. Manners