Category Archives: Algorithm

P vs NP problem (Beyond Computation)

Google and FB Dominate the Digital Advertising Market

Source: Business Insider, Apr 2017

Wieser said that both ad giants captured a combined 77% of gross spending in 2016, an increase from 72% in 2015. Facebook specifically accounted for 77% of the digital ad industry’s overall growth, he noted.

The overall US internet ad industry grew 21.8% from $59.6 billion to $72.5 billion in 2016, according to the IAB.

Related Resource: IAB, Apr 2017

Mobile advertising accounted for more than half (51%) of the record-breaking $72.5 billion spent by advertisers last year, according to the latest IAB Internet Advertising Revenue Report, released today by the Interactive Advertising Bureau (IAB), and prepared by PwC US. The total represents a 22 percent increase, up from $59.6 billion in 2015. Mobile experienced a 77 percent upswing from $20.7 billion the previous year, hitting $36.6 billion in 2016.

Solomonoff Induction

Source: Less Wrong, Jul 2012

Background

1. Algorithms — We’re looking for an algorithm to determine truth.

2. Induction — By “determine truth”, we mean induction.

3. Occam’s Razor — How we judge between many inductive hypotheses.

4. Probability — Probability is what we usually use in induction.

5. The Problem of Priors — Probabilities change with evidence, but where do they start?

The Solution

6. Binary Sequences — Everything can be encoded as binary.

7. All Algorithms — Hypotheses are algorithms. Turing machines describe these.

8. Solomonoff’s Lightsaber — Putting it all together.

9. Formalized Science — From intuition to precision.

10. Approximations — Ongoing work towards practicality.

11. Unresolved Details — Problems, philosophical and mathematical.

Big Data Assumes Past Patterns Apply to the Future

Source: Fast Company, Jan 2017

“What big data is good for,” explains Cathy O’Neil, author of last year’s Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, “is finding patterns of behavior in the past. It will never help us find something that’s completely new.”

Computational Thinking

Source: CMU, date indeterminate

“Computational Thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent.”

Cuny, Snyder, Wing

Computational thinking is a way of solving problems, designing systems, and understanding human behavior that draws on concepts fundamental to computer science. To flourish in today’s world, computational thinking has to be a fundamental part of the way people think and understand the world.

Computational thinking means creating and making use of different levels of abstraction, to understand and solve problems more effectively.

Computational thinking means thinking algorithmically and with the ability to apply mathematical concepts such as induction to develop more efficient, fair, and secure solutions.

Computational thinking means understanding the consequences of scale, not only for reasons of efficiency but also for economic and social reasons.

Seeking Patterns

Source: Digital Tonto, May 2015

Futurist and entrepreneur Ray Kurzweil considers pattern recognition so important that in his recent book, How to Create a Mind, he argued that pattern recognition and intelligence are essentially the same thing.  Expertise, in essence, is the familiarity of patterns of a specific field.

there’s a problem with patterns.  Just as they can uncover hidden meaning, they can also make us see things that aren’t really there.

Just because a pattern exists, doesn’t mean that the cause of that pattern is important or meaningful.

G. H. Hardy once wrote that, “a mathematician, like a painter or poet, is a maker of patterns. If his patterns are more permanent than theirs, it is because they are made with ideas.” Today, as we increasingly live in a world of bits rather than a world of atoms, designing patterns is how we create value.

if you believe that the most important patterns are those we have yet to uncover, then the future has no bounds.

Advancement is the discovery of new patterns.

that’s the problem with patterns.  The human mind is incapable of swallowing them whole, so we curate them instead.  Inevitably, what we recognize is our own image.  If you seek knowledge that you already believe you possess, then that’s often the most you will ever find.

 

Human Four-Dimensional Spatial Intuition in Virtual Reality

Source: Questia, Oct 2009

we show evidence that people with basic geometric knowledge can learn to make spatial judgments on the length of, and angle between, line segments embedded in four-dimensional space viewed in virtual reality with minimal exposure to the task and no feedback to their responses. Their judgments incorporated information from both the three-dimensional (3-D) projection and the fourth dimension, and the underlying representations were not algebraic in nature but based on visual imagery, although primitive and short lived. These results suggest that human spatial representations are not completely constrained by our evolution and development in a 3-D world.

Much effort has been made to challenge this cognitive limitation and to develop human four-dimensional (4-D) intuitions (Davis, Hersh, & Marchisotto, 1995; Gardner, 1969; Rucker, 1984; Seyranian, 2001; Weeks, 1985). Two basic techniques were proposed to help people obtain an intuition of 4-D space.

The first is by analogy to 3-D space. This technique has been widely used. For example, Berger (1965; Abbott, 1991) explained how a 4-D creature can enter a 3-D locked closet from the fourth dimension by describing how a 3-D creature enters a two-dimensional (2-D) enclosure from above without touching its walls.

The second technique is to lift an observer into the higher dimensional space, so that he or she can directly experience it perceptually (Abbott, 1991; Berger, 1965; Rucker, 1984; Seyranian, 2001). For example, Abbott suggested that a 2-D creature can obtain 3-D intuition when it is taken into the 3-D space and views its world from above. Although this approach is hypothesized to be the most powerful means of acquiring 4-D intuition, it was not possible to implement the technique until virtual reality was available (D’Zmura, Colantoni, & Seyranian, 2000; Francis, 2005).