Posts Tagged ‘quote’

Why “Many-Model Thinkers” Make Better Decisions

Saturday, November 24th, 2018

Why “Many-Model Thinkers” Make Better Decisions
https://HBR.org/2018/11/why-many-model-thinkers-make-better-decisions Intuitive description of #MachineLearning concepts. Focuses on practical business contexts (eg hiring) & explains how #ensemble models & boosting can make better choices

QT:{{”
“The agent based model is not necessarily better. It’s value comes from focusing attention where the standard model does not.

The second guideline borrows the concept of boosting, …Rather than look for trees that predict with high accuracy in isolation, boosting looks for trees that perform well when the forest of current trees does not.

A boosting approach would take data from all past decisions and see where the first model failed. …The idea of boosting is to go searching for models that do best specifically when your other models fail.

To give a second example, several firms I have visited have hired computer scientists to apply techniques from artificial intelligence to identify past hiring mistakes. This is boosting in its purest form. Rather than try to use AI to simply beat their current hiring model, they use AI to build a second model that complements their current hiring model. They look for where their current model fails and build new models to complement it.”
“}}

Tea if by sea, cha if by land

Sunday, November 18th, 2018

Tea if by sea, cha if by land: Why the world only has two words for
tea https://QZ.com/1176962/map-how-the-word-tea-spread-over-land-and-sea-to-conquer-the-world Both words originated from China; differences stem from whether they were globalized via Dutch sea trade or overland route HT
@gamzeandgursoy

QT:{{
“Both versions come from China. How they spread around the world offers a clear picture of how globalization worked before
“globalization” was a term anybody used. The words that sound like “cha” spread across land, along the Silk Road. The “tea”-like phrasings spread over water, by Dutch traders bringing the novel leaves back to Europe.”
“}}

Powering the internet of things | August 7, 2017 Issue – Vol. 95 Issue 32 | Chemical & Engineering News

Sunday, November 4th, 2018

Powering the internet of things
https://CEN.ACS.org/articles/95/i32/Powering-internet-things.html Great variety of sources & uses for #EnergyHarvesting devices — eg smart card readers for door & sensors for T gradients

QT:{{”
“Like Enerbee, many energy-harvesting firms remain optimistic and say the technology is improving. Most also acknowledge, as does Alta’s Vijh, that “the market for energy harvesting and the internet of things is a little slow now.” But sooner or later, he says, “it’s going to happen.””
“}}

Powering the internet of things | August 7, 2017 Issue – Vol. 95 Issue 32 | Chemical & Engineering News
https://cen.acs.org/articles/95/i32/Powering-internet-things.html

DNA Sequencing Giant Illumina Will Buy Pacific Biosciences For $1.2 Billion – Exclusive CEO Interview

Sunday, November 4th, 2018

We’re now all ILLUMINATED: DNA Sequencing Giant $ILMN Will Buy @PacBio For $1.2B, by @MatthewHerper
https://www.Forbes.com/sites/matthewherper/2018/11/01/dna-sequencing-giant-illumina-will-buy-pacific-biosciences-for-12-billion–exclusive-ceo-interview/ After raising $360M from VCs + $200M from an IPO

QT:{{”
“Pacific Biosciences was originally supposed to be an Illumina-killer. Founded in 2003 by chief technology officer, Steven Turner, who invented the firm’s basic technology with PacBio’s chief scientific officer, Jonas Korlach, PacBio emerged in 2009 boasting that it would disrupt the sequencing market, raising $360 million in venture capital and scoring a $200 million initial public offering. But its machines were too slow, expensive and unwieldy to slow down Illumina’s ascent. Shares plummeted, and even at the rich premium being offered by Illumina, shares are at half the IPO price.”
“}}

Michael Specter: The Growing Battle Over How to Treat Lyme Disease : The New Yorker

Saturday, November 3rd, 2018

http://www.newyorker.com/reporting/2013/07/01/130701fa_fact_specter

QT:{{”
“The disease is caused by the bacterium Borrelia burgdorferi. In the Northeast and the Midwest, B. burgdorferi is transmitted by the bite of a black-legged tick, Ixodes scapularis. (In the Western United States, a related tick, Ixodes pacificus, prevails, and in Europe the main vector is Ixodes ricinus.) Lyme was all but unknown until 1977, when Allen Steere, a rheumatologist at Yale, produced the first definitive account of the infection. The condition was initially thought to have been an outbreak of juvenile rheumatoid arthritis in and around Lyme, Connecticut. In 1982, Willy Burgdorfer, a medical entomologist at the National Institutes of Health’s Rocky Mountain Laboratories, determined that the infection was caused by the previously unknown spirochete borrelia. As is common in scientific practice, the bacterium was named for him: Borrelia burgdorferi.”

“The controversy over Lyme disease is unlikely to diminish until scientists resolve at least two critical, but related, questions. Can the bacteria persist in the body, causing harm and illness months or even years after treatment has ended? And can prolonged antibiotic therapy destroy the remaining bacteria?”
“}}

Dopamine receptor D2 – Wikipedia – DRD2

Saturday, November 3rd, 2018

https://en.wikipedia.org/wiki/Dopamine_receptor_D2

QT:{{”
Dopamine receptor D2, also known as D2R, is a protein that, in humans, is encoded by the DRD2gene. After work from Paul Greengard’s lab had suggested that dopamine receptors were the site of action of antipsychotic drugs, several groups (including those of Solomon Snyder and Philip Seeman) used a radiolabeled antipsychotic drug to identify what is now known as the dopamine D2receptor.[5] The dopamine D2 receptor is the main receptor for most antipsychotic drugs. The structure of DRD2 in complex with the atypical antipsychotic risperidone has been determined.[6]
“}}

NMDA receptor – Wikipedia

Saturday, November 3rd, 2018

https://en.wikipedia.org/wiki/NMDA_receptor

QT:{{”
The N-methyl-D-aspartate receptor (also known as the NMDA receptor or NMDAR), is a glutamate receptor and ion channel protein found in nerve cells. The NMDA receptor is one of three types of ionotropic glutamate receptors. The other receptors are the AMPA and kainate receptors. It is activated when glutamate and glycine (or D-serine) bind to it, and when activated it allows positively charged ions to flow through the cell membrane.[2] The NMDA receptor is very important for controlling synaptic plasticity and memory function.[3]
“}}

iPad Notebook export for Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts

Saturday, November 3rd, 2018

Some quick quotes from
{{
Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts Dehaene, Stanislas
Citation (MLA): Dehaene, Stanislas. Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts. Penguin Publishing Group, 2014. Kindle file.
}}
that I really liked

Each short quote below is in order as it appears in book

QT:{{”
These two anecdotes are reported by Jacques Hadamard, a world-class mathematician who dedicated a fascinating book to the mathematician’s mind.75 Hadamard deconstructed the process of mathematical discovery into four successive stages: initiation, incubation, illumination, and verification.

It is crucial to understand that, in this sort of coding scheme, the silent neurons, which do not fire, also encode information. Their muteness implicitly signals to others that their preferred feature is not present or is irrelevant to the current mental scene. A conscious content is defined just as much by its silent neurons as by its active ones.

As we discussed in Chapter 5, the prefrontal cortex, a pivotal hub of the conscious workspace, occupies a sizable portion of any primate’s brain—but in the human species, it is vastly expanded.45 Among all primates, human prefrontal neurons are the ones with the largest dendritic trees.46

One of these regions, called the frontopolar cortex, or Brodmann’s area 10, is larger in Homo sapiens than in any other ape.

Another special region is Broca’s area, the left inferior frontal region that plays a critical role in human language.

At a more microscopic level, the huge pyramidal cells in the dorsolateral prefrontal cortex (layers 2 and 3), with their extensive dendrites capable of receiving thousands of synaptic connections, are much smaller in schizophrenic patients. They exhibit fewer spines, the terminal sites of excitatory synapses whose enormous density is characteristic of the human brain. This loss of connectivity may well play a major causal role in schizophrenia. Indeed, many of the genes that are disrupted in schizophrenia affect either or both of two major molecular neurotransmission systems, the dopamine D2 and glutamate NMDA receptors,

Most interesting, perhaps, is that normal adults experience a transient schizophrenia-like psychosis when taking drugs such as phencyclidine (better known as PCP, or angel dust) and ketamine. These agents act by blocking neuronal transmission, quite specifically, at excitatory synapses of the NMDA
“}}

AncestryDNA(R) White Papers

Monday, October 29th, 2018

https://support.ancestry.com/s/article/AncestryDNA-White-Papers

QT:{{”
Here, we augment these DNA and pedigree-based insights even further with our new Genetic Communities feature (Figure 1.1). Instead of considering the IBD connection between each pair of customers in isolation, we simultaneously analyze more than 20 billion connections identified among over 2 million AncestryDNA customers as a large genetic network (described below in Section 3). Intuitively, because the estimated IBD connections between individuals are likely due to recent shared ancestry (within the past 10 generations), broader patterns in this large network likely represent recent shared history. The result is that we can identify clusters of living individuals that share large amounts of DNA due to specific, recent shared history. For example, we identify groups of customers that likely descend from immigrants participating in a particular wave of migration (e.g. Irish fleeing the Great Famine)
….
Ethnicity estimates are not an exact science. The percentage AncestryDNA reports to a customer is the most likely percentage within a range of percentages. In this section, we discuss how we calculate this range. It is important to keep in mind that here at AncestryDNA we continue to build upon our previous work to offer ever more accurate results to our customers.

So, for example, we might report someone as 40% England, Wales and Northwestern Europe with a confidence range of 30-60%. This means that they are most likely 40% England, Wales and Northwestern Europe but they could be anywhere between 30% and 60% England, Wales and Northwestern Europe.

As illustrated in Figure 4.1, our updated ethnicity estimation process, or algorithm, performs significantly better than our previous process for nine European regions. Since we are analyzing
single-origin people, a perfect algorithm would report back 100% for all of these cases. While not quite perfect, in each case, the updated algorithm is closer to 100% compared to the previous method. The trend is similar for the majority of the other regions (data not shown). …

Transition probabilities are really just the odds that an ethnicity will change from one window to the next.

The final ethnicity estimates customers receive are calculated by counting the proportion of the Viterbi path (weighted by recombination distance) that are assigned to a particular population in the reference panel.
“}}

related
23andMe ancestry composition white paper
https://permalinks.23andme.com/pdf/23-16_ancestry_composition.pdf

Hope, hype and heresy as blockchains enter the energy business

Monday, October 15th, 2018

Hope, hype & heresy as #blockchains enter the energy business
https://www.Economist.com/business/2018/08/02/hope-hype-and-heresy-as-blockchains-enter-the-energy-business Quote: “Digiconomist…estimates that just 1 #bitcoin transaction uses as much electricity as an average household in the Netherlands uses in a month.”