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“So there came a time in which the ideas, although accumulated very slowly, were all accumulations not only of practical and useful things, but great accumulations of all types of prejudices, and strange and odd beliefs.
Then a way of avoiding the disease was discovered. This is to doubt that what is being passed from the past is in fact true, and to try to find out ab initio again from experience what the situation is, rather than trusting the experience of the past in the form in which it is passed down. And that is what science is: the result of the discovery that it is worthwhile rechecking by new direct experience, and not necessarily trusting the [human] race[’s] experience from the past. I see it that way. That is my best definition…Science is the belief in the ignorance of experts.“
–Richard P Feynman, What is Science? (1968)[1]

TruthSift.com is a platform designed to support and guide individuals or crowds to rationality, and make them smarter collectively than any unaided individual or group. (Free) Members use TruthSift to establish what can be established, refute what can’t be, and to transparently publish the demonstrations. Anyone can browse the demonstrations and learn what is actually known and how it was established. If they have a rational objection, they can post it and have it answered.

Whether in scientific fields such as climate change or medical practice, or within the corporate world or political or government debate, or on day to day factual questions, humanity hasn’t had a good method for establishing rational truth. You can see this from consequences we often fail to perceive:
Peer reviewed surveys agree: A landslide majority of medical practice is *not* supported by science [2,3,4]. Scientists are often confused about the established facts in their own field [5]. Within fields like climate science and vaccines, that badly desire consensus, no true consensus can be reached because skeptics raise issues that the majority brush aside without an established answer (exactly what Le Bon warned of more than 100 years ago[6]). Widely consulted sources like Wikipedia are reported to be largely paid propaganda on many important subjects [7], or the most popular answer rather than an established one [8]. Quora shows you the most popular individual answer, generated with little or no collaboration, and often there is little documentation of why you should believe it. Existing systems for crowd sourced wisdom largely compound group think, rather than addressing it. Existing websites for fact checking give you someone’s point of view.

Corporate or government planning is no better. Within large organizations, where there is inevitably systemic motivation to not pass bad news up, leadership needs active measures to avoid becoming clueless as to the real problems [9]. Corporate or government plans are subject to group think, or takeover by employee or other interests competing with the mission. Individuals who perceive mistakes have no recourse capable of rationally pursuading the majority, and may anyway be discouraged from speaking up by various consequences[6].

TruthSift is designed to solve all these problems. TruthSift realizes in your browser the Platonic ideal of the scientific literature, but TruthSift applies it to everything, and makes it tangible and lightweight, extended to a much lower hurdle for publishing. On a public TruthSift diagram, members (or on a Private diagram, members you have invited), who believe they can prove or refute a statement, can post their proof or refutation exactly where it is relevant. TruthSift logically propagates the consequences of each contribution, graphically displaying how it impacts the establishment status of all the others, drawing statements established by the combined efforts in thick borders, and statements refuted in thin. Statements are considered established only when they have an established demonstration, one with every posted challenge refuted.

An example topic. The topic statement n0 is currently refuted, because its only proof is refuted. The statement menu is shown open in position to add a proof to this proof. The topic statement is gold, pro statements are blue, con statements are red. Proof connectors are black, challenges red, remarks purple, assumptions (not shown)  blue. Statements show the title, to see the body select “View Statement” or hover mouse.

Fig 1: An example topic. The topic statement n0 is currently refuted, because its only proof
is refuted. The statement menu is shown open in position to add a proof to this proof.
The topic statement is gold, pro statements are blue, con statements are red. Proof
connectors are black, challenges red, remarks purple, assumptions (not shown) blue.
Statements show the title. On the actual Topic the body can be seen by selecting
the statement and “View Statement” or hovering the mouse.

What is a proof? According to the first definition at Dictionary.com a proof is: “evidence sufficient to establish a thing as true, or to produce belief in its truth.” In mathematics, a proof is equivalent to a proof tree that starts at axioms, or previously established results, which the participants agree to stipulate, and proceeds by a series of steps that are individually unchallengeable. Each such step logically combines several conclusions previously established and/or axioms. The proof tree proceeds in this way until it establishes the stated proved conclusion. Mathematicians often raise objections to steps of the proof, but if it is subsequently established that all such objections are invalid, or if a workaround is found around the problem, the proof is accepted.

The Scientific literature works very similarly. Each paper adds some novel argument or evidence that previous work is true or is not true or extends it to establish new results. When people run out of valid, novel reasons why something is proved or is not proved, what remains is an established theory, or a refutation of it or of all its offered proofs.

The view focused on the topic statement of a Topic diagramming the discussion in Galileos: Dialogues Concerning the Two Chief World Views. The black triangle indicates other incoming edges not shown. For complex diagrams, it is often best to walk around in focused view centered on each statement in turn.

Fig 2: The view focused on the topic statement of a Topic diagramming the discussion in:
Galileo’s: Dialogues Concerning the Two Chief World Views.
The black triangle indicates other incoming edges not shown. For complex diagrams,
it is often best to walk around in focused view centered on each statement in turn.

TruthSift is a platform for diagramming this process and applying it to any statements members care to propose to establish or refute. One may state a topic and add a proof tree for it, which is drawn as a diagram with every step and connection explicit. Members may state a demonstration of some conclusion they want to prove, building from some statements they assert are self-evident or that reference some authority they think trustworthy, and then building useful intermediate results that rationally follow from the assumptions, and building on until reaching the stated conclusion. If somebody thinks they find a hole in a proof at any step, or thinks one of the original assumptions need further proof, they can challenge it, explaining the problem they see. Then the writer of the proof (or others if its in collaboration mode) may edit the proof to fix the problem, or make clearer the explanation if they feel the challenger was simply mistaken, and may counter-challenge the challenge explaining that it had been resolved or mistaken. This can go on recursively, with someone pointing out a hole in the proof used by the counter-challenger that the challenge was invalid. On TruthSift the whole argument is laid out graphically and essentially block-chained, which should prevent the kind of edit-wars that happen for controversial topics on Wikipedia. Each challenge or post should state a novel reason, and when the rational arguments are exhausted, as in mathematics, what remains is either a proof of the conclusion or a refutation of it or all of its proofs.

As statements are added to a diagram, TruthSift keeps track of what is established and what refuted, drawing established statements’ borders and their outgoing connectors thick, and refuted statements’ borders and their outgoing connectors thin so viewers can instantly tell what is currently established and what refuted. TruthSift computes this by a simple algorithm that starts at statements with no incoming assumptions, challenges, or proofs, which are thus unchallenged as assertions that prove themselves, are self evident, or appeal to an authority everybody trusts. These are considered established. Then it walks up the diagram rating statements after all their parents have been rated. A statement will be established if all its assumptions are, none of its challenges are, and if it has proofs, at least one is established. (We support challenges requesting a proof be added to a statement which neither has one added nor adequately proves itself.) Otherwise, that is if a statement has an established challenge, or has refuted assumptions, or all of its proofs are refuted, it is refuted.

To understand why a statement is established or refuted, center focus on it, so that you see it and its parents in the diagram. If it is refuted, either there is an established challenge of it, or one of its assumptions is refuted, or all of its proofs are. If it is not refuted, it is established. Work your way backward up the diagram, centering on each statement in turn, and examine the reasons why it is established or refuted.

Fig 3: An example topic.

Fig 3: An example topic.

Effective contribution to TruthSift diagrams involves mental effort. This is both a hurdle and a feature. TruthSift teaches Critical Thinking. First you think about your Topic Statement. How actually should you specify Vaccine Safety or Climate Change, so it covers what you want to establish or refute, and so it is amenable to rational discussion? There is no place you could go to see that well specified now, and can you properly assure it without properly specifying it? Next you think about the arguments for your topic statement, and those against it, and those against the arguments for, and those for the arguments for, and the arguments against the arguments against, and so on until everybody runs out of arguments, when what is left is a concise rational analysis of what is established and why. The debate is settled point by point. The process naturally subdivides the field into sub-topics where different expertise’s come into play, promoting true collective wisdom and understanding.

For TruthSift to work properly, posters will have to respect the guidelines and post only proof or challenge statements that they believe rationally prove or refute their target and are novel to the diagram (or also novel additional evidence as assumptions or remarks or tests, which are alternative connector types). Posts violating the guidelines may be flagged and removed, and consistent violators as well. Posts don’t have to be correct, that’s what challenges are for, but they have to be honest attempts, not spam or ad hominem attacks. Don’t get hung up on whether a statement should be added as a proof or an assumption of another. Frequently you want to assemble arguments for a proposition stating something like “the preponderance of the evidence indicates X”, and these arguments are not individually necessary for X, nor are they individually proofs of X. It is safe to simply add them as proofs. They are not necessary assumptions, and if not enough of them are established, the target may be challenged on that basis. The goal is a diagram that transparently explains a proof and what is wrong with all the objections people have found plausible.

For cases where members disagree on underlying assumptions or basic principles, stipulation is available. If one or more statements are stipulated, statements are shown as conditionally true if established based on the stipulations and as conditionally false if refuted based on the stipulations. The challenges to the stipulation are also shown. TruthSift supports reasoning from different fundamental assumptions, but requires being explicit about it when challenged.
Probability mode supports the intuitive construction of probabilistic models, and evaluates the probability of each statement in the topic marginalizing over all the parameters in the topic. With a little practice these allow folding in various connections and evidence. These could be used for collaborative, verified, risk models; to support proofs with additional confidence tests; to reason about hidden causes; or many other novel applications

Fig 4: Detail from a topic showing an established conclusion some may find surprising. Rebut it if you can.
Fig 4: Detail from a topic showing an established conclusion some may find surprising. Please
Rebut it if you can. Dashed edges represent citation into the literature. Title is shown for each
statement, on actual topic select “View Statement” to see body.

Basic Membership is free. In addition to public diagrams, debating the big public issues, private diagrams are available for personal or organizational planning or to exclude noise from your debate. Private diagrams have editing and/or viewing by invitation only. Come try it. http://TruthSift.com

TruthSift’s mission is to enable publication of a transparent exposition of human knowledge, so that anyone may readily determine what is truth and what fiction, what can be established by valid Demonstration and what can’t, and so that anyone can read and understand that Demonstration.
We intend the process of creating this exposition to lead to vastly increased understanding and improved critical thinking skills amongst our members and beyond. We hope to support collaborative human intelligences greater than any intelligence previously achieved on the planet, both in the public domain and for members’ private use.

And please, I’d love feedback or questions. [email protected]

1. Richard P Feynman, What is Science? (1968) http://www-oc.chemie.uni-regensburg.de/diaz/img_diaz/feynman…nce_68.pdf
2. Assessing the Efficacy and Safety of Medical Technologies, Office of Technology Assessment, Congress of the United States (1978)
http://www.fas.org/ota/reports/7805.pdf
3. Jeannette Ezzo, Barker Bausell, Daniel E. Moerman, Brian Berman and Victoria Hadhazy (2001). REVIEWING THE REVIEWS . International Journal of Technology Assessment in Health Care, 17, pp 457–466. http://journals.cambridge.org/action/displayAbstract?fromPag…aid=101041
4. John S Garrow BMJ. 2007 Nov 10; 335(7627): 951.doi:10.1136/bmj.39388.393970.1F PMCID: PMC2071976
What to do about CAM?: How much of orthodox medicine is evidence based?
http://www.dcscience.net/garrow-evidence-bmj.pdf
5. S. A. Greenberg, “How citation distortions create unfounded authority: analysis of a citation network”, BMJ 2009;339:b2680
http://www.bmj.com/content/339/bmj.b2680
6. Gustav Le Bon, The Crowd, (1895), (1995) Transaction Publishers New Edition Edition
7. S Attkisson, “Astroturf and manipulation of media messages”, TEDx University of Nevada, (2015) https://www.youtube.com/watch?v=-bYAQ-ZZtEU
8. Adam M. Wilson , Gene E. Likens, Content Volatility of Scientific Topics in Wikipedia: A Cautionary Tale 2015 DOI: 10.1371/journal.pone.0134454 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0134454
9. Kiira Siitari, Jim Martin & William W. Taylor (2014) Information Flow in Fisheries Management: Systemic Distortion within Agency Hierarchies, Fisheries, 39:6, 246–250, http://dx.doi.org/10.1080/03632415.2014.915814

New technology of machinery, new invention of machinery in the world, modern machines heavy equipment in the world 2016, amazing machine, amazing machine — what does this machine makes, amazing machines in the world compilation 2016…PLEASE ENJOY VIDEOS…ALL THE BEST AND MOST ADVANTAGE MACHINE FOR YOU…

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Latest on 3D printed Synthetic hair.


Makeup brushes haven’t changed all that much over the last century. Sure, brands have figured out how to create synthetic fibers and played around with handle placement, but otherwise, there hasn’t been a whole lot of innovation, especially compared with the developments we’ve seen in skin care and cosmetics. But that could all change thanks to the creation and testing of 3D-printed hair by researchers at the Massachusetts Institute of Technology’s Tangible Media Group.

3D-printed hair isn’t technically a new innovation; MIT unveiled the first 3D-printed hair about a year ago. What’s new is that since then, the researchers have explored the exciting possibilities of the technology. In a recently released paper, the Tangible Media Group details the creation of its Cillia program, which allows for the 3D printing of both flat and curved surfaces covered in extremely fine, tightly packed, artificial hairs. What’s so cool is just how small they can make the hairs—as tiny as 50 microns across—giving them the ability to create highly dense hairy or furry surfaces that were previously only possible in nature. And because they can get the hair that small, it allows the company to control a whole bunch of things like the length, thickness, and density of each individual hair that’s printed.

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Anyone who has heard of Bitcoin knows that it is built on a mechanism called The Blockchain. Most of us who follow the topic are also aware that Bitcoin and the blockchain were unveiled—together—in a whitepaper by a mysterious developer, under the pseudonym Satoshi Nakamoto.

That was eight years ago. Bitcoin is still the granddaddy of all blockchain-based networks, and most of the others deal with alternate payment coins of one type or another. Since Bitcoin is king, the others are collectively referred to as ‘Altcoins’.

But the blockchain can power so much more than coins and payments. And so—as you might expect—investors are paying lots of attention to blockchain startups or blockchain integration into existing services. Not just for payments, but for everything under the sun.

Think of Bitcoin as a product and the blockchain as a clever network architecture that enables Bitcoin and a great many future products and institutions to do more things—or to do these things better, cheaper, more robust and more blockchain-01secure than products and institutions built upon legacy architectures.

When blockchain developers talk about permissionless, peer-to-peer ledgers, or decentralized trust, or mining and “the halving event”, eyes glaze over. That’s not surprising. These things refer to advantages and minutiae in abstract ways, using a lexicon of the art. But—for many—they don’t sum up the benefits or provide a simple listing of products that can be improved, and how they will be better.

I am often asked “What can the Blockchain be used for—other than digital currency?” It may surprise some readers to learn that the blockchain is already redefining the way we do banking and accounting, voting, land deeds and property registration, health care proxies, genetic research, copyright & patents, ticket sales, and many proof-of-work platforms. All of these things existed in the past, but they are about to serve society better because of the blockchain. And this impromptu list barely scratches the surface.

I address the question of non-coin blockchain applications in other articles. But today, I will focus on a subtle but important tangent. I call it “A blockchain in name only”

Question: Can a blockchain be a blockchain if it is controlled by the issuing authority? That is, can we admire the purpose and utility, if it was released in a fashion that is not is open-source, fully distributed—and permissionless to all users and data originators?

Answer: Unmask the Charlatans
Many of the blockchains gaining attention from users and investors are “blockchains” in name only. So, what makes a blockchain a blockchain?

Everyone knows that it entails distributed storage of a transaction ledger. But this fact alone could be handled by a geographically redundant, cloud storage service. The really beneficial magic relies on other traits. Each one applies to Bitcoin, which is the original blockchain implementation:

blockchain_logo▪Open-source
▪Fully distributed among all users.
▪ Any user can also be a node to the ledger
▪Permissionless to all users and data originators
▪Access from anywhere data is generated or analyzed

A blockchain designed and used within Santander Bank, the US Post Office, or even MasterCard might be a nifty tool to increase internal redundancy or immunity from hackers. These potential benefits over the legacy mechanism are barely worth mentioning. But if a blockchain pretender lacks the golden facets listed above, then it lacks the critical and noteworthy benefits that make it a hot topic at the dinner table and in the boardroom of VCs that understand what they are investing in.

Some venture financiers realize this, of course. But, I wonder how many Wall Street pundits stay laser-focused on what makes a blockchain special, and know how to ascertain which ventures have a leg up in their implementations.

Perhaps more interesting and insipid is that even for users and investors who are versed in this radical and significant new methodology—and even for me—there is a subtle bias to assume a need for some overseer; a nexus; a trusted party. permissioned-vs-permissionlessAfter all, doesn’t there have to be someone who authenticates a transaction, guarantees redemption, or at least someone who enforces a level playing field?

That bias comes from our tendency to revert to a comfort zone. We are comfortable with certain trusted institutions and we feel assured when they validate or guarantee a process that involves value or financial risk, especially when we deal with strangers. A reputable intermediary is one solution to the problem of trust. It’s natural to look for one.

So, back to the question. True or False?…

In a complex value exchange with strangers and at a distance, there must be someone or some institution who authenticates a transaction, guarantees redemption, or at least enforces the rules of engagement (a contract arbiter).

Absolutely False!

No one sits at the middle of a blockchain transaction, nor does any institution guarantee the value exchange. Instead, trust is conveyed by math and by the number of eyeballs. Each transaction is personal and validation is crowd-sourced. More importantly, with a dispersed, permissionless and popular blockchain, transactions are more provably accurate, more robust, and more immune from hacking or government interference.

What about the protections that are commonly associated with a bank-brokered transaction? (For example: right of rescission, right to return a product and get a refund, a shipping guaranty, etc). These can be built into a blockchain transaction. That’s what the Cryptocurrency Standards Association is working on right now. Their standards and practices are completely voluntary. Any missing protection that might be expected by one party or the other is easily revealed during the exchange set up.

For complex or high value transactions, some of the added protections involve a trusted authority. blockchain-02But not the transaction itself. (Ah-hah!). These outside authorities only become involved (and only tax the system), when there is a dispute.

Sure! The architecture must be continuously tested and verified—and Yes: Mechanisms facilitating updates and scalability need organizational protocol—perhaps even a hierarchy. Bitcoin is a great example of this. With ongoing growing pains, we are still figuring out how to manage disputes among the small percentage of users who seek to guide network evolution.

But, without a network that is fully distributed among its users as well as permissionless, open-source and readily accessible, a blockchain becomes a blockchain in name only. It bestows few benefits to its creator, none to its users—certainly none of the dramatic perks that have generated media buzz from the day Satoshi hit the headlines.

Related:

Philip Raymond is co-chair of The Cryptocurrency Standards Association,
host & MC for The Bitcoin Event and editor at A Wild Duck.

The same laser system being developed to blast tiny spacecraft between the stars could also launch human missions to Mars, protect Earth from dangerous asteroids and help get rid of space junk, project leaders say.

Last month, famed physicist Stephen Hawking and other researchers announced Breakthrough Starshot, a $100 million project that aims to build prototype light-propelled “wafersats” that could reach the nearby Alpha Centauri star system just 20 years after launch.

The basic idea behind Breakthrough Starshot has been developed primarily by astrophysicist Philip Lubin of the University of California, Santa Barbara, who has twice received funding from the NASA Innovative Advanced Concepts (NIAC) program to develop the laser propulsion system. [Stephen Hawking Video: ‘Transcending Our Limits’ with Breakthrough Starshot].

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Nice.


/EINPresswire.com/ — SAN JOSE, CA — (Marketwired) — 05/24/16 — UltraMemory Inc. (UltraMemory) has selected NanoSpice™ and NanoSpice Giga™ from ProPlus Design Solutions, Inc., the leading technology provider of giga-scale parallel SPICE simulation, SPICE modeling solutions and Design-for-Yield (DFY) applications, to simulate its super-broadband, super large-scale memory design.

UltraMemory is developing innovative 3D DRAM chip, which includes Through Chip Interface (TCI), enabling low-cost and low-power wireless communication between stacked DARM when compared to TSV technology.

Highly accurate and high-capacity SPICE simulation was necessary because it needed to simulate several DRAM chips with analog functions. UltraMemory’s decision to adopt NanoSpice, a high-performance parallel SPICE simulator, and NanoSpice Giga, the industry’s only GigaSpice simulator, came after an extensive evaluation of commercial SPICE and FastSPICE circuit simulators. NanoSpice and NanoSpice Giga have been integrated in UltraMemory’s existing design flows to replace other SPICE and FastSPICE simulators to provide full circuit simulation solutions from small block simulation to full-chip verification.

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“We have developed a hydrogel based rapid E. coli detection system that will turn red when E. coli is present,” says Professor Sushanta Mitra, Lassonde School of Engineering. “It will detect the bacteria right at the water source before people start drinking contaminated water.”

The new technology has cut down the time taken to detect E. coli from a few days to just a couple of hours. It is also an inexpensive way to test drinking water (C$3 per test estimated), which is a boon for many developing countries, as much as it is for remote areas of Canada’s North.

“This is a significant improvement over the earlier version of the device, the Mobile Water Kit, that required more steps, handling of liquid chemicals and so on,” says Mitra, Associate Vice-President of Research at York U. “The entire system is developed using a readily available plunger-tube assembly. It’s so user-friendly that even an untrained person can do the test using this kit.”

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Visit Singularity Hub for the latest from the frontiers of manufacturing and technology as we bring you coverage of Singularity University’s Exponential Manufacturing conference. Watch all the talks from the first day here and second day here.

The software startup launching out of a garage or a dorm room is now the stuff of legend. We can all name the stories of people who got together in a garage with a few computers and ended up disrupting massive, established corporations — or creating something the world never even knew it wanted.

Until now, this hasn’t really been as true for physical things you build from the ground up. The cost of tools and production has been too high, and for top quality, you still had to go at it the traditional manufacturing route.

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If you’ve ever seen a “recommended item” on eBay or Amazon that was just what you were looking for (or maybe didn’t know you were looking for), it’s likely the suggestion was powered by a recommendation engine. In a recent interview, Co-founder of machine learning startup Delvv, Inc., Raefer Gabriel, said these applications for recommendation engines and collaborative filtering algorithms are just the beginning of a powerful and broad-reaching technology.

Raefer Gabriel, Delvv, Inc.
Raefer Gabriel, Delvv, Inc.

Gabriel noted that content discovery on services like Netflix, Pandora, and Spotify are most familiar to people because of the way they seem to “speak” to one’s preferences in movies, games, and music. Their relatively narrow focus of entertainment is a common thread that has made them successful as constrained domains. The challenge lies in developing recommendation engines for unbounded domains, like the internet, where there is more or less unlimited information.

“Some of the more unbounded domains, like web content, have struggled a little bit more to make good use of the technology that’s out there. Because there is so much unbounded information, it is hard to represent well, and to match well with other kinds of things people are considering,” Gabriel said. “Most of the collaborative filtering algorithms are built around some kind of matrix factorization technique and they definitely tend to work better if you bound the domain.”

Of all the recommendation engines and collaborative filters on the web, Gabriel cites Amazon as the most ambitious. The eCommerce giant utilizes a number of strategies to make item-to-item recommendations, complementary purchases, user preferences, and more. The key to developing those recommendations is more about the value of the data that Amazon is able to feed into the algorithm initially, hence reaching a critical mass of data on user preferences, which makes it much easier to create recommendations for new users.

“In order to handle those fresh users coming into the system, you need to have some way of modeling what their interest may be based on that first click that you’re able to extract out of them,” Gabriel said. “I think that intersection point between data warehousing and machine learning problems is actually a pretty critical intersection point, because machine learning doesn’t do much without data. So, you definitely need good systems to collect the data, good systems to manage the flow of data, and then good systems to apply models that you’ve built.”

Beyond consumer-oriented uses, Gabriel has seen recommendation engines and collaborative filter systems used in a narrow scope for medical applications and in manufacturing. In healthcare for example, he cited recommendations based on treatment preferences, doctor specialties, and other relevant decision-based suggestions; however, anything you can transform into a “model of relationships between items and item preferences” can map directly onto some form of recommendation engine or collaborative filter.

One of the most important elements that has driven the development of recommendation engines and collaborative filtering algorithms is the Netflix Prize, Gabriel said. The competition, which offered a $1 million prize to anyone who could design an algorithm to improve upon the proprietary Netflix’s recommendation engine, allowed entrants to use pieces of the company’s own user data to develop a better algorithm. The competition spurred a great deal of interest in the potential applications of collaborative filtering and recommendation engines, he said.

In addition, relative ease of access to an abundant amount of cheap memory is another driving force behind the development of recommendation engines. An eCommerce company like Amazon with millions of items needs plenty of memory to store millions of different of pieces of item and correlation data while also storing user data in potentially large blocks.

“You have to think about a lot of matrix data in memory. And it’s a matrix, because you’re looking at relationships between items and other items and, obviously, the problems that get interesting are ones where you have lots and lots of different items,” Gabriel said. “All of the fitting and the data storage does need quite a bit of memory to work with. Cheap and plentiful memory has been very helpful in the development of these things at the commercial scale.”

Looking forward, Gabriel sees recommendation engines and collaborative filtering systems evolving more toward predictive analytics and getting a handle on the unbounded domain of the internet. While those efforts may ultimately be driven by the Google Now platform, he foresees a time when recommendation-driven data will merge with search data to provide search results before you even search for them.

“I think there will be a lot more going on at that intersection between the search and recommendation space over the next couple years. It’s sort of inevitable,” Gabriel said. “You can look ahead to what someone is going to be searching for next, and you can certainly help refine and tune into the right information with less effort.”

While “mind-reading” search engines may still seem a bit like science fiction at present, the capabilities are evolving at a rapid pace, with predictive analytics at the bow.