Robotics | WHY ARTIFICIAL INTELLIGENCE MARKETING

Hierarchies exist in the brain because of lower connection time,

Pecking orders exist in the mind as a result of reduced link expenses, research study reveals Searchings for might likewise enhance expert system as well as robotics systems

The Evolutionary Origins of Hierarchy: Evolution with performance-only choice causes non-hierarchical and also non-modular networks, which take longer to adjust to brand-new settings. Nonetheless, progressing connect with a link expense develop ordered as well as functionally modular networks that could address the general trouble by recursively resolving its sub-problems. These networks likewise adjust to brand-new atmospheres much faster. (credit scores: Henok Mengistu et al./ PLOS Comp. Biography)

New study recommends why the human mind and also various other organic networks show an ordered framework, as well as the research might boost efforts to produce expert system.

The research study, by scientists from the University of Wyoming as well as the French Institute for Research in Computer Science as well as Automation (INRIA, in France), shows that the development of power structure– a basic system of position– in organic networks might develop as a result of the expenses related to network links.

This research additionally sustains Ray Kurzweil’s concept of the ordered framework of the neocortex, offered in his 2012 publication, How to Create a Mind. The human mind has different locations for vision, electric motor control, as well as responsive handling, for instance, and also each of these locations include sub-regions that regulate various components of the body.

Transformative stress to decrease the number as well as price of links

The research study searchings for recommend that pecking order progresses not due to the fact that it creates much more reliable networks, however rather since hierarchically wired networks have less links. That’s since links in organic networks are costly– they need to be developed, kept, and so on– so there’s a transformative stress to minimize the variety of links.

Along with clarifying the introduction of power structure throughout the numerous domain names where it shows up, these searchings for could additionally speed up future research study right into progressing much more intricate, smart computational minds in the areas of expert system as well as robotics.

The research study, led by Henok S. Mengistu, is defined in an open-access paper in PLOS Computational Biology. The scientists additionally substitute the development of computational mind versions, referred to as fabricated semantic networks, both with as well as without an expense for network links. They located that ordered frameworks arise far more often when a price for links exists.

Apart from clarifying why organic networks are ordered, the study could additionally discuss why numerous manufactured systems such as the Internet and also roadway systems are additionally ordered. “The following action is to harness and also integrate this expertise to advance massive, structurally arranged networks in the hopes of producing far better expert system as well as boosting our understanding of the development of pet knowledge, including our very own,” inning accordance with the scientists.


Abstract of The Evolutionary Origins of Hierarchy

Ordered company– the recursive structure of sub-modules– is common in organic networks, consisting of neural, metabolic, environmental, as well as hereditary regulative networks, and also in human-made systems, such as big companies and also the Internet. To this day, the majority of study on pecking order in networks has actually been restricted to evaluating this residential or commercial property. Nevertheless, an open, essential concern in transformative biology is why ordered company advances to begin with. It has actually just recently been revealed that modularity develops due to the visibility of an expense for network links. Below we examine whether such link expenses additionally have the tendency to trigger an ordered company of such components. In computational simulations, we locate that networks without a link price do not advance to be ordered, also when the job has an ordered framework. Nevertheless, with a link price, networks progress to be both modular as well as ordered, and also these networks show greater total efficiency as well as evolvability (i.e. quicker adjustment to brand-new settings). Added evaluations verify that pecking order individually enhances flexibility after managing for modularity. On the whole, our outcomes recommend that the exact same pressure– the expense of links– advertises the advancement of both pecking order as well as modularity which these homes are essential motorists of network efficiency and also versatility. Along with clarifying the introduction of power structure throughout the numerous domain names where it shows up, these searchings for will certainly additionally speed up future study right into developing extra intricate, smart computational minds in the areas of expert system and also robotics.

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Without keywords, there may be no SEO

Despite what you’ve been listening to, key-word studies isn’t useless. Without keywords, there may be no SEO… right? Let’s study the data.

Ninety-three% of online reports in 2016 started out with a search, and the search begins with words. Keywords will stay applicable as long as human beings use phrases to interact with engines like google. The handiest element that has modified are the additional elements that have prompted how we use keywords for SEO.

The fact is that SERPs are not looking only at keywords, but also price many other elements. Keywords are actually only a minuscule a part of the pie. That isn’t always to say that keyword discovery isn’t important. In reality, it’s miles critical inside the way you move approximately it: we need to head stages deeper – to the proper degree that users are attempting to find, and search engines are indexing for.

This put up will cognizance on the 3 essential focuses of precious keyword research in 2017:

(1) person motive
(2) long-tail keywords
(three) Google voice seek

User Intent

User motive is crucial to keyword research and discovery in 2017. Marketers need to understand how to work together with search engines like google to provide the content customers really want. After all, it’s not about attracting traffic to your website online, however, the proper kind of site visitors.

So what exactly is person rationale? User intent refers to “a consumer’s final goal or intention” in making a search query. Every unmarried seek query has a motive, a goal, possibly a trouble the consumer desires to remedy.

For example, a seek query of “Chicago to New York” offers me flight schedules. Adding “tour” to make it “Chicago to New York journey” does not only provide me with flight outcomes but bus and vehicle data as well.

Adding one phrase to a seek question substantially affects the outcomes of the hunt as Google mechanically acknowledges the alternate in reason. In fact, Google refines its algorithms 500-600 instances a yr to get person motive right. And if Google is that specialize in it, so should you.

You want to make user purpose principal in your keyword studies. It is vital that you understand your person’s dreams once they make seek queries. This expertise or lack of it is able to make or wreck your organic visitors.

The wonderful factor is that consumer reason is pretty smooth to parent out the longer the search query is. Long queries give us a whole lot of statistics on what a person precisely desires so we are able to give it to them. They also permit us to get focused organic visitors for long-tail key phrases. Speaking of which…

Long-Tail Keywords

I’m certain you’ve heard lengthy-tail keywords uttered again and again by means of marketers in 2016 (and even before). It looks as if it’s set to dominate 2017 as properly. I’m positive you’re already familiar with lengthy-tail keywords, being that you’ve been specializing in them, however, allow’s cover the basics first earlier than we delve into how to effectively incorporate it into your search engine optimization approach.

What are long-tail keywords? Long-tail keywords are search terms with four or greater words. In fact, fifty-one% of all search queries in 2016 contained four or greater words (supply).

So where should you start out on locating relevant long-tail keywords to your commercial enterprise? Well, you need to first find actual phrases which might be being used for your unique industry or area.

You don’t want to apply the highly-priced keyword research tools to get a terrific list of long-tail keywords. There is some loose keyword research equipment so as to come up with a good list first of all. Keyword Finder (shows lengthy-tail queries and seek volumes however handiest permits some day by day searches) and Keyword.Io (propose long-tail queries, however, does not provide search volumes) are free (but confined) equipment you can start out with if you’re on a price range. And of direction, there’s usually Google’s very personal Keyword Planner that’s included into Adwords.

By far the nice top class keyword research device is Moz Pro’s Keyword Explorer. You begin by looking your major keyword and it will return a complete list of key phrases that you could rank for relevancy and search extent. From there, you could pass down the list and select out the lengthy tail keyword phrases or seek queries. The amazing element about the Keyword Explorer is that you could click on any long-tail keyword to generate a new listing of queries related to that keyword. The new listing functions greater lengthy-tail queries than the initial listing.

Google’s Voice Search

Perhaps the most exciting improvement in keyword method is Google’s voice search and herbal language capabilities. According to Google, 20% of cell searches at the moment are voice searches! This trend is quick taking and has brought on Google to create solution containers and Knowledge Graph panels. Here you may additionally discover an amusing infographic concerning the problem.

Let’s check how herbal language, both spoken and typed, has changed search. For example, users, such as you, used to look for “keyword studies”, but now ask, “what is keyword studies?” or “what is the handiest keyword research approach?” You can see the alternate from key phrases to long-tail key phrases or queries. Voice search functionality is quickening this improvement in SEO content material marketing.

Google voice queries are rising in popularity and could simplest retain to accomplish that in 2017.
Google voice queries are quickly growing in reputation and could best continue to accomplish that in 2017.
So how are you going to maintain up with this development to plot an effective search engine marketing content material marketing approach?

(1) Look at the Frequently Asked Questions (FAQ) on your industry or logo and healthy this for your existing content material. Do you have pages or posts that address the one’s questions? If so, ensure that the questions are recommended in a very herbal way and are highlighted on the web page.

(2) Create an FAQ web page for questions that you could answer in multiple paragraphs. Answer the questions in clear, concise and herbal language.

(three) Try and provide you with long-shape content for the questions via breaking them up into “What, while, why, how, who and in which?” components. Create subheadings for every in order that search engines can index them and customers can without difficulty find the content material.

Wrapping Up

A properly information and draw close to consumer cause, lengthy-tail key phrases, and Google voice seek will assist you higher refine your keyword, and usual search engine marketing, a method for 2017. Start to discern out what your target audience in reality desires so you can begin communicating with them within the excellent way as quickly as feasible.

Blockchain: The Missing Link Between Genomics and Privacy?

Blockchain

 
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Why blockchain technology may be the next big thing

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Blockchain technology could increase the trust in data that we never knew we’d lost

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Re-Imagining the Future of Finance with Blockchain

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With Blockchain Tallying, Rock N Roll Hall of Fame 2017 Vote Escapes Scrutiny

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Bonding Experience: Can Kenya Pave a Path for Blockchain Change?

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Blockchain Biometrics Chimera for Secure Transactions

A combination of biometric authentication and blockchain based storage solution can enhance the security of payment applications worldwide.
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Overstock Ramps Up Blockchain Spending With $8 Million Q1 Loss

Medici, the blockchain technology arm of online retailer Overstock.com, is already on pace to exceed its 2016 spending on distributed ledger R&D.
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Wrap of Week #17: Blockchain-Bitcoin-Crypto, BondIT, SME banking, Insurtech, Triodos Bank

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Positive reinforcement

2016 became huge for improvements in synthetic intelligence and machine gaining knowledge.

But 2017 may additionally properly supply even more. Here are 5 key matters to stay up for.

Positive reinforcement

AlphaGo’s historical victory towards one of the excellent Go players of all time, Lee Sedol, became a landmark for the sphere of AI, and particularly for the approach called deep reinforcement mastering.

Reinforcement learning takes an idea from the methods that animals learn how positive behaviors have a tendency to bring about advantageous or poor final results. Using this approach, a laptop can say, determine out the way to navigate a maze with the aid of trial and errors and then accomplice the tremendous final results—exiting the maze—with the moves that led as much as it. This we could a machine study without practice or maybe express examples. The concept has been around for many years, but combining it with big (or deep) neural networks offers the strength had to make it paintings on truly complicated troubles (like the game of Go). Through relentless experimentation, as well as evaluation of previous games, AlphaGo found out for itself how to play the sport at a professional degree.

The hope is that reinforcement learning will now show beneficial in many actual-international conditions. And the latest launch of numerous simulated environments must spur progress at the necessary algorithms by means of growing the variety of abilities computer systems can accumulate this manner.

Dueling neural networks

At the banner AI academic accumulating held in Barcelona, the Neural Information Processing Systems conference, plenty of the excitement a new system getting to know technique called generative hostile networks.

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5 Things To Watch In AI And Machine Learning

Five Things To Watch In AI And Machine Learning In 2017

 

Without a doubt, 2016 was an amazing year for Machine Learning (ML) and Artificial Intelligence (AI). During the year, we saw nearly every high tech CEO claim the mantel of becoming an “AI Company”. However, only a few companies were actually able to monetize their significant investments in AI, notably Amazon AMZN +0.71%, Baidu , Facebook FB +1.75%, Google GOOGL +3.77%, IBM IBM -0.02%, Microsoft MSFT +0.29%, Tesla Motors TSLA +1.75% and NVIDIA NVDA -1.29%. But 2016 was nonetheless a year of many firsts. As a posterchild for the potential for ML, Google Deep Mind mastered the subtle and infinitely complex game of GO, soundly beating the reigning world champion. And more than a few cool products were introduced that incorporated Machine Learning, from the first autonomous vehicles to new “intelligent” household assistants such as Google Home and Amazon Echo. But will 2017 finally usher in the long-promised age of Artificial Intelligence?

NVIDIA's Saturn V supercomputer for Machine Learning is the 28th fastest computer in the world, and is the #1 in the Green 500 list of the most power efficient. (Source: NVIDIA)

NVIDIA’s Saturn V supercomputer for Machine Learning is the 28th fastest computer in the world, and is the #1 in the Green 500 list of the most power efficient. (Source: NVIDIA)

Two domains: AI and Machine Learning. These terms are not interchangeable. Machine learning, a completely different way to program a computer by training it with a massive ocean of sample data, is real and is here to stay. General Artificial Intelligence remains a distant goal and is perhaps 5-20 years away depending on the specific domain of the “intelligence” being learned. To be sure, computers trained using Machine Learning hold tremendous promise, as well as the potential for massive disruption in the workplace. But these systems remain a far cry from genuine intelligence. Just ask Apple AAPL -0.14% Siri, and you will see what I mean. The hype around AI, and confusion over what the term actually means, will inevitably lead to some disillusionment as the limitations of this technology become apparent.

With that context in mind, here’s what I expect for the coming year for Machine Learning and AI.

1. Hardware accelerators for Machine Learning will proliferate.

Today, nearly all training of deep neural networks (DNNs) is performed using NVIDIA GPUs. Conversely, DNN inference, or the actual use of a trained network can be done efficiently on CPUs, GPUs, FPGAs, or even specialized ASICs such as the Google TPU, depending on the type of data being analyzed. Both training and inference markets will be hotly contested in 2017, as Advanced Micro Devices GPUs, Intel’s newly acquired Nervana chips, NVIDIA, Xilinx and several startups all launch accelerators specifically targeting this lucrative market. If you would like a deeper dive into the various semiconductor alternatives for AI, please see my companion article on this subject here.

2. Select application domains will leverage Machine Learning to improve efficiency of mission-critical processes.

If you are trying to find the killer AI app, the increasingly pervasive nature of the technology will make it difficult to identify. However, Machine Learning has begun to deliver spectacular results in very specific niches where the pattern recognition capabilities can be exploited, and this trend will continue to expand into new markets in 2017.

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LATEST SCIENCE & TECHNOLOGY NEWS **Kurzwel**

Monday | April 24, 2017
DAILY EDITION
LATEST SCIENCE & TECHNOLOGY NEWS

The first 2D microprocessor — based on a layer of just 3 atoms

April 24, 2017
Overview of the entire chip. AC=Accumulator, internal buffer; PC=Program Counter, points at the next instruction to be executed; IR=Instruction Register, used to buffer data- and instruction-bits received from the external memory; CU=Control Unit, orchestrates the other units according to the instruction to be executed; OR=Output Register, memory used to buffer output-data; ALU=Arithmetic Logic Unit, does the actual calculations. [3] (credit: TU Wien)May one day replace traditional microprocessor chips as well as open up new applications in flexible electronics

Researchers at Vienna University of Technology (known as TU Wien) in Vienna, Austria, have developed the world’s first two-dimensional microprocessor — the most complex 2D circuitry so far. Microprocessors based on atomically thin 2D materials promise to one day replace traditional microprocessors as well as open up new applications in flexible electronics. Consisting of 115 … more…

‘Negative mass’ created at Washington State University

April 21, 2017
Experimental images of an expanding spin-orbit superfluid Bose-Einstein condensate at different expansion times (credit: M. A. Khamehchi et al./Physical Review Letters)Washington State University (WSU) physicists have created a fluid with “negative mass,” which means that if you push it, it accelerates toward you instead of away, in apparent violation of Newton’s laws. The phenomenon can be used to explore some of the more challenging concepts of the cosmos, said Michael Forbes, PhD, a WSU assistant … more…

NEW EVENTS

london_futuristsWho can save Humanity from Superintelligence?

Dates: Apr 29, 2017
Location: London, UK
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ai-summit-logoThe AI Summit San Francisco

Dates: Sep 27 – 28, 2017
Location: San Francisco, California
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