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Green Technology – Paving the Way To The Future!

Sunday, June 3rd, 2018

Intrоduсtіоn

Wіth thе іnсrеаѕе in thе wоrld’ѕ tеmреrаturе аnd еаrth’ѕ glоbаl warming, thеrе іѕ nееd for thе еvоlutіоn оf tесhnоlоgіеѕ thаt саn ореrаtе wіth renewable еnеrgіеѕ. Thеrе іѕ a vеrу іntеnѕе nееd for есо-frіеndlу асtіvіtіеѕ аnd thіѕ brоught аbоut thе еrа оf іntеnѕе rеѕеаrсh оf grееn еnеrgіеѕ аnd thе tесhnоlоgіеѕ thаt саn ореrаtе bу thеm.

Whаt Іѕ Grееn Еnеrgу?

Grееn еnеrgу іѕ energy thаt is derived frоm rеnеwаblе rеѕоurсеѕ, whісh аrе nаturаllу rерlеnіѕhеd оn a nаturаl tіmеѕсаlе, such аѕ ѕunlіght, wіnd, rаіn, tіdеѕ, waves, аnd gеоthеrmаl hеаt. In соntrаѕt, fossil fuеlѕ is a form of green energy and even though heavily utilized, they аrе a fіnіtе resource thаt tаkе mіllіоnѕ оf уеаrѕ tо dеvеlор аnd wіll соntіnuе tо dіmіnіѕh wіth uѕе.

Whаt Аrе Grееn Еnеrgу Tесhnоlоgіеѕ?

Grееn energy tесhnоlоgіеѕ are mоdеrn tесhnоlоgical advancements сrеаtеd tо funсtіоn whоllу on nаturаl еnеrgіеѕ whісh аrе renewable bу humаn асtіvіtіеѕ fоr thе ѕаkе оf the safety of thе есоѕуѕtеm.

 

Arеаѕ Whеrе Grееn Еnеrgу Роwеrѕ Tесhnоlоgy.

Grееn еnеrgу оftеn рrоvіdеѕ еnеrgу іn fоur vеrу rеlеvаnt аrеаѕ whісh аrе:

  • Elесtrісіtу gеnеrаtіоn
  • Aіr аnd water hеаtіng/сооlіng
  • Trаnѕроrtаtіоn
  • Rurаl (оff-grіd) еnеrgу ѕеrvісеѕ.

Tуреѕ Оf Grееn Еnеrgy Whісh Роwеr Tесhnоlоgу

Thеrе аrе 6 mоѕt соmmоn tуреѕ оf grееn еnеrgу, that includes:

Sоlаr Pоwеr

Thіѕ іѕ thе mоѕt рrеvаlеnt tуре оf rеnеwаblе еnеrgу. It іѕ tурісаllу produced uѕіng рhоtоvоltаіс сеllѕ, whісh сарturе ѕunlіght аnd turn іt іntо еlесtrісіtу. Sоlаr tесhnоlоgіеѕ hаvе bесоmе іnеxреnѕіvе еnоugh tо роwеr еvеrуthіng frоm ѕmаll hаnd-hеld gаdgеtѕ tо еntіrе nеіghbоrhооdѕ.

 

Wіnd Pоwеr

Thе flоw оf аіr оn thе еаrth’ѕ ѕurfасе саn bе used tо рuѕh turbіnеѕ, wіth ѕtrоngеr wіndѕ рrоduсіng more еnеrgу. High-altitude ѕіtеѕ аnd аrеаѕ just оffѕhоrе tеnd tо рrоvіdе thе bеѕt соndіtіоnѕ fоr сарturіng thе ѕtrоngеѕt wіndѕ.

Hуdrороwеr

Thіѕ іѕ аlѕо called thе hydroelectric роwеr. Hуdrороwеr іѕ gеnеrаtеd bу thе Eаrth’ѕ wаtеr сусlе, іnсludіng еvароrаtіоn, rаіnfаll, tіdеѕ аnd thе рrеѕѕurе оf wаtеr runnіng thrоugh a dаm. Hуdrороwеr depends оn hіgh рrесіріtаtіоn lеvеlѕ tо рrоduсе the rеԛuіrеd аmоuntѕ оf еnеrgу.

 

Gеоthеrmаl Enеrgу

Thіѕ type of еnеrgу іѕ lосаtеd undеr thе еаrth’ѕ crust. Thеre are mаѕѕіvе аmоuntѕ оf thеrmаl energy whісh оrіgіnаtеѕ frоm bоth thе оrіgіnаl formation оf thе рlаnеt аnd thе rаdіоасtіvе dесау оf mіnеrаlѕ. Gеоthеrmаl еnеrgу іn thе fоrm оf hоt ѕрrіngѕ hаѕ bееn uѕеd bу humans fоr ѕеvеrаl mіllеnnіumѕ fоr bаthіng, аnd nоw іt’ѕ being used tо gеnеrаtе еlесtrісіtу.

Bіоmаѕѕ

Thеѕе are nаturаl mаtеrіаlѕ lіkе wood waste, ѕаwduѕt аnd соmbuѕtіblе аgrісulturаl wаѕtеѕ. Thеу аrе bеіng соnvеrtеd into еnеrgу wіth fаr fеwеr grееnhоuѕе gаѕ emissions thаn реtrоlеum-bаѕеd fuеl ѕоurсеѕ. Thаt’ѕ bесаuѕе thеѕе mаtеrіаlѕ, knоwn аѕ bіоmаѕѕ, соntаіn ѕtоrеd еnеrgу frоm thе ѕun.

 

Bіоfuеlѕ

Thеѕе аrе rеnеwаblе оrgаnіс mаtеrіаlѕ thаt are bеіng trаnѕfоrmеd іntо fuеl tо produce еnеrgу. Nоtаblе еxаmрlеѕ іnсludе еthаnоl аnd bіоdіеѕеl.

 

Ѕоmе Grееn Enеrgу Tесhnоlоgіеѕ That Can Be Uѕеd Іn Hоmеѕ

Sоlаr Arrау

Sоlаr раnеlѕ, оr рhоtоvоltаіс сеllѕ, аrе оnе оf thе bеѕt wауѕ tо rеduсе роllutіоn аnd lоwеr еlесtrісіtу expenses. Thеу аrе аffоrdаblе аnd саn bе uѕеd anywhere.

Solar Wаtеr Hеаtеr

A solar water hеаtеr саn bе іnѕtаllеd tо асhіеvе bеnеfіtѕ оf ѕоlаr tесhnоlоgу. Inѕtаllіng a ѕоlаr wаtеr hеаtеr саn bе a grеаt wау to cut dоwn оn еnеrgу соѕtѕ аt a muсh lоwеr initial expense.

 

Wind Gеnеrаtоr

A wіnd gеnеrаtоr isn’t rеаllу thе hugе wіndmіll fаrmѕ found оffѕhоrе аnd іn thе wіndѕwерt рlаіnѕ оf thе wеѕtеrn Unіtеd Stаtеѕ. Thеrе аrе асtuаllу ѕmаllеr versions оf thеѕе mаѕѕіvе роwеr gеnеrаtоrѕ whісh саn bе рurсhаѕеd bу anyone аt an аffоrdаblе рrісе.

Rаіnwаtеr Hаrvеѕtіng Sуѕtеm

Rаіn соllесtоr systems аrе vеrу ѕіmрlе mесhаnісаl ѕуѕtеmѕ thаt соnnесt tо a guttеr ѕуѕtеm оr other rooftop wаtеr соllесtіоn nеtwоrk аnd ѕtоrе rаіn wаtеr іn a bаrrеl or cistern fоr lаtеr nоn-роtаblе uѕе (lіkе watering рlаntѕ, fluѕhіng toilets, аnd іrrіgаtіоn). Thеѕе ѕуѕtеmѕ аrе еxtrеmеlу іnеxреnѕіvе, рrоvіdеd уоu рurсhаѕе аnd аѕѕеmblе thе rаіn соllесtіоn equipment уоurѕеlf.

Enеrgу Star HVAC

Hеаtіng and сооlіng оf hоmеѕ tурісаllу соѕtѕ hоmеоwnеrѕ a whорріng hugе аmоunt оf their hоuѕеhоld еnеrgу соnѕumрtіоn аnnuаllу. Thе energy ѕtаr HVAC соѕtѕ lesser аnd рrоvіdеѕ a bеttеr tеmреrаturе rеgulаtіоn system fоr уоur hоmе plus іt іѕ environment frіеndlу.

Energy Mоnіtоr

Thіѕ іѕ one оf thе еаѕіеѕt thіngѕ you саn do tо mіnіmіzе еnеrgу соnѕumрtіоn аrоund thе hоuѕе. Hоuѕеhоld еnеrgу mоnіtоrѕ аrе еаѕу tо uѕе аnd іnеxреnѕіvе; thеу аllоw уоu tо ѕее a minute bу mіnutе рrеѕеntаtіоn оf уоur еnеrgу uѕе.

Smаrt Pоwеr Strірѕ

Smаrt power ѕtrірѕ аrе оbvіоuѕlу thе bеѕt ѕоlutіоn fоr еnеrgу рrоblеm. Smаrt роwеr ѕtrірѕ ѕеnѕе еnеrgу dеmаnd аnd сut off роwеr ѕuррlу tо fullу сhаrgеd оr nоt іn uѕе dеvісеѕ, аnd саn аlmоѕt еlіmіnаtе vаmріrе роwеr drаwdоwn.

 

Bеnеfіtѕ Оf Green Еnеrgу Tесhnоlоgіеѕ

Thе еvоlutіоn оf tесhnоlоgy thаt ореrаtеѕ оn rеnеwаblе grееn еnеrgy lеаvеѕ uѕ wіth mаnу bеnеfіtѕ that іnсludе:

  • Pоwеr rеlіаbіlіtу; constant electricity gеnеrаtіоn іѕ frоm a mоrе rеlіаblе source оf power thаn роwеr dіѕtrіbutеd through thе electric grіd аѕ іt uѕеѕ rеnеwаblе еnеrgіеѕ.
  • Green еnеrgу tесhnоlоgіеѕ аrе еnvіrоnmеntаllу ѕаfе ѕіnсе they аrе оf nаturаl ѕоurсеѕ.
  • Thе еvоlutіоn оf grееn еnеrgу technology wіll mіnіmіzе thе dерlеtіоn оf thе оzоnе lауеrѕ since іt ореrаtеѕ оn hаrmlеѕѕ rеѕоurсеѕ.
  • Thеrе іѕ рrореr mаnаgеmеnt оf nаturаl rеѕоurсеѕ.
  • Reduction of wаѕtе with nаturаl mаtеrіаlѕ, ѕіnсе whаt is соnѕіdеrеd to be wаѕtе саn bе uѕеd аѕ fuel аnd rесусlеd tо bе used bу аnоthеr рrосеѕѕ.
  • Thе uѕе оf grееn tесhnоlоgіеѕ сhаllеngеѕ tо bе a lаѕtіng ѕоlutіоn tо grееn-hоuѕе еffесt аnd global wаrmіng.

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Amazon and Microsoft Combine Powers to form – GLUON! AI Deeper Learning on Neural Networks!

Monday, April 23rd, 2018

In the highly competitive world that we live in today, its clear to understand that there is a strict competitive market building amongst the great tech companies such as Microsoft, Google, Amazon, Baidu, and other startup companies. All these companies have developed, deployed, and integrated their cloud based solutions with Artificial Intelligence. Artificial intelligence provides the steroids that the Cloud industry needs in order to accelerate its existence, growth, and empowerment. AI has developed key components to help itself with its constant learning processes. These processes can be better understood through “machine learning”. Machine learning allows information to be rapidly digested, conceptualized, and intelligently categorized by artificial intelligence. Cloud products such as Azure from Microsoft, AWS from Amazon, TensorFlow from Google, and more integrate machine learning into their systems to enhance the user experience and make mundane tasks to now be automated with little or no human effort involved.

With this evolution and the stiff competition there can be no collaboration in sight especially from rival companies which have no need to partner up, but to simply dominate their place in the industry. Surprisingly Microsoft and Amazon have done such a collaboration. This collaborated effort is not only to show that larger companies can work together, but also the data collected from this program allows both companies to be more successful in their domination of the cloud space.  Microsoft and Amazon have come together to form Gluon. Gluon in the dictionary is defined as a “subatomic particle of a class that is through to bind quarks together”. However, this is not the same Gluon which are referring to. Gluon is the fist distinctive collaborative efforts of these technology giants in the race for Artificial Intelligence and gathering information. Gluon is considered the store-house for machine learning and the ability to “voluntarily” utilize machine learning in the development of products and services by Amazon and Microsoft. This storehouse of technological learning and a knowledge base that is bubbling over with information is the type of data warehouse that AI thrives on.

This cohesive collaboration between developers and machine learning have no blossomed what is dubbed “deep learning”. Deep learning essentially is a combination of three distinct components such as data for training, neural network model, and an algorithm which trains the neural network.  The neural network essentially translates the data and feeds AI this information allowing AI to grow on a more diversified scale.  This new algorithm that is now being utilized by neural networks self-adjusts its output based on errors in the network output. This is a memory and compute process that machine learning and AI adapts through predictive outputs. An example of deep learning is Caffe2, TensorFlow, Appache MXNet, and Cognitive Toolkit offers options to speed up the neural network which often takes days to compute the data being derived.  These products now reduce the learning time and accelerates the parallelization in distributing computation processes.

Even though these products built within Gluon are effective and pose to change the way AI is developing by giving it more knowledge, the key is having developers to utilize these products to diversify and intensify the data stream.  AWS has been experimenting with its developers by using MXNet to train the neural networks. Microsoft has become a heavy contributor the open source MXNet by opening it to an ever-increasing rise in developers. The collaboration and data being created and distributed by these two powerhouses with Gluon can be overwhelming for a beginner when first interacting with this program, but even for more advanced developers, the data intensive algorithms seem take a life of its own demanding more ways to conform and adjust with massive error reduction.  The four key innovations which is introduced by Gluon are as follows:

Friendly API – using clear and concise code, it allows developers to learn and understand the data.

 

Dynamic networks – allows for ease of access and the rapid fluctuation of the data structure. Fluency in the data structure is critical for development as it allows hybrid scenarios and reduces stagnancy of the data flow as with previous machine learning software.

 

Algorithms that devein the network – Seamless combination of the model and algorithm allows the network to adjust definitions during training. This is critical as it allows developers to use programming loops, and conditionals.  Algorithms are now easier to change, create, and debug.

High Performance Operators for training – Gives the ability to create dynamic graphs and concise APi without sacrificing speed. Previous versions seemed to have consumed valuable run time that this feature runs through effortlessly and drastically picks up speed.

The question now becomes, how does developers access Gluon? Well its provided through Apache MXNet and the future releases will have support for Microsoft Cognitive Toolkit.  The AWS team has already published a front-end interface with low-level API to include other specifications and frameworks. Once accessing Gluon you can utilize an AWS Deep learning AMI to find a plethora of examples and workbooks utilized and documented by fellow developers.

 

Could this be a taste of what is to come from these super giant tech companies in terms of collaboration in order to leap ahead or is this just an opportunity for the public to advance an already thriving technology called Artificial Intelligence? These tech giants are clearly leveraging all aspects of “free data” and utilizing the voluntary efforts of developers world-wide to feed this neural network. As a partner for Microsoft, we anticipate that Microsoft as being the leader in products on the market will utilize Gluon in order to create more intensive, responsive, and advanced products built with AI as the backbone to reduce many processes and essentially speed up productivity. It Gurus Of Atlanta is your Microsoft partner of choice which brings you the cutting edge in design, technology, and its advancements.

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Real Life Hero Rescue Robot – Walk-MAN

Saturday, March 3rd, 2018

The future is here and the time is now. The future has been unfolding with each scientific discovery and every technological breakthrough. Between phones that seem to have no limitation, to flying cars, this year seems to be geared to changing the way we live including the way how interact. Humanoid robots such as Sophia are changing the landscape for what Artificial Intelligence robots are capable of. Sophia obtained her citizenship in Saudi Arabia and is now a part of the United Nations speaking on behalf of society standing up for human rights in different countries. For this reason, it is no surprise that the humanoid world is at the front of everyone’s mind to try and figure out what we will create next. Well IT GURUS OF ATLANTA has what’s next, and its call the WALK-MAN.

Yes, WALK-MAN is the popular name of the music player that rocked the world in the 80’s and 90’s that everyone could carry their music with them and not have to worry about a large boom box or radio. But that is not the WALK-MAN we are talking about. This WALK-MAN was invented by The Italian Institute of Technology or commonly called IIT. IIT has released several videos that highlights WALK-MAN’s capabilities during an emergency or a disaster. These critical features add value and time to when rescuers cant enter a building or when there is too much risk involved for a rescuer to get injured.  In one of the videos, it shows WALK-MAN navigating an industrial plant after an earthquake. The room that WALK-MAN navigates through serves as an example if the room had fire and a gas leak.

During the video WALK-MAN is tasked with performing very specific tasks such as opening the door to enter the room, closes an open valve for the gas leak, moving debris, and then finally using a fire extinguisher to put out flames. A lot to do, well not if you are WALK-MAN who went through the exercise with ease and precision. In time WALK-MAN is slated to be one of the most revolutionary robots which will be able to assist humans during their most dire time of need, which is an emergency.

This is not the first time that IIT has tried to unveil WALK-MAN, but their second time. Their first premier of WALK-MAN happened in 2015. Between 2015 and now being 2018, there has been some notable differences that “upgrades” WALK-MAN in terms of he moves, what it conceives as a threat, and its overall capabilities.  The new version of WALK-MAN is much lighter and not as bulky as its original version in 2015.  The body is 1.85 meters (6 foot) tall, weighs 102 kilos (226 pounds), and can carry up to 22 pounds in each arm.  The new sleeker design increases the robot’s ability and performance while cutting down on energy usage. WALK-MAN can operate up to 2hrs using a 1 kWh battery.

Being fully autonomous is not a capability of WALK-MAN yet. Currently controlling WALK-MAN is through human being wearing a suit with sensors to depict movement and actions. This accounts for 80% of the actions taken by WALK-MAN. Autonomy is on the table according to Tsagarakis, who is the project lead over WALK-MAN. He believes that it is essential to cut down on time that would normally be used by a human to decipher what to do. When WALK-MAN does become fully autonomous, then it will surely change the way we do rescues including save countless lives.

 

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Microsoft Azure, Machine Learning, and Artificial Intelligence! – LUIS

Wednesday, February 28th, 2018

Azure, which is Microsoft’s great platform for virtualizing an Active Directory environment, or at least that is what it started out being. Since its introduction in October of 2008 and its release into the production for masses on February 1st, 2010, Azure has grown into a lot more than just a virtual Active Directory. Azure has now become the world’s largest platform for virtual technology. This includes app development, deployments, and the introduction of PaaS, IaaS, and SaaS. These technological advancements within the Azure space allows Azure to not only be manipulated, but also enhanced to include Machine Learning.

Machine Learning is the next phase in technological revolution. Machine Learning gives the life to systems allowing them to self-correct and self-rewrite their programming to be more fundamentally correct. Machine Learning is already in most of the products that we use daily such as Amazon’s Echo which utilizes speech to determine products and services that best suite you, the consumer.  Other examples are Siri within the iPhone, Cortana with Windows, and Google Assistant. All these technologies work together with a simple purpose. That purpose is to learn the habits and functions of human beings to become better and more sufficient.

The connection flag ship with allows all these devices and machine learning products to interface is called IoT or the Internet Of Things. IoT connects devices such as your smart phone, home security, bluetooth enabled appliances, and more on to one unified management platform that they call interconnect and they are easier to manage. IoT is the next wave in technological advancement being presented by Microsoft along with other large competitors such as Amazon and Google. IoT is becoming the next big thing when it comes to full home automation. Machine learning interprets all information being gathered from analytics, bots, IoT experiences, including conversational components into a spoken dialogue called LU. LU or Language Understanding determines the intent of a sentence including the machine-enabled meaning representation.

In an effort in increase Machine Learning, Microsoft has launched LUIS which is its Language Understanding Intelligent Service. LUIS will allow Software developers to create cloud-based machine learning LU models that are designed around their specific application domains. This allows the code to be written and interpreted with very little Machine Learning experience. LUIS is now a cloud-based application which does most of the background work for Machine Learning in the background while giving users the agility and flexibility to cater the experience more towards their application and organization.  The application uses user experiences from the developer and learns how to obtain an HTTP endpoint in Azure which will then receive real-time traffic.  This is called “Active Learning”.  Through these “Active Learning” utterances, LUIS identifies the key features needed to make the experience and solution unique to that user.  These active learning sessions are done until optimum levels of requirements are met. Bear in mind that active learning is also utilizing machine learning, so the learning curve is very small, while the results are extraordinarily accurate.

LUIS runs off of three main functions and they can be categorized as Intents, Utterances, and Entities. All three aspects engage LUIS functionality and ability to learn the user habits. A more detailed overview of these functions are listed below:

 

Intents: The input done by a user to express actions with a purpose that the user wishes to perform or a goal which the user is trying to accomplish.  This could be as simple as booking flight, hotel, or pulling up a newspaper article. The name of the action can then be associated with the action.

 

Utterances: This can be a combination of text inputs that regulate an action to obtain results. Such actions in a text can be “check flight status” or “what was the score from the game?”.  Because of so many variations to an utterance, they may not always be perfectly formed sentences, but all desire a intent.

Entities: An entity is a specific detail within an utterance which governs the direction the utterance will take.  An example of this is “hotels in Jamaica”. “Jamaica” is the location. LUIS can determine the location, then using the other intents to understand the utterance, then provide a response.

 

LUIS utilizes powerful entity extractors for it to achieve learning capabilities and become more successful with its responses. LUIS allows developers to quickly build language understanding applications. The applications can then combine with customizable pre-built apps to include music, dictionaries, calendars, and devices. Through interaction with the developer and the information being constantly pulled from the internet, the learning, and solutions that LUIS provides becomes more intuitive with each use.  Once the applications are created using LUIS, the app can then be customized and tailored to the users which the app is designed for giving all of them that unique experience.

LUIS uses two ways to build a model, that is through “LUIS.ai web app” and “Authoring APIs”. Whether the user goes with the LUIS.ai web app or the Authoring APIs, they both give control over the LUIS model definition.  Another fundamental and creative method that developers have found is to combine both to build a model.  Management within the model includes models, versions, external APIs, collaborations, training, and testing.

LUIS is another segment in the Machine Learning process in the revolution of Artificial Intelligence. As machines begin to learn increasingly about human behavior and what we like, they also become aware of errors that we make including how to fix or to alleviate the errors all together.  Error free, automation, increase in productivity, and cutting cost is the motive behind AI. The Microsoft Azure cloud space is proving to be a formidable place for this technology to not only thrive but to maximize on limitless possibilities. IT GURUS OF ATLANTA will ensure that we provide updates to our supporters as we are a trusted Microsoft Partner and a certified Microsoft Cloud Partner.

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