Amazon Web Services (AWS) is one of the most prolific web service platform in the world. In fact, it’s estimated that over half of the world’s small and medium businesses have adopted the technology platform as their platform of choice when it comes to dealing with cloud services for their needs. AWS provides one of the most diverse platforms supporting Artificial Intelligence, Machine Learning and even rudimentary data storage. They provide their different services as deployable modules which allow companies to deploy and terminate instances as they need to.
AMD is one of the latest additions to the platforms array of instance which can be deployed. The new AMD EPYC instances will be powering the Amazon Elastic Compute Cloud (EC2) C5a instances. These instances will be powered by AMD’s 2nd generation server grade EPYC processors which also power one of the largest supercomputers in the world. The AMD EPYC processors will be able to run at frequencies of up to 3.3GHz and will be able to provide users with high performance x86 processing for large compute workloads. This includes batch processing, distributed analytics, data transformations, log analytics and web applications.
The new EPYC powered C5a instance joins the increasing number of AMD powered instances available on AWS. It will be available in eight configurations with up to 96 virtual CPUs (vCPUs). The new AMD EPYC powered instance also delivers on AMD’s promise of being able to deliver high performance compute at affordable prices. On AWS, the new C5a instance is the one of the lowest cost per x86 vCPU in Amazon’s portfolio.
The C5a instance is already available in AWS U.S. East, AWS U.S. West, AWS Europe and AWS Asia Pacific regions. AMD also has five other instances already available on AWS under the EC2 catalogue: M5a, M5ad, R5a, R5ad and T3a. These instances also provide users with compute capabilities that are catered to their needs and price points.
Artificial Intelligence and Machine Learning (AI and ML) technologies have come a long way since its first inception. Who would have thought that we would have a working model of actual computer-based assistants that can do things like manage our schedules? Who would have thought that we could even use these assistants to manage our homes? These things can even be used to diagnose cancer patients, something impossible without doctors even five years ago.
Amazon Web Services (AWS) is at the forefront of AI and ML technology. As one of the world’s largest technology innovators, they would naturally be at an advantage to feed enough data to the technology and accelerate their development. Because they are also one of the largest technology firms any man has ever seen, they are also at an advantage in placing AI and ML in places and applications we may never have imagined.
Linguistics is one segment that has benefitted greatly from technologies today. Linguistics, if you think about it is also one of the most complex things that us humans can create and understand. The context of it and interpretation can be affected by plenty of things too. Linguistics is affected by area, culture, community, heritage, and even lineage.
For example, there are differences between French spoken in France and Canada. There are even subtle differences between French spoken in France and Monaco, or even Switzerland. The most common language of all, English has differences even in spelling and context in Britain, the Americas, and even Australia. English spoken today is also a distinct form of the language that was spoken 50 years ago.
The progression of technology in linguistics have progressed through years and years of feeding all these data into it. That has allowed us to communicate with global communities with more ease than peeling an orange. AWS has taken it a little further than that though. They have gone beyond spoken or written languages. Through something called AWS DeepLens, they have developed translation algorithms to sign languages.
While that technology might sound like it is as simple as gesture controls, it is plenty more than that. Yes, it is technically gesture control and recognition. But it is way larger and more complex than just a solution for end-point devices. The trick is to teach the native algorithm to recognise all the available sign words and even alphabets. The AWS DeepLens Community projects so far has learnt to recognise most of the alphabets in the American Sign Language.
But technology also goes beyond just recognising alphabets to understanding proper words with the algorithm in Amazon Alexa. It is not just about communicating with your friends anymore. It is about using the platform as a home assistant tool, a customer service tool, a command center, and user defined PC experience that mimics voice control and command for us. Instead of using voice though, its all in the gestures.
The tool they use is called Amazon Transcribe. It works just like any transcribe apps you can find in the market. It supports up to 31 languages currently with more being added by time. It even supports ASL as a component to create text from sign language.
Simple communication is just the beginning for the technology though. AI and ML still has a long way to go even in the medical field. Just like the human race, the technology gets better everyday though. If you really think about it, the technology is not that new in the first place. We have embarked on the journey of having machine built and defined assistants since we started developing computers to help us with simple and complex mathematical problems.
It is just that simple mathematical problem solver has become something much bigger today. Who would have thought that we would let computers fly a commercial airplane? Who would have thought that cars can drive themselves today? Who would have thought that we could hire a private translator without spending any money or any time? You just have to look into your pocket.
Sports, data analytics and machine learning. Three words you would never expect to be in the same sentence, right? Well, what if we told you that they already are in the same sentence in sports teams the world over. That’s right, we’re already seeing the inclusion of data analytics and machine learning in sports – some even as early as 15 years ago. You’d be surprised how advanced things have gotten when it comes to data analytics and sports; we’re even seeing companies use Amazon Web Services (AWS) to help deal with and store the data.
In sports such as the F1, American football and even rugby we’re seeing more and more decisions being made when taking into consideration probabilities and numbers generated by machine learning. In fact, one of the sports most adept at using data is the Formula 1. Teams generate up to 600GB of data per lap from the 200 to 300 sensors in the cars. When it comes to the American NFL (National Football League) each player is analysed based on over 100 data points. These data points drive the plays we, as fans, cheer and look for when we watch the athletes play.
Dilemma: Where to store the data? How to capitalise on it?
When it comes to dealing with the data generated from these sports, the first dilemma is where to store the data. Of course, Amazon Web Services has a slew of container and data lake services such as Amazon S3 storage and more these teams are already using to store their data. However, just keeping the data in the cloud isn’t enough. They will need to run through and analyse the data for it to truly be useful to the teams. That’s where machine learning comes in.
While it might seem like a brand-new paradigm, we can assure you, that it’s been happening behind the scenes for quite a while. Teams in the F1, NFL and even rugby have been collecting data and analysing them to help players perform better, drivers drive better and engineers optimise their technology further. In fact, there are companies out there such as Pro Football Focus that actually process and analyse the data in real time. In fact, at AWS Re:Invent, Cris Collinsworth, CEO and Co-Founder of Pro Football Focus, said that what used to take coaches around two to three days to analyse is done in less time. He said that with this improvement, coaches are given more time to strategize and tweak their plays to help their teams win.
Photo by Chris Peeters from Pexels
The data collected during the races of the F1 doesn’t just go to the cloud for storage. Analysts on the ground are constantly looking at it to help tweak and make critical decisions for that edge. In fact, the data plays a big role in the teams pitting and undercutting strategies in a race. The engineers are also using this data to help with their car design and tweaking between races. However, the F1 has a pretty good head start compared to other sports out there. They’ve been using data analytics in their sport for over a decade now and have been able to use it to help with performance. However, that isn’t the only way they use their data, they also use it to create new regulations that affect the whole game and the welfare of the drivers.
Machine Learning in Capitalising on Collected Data
“We don’t do magic. We use technology to make decisions.”
Rob Smedley, Expert Technical Consultant, Formula 1
With the advent of machine learning in the past few years, the work of analysing the data has been made even easier. Using services like Amazon SageMaker, companies and teams are able to take advantage of the numerous data points in real time. Machine learning algorithms can churn out predictions and probabilities based on the collected data near instantaneously.
That said, the data generated by the machine learning algorithms is only half the picture. It informs the coaches and players of not only the probabilities and possibilities but also what could be done to help give the teams an edge over the competition. The decision making process on the pitch or track is no longer only a question of gut instinct, it’s about tempering and guiding the gut instinct with mathematics.
“The teams that are really embracing the new approach are going to win the championships”
Cris Collinsworth, Co-founder and CEO, Pro Football Focus. and Broadcaster for NBC Sports Sunday Night Football
We are at the crossroads of a change in sports paradigms. Coaches are beginning to accept that the data being processed by machine learning algorithms as guides for their game time decisions. The game is changing based on how teams are able to use and optimise machine learning to get the edge they need during game time.
Creating New Fan Experiences
That said, machine learning isn’t just giving the edge during game time. It’s also being used to create new fan experiences. Watching sports can become a pretty mundane experience for some. However, using machine learning and data analytics, broadcasters can create new experiences for fans to keep them more engaged.
In the United States, broadcasters have been experimenting using data lakes and machine learning to enhance the sports viewing experience. This isn’t just restricted to F1, NFL, NBA or the MLB. It’s across the board. These broadcasters are using machine learning to create overlays and explanations of complexities that help fans better understand the sport. In fact, with the amount of data they have at their fingertips, shout casters and commentators are able to see plays before they happen or even suggest some that would have led to a better outcome. These hints of information are also opening up the sports world to new audiences. It is also creating a more engaging experience for long time sports viewers and fans.
Given the amount of data being collected, it also comes as no surprise that broadcasters and even teams are looking into giving fans a better experience via a second screen. They are looking at what information would make sense and enhance the experience for viewers. Of course, raw data isn’t the answer but the data processed by machine learning algorithms are able to give a better understanding and appreciation to fans. In fact, they expect that it would engage a whole new type of viewer.
“You still need the human element”
Rob Smedley, Expert Technical Consultant, Formula 1
With all the emphasis on machine learning and data analytics, it would seem that sports will be reduced to 1s and 0s. However, as Rob Smedly highlighted, artificial intelligence and machine learning can never replace the driver or player. In fact, the thing that makes sports engaging is the human element in the game. It’s about how athletes are able to push boundaries of human performance and how we use the data to improve, not only the game, but also other aspects of human life.
*This article is contributed by Myles Hosford, Head of Security Architecture, ASEAN, AWS*
Cybersecurity professionals see some threat
actors or outside-parties as the enemy. However, challenging this mindset is
important; you can better protect your organization against outside-parties if
you understand how they think and operate. With this in mind, businesses around
the globe have turned to hackers to test security infrastructure and develop
stronger, more robust security practices.
Before integrating penetration testing
into your security policy, it is important to understand the different types of
hackers that exist. Each group has differing motivations, and you must be clear
on which of their skills can be used to your organization’s advantage.
Black hat
Photo by Luca Nardone from Pexels
Black hat hackers are cybercriminals
motivated by personal or financial gain. They range from teenage amateurs to
experienced individuals or teams with a specific remit. However, over recent
years, several high profile blackhat hackers have refocused on using their
cyber skills to protect organizations. An example is Kevin Mitnick aka Condor,
who was just sixteen years old when he gained access to a Department of Defense
computer. Following this and numerous
other hacks, Mitnick spent five and a half years in prison. Upon his release
set up his own company, Mitnick Security Consulting, which now runs penetration
tests for clients.
The issue of whether to work
with a previous black hat hacker is a contentious one. Some, including David Warburton, senior threat evangelist at F5 Networks,
believe that hiring ex-hackers is critical in staying ahead of the threat
landscape. However, others are concerned about allowing this group access to
corporate systems and customer data. The latter group should, however, consider
other approaches to working with hackers.
White hat
Photo by Reza Rostampisheh on Unsplash
Often referred to as ethical hackers,
white hat hackers are employed by organizations to look for vulnerabilities in
security defences. Despite using the same tactics as black hat hackers, this
group has permission from the organization making what they do entirely legal.
While they use their knowledge to find ways to break the defences, they then
work alongside security teams to fix issues before others discover them.
Many of the biggest organizations in
the world, including General Motors and Starbucks, are turning to white hat
hackers to help identify fault lines and proactively enhance security posture.
White hat hacking can offer an interesting and lucrative career path for people
with technical skills. Drawing attention to the important role white hat
hackers play can encourage more talented individuals to take a positive path
instead of becoming black hat hackers.
Nurturing
talent
There are many programmes in place to
find, encourage and support the next generation of white hat hackers. An
example, supported by AWS, is r00tz Asylum, a conference dedicated to teaching
young people how to become white-hats. Attendees learn how hackers operate and
how cybersecurity experts defend against hackers. The aim is to encourage
people with technical expertise to use it for good in their career. By equipping aspiring cybersecurity
professionals with knowledge and skills, they can bake security into
infrastructure, from the ground up. AWS’s support for r00tz is our chance to
give back to the next generation, providing young people who are interested in
security with a safe learning environment and access to mentors.
Building on
solid foundations
Photo by Ramin Khatibi on Unsplash
For those responsible for maintaining
customer trust and protecting data, an end to end approach to security is
critical. As we have seen, working with ethical hackers is a powerful way to
view security posture from a cyber-criminal’s perspective to identify and
tackle vulnerabilities. However, it’s also important to remember that security
needs to be baked in throughout an organization’s infrastructure. This is where
partnering with a cloud platform can be beneficial; the best of these are
developed to satisfy the needs of the most risk-sensitive organizations. Cloud
platforms also offer automated security services, which can proactively manage
security assessments, threat detection, and policy management. In so doing,
these platforms take on a lot of the heavy lifting for security professionals,
including ethical hackers.
*This article is contributed by Myles Hosford, Head of Security Architecture, ASEAN, AWS*
As the world
becomes more interconnected, opportunities for companies and individuals to
interact and transact across borders, time zones, and channels grow quickly. To
make sure that these transactions run smoothly, proactive management –
specifically to ensure the minimization of cost, lowering of risk, and the
elimination of inefficiencies – is needed.
Distributed
ledger technology (DLT) such as blockchain helps simplify transactions and
conduct efficient, secure interactions with multiple independent parties around
the globe. All without the need for a third-party intermediary. These
transactions can vary from sending anything from farm data, to banking and
contract transactions.
Use case: Empowering farmers to sell field data
transparently
Farmers collect
large volumes of data with each step in the planting and harvesting process.
Licensed data – data that qualifies as intellectual property of the farmer such
as which crops to plant or how many seedlings – can be anonymized, sold to
third parties and offer the agricultural industry with real-time insights on
farms across the world. However, farmers are unsure how to monetize this crop
data.
As farmers are
unsure how to monetize their crop data, Farmobile addresses these challenges through a
blockchain-based exchange, built on AWS. The solution empowers farmers to licence
data to approved buyers and includes account set up, creation, confirmation,
execution of the offer, and delivery of the digital asset. They can seamlessly
sell single-use licenses while keeping their farm’s identity completely
anonymous. However, farmers have full visibility into the identities of
potential data buyers, such as agronomists, equipment producers, and retailers,
and are free to decline offers.
Use Case: Boosting financial inclusivity
Another case
study for Blockchain technology is the financial sector in the Philippines.
Here, rural banks lack the resources of larger banking institutions, making it
nearly impossible for them to thrive or survive. This has left a large majority
of rural-based Filipinos with little or no banking access.
Photo by David McBee from Pexels
UnionBank,
a pioneer in its use of blockchain technology, joined forces with ConsenSys,
an AWS Partner , to build a blockchain solution that would resolve this issue.
The new, blockchain-based solution created a decentralized, cost-efficient, and
near real-time network, allowing for the execution of domestic payments without
relying on existing banking infrastructure and intermediaries.
The blockchain
solution introduced means that rural banks no longer have to shoulder the
burden of manually processing back-office transactions, freeing up staff to
serve more customers. As such, the technology not only increased banking access
and inclusivity but drove sustainable, future banking practices.
Use case: Limiting contract disputes in the oil and gas industry
Another example
comes from the oil and gas industry. Moving resources through the oil and gas
supply chain involves many stakeholders, including landowners, governments, oil
and gas company operators, surveyors, and financial institutions. One critical
step occurs between those mining the oil and royalty owners on whose land the
oil is mined. Checking royalty transaction payments is a lengthy, manual
process where stakeholders must agree to contract terms upfront. However, those
terms are often interpreted differently on either side, often leading to
disputes.
GuildOne,
believed companies needed more efficient, secure, and cost-effective ways to
execute a royalty contract transaction. They developed a solution through which
contract terms are capable of being replicated, and consensus agreed using
blockchain technology. By doing so, they mitigated the possibility of disputes
and eliminated a large chunk of the expense of contract administration.
To build its
royalty ledger and to meet the stringent privacy and security needs of its
stakeholders, GuildOne chose to use R3’s Corda — a blockchain platform built for
business and longevity — on AWS. Believing that the security capabilities
gained would be vital in enabling rapid adoption of the royalty ledger solution
in the oil and gas industry.
The future of blockchain technology solutions
Blockchain
solutions are transforming the ways companies and individuals do business,
locally and globally, by simplifying transactions and increasing their
efficiency. Those looking to take advantage of the technology should partner
with cloud providers capable of scaling up while delivering cybersecurity controls
and standards to protect from external attacks. With Amazon Managed Blockchain,
it eliminates the overhead required to create the network and automatically
scales to meet the demands of thousands of applications running millions of
transactions. Once a network is up and running, Managed Blockchain makes it
easy to manage and maintain the blockchain network
by managing its certificates and letting customers easily invite new members to
join the network.
5G is fast becoming the norm in the tech industry as more countries see the rollout of their own 5G networks. Back at AWS re:invent, Amazon Web Services made a significant announcement, in partnership with Verizon, which made it the first company to have 5G edge computing services. AWS Wavelength is a first of its kind service which brings AWS services closer to developers and, more importantly, end users.
AWS Wavelength will see an initial rollout to 69 sites in the United States. Verizon and AWS have already been hard at work developing and fine tuning the service in Chicago. There companies such as Bethesda Softworks and the National Football League have been developing on and utilising Wavelength to deliver new, enhanced experiences to their users. This includes interactive experiences which may be the next generation of gaming and sports.
AWS Wavelength essentially brings the company’s full suite of services to the 5G Edge. The technology allows telecommunications providers and AWS to deploy remote containers fitted with all of its services. This allows developers to develop with real time experience and with single digit millisecond latency. They will then be able deploy whole new experiences to end users.
The deployment of Wavelength marks a paradigm shift which empowers edge computing like never before. It allows real time compute with large data packets which will find its applications in things like autonomous vehicles and even Smart City management. The deployment of Amazon’s full suite of web services will allow developers to deploy unique experiences for end users which take advantage of the low latency and high data volume. This in addition to the exponential increase in the number of devices each base station is able to handle will enable IoT technologies as well. The availability of machine learning interfaces at the 5G edge enables developers to develop more complex applications with further ranging implications.
Source: AWS
Developers won’t need to familiarise themselves with a new interface; Wavelength comes with the same interface developers are used to in their AWS dashboard. In fact, they will simply need to activate instances of AWS services such as EC2, ECS and more which suit their needs at a Wavelength availability zone to use the service.
AWS Wavelength is available in an initial 69 availability zones in 25 AWS Regions. The initial rollout in the United States will be done in partnership with Verizon. However, the company has committed to new availability zone in South Korea (SK Telcom), Japan (KDDI) and Europe (Vodafone) in 2020.
You would think that when it comes to making compositions and music, you’d need a really good ear and knowledge of the arts. Not so much with Amazon Web Service’s new AI (Artificial Intelligence) service focused on creating musical pieces with a keyboard! DeepComposer is the latest in a series of Machine Learning focused services that AWS has introduced since it’s announcement of DeepLens at Re:Invent 2017.
The new music based AI is a 32 key, 2 octave keyboard which will allow developers to familiarise themselves with using Generative AI. The simple application of Generative AI in DeepComposer will take short riffs and generate a full compositions.
A brief diagram explaining how AWS’s DeepComposer works. (Source: AWS)
The DeepComposer generative AI will be able to layer and generate songs based on pre-trained models or even user defined models. The pre-trained models are able to generate based on algorithms developed by training the AI with large musical data sets. The user defined models give users better control of the generative AI. Users will be able to define multiple parameters including the Architecture and Discriminator. The latter allows the AI to distinguish between the genres and determine the overall composition.
Being a machine learning model, DeepComposer is continually learning to identify music types. The AI will improve with time as it learns and generates more music based on the models and riffs. It will also be able to generate music which mimics a defined model. Amazon’s release touts, ” you have to train as a counterfeiting expert in order to become a great counterfeiter “.
DeepComposer isn’t just linked to the physical keyboard. It also has a digital keyboard interface which allows users to compose on the go. Using this approach, AWS is hoping that Generative AI models are made more approachable for those looking to explore their applications.
The new feature is currently available for preview on AWS at the DeepComposer website. Also on the website is a FAQ to address some of the questions that new users may have.
It would come as no surprise is the first thing that comes to your mind when your hear “Amazon” is the popular online marketplace or the largest rain forest in the world. What if we told you that there is another Amazon that you should know of – Amazon Web Services (AWS). Yes. This Amazon is related Amazon.com but their reach is far, far greater than just an online marketplace. AWS is a subsidiary of Amazon.com which powers most of the internet. Yep. You read that right! AWS provides the backbone for a majority of the world’s websites, apps and services. They provide a slew of cloud computing infrastructure services which allow many websites, apps and businesses to scale and accommodate sudden spikes in their usage as well as the backbone for cloud based compute services.
Now that you have a general idea of who they are. We’re pretty sure that you’re wondering why you should be paying attention to this company. From what we’ve outlined, it seems like AWS is a very corporate service. So why would the regular joe need to know about it? Here’s three of the compelling reasons you should.
1. Nearly Half of the world’s Cloud Computing Services and Platforms Run AWS
AWS is one of the most omnipresent service providers in the world. Apps, websites, banks and more are using AWS to drive digitalization of their businesses. In fact, in his keynote, Andy Jassy, CEO of AWS, shared that about 47.8% of all services using cloud computing run on the platform. This number puts them far ahead of their nearest competition, Microsoft Azure (15.5%) and Alibaba Cloud (7.7%).
Q3 Cloud Computing Market Share estimates from Canalys. Taken from https://www.canalys.com/newsroom/global-cloud-market-Q3-2019
This also
means the company’s platform and services are being adopted at a rate much
higher than its competition. The AWS platform has an edge over its competition
thanks to the extensive services and granularity of the customisation that the
service offers.
2. More Companies are adopting Cloud Computing to
better serve customers
We’ve
talked about Industry 4.0 a whole lot over the past year and the truth is,
we’re only at the cusp of it. In the next few years, we will be seeing more and
more companies adopt cloud computing as one of their main tools to serve their
customers. You may not see this being announced publicly, but nearly all the
services that you use from Agoda to Facebook have a cloud computing component
to it; whether it’s to hyper personalise their offerings or to have
redundancies that will help with making things more seamlessly. With this
increased adoption, it may be time to know a little more about how these
services are provided.
3. AWS has one of the most complete Cloud Computing
services
Amazon Web Services is one of the most complete cloud computing platforms available now. In fact, the company is ahead of the curve when it comes to providing the latest and greatest in cloud computing. AWS currently has over 165 modules or services which it offers its customers. Each of these services can be selectively deployed to meet their customers’ unique needs. With their recent announcement at their annual Re:invent conference, the number of services offered by AWS has grown further.
AWS service categories as listed on aws.amazon.com
Very briefly, the company’s offerings span everything from storage, machine learning, artificial intelligence and data processing. The company has announced even more services with a strong focus on allowing its customers to adopt edge computing and better manage and process their data which is being stored in the cloud and even on premises with their new AWS Outpost.
With the companies adoption across the internet becoming more and more popular, AWS is set to become one of the largest cloud computing providers in the world. They’ve even made it into things like the Formula 1 (F1) and National Football League (NFL). You may even see them helping your self driving vehicles in the near future. With that in mind, prepare to find out more about AWS and how they are changing the state of the internet.
