The opportunities of the modern market, with itseCommerceand multiplicity of brands, provide customers with a choice they never had before. So companies strive to evolvetheir marketing strategy in order to have a leading position in the race with their competitors. But how to place more ads and send messages to customers without scaring them off? And how to convert a doubting potential client into a loyal one?
This is possible with marketingautomation software. Such software offers companies tools that help them set up a steady personalized communication with each of their contacts and thus, win their trust and make them regular buyers. How?
A marketing automation platform structures all the information about customers, using their personal data and preferences. The latter is tracked across social media, the websites visited by a person, the history of purchases, etc. Based on this, the system assesses the readiness of the prospect to make a particular purchase in a given time. Notifications are sent to sales representatives then, so they can contact the right person at the right moment.
In addition, the above technology automatically creates attractive personalized content that appeals to customers. Each of them receives messages exactly the way they want, whether by email, SMS, or via various apps. The ads appear in a non-intrusive way to catch the interest of the audience. This helps to generate new leads and nurture clients until they are ready to purchase.
Marketing automation solutions vary depending on the vendors and features. They can be on-premises or cloud-based. The latter solutions are easier to implement as they don’t require a physical server and provide their users with remote access. Whatever the approach is, all platforms have the same basic tools. These refer to:
email creating and sending;
sending mobile messages and notifications;
social media posting;
launching ads.
The above processes run automatically. This frees the staff from repetitive time-demanding actions and gives them space to develop new strategies and take care of high-priority tasks. The technology works well not only for large enterprises with multiple workflows. It also helps small businesses to expand and do more with a limited budget and resources.
Another substantial advantage of marketing automation is its simplicity. The providers offer a user-friendly interface, so the required actions can be performed just in a few mouse clicks. Additionally, all the information about customers and their interaction with the brand is gathered in one place. This makes it easy for the marketing and sales departments to better organize their work.
To introduce marketing automation into your business processes, you need to take a few necessary steps.
Firstly, marketing, sales, and customer support teams should work together. If their managers don’t cooperate while introducing the new technology, they won’t use it later. So the departments need to develop a common strategy for the implementation of marketing automation.
Secondly, the application of the platform’s tools should be carried out from the simple to the complex. Marketing automation has a lot to give to its users. Pursue just one objective for a start and gain pace along the way.
When it comes to an iOS app development company, the best companies are always in pursuit of the next trend that is going to hit the market. These services are everywhere, but staying on top of these trends can be relatively challenging. iOS app development has come a substantially long way, and it is exciting to see what will be Apple’s next invention. It is why we have put up some of the top iOS development trends that are most likely to dominate this year. So, below are the trends that we can expect to rule 2021.
IoT Mobile Apps
With time, the Internet of Things has made its way into our lives. It has gone from being remotely there to almost becoming a significant part of our daily lives. Thus, it is not a surprise that the IoT will allow devices to connect and exchange data. Therefore, it is likely that IoT mobile apps will be a dominating trend this year.
The IoT lets are users share data across devices with ease. Irrespective of the industry, mobile apps that implement the Internet of Things are highly beneficial for the users. Automatic control is a great advantage for the apps that facilitate the Internet of Things, and the same goes for the iOS apps. Thus, it is likely that these iPhone apps will see a drastic increase in 2021.
iOS Apps with Cloud Integration
When we are talking about the top iOS development trends that are likely to dominate in 2021, it is impossible to miss out on cloud integration. Cloud integrated iOS is undoubtedly on the top of these trends. Cloud allows people to not only store but also share their data with total security.
A mobile app with cloud integration is the main goal for many iOS developers since they already see it. Till now, cloud integration has been highly influential, productive, and it has a significant impact on the users and developers providing these services. Therefore, developers who are working on iOS apps should highly consider using cloud integration while building apps.
Integration and Wearable Apps
For the developers working on iOS apps, it is a must to know that integration and wearable apps are very likely to rise as a trend in 2021. Even if we consider the Apple Watch, its use has been increasing over the years, and it is becoming more mainstream with time. Here are some apps that people use daily:
Email and reminders
Fitness tracking apps and heartrate monitors
Messaging apps
There are many new developments in this sector. Thus, wearable apps are always coming up with something new, especially in healthcare and fitness. Furthermore, the wearable device market’s total expenditure was a whopping 52 billion USD in 2020. It is a sign that more app users are resorting to wearable apps with time. Thus, iPhone app developers should consider it and work on the wearable apps sector.
Apps with Mobile Payment
Mobile payments are rising with time, and it has increased even more since the onset of no-contact transactions because of COVID-19. Smartphones are a unique way to make payments on the go. According to a study, approximately 87 million people are using Apple Pay all over the world. For the developers working on iOS app development, apps using mobile payment methods and Apple Pay will increase. The E-Commerce and Online Banking sector is also witnessing an increase over the years and online payment options. Users love using payment apps that are convenient, easy to use on the go, and secure.
5G Tech Apps
We cannot deny the fact that 5G will have a significant impact on the app trends of 2021 for iOS app development. Especially for developers, it will change how they develop apps. It means that the speed and efficiency of app development will drastically improve.
Not only that, but it is also most likely to reduce latency by almost ten times. If we compare 4G with 5G, 5G is nearly a hundred times faster, but it also depends on the mobile network operator. All in all, 5G technology is going to boost the functionality of iOS apps majorly. Thanks to it, developers will now be able to add extra features without it harming the app’s performance.
Beacon Tech Apps
Many industries use beacon technology in their apps, from healthcare to hospitality to retail. It is because beacons drastically increase the functionality of any mobile app. Not only that, but beacons can also help track the behavior of a buyer in a particular store, making things easy for the seller. For instance, it can track if a customer is spending so and so time in a specific shop section. After that, it can trigger a push notification that will announce a sale in the near future on the same products.
AR and VR
These are two of the most noteworthy trends that have taken the tech industry by storm. It helps us perceive information in different ways. We can use Augmented and Virtual Reality for many practical reasons like shopping, searching for data, improving ourselves, and so on. One such example of a company using AR and VR is L’Oreal.
It uses AR by offering its buyers to try different hairstyles using their app “Style My Hair.” Using such methods also helps them increase their sales. Similarly, Google Maps’ “True View” also makes use of AR. With it, you can find out the exact location of a person by using your phone’s camera and pointing it at the nearest buildings. There is extensive usage of AR and VR in mobile app development. Thus, it is highly likely to dominate the iOS app development market as a trend in 2021.
Final Words
So, these were some of the top trends that are most likely to dominate 2021 for iOS app development. Considering these trends and working accordingly for iOS app development will be highly beneficial.
GENERAL PRACTITIONERS navigational systems appear to be part of the “in” thing nowadays. This is even more evident with the appearance of cost-effective mobile GENERAL PRACTITIONER gadgets which you can buy for less than a hundred dollars. The only problem with an economical price is that it comes with a “reward” – the possibility of using a fractured software application.
You are possibly conscious that almost all types of electronics come with a Chinese brand as well as portable GPS tools are no exemption. These versions take on the “well-known” OEM producers but are getting a reasonable share of the marketplace as a result of their less expensive rate.
To keep these prices at the lower end, equipment distributors disperse these units minus the certified software – and also this is where the issues begin.
Your Mobile GPS Device as well as Cracked Software Application
Distributors of mobile GPS gadgets originating from China obtain these devices at wholesale rates without any qualified software. It would certainly be up to you as the consumer to choose the very best GENERAL PRACTITIONER software for your demands that would certainly be compatible with your system.
Trustworthy distributors may give systems mounted with functioning analysis software applications, so you can check the capability of the unit. You can have the choice to purchase the full variation of this software application or pick an additional one according to what you choose.
The trouble is, some underhanded suppliers would attempt and also set up broken or pirated software programs in their units, passing them off as the actual thing. The consumers like you would certainly wind up with a gadget that breaks down on you – normally throughout a time when you truly need it one of the most.
To ensure that you won’t obtain caught out in the open with the badly-working Crack Software Download, you ought to only purchase mobile GENERAL PRACTITIONER devices from a credible source. Other than that, you can do specific activities by yourself to make it certain that you’ll just get a real and reputable software application for your requirements.
Available Resources on the Right Course
How do you find out if your provider is selling you pirated software applications for your mobile GPS gadget? Trustworthy distributors typically use twelve-month warranties on the items they offer. Other than that, the items also lug a Quality assurance seal guaranteeing you that you’ll only obtain great items.
If you have doubts, however, you can always inspect the serial number that goes with your software application and also confirm it versus the maker’s database. The software program supplier would be most ready to aid you hereof. That’s the appeal of a genuine or exclusive software program – excellent consumer support and also service.
You can likewise tackle the open-source path for your portable GPS software application requires. Open-up resource applications are offered which you can utilize for your gadgets as long as they work. There is free software readily available which you can make use of such as Waze. You might not obtain all the extra functions with the cost-free software application, however, the majority of your mobile GENERAL PRACTITIONER tool’s standard functions would certainly be supported.
Do Something About It: Make a Stand against Software Program Piracy
If you’re a business owner engaged in marketing portable GPS systems online or with a physical shop, you must watch out for offering items mounted with pirated software. Not only would this be unlawful, but it can likewise damage your online reputation in this service – as well as shed your beneficial customers permanently.
Make a stand and also offer just authentic navigational gadgets and software applications. You need to additionally report unscrupulous providers using fractured software applications on their products. Not only will you do your consumers great excellent, yet the entire sector also.
Energy has dominated the news the last couple of weeks. On the one hand, the Colonial Pipeline hack exposed the vulnerability of the networks that control our energy distribution system, resulting in a surprise counterattack by the US on the hackers that both locked them out of their own system and apparently also made it impossible to get to their own digital currency.
On the other hand digital currencies have seen a major run-up, especially in Ethereum and Dogecoin, as speculators have bid prices of these blockchain-based coins into the stratosphere, likely in response to the possibility that increased commodity prices are an early warning of hyperinflation (which is grist for another editorial).
Similarly, Non-Fungible Tokens (NFTs) have taken the art and speculative realms by storm, employing blockchains to make a digital copy of a piece of artwork unique, something that is almost impossible to otherwise manage in the digital realm. DSC Contributing Writer Stephanie Glen has written a must-read article this week that explains what NFTs are, why they are attractive, and why they also may be yet another tulip mania.
This site is devoted in great part to machine learning and other computationally intensive technologies, but it is always worth remembering that computation always comes at a cost, and that cost is almost invariably energy-related. There is a perception that technology is “green”, but it’s more accurate to say that technology is simply distributed, which means that most people do not see the direct impact of technology, because the place where the power is generated is typically quite far away from where it is consumed.
This means that we as data professionals need to recognize that we need to keep our algorithms as efficient as possible, perhaps trading some (usually spurious) accuracy for less power-hungry applications. Moreover, we should advocate the notion that the energy involved in creating better models (or calculating primes to ensure rarity) be better accounted for in our businesses, rather than simply dumped out the back door onto society’s commons. If the speculators who have dreams of becoming rich on Ethereum or Dogecoin or some dubious NFT scheme had to factor in the costs of data mining for primes, they may find that the benefits aren’t worth it.
This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think.
Predicting the length of time it will take to get a Machine Learning (ML) project into production can be tricky. If there is an issue, more often than not, it is likely related to a disconnect between engineering and the data science team. Collaboration between data science and engineering is critical for ML projects, but it is often a challenge.
Although data scientists and engineers both work with code and machines – their roles and mindsets are different. Data scientists extract knowledge and insights from data, while software engineers build products and systems. Data scientists can spend considerable time creating and tweaking data models and algorithms to get an ideal result, which makes their work more experimental and iterative than software development. Engineers are responsible for building functionality around the ML data models and getting products into production within a set timeframe.
The model development portion of an ML project is considered the ‘research phase’ and is where many ML projects get stalled due to continual model adjustments. Therefore, it can be extremely beneficial for data scientists to think in engineering terms, which often leads to a faster production cycle.
When it comes to ML project management, one can separate the process into three stages: Proof of Concept (PoC) or the research phase, the Demo phase, and the Engineering phase. In this article, we examine these different phases and how one can handle them to ensure smooth and timely delivery of projects. The resulting protocol can also ensure better estimates of time for production deployment.
Based on several years of experience handling various ML projects as part of a data science team, we have created a number of heuristic rules that one can follow to ensure smooth, predictable and faster time to production.
PoC Phase
Almost all ML projects require a PoC phase. PoC ensures a reasonable performing model, apart from ensuring feasibility.
Rule 1:Time bound PoC efforts
Since the PoC is essentially a research effort, it can go on for an undetermined time for two main reasons: 1) data scientists are never done searching for a better model and 2) ML models have a multitude of hyper-parameters to adjust and refine. Therefore, it is essential to set and stick to a pre-determined timeframe to complete the PoC. This reality also drives the need for Rule 2.
Rule 2:Set Expectations of PoC beforehand
Start by clearly defining the output of the PoC either in terms of metrics or a set of feature behaviour. One could argue that by clearly defining Rule 2, Rule 1 is unnecessary. But, Rule 2 will only be operational if the problem can actually be solved. Therefore, Rule 1 ensures the team does not go beyond a certain number of retries before giving up.
So how do you estimate the appropriate amount of time to develop a PoC? This takes experience and can evolve, but as a rule of thumb:
5 months for problems involving classical learning techniques or problems involving transfer learning
3 months for problems involving proven deep learning techniques
Demo Phase
Once the viability of the ML project is ensured, demonstration of the work becomes important. This also sets the path for Minimum Viable Product (MVP).
Rule 3:Demonstrate PoC effort to all the stake-holders
Involving the stakeholders has a number of impacts for MVP:
Defining future course of the product
Defining or re-defining supported features
Redefining ML metrics
Defining how it fits into an existing product or in the case of a new product, what the final product will look like
Though stakeholders differ from project to project, the minimum stakeholders should include:
Product Owners:Person(s) who defined the product at the first place (CTO/CEO)
Data Science Team:Team involved in PoC and subsequently, person(s) taking it to production
Engineering Team:This team helps define the feasibility of a product
Dependents:Mostly UI/UX team which takes the product to end-users. In some case, UI/UX may be combined with the Engineering team
The quality of demonstration becomes important as this is the project buying phase: the better the demonstration, the higher the chance of the project being approved. Data science is all about creating stories and this is the phase where the stories should speak clearly. These data science stories, combined with business understandable visualizations, are direct indicators of a successful demo. In addition to the model demo, a snapshot of how the PoC would be taken to production should also be presented by the engineering team and other dependents.
The demo phase should not last more than a month and should be time-boxed. Delaying a demo will result in higher chances of the PoC landing in the scrap yard or pre-empted by higher priority projects.
Engineering Phase
Once the project is in an approved phase, the next step is to take the PoC to production. Taking a PoC into production needs to be handled carefully, since the underlying product sometimes becomes the face of the company.
Rule 4: Set the Requirements Clearly
Setting clear requirements is important as it not only defines the goals for the data science team, but also for all the team/parties on which ML project is dependent on. The following factors should be accounted for:
The requirements should also determine if the final model’s performance does not meet the expectations either due to data unavailability or unforeseen model limitations. In such situations, one can still deploy to a limited set of users to validate the feature, as discussed under Rule 7.
Rule 5: Define Clear Timelines and the Design
Defining timelines for the data science team ensures the project is being tracked and brought to closure within an estimated time. It also sets a product launch time and therefore, timelines should be set carefully, accounting for unknowns. Timelines should also be accordingly defined for dependents to ensure all the parties work in parallel. A regular, agile-type tracking is required to identify blockers early and bring them to closure – before they start to over-power the project.
The allocation of sufficient time for QA and code reviews is often ignored in timelines. Code reviews ensure quality and code coverage, before QA takes over. QA defines the product stability and therefore, should be accounted for appropriately during the planning stage.
Timelines should include integration points clearly. In cases where a dedicated engineering team is available, at least one engineer from the product team should work together with the ML engineer to ensure smooth and faster integration with the system.
Design constitutes an integral part of the system and a well thought through system design ensures future changes, apart from system robustness. Timelines should allocate sufficient time for design which varies depending on whether it is entirely a new feature/product or an add-on feature. Various aspects to consider while designing:
Modularity and Reusability
Scalability
Accommodability for improved ML models
Ease of use by end users
Most of the ML projects take roughly 6-8 months to go to production.
Rule 6: Pre-launch Demo with All Stake Holders
A pre-launch demo is a good way to make sure the final product is consistent with what was agreed upon during start of the project. It also ensures the team accommodates minor changes resulting from final product observations and the resulting discussions. A pre-launch demo is also a counter-check on the business metrics defined earlier. Therefore, the pre-launch demo should be completed nearly a month before the launch.
Rule 7: Phase-wise Product Deployment
Deployment should be completed in phases to ensure user feedback is accounted for incrementally, thereby further ensuring product quality and stability. The specific phase-wise approach will differ depending on the type of ML project, but generally includes:
Selective User Deployment:Pre-define the users to whom the product will be available. Typically these are internal users, who are less risky to the business and will provide detailed feedback.
A/B Testing Deployment:This phase is used to show the feasibility of the ML solution against an existing solution, which in most cases, would be a heuristic or a rule-based approach. The product is exposed to end-users in selective way to judge the performance of the ML model.
Final Deployment:In this phase, the product is exposed to all users and/or all organizations.
The final deployment may take 2-6 months, depending upon different deployment phases involved, but plan on a minimum of 2 months.
After considering all the phases and steps, it’s clear that an ML project can take roughly 10-12 months from PoC to production. To ensure the project is delivered within the allotted timeframe, start with clear requirements and well-defined business metrics. Also allow sufficient time for QA and a phased deployment schedule. By following the framework above, the probability of delivering your ML project on time can increase dramatically.
Augmented reality technology has evolved into one of the most effective distance learning solutions. It enables tutors to both educate and engage their students in the learning process. It is partly due to the fact that the modern generation became acquainted with technology at a young age. What do you know about the applications of augmented reality in distance education?
Technology is already ingrained in the lives of today’s students. They have been using it since they were children. During the lockdown, most educational institutions switched to distance learning, believing that it was the best way to educate and stay safe during the pandemic.
Nonetheless, because they had no prior experience, this move proved to be difficult. It also caused many problems, such as efficient education and keeping students engaged at all times. Augmented reality has evolved into a helpful tool, allowing for the resolution of the majority of problems.
Augmented reality (AR) is a modern technology that allows a person to connect the physical and virtual worlds and interact with them in real time. It allows virtual objects to be integrated into the physical world and the educational process as well.
There are numerous benefits to using augmented reality in distance learning:
It allows students to share content regardless of their location.
It gives students quick access to high-quality, fun, and interactive experiences.
It increased students’ motivation, attention, and confidence.
It does not always necessitate the installation of a specific app or a monthly subscription, etc.
It enables students to take virtual field trips, transporting them to a physical, real-world location thousands of kilometres away.
AR has the potential to alter how students interact with visual graphic experiences. It is capable of integrating computer-generated graphics into the real-world environment displayed on the screen.
Getting Practical Skills In Addition To Theory
It seems that distance learning appears to be all about theory, with no room for practice. Nonetheless, AR provides the ability to model a specific situation using a graphic representation of the environment. For example, students learn the topic regarding the lives of savannah tribes. They can travel far into the past and feel as if they are living in those days, thanks to augmented reality.
The learning process of pilots is another good example of AR use. Using it, students learn to apply their theoretical knowledge into practice. It helps to save their lives in the event of a mistake and practice a specific situation more than once.
Presentation Of Complex Phenomena and Subjects
There are concepts and phenomena in the educational program that are difficult to imagine without visual presentation. For example, cell structure of plants, molecule structure, interaction of chemical components at the molecular level, cell decay, etc. AR enables students to comprehend not only the structure of specific elements, but also to investigate how forest plants and animals interact to create a sustainable environment.
Virtual Trips And Language Advancement
A student can, for example, use special AR glasses (such as Epson Moverio or Everysight Raptor) and a downloaded app to immerse himself in Paris and learn French. Furthermore, after an exciting trip, he can take a test to assess his progress in learning a language. For example, the educational company Unimersiv created the House of Languages program. After installing it on a smartphone, a student can study foreign languages, visit virtual museums, airports, and cafes, among other things.
Taking a virtual trip is especially beneficial when a student is required to write an essay or report on a specific topic. Given that many museums and libraries had locked their doors during the pandemic, it proved to be a difficult task.
Using the AR technology, a student can get a complete understanding of the subject under study, which eases handling any type of writing. If you are still having trouble coming up with ideas, you can check out the top writers list to get writing help.
Thanks to the reviews of essay writing services, you can find the most appropriate variant with the best writing reviews rating. It will save youy time and provide you with a high-quality piece of writing.
A growing number of colleges and universities are experimenting with AR in these areas and using it to support curriculum delivery. AR engages students’ senses and enhances learning by immersing them in a world rich in both information and experience: it provides a learning experience that students are unlikely to forget.
Do what you do best and partner for the rest! This fits well with how Telcos are approaching the digitalization wave. Telecom leaders are aware of what value partners can bring in, as CSPs look to expand their value chain and revenue streams by exploring cross-industry business opportunities. In fact, the partnership strategy is not new to Telcos, but it indeed is becoming prominent with the evolving business models as 5G, IoT becomes mainstream. The partner ecosystem enables CSPs to accelerate innovation, increase agility and lower the operating cost by offsetting pressure from traditional services.
Many CSPs have seized partnerships with other industry verticals to capitalize on the 5G promise. Deutsche Telecom (DT) announced a 5G partnership to support the smart industries and accelerate digitalization in the industry. Reliance Jio, the Indian operator, has also transformed itself into a digital service provider by offering an array of services under the JIO brand (see figure 1).
Telia created Division X, a separate business to focus on emerging businesses such as IoT, 5G, and AI by creating a digital ecosystem-enabled platform to monetize joint offerings with partners.
We have seen both horizontal as well as vertical expansion by operators to add more value to the telco value chain. The diversification of services through collaboration and co-creation with the B2B2X business model is the new norm.
So how well are Telcos able to adopt the partner ecosystem strategy? We might have seen some progress, but how can they accelerate this to match the evolving customer needs. A telco needs to define its role in the evolving value chain and ensure a successful transition into the role of an enabler or provider of new-age services.
What Telcos can bring to the table?
Playing to their strength while adopting the digital transition.
CSPs need to constantly keep innovating the service offerings to entice the digitally savvy enterprise and end consumers. They need to start thinking like Google, Amazon, or any webscale organization to embrace an end-to-end digital transformation. The traditional way is not sustainable and demands a reshuffle of the strategic focus and priorities set in the past. Telcos need to move away from connectivity providers’ perception and start leveraging their core competencies such as solid customer base, insights into customer needs, network assets as a platform to digital disruptors, and more.
The way forward strategy: Partner Ecosystem development
Telcos have been working with interconnect partners, roaming, MVNOs, other value-added service providers. But these partnerships are low involvement, with limited monitoring and contract management requirements. The new partnerships are becoming more complex and dynamic with the diversification of services and partners from across industry verticals.
One of the reports by a leading analyst organization revealed that CSPs are already cut out of strategic engagements and solution building with enterprise partners. CSPs are playing a secondary supplier role in 40% of enterprise 5G deals are signed. To capitalize on these opportunities, CSPs need to strengthen their position by creating a robust digital partner ecosystem that can deliver value with their offerings.
Where the telcos will see the most opportunities in near time:
Research shows that key industry verticals such as industrial automation, healthcare, connected cars, intelligent homes represent a $1 trillion opportunity by 2023. Telcos will support a wide variety of use cases with network slicing, edge computing and AI. However, a clear monetization strategy will have to be in place for these new revenue streams.
Healthcare: As there is a radical shift towards digital health services, it has created an opportunity for Telcos to offer telemedicine solutions for remote health monitoring and health management for people with chronic diseases. The low latency and ultra-reliable connectivity will provide accurate feeling and tactile interaction in remote surgical procedures.
Mobility: As the 5G networks roll out across cities and bring together existing wireless networks, they can provide real-time, end-to-end visibility into the transportation systems. Increased fleet visibility will also translate into better safety and reliability for travelers.
Gaming: 63% percent of gamers play with other fellow gamers. In fact, massively multiplayer online games make up the most popular gaming genre globally, but most gamers, especially multiplayer gamers, must deal with lag. By utilizing 5G end-to-end network slicing, operators can create a low latency-focused slice to offer an enhanced gaming experience, while a separate high bandwidth slice can be created for video streamers within the same mobile network.
Smart Industries: 5G will help create a more agile, fully connected, and automated end-to-end manufacturing experience from design to distribution. Supported by the unprecedented levels of AI/ML and automation, the smart industries will make faster decisions and quickly adjust to changes in near real-time.
While the opportunities are immense, Telcos alone cannot deliver the success that 5G promises. CSPs need to establish successful partnerships with digital incumbents and innovative startups on both the technology and service front to deliver the 5G promise to its enterprise and end consumers. One certain thing for success is, CSPs need to bring cooperation and collaboration with partners at the center of value creation.
Subex is hosting a live webinar on “Innovating and Accelerating Growth in the 5G world” that will take a deep dive into the new generation of the partner ecosystem and how it can help CSPs deliver the 5G promises.
Non-Fungible Tokens (NFTs) are taking the art world by storm.
A large number of serious problems outweigh any positives.
Two infographics to explain the process and issues.
The above image shows how an object of value, like an artwork, music file or GIF, can be “minted” and sold via a non-fungible token (NFT). An NFT is much like a certificate of authenticity. But instead of a physical certificate, you own a token: a unique piece of data on a blockchain. NFTs work as public ledgers, recording each transaction associated with the sale of an artwork. When you purchase an NFT, you’re essentially purchasing a tamper-proof digital receipt.
An NFT is not:
An artwork—digital or otherwise. The buyer doesn’t actually possess the original item at all. It is permanently stored elsewhere. A secure method is to attach the work permanently to an Ethereum blockchain—containing the work, the unique identifier, and an ownership record. However, it is possible to store the art on a server separate from the NFT.
A right to copy, disseminate, or display the artwork [1]. The creator of the artwork usually keeps these rights. The buyer’s only right is that of ownership of an “original copy.” For example, the artwork in the image above is a portrait of Diana Ross I created in 2013. I sold the physical artwork and kept a digital copy. I could sell ownership of the digital image with an NFT (but I’m not going to, because of the environmental concerns outlined below).
An exclusive digital version of the artwork. Digital artist Beeple made history earlier this year when Christie’s auction house sold an an NFT consisting of 5,000 of his illustrations for over $69 million. However, he posted each element of the art on Instagram; Anyone can download a free copy, albeit without that prized COA.
NFTs do have a couple of positives: They provide a solution to tracking digital artwork and verifying ownership. In addition, the technology is enabling artists to make up for lost income due to the pandemic lockdowns. However, the marketplace is suffering from a deluge of criticism for a variety of issues.
The Bad…and The Ugly
At the top of the list: Environmental issues: There is hot debate about how much energy is used specifically with NFTs. But we do know that its close companion, cryptocurrency, uses more energy than the whole of Denmark (or Argentina). A sale of just six NFTs is estimated to use ten times the energy that an average American uses in a month [2].
Many other issues are plaguing the inchoate technology:
Fraud: Forgery is a pervasive problem for physical art collectors, and it has infiltrated the digital market as well. The lack of legislative control adds another layer of risk.
Ownership issues: Paying several thousand dollars for a virtual “token” falls into the realm of legal quagmire. From a legal perspective, it isn’t clear who owns what.
Prohibitive costs for artists: Artists can lose money due to “gas” and other fees associated with selling on Ethereum, even with minimum prices in the hundreds of dollars range. Cryptocurrency fees can be so unpredictable and difficult to comprehend that some artists are losing before they even post a work for sale. One artist reported on Reddit that “Fees out the behind” for money transfers caused him to lose $45 before he could even list his artwork [3].
The bubble is about to burst. The NFT marketplace has also been dismissed by many market professionals as a “collector” bubble. Remember the Beanie Babies craze? Decades ago, these small cloth toys traded for thousands of dollars. Most are now worthless. James Surowiecki, a columnist for The Slate and The New Yorker, states that investing in collectibles is “far more lucrative when you get on it early”– and that time has passed. “There’s the very real possibility that the whole thing will crash,” he says [4].
Tech Issues: NFTs are contributing to a global silicon chip shortage [5]. In addition, some buyers of NFTs aren’t aware of where their art is digitally stored. If it’s on a private server that crashes, the token will become worthless.
It’s doubtful that so many resources should be used for something that adds dubious value to the human experience. Until the serious problems with NFTs are fixed, visit a local art gallery and support your local artist.
Digital Twins are increasing in usage but are often used in multiple contexts and in a simplified manner. Most references to the Digital Twin actually refer to a Digital shadow i.e. maintaining a digital copy of a physical object that is updated periodically. In a more complete sense, the Digital Twin concept relates to simulation and interaction of complex, multiple physical objects in a digital environment (typically for Engineering and Construction)
I am interested in the idea of Digital Twin because my teaching at the #universityofoxford applies more to AI in engineering (as opposed to say financial services).
Also, Digital Twins relate to the idea of Physics based modelling in Engineering. A wind tunnel is an example of Physics based model. Hence, one could think of a corresponding digital entity to the physical model which simulates the behavior of the model in a digital sense.
For this reason, digital twins are one of the best conceptual mechanisms for incorporating artificial intelligence into large-scale, dynamic engineering problems – especially considering existing ideas of physics-based modelling in engineering.
Digital twin technology is already used in various industrial sectors such as aerospace, infrastructure and automotive.
A paper I recently read talks about how Digital twins can be implemented through surrogate modeling.
The paper uses a discrete damped dynamic system to explore the concept of a digital twin.
The paper uses Gaussian process (GP) emulator within the digital twin technology is explored. GP has the inherent capability of addressing noisy and sparse data.
GP is a probabilistic machine learning technique that attempts to infer a distribution and then use that distribution to predict unknown points.
GP has two distinct advantages over other surrogate models:
GP is a probabilistic surrogate model, it is resistant to overfitting.
GP can measure the uncertainty which can then be used in the decision-making process
Additional notes from the paper
GP not only model and also the example (spring) is a relatively simple one for explanation
As IoT proliferates, digital twins would get more complex based on increasing data being reflected in the virtual world from the physical world
Digital twins / surrogate modelling approach suits dynamically evolving systems
Typically, the digital twin starts from an ‘initial model’ which is often a physics-based model.
Over time, as more and more components can be modelled virtually, digital twins of larger (composite) objects would become the norm ex aircraft, automobiles etc
“If you make decisions based upon averages, at best, you’ll get average results”
During the 1950s[1], United States Air Force pilots were having trouble controlling their planes. The problem turned out to be the cockpit, or more specifically, the fact that the cockpit had just one design: one designed for the 1920’s average pilot. The Air Force concluded that they simply needed to update their measurement of the average pilot, adjust the cockpit accordingly, and the pilot handling troubles would go away.
With the help of Lieutenant Gilbert Daniels, the Air Force measured more than 4,000 pilots across 10 size dimensions. The air force had assumed that the vast majority of pilots would fall within average across the 10 dimensions. In reality, none – none – fell within average across the 10 dimensions; that is, out of 4,000 pilots, zero of them were “average” (see Figure 1).
Figure1: The Danger of Making Decisions Based Upon Averages
The Air Force’s “aha” moment?
If the cockpit was designed for the average pilot, it was actually designed for no pilot
Todd Rose came up with the Jaggedness Principle of individuality. The Jaggedness Principle asserts that measuring a collection of traits across a sufficiently large number of individuals, roughly half of individuals will be above average, and roughly half will be below average for any particular trait. And that across all the traits, few (if anyone) will actually be “average” (notice the “jagged” line for each individual in Figure 2).
Since no one is “average”, why do organizations continue to make decisions based upon averages? We have spent much of our university education and professional career being taught to make decisions based upon averages – average churn rate, average click rate, average market basket size, average mortality rates, average COVID19 infection and death rates. And maybe when your data analytics tool of choice was a spreadsheet, then the best we could do was using averages to make overly generalized policy and operational decisions. But the world is changing… and changing rapidly!
Unfortunately, averages don’t provide the level of granularity necessary to make precision decisions that drive the optimization of the organization’s key business and operational use cases. “On average” is not how successful companies will survive in a world of continuous transformation.
Fortunately, Big Data, Data Science, Analytic Profiles, and Nanoeconomics provide the foundation for changing the organization’s decision-making frame. It’s time for organizations – and management teams – to “Cross the Analytics Chasm” from making overly-generalized operational and policy decisions based upon averages, to making granular, precision decisions using big data and data science (see Figure 3).
Some critical concepts for crossing the Analytics Chasm include:
Nanoeconomics. Nanoeconomics is the important concept guiding organizations across the Analytics Chasm. Nanoeconomics is economic theory of individual entity (asset) predicted propensities, whether the entity (asset) be human (doctor, nurse, technician, operator, teacher) or device (wind turbine, automobile, chiller, compressor). See Figure 4.
Figure4: Nanoeconomics is economic theory of individual entity predicted propensities
Nanoeconomics is based upon identifying and codifying individual asset (human or device) predictable propensities, tendencies, patterns, and relationships. And from those predicted propensities, organizations can make informed, precision decisions that seek to optimize the organization’s key business and operational use cases.
Analytic Profiles. Those predictive propensities are captured in Analytic Profiles (or asset models) that facilitates the application of those customer, product, and operational propensities against the organization’s key business and operational use cases (see Figure 5).
“If you make decisions based upon averages, at best, you’ll get average results”
Crossing the Analytics Chasm requires a mind shift in how organization’s make decisions. Making decisions based “on average” is not how successful companies will survive in the age of digital economic transformation. Organizations need to embrace the power of nanoeconomics – the economics of individual entity (human or device asset) predicted propensities.
Organizations can couple the concept of nanoeconomics with Analytic Profiles to leap across the Analytics Chasm in transitioning from decisions based on averages, to decisions based upon predicted propensities. And as a result, these organizations can become more effective at leveraging data and analytics to power their business and operational models (see Figure 6).
The valuable data and analytic concepts mastered to cross the Analytics Chasm – nanoeconomics and analytic profiles – positions the organization to exploit the economic potential of data and analytics and transverse the Big Data Business Mode Maturity Index to re-invest business and operational processes, dis-intermediate customer relationships, and transform industry value creation processes.
But ya gotta start by getting over that darn Analytics Chasm…
[1] Story taken from the Harvard Graduate School of Education article “Beyond Average”
