• The network offers zero-trust networking for Secure Access Service Edge, Identity and Access Management, and Privileged Access Management by providing end-to-end encryption and treating all users with the least privileged access.
• Over 2,000 organizations have used Tailscale’s technology, with over 2.5 million connected devices.

Recently, Tailscale Inc., a company offering corporate virtual private networks using mesh networking technology, launched its zero-trust networking solution for business clients, enabling them to ensure that each connection is authenticated and that all traffic is end-to-end encrypted.

To address the problems with secure connectivity brought on by conventional VPN services, which rely on centralized servers to provide monitoring and access management, Tailscale was founded in 2019. The business uses mesh networking, which enables devices to connect via nodes and decentralizes access while boosting network speed, scalability, and reliability.

The Tailscale solution also offers quick setup, almost no configuration, and simple connectivity for new devices. The network offers zero-trust networking for Secure Access Service Edge, Identity and Access Management, and Privileged Access Management by providing end-to-end encryption and treating all users with the least privileged access.

Avery Pennarun, Co-founder and CEO of Tailscale, said, “The big conundrum with zero trust is, how do you lock down access without bringing productivity to a screeching halt and overhauling your entire tech stack? Tailscale is the zero trust easy button enterprises have been looking for. Unlike other solutions, we work with your existing infrastructure so it can be set up within minutes — a powerful tool to protect against unauthorized access and data breaches.”

Tailscale already integrates with a wide range of identity services for user authentication, including Okta, Azure AD, and Google. The business has added OpenID Connect-compliant connectivity for enterprise customers with complex identity requirements or who self-host their solutions. Customers can then connect to identity providers like GitLab, JumpCloud, Auth0, and Duo.

With improved real-time logs, enterprise information technology teams can monitor and analyze traffic as part of their security procedures. In contrast to other network connections, Tailscale network activity can be connected to users’ identities, allowing for detailed attribution of traffic and a better understanding of potential security issues.

Customers can also authenticate and encrypt secure shell connections between devices using the company’s enterprise solution. Additionally, organizations can record shell commands, such as Tailscale SSH, by streaming the session logs to another network node. The recordings are end-to-end encrypted, making them only accessible to authorized users. Even Tailscale cannot access the recordings.

Insight Partners and CRV led the USD 100 million funding round for Tailscale last year. Over 2,000 organizations have used Tailscale’s technology, with over 2.5 million connected devices. Its private networking is used by businesses like Instacart, the language-learning company Duolingo Inc., and the Japanese e-commerce company Mercari Inc. to secure their operations and sensitive data.

• Hyper-automation is a term used to describe the integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and other forms of intelligent automation to automate complex business processes end-to-end.
• Due to the growing interest in this technology phenomenon, Gartner anticipates that by 2024, businesses will have implemented at least three of the twenty software solutions that enable hyper-automation.

We were taught that steam engines were deployed to automate the textile industry during the First Industrial Revolution towards the end of the 18th century. The Second Industrial Revolution brought electricity and the internal combustion engine, while the Third Industrial Revolution brought digitalization and process automation around the end of the 20th century.

The Fourth Industrial Revolution is now here, bringing in robotics, the Internet of Things (IoT), and Artificial Intelligence (AI). It is a process that is compelling developed nations to engage in reindustrialization to regain their self-sufficiency in consumer goods production. Hyper-automation, also known as digital process automation (DPA) or intelligent process automation (IPA), is one of the most significant technological developments of the next several years in this new setting.

Hyper-automation is a term used to describe the integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and other forms of intelligent automation to automate complex business processes end-to-end.

This process is the next phase of automation, where the focus is on automating repetitive and manual tasks and creating a fully automated system that can handle complex workflows, unstructured data, and decision-making processes.

Hyper-automation combines multiple automation technologies and tools to create a cohesive system that can work seamlessly across different functions and departments. By automating complex processes, hyper-automation can help organizations reduce costs, improve operational efficiency, enhance customer experience, and gain a competitive advantage in the market.

Hyper-automation Outperforms RPA in Every Way

Although hyper-automation tries to automate the entire process, i.e., in sizeable enterprise-level transformation, RPA employs software bots to automate specific activities. In essence, it aids in an organization’s digital transformation process.

RPA is enhanced by hyper-automation in terms of:

• The tools used are task-based and constructed on individual bots for RPA, whereas technology sequencing is used for hyper-automation.
• Internal workings: Hyper-automation is a network of technologies, platforms, and systems, unlike RPA, which is platform-specific.
• The end result: While hyper-automation produces intelligent, flexible, and efficient processes, RPA produces efficient processes.
• Future potential: Whereas automation can only be used for specialized, isolated use cases, hyper-automation can automate practically every aspect of a business.

RPA, AI, low-code application platforms (LCAP), and virtual assistants can all be used to quickly identify, evaluate, and automate as many processes as it is very practical. Gartner predicts that by 2024, businesses will have implemented at least three of the twenty software solutions that facilitate hyper-automation, due to the increasing interest in this technology phenomenon.

How Can Hyper-automation Transform Business in 2023

You might wonder what makes hyper-automation such hype. Well, it has the potential to revolutionize business operations tremendously. According to CRM Consultant, GlobeNewswire estimates that the present value of the worldwide hyper-automation market is USD 549.3 million, with a predicted CAGR of 22.79% to reach USD 2,133.9 million by 2029. Here are seven ways how hyper-automation will transform business in 2023:

Faster and more accurate decision-making

Hyper-automation will enable businesses to make faster and more accurate decisions by automating the analysis of large amounts of data. AI and machine learning algorithms can analyze data and provide insights to decision-makers, enabling them to make more informed decisions. This will improve operational efficiency, reduce errors, and increase the speed of decision-making.

Hyper-automation guarantees an implementation path devoid of errors. In addition, it provides superior analytic solutions that can be utilized to gain insights and comprehend organizational trends on a broader scale. Executives can monitor and distinguish what is successful and what is not.

Improved customer experience

Hyper-automation automates complicated business operations. Hyper-automation will transform the way businesses interact with customers. With the help of AI-powered chatbots, companies can provide personalized and efficient customer service around the clock. This will lead to improved customer satisfaction, loyalty, and retention.

To improve CX, you can also obtain insights into customer behaviors and analyze agent interactions, the number of complaints, and the frequency of first-time problem resolution, among other things.

Reduced costs

Hyper-automation will enable businesses to automate repetitive tasks like data entry, invoice processing, and report generation. This will reduce the need for human intervention, thereby lowering labor costs. Additionally, automation reduces errors and increases efficiency, lowering operational costs.

According to Gartner, combining hype-automation technologies with redesigned operating processes will reduce costs by 30% by 2024.

Accelerate the pace of innovation

Hyper-automation serves as the key driver for digital transformation. Low-code platforms allow businesses to design and improve processes, replace outdated systems without fear of data loss, and deploy new apps within weeks.

Establish a centralized truth source

The new hybrid cloud standard has made system integration a requirement for digital transformation. The concept of hyper-automation is predicated on the integration of software and processes. This results in seamless exchanges between on-premise equipment and data storage. Consequently, this architecture enables systems to connect and communicate easily, resulting in enhanced data accessibility through consolidation, even in a highly diverse environment.

Improved compliance

Hyper-automation will help businesses comply with regulations and standards by automating compliance-related tasks like data privacy and security. This will reduce the risk of non-compliance, fines, and reputational damage.

Competitive advantage

By utilizing hyper-automation, businesses can obtain a competitive advantage by responding to changing market conditions and innovating more rapidly than their competitors. From basic automation to enhanced artificial intelligence and machine learning, organizations must reach hyper-automation and deep learning.

Conclusions

With the increased usage of low-code/no-code tools, digital twins, and mass-market robotic process automation, the hyper-automation strategy is predicted to reach new heights and impact the corporate world in 2023. By merging AI with automation, businesses can focus on more inventive solutions that reliably satisfy ever-changing market demands.

• The work of developers is made more difficult by the fact that many conventional databases were not created with AI model embeddings in mind and amp; the Chroma database addresses this issue.
• Chroma claims to use the most recent funding round to develop new features. The startup is preparing a feature that will enable developers to judge whether the database’s retrieved information is pertinent to a particular query, among other additions.

Chroma Inc., a provider of databases, recently disclosed that it has secured USD 18 million in seed funding.

Quiet Capital headed the investment. In addition, executives from over a dozen other tech firms, including Hugging Face Inc. and publicly traded database maker MongoDB Inc., contributed. A pre-seed round of undisclosed size was previously closed by Chroma last year.

Chroma creates an open-source database intended to power applications for artificial intelligence. The database has been downloaded over 35,000 times since it was made available less than two months ago.

Decisions made by AI models are based on a data bank. For instance, a shopping recommendation model might keep a database of the most recent product listings. Information about hacker activity is stored in neural networks designed for cybersecurity tasks.

AI models store their data as abstract mathematical structures known as vectors rather than in their raw form. An embedding is a collection of such vectors. The open-source database for Chroma, also known as Chroma, was created specifically to house the embeddings of AI models.

In a recent blog post, Huber and his founding partner Anton Troynikov wrote, “Developers use Chroma to give LLMs pluggable knowledge about their data, facts, tools, and prevent hallucinations. Many developers have said they want ‘ChatGPT but for my data’ — and Chroma provides the ‘for my data’ bridge through embedding-based document retrieval.”

The ability of embeddings to highlight similarities between the data points they store is one of their most crucial features. For instance, an embedding with vectors representing phones can draw attention to phones with similar prices. On the other hand, it is also possible to identify phones with a sizable price disparity.

The ability of embeddings to draw attention to similarities between data points is crucial to the operation of AI models. For instance, recommendation models produce shopping recommendations by looking up comparable goods based on a user’s purchase. When detecting malware, neural networks search for the network activity that resembles well-known hacking techniques.

Additionally, embeddings allow for the detection of differences between two objects, which is useful for AI applications. In the cybersecurity market, a few AI-powered breach prevention tools work by identifying how users frequently use an application. Then, these tools search for an activity that differs from a customer’s typical access patterns.

The work of developers becomes more complicated because many conventional databases were not created with AI model embeddings in mind. The database created by Chroma addresses that problem. The startup claims that because its platform is specially tailored to store AI embeddings, it can offer relatively straightforward developer experiences.

Specialized algorithms transform the data an AI model consumes into embeddings it can process. Chroma claims that its database has features that make using such algorithms simpler. As a result, software teams now need more manual work.

Chroma supports many algorithms made for open source embedding generation. It can also simplify using various paid tools in the group, such as OpenAI LLC’s cloud-based embedding service. Developers can use their unique algorithms if they have more complex requirements.

Chrome provides an in-memory mode to expedite queries. Databases typically store information on disk or flash storage and only access it from memory when necessary. An in-memory system doesn’t wait to retrieve data from storage; instead, it keeps information in RAM immediately, speeding up computations.

Chroma claims to use the most recent funding round to develop new features. The startup is preparing a feature that will enable developers to judge whether the database’s retrieved information is pertinent to a particular query, among other additions. A managed, commercial version of its database is also being created; it will debut in the third quarter.

• For third-party apps that offer AI experiences, Discord is already a popular choice. Discord, an AI-enabled app, has been utilized by more than 30 million users.
• To determine if what the user said violates the rules and flag it for moderators, the AutoMod AI can translate other languages.

Discord Inc., a voice, video, and instant messaging app, recently launched several new experimental updates using the power of artificial intelligence that will alter how users socialize on the app.

These updates include enhancing the server’s automatic moderation service, bringing its chatbot, Clyde, to life through artificial intelligence, and enabling users to view summaries of previous conversations.

For third-party apps that offer AI experiences, Discord is already a popular choice. Discord, an AI-enabled app, has been used by more than 30 million users. One of the biggest servers on Discord, with more than 13 million users, is the AI art generator Midjourney.

Anjney Midha, Discord’s vice president of the platform ecosystem, said, “On Discord, you can use AI to supercharge your conversation with friends – you can brainstorm together, create together, make memes together. It’s both entertainment and utility, fun and informative.”

To power its new AI tools, the company partnered with OpenAI LLC, the creator of ChatGPT chatbot. Generative AI, a type of artificial intelligence that can hold conversations, conduct research, and even make recommendations, is what ChatGPT uses.

Thanks to Discord, Clyde, a helpful bot and occasional logo that appears to provide information when users encounter errors, now uses that technology. The following week, Clyde will act almost like a different chat participant and become more helpful than ever.

Users can communicate with the new Clyde in any channel by entering “@Clyde” in a server with this new AI update. He will respond to them just like any other user, answering questions about anything from where to eat out to what movies to watch to who plays the lead character in that hit TV show (because you’re too lazy to open Google).

Like any other user, Clyde can use animated GIFs and emojis and will do so when requested.

AI capabilities were a significant addition to AutoMod as well. By enabling server moderators to automatically identify and block potentially harmful speech occurring in their channels, this feature was introduced last year to lessen their workload and enable them to take the necessary action. It uses keywords and phrases that sometimes lack context and nuance.

With the help of OpenAI’s technology, AutoMod can read the server’s posted rules, apply them to messages as they arrive, and comprehend the context in addition to keyword usage. The difference between doing this and flagging a user who said, “I hate parrots, they should die,” and asking another, “What are common ways parrots die so I can stop that from happening?”

To determine if what the user said violates the rules and flag it for moderators, the AutoMod AI can translate other languages. The original AutoMod would not have been able to do this because it would have had to use keywords very strictly to look for potentially harmful or illegal content.

Users who have been away from chats for a while will be able to catch up with them thanks to new AI-generated conversation summaries. These summaries will group related topics and offer concise summaries of what was discussed, along with participant icons. Then users can click on them to go to the context and continue reading.

Multiple topics frequently arise on servers with many players or even a small group of talkative friends. When they return, they can rejoin the conversation or comprehend what might have been discussed without feeling cut off.

The Discord team is developing a collaborative whiteboard with an AI twist as one of the upcoming features. The ability to draw on a whiteboard, choose a region, type text to describe what they want, and have a text-to-image generator turn what they drew into visuals will be available to users who join a session.

Discord also launched an AI Incubator as part of its USD 5 million ecosystem fund, which it announced in October, focusing on bringing the best AI offers to its community and developers. The new incubator will provide office space for development teams, cloud computing credits for services, and early access to Discord platform features, in addition to financial grants to support the creation of additional AI tools for Discord.

• Confidential computing is a feature that many server processors from Intel Corp. and Advanced Micro Devices Inc. support.
• Only the application to which it is assigned has access to a CCE. The isolated memory pool used by that application cannot be used by other workloads running on the same server.

According to a press release, Edgeless Systems GmbH has raised USD 5 million in funding. The startup uses confidential computing technology to make Kubernetes clusters more secure.

SquareOne led this round of seed funding. Multiple angel investors participated, including founders of Netlify Inc., a developer tooling startup estimated to be worth two billion dollars in 2021.

Confidential computing is a feature that many server processors from Intel Corp. and Advanced Micro Devices Inc. support. The technology enables encryption algorithms to isolate a portion of a server’s memory. While processing, sensitive application data can be stored in isolated memory pools called confidential computing environments or CCEs.

Only the application to which it is assigned has access to a CCE. The isolated memory pool used by that application cannot be used by other workloads running on the same server. Additionally, neither the hypervisor nor the underlying operating system has access.

CCEs have a remote attestation feature as an extra security precaution. It enables businesses to ensure that a CCE is set up properly before handling sensitive data.

Constellation is an open-source, private computing platform created by German company Edgeless Systems. The startup claims that Constellation enables businesses to set up Kubernetes clusters in a separate CCE memory environment. A Kubernetes cluster is more difficult for hackers to compromise if it is separated from the rest of a company’s infrastructure.

All of the main cloud platforms are supported by Constellation. Edgeless Systems claims the platform can be set up with only a few commands and doesn’t require significant coding changes to current workloads.

Felix Schuster, Chief Executive Officer of Edgeless Systems said, “We are turning the public cloud into everyone’s private cloud. By encrypting data all the time, even at runtime, and providing the best possible protection against infrastructure-based threats like malicious admins or co-tenants, Edgeless Systems can transform the way developers build and secure their public cloud workloads.”

Constellation is one of many tools that Edgeless Systems provides to make it simpler for developers to use CCE technology.

MarbleRun, a tool for managing workloads in Kubernetes clusters powered by CCE, is one of the startup’s products. Between workloads, MarbleRun can establish encrypted network connections and verify the accuracy of their security configurations. The tool can automatically restore a software component in case of an outage.

Edgeless Systems provides EdgelessDB for managing application data. It is an open-source database explicitly designed to operate in private computing settings. EGo, a software development kit created by the startup, makes it simpler to create secure computing applications in the Go programming language.

According to Edgeless Systems, hundreds of developers at organizations like Intel, IBM Corp., and Robert Bosch GmbH use its software. With its recently announced new funding round, the startup will continue to grow and expand its market. Edgeless Systems plans to invest in the creation of new features as well as grow its sales and marketing teams as part of the effort.

• A Kubernetes application’s storage allocation is frequently modified by administrators based on the volume of data it is holding.
• Ondat offers tools that assist in lowering the risk of data loss in Kubernetes environments in addition to its core feature set.

Ondat, a startup with venture capital backing and a data storage platform for Kubernetes, will be acquired by Akamai Technologies Inc., the company announced recently.

The deployment and maintenance of software container applications are made simpler by Kubernetes. However, it has few features for controlling the data used by those applications. As a result, businesses that use Kubernetes frequently implement external storage tools like the platform from Ondat.

Adam Karon, chief operating officer and divisional manager of the cloud computing division of Akamai, said, “Storage is a key component of cloud computing and Ondat’s technology will enhance Akamai’s storage capabilities, allowing us to offer a fundamentally different approach to cloud that integrates core and distributed computing sites with a massively scaled edge network.”

London-based startup Ondat, formally known as StorageOS Inc., has received USD 20 million in funding. Major corporations like DHL and Lloyds Bank plc, one of the biggest financial institutions in the UK, are among its clients. The deal’s financial details were kept private by the companies.

A Kubernetes application’s storage allocation is frequently modified by administrators based on the volume of data it is holding. Ondat greatly automates the procedure, which reduces the time spent. Its platform also compresses and encrypts data from applications to conserve storage space.

Ondat offers tools that assist in lowering the risk of data loss in Kubernetes environments in addition to its core feature set. Using a checksum algorithm, the startup’s platform automatically checks a company’s data for potential errors. Additionally, it enables businesses to make numerous standby copies of their data and bring them online if the primary copy is unavailable due to an outage.

Multiple servers are typically present in Kubernetes clusters. Performance may be slowed when Kubernetes deploys an application and its data to two servers. When an application is set up in this way, it must make time-consuming network requests to get the data it needs to perform processing tasks.

Ondat’s platform can assign each workload to the server that houses its data to maximize performance. By eliminating the need for data to cross the network, performance is improved. The platform accelerates the procedure when data travel is unavoidable by compressing network traffic.

Ondat is one of many storage platforms targeted at Kubernetes that are available. The startup claims that the platform’s emphasis on usability is one of its key differentiators. The software can be configured with just a few commands and is packaged into a container, which makes deployment relatively simple.

By March 31st, Akamai hopes to have the acquisition completed. The business will incorporate the technology with its just-launched Akamai Connected Cloud platform. The platform is an edge computing service built using resources from Linode LLC, a hosting company in Philadelphia that the business purchased for USD 900 million last year.

Sathya Sankaran, founder and CEO of CloudCasa, said, “The acquisition of Ondat by Akamai is another indication that Kubernetes is entering the mainstream for enterprises deploying stateful business applications on Kubernetes environments in public clouds. Ondat fills the distributed storage management gap in the Linode Kubernetes Environment for the Akamai Connected Cloud.”

The Akamai Connected Cloud, unveiled last month, consists of edge computing sites set up in various global cities. To reduce customer latency, developers can deploy their applications at those locations. To speed up network access, a company with users in a specific city may choose to deploy its application there.

By the end of the year, Akamai wants to have edge computing locations for its Connected Cloud in more than 50 cities worldwide. The business also intends to construct “enterprise-scale core cloud computing sites” in the United States and Europe. They will provide various capabilities, including computing, storage, and database services.

• Due to their theoretical capability to perform calculations that are extremely challenging or impossible for current “classical” computers, quantum computers have long been hailed as the computing technology of the future.
• In-depth instructions for creating a practical error-corrected quantum computer have now been released by Google researchers. It is currently developing a single logical qubit, with the intention of scaling it up later.

Google LLC has made a significant advancement in its efforts to build a practical quantum computer.

The business recently claimed that its discovery would help it get past the problem of “quantum errors,” which is one of the main obstacles to building a functional quantum computer.

Due to their theoretical capability to perform calculations that are extremely challenging or impossible for the current “classical” computers, quantum computers have long been hailed as future of computing. The difficulty is that errors are likely to occur on quantum computers, rendering their calculations unreliable. However, Google claims to have discovered a solution to fix these mistakes. It claims this discovery is a significant step towards building useful quantum machines.

Researchers at Google Quantum AI claim to have found a way to reduce quantum computer error rates as system size increases exponentially, allowing them to reach the “break-even point.” The business claims it is now certain that it will eventually be able to produce quantum computers with “commercial value.”

The quantum mechanics they employ to store information and carry out calculations is what gives quantum computers their potential. The basic units of information in a traditional computer are called bits and stored as a string of ones and zeros. However, “qubits” used in quantum computing can be either ones, zeros, or both at once. Thanks to this special ability, they can carry out more powerful and intricate computations than traditional computers.

Even though quantum computers function in theory, their development has been slow. That’s because they operate by manipulating those qubits through quantum algorithms. Because the qubits are so delicate, they can become unstable and produce errors when exposed to heat, vibrations, or even stray light rays.

Until now, the issue has gotten worse as quantum computers get more powerful. It’s a challenge because effective quantum algorithms require many qubits to process data. Quantum error correction is therefore required to close the gap.

Google researchers have found a way to suppress quantum errors by scaling a surface code across a “logical qubit,” a collection of several physical qubits that work better together as a unit resistant to errors. In a study, a research team under the direction of Dr. Hartmut Neven, engineering director at Google Quantum AI, built a quantum processor with 72 qubits to demonstrate Google’s discovery. With two different surface codes, the team tested it. One was applied to 49 physical qubits, that became a logical qubit, while the other was only used with 17 qubits. The experiment demonstrated that the larger surface code, which used 49 qubits, outperformed the smaller one by a wide margin.

Google CEO Sundar Pichai stated in a blog post that the discovery represents a “significant shift in how we operate quantum computers.” The physical qubits in a quantum processor are treated as a group, or as a single logical qubit, instead of being worked on individually.

He added, “By encoding larger numbers of physical qubits on our quantum processor into one logical qubit, we hope to reduce the error rates to enable useful quantum algorithms.”

Google researchers have now released in-depth instructions for creating a practical error-corrected quantum computer. It is currently developing a single logical qubit to scale it up later.

According to Pichai, the achievement puts Google on the path to creating a quantum computer that “tangibly benefits the lives of millions.” He said the business now thinks it’s feasible to develop quantum computers that could be used for the following: speed up physics research in ways that people haven’t yet imagined, identify molecules for new medicines, produce fertilizer with less energy, and discover more sustainable energy sources.

However, Pichai cautioned that such a day is still some way off. To scale quantum machines to the thousands of logical qubits necessary to realize its vision, Google will need to reach even more technical milestones. Only then will quantum computing be able to realize its full potential. He said, “There’s a long road ahead — several components of our technology will need to be improved, from cryogenics to control electronics to the design and materials of our qubits.”

• Qualcomm’s Aware platform furnishes ubiquitous global connectivity, intelligently optimizing and fusing location technologies with critical device management, sensor alerts, and control features.
• The platform stretches beyond silicon, encompassing technology from PoLTE Corp and Skyhook Wireless Inc., both companies purchased by Qualcomm in 2022.

Qualcomm Technologies Inc. recently disclosed a new platform designed to enable enterprises and developers to harness real-time information and data insights so they can speed up their digital transformation programs.

The Qualcomm Aware Platform strives to respond to the issues wherein system design complexity and ecosystem fragmentation frequently prevent “internet of things” deployments from reaching their full potential. According to the company, Qualcomm Aware integrates silicon and an extensive ecosystem of hardware and software partners with a developer-friendly cloud framework to provide services for directing assets that need accurate, time-sensitive, and critical decision-making.

The platform furnishes ubiquitous global connectivity, intelligently optimizing and fusing location technologies with critical device management, sensor alerts, and control features. The integration is said to provide a new baseline for IoT and help enterprises handle operational efficiencies.

Critical use cases that Aware can serve consist of utility asset monitoring, cold chain distribution, warehouse and inventory management, and cargo shipment tracking.

The Aware service is based upon three main pillars:

• Technology leadership
• Enablement of an expansive ecosystem of software and hardware partners
• Application programming interface-first architecture and developer-convenient tools that empower interoperability with leading enterprise software tools and partner clouds

The platform stretches beyond silicon, encompassing technology from PoLTE Corp and Skyhook Wireless Inc., both companies purchased by Qualcomm in 2022. The technology consists of proprietary positioning techniques and a solid global signal database of about eight billion wireless Media Access Control or MAC addresses, and hundreds of millions of cells.

As per the company, in combination with Qualcomm’s current location technology, the acquired technology empowers Aware to provide ubiquitous and intelligent location capabilities in an always-on and low-power manner. The platform is designed intelligently to work in challenging signal environments, consisting of underground, indoors, and when devices are offline, furnishing critical awareness and condition monitoring context to enterprise solutions for operational visibility and end-to-end asset.

The platform comprises the encryption and mutual authentication of all device-to-cloud communications and cloud security tools. It also supplies device management, global cellular connectivity, and provisioning.

Senior vice president and general manager of connected smart systems at Qualcomm, Jeff Torrance, said, “Qualcomm Technologies is rapidly diversifying with new opportunities. Qualcomm Aware is designed to support organizations across industries with a scalable, cost-effective, capital-efficient investment solution that delivers an accelerated time to market, simplifies digital transformation, and delivers transformative insights needed to mitigate risk, make more informed business decisions, and navigate challenges across industries.”

Initial launch partners include BASF SE, Ferrero International S.A., Foxconn (Hon Hai Precision Industry Co. Ltd.), Anheuser-Busch InBev SA/NV, Cognizant Technology Solutions Corp, Microsoft Corp, Maersk A/S and Mondelēz International Inc., among others.

The availability of Qualcomm Aware is expectedly later this year.

• Proof of Replication (PoRep) is a consensus mechanism demonstrating to blockchain networks that a miner can maintain and present a copy of data on a particular storage location.
• The three challenges, namely Sybil Attack, Outsourcing Attack, and Generation Attack, are all related to replication. The ability to overcome these three challenges differentiates PoRep from other kinds of proofs.

The advent of technology has touched many segments, including IT, virtualization, networking, cloud computing, storage service, cyber security, blockchain, etc. Users and businesses are flooded with various service providers in this competitive age. People demand trust, decentralized financial solutions, and transparency when it comes to blockchain. In this public network, developers, node operators, miners, and investors are active players.

A consensus mechanism is needed between these players to stay up-to-date and in consonance with the changes that keep happening. Proof-of-replication (PoRep), in short, is a process to convey that everything is going according to the regulations.

What is Proof-of-Replication (PoRep)?

Proof of Replication (PoRep) is a consensus mechanism demonstrating to blockchain networks that a miner can maintain and present a copy of data on a particular storage location. This combines proof-of-reliability (PoR) with proof-of-space (PoS), two additional mechanisms.

Users can save data on a server, verify that it is stored, and then retrieve it, thanks to proof-of-reliability (PoR). A user can outsource storage space to a network server using proof-of-space (PoS), a kind of proof-of-capacity (PoC).

Many proof-of-space (PoS) systems let users display the availability of space for junk files. Proof of Replication (PoRep), as opposed to PoS, enables the user to store significant data in such places. Additionally, it makes efficient data retrieval possible. Proof of Replication (PoRep) incorporates Proof of Reliability (PoR) into Proof of Space (PoS). The combined technique has many advantages for decentralized storage networks and cloud computing.

It includes a one-time setup of security parameters. Then pre-processing of algorithms and data inputs happens. After processing data, the replication and extraction of data are done. Then the main part comes into the picture, data that is replicating a proof. After proof replication, the PoRep verifier outputs a decision to accept or reject a proof. Verifiers and provers use this procedure.

Running a proof of replication (PoRep) is almost always more expensive than running a proof-of-space (PoS). This is because of PoRep’s ability to access these files and store both useful and junk information. Fortunately, PoRep shares characteristics with other pricing models.

PoRep operates on arbitrary data; while doing so, it follows below significant steps:

• PoRep Set Up
• PoRep Preproc
• PoRep Replicate
• PoRep Extract
• PoRep Prove
• PoRep Poll
• PoRep Verify

The rising use of cloud computing and the increasing need for decentralized storage networks compels recipients to take the help of PoRep.

Let’s Walk into the Benefits of PoRep

The PoRep is dynamic, publicly verifiable, non-outsourceable, and transparent. In addition to supporting retrievability, it is also time-bounded. Here are some of the benefits –

Effective channel: PoRep creates an effective channel for the user to have a server to keep duplicate copies of data. At the same time, it prevents users from storing the same kind of file on different servers.

Prevent outsourcing: The PoRep was created to stop a miner from delegating responsibility for certain tasks to other miners. Because proof verification verifies a prover’s identity, a miner cannot claim ownership of another miner’s work.

Transparency: PoRep provides maximum transparency as they are publicly verifiable unless the data is private or surreptitiously generated data. It allows any party to have access to data with a validating key.

User-friendly: PoRep is highly user-friendly, enabling users to extract and update the stored data without restarting the protocol. Hence, specific steps are not required to obtain data. To avoid imposters, users show and prove full PoRep in a specified time.

Protection from alien attacks: PoRep is highly effective in the cloud and decentralized storage network systems. It assures the user that stored files have a replica and is independent of the original file in a realistic manner. It creates a unified decentralized storage network where users and servers are protected from outsourcing attacks, Sybil attacks, and generation attacks.

Publicly verifiable: A PoRep includes a one-time setup run by a trusted party or a committee that runs a multi-party computation (MPC) protocol to generate the public parameters. This transparent PoRep process does not possess any private information. This is a one-time, independent setup where a trusted party that runs that particular setup will no longer be involved in the interactive protocols. It is a trusted channel for publicly verifiable data replication security.

Other Kinds of Proof Vs. Proof of Replication

The PoRep is a powerful method to operate arbitrary or random data. Other schemes like PoS and PoRet have significance but do not completely fulfill users’ expectations. So, PoRep is an extended version of all schemes to hop with the replication, deduplication, Sybil identities, and networks.

The three challenges, namely Sybil Attack, Outsourcing Attack, and Generation Attack, are all related to replication. The ability to overcome these three challenges differentiates PoRep from other kinds of proofs.

To cope with the different attacks, we can create different strategies to construct proof-of-replication protocols. Some protocols may be trusted hardware; some may be time-bound protocols. Using time-bound PoRep and relying on local time from the verifiers’ perspective, we create the most suited decentralized storage network.

While making a PoRep strategy against Sybil attack, ensure that encoding is distinguishable and incompressible. While making a PoRep strategy against outsourcing and generation attacks, it is important to make sure that the prover does not get a replica just in time. It is the time between receiving the challenge and producing the proof.

Wrapping Up

The development of blockchain is very much needed at this point in time. As the technology is widening its branches all over the way, transparent, concise, more user-friendly, and trustful decentralized solutions are needed to have more reliability to opt over traditional centralized financial solutions.

• The launch comes almost a year after Akamai bought Linode LLC, a provider of infrastructure-as-a-service, for about USD 900 million.
• Akamai claims that all cloud services will adhere to ISO, SOC 2, and HIPAA standards and that introducing the Akamai Qualified Computing Partner Program would increase their utility.

Akamai Technologies Inc., which provides content delivery network and offers cloud services, recently, announced that it is launching a huge distributed edge and cloud platform called Akamai Connected Cloud.

It is made to work with cloud computing, content delivery, and security in a way that brings experiences and applications closer to end users and protects them better from security threats. The launch comes almost a year after Akamai bought Linode LLC, a provider of infrastructure-as-a-service, for about USD 900 million.

Linode was a competitor to cloud services like Amazon Web Services Inc. and Google Cloud, but it was much smaller and less complicated. It was made for small developers who didn’t want to be stuck with big companies or get nasty surprises when they got their bills. At the time of the purchase, Akamai said it would use Linode’s features to make itself the “world’s most distributed compute platform.”

The result is Akamai Connected Cloud, and the company is adding core and distributed sites to the same infrastructure backbone that powers its existing edge network. This is a huge step forward for the company’s cloud capabilities. It is meant to offer developers a distributed platform for building, running, and securing next-generation applications.

Akamai says its Connected Cloud service is available in 4,100 locations in 134 countries. This puts compute, storage, database, and other essential cloud services closer to large populations, industries, and IT centers. It said that as a result, developers can now build and deploy more efficient cloud workloads with a latency of fewer than ten milliseconds and global reach.

The company wants to work with customers in the media, gaming, software-as-a-service, retail, and government industries. It hopes to win them over not only with the features of Akamai Connected Cloud but also with low prices.

It said that it has been able to use the power of its existing network to bring CDN-like economics to cloud data transfer and lower the cost of cloud egress. Because of this, it can offer much lower egress rates than other cloud service providers, the company said.

Tom Leighton, co-founder, and CEO of Akamai, claimed that because of his company’s expertise in edge computing, anything from content to cybersecurity could be scaled, bringing customers’ digital experiences closer to them while keeping threats farther away. He said, “Our customers know us and trust us for this scale. Now we plan to scale cloud computing, to provide customers with better performance at a lower cost.”

Eleven primary cloud computing facilities are already part of Akamai’s cloud infrastructure, dispersed around the United States, Europe, and Asia. Three additional sites will be added and ready to use by the end of the second quarter. After constructing ten additional core locations the following year, they will offer all of Linode’s cloud computing services. At the same time, Akamai intends to establish new “distributed” sites in more than 50 cities around the world this year, providing essential cloud computing services to more “difficult-to-reach” areas that conventional cloud providers currently neglect.

Akamai Chief Operating Officer and General Manager Adam Karon defined Akamai Connected Cloud as a “continuum of computation” that extends from the core to the edge, along with security, CDN, and round-the-clock support, in a blog post.

He added, “With Linode’s developer-friendly DNA, you’ll find it simpler to deploy distributed applications. Developers will be able to use these capabilities for applications, workloads, and use cases that haven’t been imagined yet.”

Akamai claims that all cloud services will adhere to ISO, SOC 2, and HIPAA standards and that introducing the Akamai Qualified Computing Partner Program would increase their utility. According to the statement, this is to provide customers with various services from third-party compatible with the Akamai Connected Cloud.

To deliver workloads across a broader range of computing and geography, the next generation of cloud computing, according to International Data Corp. analyst Dave McCarthy, will require changing how enterprises and developers think about bringing their applications and data closer to customers.

McCarthy said, “Akamai’s innovative rethinking of how this gets done — and how it is architecting Akamai Connected Cloud — puts it in a unique position to usher in an exciting new era for technology and to help enterprises build, deploy and secure distributed applications.”