Monday, June 26, 2023


In today's fast-paced financial landscape, staying ahead requires leveraging cutting-edge technologies. Machine learning, a branch of artificial intelligence, has emerged as a game-changer in the financial industry. By harnessing the power of vast data sets and advanced algorithms, machine learning is transforming predictive analytics and revolutionizing risk assessment. This article explores the exciting intersection of machine learning and finance, highlighting its potential to enhance decision-making, improve efficiency, and mitigate risks.

The Power of Predictive Analytics:


Machine learning algorithms have the ability to analyze massive volumes of financial data with remarkable speed and accuracy. This enables institutions to uncover hidden patterns, make data-driven predictions, and gain valuable insights into market trends. Predictive analytics powered by machine learning is empowering financial professionals to make informed investment decisions, optimize portfolios, and identify emerging opportunities.


Risk Assessment and Management:


Risk assessment is a critical component of financial operations. Machine learning algorithms can analyze historical data, market conditions, and other relevant factors to identify potential risks and assess their impact. By leveraging sophisticated models, financial institutions can enhance risk assessment processes, identify anomalies, and implement proactive risk management strategies. Machine learning also enables real-time monitoring, fraud detection, and the identification of suspicious activities, ensuring robust security measures.

Algorithmic Trading and Quantitative Finance:

Machine learning algorithms play a pivotal role in algorithmic trading and quantitative finance. These technologies can analyze vast amounts of market data, identify patterns, and execute trades with speed and precision. Machine learning models are used to develop trading strategies, optimize trade execution, and minimize risks. By automating trading decisions based on data-driven insights, machine learning has the potential to enhance profitability and reduce human bias in financial markets.

Customer Personalization and Financial Services:


Machine learning enables financial institutions to deliver personalized services and tailored recommendations to customers. By analyzing customer behavior, preferences, and historical data, machine learning algorithms can provide personalized investment advice, customized financial plans, and targeted product offerings. This enhances customer satisfaction, fosters long-term relationships, and improves overall customer experience in the financial industry.



Ethical Considerations and Regulatory Compliance:

As machine learning continues to drive innovation in finance, it is crucial to address ethical considerations and ensure regulatory compliance. Transparency, fairness, and accountability are paramount when utilizing machine learning algorithms in financial decision-making. Striking the right balance between innovation and responsibility is essential to maintain trust and protect the interests of all stakeholders.

Machine learning is reshaping the landscape of finance, empowering professionals to make data-driven decisions, enhance predictive analytics, and manage risks more effectively. From predictive modeling and risk assessment to algorithmic trading and customer personalization, the applications of machine learning in finance are vast and promising. Embracing this transformative technology has the potential to unlock new opportunities, improve efficiency, and drive sustainable growth in the financial industry.

7th Edition COMMS | 27-28 July 2023 | Delhi, India.





#MachineLearning
#ML
#ArtificialIntelligence
#AI
#DataScience
#DeepLearning
#NeuralNetworks
#DataMining
#BigData






NewsTechnologyArtificial IntelligenceRoboCat, a self-learning agent developed by Google DeepMind, is set to revolutionise robotics RoboCat, a self-learning agent developed by Google DeepMind, is set to revolutionise robotics

 



The world is increasingly adapting to the changing tides of technology. Even as artificial intelligence continues to make strides across industries, robotics is not far behind. Google DeepMind has just introduced a new dimension to robotics. The latest creation known as RoboCat has the capability to perform various tasks through diverse robotic arms.



RoboCat can be categorised as a different league of robotics owing to its unique ability to ‘tackle and adjust’ to various tasks using different types of robots in real-world scenarios. This is something that Google DeepMind claims have never been achieved before in robotics.
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In its official post, DeepMind said that most robots are programmed to perform specific tasks. However, with the advances in AI, robots may be able to perform more tasks. It said that the progress in general-purpose robots is considerably slow due to the time consumed in gathering real-world training data.


What is RoboCat?



DeepMind claims RoboCat is a self-improving AI agent for robotics. It learns to perform a wide range of tasks across different arms and then generates new training data on its own to improve itself.

Several researchers in the past have explored robots that can learn to multitask at scale and comprehend large language models along with the real-world capabilities of a helper robot. According to Google DeepMind, RoboCat is the first agent to perform and adapt to multiple tasks and to do the same across different real robots.


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The company claims that RoboCat learns at a much faster rate than other state-of-the-art models. RoboCat can learn a new task with as few as 100 demonstrations as it draws heavily from a diverse dataset. Google DeepMind claims that this will help in accelerating robotics research as it nearly dissolves human intervention when it comes to training. The company sees it as a step forward in the creation of general-purpose robots.

How does RoboCat learn and improve itself?

RoboCat is based on Google DeepMind’s multimodal model Gato that can process language, images, and actions from simulated and physical environments. The company claimed that it infused Gato’s architecture with a large training dataset which is sequences of actions and images of various robot arms that are solving hundreds of tasks. After this round, the company said that it launched RoboCat into a self-improvement training cycle with a set of unseen tasks. The learning of new tasks took place in five steps.

“The combination of all this training means the latest RoboCat is based on a dataset of millions of trajectories, from both real and simulated robotic arms, including self-generated data. We used four different types of robots and many robotic arms to collect vision-based data representing the tasks RoboCat would be trained to perform,” the company said in its official post.

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RoboCat is essentially an agent that is a visual goal-conditioned decision transformer that has been trained on video clips of hundreds of tasks being done. The data is gathered from a vast set of real-world robot arm types and simulated environments.

The most noteworthy facet of this agent is that it continues to learn and improve itself with each new task. The first model reported a success rate of nearly 36 per cent on previously unseen tasks after being presented with 500 demonstrations. However, Google DeepMind says that after RoboCat learned more tasks, its success rate more than doubled. The versatility, adaptability, and multimodal capabilities of RoboCat can have far-reaching benefits in the field of robotics.

7th Edition COMMS | 27-28 July 2023 | Delhi, India.



#Robotics
#Robots
#AI
#ArtificialIntelligence
#Automation
#Tech
#Innovation
#Technology
#FutureTech
#RoboticsResearch
#RoboticsNews

Sunday, June 25, 2023

Over 80% Enterprises Benefit From Edge Computing, Reveals Report






A study revealed that over 80 per cent of organisations have seen their edge investment expectations met or exceeded.

However, despite the positive outcomes, concerns about the capabilities of current infrastructure to support edge computing have risen by nearly 40 per cent of organisations planning edge deployments.

The NTT report highlighted that organisations perceive the fragmented management of compute, connectivity, and IoT devices as a major hindrance in harnessing the full potential of edge technology. In contrast, those organisations adopting a combined approach of private 5G and edge technologies reported the highest benefits, surpassing those with a legacy segregated approach or no adoption at all.

Another key finding was that while most organisations believed their network infrastructure could handle their current edge requirements, approximately 40 per cent of enterprises planning edge deployments acknowledged the need to upgrade their network to accommodate the anticipated surge in connected devices and applications. In fact, nearly two-thirds of enterprises that had already deployed edge solutions had undertaken a wide area network refresh.

To navigate the complexities of edge adoption, organisations are increasingly seeking partnerships. The report revealed that 88 per cent of organisations expect their dependence on third-party edge services to grow in the next 24 months. Moreover, 90 per cent expressed a preference for a single partner offering a central point of accountability, and 94 per cent considered having more managed service options as a top factor in facilitating edge consumption.

The study also highlighted the motivations behind edge adoption in different industries. Manufacturing firms ranked operational efficiency and data security as the top drivers, while healthcare, transportation, and manufacturing organisations reported meeting or exceeding expectations in terms of improving supply chain efficiency and employee safety.

The findings of the report emphasize the growing importance of edge computing in achieving business outcomes and driving digital transformation. As organisations continue to invest in edge technologies, the need for robust network infrastructure, strategic partnerships, and holistic management becomes crucial to fully harness the benefits of edge computing.



#EdgeComputing
#EdgeAnalytics
#InternetOfThings
#RealTimeProcessing
#CloudComputing
#DigitalTransformation
#EdgeDevices
#EdgeInfrastructure
#LowLatency
#SmartCities





Thursday, June 22, 2023

Global IoT Communication Protocol Market to Surpass US$ 24.6 Billion by 2032 Amid Skyrocketing Demand for Smart Home Appliances


 




The IoT communication protocols Market is anticipated to be worth US$ 24.6 Billion. In 2022, its estimated value was US$ 15.9 Billion. It is likely to see a CAGR of 4.5% between 2022 and 2032.

Consumer electronics are integrating IoT communication protocols as they enable connectivity and communication across devices. These facilitate the development of connected and smart products further.

These protocols give data sharing a defined structure and make it possible for devices from various manufacturers to work together. They aid in guaranteeing smooth data transport and communication. Smart home appliances frequently use Wi-Fi to connect to a network at home and the internet.

Bluetooth is often utilized in audio and wearable technology. Consumer gadgets using IoT connection protocols can perform functions, including remote monitoring & control, real-time data analysis & feedback, and improved device compatibility.

Competitive Landscape:


Leading developers of IoT communication protocols are concentrating on creating real-time protocols to support IoT applications. Applications, including industrial automation, smart grid management, and autonomous cars would call for quick and dependable data interchange.

Several additional businesses are creating protocols that can be connected with cloud services. They want to make it possible for IoT systems and devices to be managed, monitored, and have their data analyzed remotely. Scalable protocols that can handle a lot of devices and data traffic are still being developed.

Standardization and interoperability through IoT communication protocols are vital as IoT devices are being adopted more widely in consumer electronics. These are improving user experience and enabling smooth data sharing and communication between devices.

Demand for IoT communication protocols that can support edge devices is expected to increase. Edge computing, which involves processing IoT data at the edge of networks, is set to become highly popular by 2032.

Edge computing decreases the quantity of data that needs to be transferred to the cloud and allows for real-time data processing. It can lower the need for bandwidth while enhancing device performance.

The usage of IoT has increased significantly recently all across the world. Around 8.6 billion IoT devices were present worldwide in 2019. As IoT device adoption accelerated globally, the total number of IoT devices was estimated to be 12.14 Billion in 2022.

By 2030, it is predicted that there will be about 23.14 Billion IoT devices on the planet. The need for IoT communication protocols is anticipated to rise over the course of the assessment period, as there are more IoT devices in use



#CommunicationProtocols
#NetworkingProtocol
#DataTransmission
#ProtocolStandards
#MessageFormat
#ErrorHandling
#NetworkRouting
#ProtocolDesign
#SecurityProtocols
#Interoperability
#ProtocolStack
#InternetProtocol
#TCP/IP
#WirelessCommunication
#IoTProtocols
#NetworkSecurity





Breaking Free from Invoicing Fraud with Blockchain




Blockchain is already transforming the way businesses operate, particularly when it comes to invoicing.

From the million-dollar scam that cost tech giants like Google and Facebook $122 million many years ago to the PayPal invoice scam doing rounds this year, scamsters have only become more sophisticated and convincing in their approach. Moreover, technological advancements making Artificial Intelligence more accessible today make things trickier. Increasingly, blockchain technology is being called on to help prevent fraud.

Think about how ChatGPT, given its ability to generate humanesque text, and trained on large amounts of data, including invoices and financial documents, can be misused to conduct invoicing fraud. This makes it easier for bad actors to impersonate legitimate businesses and create fraudulent invoices to trick others into paying for something they did not receive. The fascinating thing about technology, however, is that it’s double-edged- capable of good and bad. Let’s look at how technology, in the form of blockchain, has come through to turn things around for invoicing.

How blockchain changes the narrative

As one of the most valuable sources of information that forms the foundation of doing business, invoices also run the risk of being misused. It’s not surprising that the world of invoicing is tainted with liquidity risks, operational costs, disputes, losses, and fraud. But the good news is that blockchain technology and its limitless potential can make a huge difference in this situation.
Automating processes through smart contracts

Blockchain technology allows for the use of another tool that has emerged as a game changer in the industry- smart contracts, which are self-executing contracts with the terms of the agreement written into the code itself. They can be programmed to automatically verify and execute invoices based on predefined conditions, including payment terms and delivery requirements. Smart contracts and invoicing go together like a natural match and reduce the risk of invoice fraud by ensuring that invoices are only processed if they meet specific criteria.

By automating the invoicing process, blockchain eliminates the need for manual processing and, as a result, also significantly reduces the time required to complete transactions. Besides the fact that time is money and that this makes processes extremely cost efficient, it also reduces the risk of fraud by creating a more efficient system that is less susceptible to human error.

What’s more, smart contracts also help create more flexible and customized invoicing processes that can seamlessly adapt to the needs of businesses and their customers. One may wonder if smart contracts can accommodate complex invoicing arrangements. That’s possible too. Consider variable pricing based on usage or other conditions- a smart contract could be used to automatically adjust pricing with respect to changes in the price of raw materials or even execute discounts or penalties based on specific performance metrics. Now, imagine the plight of having to manually adjust and implement these arrangements, that too with the possibility of error and manipulation always lurking around. From the creation of the invoice to its payment, blockchain ensures the authenticity of an invoice at various stages. We’re starting to see how smart contracts make invoicing smart!

Doubling up the vigilance: What happens when IoT is added to the mix?

The Internet of Things (IoT) may have seemed like it was straight out of fiction many years ago, but today it’s very much part of our reality. That being the case, what does it mean for invoicing? Long story short, IoT sensors enable the collection of real-time data from physical objects and environments, designed to measure various types of data, such as temperature, humidity, pressure, light, motion, sound, etc., and send that data to a network or cloud-based platform for processing analysis and decision making.

Using IoT sensors to collect data such as temperature, weight, and location of products or services being delivered, and leveraging blockchain technology to automatically record it on the ledger, translates to an extremely efficient method to verify the authenticity of an invoice. Real-time data on the status of deliveries allows for the tracking of shipments to ensure that the invoiced products or services are actually delivered. What this also does for the businesses is equip them to optimize their inventory management, thereby reducing the risk of overstocking or running out of stock. Subsequently, this can help prevent fraudulent billing as well as reduce the likelihood of invoicing errors stemming from incorrect inventory data. In short, something seemingly simple as automatically recording the delivery of goods and services can significantly reduce the risk of errors or fraud.

It’s not always fake invoices and phishing attempts- sometimes, it could be just errors

It doesn’t necessarily have to be fraud or phishing, but even unintentionally made errors could cause confusion and reason for mistrust between two parties involved in a business transaction. Implementing a blockchain invoicing system will enable businesses to create a transparent and auditable record of all transactions related to what they offer, which can then be accessed and verified by all relevant parties. By ensuring that the invoice amount accurately reflects the previously agreed-upon terms and conditions of the service, this arrangement also helps prevent instances of overcharging.

Similarly, IoT devices can be used to monitor the usage of services in real time. Think about how smart meters, powered by IoT, could be used to track the usage of resources like electricity, water, or gas to provide accurate data, which can be compared against the invoiced amount to verify if it’s a fair charge. It’s interesting to note how smart contracts could also be incorporated into this setting to ensure that invoicing is accurate and timely, in addition to preventing the risk of overcharging. A smart contract could be used to automatically generate an invoice when a certain amount of usage is reached or to trigger a payment when a certain condition is met.

The future of invoicing looks promising, thanks to blockchain technology

With its immense potential to significantly reduce the time, cost, and risk associated with transactions, blockchain is already transforming the way businesses operate, particularly when it comes to invoicing. Moreover, when combined with other emerging technologies like the Internet of Things (IoT), blockchain technology acts as a catalyst for making invoicing processes as efficient, seamless, and transparent as possible.


#BlockchainTechnology
#Crypto
#Cryptocurrency
#Decentralization
#SmartContracts
#DistributedLedger
#DigitalCurrency
#BlockchainSolutions
#BlockchainDevelopment

Digital Marketing Trends That Help You Stay Ahead


 


Virtual Reality and Augmented Reality

Virtual reality and augmented reality are revolutionizing the world of digital marketing, gaining immense popularity. VR offers captivating and immersive experiences, transporting customers to virtual realms. In contrast, AR seamlessly merges digital content with the real world, creating interactive and engaging encounters that blend physical and digital environments. These cutting-edge technologies help businesses increase customer engagement and ultimately sales. Although it may seem distant for small businesses, global brands are already capitalizing on these opportunities.

Take automobile manufacturers like Audi and Volvo, for instance. They've created virtual showrooms where customers can explore and personalize their dream car models. Users can virtually step inside the vehicle, closely inspect its features, and even do a simulated test drive. This groundbreaking approach allows customers to immerse themselves in the product without the need to physically visit a dealership.

The Rise of Social Commerce

Social commerce is not simply about selling products online; it's a dynamic approach that integrates shopping with social interactions on various social media platforms. By leveraging influencers' and users' content, such as captivating photos, engaging videos, and authentic customer reviews, companies can elevate their sales strategies beyond traditional online methods. This innovative approach enables businesses to create emotional connections with customers, leading to increased sales directly on social media. Moreover, the interactive nature of social commerce allows customers to ask questions, seek clarification, and engage with brands through comments or dedicated Q&A sections. This not only instills confidence in purchase decisions but also provides valuable insights to companies, empowering them to better understand the market. Social media platforms thus serve as vibrant hubs where potential customers can engage with and learn from those who have already experienced the product firsthand.
The Rise of Voice Search


The rise of AI virtual assistants like Siri and Google Assistant, along with the advent of smart speakers such as Amazon Alexa and Google Home, has propelled the popularity of voice search. This trend has witnessed exponential growth, with research indicating that 72% of consumers have used a voice assistant. Notably, these voice-based inquiries often adopt a more conversational style, necessitating a corresponding conversational response. To effectively optimize your content for voice search, it becomes imperative to align it with these conversational patterns and provide concise, clear answers. AI assistants heavily rely on extracting information from featured snippets or the top positions in search engine results. Consequently, it is crucial to anticipate frequently asked questions relevant to your topic and ensure that your content offers direct and informative responses.

The Rise of Conversational Marketing

Since 2015 and continuing to the present day, conversational marketing has emerged as a crucial trend. This innovative approach revolves around actively engaging and interacting with customers in personalized, real-time conversations. Leveraging channels such as chatbots, live chat, and messaging apps, businesses can cultivate meaningful customer experiences and drive conversions. Today's digital-savvy consumers demand superior online experiences, personalized content, and prompt responses to their inquiries. Their impatience with searching for information or navigating websites necessitates immediate attention. By implementing a conversational marketing strategy, your business can not only enhance the customer experience but also generate high-quality leads. Adopting chatbots for conversational marketing proves to be a valuable investment, as routine queries can be promptly addressed without overwhelming your customer service representatives' email inboxes, saving both time and money.

Some Last Words

Digital marketing is undergoing a transformation with the advancements in technology, and there isn't a single solution that fits all scenarios. As time goes on, consumer priority changes, and it's necessary to adapt your strategy accordingly. These trends have brought about a revolution in how businesses interact with their customers, offering personalized and immersive experiences that nurture stronger connections. To stay ahead in the dynamic and ever-evolving digital marketing world, it's crucial for marketers to embrace these trends and make use of emerging technologies. By grasping and capitalizing on these trends, businesses can improve customer engagement, increase conversions, and ultimately succeed in the digital realm.


#DigitalMarketing
#MarketingStrategy
#OnlineMarketing
#SocialMediaMarketing
#ContentMarketing
#SEO (Search Engine Optimization)
#SEM (Search Engine Marketing)
#PPC (Pay-Per-Click)












CLOUD COMPUTING, OPEN FINANCE AND THE FUTURE OF BANKING


 



Challenges and challengers

Incumbent banks no longer enjoy a monopoly in the financial-services space, pressuring the sector into creating agile products and ecosystems that meet a widening range of ever-changing consumer demands and experiences unique to customers. It is specialist, digital and challenger banks as well as third-party fintechs (financial-technology firms) that are providing the solutions, whether in partnership or competition with those incumbents.

As the market becomes increasingly saturated, banks are being forced to rethink how to maintain their competitive edges and adapt their product-development models. Instead of building solutions prior to finding buyers within captive audiences (in other words, their existing customer bases), banks are faced with identifying the needs of their consumers and then working backwardsto find the best solutions. This is often referred to as “front-to-back development”—a process reinforced by external pressures, such as evolving regulatory landscapes. To remain compliant, banks have to be increasingly agile. And for international banks, that means complying with varying regional and national standards.

New regulations concerning data are also arising. To create the highest-quality experiences, whether in fraud prevention or user personalisation, a large amount of data is required. And as data sharing develops, from open banking to open data, banks are grappling with another layer of complexity in their compliance operations.

All in all, banks are facing a range of pressures as they look to the future, which can be summarised into four key priorities:Ensuring security, compliance and resilience and meeting all global standards,
Improving customer experiences in a saturated market,
Optimising the use of internal data by enriching it with external data and
Building front-to-back digital infrastructure.

Of course, technological improvements—what we would perceive as the fifth wave of technological development in banking—provide obvious solutions to all these pressure points.

The fifth wave: banking on the cloud

The first wave of banking technology revolved around replacing manual processes with machines, including mainframe computers and card machines. Much of this technology was used for record-keeping, general ledgers and calculations, resulting in banks being able to handle larger transactions with greater efficiency. The second wave saw the introduction of mini-computers and terminal desktops, furthering efficiencies and developing automation for branches and back-end operations. And the third wave, during which software began to play a more vital role, involved banks running on servers accessed by clients over networks.

The fourth wave was marked by the emergence of service-oriented architecture (SOA) and object-oriented programming, which paved the way for developing applications (apps). Here, consumer demands started shaping the banking industry, with increasing expectations that consumer banking should be done anytime and anywhere via apps and other branded front-end capabilities. This wave also saw the shift toward more modular and flexible software architectures that could be used on a wide range of devices.

This brings us to the fifth and current wave, inspired by all-around services such as Amazon. Beginning with the rise of cloud computing—complementing a more flexible, incremental software-development style—the fifth wave has seen the creation of marketplaces and ecosystems that bring together a range of services and applications, allowing consumers and corporations alike to access a full suite of capabilities in one location.

The fifth wave of banking technology paves the way for a new, innovative era in which interlinking software can create seamless services by updating and tying together existing legacy infrastructures. This wave also allows for greater connectivity and collaboration, as well as the emergence of new business models and opportunities.

Why put banking on the cloud?

While historically, larger “high street” banks have enjoyed a monopoly on innovation in the banking-technology space (especially in the earlier waves)—partly due to the scale of investment required—cloud-based banking, with its minimal upfront costs, changes everything.

Cloud banking enables banks of any size to receive on-demand delivery of hosted computing services—from servers and data storage to communication tools and applications—via the internet. The benefits of banking on the cloud—including mass scalability, better compliance, improved market time and easier integration—make the transition not only viable but advantageous.

Regulatory compliance alone justifies the commitment, with cloud-based banking technology aiding privacy, open finance and jurisdiction-related regulatory standards. Indeed, many cloud-based ecosystems come with controls in place to ensure that all products are already fully compliant with any relevant regulations.

Furthermore, a bank can leverage these capabilities via a single ecosystem by using a cloud platform with third parties and fintechs already integrated rather than enduring the arduous process of integrating with each fintech on outdated systems. And should a new fintech or challenger develop a desirable product, the composability of cloud banking means that the new solution can simply be added to the existing suite rather than requiring another full round of integration.

Finally (in terms of benefits), a bank can take new offerings to market faster by offering a pre-integrated ecosystem through a marketplace. While partnering with a fintech (by a bank alone) can take months from germination to implementation, adding a new partner or product via a cloud-based banking system by leveraging its existing marketplace can reduce the processing time to a few weeks. In an increasingly competitive consumer-banking arena, such speed is invaluable when competing head-to-head with challengers.

Cloud and open finance: a terrific combination

Imagine this: You’re a bank trying to onboard an SME (small or medium-sized enterprise) for financing. Just a few years ago, this meant the SME would have to travel to a physical branch to hand over up to 20 documents. These documents would likely include a certificate of incorporation, tax statements and proof of address, among other items. They would be passed to the back office and credit department, where they would be run through a handful of algorithms. Finally, a credit decision would be taken depending on the customer and the facility required: all in all, a process typically taking more than 10 days.

With a cloud-based open-finance platform, the SME no longer needs to enter a physical branch. Instead, the SME is directed to a simple, branded microsite or even a mobile app to start their self-onboarding. They can enter basic information, such as a phone number or tax identification, which can be leveraged (with their consent) via a third party to extrapolate further data, perhaps from a government website. Verified data can then be obtained via a simple API (application programming interface).

The process can also be scaled, meaning that data can be gathered from various sources via separate APIs and then triangulated. And this combined data can then be used to make a credit decision using an algorithm. Rather than taking 10 days, approval in principle can be provided in an hour—and without the arduous process of the SME organising and handing over physical documentation.

Looking to the future

The wave of innovation sweeping the financial services sector is unlikely to slow down. Indeed, it is speeding up as more banks move toward fifth-wave cloud computing. Cloud-based open-finance platforms are set to become the norm, while in the next few years, it is likely that APIs and architecture will continue to break new ground and shape the banking services of the future.

Looking further ahead—and perhaps even into the components that could build the sixth wave of banking technology—quantum computing could be harnessed to solve problems and build algorithms too complex for current computer models. Furthermore, the growth of the Internet of Things (IoT) could extend banking ecosystems further and provide even smoother customer experiences.

Whether Wave Six will see an explosion of interlinked online services, wearable tech or quantum computing, one thing remains clear: Cloud computing is the stepping stone to the future that banking so desperately needs.


#CloudComputing #CloudServices #CloudTechnology #CloudInfrastructure #PublicCloud #PrivateCloud
#HybridCloud #MultiCloud #CloudMigration  #CloudSecurity







































MongoDB readies its Atlas database service for new workloads


 



At its MongoDB.local NYC event, MongoDB today announced a slew of product releases and updates. Given the company’s focus on its fully managed Atlas service, it’s no surprise that the majority of news focuses on that platform, with improved support for AI and semantic search workloads, dedicated search nodes to better enable search use cases and new capabilities to process streaming data, among others.

Andrew Davidson, MongoDB’s SVP of product, told me that this is a continuation of the work the company has been doing on Atlas in recent years. “With Atlas, we can deliver capabilities much more quickly,” he said. “We’re able to add the power of search and time series and drive a wider variety of workload shapes.” He argues that as businesses are forced to do more with fewer resources — all while developers are expected to build more applications and do so faster — expanding Atlas’ capabilities is a natural evolution for MongoDB. “We think that this is totally our moment, because we come in with our developer data platform vision, saying: we want to enable a builder to express the vast majority of the features in the vast majority of their applications with respect to their operational data needs. That’s why we keep investing in all of these key primitives and capabilities,” he explained.

Vector search is maybe the most obvious example here. For companies that want to use large language models (LLMs), translating their data into vectors and storing them is key to customizing foundation models for their needs. In addition, vector search also enables new workloads on Atlas, like text-to-image search, for example. “We think that, of course, a developer data platform that specializes in operational data should also be able to then express indexes that let you efficiently query the vector summaries of that data,” said Davidson.


Likewise, stream processing is a capability that hasn’t traditionally been the focus of MongoDB’s document model. For a while now, MongoDB has been offering its Aggregation Framework, which allows developers to perform transformations on a stream of documents that comes out of a database. “We realized, ‘holy moly, that’s a perfect metaphor for being able to conceptualize transformations on a stream coming off Kafka,'” Davidson explained.

Another new feature here is support for querying data in Microsoft Azure Blob Storage with MongoDB Atlas Online Archive and Atlas Data Federation. MongoDB previously launched support for AWS. While MongoDB would obviously prefer it if everybody hosted their data in MongoDB, the reality is that most enterprises will continue to use multiple systems. Atlas Data Federation makes it easy for developers to read and write data from and to Atlas databases and third-party cloud object stores, which then makes it easier for them to generate and combine data streams from multiple data sources to power their applications.

Some of the other new features MongoDB is launching this week include Atlas Search Nodes, which are dedicated nodes for scaling search workloads independent of the database, as well as improvements to how the database handles enterprise-scale time series workloads.

“The new MongoDB Atlas capabilities announced today are in response to the feedback we get from customers all around the world — they love that their teams are able to quickly build and innovate with MongoDB Atlas and want to be able to do even more with it across the enterprise,” said Dev Ittycheria, MongoDB’s president and CEO. “With the new features we’re launching today, we’re further supporting not only customers who are just getting started, but also customers who have the most demanding requirements for functionality, performance, scale and flexibility so they can unleash the power of software and data to build advanced applications to transform their businesses.”





Wednesday, June 14, 2023

Specialty optical fibers for advanced sensing applications

 




Optical fiber technology has changed the world in enabling extraordinary growth in world-wide communications and sensing. Optical fiber sensors offer favorable advantages such as immunity to electromagnetic interference, lightweight, small size, high sensitivity, large bandwidth, reliable and robust performance, ability to withstand harsh environment, and ease in implementing multiplexed or distributed sensors. To date, optical fiber sensors have been widely used and explored for civil engineering, environmental monitoring, agricultural engineering, biomedical engineering, etc. Notably, the research on specialty optical fibers is playing a critical role in enabling and proliferating the optical fiber sensing applications. Special fiber sensing technology includes various types of fiber, such as single-mode fiber, multi-mode fiber, multi-core fiber, fiber grating, microstructure fiber, etc. Compared with traditional optical fiber sensors, sensor performance such as sensitivity could be significantly improved by utilizing specialty fiber-based sensors.


A review article named "Specialty Optical Fibers for Advanced Sensing Applications" was published in Opto-Electronic science by Professor Perry Ping Shum’s research team in the Optoelectronics Intellisense Lab of Southern University of Science and Technology Issue 2, 2023. The paper overviews recent developments in specialty optical fibers and their sensing applications. The specialty optical fibers are reviewed based on their innovations in special structures, special materials, and technology to realize lab in/on a fiber. An overview of sensing applications in various fields is presented. The prospects and emerging research areas of specialty optical fibers are also discussed.

 

Fabrication of semiconductor fibers with other cross-section geometries.
Optoelectronic fibers with interdigital electrodes by the capillary breakup.
Fabrication of graphene nanoribbons via cold drawing method and achieve a low-cost, simple, and fast method for producing monolayer graphene nanoribbons.
   

Wearable sensing technologies have benefitted from the development of specialty optical fibers, ranging from fiber microstructure-based sensors, fiber interferometer-based sensors, polymer optical fiber (POF) sensors and micro/nano fiber (MNF) sensors. Optical fiber shape sensors based on special fibers and fibers with advanced structures have achieved performance enhancement and attracted growing interest in developing applications in medical treatment, soft robots, and structural behavior monitoring. Besides point sensing applications, distributed fiber sensors have been widely used for industrial applications, such as monitoring of gas pipelines, underwater security, loaded beam structure, earth phenomena, power transformer and downhole environments. Specialty fibers are also widely used in biosensor instrumentation for life sciences related clinical, and research applications.



Perry Ping Shum

chair professor of the Department of Electrical and Electronics Engineering, Southern University of Science and Technology, Director of Guangdong Key Laboratory of Integrated Optoelectronics Intellisense,national distinguished expert, IEEE Fellow, Chinese Optical Society Fellow, SPIE Fellow, OSA Fellow, Chairman of IEEE Photonics Society Guangdong Branch, Vice Chairman of the Optical Communication and Information Network Professional Committee of the Chinese Society of Optical Engineering. He has published nearly a thousand academic papers with more than 19,000 citations, and a H-index of 66. In recent years, as the person in charge, he has been granted research funds of more than RMB 50 million. He served as Director of the NTRC, OPTIMUS, and COFT, and Dean in charge of education in Nanyang Technological University, Singapore. During this period, NTU-COFT, a world-class fiber research/processing center, was created, which enabling Singapore to have the ability to manufacture special fiber optics, special fiber lasers and sensors for the first time. He chaired several major international conferences, including CLEO-PR | OECC| PGC 2017; and initiator of international conferences such as ICICS, PGC, ICOCN, ICAIT, OGC, etc. He has created the special project “Enabled learning: Escape Room Design”, which has been reported by Channel News Asia, Channel U, Channel 8, Channel 5, Zaobao, in four different languages. His doctoral students/postdoctoral students trained have awarded the National Science Fund for Distinguished Young Scholars, the National Specially Appointed Young Expert, the National Natural Science Foundation of China Youth Fund. Two high-tech enterprises in the field of optoelectronics were established or supported by his team (1 of which has been listed). His team also cooperate closely with universities and institutes worldwide


Guangdong Key Laboratory of Integrated Optoelectronics Intellisense 

Guangdong-Hong Kong-Macao Greater Bay Area, aiming to build a world-class platform for optoelectronic intellisense technology innovation, and promote the technological innovation and high-quality and rapid development of industries in related fields in China and Guangdong Province. The laboratory director is Professor Perry Ping Shum of Southern University of Science and Technology. The key lab will follow the urgent needs of national strategy "Digital China" and the development of information industry, relying on Southern University of Science and Technology and key disciplines of Electronic Science and Technology. The key lab launches the research based on the existing academic platform, construction with "optoelectronics intellisense" + "communication network", featuring integration of academic research and talent training base. It carries out a full-chain research pattern of "Key materials – Core sensors- Intellisense equipment - System network", connecting the environment from the physical layer to the application layer, to help build a ubiquitous intelligent infrastructure system. The main research directions cover the following four aspects: Integrated optoelectronic functional materials and devices, high-performance integrated optoelectronics sensors, optoelectronics intellisense equipment and systems, and communication sensing integrated network.





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