The post The Evolution of Passwords and Digital Security in the 21st Century appeared first on Techso Mogro.
]]>Over the years, I have searched for alternatives to the endless password-related security issues that come with being online. I use a single-sign-on app today. But now, passwords and digital security are about to be more complicated by artificial intelligence (AI) and quantum computing.
That’s why Maggie’s article is appropriate for the times.
For decades, passwords have been the primary gatekeepers of our digital lives. They protect
everything from personal email accounts and financial information to workplace systems and cloud-based applications. Despite their widespread use, passwords have also become the weakest link in protecting our cyber presence, creating challenges for all.
As digital technology continues to change rapidly, the future of authentication needs to adapt as well. Advancements in AI, biometrics, cloud computing, and identity management are already transforming how people access digital services. Quantum computing going mainstream will mean much of what we use today to secure our digital presence will no longer work.
Rather than relying solely on complex strings of characters, future authentication systems need to focus on creating convenience and security, and a seamless online experience.
Recently, you may have experienced two-factor (2FA) or multi-factor authentication (MFA) requests to confirm you are who you say you are. This is becoming quite common in banking apps. 2FA and MFA send push notifications to a second device or to an email address that you have identified as a trusted alternative. Usually, 2FA involves a string of characters or a set of pictures that require you to identify which ones share a common object.
Then there is the arrival of passwordless authentication that eliminates the need to memorize endless passwords. Because security experts and technology companies are increasingly aware of their vulnerability to phishing attacks, credential theft, and human error, they are removing passwords from the authentication process altogether.
Instead, passkeys are emerging as one of the leading solutions. These cryptographic credentials are stored securely on trusted devices and verified through biometric methods such as facial recognition, fingerprint, voice, or device PIN. To access a site or app, one or more of these may be needed. Passkeys are resistant to phishing attacks and do not require users to remember complicated routines.
Biometric measurements, mentioned above, are increasingly becoming important for online security. Fingerprint scans, facial recognition, iris scans and voice identification (the stuff of spy movies in the past) are becoming more accessible. Smartphones today incorporate biometric security features.
Adding AI to biometrics is likely the next evolutionary step. Rather than relying on a single biometric trait, AI authentication systems could analyze multiple factors simultaneously. This layered approach would make it significantly more difficult for cybercriminals to impersonate legitimate users.
Single sign-on (SSO) technology allows users to access multiple applications and services using one verified identity. Instead of memorizing or managing dozens of unique passwords for different platforms, SSO can securely authenticate a user once to gain access to authorized systems. It addresses password fatigue and can incorporate biometric verification, passkeys, and contextual intelligence. Authentication systems will evaluate factors such as device trust, user location, network security, and behavioural patterns before granting access.
AI will dramatically influence how passwords and authentication systems evolve. Traditional passwords treat every login attempt the same way, but future systems will continuously
analyze risks in real time. AI-powered security platforms will identify suspicious behaviours that may indicate compromised accounts. For example, if a user suddenly attempts to access a system from an unfamiliar location, unusual device, or unexpected time zone, the system can require stronger verification before granting access.
These adaptive authentication methods create a more intelligent security environment. Rather than requiring users to complete multiple authentication steps every time they sign in, security measures will adjust dynamically based on risk levels. This balance between convenience and protection is expected to become a defining characteristic of future authentication systems.
Another significant advancement will be the development of comprehensive digital identity
ecosystems. Instead of creating separate accounts for every website and application, individuals may maintain trusted digital identities that can be verified across multiple services.
Governments, financial institutions, healthcare providers, and employers are likely to participate in secure identity frameworks that streamline authentication while protecting sensitive information.
If any single technology advancement can throw a wrench into online security, it will be quantum computing. Passwords, 2FA, MFA, biometrics, digital authentication and AI-enhanced systems will all need a complete overhaul. Two algorithms used in quantum computing, Shor’s and Grover’s, can break all current cryptographic applications. They effectively will kill traditional password security’s effectiveness.
Biometric systems will be compromised because facial, fingerprint, voice, and iris scan stored templates wouldn’t hold up to quantum-enabled native decryption capability. Tokens and passkeys would equally be useless.
Quantum-based security remains a relatively new endeavour. Google and Apple are working on hybrid cybersecurity solutions. Governments are also looking at prioritizing research into quantum encryption apps. Needless to say, with quantum computing ever closer to being a useful adjunct to traditional computing platforms, there is a sense of urgency to develop algorithmic breakthroughs capable of addressing the switch from bits and bytes to qubits.
Quantum computing will need new types of cryptography because, surely, cybercriminals will be working in parallel to break these systems.
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]]>The post Quantum Computing Making Its Presence Known in Finance appeared first on Techso Mogro.
]]>For those who see the arrival of quantum computing as a coming Q-apocalypse for every system and application protected by passwords and encryption algorithms, it is interesting to see how banking and finance are forging ahead with qubits added to bits and bytes.
It is also interesting to read about how quantum devices may travel that last mile to close the gap to achieve widespread adoption, not just in the financial sector, but rather, globally.
I will let Harper tell you more.
Quantum computing has captured the imagination of the financial sector, promising breakthroughs in portfolio optimization, risk modelling, derivative pricing, and fraud detection. Yet behind the headlines lies a more nuanced reality.
Today’s quantum systems are fragile, noisy, and constrained. The gap between theoretical advantage and practical deployment remains significant. Understanding error correction, the techniques used to detect and fix mistakes in quantum computations, and noise, the unwanted disturbances that cause quantum bits or qubits to lose coherence and produce incorrect results, is essential. Anyone evaluating how quantum technologies need to be well-armed with knowledge about quantum strengths and weaknesses.
Many financial problems are mathematically complex and computationally intensive. Classical computers often struggle with tasks such as simulating large correlated systems, solving optimization problems with many variables, or pricing complex derivatives under uncertain conditions. Quantum computing, supposedly, can offer faster results for these problems using fundamentally different computing mechanisms than conventional systems.
For example, Monte Carlo simulations, widely used in options pricing and risk assessment, could benefit from quantum algorithms that reduce the number of samples required to achieve a given level of accuracy. Similarly, portfolio optimization problems—where firms aim to allocate capital efficiently across thousands of assets—can be framed as combinatorial challenges that quantum computers may solve more efficiently using techniques like quantum annealing.
So, which banks are dipping their toes into quantum computing waters? A number have launched pilot projects. These include: JPMorgan Chase, which is using quantum algorithms for derivatives pricing and optimization; Goldman Sachs, using options pricing and Monte Carlo acceleration research; Barclays, HSBC, and BBVA, doing exploratory work in risk modelling and portfolio optimization
Unlike classical bits, which are stable and deterministic, qubits are highly sensitive to the environment. The quantum folk call these noise. They include small interactions with external factors, such as temperature fluctuations, electromagnetic interference, or imperfections in control signals. All can disturb a qubit’s state, and it is one of the primary barriers to widespread quantum adoption.
Noise in financial transactions and calculations introduces unacceptable uncertainty, leading to computational errors that cause significant financial losses. This can happen when calculating derivative values or when modelling probability distributions. Both would produce disastrous outcomes.
The current state of quantum computing devices is classified as NISQ, meaning Noisy Intermediate-Scale Quantum systems. They can perform limited calculations but lack the stability required for large-scale, fault-tolerant computation. For financial firms, this means that while experimentation is possible, mission-critical workloads remain out of reach.
To deal with noise, quantum systems researchers have developed quantum error correction (QEC) techniques. Unlike classical error correction, which typically involves duplicating bits, QEC encodes a single logical qubit into many physical qubits. This allows the system to detect and correct errors without directly measuring the quantum state, which would otherwise collapse it.
The overhead needed is substantial. Maintaining one reliable logical qubit can require hundreds or even thousands of physical qubits. The current quantum processor limitations, often only ten to a few hundred qubits, pose a scalability problem.
For financial applications, it means that the algorithms needed to deliver real results are not yet feasible. Even relatively modest tasks in portfolio optimization or risk analysis could demand error-corrected systems far beyond current capabilities.
Despite these challenges, progress is ongoing. Advances in hardware design, improved qubit coherence times, and more efficient error correction codes are steadily reducing the gap. Still, the timeline for fully fault-tolerant quantum systems remains uncertain, and financial institutions must plan accordingly.
Banks and financial institutions are not installing these devices on site because quantum systems require super-cold millikelvin temperatures, vibration isolation, and shielding from other external noise. The result is that quantum devices are being accessed through the Cloud using software and hardware-as-a-service models.
Right now, the world of finance is using quantum computing solutions from a limited number of vendors, including:
Current qubit limitations and environmental concerns are leading to hybrid quantum-classical computing solutions, with the latter handling the bulk of computation, while the quantum processors are used selectively to handle specific subroutines where they can provide an advantage.
For example, a risk management platform might use classical methods to generate baseline scenarios, then apply quantum techniques to refine certain optimization steps. Similarly, in fraud detection, quantum algorithms could assist with pattern recognition in high-dimensional datasets, while classical systems manage data ingestion and preprocessing.
This is where quantum computing software plays a crucial role. These platforms act as bridges between hardware and application layers, enabling developers to design, simulate, and test quantum algorithms in real-world contexts. By abstracting hardware complexities, they allow financial engineers to experiment without needing deep expertise in quantum physics.
Real-world trials have already demonstrated incremental gains. Some firms have reported improvements in sampling efficiency for Monte Carlo simulations, while others have explored enhanced optimization techniques for asset allocation. However, these gains are often modest and situational rather than transformative.
The financial industry has a history of adopting advanced technologies quickly, but also of overestimating short-term impact. Quantum computing is no exception. While the long-term potential is significant, current systems are not ready to replace classical infrastructure.
Executives and technologists must balance enthusiasm with realism. Investments in quantum research and partnerships can provide valuable insights and strategic positioning, but expecting immediate returns is risky. Instead, organizations should focus on building internal expertise, identifying niche applications, and monitoring technological progress.
A practical example is stress testing. Regulators require banks to simulate extreme economic scenarios, which can involve massive computational workloads. While quantum computing could eventually accelerate these simulations, current systems cannot yet deliver the required scale or reliability. Firms that understand this limitation can invest wisely rather than chasing premature deployment.
When will quantum computing go mainstream? For the financial sector, it depends on breakthroughs in hardware. Fault-tolerant quantum computers that run in ambient room-temperature conditions are the desired solution, and it may not be that far away.
A new quantum computer system, Aurora, developed by Xanadu, a Canadian company, is the first scalable photonic quantum device capable of operating at room temperature. Overcoming that severe environmental precondition to widespread adoption is a game-changer.
Meanwhile, using smaller photonic quantum devices interconnected by fibre optics represents another significant advance. Christian Weedbrook, the founder and CEO of Xanadu, states, “Photonics really is the best and most natural way to both compute and network. We now could, in principle, scale up to thousands of server racks and millions of qubits.”
Aurora means more near-absolute-zero temperatures and noise-cancelling hardware restrictions. Aurora uses networked photonic chips that can be distributed across large area networks connected through a fibre-optic mesh. It means, instead of a single large quantum computer, you have a network of smaller and simpler quantum devices that are easier to error correct individually while being fully interconnected and operating at light speed.
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]]>The post Blurring the Lines Between Biology and Technology appeared first on Techso Mogro.
]]>Well, it appears that the future is arriving sooner than the 24th century, with our ability today to engineer biology, blurring the lines between what is alive and what is not.
Research into bio-digital convergence is going well beyond creating robots with humanoid characteristics, as today’s universities and computing companies work on:
New research is adding intelligent functionality to bio-hybrid designs. Lines of investigation are creating bio-implants that respond to the human body’s natural growth, mobility and strength.
It’s not the six-million-dollar man of science-fictional lore. In fact, it is something even better.
For AI today, let alone regular computer systems, a big constraint is the power requirement. Human brains are far more energy-efficient than computers. An average adult brain uses 20 watts of power or less than a third of a kilowatt-hour (kWh) daily. It uses 25% of the energy to perform maintenance, and the remaining 75% for information processing.
To feed the brain’s daily energy requirements, the average human consumes about 260 kilocalories (kcal) daily. That’s between 10 and 13% of the total kcals consumed daily to maintain weight.
An average desktop computer, if left on for 24 hours, consumes between 1.5 and 12 kWh, depending on the work. High-end gaming desktops and data servers consume even more energy. Gamers can use as much as 16.8 kWh daily.
For energy comparisons between the biological and the electrical, 1 kWh equals 860 kcal, that more than 3 times greater than the chemical energy an average brain consumes daily.
I share with you the work being done in biodigital convergence that recently caught my eye.
At Northwestern University, printed artificial neurons are communicating with human brain cells. Researchers have invented a device that interfaces with brain cells, demonstrating biocompatibility.
What have these researchers invented? It is a device containing synthetic flexible neurons fabricated using aerosol-jet printing. The device produces electrical pulses that match natural neuronal activity, triggering responses from living neurons.
What the researchers want to achieve is a brain-machine interface in the form of neuroprosthetics that can communicate with the nervous system to restore vision, hearing and movement.
A Swiss company, FinalSpark, has invented a bioprocessor using stem cells to build human brain organoids connected to neuromorphic chips. These organoids are kept in an incubator, keeping them alive for approximately 100 days. So far, the company has built more than 1,000 organoids to produce 18 terabytes of data.
The bioprocessor is almost a million times more energy efficient than current digital processors because the organoids are each a mini-brain. It operates in the cloud, serving as a living neuromorphic computing platform for AI and biocomputing applications.
FinalSpark’s ultimate goal is to build a living biocomputer that is scalable in size because you can add as many organoids as you like. The company sees it as a far more manageable computing platform for AI because of its low energy requirements.
Our final story today looks at deoxyribonucleic acid (DNA). We have only known about the double-helix molecule for 73 years. When first discovered, it changed our understanding of genetics.
By unravelling the double helix of nucleotides, we began to understand ourselves within the living world that surrounds us. Knowing about DNA made it possible for us to better diagnose diseases, develop new treatments, and begin to manipulate genetic information to our advantage. Knowing about DNA spurred the next agricultural revolution, producing the crops that today feed the world.
But there are more ways to use the molecule of life. One of those ways involves DNA for data storage, translating binary code into Adenine, Cytosine, Guanine and Thymine, the A-C-G-T nucleotides that make up the double helix. Data is encoded into a DNA sequence. Decoding turns it back into bits. The difference from current data storage to DNA storage is that the medium is biological rather than magnetic or optical. A gram of DNA can store a Petabyte (1,000 Terabytes) of data for hundreds of years while remaining stable throughout.
Consider the massive data storage and processing needs of today’s global digital economy, and the advent of artificial intelligence (AI), and you begin to see why DNA data storage makes sense. Data warehouses would take up no more space than a single desk drawer being built to facilitate AI, if converted to using DNA.
Challenges remain. These include:
A positive is that the expanding capabilities of AI should help to overcome some of the DNA storage challenges. Meanwhile, new chips will be needed. Synthesizing DNA strands will need to be improved to reduce error rates.
Where will DNA storage be used?
It won’t be for local desktop computing. Instead, it will serve best for archival, long-term storage requiring data preservation for decades and even centuries.
How close are we to seeing DNA storage become a reality?
An alliance of academic and high technology companies have formed the DNA Data Storage Alliance. Companies include Twist Bioscience, Illumina, Western Digital, and Microsoft. Active research is being done at The Wyss Institute, University of Washington, EPFL, ETH Zurich, and imec, the latter a Belgium-based independent research and innovation centre. Wyss predicts the first pilot archival DNA storage system will be here before the decade is out.
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]]>The post Green Trading and Algorithms Are Changing How We Trade and Invest appeared first on Techso Mogro.
]]>In her writing, she tries to explain complex concepts to readers. In her free time, Harper enjoys music and looking for the small details in life that make it more enjoyable. In this contribution, Harper looks at how algorithms are changing how trading and investments are managed.
Investing is often framed as a purely financial activity, but behind every trade is an infrastructure that consumes real resources. Data centers process market feeds, servers execute orders, and networks transmit information across the globe in milliseconds. As investing becomes more automated, these hidden systems grow more complex and more energy‑intensive.
Much of this automation takes the form of algorithmic trading, in which computer programs execute trades based on predefined rules such as price movements, timing, volume, or risk thresholds. These systems are designed to remove manual intervention from the trading process, allowing transactions to occur faster and more consistently than human decision‑making alone. Increasingly, some of these algorithms also incorporate artificial intelligence (AI) or machine learning (ML) models that adapt to patterns in market data, refining strategies over time and further shaping how trades are executed.
At the same time, algorithmic trading is frequently credited with making markets more efficient. By reducing human error and optimizing execution, automated systems can lower certain forms of waste. The question, then, is not whether algorithms change the resource demands of investing, but whether and how these changes ultimately support a greener financial ecosystem.
Although trading feels intangible to the end user, it relies on a physical backbone. Exchanges, brokers, and analytics platforms operate vast computing infrastructures to handle real-time data and transactions. As algorithmic strategies proliferate, the demand for low-latency processing and constant uptime increases.
This has led to a rise in energy consumption tied to financial activity. Servers must run continuously, cooling systems must regulate heat, and redundant systems must be maintained to ensure reliability. From an environmental perspective, the efficiency of these systems matters as much as the strategies they support.
Understanding green trading begins with recognizing that digital does not mean resource–free. It means the environmental impact is concentrated behind the scenes.
Despite the growing computational footprint, algorithmic systems can reduce other forms of inefficiency. Manual trading often involves delayed execution, redundant actions, and emotional overreactions that amplify volatility. Automated strategies, by contrast, follow predefined rules with consistency.
This consistency can translate into fewer unnecessary trades, tighter spreads, and more efficient price discovery. When algorithms are designed to minimize churn rather than chase marginal gains, they can lower transaction volumes without sacrificing performance. Fewer trades mean fewer system calls and less computational overhead per unit of value created.
Automation also reduces human travel and infrastructure associated with traditional trading floors. As markets have moved from physical spaces to digital platforms, resource use has shifted from buildings and commutes to centralized computing, creating opportunities for optimization at scale.
One of the defining features of algorithmic trading is speed. Strategies compete in milliseconds, pushing firms to invest in faster hardware, specialized networks, and proximity to exchanges. This arms race can increase energy consumption as firms pursue ever-lower latency.
From a sustainability perspective, this raises important questions. Does marginally faster execution justify higher resource use? Or could markets function just as well with slightly slower, more energy–efficient systems?
Some market participants are beginning to explore this balance. Rather than maximizing speed at all costs, they focus on robustness, reliability, and energy efficiency. In this model, green trading is not about eliminating algorithms, but about redefining performance metrics to include environmental impact.
For instance, in major U.S. equities markets, such as those centred in New Jersey data centers, some firms are choosing to trade in milliseconds rather than microseconds, instead of colocating for ultra‑low latency, thereby reducing the need for energy‑intensive hardware and constant infrastructure upgrades while still maintaining strong overall performance.
Not all algorithmic systems are equally resource-intensive. Advances in software optimization allow newer models to achieve better results with less computational power. Efficient code, improved data sampling, and smarter signal selection can reduce the need for constant, high–frequency processing.
Machine learning models are also evolving. Instead of retraining continuously on massive datasets, some systems update incrementally or rely on selective inputs. This approach reduces processing demands while maintaining strategic relevance.
Even the best AI crypto trading bot is constrained by its design and deployment. Performance depends not only on market logic, but on how efficiently the system uses data, executes trades, and manages idle time. Efficiency gains at this level can meaningfully reduce the overall resource footprint of automated investing.
Another factor shaping the environmental impact of algorithmic trading is where and how systems are hosted. Cloud infrastructure allows firms to scale resources dynamically, reducing the need for overbuilt, always–on hardware. When paired with renewable energy sources, centralized data centres can be more efficient than distributed, on–premise setups.
However, centralization also concentrates energy demand. The sustainability of algorithmic trading increasingly depends on the energy mix powering these facilities. Regions investing in clean energy can support greener financial infrastructure, while others may lag.
Investors and firms rarely see this layer directly, but their choice of platforms, hosting regions, and partnered providers collectively influences the environmental profile of modern investing.
Green trading is not solely a technical challenge; it is a question of responsibility. As automation abstracts decision–making away from individuals, accountability becomes more diffuse. Who is responsible for the energy footprint of a strategy? Is it the developer, the platform, or the investor deploying it?
Increasingly, transparency is part of the answer. Understanding how systems operate, what resources they consume, and what tradeoffs they embody allows participants to make more informed choices. Just as investors evaluate risk and return, they may begin to evaluate efficiency and sustainability.
This shift does not require abandoning algorithmic systems. It requires expanding the definition of performance to include long–term impact alongside short–term gains.
Green trading is not about rejecting automation, but about using it more thoughtfully. Smarter algorithms, efficient infrastructure, and a willingness to balance speed with sustainability can reduce waste without undermining market function.
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]]>The post Why Edge Computing Resilience Relies on Proactive Power Management appeared first on Techso Mogro.
]]>Why are organizations moving away from the old mainframe, big iron model to edge computing? Moving the data workload closer to the end user in support of low-latency applications and IoT networks is proving to be more efficient.
Today, edge computing has become the backbone of modern digital operations. IT teams are investing heavily in hybrid cloud observability and automated deployment pipelines, ensuring software runs smoothly across thousands of remote nodes.
There is, however, a fundamental layer that software tools cannot monitor back to health once it fails. That layer is the physical power supply. In the rapid push for digital transformation in places like Australia and elsewhere, proactive power management is often the neglected subject, treated as an afterthought until a sudden outage brings the network to an immediate halt.
When a centralized data centre experiences a power disruption, massive redundant generators automatically engage to keep servers online. Edge environments rarely have this luxury. A power spike or micro-outage at a remote telecommunications node affecting business operations can immediately knock critical applications offline. Think of the impact on emergency operations in regional hospitals when such power outages occur.
The financial impact of these power disruptions is staggering. According to comprehensive industry research detailing the cost of downtime, Gartner and Ponemon Institute data reveal that an IT outage can cost a business anywhere from $5,600 to nearly $9,000 per minute, depending on the size of the company.
For an enterprise running real-time edge analytics or automated manufacturing processes, even a brief disruption creates severe financial, reputational, and operational consequences. Every minute spent attempting to restore a remote network node translates directly to lost revenue and diminished customer trust.
Unlike the carefully controlled climate of a primary tier-four data facility, edge computing hardware is frequently deployed in harsh, remote, or non-traditional environments. These distributed networks face unique physical threats. Operations teams must account for everything from fluctuating ambient temperatures and high dust levels to inconsistent local utility grids that are prone to voltage drops.
This reality highlights the critical importance of foundational hardware protections. Building envelope efficiency in edge computing units and maintaining hardware lifespan and network reliability outside of traditional facilities requires incredibly smart infrastructure design. Robust building envelopes and intelligent cooling mechanisms are necessary first steps, but they must be paired with an equally resilient power strategy to prevent catastrophic hardware failure from local grid fluctuations.
To mitigate these risks effectively, IT operations teams must move away from reactive troubleshooting and standardize their physical hardware deployments across all edge locations. Localized compute nodes need immediate protection against voltage sags, sudden surges, and rolling blackouts. Standardization simplifies maintenance protocols and ensures a consistent baseline of reliability across the entire distributed network.
Deploying enterprise-grade backup power is the most effective way to ensure smooth operations in these vulnerable locations. For example, standardizing remote power infrastructure using power backup technology like Eaton UPS ensures that distributed networks receive clean, uninterrupted power to either safely shut down critical software systems or bridge the gap until secondary utility power comes back online. By treating the physical hardware layer with the same importance as software observability, organizations can drastically reduce their vulnerability to unpredictable local power anomalies.
Building a resilient edge infrastructure requires a strategic, unified approach to power management. Operations teams should consider the following foundational steps when designing and maintaining their distributed networks:
Ultimately, the long-term success of a distributed network depends on much more than just advanced software deployment tools and hybrid cloud observability. True resilience is built from the ground up. By acknowledging the physical realities of remote deployments, understanding the steep financial risks associated with system downtime, and investing in high-quality continuous power hardware, operations teams can create edge computing environments built to last.
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]]>The post What Killer Applications Are Driving Quantum Computing? appeared first on Techso Mogro.
]]>When recently asked about the technology, IDTechEx Technology Analyst and quantum expert Noah El Alami made his picks for this emerging field, with quantum computing hardware sales expected to reach USD $21 billion in the next two decades. A recent IDTechEx report listed the industries where quantum computing can provide real solutions. The graphic in that report appears below.
Quantum computing use cases already exist for chemistry and material sciences, developing new pharmaceuticals and electric vehicle batteries. Vancouver-based D-Wave, an early quantum computing platform, has developed a growing ecosystem of quantum algorithm developers to support food, manufacturing, space, and telecommunications clients.
But where quantum computing talk presently centres is in cybersecurity. Why? Because quantum technology is seen as a game-changer, capable of breaking state-of-the-art encryption used today for internal digital security and on the Internet.
There are, however, other quantum technology headlines in the news that I thought were worth taking a look-see.
Ultra-fast photonic chips are about to kickstart quantum computing. At Julius Maximilian University and the University of Würzburg in Germany, researchers are inventing a photonic chip to control and direct quantum light beams used to carry data without degradation. Led by Andreas Pfenning and Sven Höfling, the chip generates photons where and when needed to allow for synchronous transmission.
The technology is critical in developing complex quantum circuits. The new chip can control light signals at high speed. It integrates home-grown crystals using thin-film molecular beam epitaxy fabrication with a barium titanate ferroelectric material to generate quantum light on a chip.
Applications include telecommunications and optical signal processing.
At the Institute for Quantum Computing at Canada’s University of Waterloo, a PhD student, Alex Maierean, who is also CEO of Phantom Photonics, a new startup, is developing quantum sensors to operate in extreme environments where GPS cannot go. This includes the insides of buildings, the deep ocean, caves, and outer space.
The technology uses quantum LiDAR-style sensors and single-photon detection for high-precision remote sensing and navigation. A tailored light pulse that gets sent and returns is captured, providing detailed timing and phase information to reconstruct location and distance.
This makes it possible to map GPS-inaccessible geography, enabling the building of high-resolution 3D maps for practical applications such as:
Submarine and underwater drone navigation without the requirement for resurfacing to acquire GPS coordinates.
Spacecraft navigation where satellite positioning, navigation and timing systems like GPS are intermittent or intentionally blocked.
Maierean is currently working on getting the device down to chip size.
Quantum computing is being used to accelerate commercial fusion technology, specifically in creating plasma simulations to help develop next-generation reactor containment chambers.
In 2025, the Electric Power Research Institute (EPRI), an independent, non-profit energy research and development organization, announced the Fusion Quantum Challenge aimed at showcasing new ideas to advance commercial fusion energy technology.
Three winners were announced. First-place was awarded to a team from Missouri State University, led by Dr. Ridwan Sakidja, Professor of Physics, Astronomy and Material Sciences. They used quantum computing to focus on sparse approximations to reduce the complexity of data structures while retaining essential properties, as well as compression techniques to design new materials for use in commercial fusion reactors.
Other winners included an independent researcher, Justus Lau, for his submission focused on radiation-resistant materials. In the plasma category, Kory Burns from the University of Virginia was the winner, applying quantum-driven controls to stabilize plasma within fusion chambers.
Gilles Brassard, a pioneer in quantum cryptography from the University of Montreal, and Charles H. Bennett, an IBM Fellow and American physicist, are co-recipients of this year’s Turing Award. Described as the Nobel Prize of computing, it is given to individuals who have advanced the theoretical and practical applications of the world of computing.
Brassard and Bennett were recognized for their foundational work in quantum information science, quantum teleportation and entanglement. In 1984, the two invented the BB84 protocol, enabling secure key exchanges across insecure channels. It remains in use to this day for fibre-optic, free-space and real-world quantum key distribution (QKD) networks.
The award, which comes with USD $1 million, marks the first Turing given for work in quantum computing, a good way to end this posting.
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]]>The post While On Vacation In The Bahamas I Observed One Computing Password Crunch After Another appeared first on Techso Mogro.
]]>I know that online identification and passwords are there to stop cybercriminals. Today, more companies require two-factor identification. In the last month alone, I think I have been asked more than a dozen times to check my mobile phone for a text message containing a unique string of numbers that I then have to type in to prove I am me.
My phone has a fingerprint and facial recognition ID. The facial recognition doesn’t work in low-light conditions, and the fingerprint has never worked. Instead, I type in a personal identification number or PIN more often than not to get my mobile online presence started.
I could blame my increasing frustration on old age. I am 77. There is more to this, however, than my senescence. A February 23, 2026, article in The Register, a British tech newsletter that appears regularly in my inbox, describes the growing password challenge.
Did you know that passwords were first used in 1961? Since MIT introduced them, Rupert Goodwins writes, “We are now stuck with this godless world. Passwords have hit retirement age, yet show no signs of going away, voluntarily or forcibly. They are, unhappily, getting worse at their job.”
Why is that? Escalating cyber threats is the common refrain of computer geeks. They describe artificial intelligence (AI) as the latest threat. It is no longer just human cybercriminals doing the phishing, stealing online credentials, and compromising machine identities.
Today, online access to your financial information, business dealings, health records, and more comes with layered defences like multi-factor authentication (MFA) and anomaly detection. One of the reasons I looked for a universal single sign-on (SSO) application for my desktop computing system was to avoid the hassle of repeatedly having to remember my passwords for the innumerable apps I have accumulated over the past 40 years.
I remember in my working life being introduced to the security dongle. These small pieces of hardware were similar to the streaming devices used for television today to access Netflix, Prime, Britbox and AppleTV. The dongle plugs into a computer USB port. It contains software bearing a unique ID or encrypted data and with the user responding to several identification and validation requests. Dongles are like house keys but harder to copy. The downside to them is if you forget to keep it with you, you are blocked, whether working on site or at home.
That’s why SSO is more attractive to the average user seeking a level of security that bypasses the multiple layers of security defences we face today just to access online banking and other common activities.
When I started using SSO, I thought my userID and password issues would vanish. They have not. Why? If an SSO gets breached, it is even less secure than the multiple identifiers and passwords you may have written down on Post-It notes stuck to your desk or computer screen. Instead of one breach, you create a massive single point of failure, giving cybercriminals access to everything in your online identity.
Goodwins writes about the most recent “new wrinkles” in password security. He describes them as “too-clever-by-half.” The goal is to create invulnerability with AIs now being used to generate strong passwords. Goodwins says they may look “invulnerable to compromise,” but “are less than perfect after all.”
Digital sovereignty is a growing issue in the Trumpian world, with Microsoft, Apple and Google, all American companies, providing most of the online services we rely on today. Goodwins asks what if these American companies withdraw access to their services on the behest of the President? Annoying Trump could see all your passwords and access vanish.
Then there is the question of establishing a single security standard for SSO. Companies like Google, Microsoft and Apple profit from proprietary systems even for SSO. They don’t want a universal standard that infringes on profits.
It will get worse before it gets better states Goodwins. AI will ensure the former. He argues that using AI to generate passwords means introducing “a global orgy of info-swapping among the robots, without a single silicon condom in sight.” Goodwins advises not to use it rather than share your digital presence “with a universe of mischievous djinn.” If you don’t understand the reference, Goodwins suggests you watch the Sorcerer’s Apprentice, a scene in Disney’s movie, Fantasia, as a refresher.
Today’s two-factor identification is an attempt to overcome the weaknesses of current password-based online security. Using two-factor means the perpetuation of older security identifiers like the PIN. It still works when backed up by three-strike security lockouts. What’s obvious to me, however, after this last week’s online challenges, is that nobody I ended up talking to asked me for my PINs. So, clearly, many working in banking and the airline industry no longer see PINs as reliable.
Meanwhile, a fingerprint sensor on your mobile device is not easily duplicated without purchasing an extra device to plug in. And facial recognition doesn’t easily transfer from a mobile phone to a desk or laptop. In addition, facial recognition faces numerous hurdles. It is skin-tone biased, recognizing lighter complexions than darker. It works better with men than women, younger than older. It performs poorly in low lighting or if the face it is viewing is on an angle. And finally, AI can be used to generate a 3D mask that fools the best facial recognition systems available today.
Goodwins states, “passwords are broken.” He advises that security specialists need “to sit in a room for a month and fix it.” That shouldn’t involve using an AI band-aid, but rather requires agreement on creating a universal standard. In the meantime, I continue to use my SSO at home and Google when I travel and feel the frustration that comes when these security ID systems break.
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]]>The post The 2001 HAL Glass Memory Becomes A Reality appeared first on Techso Mogro.
]]>In Stanley Kubrick’s 2001: A Space Odyssey, the spaceship Discovery featured a central computer called HAL. The name was created by transposing the letters from IBM, shifting each by one character. I became H, B became A, and M became L. On the mission to Jupiter, HAL goes rogue, killing the crew except for David Bowman, who stops the computer by removing a series of clear glass panels from the computer’s central processor to degrade its AI functions.
The technology depicted in the movie was non-existent in 1968 at the time of its release. Today, however, glass-based data storage is being demonstrated as feasible, with Microsoft’s Project Silica describing high-density, long-term archival use. An article entitled “Laser writing in glass for dense, fast and efficient archival data storage” appeared this month in the online edition of the journal Nature, describing how optical storage using glass and lasers can create long-term, robust media capable of surviving for millennia.
The article discusses the progress since 2019 to extend the shelf-life of data storage media using quartz/borosilicate glass. Femtosecondoptical archival storage technology involves directing a femtosecond laser to write in glass, creating archival storage called Silica. Using it, Sillica is capable of archiving 4.8 Terabytes of data per 120 square millimetres in a 2 millimetre-thick piece of glass.
This development cannot come soon enough as cloud storage and AI necessitate an enormous increase in demand, making past technologies inadequate.
When I was an undergraduate in university, I was allowed to work with the University of Toronto’s IBM mainframe computer. My project tracked the coins found in buried caches discovered along the Mediterranean Sea during the period of the Roman Empire. I used Hollerith cards to create the program. Each card featured 80 columns by 12 rows and stored a limited amount of data. To create and run my program, I punched holes into the appropriate places on the card. I then stacked them in the right order and fed them into the IBM in a process called batch processing. That produced a printout. The cards stored the program. That was the state of the technology in 1968, when 2001 produced a vision of an entirely different futuristic storage medium.
Data storage today suffers from best-before dates. The hard disk drive in your computer or laptop provides archival storage of pictures and files. It is a ROM drive, meaning it provides read-only memory. These drives use magnets to align digital bits and bytes. On my home desktop, I have a 2-Terabyte hard drive and a similar-sized backup drive. I also have a DVD-CD-ROM drive to store removable data. The average shelf life of hard disks is 3 to 10 years because the motors used to spin them eventually fail.
On the Chromebook I am using to write this posting while away on vacation, I have a flash drive. Flash drives are solid-state ROM technology that retain information without power, relying on floating-gate transistors to trap bits and bytes. Flash drives are faster in retrieving archived material than hard disks. Typically, however, flash drives on personal laptops are never as capacious as hard drives. The typical Chromebook flash drive stores between 32 Gigabytes and 128 Gigabytes of data. Larger flash drives can be found in data centres catering to cloud computing and artificial intelligence (AI). These drives can approach 200 Terabytes in capacity.
Another form of temporary storage is called RAM, random-access memory. Temporary storage of this type comes in two flavours, DRAM and SRAM. This type of memory uses semiconductor capacitors and transistors for handling data in the moment. Computers without DRAM and SRAM cannot work. That’s because DRAM and SRAM provide the working memory for the computer’s central processor (CPU). Today, DRAM and SRAM offer high-density, low-cost memory, but remember things only in the moment, not permanently.
Other forms of data storage include magnetic tape, the same type of tape that gets used in tape recorders. Magnetic tape is still used for archival cold storage. If you still have a Blu-ray video and music player, this technology uses laser discs with phase-change layers embedded in polycarbonate. They are great for watching movies or playing video games.
In my past business life, I worked for Legacy Storage Systems, a company that built storage arrays. One of these was called a RAID, which stands for Redundant Array of Independent Disks. RAIDs combine multiple hard, magnetic tape and solid-state storage distributed in an array managed by software and hardware controllers to provide quick data retrieval.
| Technology | Typical Shelf Life (Unpowered/Cold Storage) | Notes |
|---|---|---|
| Flash Drives | 1-10 years | Charge leakage; shorter offline. |
| Hard Drives | 3-10 years | Lubricant dry-out, motor failure risks. |
| Magnetic Tape | 15-30+ years | Optimal if cool/dry; enterprise choice. |
| Optical Laser (CD/DVD) | 2-10 years (standard); 50+ (M-Disc) | Dye degradation vs. stable layers. |
| Blu-ray disks | 20-50+ years | Inorganic layers resist decay. |
| Glass/5D Optical | 1,000-10,000+ years | Heat/fire resistant; archival future. |
Within this decade, expect glass storage to replace the above-mentioned data storage technologies. There are still issues to overcome, but glass storage is just too promising in its potential to meet growing data needs. The lifespan alone makes glass for cloud computing the best in show.
Instead of pixels, data is encoded using voxels. Data reads and writes use polarized light microscopy, a recent innovation.
Glass is an abundant material. Other than its newness, adoption will only be held up by glass storage feeds and speeds. Glass storage access is slower than the older technologies mentioned above, but I suspect that problem will soon vanish.
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]]>The post Have You Heard About The Internet Archive And The Wayback Machine? appeared first on Techso Mogro.
]]>“Is there a place on the Web that serves as a universal library containing all the content ever posted to sites or digitized from print media, books, film and video, and audio sources that is free and open for anyone to use?”
My follow-up question was:
“Can I add my content to it?”
The search results pointed to the Internet Archive and Wayback Machine. I had to learn more and set up an online account, which cost nothing.
So, what is the Internet Archive?
It is a non-profit digital library representing the largest collection of existing digitized print materials, web pages, software, music, audiovisuals and other forms of media. It is an online resource that is searchable and open for general public use.
The Archive began back in 1996. The first web page was captured on May 10th of that year. Today, it is being continually updated by web crawlers. The first of these was launched in October 1996.
A web crawler, if you are unfamiliar with the term, is a piece of software code or algorithm written to seek out keywords on specific subjects, topics, and phrases, and deliver the results to a designated recipient.
If you want to use your own web crawler, you don’t need to write any code. Google Alerts provides a free web crawler-based service that dumps the results from keyword and phrase searches into your email.
The Archive’s Wayback Machine lets users access more than one trillion websites found in the Internet Archive. The Archive contains older versions of websites as well as current ones. The older dates back as far as 1996. Imagine how much the content and appearance of corporate websites have changed since 1996.
The Archive’s content is enormous. A quick inventory currently includes:
If you choose to become a user, you can add to the Archive’s content, a way to permanently preserve your digital legacy.
Using the Wayback Machine for searches is easy. Type the URL or words associated with a site you seek, and it delivers.
Use Internet Archive Scholar to see research papers and articles dating back to the 18th century. Internet Archive Scholar offers a distinct advantage over services like Google Scholar, which I often use for background research for this blog. The problem with the Google tool is that the results I usually get are abstracts that summarize the research, while the entire article remains inaccessible behind a paywall. That doesn’t happen with Internet Archive Scholar.
My search for universal knowledge repositories came across another source, the Universal Digital Library. This is a project with similar ambitions to the Internet Archive. Its focus is the digitization of academic books and documents, and was started by Carnegie Mellon University. Today, new contributions come from 8 other American, 8 Chinese, and 10 Indian universities and European academic institutions as well.
The initial Carnegie Mellon goal had been to digitize a million books in multiple languages by 2007. Today, that number has grown to more than 1.5 million in more than 20 languages, with the library increasing by 7,000 new books daily.
A repository where you can share your knowledge and digital legacy that shouldn’t be forgotten is Wikipedia. This is the online encyclopedia published in 343 languages. It has killed off most encyclopedia publishers except for Britannica and World Book.
Having grown up in a house with both of these print encyclopedias on our shelves, it is hard to believe that the former no longer publishes a print edition while the latter still does, the last one standing, backed by the largesse of Berkshire Hathaway.
Elon Musk recently launched his Wikipedia killer, Grokipedia, an attempt to supplant the former because he claims it has an inherent liberal bias. The recent launch of Grokipedia contains 800,000 entries and pales in comparison to the millions of entries to be found in Wikipedia. It also appears to have used its AI to grab Wikipedia content. That is bound to be a lawsuit in the making.
Wikipedia also offers a repository of freely usable media, called Wikimedia. This is another place where you can deposit your digital legacy of images, videos, sounds, diagrams and more.
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]]>The post From Spreadsheets to AI: Tax Season is Being Digitally Transformed appeared first on Techso Mogro.
]]>Artificial intelligence (AI), automation, and cloud-based accounting tools are transforming the process into something faster, more accurate, and far less stressful. And this change isn’t just happening in the United States. Canada, the European Union, Australia, and many other regions are also modernizing how taxes are prepared and filed.
Not long ago, most taxpayers and accountants relied heavily on manual data entry. Every expense had to be recorded by hand, and one small mistake could throw off an entire return. Those spreadsheets were useful, but they required constant attention and updates.
Now, modern tax software connects directly to your bank accounts, payment platforms, and accounting systems. Instead of entering every number yourself, transactions are imported automatically, categorized correctly, and ready for review. This not only saves time but also helps you avoid the common errors that come from doing everything manually.
AI is changing the way you approach taxes. In the U.S., tools like TurboTax and H&R Block already use AI to identify possible deductions, detect inconsistencies, and suggest personalized advice. But the benefits go well beyond one country.
In Canada, AI-powered platforms such as Wave and FreshBooks help small business owners calculate GST/HST, track expenses, and project their tax obligations.
Across the European Union, AI helps manage complex VAT rules that differ between member countries.
In Australia, the Australian Taxation Office uses machine learning to catch fraudulent filings and improve refund accuracy.
These innovations mean you can spend less time trying to interpret tax codes and more time focusing on strategy and growth. AI takes care of the repetitive work while you focus on the bigger picture.
Many countries are moving toward real-time tax reporting. Instead of waiting until the end of the year to submit everything at once, systems now collect and process information as it happens. The EU’s e-invoicing and VAT reporting requirements, Canada’s digital filing systems, and Australia’s Single Touch Payroll initiative are all examples of this shift.
If you’re running a business, this means you need to stay organized and up to date throughout the year. Cloud accounting software helps you do exactly that by syncing your data, generating reports instantly, and keeping everything ready for submission when needed.
The digital transformation of tax season has been especially helpful for freelancers, contractors, and small business owners. They no longer have to juggle multiple tools or guess which expenses qualify as deductions. Many tax apps now handle income tracking, expense categorization, and quarterly payment estimates automatically.
If you are self-employed, tools that simplify tax management can save countless hours. For instance, in the U.S., you can use a self-employed taxes calculator to get a clearer picture of your estimated liabilities and deductions.
Similar tools in Canada and Australia help with GST and BAS submissions so that you can stay compliant without the stress.
Remote work and global freelancing have made tax compliance more complicated. You might be based in Canada but work with clients in the U.S., or live in Australia while managing contracts in the EU. Each country has its own tax treaties, currencies, and reporting standards, which can make things confusing.
AI-driven software can help you navigate all of that. Many tools now recognize where income comes from, calculate the right withholding rates, and generate the correct forms for each jurisdiction. They can even help determine your tax residency status. This is becoming more important as global tax coordination increases through efforts like the OECD’s Common Reporting Standard.
Of course, digital transformation also means you need to think about data security. When your financial information lives in the cloud, you have to make sure it’s protected. The best tax software now includes encryption, two-factor authentication, and regular security audits.
Governments are also taking privacy more seriously. Regulations such as the EU’s GDPR and Australia’s Privacy Act establish clear guidelines for the collection and storage of data. When you choose a reputable tax platform, you’re not just saving time—you’re also protecting your personal and financial information.
The next big step in this transformation is predictive tax intelligence. Instead of simply reacting to your financial situation, AI tools will soon be able to predict it. Imagine software that estimates next year’s tax liabilities based on your spending habits, or suggests adjustments that could lower your taxes before you even file.
You’ll see this kind of technology expand globally as more tax authorities embrace automation. From the U.S. to Australia, the goal is the same: to make tax filing easier, faster, and more transparent for everyone.
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