Daily Tech Digest - November 04, 2023

AI’s Role In Payments 3.0: Balancing Innovation With Responsibility

At the core of the responsible use of AI is protecting individuals and their data. After all, one of the most personal things to people is their financial information. It’s critical for businesses to work with data experts who know how to keep customers’ private information safe while appropriately using payment data to enable personalized service. ... One of AI’s greatest advantages is its ability to scan and analyze large amounts of data and suggest or implement improved experiences related to payments. One could argue that AI might be able to handle these tasks better than humans, not only because it can do so quickly, but because it eliminates the biases humans can impose. However, is that true in practice? Unfortunately, not always. Responsible AI depends upon first choosing the most accurate, audited and unbiased data sources available. Then it must use a system of audits during the development and implementation of the machine learning model—and frequently thereafter—to detect and correct for inappropriate biased decision-making. Another compelling reason to weed out AI bias is compliance.

Decoding Kafka: Why It’s Worth the Complexity

First off, learning Kafka requires time and dedication. Newcomers might take a few days or weeks to grasp the basics and months to master advanced features and concepts. In addition, you need to constantly monitor and learn from the cluster’s performance as well as keep up with Kafka’s evolution and new features being released. Setting up your Kafka deployment can be challenging, expensive and time-consuming. This process can take anywhere between a few days and a few weeks, depending on the scale and the specifics of the setup. You may even decide that a dedicated platform team will need to be created specifically to manage Kafka. ... Kafka is more than a simple message broker. It offers additional capabilities like stream processing, durability, flexible messaging semantics and better scalability and performance than traditional brokers. While its superior characteristics increase complexity, the trade-off seems justified. Otherwise, why would numerous companies worldwide use Kafka? 

The Tech Gold Rush Is Over. The Search for the Next Gold Rush Is On

The tech industry will probably shrink, too. It will still be an important part of the economy, but employing fewer people and offering more normal returns. Now the question is where young people seeking wealth will turn next. From the Age of Discovery to the actual California Gold Rush to Snapchat, it is human nature to chase fortune where you can. It is a large part of what moves an economy forward. But these modern settings for this quest — the finance industry and the tech industry — are unusual in that they attracted many people who expected to get rich even if they lacked two things normally associated with extreme wealth creation: creating lots of value for the economy, and taking smart risks. The fact that anyone thinks things should be different is simply the result of the historically low interest rates of the last few decades, which helped make capital incredibly cheap. The price of capital is the price of risk, and if it is effectively zero, then it stands to reason that easy fortunes can be made risk-free.

To Improve Cyber Defenses, Practice for Disaster

The primary challenge organizations face when executing crisis simulations is determining the right level of difficulty, says Tanner Howell, director of solutions engineering at RangeForce. "With threat actors ranging from script kiddies to nation-states, it's vital to strike a balance of difficulty and relevance," he says. "If the simulation is too simple, it won't effectively test the playbooks. Too difficult, and team engagement may decrease." Walters says organizations should expand simulations beyond technical aspects to include regulatory compliance, public relations strategies, customer communications, and other critical areas. "These measures will help ensure that crisis simulations are comprehensive and better prepare the organization for a wide range of cybersecurity scenarios," he notes. Taavi Must, CEO of RangeForce, says organizations can implement some key best practices to improve team collaboration, readiness, and defensive posture. "Managers can perform business analysis to identify the most applicable threats to the organization," he says. "This allows teams to focus their already precious time around what matters most to them."

Going All-in With Evergreen Cloud Adoption Brings Its Own Challenges

An evergreen IT strategy on the other hand, keeps a cloud-based system online throughout to allow businesses to conduct smaller, more regular updates either weekly, biweekly, or monthly. These can be planned at optimum times to avoid downtime, support business continuity, and mitigate against lost profits. Disruption during transformation is always a risk, so incremental changes also mean pockets of disruption can be easier to isolate and resolve. An MSP expert has the time and knowledge necessary to craft a bespoke strategy for transformation, which builds-in operational resilience and offsets potential downtime. For instance, round-the-clock help desks mean businesses can access support and advice whenever they need it, to allow the fastest resolution of problems during the onboarding process. ... MSPs don’t just offer support during implementation, they have a wealth of systems knowledge that businesses can exploit to assist with automation updates, faults, and internal process reviews. For industries such as the manufacturing sector, downtime can account for 5% – 20% of working time, with lost productivity costing up to £180 billion a year. 

Ongoing supply chain disruption continues to take its toll

Firstly, there appears to have been little sign of easing on some supply chains, with 86 percent of respondents stating that they had experienced supply chain volatility over the past year, slightly down on the same level who reported the same in winter 2022 but similar to the levels who reported the same one year ago. Once again, the professionals responsible for delivering new data center facilities to the market have been significantly impacted by the volatility in the supply chain. Our survey of developer/investor respondents revealed that some 91 percent of them confirmed being significantly affected. This figure represents an increase from the 82 percent reported six months ago and 83 percent recorded a year ago. Notably, among our DEC stakeholders, the impact was more pronounced, with 93 percent expressing their strong agreement, compared to 70 percent in Q4 2022. Amongst our service providers, there is still a high level of agreement regarding this disruption albeit with some marginal easing; 92 percent stated that they had experienced such supply chain problems, down from 97 percent reported six months ago.

What Does a Data Scientist Do?

As the field of Data Science continues to evolve, so too does its potential to transform industries and revolutionize decision-making processes. While descriptive and predictive analytics have long been utilized to gain insights from historical data and make informed predictions about future outcomes, the current emphasis is on prescriptive analytics. Prescriptive analytics takes data analysis to a whole new level by not only providing insights into what might happen but also offering recommendations on how to make it happen. By leveraging advanced algorithms, machine learning techniques, and artificial intelligence, data scientists can now go beyond simply understanding patterns and trends. They can provide actionable suggestions that optimize decision-making processes. The impact of prescriptive analytics is far-reaching across numerous sectors. For example, in healthcare, it can help physicians determine personalized treatment plans based on patient data. In supply chain management, it can optimize inventory levels and streamline logistics operations. In finance, it can assist with risk management strategies.

From automated to autonomous, will the real robots please stand up?

Today's robots, despite not being as versatile as Mr. Data, are generally quite useful and functional. These include industrial robots, medical robots, military and defense robots, domestic robots, entertainment robots, space exploration and maintenance robots, agricultural robots, retail robots, underwater robots, and telepresence robots that help people participate in an activity from a distance. My personal interest has been focused on robots available and accessible to makers and hobbyists, robots that can empower individuals to build, design, and prototype projects previously only feasible by those with a shop full of fabrication machinery. I'm talking about 3D printers, which build up objects from layers of molten plastic; CNC devices, which often cut, carve, and remove wood or metal to create objects; laser cutters, which are ideal for sign cutting, engraving, and fabricating very detailed parts and circuit boards; and even vinyl cutters, for carefully cutting light, flexible material in intricate patterns. These machines are programmed using CAD software to define -- aka, design -- the object being built.

The Misleading Use of the ‘Technical Debt’ expression

The good software is the one that makes users happy and has the capacity to be improved when necessary. However some tech debts emerge from this need to evolve the product. I believe when the software does a good job in solving some problem, it probably needs to scale — as more people would use it, more features would be necessary and so on. When that happens, some characteristics of your software will need to change and something that usually works just fine will become a debt — If you don’t care about it soon enough — your great software will be called a legacy one. Software and its architecture should be evolutive. It’s important to map tech opportunities because it can become a technical debt in future but we don’t need to apply all immediately if it doesn’t fit the current scenario. I’ve seen many managers saying ‘From now on let’s never do a feature with debts’ — that’s for me a very incomprehension of the reality in software development. You will need to make debts to achieve the delivery time and have success in software and your business, but also debts will always emerge from a change of scenario of your product. 

What is a digital twin? And why is everyone suddenly building one?

Digital twins of vehicles, factories, and cities already exist, but could there ever be a digital twin of you? It’s very possible, especially as health technology, biosensors, and AI’s predictive ability improves. It’s easy to envision a day when every person has a digital health twin that mirrors our physical and genetic makeup and lifestyle, a doppelgänger we can feed hypothetical inputs to and see what effects they may have on our bodies. For example, a digital health twin could show us how adhering to a vegetarian diet would impact our specific body over the next 20 years. Or our digital health twin might be able to reveal the effect that physical stresses on our body will have as we continue to age—what another 10 years of sitting at our desk at work will do to our spine, for example. A digital twin may even be able to show us which treatment option for a disease is better for us by revealing how our unique body would react if we chose to undertake one treatment plan instead of another, say medication versus surgery. All this sounds far-fetched, but the digital twinning of organs and entire bodies is already well underway in the healthcare industry.

Quote for the day:

“Failure defeats losers, failure inspires winners.” -- Robert T. Kiyosaki

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