One issue relating to neural network–based machine learning–enabled AI applications in investment management is the black box issue, in which the workings of an algorithm are not understood by its user or other stakeholders and lead to potentially unintended actions or consequences. This is a well-known headache for regulators trying to ensure market stability. Although some attempts have been made to check the source code of algorithmic traders, the most effective protection against algorithmic errors are circuit breakers on markets that limit the amount of damage a failing algorithm can cause. We highlighted in our response to the EC’s consultation document on fintech an example from the world of algorithmic trading on the use of circuit breakers as a de facto solution to the black-box challenge.
The better you know how to manage projects, the easier it will be to take on bigger challenges and contribute more to your company’s success. While it’s important to develop effective project management skills, you must also learn to recognize the common pitfalls that can quickly derail your efforts. By doing so, you’ll have no problem dodging these issues and letting your superior project management skills speak for themselves. Always take a top-down project management approach. This will ensure you’re focused on who will be leading various elements of the project. While every other part of your project is important (e.g., who else will be involved, what resources you’ll need, etc.), if you don’t pick strong team leads, you may be setting the project up for failure.
Common issues include the lack of cloud economics and complexity leveraging existing infrastructure. While these experts are certainly raising real concerns about the validity of SDS, we must first take a step back to understand the root of these issues; that being said: the promise of the technology will only align with the hype once the industry can agree on a set definition of what it means to be truly “software-defined.” In order to bridge the gap of understanding these nuances of SDS, we need to agree on a definition once and for all to help us look past the industry jargon to truly get to the heart of the technology and how it can help companies achieve simple and cost-effective data management. Simply put, SDS is an approach to data storage in which the programming that controls software-relate tasks is decoupled from the physical storage hardware.
We've seen the steady shift from SMAC (social, mobile, analytics, cloud) that dominated this list at its inception to one that is more focused on artificial intelligence, Internet of Things, distributed ledgers, immersive digital experiences (AR/VR), edge computing, low code tools, and much more. That's not to say that essentially mainstream technology bases like public cloud, cybersecurity, or big data are staid and therefore are about to come off the list. In fact, they are shifting and evolving more now than ever before and should remain at the top of the technologies that most enterprises should be watching very closely today. Based on my analysis then, here is the short list of enterprise technologies that organizations should be tracking for building skills, assessing their strategic and tactical impact, experimenting with
Lauded as the next level of distributed internet, blockchain technology enables unprecedented transparency, efficiency and flexibility in terms of facilitating transactions without a centralized control or administration. The distributed ledger technology has powered cryptocurrencies like bitcoin and Ethereum, but it is not only in fintech where it shines. Any industry that involves peer-to-peertransactions will also benefit from its distributed and peer-authenticating features. By taking away the middleman or central authority in establishing trust, and by ensuring that transactions can be audited by any and all parties involved, the blockchain provides a perfect way to protect against potentially fraudulent transactions. That's not to say that the tech is immune from external security threats, but the system in itself provides trust intrinsically.
Consumers should probably go ahead and assume that at one point or another their personal details will likely be breached by an online hacker. With nearly every possible part of people’s lives living online, and sometimes on an internal intranet as well, it’s safe to assume that nothing is truly, well, safe. Beyond the usual advice of changing passwords upon hearing of data breaches, there’s not much consumers can really do. With all of this knowledge, the issue that then arises for retailers is how to bring consumers back to the path of purchasing — post-hacking occurrence. Typical advice from top security officials can range from being careful with third-party vendors to giving consumers more control over their own data. Consumer insights company Diginomica’s co-founder, Jon Reed, offered a succinct way for retailers to gain and maintain consumer trust.
Thibodeaux clarified that a tech skills gap isn't necessarily the problem, but instead a confidence gap is what prevents many women and people of color from pursuing jobs in the tech industry. TechRepublic's Hope Reese noted the women's confidence gap after watching University of Louisville's inaugural Women in Leadership Forum. "The panel agreed that women often judge themselves too harshly as well, saying 'I'm not good enough,' far more often than men," Reese wrote. A lack of career information is another big reason many professionals don't join the tech workforce, Thibodeaux said. Organizations need to provide career information to a more diverse group, Thibodeaux added, or else these individuals won't know to pursue tech careers.
Shipping a new version of a distributed, scale-out file system every two weeks means we have to be confident that every commit to our codebase is bug-free. To that end, we have tens of thousands of unit tests, thousands of integration tests, and many hundreds of full system tests that batter every build to verify that we haven’t introduced a regression. While these kinds of tests are important, we wanted to go further. We wanted a way to execute everypossible path through a particular piece of code so that we could verify that, no matter what, the behavior of the code was correct and the invariants of the system held. A traditional approach to this problem might be to manually inspect code coverage reports and craft individual tests to exercise each branch of the code, but this is brittle because it requires a human to inspect the code coverage and it often involves a complex, sometime convoluted test to exercise the uncovered code.
Before going any further, I’ll quickly explain the concept of code coverage for those not familiar, without belaboring the point. Code coverage measures the percentage of code that your automated unit test suite executes when it runs. So let’s say that you had a tiny codebase consisting of two methods with one line of code each. If your unit test suite executed one method but not the other, you would have 50% code coverage. In the real world, this becomes significantly more complicated. In order to achieve total coverage, for instance, you must traverse all paths through control flow statements, such as if conditions, switch statements, and loops. When people debate how much code coverage to have, they aren’t debating how “correct” the code should be. Nor do they debate anything about the tests themselves, such as quantity or quality.
A cyber disruption has a duration to it, a reach to it that is bigger than a typical incident. Where an incident might last hours, a cyber disruption would last days, weeks, months. Where a cyber incident might affect a certain application or certain small set of users, a cyber disruption has a bigger reach than that—it could affect an entire enterprise, it could affect a region, a city. The importance that we place on this in the first part of this document is differentiating a disruption—it’s very significant kind of an event. And we anticipate that it will probably be related to some major part of our infrastructure—power disruption, water, sewer, gas delivery, communications. I put a scenario in the front end of that report that describes loss of power and what happens if you find that you don’t have communications either, with no cell or mobile service. A very significant kind of event.
Quote for the day: "It doesn't matter how much we know, what matters is how clearly others can understand what we know." -- Simon Sinek