November 12, 2020

The Ever-Expanding List of C-Level Technology Positions

In decades past, it was relatively uncommon for IT leaders to be part of the top tier of executive management. Even those who held the title of chief information officer (CIO) often reported to someone other than the chief executive officer (CEO). But digital transformation has changed that. As enterprises seek new ways of doing business, CIOs have begun playing a bigger role in directing the overall strategy of the business. Several different surveys have found that more than half of CIOs now report to CEOs, and many CEOs list their CIOs as one of their most trusted advisors. ... However, while they might not be ascending to the top job, IT leaders are finding more opportunities to join the executive team. The twin trends of digital transformation and the rise of big data analytics has led many enterprises to create new C-level positions directly related to technology. In fact, some industry analysts have begun to wonder if organizations have created too many new C-level technology roles. Some are forecasting that in the years ahead enterprises might be re-vamping their org structure to cut back on these new C-level positions. But for now, IT leaders seem to have more opportunities to fill C-level roles than ever before.

Applying Lean and Accelerate to Deliver Value: QCon Plus Q&A

It is important to understand that delay degrades the economic value of what we deliver - there is a cost to delays, and it can be significant. Think about the loss of opportunity or revenue if a software product is delivered late, especially in a highly competitive market segment. Delays also slow down feedback, which makes it harder to adapt to new information. You can also incur significant risk of outages or customer turnover if features are delivered late. With this in mind, just as we spend so much time optimizing and tuning the latency and throughput of our software systems, we should spend time to optimize and tune the latency and throughput of our development process. It turns out when you look at the math and dynamics of product delivery pipelines, the biggest contributor to delay is letting queues back up. Unlike in manufacturing, these queues are invisible in software development, so it is important that we make an effort to make them visible, and then address them quickly and aggressively. Two powerful ways to reduce queues are limiting work in progress and keeping your batch sizes small.

Banking Trojan Can Spy on Over 150 Financial Apps

The Kaspersky researchers first came across the Ghimob Trojan in August while examining a Windows campaign related to another malware strain circulating in Brazil. "We believe this campaign could be related to the Guildma [Brazilian banking Trojan] threat actor for several reasons, but mainly because they share the same infrastructure," according to the report. "It is also important to note that the protocol used in the mobile version is very similar to that used for the Windows version." Unlike other types of Android-focused malware, the Ghimob Trojan does not disguise itself as a legitimate app that is hidden within the official Google Play Store. Instead, the fraudsters attempt to lure victims into installing a malicious file through a phishing or spam email that suggests that the recipient has some kind of debt, according to the report. The message includes an "informational" link for the victim to click on, which starts the malware delivery. The malicious link is usually disguised to appear as either a Google Defender, a Google Doc or a WhatsApp Updater, according to the report. If opened, it installs the Ghimob Trojan within the device. The malware's first step is to check for any emulators or debuggers which, if found, are terminated.

How to stress-test your business continuity management

“You really need to be in a position to mitigate against any potential risks both before a system is live, and afterwards, so there are no nasty surprises. End to end testing of every platform, both independently and in terms of its integration with the wider network of systems, is therefore critical. However this needs to be balanced against the need to deliver with speed and certainty – so strong automated testing should be seen as a standard component of your production systems. “This will usually be provided by an independent quality assurance specialist. At Expleo we actually automate this process for clients to account for the complexity and speed of the technology and release cycles. Automated testing not only safeguards quality, but also adds value by providing immediate speed and efficiency gains. “First, ML cuts through the testing workload and sieves the data at scale, surfacing the highest-priority test cases. Then, AI analyses this data in real-time, so we can respond to risks before they become issues. This is used as the basis for predictive analysis – so you can predict where risk is going to emerge and mitigate it in the most cost effective way.”

What's next for AI: Gary Marcus talks about the journey toward robust artificial intelligence

Marcus points out this is a really deep deficiency, and one that goes back to 1965. ELIZA, the first expert system, just matched keywords and talked to people about therapy. So there's not much progress, Marcus argues, certainly not exponential progress as people like Ray Kurzweil claim, except in narrow fields like playing chess. We still don't know how to make a general purpose system that could understand conversations, for example. The counter-argument to that is that we just need more data and bigger models (hence more compute, too). Marcus begs to differ, and points out that AI models have been growing, and consuming more and more data and compute, but the underlying issues remain. Recently, Geoff Hinton, one of the forefathers of deep learning, claimed that deep learning is going to be able to do everything. Marcus thinks the only way to make progress is to put together building blocks that are there already, but no current AI system combines. ... A connection to the world of classical AI. Marcus is not suggesting getting rid of deep learning, but using it in conjunction with some of the tools of classical AI. Classical AI is good at representing abstract knowledge, representing sentences or abstractions. The goal is to have hybrid systems that can use perceptual information.

Passage of California privacy act could spur similar new regulations in other states

The COVID-19 crisis has derailed a lot of legislative activity across the country, making it difficult to get a solid sense of where privacy initiatives are headed. “The challenge you're going to find is that post-pandemic most of the state legislatures said anything that's not COVID related is not being considered,” Stockburger says. After the pandemic recedes from its urgent priority status, many states could kick new legislative efforts into gear. “Next year, that's when you're going to see big new developments and introductions,” he says. ... Another question that remains is whether the federal government will step in to create a more consistent privacy law framework. In the past, Silicon Valley giants stood staunchly opposed to the stringent provisions of the CCPA and sought a national privacy law to preempt and water down the CCPA’s requirements. However, their resistance has weakened over the past several years. “At the federal level, there's just a real challenge in getting any type of omnibus legislative efforts pushed through,” Stockburger says. “That’s been a challenge since probably 2016 when the Democrats got whooped in the midterms, and since then, we've had divided Congress.”

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