Daily Tech Digest - July 01, 2020

The Future Of Work Is Not What You Think

Technology in its broadest sense is having profound impacts on society – both good and bad – as it always has. Much of the research points towards an acceleration of technology impact and much more profound structural changes than before. The growth of platforms, ecosystems, what I call ‘self’ technologies – those technologies that complete actions ‘by themselves’ (AI, ML, RPA, Blockchain, Nano, Smart etc) – along with advances in biotechnology are already having strong impacts to how society works, and how work works. The explosion of data and the promise of quantifying everything is already creating new challenges around privacy and what is appropriate to track and measure. Many of these technologies are creating fundamental contradictions that are new and will need significant creative thinking in how best to extract true net benefits. Platform technologies supported by growing ecosystems are fully enabling sustainable ‘Self Careers’. A plethora of tools are widely available for individuals to deliver quality output, design their employment journeys and create their own portfolios of work.

Challenges facing data science in 2020 and four ways to address them

Despite the popularity of open-source software in the data science world, 30% of respondents said they aren't doing anything to secure their open-source pipeline. Open-source analytics software is preferred by respondents because they see it as innovating faster and more suitable to their needs, but Anaconda concluded that the security problems may indicate that organizations are slow to adopt open-source tools. "Organizations should take a proactive approach to integrating open-source solutions into the development pipeline, ensuring that data scientists do not have to use their preferred tools outside of the policy boundary," the report recommended. ... Ethics, responsibility, and fairness are all problems that have started to spring up around machine learning and artificial intelligence, and Anaconda said enterprises "should treat ethics, explainability, and fairness as strategic risk vectors and treat them with commensurate attention and care."  Despite the importance of addressing bias inherent in machine learning models and data science, doing so isn't happening: Only 15% of respondents said they had implemented a bias mitigation solution, and only 19% had done so for explainability.

Apple Watch, Fitbit data can spot if you are sick days before symptoms show up

The current study, which is a collaboration between Stanford Medicine, Scripps Research, and Fitbit, will use data gathered from the wearables to create algorithms that can detect the physiological changes in someone that show they're coming down with an infection, potentially before they even know they're sick. Once the signs of infection -- such as an increase in resting heart rate -- have been detected, the user will be alerted through the app that they may be getting sick, allowing them to self-isolate earlier and so spread the infection to fewer people. The lab has been investigating the potential of wearable devices to shed light on changes in users' health for some years. Researchers published a study in 2017 that showed devices could pick up changes in physical parameters before the wearer noticed any symptoms.  The algorithm from that research, known as 'change of heart', detected that changes in heart rate could signal an early infection, and the lab is now building on that research for the current pandemic. "We continued to improve the algorithm, then when the COVID-19 outbreak came, as you might imagine, we started scaling at full force," Michael Snyder, professor and chair of genetics at the Stanford School of Medicine, told ZDNet.

9 career pitfalls every software developer should avoid

It seems easy and safe to become an expert in whatever is dominant. But then you’re competing with the whole crowd both when the technology is hot and when the ground suddenly shifts and you need an exit plan. For example, I was a Microsoft and C++ guy when Java hit. I learned Java because everyone wanted me to have a lot more experience with C or C++. Java hadn’t existed long enough to have such requirements. So I learned it and was able to bypass the stringent C and C++ requirements, and instead I got in early on Java. A few years back, it looked like Ruby would be ascendant. At one point, Perl looked like it would reach the same level that Java eventually did. Predicting the future is hard, so hedging your bets is the safest way to ensure relevance. ... “I’m just a developer, I don’t interest myself in the business.” That’s career suicide. You need to know the score. Is your company doing well? What are its main business challenges? What are its most important projects? How does technology or software help achieve them? How does your company fit into its overall industry? If you don’t know the answers to those questions, you’re going to work on irrelevant projects for irrelevant people in irrelevant companies for a relatively irrelevant amount of money.

Top 13 Challenges Faced In Agile Testing By Every Tester

Speaking strictly in business terms, time is money. If you fail to accommodate automation in your testing process, the amount of time to run tests is high, this can be a major cause of challenges in Agile Testing as you’d be spending a lot running these tests. You also have to fix glitches after the release which further takes up a lot of time. Automation for browser testing is done with the help of Selenium framework, in case you’re wondering, What is Selenium, refer to the article linked. ... Most teams emphasize maximizing their velocity with each sprint. For instance, if a team did 60 Story Points the last time. So, this time, they’ll at least try to do 65. However, what if the team could only do 20 Story Points when the sprint was over?  Did you realize what just happened? Instead of making sure that the flow of work happened on the scrum board seamlessly from left to right, all the team members were concentrating on keeping themselves busy. Sometimes, committing too much during sprint planning can cause challenges in Agile Testing. With this approach, team members are rarely prepared in case something unexpected occurs.

Brute-Force Attacks Targeting RDP on the Rise

Since the start of the COVID-19 pandemic, the number of brute-force attacks targeting remote desktop protocol connections used with Windows devices has steadily increased, spiking to 100,000 incidents per day in April and May, according to an analysis by security firm ESET. By waging brute-force attacks against RDP connections, attackers can gain access to an IT network, enabling them to install backdoors, launch ransomware attacks and plant cryptominers, according to ESET's analysis. RDP is a proprietary Microsoft communications protocol that allows system administrators and employees to connect to corporate networks from remote computers. With the COVID-19 pandemic forcing employees all over the world to work at home, many organizations have increased their use of RDP but have overlooked security concerns. "Despite the increasing importance of RDP, organizations often neglect its settings and protection. When employees use easy-to-guess passwords, and with no additional layers of authentication or protection, there is little that can stop cybercriminals from compromising an organization's systems," Ondrej Kubovič, a security analyst with ESET, notes in the report.

3 CIOs talk driving IT strategy during COVID-19 pandemic

As a result of the COVID-19 pandemic, many IT leaders faced the challenge of having to transition organizations to a work-from-home environment. In less than 10 days, the technology and operations team at Travelers Insurance managed to get the company that has offices all over the world to being fully online, with almost 100% of employees working remotely, said Lefebvre. In addition to having access to digital capabilities, the IT department's ability to respond quickly and effectively was in large part a result of building and engineering a culture with deep expertise, according to Lefebvre. Tariq echoed this during the online panel, describing the importance of having a culture that allows for a more innovative mindset as part of the IT strategy. "Like any organization, we focus on results, [but] we also equally focus on creating a healthy and inclusive culture -- a culture where every team member feels that they have a voice, they are heard, where they can be themselves and be their best," he said. "[It's] a culture that is focused on continuous improvement and value generation. When you do that, magic happens."

Smart cities will track our every move. We will need to keep them in check

For James, it is key to make sure that citizens trust the organizations that own the data about them. "We need to know how this data is governed, who owns it, and who has access to the platform that does it," he says. "Otherwise, there is a risk that you won't bring citizens along with you." James points to the smart city initiative led by an Alphabet-owned urban design business in Toronto. The project was recently axed due to the economic uncertainty caused by the pandemic, but was already running into a series of problems because of backlash from privacy concerned leaders who were worried about surveillance. Ensuring public trust, therefore, is critical; and especially because the cost of abandoning smart city technology, in the context of COVID-19, will be far greater than in normal times. "You have to think about what happens, in the long term, if you don't implement these processes," says James. Smart sensors and IoT devices won't only be used by city planners to monitor the immediate impact of measures linked to the pandemic. In the next few months, they will also be key to the recovery of local businesses, as policy makers start identifying where residents work, shop, eat out or go for drinks.

For data scientists, drudgery is still job #1

Despite all the advances in recent years in data science work environments, data drudgery remains a major part of the data scientist’s workday. According to self-reported estimates by the respondents, data loading and cleaning took up 19% and 26% of their time, respectively—almost half of the total. Model selection, training/scoring, and deployment took up about 34% total (around 11% for each of those tasks individually). When it came to moving data science work into production, the biggest overall obstacle—for data scientists, developers, and sysadmins alike—was meeting IT security standards for their organization. At least some of that is in line with the difficulty of deploying any new app at scale, but the lifecycles for machine learning and data science apps pose their own challenges, like keeping multiple open source application stacks patched against vulnerabilities. Another issue cited by the respondents was the gap between skills taught in institutions and the skills needed in enterprise settings. Most universities offer classes in statistics, machine learning theory, and Python programming, and most students load up on such courses.

Deep learning's role in the evolution of machine learning

"There are many problems that we didn't think of as prediction problems that people have reformulated as prediction problems -- language, vision, etc. -- and many of the gains in those tasks have been possible because of this reformulation," said Nicholas Mattei, assistant professor of computer science at Tulane University and vice chair of the Association for Computing Machinery's special interest group on AI. In language processing, for example, a lot of the focus has moved toward predicting what comes next in the text. In computer vision as well, many problems have been reformulated so that, instead of trying to understand geometry, the algorithms are predicting labels of different parts of an image. The power of big data and deep learning is changing how models are built. Human analysis and insights are being replaced by raw compute power. "Now, it seems that a lot of the time we have substituted big databases, lots of GPUs, and lots and lots of machine time to replace the deep problem introspection needed to craft features for more classic machine learning methods, such as SVM [support vector machine] and Bayes," Mattei said, referring to the Bayesian networks used for modeling the probabilities between observations and outcomes.

Quote for the day:

"Don't be buffaloed by experts and elites. Experts often possess more data than judgement." -- Colin Powell

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