The decision on this complaint has clear implications for any website based in France that’s currently using Google Analytics — or, indeed, any other tools that transfer personal data to the U.S. without adequate supplementary measures — at least in the near term. For one thing, the CNIL’s decision notes it has made “other” compliance orders to website operators using Google Analytics (again without naming any sites). While, given joint working by EU regulators on these 101 strategic complaints, the ramifications likely scale EU-wide. The CNIL also warns that its investigation — along with the parallel probes being undertaken by fellow EU regulators — extends to “other tools used by sites that result in the transfer of data of European Internet users to the United States”, adding: “Corrective measures in this respect may be adopted in the near future.” So all U.S.-based tools that are transferring personal data are facing regulatory risk. We’ve asked the CNIL which other tools it’s looking at and will update this report with any response.
One of the challenges to securing dApps in the new Web3 world is engaging security professionals in a meaningful way. A number of the cybersecurity experts I follow on Twitter have been dismissive of Web3 and blockchain technologies as fads at best and scams at worst. I asked Spanier what it will take to get more of these folks to engage with Web3. “For security professionals, here’s some advice to figure out if blockchain security interests you,” he replied. “Treat your initial plunge as an exploratory journey. Look at different security issues that have manifested themselves in the past, be they with smart contracts or core blockchains. These projects are mostly open, so you can look at their Github issues and patches. Review vulnerability write-ups and deconstructions of previous attacks. Projects affected by a compromise will typically post detailed write-ups. This would be a good start.” There’s a lesson for developers here too. Because so much of what’s being developed for Web3 is done in a very public way, there’s an opportunity to avoid the mistakes of others. As you develop, consider doing a review of mistakes made by others a part of your release process.
It goes without saying that not everyone can be a maestro of everything. This is hugely applicable to IT businesses who tackle complex tasks across several technologies and infrastructures on a daily basis. It becomes clear that IT companies that are running their strategies solo are not taking advantage of the possible strengths of great IT vendor collaborations, which include several experts of different skills across a plethora of applications. In other words, it is likely that an IT partner with an appropriate talent and skillset would be capable of selecting the right solution for the client, rather than the primary vendor that does not specialise in the client’s specific needs. In order to develop and nurture productive partnerships, IT businesses must know when to partner, who to partner with, and how to add value to a partnership. A partnership is a value added relationship that develops over time based on the foundation of trust. It takes equal endeavour from both sides to evolve into an IT partnership enabling both the parties to share their ideologies, work cultures, expertise, and strategies.
A key agile principle to me is “Embrace Change”, the subtitle of XP Xplained by Kent Beck. Change is continuous in our world and also at work. Accepting this fact makes it easier to let go of a decision that was taken once under different circumstances, and find a new solution. To change something is also easier if there is already momentum from another change. So I like to understand where the momentum is and then facilitate its flow. We had a large organizational change at the beginning of 2020. Some teams were newly created and everyone at MOIA was allowed to self-select into one of around 15 teams. That was very exciting. Some team formations went really well. Others didn’t. There were two frontend developers who had self-selected into a team that had less frontend work to do than expected. These two tried to make it work by taking over more responsibility in other fields, thus supporting their team, but after a year they were frustrated and felt stuck. Recognizing the right moment that they needed support from the outside to change their team assignment was very important.
Biometric authentication technology has been an important industry trend for years, especially in 2021 due to the latest AI innovations available on the market. According to IBM, 20% of breaches are caused by compromised credentials. Worse, it can take an average of 287 days to identify and respond to a data breach. AI-based security is increasing in usage and will be necessary to remain competitive in any industry. IBM reports that as of 2021, 25% of businesses have completed deployment of AI-based security, while 40% are partially deployed. The remaining 35% have not begun this process, and if your business falls into this category you may be placing your clients at great risk for dangerous data breaches. Investing in AI-based security can save a business up to $3.81 million in 2021. Being able to use artificial intelligence to identify and automatically respond to data breaches is incredibly important for protecting the data and privacy of a company and its customers. AI biometrics authentication provides yet another safeguard against a data breach that is essential for businesses of any scale.
Recently, researchers have developed EEG sensors made from graphene, which offers excellent conductivity and biocompatibility. Graphene-based biosensors, however, often have low durability, corroding upon contact with sweat, and exhibit high skin-contact impedance that hampers the detection of signals from the brain. A novel graphene-based biosensor developed at the University of Technology Sydney aims to overcome these limitations, detecting EEG signals with high sensitivity and reliability – even in highly saline environments. The sensor, described in the Journal of Neural Engineering, is made from epitaxial graphene (EG) grown on a silicon carbide (SiC)-on-silicon substrate. This structure unites graphene’s favourable properties with the physical robustness and chemical inertness of SiC. “We’ve been able to combine the best of graphene, which is very biocompatible, very conductive, with the best of silicon technology, which makes our biosensor very resilient and robust to use,” says senior author Francesca Iacopi in a press statement.
Our immediate observation was that most of the experiments ran multiple versions (in extreme cases, up to hundreds) of their investment model in parallel. In almost all the cases, the authors presented their highest-performing model as the primary product of their experiment – meaning the best result was cherry-picked and all the sub-optimal results were ignored. This approach would not work in real-world investment management, where any given strategy can be executed only once, and its result is unambiguous profit or loss – there is no undoing of results. ... Despite all their imperfections, empirical evidence strongly suggests humans are currently ahead of AI. This may be partly because of the efficient mental shortcuts humans take when we have to make rapid decisions under uncertainty. In the future, this may change, but we still need evidence before switching to AI. And in the immediate future, we believe that, instead of pinning humans against AI, we should combine the two. This would mean embedding AI in decision-support and analytical tools, but leaving the ultimate investment decision to a human team.
Trivedi points out there are many places where people can begin their journey, starting in core-IT or IT engineering, before moving into automation engineering/SRE (Site Reliability Engineer). “Another good path is to start off in the technical support organization and learn more about the technical side of the product,” he says. “Then, you can take on small projects that help automate support issues, and you can use this experience to move into SRE. Many people also start off in a systems administration role before moving into an engineering role.” Nirmal said entry-level professionals with an understanding and passion for automation technologies have “endless opportunities” to embark on a career path that provides tremendous value to a company's digital transformation and future growth. A key change in how organizations are approaching automation is through expanded use of AI and machine-learning. This means IT workers must have knowledge of how AI advances automation and allows for more informed decisions that improve outcomes.
An overly intense focus on a goal can lead to what cognitive psychologists call goal neglect. That may seem counterintuitive to the average goal-oriented MBA or entrepreneur, but take, for example, the dynamic at work in micromanagement. Often, when leaders micromanage employees, an intense focus on task performance distracts those leaders from the larger goals of the company. They obsess over the trees and neglect the forest—and drive employees crazy while they’re at it. Where you direct your focus is a function of the brain’s attention system. This system has three subsystems, which Amishi Jha, a professor and the director of contemplative neuroscience for the Mindfulness Research and Practice Initiative at the University of Miami, describes as the flashlight (or orienting system), which enables you to selectively direct and concentrate your attention; the floodlight (or alerting system), which enables you to take in the larger picture; and the juggler (or executive function), which enables you to align your actions to your aims. “What happens with goal neglect is that the flashlight is pointed very intently, but the floodlight is not quite working,” she told me in a recent Zoom interview.
Federated governance is a balancing act. While a producer of a data product should have full autonomy to build, populate and publish in any way they see fit, they must also ensure that it is in a form that is easy and reasonable for consumers to access and use. There are many parallels that can be drawn between the microservices domain and the data mesh domain: Both empower users to select the tools and technology that is best suited for their use cases while simultaneously offering resistance to technological sprawl, confusing implementations and difficulty in usage. For example, a microservice platform may restrict the languages that developers may use to a specific subset. In the data mesh, a similar analogy would be to restrict the format of data products such that only one or two mechanisms are the usable standards. In both cases, the goal isn’t to make life more difficult for the creators, but rather to limit the technological sprawl and implementation complexity, particularly if existing technologies and standards are more than sufficient to meet the product needs.
Quote for the day:
"The actions of a responsible executive are contagious." -- Joe D. Batton