It’s no longer enough to rely upon a small pool of employees to drive, inform and implement widespread change. Meeting these ambitious targets will only be possible if they are accompanied by a top-down mentality to change alongside a groundswell of employee support. Ultimately, responsibility for change needs to fall under the remit of the entire workforce, not just one individual. At the heart of this is ensuring that the sustainability function is not siloed from the rest of the business, acting as its own separate entity with different KPIs or activations. To be successful, it needs to permeate the wider business and encourage others to embrace a ‘sustainability by design’ mindset with new policy, direction and solutions. At first, this might mean that meetings should have a dedicated sustainability champion, whether that is the CEO or Chief Sustainability Officer, as outlined in the above research, or another individual, who knowledge-shares, coaches other employees and ensures the business is on track against its targets.
The ministry told the Belgian newspaper that the cyberattack stemmed from Apache's Log4j - which provides logging capabilities for Java applications and is widely used, including for Apache web server software. Belgian Commander Olivier Séverin also told the outlet, "All weekend our teams have been mobilized to control the problem, continue our activities and warn our partners." Taking to Facebook in the wake of this recent attack, the Ministry of Defense writes, "Due to technical issues, we are unable to process your requests via mil.be or answer your queries via Facebook. We are working on a resolution and we thank you for your understanding." Representatives for both the ministry and Defense Minister Ludivine Dedonder did not respond to Information Security Media Group's request for comment. Belgian officials also did not elaborate on the attack's specifics with De Standaard. The Belgian incident is one of the first high-profile attacks stemming from the Log4j vulnerability, although cybersecurity experts have warned of active scanning and exploitation of the remote code execution vulnerability.
With advancements in technology, trust has become a vital factor in human-technology interactions. In the past, people trusted technology mainly because it worked as expected. However, the emergence of Artificial Intelligence solutions does not remain the same due to the following challenges: Openness: AI-based applications are built to be adaptive and reactive, to have an intelligence of their own to respond to situations. Anyone can put it to good use or apply it for nefarious purposes. Hence, people have some reservations about trusting AI-based solutions. Transparency: One of the significant issues impacting human trust in AI applications is the lack of transparency. AI developers need to clarify the extent of personal data utilized and the benefits and risks of using the application to increase trust. Privacy: AI has made data collection and analysis much easier; however, the end-users have to bear the brunt, as the collection of humongous amounts of data by companies worldwide may end up jeopardizing the privacy of the user(s) whose data is being collected.
With the rising cost and complexity of modern software development practices, businesses will increasingly require a comprehensive, fully integrated security platform with fewer disparate tools. This platform supports pervasive, or continuous, security because it: Starts in the design phase with threat modeling, ensuring that only secure components are incorporated into the design. This shifts security even further left so that DevSecOps now becomes SecDevOps ensuring software is ‘secure by design’. Is fully integrated, but also open to new technology plugins, to provide comprehensive coverage analyzing every possible dimension of the code. This ‘single pane of glass’ approach empowers security professionals and developers to understand risk, prioritize remediation efforts, and define and monitor progress objectives across multiple dimensions. Delivers a frictionless developer experience that enables security analysis to meet developers where they work – within the IDE, CI/CD pipelines, code and container repositories, and defect tracking systems.
Bigger is better—or at least that’s been the attitude of those designing AI language models in recent years. But now DeepMind is questioning this rationale, and says giving an AI a memory can help it compete with models 25 times its size. When OpenAI released its GPT-3 model last June, it rewrote the rulebook for language AIs. The lab’s researchers showed that simply scaling up the size of a neural network and the data it was trained on could significantly boost performance on a wide variety of language tasks. Since then, a host of other tech companies have jumped on the bandwagon, developing their own large language models and achieving similar boosts in performance. But despite the successes, concerns have been raised about the approach, most notably by former Google researcher Timnit Gebru. In the paper that led to her being forced out of the company, Gebru and colleagues highlighted that the sheer size of these models and their datasets makes them even more inscrutable than your average neural network, which are already known for being black boxes.
A number of cloud experts suggest that centralizing your application data is the right model for managing a large dataset for a large application. Centralizing your data, they argue, makes it easier to apply machine learning and other advanced analytics to get more useful information out of your data. But this strategy is faulty. Centralized data is data that can’t scale easily. The most effective way to scale your data is to decentralize it and store it within the individual service that owns the data. Your application, if composed of dozens or hundreds of distributed services, will store your data in dozens or hundreds of distributed locations. This model enables easier scaling and supports a full service ownership model. Service ownership enables development teams to work more independently, and encourages more robust SLAs between services. This fosters higher-quality services and makes data changes safer and more efficient through localization.
The fact that the Federal Government is suddenly placing such a high priority on cyber security is telling, and the directive is worth paying attention to, even for private sector organizations. If federal agencies shore up their cyber defenses in accordance with the new directive, then at least some cybercriminals will likely turn their attention toward attacking private sector targets. After all, it is likely that some of the known vulnerabilities will continue to exist in private companies, even after those vulnerabilities have been addressed on systems belonging to the federal government. With the end of the year rapidly approaching, IT professionals should put cyber security at the top of their New Year's resolutions. But what specifically should IT pros be doing to prepare for 2022? CISA differentiates between known vulnerabilities and vulnerabilities that are known to have been exploited. Likewise, IT pros in the private sector should focus their efforts and their security resources on addressing vulnerabilities that have been exploited in the real world.
In a major breakthrough, scientists have discovered an entirely different form of biological reproduction and applied it to create the first-ever, self-replicating living robots. This research was conducted by scientists at the University of Vermont, Wyss Institute for Biologically Inspired Engineering at Harvard University, and Tufts University. This team had created “Xenobots” last year and discovered that these computer-designed and hand-assembled organisms can swim out into their tiny dish, look for single cells, gather them together and assemble “baby” Xenobots in their mouth. After a few days, these become new Xenobots that look and move just like themselves. ... 2021 has been a transformative year for large language models, with all the major names in tech bringing in path-breaking new systems. Just days back, DeepMind introduced a 280 billion parameter transformer language model called Gopher. DeepMind’s research went on to say that Gopher almost halves the accuracy gap from GPT-3 to human expert performance and exceeds forecaster expectations.
Use milestones and deadlines to gauge your team’s progress instead of tracking time. One challenge of remote work is “appearing” to be productive and present to the management team. However, measurement should not be seen as a punitive exercise to catch people out – it should guide employees toward completing their goals. Most workers don’t work the entire eight hours they’re in the office either, as they’re often engaging in spontaneous meetings and meaningful moments of connection with colleagues. Managers should disregard time as a measure of productivity and trust their employees to do their job to the best of their ability. If goals are being met but the employees feel distant because they don’t need to collaborate as much or that they need to “appear busy,” then the goals are too easy and need to be readjusted. Be careful to keep engagement and communication high – otherwise, you can end up with the “watermelon effect” – good “green” performance, but below the surface, there’s a big chunk of red, which represents a poor employee experience.
A crucial characteristic of successful digital CEOs is that they can step back far enough from their current business to reimagine where transformative — not incremental — value is possible. We find that these CEOs spend a lot of time visiting companies and staying abreast of how new trends are generating value. That helps them to look at their own assets with fresh eyes and see where there’s new value. Steve Timm, the president of Collins Aerospace, finds transformative value in being able to thoughtfully reimagine the business model. “Many CEOs have domain experience and they don’t want to get outside of that,” he told us during an interview. “They’re not thinking about redefining the broader architecture or ecosystem. We need to redefine the boundaries where value can come from.” With clarity on the business model established, targeting a domain — for example, a complete core process or user journey — has emerged as a critical element for focusing energies in a digital transformation.
Quote for the day:
"The problem with being a leader is that you're never sure if you're being followed or chased." -- Claire A. Murray