CISOs should also be looking for common signs of burnout itself which team members might be exhibiting, including: A sharp drop in quantity and timeliness of output; A general lack of energy and enthusiasm around job functions; Continual signs of anxiety and stress; An extreme irritability toward co-workers and duties; and Significant changes in social patterns with co-workers. If some of these characteristics are present, the CISO has a few options for addressing them. One is to examine possible workload issues. Even the most resilient team members can burn out if the workload is crushing. If a staffer is exhibiting signs of burnout, an assessment can be made as to whether certain tasks should be spread out among other staffers, if possible. When taking this route, it's important for the CISO to let team members know that this is being done to gain more scale, not as a punitive measure. If the burnout signs point to an especially stressful infosec assignment, such as protecting assets from threats that are rapidly increasing, a discussion regarding giving the staffer more support may help them feel less alone in a challenging situation.
Researchers at the University of California, Los Angeles, and Google investigated the problem in a recently published study titled “Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research.” They found that there’s “heavy borrowing” of datasets in machine learning — e.g., a community working on one task might borrow a dataset created for another task — raising concerns about misalignment. They also showed that only a dozen universities and corporations are responsible for creating the datasets used more than 50% of the time in machine learning, suggesting that these institutions are effectively shaping the research agendas of the field. “SOTA-chasing is bad practice because there are too many confounding variables, SOTA usually doesn’t mean anything, and the goal of science should be to accumulate knowledge as opposed to results in specific toy benchmarks,” Denny Britz, a former resident on the Google Brain team, told VentureBeat in a previous interview. “There have been some initiatives to improve things, but looking for SOTA is a quick and easy way to review and evaluate papers. Things like these are embedded in culture and take time to change.”
The good news is that the serverless option is exactly what we’re looking for at this stage, even if the product isn’t open-source. That’s because we want something that can scale in terms of storage and query performance without necessitating dedicated maintenance efforts. And so the ideal option when getting started is a serverless managed offering — this is true for all of our components that necessitate elasticity, not just the data warehouse. ... And so it would make sense for us to leverage BigQuery as our data warehouse for this platform, but this doesn’t generalize the choice since in other scenarios it may be more interesting to opt for another option. When picking your data warehouse, you should take into account factors like pricing, scalability, and performance and then pick the option that fits your use case the best. ... In an ELT architecture, the data warehouse is used to store all of our data layers. This means that we won’t just use it to store the data or query it for analytical use cases, but we’ll also leverage it as our execution engine for the different transformations.
A team of researchers with Japan's NTT Corporation, the Tokyo University, and the RIKEN research center have announced the development of a full photonics-based approach to quantum computing. Taking advantage of the quantum properties of squeezed light sources, the researchers expect their work to pave the road towards faster and easier deployments of quantum computing systems, avoiding many practical and scaling pitfalls of other approaches. Furthermore, the team is confident their research can lead towards the development of rack-sized, large-scale quantum computing systems that are mostly maintenance-free. The light-based approach in itself brings many advantages compared to traditional quantum computing architectures, which can be based on a number of approaches (trapped ions, silicon quantum dots, and topological superconductors, just to name a few). However, all of these approaches are somewhat limited from a physics perspective: they all need to employ electronic circuits, which leads to Ohmic heating (the waste heat that results from electrical signals' trips through resistive semiconductor wiring).
Both state and national laws protecting consumer privacy are expected in 2022 by Trevor Hughes, president and CEO of the International Association of Privacy Professionals (IAPP). “The trendlines for privacy that formed in 2021 will accelerate and will bring new risks and complexity for organizations,” Hughes explained. “More national laws will be passed. More state laws will be passed. More (and heftier) enforcement will occur.” The trade-off for business is that privacy protections will be something that end users are more concerned about. ... “Social engineering will continue to work pretty dang well,” Stairwell’s Mike Wiacek said about 2022. “Social engineering is one of the most difficult security issues to address because no compliance, governance or risk-management action can address the fact that people are imperfect and susceptible to being duped.” ... Email will be increasingly targeted in 2022 with targeted, high-quality spear-phishing attempts, and will require a change in defense tactics, according to Troy Gill, senior manager of threat intelligence with Zix | App River.
It’s no secret that tech companies are some of the biggest holders of user data, and — less surprisingly — a frequent target of government data requests that seek information for criminal investigations. But Microsoft this year warned of the growing trend of the government attaching secrecy orders to search warrants, gagging the company from telling its users when their data is subject to an investigation. Microsoft said one-third of all legal orders come with secrecy provisions, many of which are “unsupported by any meaningful legal or factual analysis,” according to the company’s consumer security chief Tom Burt. Microsoft said secrecy orders were endemic across the entire tech industry. In April, the FBI launched a first-of-its-kind operation to remove backdoors in hundreds of U.S. company email servers left behind by hackers weeks earlier. China was ultimately blamed for the mass exploitation of vulnerabilities in Microsoft’s Exchange email software, which the hackers used to attack thousands of company email servers around the U.S. to steal contact lists and mailboxes.
Critical to any model’s success is the dataset’s quality. Instead of relying on ready-made data collections, we employ a manual process for Reflexion.ai to replicate real-world use cases better. We also have firm guidelines in place to ensure that the collected data is usable. This data requirement sparked the need for a validation exercise to help weed out abnormalities detected early in the process. This exercise helps us avoid backtracking later when the model fails accuracy tests. Since manually validating every data point is tedious, we explored options to automate the process. We realized Azure Machine Learning could help. Azure Machine Learning helps us develop scripts to automate initial dataset checks. We also benefit from the collaboration of notebooks and datasets, making it easier for multiple developers to work parallelly. This workflow assures us that the dataset is always in perfect condition, allowing our developers to focus on other aspects of the operation whenever a need for optimization arises. Data collection is an inherently iterative process, and in turn, the datasets are constantly evolving.
I/CD is an abbreviation for Continuous Integration/Continuous Deployment (or Continuous Delivery). Much ink has been spilled defining the scope of each of these terms. We will prioritize basic concepts over comprehensive coverage, and show how we can apply those basics to our simple pipeline. Continuous Integration (CI) refers to using automation to frequently build, test, and merge code changes to a branch in a shared repository. The basic motivation behind CI is pursuing faster developer iteration and deployment of changes compared to a process with larger, infrequent integration events where many changes, often from multiple developers, are de-conflicted and tested at the same time. Continuous Deployment (CD) refers to using automation to frequently redeploy a software project. The basic motivation behind CD is freeing operations teams from executing time-consuming and error-prone manual deployment processes while getting changes out to users quickly. For our batch processing pipeline, deployment simply means re-running the pipeline to update the database when changes are pushed to main.
The ultimate edge location, though, will continue to be in the phones and laptops. Web app developers continue to leverage the power of browser-based storage while exploring more efficient ways to distribute software. WebASM is an emerging standard that can bring more powerful software to handsets and desktops without complicated installation or permissioning. Computer scientists are also working at a theoretical level by redesigning their algorithms to be distributed to local machines. IBM, for instance, is building AI algorithms that can split the jobs up so the data does not need to move. When they’re applied to data collected by handsets or other IoT devices, they can learn and adapt while synchronizing only essential data. This distributed buzzword is also more commonly found in debates about control. While the push by some to create a distributed web, sometimes called Web3, is driven more by political debates about power than practical concerns about latency, the movement is in the same general direction.
Unlike Web 2.0 applications like Medium, Web 3.0 eliminates the middle man. There’s no centralized database that stores the application state, and there’s no centralized web server where the backend logic resides. Instead, you can leverage blockchain to build apps on a decentralized state machine that’s maintained by anonymous nodes on the internet. By “state machine,” I mean a machine that maintains some given program state and future states allowed on that machine. Blockchains are state machines that are instantiated with some genesis state and have very strict rules (i.e., consensus) that define how that state can transition. Better yet, no single entity controls this decentralized state machine — it is collectively maintained by everyone in the network. And what about a backend server? Instead of how Medium’s backend was controlled, in Web 3.0 you can write smart contracts that define the logic of your applications and deploy them onto the decentralized state machine. This means that every person who wants to build a blockchain application deploys their code on this shared state machine.
Quote for the day:
"My philosophy of leadership is to surround myself with good people who have ability, judgment and knowledge, but above all, a passion for service." -- Sonny Perdue