The primary challenge to successful quantum computing lies within the technology itself. In contrast to classical computers, a quantum computer employs quantum bits, or qubits that can be both 0 and 1 at the same time, Jagannathan says. Such two-way states give quantum computer its power, yet even the slightest interaction with their surroundings can create distortion. "Correcting these errors, known as quantum error correction (QEC), is the biggest challenge and progress has been slower than anticipated," he says. There's also an important and possibly highly destructive aspect to quantum technology. "In addition to [a] wide range of benefits . . . it is also expected that [cybercriminals] will someday be able to break public key algorithms that serve as a basis for many cryptographic operations, like encryption or digital signatures," says Colin Soutar, managing director and cyber and strategic risk leader with Deloitte & Touche. "It's important that organizations carefully understand what exposure they may have to this [threat] so that they can start to take mitigation steps and not let security concerns overshadow the positive potential of quantum computing," says Soutar
Data drives businesses growth and provides valuable insights prior to any conclusive decision making. As the enterprises scale, many challenges surface. For instance, working professionals, including data scientists, analysts, engineers, join in with different skill-sets and tools. Different people, different tools, different working styles – all these lead to a major bottleneck. Business segments are in dire need of data management to create contextual insights, now is the time to improve the quality and speed of data streaming into the organisation and get leadership commitment to support and sustain a data-driven vision across the company. This is where DataOps (data operations) come in handy. For instance, users can integrate their tables from Databricks with Atlan in a series of steps. Initially there are some prerequisites for establishing a connection between Atlan and Databricks Account: Go to the Databricks console and select “Clusters” from the left sidebar; Select the cluster you want to connect with Atlan. The cluster should be in a Running state for the Atlan crawler to fetch metadata from it; Click on “Advanced Options” in the “Configuration” tab.
They're offering a service and they sit somewhere on the darker side of the internet and they offer what's called ransomware-as-a-service. They recruit affiliates or essentially sub-contractors who come in, who use their platform and then attack companies. And in the case of DarkSide, if you actually logged into the infrastructure and take a look at it, which is something we in the research community actively do, they had a very polished operation. They provide technical support for their affiliates who are breaking into companies. They provide monetization controls so that an affiliate can go in and see how much has been paid and what's outstanding and manage the money and all that. They're basically like companies and that's the challenge with ransomware now is it's moved from this sort of opportunistic thing where there were a few criminals scattered around the world doing this, to being these as-a-service operations that basically mean any enterprising criminal can get access to ransomware for, I've seen it for less than $100, and then use that to infect stuff. And obviously at the lower end, you're talking about things that aren't very sophisticated. The problem is it doesn't need to be sophisticated.
The most robust method to reduce overfitting is collect more data. The more data we have, the easier it is to explore and model the underlying structure. The methods we will discuss in this article are based on the assumption that it is not possible to collect more data. Since we cannot get any more data, we should make the most out of what we have. Cross validation is way of doing so. In a typical machine learning workflow, we split the data into training and test subsets. In some cases, we also put aside a separate set for validation. The model is trained on the training set. Then, its performance is measured on the test set. Thus, we evaluate the model on previously unseen data. In this scenario, we cannot use a portion of the dataset for training. We are kind of wasting it. Cross validation allows for using every observation in both training and test sets. Ensemble models consist of many small (i.e. weak) learners. The overall model tends to be more robust and accurate than the individual ones. The risk of overfitting also decreases when we use ensemble models. The most commonly used ensemble models are random forest and gradient boosted decision trees.
There is a widespread misconception in most industries that older employees are not “digital savvy” and are afraid to learn new things when it comes to technology, Miklas adds. “This assumption often results in decisions that can result in being sued for age discrimination, especially when the older worker is passed over for promotion, not hired, or terminated,” he says. One issue that arises more in age discrimination claims than other types of discrimination is an employer’s use of selection criteria for hiring, promotion, or layoff decisions that are susceptible to assumptions about age, says Raymond Peeler, director of the Coordination Division, Office of Legal Counsel at the U.S. Equal Employment Opportunity Commission (EEOC). “For example, an employer making determinations about workers based on ‘energy,’ ‘flexibility,’ ‘criticality,’ or ‘long-term concerns’ are susceptible to employer assumptions based on the age of the worker,” Peeler says. The EEOC is responsible for enforcing federal laws that make it illegal to discriminate against job applicants or employees because of a person’s race, color, religion, sex, national origin, disability, genetic information, or age.
Self-appointed as ‘The People’s Network,’ the existing LoRa-based Helium Network is live with 28,000+ hotspots devices deployed in over 3,800 cities worldwide, and there are 200,000+ hotspot devices on backorder from various manufacturers. Helium aims to take that experience and apply it to a new tier of 5G connectivity that is enabled by the unique CBRS spectrum, 3550 MHz-3700 MHz, which the US Federal Communications Commission has made available on three tiers of access, two of which are open to non-government users. Though the Priority Access level is licensed, General Authorized Access permits open access for the widest group of potential users and use cases. Using gateways from Helium partner FreedomFi, hotspot hosts – including individual consumers – will have the option to earn Helium’s own HNT cryptocurrency, in part by offloading carrier cellular traffic to their 5G hotspots. The FreedomFi Gateways will be compatible with Helium’s existing open-source blockchain and IoT network and will by default act as a Helium hotspot, also mining rewards for proof of coverage and data transfers on the IoT network.
In a panel discussion on whether UAE fintech is going global, Ellen Moeller, head of EMEA partnerships at Stripe, a San Francisco-based company that offers software to manage online payments, said key areas of interest for fintechs included ensuring that transactions were a “very frictionless experience” for consumers. “They’re used to calling a taxi from the touch of a button,” she said. “Why shouldn’t it be so simple when we’re talking about financial services? There’s a lot of opportunity for innovation for fintech. “The final piece is regulators and central banks embracing this innovation. I think we’ve only scratched the surface of fintech innovation and there’s lots more to come.” She added that the UAE “has all the right ingredients” to be a world-class technology and fintech hub, including a deep pool of talent and good investment climate. “We’ve seen the UAE do a remarkable job at fostering fintech,” she added. The region is seeing rapid growth in the number of tech start-ups in a range of fields, according to Vijay Tirathrai, managing director of Techstars, a company in the US state of Colorado, that supports tech start-ups.
Quantum computers are expected to greatly outperform the most powerful conventional computers on certain tasks, such as modeling complex chemical processes, finding large prime numbers, and designing new molecules that have applications in medicine. These computers store quantum information in the form of quantum bits, or qubits — quantum systems that can exist in two different states. For quantum computers to be truly powerful, however, they need to be “scalable,” meaning they must be able to scale up to include many more qubits, making it possible to solve some challenging problems. “The goal of this collaborative project is to establish a novel platform for quantum computing that is truly scalable up to many qubits,” said Boerge Hemmerling, an assistant professor of physics and astronomy at UC Riverside and the lead principal investigator of the three-year project. “Current quantum computing technology is far away from experimentally controlling the large number of qubits required for fault-tolerant computing. ...”
The most compelling reason for building a cyber range is that it is one of the best ways to improve the coordination and experience level of your team. Experience and practice enhance teamwork and provide the necessary background for smart decision-making during a real cyberattack. Cyber ranges are one of the best ways to run real attack scenarios and immerse the team in a live response exercise. An additional reason to have access to a cyber range is that many compliance certifications and insurance policies cite mandatory cyber training of various degrees. These are driven by mandates and compliance standards established by the National Institute of Standards and Technology and the International Organization for Standardization (ISO). With these requirements in place, organizations are compelled to free up budgets for relevant cyber training. There are different ways to fulfill these training requirements. Per their role in the company, employees can be required to undergo certifications by organizations such as the SANS Institute.
It’s important to take a look at the hiring strategy, and make sure that it attracts a diverse talent pool. Nabila Salem, president at Revolent Group, commented: “For the tech industry, there is more than just a moral imperative to solve the issue of missing equity. The lack of diversity within the tech sector also compounds upon a very real business challenge for organisations: a lack of available talent. “The consequences of not plugging this skills gap are of great concern: GDP growth across the G20 nations could be stunted by as much as $1.5 trillion over the next decade, if companies refuse to adapt to the needs that tech presents to us. “One way to overcome this is to invest in new, diverse talent to help solve both the skills gap and the lack of representation in tech. New, innovative programs like the Salesforce training provided by Revolent specialise in fuelling the market with the diverse, highly skilled new talent it so desperately needs. “There is an opportunity here, to address the issue of a lack of representation and an overall skills gap, all at once. Companies must be open to the idea that the average applicant is not as homogenous as they think. ...”
Continuously verifying documentation means making sure that the current state of the documentation matches the current state of the codebase, as the code evolves. In order to keep the docs in sync with the codebase, existing documentation needs to be checked against the current state of the code continuously and automatically. If the documentation diverges from the current state of the code, the documentation should be modified to reflect the updated state (automatically or manually). Continuously verifying documentation means that developers can trust their documentation and know that what’s written there is still relevant and valid, or at least get a clear indication that a certain part of it is no longer valid. In this sense, Continuous Documentation is very much like continuous integration - it makes sure the documentation is always correct, similar to verifying that all the tests pass. This could be done on every commit, push, merge, or any other version control mechanism. Without it, keeping documentation up-to-date and accurate is extremely hard, and requires manual work that needs to be repeated regularly.
Quote for the day:
"Without courage, it doesn't matter how good the leader's intentions are." -- Orrin Woodward