By learning from each image pair, the tensor network tweaked the parameters of its own calculations, successively enhancing its ability to create holograms. The fully optimized network operated orders of magnitude faster than physics-based calculations. That efficiency surprised the team themselves. “We are amazed at how well it performs,” says Matusik. In mere milliseconds, tensor holography can craft holograms from images with depth information — which is provided by typical computer-generated images and can be calculated from a multicamera setup or LiDAR sensor (both are standard on some new smartphones). This advance paves the way for real-time 3D holography. What’s more, the compact tensor network requires less than 1 MB of memory. “It’s negligible, considering the tens and hundreds of gigabytes available on the latest cell phone,” he says. The research “shows that true 3D holographic displays are practical with only moderate computational requirements,” says Joel Kollin, a principal optical architect at Microsoft who was not involved with the research. He adds that “this paper shows marked improvement in image quality over previous work,” which will “add realism and comfort for the viewer.”
While demand for data science talent is through the roof, there are not enough skilled professionals available to take on those roles. One primary reason for this is the lack of clarity on the skills required for different roles within the field of data science. Most companies look for individuals possessing certain specialized skill sets rather than the ‘jacks-of-all-trades’. In order to prepare for the best opportunities and avoid getting tagged as a ‘generalist’, one needs to first appreciate the nuances that make these different roles unique. For instance, how is a data scientist different from a data engineer or a data analyst? Contrary to popular perception, these roles are not interchangeable. For instance, a data scientist is one who employs advanced data techniques such as clustering, neural networks, decision trees to help derive business insights. Apart from the requisite coding skills, data scientists typically need to be adept at programming languages such as Java, Python, SQL, R, and SAS. In addition, they require working knowledge of Big Data frameworks such as Hadoop, Spark, and Pig. Data scientists also need to be familiar with new technologies such as deep learning, machine learning, etc.
Even in the world of cloud native and containers, a standard operating environment matters. The set of criteria that should be used to evaluate container base images is quite similar to what we’ve always used for Linux distributions. Evaluate things like security, performance, how long the life cycle is (you need a longer life cycle than you think), how large the ecosystem is, and what organization backs the Linux distribution used. (See also: A Comparison of Linux Container Images.) Start with a consistent base image across your environment. It will make your life easier. Standardizing early in the journey lowers the cost of containerizing applications across an organization. Also, don’t forget about the container host. Choose a host and standardize on it. Preferably, choose the host that matches the standard container image. It will be binary compatible, designed and compiled identically. This will lower cognitive load, complexity of configuration management, and interactions between the application administrators and operations teams responsible for managing the fleet of servers underlying your containers.
Like any microservice, the block store and p2p are simple (and easy to understand) programs. What makes these microservices special is that they are the first blockchain-specific microservices ever to be open-sourced. As you can see, we love open-source software development and decentralization but we’ve been saddened to see how many projects that claim to share these values pursue subtle (and not so subtle) ways of ensuring that they maintain control while developing most of their software behind closed doors. As one of the most experienced dApp and blockchain developers in the world, we understand better than most how difficult it can be to develop in the open, especially when you don’t have the right tools. So we’ve been delighted to find that by launching and designing Koinos the right way, we’ve found ourselves in a position where it makes perfect sense to continue developing it in the right way too; out in the open. The modularity of Koinos means that the more chefs that are in the kitchen, the better. We want developers to begin digging into the code as soon as possible and helping to make it into the protocol they truly need, instead of the protocol we believe they need.
While only a third of operators worldwide said finding the right talent was hindering their plans for digital development, the majority of such businesses were located in emerging markets such as Latin America, Africa and the Middle East. This, according to the survey, highlighted a pronounced skills gap between developed and emerging markets, with the latter still struggling to find the skills needed to facilitate digitisation. Just under half (46%) stated that cost was the biggest issue to realising transformation ambitions, suggesting that the path to digitisation was desirable and investment is ready. A surprisingly low number of telcos viewed return on investment (ROI) as a barrier, with only two-fifths of respondents expressing concerns that a return might not be easily established. Given that the telecommunications industry is traditionally very ROI-focused, the report authors said this suggested there was a great deal of confidence in the path towards digitisation if the aforementioned barriers could be overcome. In conclusion, the report said the findings implied a phased approach towards digitisation was in the best interests of telcos worldwide to ensure interoperability between technology and services and maintain what was described as a “seamless” customer experience.
The research shows this challenge is compounded by the amount of time employees spend using messaging and collaboration apps: time spent on tools such as Zoom and Teams has increased by 13% in the US since the start of the pandemic. This means employees are spending, on average, two and a half hours every day on these applications, with 27% of US employees spending more than half the working week on these tools. A significant amount of business is now routinely conducted on these channels and employees are taking agreements as binding. For example, as a result of receiving information over messaging and collaboration tools, almost 24% of US employees have accepted and processed an order, 25% have accepted a reference for a job candidate, and 20% have accepted a signed version of a contract. Sensitive data is being shared on these tools even though 39% of US employees have been reprimanded by bosses. These admonishments may have been in vain, however, as 75% of all US workers say they would continue to share this type of information in the future.
Right now, organizations are investing heavily in the chief data scientist role. This individual manages a range of data-driven functions, including overseeing data management, creating data strategy, and improving data quality. They also help their organizations extract the most valuable and relevant insights from their data, leveraging data analytics and business intelligence (BI). In this capacity, the chief data scientist has a far deeper understanding of how AI and machine learning (ML) can improve data management than the CTO, who has a broader knowledge base but not the deeper expertise. This is critical as ML has emerged as a key driver in improving data quality and access as navigating the journey from big data ideas to real-world machine learning implementation is a challenging endeavor. In this scenario, the chief data scientist serves as the trusted navigator, understanding that data is the fuel for key initiatives, knowing the non-deterministic risk of developing those capabilities. Moreover, this individual can manage the expectations of C-suite executives, helping them better understand the reality of what ML can accomplish while mitigating the risks associated with data-driven initiatives.
Companies know that their executives are targets. In our digital risk survey, we found that 25% of enterprises cite executives' personal social media as a major risk factor to the company's overall security. And they know that the consequences of an executive cyberattack would be severe. In our poll, 70% of respondents said their company would suffer brand or reputational damage. Half of the respondents predicted potential risk to shareholder value. One in three enterprises are most fearful of impersonation or fake accounts. One in four are most worried about the possibility of an account takeover. However, despite awareness of the threats, the sophistication of executive social media risk management is lagging. ... The new generation of cloud channels is very different. Tools like Twitter and LinkedIn live across multiple devices. They cross between professional and personal spheres. They generate interactions at unprecedented volume and velocity — and out of the box, security teams have no visibility. Today, all executives leverage social media, and they are bombarded by social media cybersecurity threats. Security teams know that banning these tools isn't an option. Why? Because people will use them anyway.
In many ways, cybersecurity roles should be fair game for hiring and promotion because of the importance of code versus gender but that is not always the case in practice. “Behind the screen, in theory, everyone is equal,” she says. “Clearly that is not what is happening.” Guerrieri would like to see more networking among women in cybersecurity to facilitate the creation of support systems to encourage them to remain and thrive in this career path. Some women have seen opportunities in cybersecurity emerge in response to the pandemic, says Sabrina Castiglione, CFO at Tessian, an enterprise email security software provider. Her company recently conducted a survey that included responses from 200 female cybersecurity professionals, 100 in Britain and 100 in the United States. Castiglione says some of the responses showed an increased sense of job surety among women in cybersecurity as the world coped with the COVID-19 pandemic. “In cybersecurity, women are saying they feel more secure or that with the impact of the pandemic, their job security has actually increased,” she says. Of the women respondents to the survey, 49% felt more secure in their jobs, Castiglione says.
“What are we actually trying to achieve?” is an incredibly important question. When we're thinking about technology choices and technology styles, we want to be stepping back just from “I'm doing Cloud-native because that's what everybody else is doing” to thinking “what problem am I actually trying to solve?” To be fair to the CNCF, they had this “why” right on the front of their definition of Cloud-native. They said, "Cloud-native is about using microservices to build great products faster." We're not just using microservices because we want to; we're using microservices because they help us build great products faster. ... Cost savings, elasticity, and delivery speed are great, but we get all of that just by being on the Cloud. Why do we need Cloud-native? The reason we need Cloud-native is that a lot of companies found they tried to go to the Cloud and they got electrocuted. It turns out things need to be written differently and managed differently on the cloud. Articulating these differences led to the 12 factors. The 12 factors were a set of mandates for how you should write your Cloud application so that you didn't get electrocuted.
Quote for the day:
"Leadership involves finding a parade and getting in front of it." -- John Naisbitt