It’s easy to see why the blockchain idea evokes utopian hopes: at last, technology is sticking it to the Man. In that sense, the excitement surrounding it reminds me of the early days of the internet, when we really believed that our contemporaries had invented a technology that was democratising and liberating and beyond the reach of established power structures. ... What we underestimated, in our naivety, were the power of sovereign states, the ruthlessness and capacity of corporations and the passivity of consumers, a combination of which eventually led to corporate capture of the internet and the centralisation of digital power in the hands of a few giant corporations and national governments. ... Will this happen to blockchain technology? Hopefully not, but the enthusiastic endorsement of it by outfits such as Goldman Sachs is not exactly reassuring. The problem with digital technology is that, for engineers, it is both intrinsically fascinating and seductively challenging, which means that they acquire a kind of tunnel vision: they are so focused on finding solutions to the technical problems that they are blinded to the wider context.
Experts say battery life is getting better in consumer electronics—through a combination of super-efficient processors, low-power states, and a little help from advanced technologies like silicon anode. It’s just not necessarily getting 10 times better. Conventional lithium-ion batteries have their energy density limits, and they typically improve by single-digit percentages each year. And there are downsides to pushing the limits of energy density. “Batteries are getting a little bit better, but when batteries get better in energy density, there’s usually a trade-off with cycle life,” says Venkat Srinivasan, who researches energy storage and is the director of the Argonne Collaborative Center for Energy Storage Science. “If you go to the big consumer electronics companies, they’ll have a metric they want to achieve, like we need the battery to last for 500 cycles over two or three years. But some of the smaller companies might opt for longer run times, and live with the fact that the product might not last two years.”
Asked why they're looking to move their legacy data off-premises and to the cloud, 46% of the executives cited regulatory compliance as the top reason. Some 38.5% pointed to cost savings as the biggest reason, while 8.5% mentioned business intelligence and analytics. The survey also asked respondents to identify the features and benefits that would most influence them to move their legacy data to the cloud. The major benefit cited by 66% was the integration of data and legacy archives. Some 59% cited the cloud as a way to centrally manage the archiving of all data including data from Office 365. Other reasons mentioned included data security and encryption, advanced records management, artificial intelligence-powered regulatory and compliance checking, and fast and accurate centralized search. Of course, anxiety over cyber threats and cyberattacks also plays a role in the decision to migrate legacy data. Among the respondents, 42% said that concerns over cybersecurity and ransomware attacks slightly or significantly accelerated the migration plans.
IT and enterprise management in general is getting wise to the fact that a solution that “works” or “seems innovative” does not really tell you why operations cost so much more than forecast. Today we need to audit and evaluate the end state of a cloud solution to provide a clear measure of its success. The planning and development phases of a cloud deployment are great places to plan and build in audit and evaluation procedures that will take place post-development to gauge the project’s overall ROI. This end-to-beginning view will cause some disturbance in the world of those who build and deploy cloud and cloud-related solutions. Most believe their designs and builds are cutting edge and built with the best possible solutions available at the time. They believe their designs are as optimized as possible. In most instances, they’re wrong. Most cloud solutions implemented during the past 10 years are grossly underoptimized. So much so that if companies did an honest audit of what was deployed versus what should have been deployed, a very different picture of a truly optimized cloud solution would take shape.
When applications are built on top of a blockchain, these applications are inherently decentralized — hence referred to as dApps (decentralized applications). Most dApps today leverage a Layer 1 (L1) blockchain technology like Ethereum as their primary form of storage for transactions. There are two primary ways that dApps interact with the underlying blockchain: reads and writes. Let’s use an NFT and gaming dApp that rewards gamers who win coins that they can then use to purchase NFTs as an example: Writes are performed to an L1 chain whenever a gamer wins and coins are added to their wallet; reads are performed when a gamer logs into the game and needs to pull the associated NFT metadata for their game character (think stats, ranking, etc.). As an early-stage dApp building the game described above, writing directly to Ethereum is prohibitive because of slow performance (impacting latency) and high cost. To help developers in the dApp ecosystem, sidechains and Layer 2 (L2) solutions like Polygon improve performance.
“We need a public-private partnership to identify a list of critical open source projects — with criticality determined based on the influence and importance of a project — to help prioritize and allocate resources for the most essential security assessments and improvements,” Walker wrote. The blog post also called for an increase in public and private investment to keep the open-source ecosystem secure, particularly when the software is used in infrastructure projects. For the most part, funding and review of such projects are conducted by the private sector. The White House had not responded to a request for comment by time of publication. “Open source software code is available to the public, free for anyone to use, modify, or inspect ... That’s why many aspects of critical infrastructure and national security systems incorporate it,” wrote Walker. “But there’s no official resource allocation and few formal requirements or standards for maintaining the security of that critical code. In fact, most of the work to maintain and enhance the security of open source, including fixing known vulnerabilities, is done on an ad hoc, volunteer basis.”
By leveraging AI to automate the identification of the specific lines of code that require attention, developers can simply ask this AI-driven knowledge repository where behaviors are coming from—and quickly identify the code associated with that behavior. This puts AI squarely in the position of intelligence augmentation, which is key to leveraging its capabilities. This novel approach of AI reinterprets what the computation represents and converts it into concepts, therefore “thinking” about the code in the same way humans do. The result is that software developers no longer have to unearth the intent of previous developers encoded in the software to find potential bugs. Even better, developers are able to overcome the inadequacies of automated testing by using AI to validate that they haven’t broken the system before they compile or check in the code. The AI will forward simulate the change and determine whether it’s isolated to the behavior under change. The result is the bounds of the change are confined to the behavior under change so that no unintended consequences arise.
The next generation of VR headsets will collect more user information, including detecting the stress level of the user, and even facial recognition. “We’re going to see more capabilities and really understanding the biometrics that are generated from an individual, and be able to use that to enhance the training experience,” he says. That data collection will enable a feedback loop with the VR user. For example, if an enterprise is using VR to simulate a lineman repairing a high-voltage wire, the headset will be able to detect the anxiety level of the user. That information will inform the enterprise how to personalize the next set of VR lessons for the employee, Eckert says. “Remember, you’re running nothing more than software on a digital device, but because it senses three dimensions, you can put input through gesture hand control, through how you gaze, where you gaze. It’s collecting data,” he says. “Now that data can then be acted upon to create that feedback loop. And that’s why I think it’s so important. In this immersive world that we have, that feedback …will make it even that much more realistic of an experience.”
Data virtualization does not purport to eliminate the requirement to transform data. In fact, most DV implementations permit developers, modelers, etc., to specify and apply different types of transformations to data at runtime. Does DAF? That is, how likely is it that any scheme can eliminate the requirement to transform data? Not very likely at all. Data transformation is never an end unto itself. It is rather a means to the end of using data, of doing stuff with data. ... Because this trope is so common, technology buyers should be savvy enough not to succumb to it. Yet, as the evidence of four decades of technology buying demonstrates, succumb to it they do. This problem is exacerbated in any context in which (as now) the availability of new, as-yet-untested technologies fuels optimism among sellers and buyers alike. Cloud, ML and AI are the dei ex machina of our age, contributing to a built-in tolerance for what amounts to utopian technological messaging. That is, people not only want to believe in utopia -- who wouldn’t wish away the most intractable of sociotechnical problems? -- but are predisposed to do so.
Quote for the day:
"Authority without wisdom is like a heavy axe without an edge, fitter to bruise than polish." -- Anne Bradstreet