The idea of the metaverse gives enterprise software developers a roadmap to build software based on a single digital identity where companies join a network to connect with other companies who have likewise joined. This is not a system that belongs to any one company, but an environment where all companies are equal. Why do businesses want to connect? Because that’s the nature of a business at its essence, connecting with customers, suppliers, and any stakeholder to establish an expectation of value from the relationship, and then measure the realization of this value over time. This thinking is not inconsistent with the idea of a VR environment where a business user engages with some part of their business in an immersive environment. But we are setting the aperture much wider to say the entire business should be thought of as being part of the metaverse, and all of the data that exists about that business can be aimed at that digital identity to create a digital twin for the entire business. Then this digital business can connect with other businesses to do what businesses do — exchange value — but is now supported by a persistent, interoperable, collaborative digital space that is co-created and co-owned by those companies who have joined the metaverse.
We develop cognitive biases based on our life experience. Just as we expect teachers to be good with kids and surgeons to have a steady hand, we also hold behavioral expectations for our leaders. Today’s emphasis on servant leadership has us all believing that leaders are heroes, existing to serve the people and their every action should be a selfless gesture. Then, when they fail to act in accordance with our beliefs, we become disillusioned — the hero has fallen and everything they ever did, good or bad, gets lumped into one big giant disappointment. That’s a lot of burden for a leader to bear. Instead of looking at leaders as one whole unit, we need to see them as a collection of basic human traits. We forget that within every leader is a person, with flaws and imperfections. Instead of putting the whole person on a pedestal as some kind of one-size-fits-all embodiment of goodness, just admire them for their strengths. Unpack what you like about them without discarding the whole leader. Take the good they accomplished for what it is, but don’t blame humans for not being angels.
Today’s remote or hybrid work model poses a whole new set of security challenges. Many companies can minimize risk by leveraging a multicloud strategy, but the risk associated with malware or ransomware can compromise crucial corporate and customer data. Despite this, according to a report from Menlo Security, only 27% of organizations have advanced threat protection in place for all endpoint devices with access to company data. It’s crucial that companies deploy advanced cybersecurity software and also train employees on acceptable use of public or home-based Wi-Fi usage. While enterprise data provides the fuel that drives accurate AI, it’s important that data scientists ensure that bias doesn’t creep into the algorithms that are developed. Data should be analyzed to ensure that it is diverse and doesn’t lead to any decisions that could provide an unfair advantage to certain populations. As an example, AI that helps to determine the best suppliers to work with should be trained with diverse supplier data. Speaking of suppliers, it’s not enough that data has proper governance within the organization.
Amazon Aurora Serverless v1 changed everything by enabling customers to resize their VMs without disrupting the database. It would look for gaps in transaction flows that would give it time to resize the VM. It would then freeze the database, move to a different VM behind the scenes, and then start the database again. This was a great starting point, explains Biswas, but finding transaction gaps isn't always easy. "When we have a very chatty database, we are running a bunch of concurrent transactions that overlap," he explains. "If there's no gap between them, then we can't find the point where we can scale." Consequently, the scaling process could take between five and 50 seconds to complete. It could sometimes end up disrupting the database if an appropriate transaction gap could not be found. That restricted Aurora Serverless instances to sporadic, infrequent workloads. "One piece of feedback that we heard from customers was that they wanted us to make Aurora Serverless databases suitable for their most demanding, most critical workloads," explained Biswas.
VMware Inc. several years ago cleaned up its fuzzy cloud strategy and partnered up with everyone. And you see above, VMware Cloud on AWS doing well, as is VMware Cloud, its on-premises offering. Even though it’s somewhat lower on the X-axis relative to last quarter, it’s moving to the right with a greater presence in the data set. Dell and HPE are also interesting. Both companies are going hard after as-a-service with APEX and GreenLake, respectively. HPE, based on the survey data from ETR, seems to have a lead in spending momentum, while Dell has a larger presence in the survey as a much bigger company. HPE is climbing up on the X axis, as is Dell, although not as quickly. And the point we come back to often is that the definition of cloud is in the eye of the customer. AWS can say, “That’s not cloud.” And the on-prem crowd can say, “We have cloud too!” It really doesn’t matter. What matters is what the customer thinks and in which platforms they choose to invest. That’s why we keep circling back to the idea of supercloud. You are seeing it evolve and you’re going to hear more and more about it.
Smart contracts are one of the applications of blockchain that can vastly help companies in securing a deal. By using smart contracts, companies can form an electrical code that assists organizations to develop a venture in a conflict-free manner. Unlike traditionally, if a company tries to change the terms of the contract or denies to release a payment, everybody on the network can leverage the technology’s transparency to view the same, and the contract’s code automatically freezes the deal. The agreement would not continue further until the company pays the due or goes back to keeping up with the guidelines. This smart management of contracts helps businesses to maintain operations functioning without any friction. As blockchain is a technology that increases transparency, keeping track of the incoming and outgoing products from the site can be managed efficiently by everyone on the network. Every time a product halts at a specific gateway, the same gets documented and inserted into the blockchain ledger. This documentation increases transparency on cargo status and ensures they reach retailers on time and intact in condition.
TRE is becoming a commonly used acronym among the science and research community. In general, a TRE is a centralized computing database that securely holds data and allows users to gain access for analysis. TREs are only accessed by approved researchers and no data ever leaves the location. Because data stays put, the risk of patient confidentiality is reduced. ... TREs are becoming the architectural backbone for health data in many research organizations. While this is a step in the right direction, many TREs still can’t speak to colleagues from other organizations, or even other departments within their own organization. ... As the genomic sector continues to grow, the capability of TREs to communicate will allow researchers and scientists to effectively collaborate to overcome life threatening diseases and diagnosis by breaking down the “silos” of health data. That doesn’t mean moving data. Life sciences data sets are too large to move efficiently – and to complicate matters, many data security regulations forbid data to leave an organization, state or nation.
As an RL agent collects new data and the policy adapts, there is a complex interplay between current parameters, stored data, and the environment that governs evolution of the system. Changing any one of these three sources of information will change the future behavior of the agent, and moreover these three components are deeply intertwined. This uncertainty makes it difficult to back out the cause of failures or successes. In domains where many behaviors can possibly be expressed, the RL specification leaves a lot of factors constraining behavior unsaid. For a robot learning locomotion over an uneven environment, it would be useful to know what signals in the system indicate it will learn to find an easier route rather than a more complex gait. In complex situations with less well-defined reward functions, these intended or unintended behaviors will encompass a much broader range of capabilities, which may or may not have been accounted for by the designer. ... While these failure modes are closely related to control and behavioral feedback, Exo-feedback does not map as clearly to one type of error and introduces risks that do not fit into simple categories.
Data-centric AI is evolving, and should include relevant data management disciplines, techniques, and skills, such as data quality, data integration, and data governance, which are foundational capabilities for scaling AI. Further, data management activities don’t end once the AI model has been developed. To support this, and to allow for malleability in the ways that data is managed, HPE has launched a new initiative called Dataspaces, a powerful cloud-agnostic digital services platform aimed at putting more control into the hands of data producers and curators as they build intelligent systems. Addressing, head on, the data gravity and compliance considerations that exist for critical datasets, Dataspaces gives data producers and consumers frictionless access to the data they need, when they need it, supporting better integration, discovery, and access, enhanced collaboration, and improved governance to boot. This means that organisations can finally leverage an ecosystem of AI-centric data management tools that combine both traditional and new capabilities to prepare the enterprise for success in the era of decision intelligence.
Traditionally, top-down leadership comes to those who either already have power or the ability to purchase it. Since everyone has equal shares in a DAO, authority is not "given" to anyone. Instead, it's earned by the merits of the proposals made. This creates an organization that follows the guidance of someone people are voluntarily following. This always yields better results, whether through growth, innovation or higher profits. This style of leadership is something all good leaders can practice. Even if they didn't "earn" their role in the same way, they can earn the trust and loyalty of their team through their actions. ... Modern corporations are like enormous ships that require huge amounts of time and effort to change course. There is endless red tape and bureaucracy to navigate before any real change can be implemented. Because DAOs are more democratic, changes can be proposed and implemented with relatively little hassle. While DAOs are primarily based on the division of funds, leaders can still note how the process works and see how efficient it is. The level of efficiency DAOs create is something that great leaders can seek to replicate in their own organizations.
Quote for the day:
"Challenges in life always seek leaders and leaders seek challenges." -- Wayde Goodall