“We do a terrible job of understanding and navigating the emotional journey of change,” says Wanda Wallace, leadership coach and managing partner of Leadership Forum. “This is where leaders need to get smart.” While some people may welcome it, “change is also about loss — loss of my current capability while I learn new ones, loss of who I go to to solve a problem, loss of established ways of doing things,” says Wallace. “Even if someone loves the rationale for the change, they still have to grieve the loss of what was and the loss of the ease of knowing what to do even if it wasn’t efficient.” It also involves fear. “This is usually labelled as ‘resistance,’ but I find many times it is fear of not being able to learn the new skills, not being as valued after the change, not feeling competent, not being at the center of activity the way they were before the change,” says Wallace. She advises IT leaders to name those fears, acknowledge them, and talk about the journey of learning — not just from the C-suite, but at the manager level.
During EDA, one of the first steps to undertake should be to check for and remove constant features. But surely the model can discover that on its own? Yes, and no. Consider a Linear Regression model where a non-zero weight has been initialized to a constant feature. This term then serves as a secondary ‘bias’ term and seems harmless enough … but not if that ‘constant’ term was constant only in our training data, and (unbeknownst to us) later takes on a different value in our production/test data. Another thing to be on the lookout for is duplicated features. This may not be blatantly obvious when it comes to categorical data, as it might manifest as different labels names being assigned to the same attribute across different columns, e.g. One feature uses ‘XYZ’ to denote a categorical class that another feature denotes as ‘ABC’, perhaps due to the columns being culled from different databases or departments. pd.factorize() can help identify if two features are synonymous.
Consciousness is at times mentioned in conversations about AI. Although inseparable from intelligence in the case of humans, it isn’t clear whether that’d be the case for machines. Those who dislike AI anthropomorphization often attack the notion of “machine intelligence.” Consciousness, being even more abstract, usually comes off worse. And rightly so, as consciousness — not unlike intelligence — is a fuzzy concept that lives in the blurred intersection of philosophy and the cognitive sciences. The origins of the modern concept can be traced back to John Locke’s work. He described it as “the perception of what passes in a man’s own mind.” However, it has proved to be an elusive concept. There are multiple models and hypotheses on consciousness that have gotten more or less interest throughout the years but the scientific community hasn’t yet arrived at a consensual definition. For instance, panpsychism — which comes to mind reading Sutskever’s thoughts — is a singular idea that got some traction recently.
The focus of this article so far has been on how blockchains combine cryptography and game theory to consistently form honest consensus—the truth—regarding the validity of internal transactions. However, how can events happening outside a blockchain be reliably verified? Enter Chainlink. Chainlink is a decentralized oracle network designed to generate truth about external data and off-chain computation. In this sense, Chainlink generates truth from largely non-deterministic environments. Determinism is a feature of computation where a specific input will always lead to a specific output, i.e., code will execute exactly as written. Decentralized blockchains are said to be deterministic because they employ trust-minimization techniques that remove or lower to a near statistical impossibility any variables that could inhibit internal transaction submission, execution, and verification. The challenge with non-deterministic environments is that the truth can be subjective, difficult to obtain, or expensive to verify.
When service mesh first came out, Kubernetes was in such a fervor -- it had been three or four years, so people had gone through the high of it, and saw the potential, and then there was a little bit of a lull in the hype when it hadn't really exploded in terms of usage. So when service mesh came out, for certain people, it was just like, 'Oh, cool, here's the new thing.' And it was new, 1.0 sort of stuff. If you fast forward, now, four years from that, Kubernetes is now at the point where it's super stable, it's being released less often. You have a lot more companies who are deploying Kubernetes [that are] starting to build new applications. We saw a lot of companies [during] the pandemic build new applications at a faster rate than they did before. [Solo.io customer] Chick-fil-A is an example -- at their thousands of stores as a franchise, before, most people parked their car, went in the store, then came out. Nowadays, the first interaction everybody has with the store is, 'I go on the app, I place my order, I get my loyalty points.'
One of the more interesting composability projects to emerge in Web3 is Ceramic, which calls itself “a decentralized data network that brings unlimited data composability to Web3 applications.” It’s basically a data conduit between dApps (decentralized applications), blockchains, and the various flavors of decentralized storage. The idea is that a dApp developer can use Ceramic to manage “streams” of data, which can then be re-used or re-purposed by other dApps via an open API. Unlike most blockchains, Ceramic is also able to easily scale. A blog post on the Ceramic website explains that “each Ceramic node acts as an individual execution environment for performing computations and validating transactions on streams – there is no global ledger.” Also noteworthy about Ceramic is its use of DIDs (Decentralized Identifiers), a W3C web standard for authentication that I wrote about last year. The DID standard allows Ceramic users to transact with streams using decentralized identities.
A significant part of its evolution also includes making its attacks and infrastructure more durable against detection, including continuously improving its persistence capabilities, evading researchers and reverse engineering, and finding new ways to maintain the stability of its command-and-control (C2) framework. This continuous evolution has seen Trickbot expand its reach from computers to Internet of Things (IoT) devices such as routers, with the malware updating its C2 infrastructure to utilize MikroTik devices and modules. MikroTik routers are widely used around the world across different industries. By using MikroTik routers as proxy servers for its C2 servers and redirecting the traffic through non-standard ports, Trickbot adds another persistence layer that helps malicious IPs evade detection by standard security systems. The Microsoft Defender for IoT research team has recently discovered the exact method through which MikroTik devices are used in Trickbot’s C2 infrastructure.
JSON documents can be large and contain values spread across tables in your relational database. This can make creating and consuming these APIs challenging because you may need to combine data from several tables to form a response. However, when consuming a service API, you have the opposite problem, that is, splitting a large (aka massive) JSON document into appropriate tables. Using custom-written code to map these elements in the application tier is tedious. Such custom code, unless super-carefully constructed by someone who knows how databases work, can also lead to many roundtrips to the database service, slowing the application to a crawl and potentially consuming excess bandwidth. ... The free-form nature of JSON is both its biggest strength and its biggest weakness. Once you start storing JSON documents in your database, it’s easy to lose track of what their structure is. The only way to know the structure of a document is to query its attributes. The JSON Data Guide is a function that solves this problem for you.
Top leadership has historically been responsible only for numbers and the bottom line. Profitability and utilization numbers are still important, but they generally do not motivate employees outside of the leadership, shareholders, and the board. Similarly, the feelings and well-being of the staff have long been the primary responsibility of the HR team. This no longer works in a company that is growing sustainably. The “Great Resignation” indicates that well-being has taken on a new level of critical importance. Arguably, a key contributor to this phenomenon has been employees’ lack of emotional connection to their employers. How can leaders help people feel their connection to the organization when they are physically separated? Empathy is the answer. The understanding of the empathetic leader bridges gaps and is a key component in communicating the personal role people have in the strategy of the company. In short, empathy is not just a tactic. Genuine concern for people is the ultimate business strategy for growth.
The Cloud Native Computing Foundation (CNCF) defines it as “scalable applications in modern, dynamic environments such as public, private, and hybrid clouds” – characterised by “containers, service meshes, microservices, immutable infrastructure, and declarative APIs.” However, cloud-native computing is more than just running software or infrastructure on the cloud, as cloud-only services still requires constant tweaking whenever you deploy applications. With cloud-native technology however, your applications run on stateless servers and immutable infrastructure that doesn’t require constant modification. According to a 2020 Cloud Native Foundation Survey, 51% of respondents stated improved scalability, shorter deployment time, and consistent availability as the top benefits for using cloud-native technology in their projects. Furthermore, Gartner claims more than 45% of IT spending will be reallocated from legacy systems to cloud solutions by 2024.
Quote for the day:
"Leaders are people who believe so passionately that they can seduce other people into sharing their dream." -- Warren G. Bennis,