Unfortunately, we don’t know whether secure cryptography truly exists. Over millennia, people have created ciphers that seemed unbreakable right until they were broken. Today, our internet transactions and state secrets are guarded by encryption methods that seem secure but could conceivably fail at any moment. To create a truly secure (and permanent) encryption method, we need a computational problem that’s hard enough to create a provably insurmountable barrier for adversaries. We know of many computational problems that seem hard, but maybe we just haven’t been clever enough to solve them. Or maybe some of them are hard, but their hardness isn’t of a kind that lends itself to secure encryption. Fundamentally, cryptographers wonder: Is there enough hardness in the universe to make cryptography possible? ... Most cryptographers, Ishai said, believe that at least some cryptography does exist, so we likely live in Cryptomania or Minicrypt. But they don’t expect a proof of this anytime soon. Such a proof would require ruling out the other three worlds — and ruling out Algorithmica alone already requires solving the “P versus NP” problem, which computer scientists have struggled with for decades.
Chomsky sparked a reorientation of psychology toward the brain dubbed the cognitive revolution. The revolution produced modern cognitive science, and functionalism became the new dominant theory of the mind. Functionalism views intelligence (i.e., mental phenomenon) as the brain’s functional organization where individuated functions like language and vision are understood by their causal roles. Unlike behaviorism, functionalism focuses on what the brain does and where brain function happens. However, functionalism is not interested in how something works or if it is made of the same material. It doesn’t care if the thing that thinks is a brain or if that brain has a body. If it functions like intelligence, it is intelligent like anything that tells time is a clock. It doesn’t matter what the clock is made of as long as it keeps time. ... Unfortunately, functions do not think. They are aspects of thought. The issue with functionalism—aside from the reductionism that results from treating thinking as a collection of functions (and humans as brains)—is that it ignores thinking.
PeopleLens was developed over two years by a team of Microsoft engineers and computer scientists. The aim was to create a machine learning system to help blind people navigate their social surroundings by identifying people and objects in photos. The team used a dataset of images annotated with labels indicating the presence of people and objects. They then used deep learning algorithms to train a computer vision model that could identify these labels in new images. ... The system uses computer vision algorithms to help the blind person understand their social surroundings. PeopleLens firstly identifies people in a scene and then provides information about them, such as their name and position. The PeopleLens platform consists of a wearable device and a cloud-based service. The device captures images of the surrounding environment and sends them to the cloud-based service, where they are processed by the machine learning algorithms. This information is then used to generate descriptions of the surrounding environment sent back to the wearable device.
If we shape the domain boundaries right, groups of related business concepts that change together will belong together and there will be fewer social and technical dependencies. Shaping good domain boundaries isn’t always a trivial task. When you stay high-level, you can easily fool yourself into thinking something is a sensible domain like the “customer domain” (this is usually something which connects to everything about the customer and results in a very tightly coupled system). I recommend using techniques like Event Storming and Value Stream Mapping to really get into the details of how your business works before attempting to define domain boundaries. Event Storming is a technique where you map out user journeys and business processes using sticky-notes. There aren’t too many rules, it’s a lo-fi technique which increases participation due to a very small learning curve. There is one rule though: processes are mapped out using domain events which represent something happening in the domain and are phrased in past tense, for example, ETA Calculated, Order Placed, Claim Rejected, and so on.
John Kodumal, CTO and cofounder of LaunchDarkly, says, “Technical debt is inevitable in software development, but you can combat it by being proactive: establishing policy, convention, and processes to amortize the cost of reducing debt over time. This is much healthier than stopping other work and trying to dig out from a mountain of debt.” Kodumal recommends several practices, such as “establishing an update policy and cadence for third-party dependencies, using workflows and automation to manage the life cycle of feature flags, and establishing service-level objectives.” ... “The first and most important is proper planning and estimating. The second is to standardize procedures that limit time spent organizing and [allow] more time executing.” Most development teams want more time to plan, but it may not be obvious to product owners, development managers, or executives how planning helps reduce and minimize technical debt. When developers have time to plan, they often discuss architecture and implementation, and the discussions tend to get into technical details. Product owners and business stakeholders may not understand or be interested in these technical discussions.
Today, many larger banks give you the option of depositing checks through your smartphone. Instead of actually walking to a bank, you can do it with just a couple of taps. Besides the obvious safeguards when it comes to accessing your bank account through your phone, a check also requires your signature. Now banks use AIs and machine learning software to read your handwriting, compare it with the signature you gave to the bank before, and safely use it to approve a check. In general, machine learning and AI tech speeds up most operations done by software in a bank. This all leads to the more efficient execution of tasks, decreasing wait times and cost. ... And while we are on the subject of banking, let's talk about fraud for a little bit. A bank processes a huge amount of transactions every day. Tracking all of that, analyzing, it's impossible for a regular human being. Furthermore, how fraudulent transactions look changes from day to day. With AI and machine learning algorithms, you can have thousands of transactions analyzed in a second. Furthermore, you can also have them learn, figure out what problematic transactions can look like, and prepare themselves for future issues.
The first problem is the disconnect, really chasm, it creates between the data consumer (analysts/data scientists) and the data engineer. A project manager and a data engineer will build pipelines upstream from the analyst, who will be tasked with answering certain business questions from internal stakeholders. Inevitably, the analyst will discover that data will not answer all of their questions and that the program manager and data engineer have moved on. The second challenge arises when the analyst’s response is to go directly into the warehouse and write a brittle 600 line SQL query to get their answer. Or, a data scientist might find the only way they can build their model is to extract data from production tables which operate as the implementation details of services. The data in production tables are not intended for analytics or machine learning. In fact, service engineers often explicitly state NOT to take critical dependencies on this data considering it could change at any time. However, our data scientist needs to do their job so they do it anyway and when the table is modified everything breaks downstream.
It’s important to understand some of the reasons why crypto has received the “boys club” reputation so we can smash it. At its core, I believe that because crypto was billed as a risky investment at the start. Women, who are naturally more risk averse, shielded away from the initial wave. Today, the gap between men and women in crypto aligns with the legacy of traditional investment verticals skewing toward men. ... In order for the movement to grow and gain legitimacy, we need everyone involved. I’d like to challenge men involved in Web3 to think of a woman they can invite to their next meeting. And, I’d like to challenge women to ask questions and see this opportunity as a way to align their wealth with men. This is a moment in which you can change the course of female wealth not just today, but well into the future. There are many women now joining the movement inviting others in, as well. It’s starting. And, I’m so pleased to be at the forefront of the shift. Web3 is making its debut in traditionally female venues now. Look no further than Shopify, the online sales platform, which reports 52% of its customers are women, is creating a marketplace for NFT sales.
CEOs who live up to Doctorow’s caricature by shutting down their emotions and coldly making decisions that harm people also incur a personal cost. Hougaard adds: “You turn into someone who you probably won’t like.” Often, empathy is touted as the antidote to mean business. But Hougaard thinks that an approach to leadership based solely on empathy has its own adverse side effects. “Leaders can literally take on the suffering of the people that they are inflicting suffering on and experience empathy burnout,” he explained. “Many CEOs tell me that they make multibillion-dollar decisions and sleep fine at night. But when they have to give tough feedback to employees or restructure the workforce, they don’t sleep for weeks.” They’re missing sleep because they don’t realize that empathy is only the first step in dealing with emotionally fraught people issues. “The mantra here is: connect with empathy but lead with compassion,” said Hougaard. “Empathy is nice for people, because they’re not alone anymore, but it’s not really helping them to get out of their suffering. Compassion is an intention. ...”
It is important to demystify what organizational culture means and how it impacts business outcomes, customer success, and employee satisfaction. It doesn’t have to be a top-down narrative that’s adopted universally: culture can be created at a team level. Managers have a huge influence on the subculture of their part of the organization. Managers can proactively opt to create a positive organizational culture. Adopting an open leadership mindset combined with open management practices evidently impacts key outcomes like customer satisfaction, employee engagement, innovation, and profitability. For an employee, the organization begins with their manager. Managers need to ask “What is the experience I am creating for my team?” Ask basic questions like “when do we want to meet?” and “how do we want to organize ourselves?” If there are bigger decisions to be made, consider how teams could be involved. Now, more than ever, employees are looking for empathy from their executives, to be consulted on their future, not just to have a meaningful say in the decisions that affect them, but what’s being decided on in the first place.
Quote for the day:
"Open Leadership: the act of engaging others to influence and execute a coordinated and harmonious conclusion." -- Dan Pontefract