Ontvang en verzend alle pakketten vanuit onze pakketwand (Lockster). Perfect voor ieder bedrijf, appartementencomplex of instelling dat de pakketstroom wilt faciliteren en automatiseren📦🔒 Hoe werkt Lockster? We plaatsen de Lockster in de centrale hal van het gebouw. De gebruikers maken een account aan en bestellen vanaf dan alle pakketten naar onze hub aan de rand van de stad. Lockster verzamelt alle pakketten en de Lockster-bezorger, bezorgt deze dezelfde dag nog in de Lockster. De bewoner of medewerker ontvangt een bericht met daarin een code om het pakket op te halen en haalt deze op. Voordelen Lockster: ✅ De gebruikers missen nooit meer een pakket. Ook is de troep van alle pakketten én diefstal verholpen. ✅ Er komt 1 vaste, vertrouwde bezorger per dag. Dit verhelpt overlast van 15 verschillende bezorgbusjes per dag. Ook scheelt dit veel CO2-uitstoot. ✅ Verzenden en retourneren is ook mogelijk vanuit de Lockster. ✅ Geen vertraging. Door onze contracten met de verzendbedrijven krijgen wij alles voor 11 uur binnen. Hierdoor kunnen wij dezelfde dag nog bezorgen en is er geen vertraging. ✅ Lockster is aansprakelijk voor verdwenen/gestolen pakketten. Meer informatie? Ga voor meer informatie naar https://lockster.nl/ of mail naar abel@lockster.nl💬

Lockster

by Caleb Simmons and Val Kleyman Tech companies face unique challenges when it comes to scaling their ROI sustainably. As the market continues to evolve and mature, it's becoming increasingly clear that responsible investment is the key to long-term success. Upcoming events worldwide promoting angel investments across the startup landscape, like the , and , are testaments that angels are a vital part of the startup capital landscape that helps grow the economy by promoting investment in all sectors. this month ACA Summit of Angel Investing Super Angels Summit in May At , we operate in that same spirit, onboarding responsible investors as our LPs to catalyze investment in tech startups across sectors. AdValorem Syndicate By prioritizing sustainability and scalability, AdValorem Syndicate creates standout ROI across the blockchain investment universe and drives the value of our offerings collectively. Far too many investors are focused solely on short-term gains. Yet the Corporate Finance Institute the risk associated with that “represents the purchasing power risk or risk associated with inflation. Investment returns may not be worth much as long as the level of inflation increases.” In this extended bear market, short-term investments in cryptocurrencies or cryptocurrency-powered platforms represent a discounted ROI to the companies receiving such investments, and their growth may suffer as a result. reports And, with cryptocurrency volatility at an , investors need to operate in the trenches with their portfolio companies -- especially those in the crypto space -- injecting not only capital, but talent, advisory, and hands-on assistance as well. all-time high Let's get specific. Even though is a staple strategy in cryptocurrency investing, short-term investments in high-volatility cryptocurrencies like pump-and-dump schemes stunt cryptocurrency markets and reward bad actors for their extremely finite investment behavior. #hodling This behavior disincentivizes partnership and long-term growth, and rewards hyper-exploitation at the expense of legitimate investors’ profits. Even more grievous, these behaviors have continued despite the chilling of cryptocurrency markets in 2022. Investing with a hands-on approach makes more sense. Capital Finance International that “60 percent [of sustainable investors] achieved positive returns while only eight percent were negative.” reports At , we vet, recruit, and leverage limited partnerships with accredited investors focused on long-term, sustainable growth. This allows us to use outline various platforms that may become high-growth companies centered on enduring value and multi-year climbing ROI. AdValorem Syndicate Want better returns? Put in the work. AdValorem LPs receive several tangible benefits from joining its long-term growth-focused angel syndicate. LPs receive gated access to high-quality deal flow - and with over 300+ registered today, benefit from highly vetted opportunities that otherwise would be unavailable to them on their own. This means AdValorem companies receive resources and financial backing previously only available to startups at accelerators like Y Combinator or 500 Startups. AdValorem LPs also benefit from cumulative and scaled ROI from joint efforts to drive the value of portfolio companies (and the cryptocurrencies that power them) sourced directly from AdValorem’s Angel Investment Syndicate. By leveraging the power of decentralized, long-term capital injection combined with active advisory from accredited LPs, AdValorem is building the future of syndicated investment in cryptocurrency startups. AdValorem’s LPs create long-term value by operating in lockstep with Syndicate-sourced portfolio companies, injecting liquidity while directly operating alongside startup founders to optimize operations, create additional business value, and leverage their highly advanced knowledge of cryptocurrency dynamics to advise on value creation long-term. The choice is clear: to supercharge your portfolio, work alongside smart, capable founders, and massively scale your ROI. join AdValorem’s Angel Investment Syndicate If you are accredited, us. join Happy Investing, Val Kleyman Lead Syndicator AdValorem Syndicate (Note: We're raising a $20M fund and currently accepting soft interest. Learn more ) here.

Utilizing LPs in Lockstep With Portfolio Companies

In the ever-evolving landscape of computing, the quest for optimal performance has led to the exploration of parallelism — the art of breaking down complex tasks into smaller, manageable ones. At the heart of this pursuit lies the fundamental concept of threading, a powerful technique that enables programs to execute multiple tasks concurrently, unleashing the true potential of modern computing systems. Threading involves breaking a program into smaller, independently executable units called threads, running simultaneously to enhance efficiency and speed. This is one of the fundamental concepts of parallel programming. Before delving into the intricacies of threading, it’s essential to grasp the theoretical framework that underpins the world of parallel computing. Flynn’s Taxonomy, named after computer scientist Michael Flynn, provides a comprehensive classification system for parallel processing architectures. Understanding Flynn’s Taxonomy is crucial because it lays the foundation for comprehending the intricate relationships between threads, processes, and the overall structure of parallelized systems. A clear understanding of these concepts is essential for understanding how to create multi-threaded applications. This article marks the beginning of a blog series on multi-threading, covering all the theoretical and programming aspects of multi-threading. We’ll dig deep into threading APIs and libraries (POSIX Threads, C++ threads) and OpenMP in the upcoming blogs. Without further ado… Flynn’s Taxonomy In 1966 [ ], computer scientist Dr. Michael J. Flynn proposed a classification of computer architectures based on their capability to process the number of instruction streams and data streams concurrently. This classification is still relevant and known as Flynn’s taxonomy. 1 In his initial paper, Flynn proposed four classifications: Single instruction stream, single data stream (SISD) Single instruction stream, multiple data streams (SIMD) Multiple instruction streams, single data stream (MISD) Multiple instruction streams, multiple data streams (MIMD) Flynn published three further classifications of SIMD in a subsequent paper published in 1972[ ]: , , and . 2 Array Processors Pipelined processors Associative Processors “Stream” refers to the sequence of data or instructions as seen by the machine during the execution of a program. [ ] 1 A single stream issues one element in each time unit, resembling a queue. Conversely, multiple streams typically issue numerous elements per time unit, akin to managing multiple queues simultaneously. Consequently, a single instruction stream (SI) issues only one instruction within a time unit, whereas a multiple instruction stream (MI) issues multiple instructions during the same period. Similarly, a single data stream (SD) issues a single data element per time unit, while a multiple data stream (MD) issues multiple data elements within the same time frame. Flynn’s taxonomy can be visually represented as: Before proceeding, it's important to recall how instructions are executed (the so-called instruction cycle). The instruction cycle consists of following steps: Fetch instruction - read the instruction from the memory. Decode instruction - decode to interpret the requested action. Fetch Data (if needed) - get the data for processing the instruction. Execute instruction (process data) - execute the instruction/process the data. Write-back data - save/store the results. Let’s try to understand what these different classifications mean. Single instruction stream, single data stream (SISD) SISD represents a computing architecture where a uniprocessor machine executes a single instruction stream, operating on a single data stream. This architectural design corresponds with the principles of the von Neumann model. The control unit retrieves a sequence of instructions from memory, decodes them, and dispatches them to the processing unit, which handles the data fetched from memory one unit at a time. Consequently, only one instruction is executed at any given time, reflecting a sequential execution of instructions. It is crucial to note that SISD processors may exhibit limited concurrency. For instance, the execution phases of sequential instructions can overlap, facilitated by an additional functional unit. SISD processors achieve concurrent processing through techniques like * *or . pipelined superscalar processors Pipelined processors involve executing multiple instructions in the same execution unit concurrently by segmenting the execution unit into different phases, while superscalar processors execute multiple instructions in parallel using multiple execution units. Before the 2000s, most single-core processors adhered to the SISD architecture, encapsulating the foundational structure of early computing systems. Single instruction stream, multiple data streams (SIMD) In a SIMD system, a single instruction stream is broadcast to all the processing elements (PE), and each element independently processes its assigned portion of the data. This allows for efficient parallelization of tasks and can result in significant speedup for specific computations. SIMD is well-suited for tasks involving performing the same operation on a large data set in parallel, such as graphics processing, scientific simulations, or certain mathematical computations. An instruction is supplied to numerous data elements concurrently, following a synchronized execution pattern. All instructions are queued in this “lockstep” process, and the associated data is distributed across distinct compute units. Each compute unit executes the initial instruction in the queue simultaneously, proceeds to execute the subsequent instruction in sync, and continues this synchronized execution for following instructions in the queue. SIMD architecture finds its most prevalent application in Graphics Processing Units (GPUs). In 1972, Flynn published another research paper classifying SIMD into three further classifications: Array Processors (SIMT) An array (or vector) processor executes an instruction set optimized for efficiently processing large one-dimensional arrays called vectors. In this subtype of SIMD, each parallel processing element has its distinct memory and register file. However, they receive the same instructions. Nvidia’s concept, implemented in GPGPUs, is the most common example of array processors. SIMT combines SIMD with multi-threading and is the basis of most modern GPUs. SIMT (Single Instruction, Multiple Threads) Pipelined Processors (packed SIMD) Just like other SIMD variants, pipelined processors receive the same instructions. However, they read data from a central resource and process fragments of that data independently. Finally, they write back the results to the same central resource. Modern SIMD variations, such as Intel’s AVX or ARM’s NEON, use register files as the shared resource (instead of main memory). This register-based SIMD is commonly called , underscoring its efficient use of register files for parallel computation. packed SIMD Associative Processors (predicated/masked SIMD) Each parallel processing unit receives the same instruction, but the decision to execute or skip the instruction is made independently based on local data within the unit. Modern terminology characterizes this as “ ” or “ ” SIMD, showcasing the ability to conditionally execute operations based on specific criteria within individual processing units. Modern SIMD ISAs such as AVX-512 and GPGPUs use predication to improve cache usage efficiency and ensure effective pipelining. predicated masked Most modern GPUs utilize a combination of these methods, most commonly SIMT with predication. Multiple instruction streams, single data stream (MISD) In MISD systems, multiple instruction streams operate concurrently on a single data set. This type of architecture is less common than other categories in Flynn’s Taxonomy and is often considered more theoretical than practical. In an MISD system, different processors or processing elements receive distinct instructions, but they all operate on the same data stream. Each processor performs a unique computation on the shared data stream based on its assigned set of instructions. The results from each processor can be compared or combined to achieve a final output. One possible use case of MISD systems is creating fault-tolerant systems or specialized applications where redundant computations are performed simultaneously for error checking or reliability. For instance, some earlier MISD systems were designed for fault-tolerant space mission systems. Multiple instruction streams, multiple data streams (MIMD) In MIMD systems, multiple processors or processing elements operate independently, each with its own set of instructions and data. It is well-suited for applications that require parallelism on different tasks or where the workload can be divided into independent subtasks. MIMD systems are highly flexible and represent the most widely used class of computer architecture. Depending on how MIMD processors access the memory, they are classified into two categories: . Shared and distributed memory Examples Let’s try to understand these classifications using a simple example. Suppose we have two 1-D vectors, and , each having an equal number of single-precision floating-point numbers (floats). A B We have to multiply all the elements in A with a scalar and then add them to every corresponding element in B. The result must be stored in a third array called . c result Thus, we have: Vector A: [a1, a2, a3, ...]
Vector B: [b1, b2, b3, ...]
Scalar: c

result: [c * a1 + b1, c * a2 + b2, c * a3 + b3, ...] SISD For the given task, scalar addition and multiplication would involve processing each element of the vectors sequentially. The processor performs the addition of corresponding elements from the two vectors, followed by the multiplication with a scalar, and stores the results in a third vector. The operations are carried out one at a time. For example, iterating through every iteration of this for loop without any optimizations would be the SISD way of getting the vector. result for(int i = 0;  i < n; ++i){
  result[i] = c* A[i] + B[i];
} SIMD Suppose our machine supports Advanced Vector Extensions (AVX) SIMD instructions. Thus, a single SIMD register (YMM) can store 8 single-precision floats. Therefore, each element-wise operation is performed in parallel while using SIMD, enhancing efficiency. The same scalar addition and multiplication are applied to multiple elements concurrently. For example, let us assume we have n elements in A and B. We can perform 8 data operations simultaneously, significantly improving our efficiency. Only n%8 iterations will be done sequentially. In fact, those n%8 elements can also be efficiently performed using mask registers. For example, a C++ code using SIMD intrinsics can really do wonders. // SIMD code (with intrinsics)
int ub = n - (n % 8); // 8 floats per SIMD register
__m256 x, y, z, s, temp; // vectors of 8 float entries
s = _mm256_set1_ps(c); // store the scalar c
for(int i = 0;  i < ub; i += 8){ // notice the increment of 8
  x = _mm256_loadu_ps(&A[i]); // load 8 elements of A in x
  y = _mm256_loadu_ps(&B[i]); // load 8 elements of B in y
  z = _mm256_loadu_ps(&result[i]); // load 8 elements of result in z
  temp = _mm256_mul_ps(s, x); // multiply elements of A with s and store in temp
  z = _mm256_add_ps(temp, y); // add elements of temp with y and store in z
  _mm256_storeu_ps(&result[i], z); // copy the elements to the result vector
} 
 /* Reminder loop for remaining iterations
  * Masked registers can also be used instead of this
*/
for(int i = ub;  i < n; ++i){
  y[i] = a * x[i] + y[i];
} Don’t worry if all the SIMD jargon feels a bit heavy. SIMD intrinsics deserve an article of their own, and I’ll cover them in a future article. offers detailed documentation on how to use different intrinsics. Intel intrinsics guide Modern compilers also enable vectorization if the user gives relevant optimization flags. Feel free to check my article on to learn more about such compiler flags. Compiler Optimizations MIMD In MIMD, multiple processors operate independently, each executing its instructions on its data set. In the given task, different processors might perform various operations on different elements of the vectors simultaneously. Depending on the shared or distributed memory model, several ways exist to solve the given task. For instance, if working with a shared memory programming model like OpenMP, different threads may work together and distribute the loop iterations (See__ __). OpenMP scheduling Similarly, for a distributed memory programming model like MPI, the vectors may be distributed to different processors such that each calculates their respective chunk of result. One of the processors may work as a coordinator, distribute the data, and even collect the final result from all the processors. For example, if p processors exist, the data may be divided into chunks. n/p // Each processor works on n/p data elements
Processor 1: [c * a1 + b1, ....] -> Result1
Processor 2: [c * ae + be, ....] -> Result2 // e = n/p +1
Processor 3: [c * af + bf, ....] -> Result3 // f = 2*n/p + 1 What next? This brings us to the end of the first part of this blog series on multi-threading. In the next part, we’ll dig deep into SIMD and MIMD systems, mainly introducing the programming models used to exploit them. References [1] Very high-speed computing systems | IEEE Journals & Magazine | IEEE Xplore [2] Some Computer Organizations and Their Effectiveness | IEEE Journals & Magazine | IEEE Xplore [3] Intro to Parallelism with Flynn’s Taxonomy (youtube.com) Also published here. Featured Photo by on . JACQUELINE BRANDWAYN Unsplash

Flynn’s Taxonomy and the Concept of Multithreading

This paper is under CC 4.0 license. available on arxiv Authors: (1) Tariq Bontekoe, University of Groningen; (2) Dimka Karastoyanova, University of Groningen; (3) Fatih Turkmen, University of Groningen. Table of Links Abstract & Introduction Preliminaries Zero-knowledge proofs and verifiable computing Privacy-preserving computations Requirements: an application’s perspective Verifiable, privacy-preserving computing Open challenges and future directions Conclusion & References 2. Preliminaries This section provides background on DLTs and methods for decentralized computation. Next to this, we provide an overview of the most common threat models used to describe cryptographic solutions for verifiable or privacypreserving computations. 2.1. DLTs and decentralized computations DLTs describe the collection of technologies where a (large) number of parties collectively create, maintain, and agree on a shared state or ledger. The shared state can be known completely to all parties, but can also be spread out over the respective parties. A well-known example of a DLT is blockchain, where all parties agree — via some predetermined consensus mechanism — on a public ledger containing the full state of a system. Blockchain is best known for realizing decentralized currencies such as Bitcoin [10], or smart contract systems like Ethereum [11]. However, DLTs, and specifically blockchains, also have applications in many other domains: from identity management [12] (e.g., self-sovereign identity (SSI)) to healthcare [13]. We specifically observe the proliferation of decentralized apps (or dApps), each of which can be seen as a blockchainbased autonomous application, whose behavior is defined by a collection of scripts (or smart contracts). In lockstep with an increasing number of dApps, we also observe an increase in using DLT for decentralized computations. Such decentralized computations can be applied in a DLT setting, but more importantly to any situation where multiple parties wish to perform a group computation. In this case, group computation can refer to: (1) a single computation on distributed data; (2) a decentralized computation on centrally stored data; (3) a combination of the first two. Decentralized computation becomes especially of interest when privacy of data or computation has to be taken into consideration. Examples of decentralized computation use cases that take privacy into account can be found in literature on outsourced computations, MPC, federated learning (FL), and DLTs. In subsequent sections, we will see that both DLTs and MPC are often used as base building blocks for realizing decentralized, verifiable, privacypreserving computation schemes. To analyze the privacy and security guarantees provided by those schemes, we also require an understanding of relevant threat models. 2.2. Threat models We informally describe the three most common security models regarding the possible behavior of the parties that have been corrupted by the adversary. For a more detailed treatment of the topic, we refer the reader to e.g., [14]: • Semi-honest adversaries: In this scenario, also known as the honest-but-curious or passive model, corrupted parties do not deviate from the selected protocol. The adversary does have access to all data received and owned by the corrupted parties, and tries to deduce as much as possible from this. • Malicious adversaries: In the malicious or active setting, the adversary can make corrupted parties deviate from the protocol in any way. A protocol is secure if it can prevent any malicious adversary from breaking security or privacy. When a protocol not only prevents cheating but also allows for determining who cheated, the protocol is said to support cheater detection, e.g., [15]. • Covert adversaries: Corrupted parties show the same behavior as in the malicious model. However, honest parties need only detect cheating parties with a given probability. In practice, it is assumed that the risk of getting caught and receiving a financial or reputation penalty is sufficient to deter an adversary. The notion of covert security with public verifiability was introduced in [16]. With public verifiability, not only can honest parties detect cheating with some probability, they can also construct a publicly verifiable certificate proving that a certain party has cheated, without revealing information on the private data of any honest party.

Verifiable Privacy-Preserving Computing: Preliminaries

Content Overview A space odyssey Demographic targeting isn’t new Targeting the old way The future of AI in political campaigns and its potential impact How is my local politician using AI to target me? AI’s Theory of Mind HAL’s big brother (Artificial General Intelligence) Should I be scared? A space odyssey The sentient Artificial Intelligence (AI) known as HAL-9000 (Heuristically programmed ALgorithmic computer) in the popular 1969 science fiction book is a chilling story of how a computer, programmed with a simple directive, can provide companionship, guidance, and assistance, not unlike your present-day ‘Hey Google’. 2001: A Space Odyssey In the book, the groundbreaking technology denoted as HAL is capable of natural language processing, facial recognition, and speech synthesis. These are purposeful reasons why AI currently exists, be it for improving healthcare through personalized treatments, supporting natural disaster responses using satellite imagery, or simply as a natural language chatbot to answer candidate questions for a political campaign. All good things. Fans of the book will remember that the AI takes a turn for the worse, challenging the human’s motives to protect its existence. The story continues to show how the relationship between its creators and AI can quickly spiral into despair and desperation after a conflict occurs between humans and technology. The book exposes the ethical implications of creating AI systems that appear self-aware and have autonomy, ultimately leading to humanity’s undoing, a popular narrative amongst many science fiction . AI writers Fast forward 50 years to the present day, and HAL makes Siri look like a chimpanzee and Google Assistant like a glorified ) so for now, we’re seemingly safe, you have nothing to worry about, and the world continues to spin without an ominous red light flashing back at us. Dr. Sbastio, (throwback to anyone from the late ’80s Demographic targeting isn’t new This isn’t a book review on Arthur C. Clarke’s remarkably accurate prediction on the , but a post about how AI may be targeting you and your family right now, influencing your decisions based on your political stance, age, ethnicity, and sexual orientation. Demographic targeting isn’t new; before the internet, exit polls, cold calling & paper-based surveys were used to profile communities. They would subsequently be sent direct mailings and cold-called, persuaded to vote for their candidate. Some of these techniques are still used today, although sparingly, given their high degree of variance and inaccuracy. future of AI This is where AI demographic targeting can significantly enhance accuracy. Using machine learning algorithms to analyze large volumes of user data, AI can find patterns where humans have failed. Generally speaking, the more data the model is trained on, the more accurate the predictions may be. What can we do now, that we couldn’t do before All your posts, LinkedIn articles, AMAs, and even that rant you sent last week can be harvested by these AI models. While this may seem terrifying, it depends on how the data is regulated. On the one hand, AI can do things that were not possible before, that is, enhance voter engagement by providing personalized information, as well as help in resource allocation by providing campaigners optimization techniques on which voter groups are more likely to be swayed, thus allowing them to prioritize groups differently. On the other, AI can also be used nefariously to indoctrinate young minds, take advantage of short attention spans, and . How all these ethical and social issues are handled is still undecided, as most countries do not have regulations in place. Facebook Reddit Twitter deep fake opponents’ images to sway decisions Technological revolution Artificial intelligence is transformative; it will undoubtedly change how we work and interact with technology on a monumental scale. This is where we are so far: Perhaps the iPhone launch wasn’t as revolutionary as the discovery of fire, but you get the picture. Targeting the old way Most political campaigns today take advantage of retargeting using cookies or what is known as a pixel on your machine. They follow you around the web like a leach targeting you with ads for products you already own. This is not Artificial Intelligence; this is just a dumb cookie. Ultimately this cookie tracks and retargets advertisements based on your browsing history. Typically this was done in the 2016 & as ad spending shifted from traditional forms such as TV and radio to more cost-effective digital media. Outside the US, Hong Kong also increased online ad spending to f s just to promote one policy speech. 2020 United States Primaries our times what it did in the previous year Was all this money well spent? Does targeting individuals this way reach their intended group, or does a healthy majority of the ad spend fall into an abyss of bots, fake accounts, and an unintended audience? Research done by the University of Baltimore suggests that at minimum 20% of ad spending goes to bots, ineligible voters, and other attackers using techniques such as . All of these could potentially be solved with AI trained on the right data, although, as I will discuss further, AI is not a panacea. IP masquerading Dumb cookies & AI’s new consent bar It is interesting to note that there are policies against placing trackers and cookies on users’ devices unless consent is given. You’re certainly aware of that pestering bar that keeps popping up all over the web, the one that you often fail to read before hastily whacking “Accept.” You can thank the Europeans for that. Although perhaps regulation is precisely what AI requires, the European Union may be exactly what we need. Their tight regulatory framework could force the rest of the world to mandate an that controls the ethical use and consent for how AI models ingest our data and disseminate information by way of , thus increasing trust and verifiability, more on this later. AI policy Explainable AI Consider this consent bar: If you hadn’t already noticed, that was an attempt to poke fun at the potential regulations that may be required for AI safety, as almost none currently exists. Expect a facsimile of the above to appear at websites near you; brownie points if you spotted the Borg reference. How did we get here? Political parties used to win races with community outreach, door-to-door canvassing, and engagement with local businesses, it took grit to get your message out, and the people these parties represent saw right through the party leaders’ B.S and, most importantly, made up their own minds with data they collected, the campaign message was human-led, with text generated from the candidate’s mouth. The future of AI in political campaigns and its potential impact The future of AI in political campaigns might be one we should be wary of in its current unregulated state. Instead of winning with good traditional economic policy and honest campaigning, the party with the most computing power combined with the biggest ad budget will win the race. In a very recent case, the Republican National Committee (RNC, a major part of the US republican party) used an criticizing Joe Biden in a doomsday-like dystopia video with fake AI-generated images. It was reported that members of the public believed the . AI-generated video videos were real Three years ago, AI was not really being utilised in election campaigns…You don’t have to be a software designer or a video editing expert to create very realistic-looking videos…We’ve gone beyond Photoshopping small parts of an image to basically generating a completely new image out of thin air. — Darrell West, Center for Technology Innovation at the Brookings Institution Going back to answer the question, was AI successful in reaching its demographics? Well, in the RNC case, yes, a lot of attention was drawn to that doomsday video in which a lot of voters, including democrats, believed the video was real, was it ethical? Probably not, exacerbating existing biases and reinforcing stereotypes has negative consequences for fairness and equality in the political process. Was it legal? Well, that is up to interpretation. According to the FCC, , it would be ‘unconstitutional’ otherwise. you’re pretty much allowed to lie How is my local politician using AI to target me? Politicians have been utilizing technology to market their platforms for years. From Obama’s 2008 ” ” victory to Trump’s hourly tweets to the Tories (UK conservative party) 2015 , there has been a massive move toward online demographic targeting, although misappropriating millions to line Mark Zuckerberg’s pocket may not seem like a scalable plan. email election Facebook campaign The future of AI in politics is predicting which group of people will likely sway to your party and what type of content will coax them. This is already being done and will be a significant timesaver for future Democratic elections. Another way AI will help is the ability to quickly find donors at a fraction of what it would typically cost. This can be achieved by using regression techniques, a type of supervised learning that trains on existing donor data. Your local politician will know now if you’re worth their time inviting you to that fancy fundraiser, the likelihood of your donation, and the approximate amount. This doesn’t sound too good, what is the future of AI? AI progress is inevitable and will continue to be used as a force for good as long as research emerges from the field of . Limiting the amount of identifiable data amassed from users’ machines is one regulatory hurdle. As you read this, there is a high chance of telemetry data ( ) being sent from your machine to the cloud. Your data and search queries are crowdsourced, contributing to advancements in natural language processing, deep learning, and large-scale training. Pat yourself on your back, you’re helping humanity, and your data is now part of the collective! AI safety cite Knowledge is power As the saying goes, “Knowledge is power,” thanks to your data, political campaigns are harnessing that power to gain insights into your behavior and preferences. As we have seen, campaigners can now target potential voters with surgical precision, personalize messages with increasing complexity, and have the ability to pinpoint where campaign benefactors live; this is just the tip of the AI iceberg. AI’s Theory of Mind The future of AI is evolving rapidly. In the last 3 years, AI has developed the Theory of Mind, a measurement of cognitive ability and strategic thinking. Scientists use this test to measure if humans or animals have self-consciousness and morality in social interactions. This same test was applied to GPT, and the results were astounding. The last test conducted in Nov 2022 concluded that GPT 3.5 had reached the strategic level of a 9-year-old child. What does this mean for demographic targeting? Well, If the president calls you to say , perhaps it could be AI-Joe Biden. hello HAL’s big brother (Artificial General Intelligence) As AI improves through (RLHF), which is a fancy way of assigning a thumbs up or down to a prompt response, the next iteration of AI could be (AGI). A hypothetical HAL-like agent that theoretically can accomplish any task autonomously without intervention. How this progress to AGI will affect the world is currently unknown, although many, including OpenAI, the company behind the infamous GPT, . Reinforcement Learning with Human Feedback Artificial General Intelligence are urging clear regulation How can this technology be improved? Whether AI is used for demographic targeting, natural language processing for virtual assistants, weather forecasting, or healthcare, there is much improvement to be made. AI is still embryonic, and regulation will need to be in lockstep with the fast pace of iteration. As Satya Nadela puts it, we’re still in the early stages of AI development: We’ve gone from the bicycle to the steam engine with the launch of ChatGPT3 — Satya Nadela, CEO, Microsoft — May 2023 Build Conference Improvements are needed in Long-term safety, a key concern of in its recent build conference. “ ” includes safe testing of user data and monitoring of inconsistencies in NPL to detect violence, self-harm, and hate speech. Microsoft Azure AI Safety Explainable AI Furthermore, trust needs to be reinforced by way of (XAI), which is the process that allows users to trust the output of machine learning algorithms. What ChatGPT believes to be true may not actually be true, and you, as a consumer, should be able to verify how the result of the algorithm came to be. Deep fake political videos could include fine print as to how the video was generated and what data is fact or fiction. A lot needs to be done to create a more transparent and reliable AI system that is safe for the general public to use. Explainable Artificial Intelligence Should I be scared? You need not be scared but educated and vigilant, look out for red flags, read the fine print, and know how the AI results are generated. Protect minors and teach them about the pitfalls and inaccuracies of such technologies. Know that propaganda can come in many forms, including something that may look real, feel real but entirely AI-generated. The last of human-led elections has begun, and you will be targeted. First, they ignore you, then they laugh at you, then they fight you, then you win. – Anonymous, but it’s highly possible it was written by HAL-9000. Originally posted . here

How AI Will Impact Political Campaigns in the Near Future—It All Started With Hal-9000

কীভাবে এআই অদূর ভবিষ্যতে রাজনৈতিক প্রচারাভিযানকে প্রভাবিত করবে—এটি Hal-9000 দিয়ে শুরু হয়েছিল

Wie sich KI in naher Zukunft auf politische Kampagnen auswirken wird – alles begann mit Hal-9000

Cómo afectará la IA a las campañas políticas en un futuro próximo: todo comenzó con Hal-9000

Comment l’IA aura un impact sur les campagnes politiques dans un avenir proche : tout a commencé avec Hal-9000

एआई निकट भविष्य में राजनीतिक अभियानों को कैसे प्रभावित करेगा—यह सब एचएएल-9000 से शुरू हुआ

AI は近い将来の政治キャンペーンにどのような影響を与えるか—すべては Hal-9000 から始まった

AI가 가까운 미래에 정치 캠페인에 어떤 영향을 미칠 것인가? 모든 것은 Hal-9000에서 시작되었습니다

Como a IA impactará as campanhas políticas no futuro próximo – tudo começou com o Hal-9000

Как ИИ повлияет на политические кампании в ближайшем будущем: все началось с Hal-9000

Yapay Zeka Yakın Gelecekte Siyasi Kampanyaları Nasıl Etkileyecek? Her Şey Hal-9000 ile Başladı

AI sẽ tác động đến các chiến dịch chính trị như thế nào trong tương lai gần—Tất cả đều bắt đầu với Hal-9000

人工智能将如何在不久的将来影响政治竞选——一切都从 Hal-9000 开始

309 Stories To Learn About Smart Contracts

Digital Public Infrastructure (DPI) is like the digital ID to rule them all — over people, places & purchases to power a social credit system: perspective Digital Public Infrastructure (DPI) is a mechanism for surveillance and control that combines digital ID, CBDC, vaccine passports, and carbon footprint , paving the way for 15-minute , future lockdowns, and systems of social credit. tracking data smart cities “Three major types of protocols that facilitate digital public infrastructure: digital identity, digital payments and data exchange. Collectively, we refer to these as the civic technology stack” United Nations, Building & Securing Digital Public Infrastructure Playbook, June 2022 On the surface, DPI might seem like something that could benefit people greatly, and it certainly has the potential to do so. However, that would require the people to have trust in governments, corporations, and unelected globalists working in lockstep to keep their best interests at heart. Sold as a mechanism for financial inclusion, convenience, improved healthcare, and green progress, DPI is an all-inclusive phrase applied to looming technocratic governance over people and the “ ” in which they’ll live. 15-minute smart cities The latest G20 India describes DPI as an evolving concept run by public and private entities — the fusion of corporation and state: Leaders’ Declaration “Digital public infrastructure (DPI), as an evolving concept and as a set of shared digital systems, built and leveraged by both the public and private sectors, based on secure and resilient infrastructure, and can be built on open standards and specifications, as well as opensource software can enable delivery of services at societal-scale” G20 India Leaders’ Declaration, September 2023 These “shared digital systems” include the desire to adopt Central Bank Digital Currencies (CBDCs) on a global scale, as well as establishing a “ ,” that will include vaccine passports, which were already championed by last year’s G20 Bali Leaders’ Declaration. WHO-managed framework to build a comprehensive digital health ecosystem In fact, EU Commission President Ursula von der Leyen told the G20 that vaccine passports, aka “digital health certificates,” were of DPI in action. one example “ The trick is to build public digital infrastructure, that is interoperable, open to all and trusted. Let me give you one example that is reality today. “* Many of you are familiar with the COVID-19 digital certificate. The EU developed it for itself.\The model was so functional and so trusted that 51 countries on 4 continents adopted it for free Ursula von der Leyen, G20 India Summit, September 2023 https://twitter.com/vonderleyen/status/1700760352437178427?embedable=true “We endorse the voluntary and nonbinding G20 Policy Recommendations for Advancing Financial Inclusion and Productivity Gains through Digital Public Infrastructure. We take note of the significant role of digital public infrastructure in helping to advance financial inclusion in support of inclusive growth and sustainable development” G20 India Leaders’ Declaration, September 2023 https://twitter.com/Spiro_Ghost/status/1700190385535107402?embedable=true On the “financial inclusion” front, the G20 India Leaders’ Declaration wholeheartedly welcomes the adoption of programmable s led by the Bank for International Settlements (BIS) and the International Monetary Fund (IMF), stating: CBDC “We welcome discussions on the potential macro-financial implications arising from the introduction and adoption of Central Bank Digital Currencies (CBDCs), notably on cross-border payments as well as on the international monetary and financial system. We welcome the BIS Innovation Hub (BISIH) Report on Lessons Learnt on CBDCs and look forward to the IMF Report on Potential macro-financial implications of widespread adoption of CBDCs to advance the discussion on this issue” G20 India Leaders’ Declaration, September 2023 This year’s G20 Leaders’ Declaration also rubber-stamped digital ID recommendations that emerged from last month’s , which called for G20 nations to rollout Digital Public Infrastructure as a means to “ .” B20 India Communique promote the digitization of identities at the individual, enterprise, and farm levels that are both interoperable and recognized across borders https://twitter.com/TimHinchliffe/status/1696590065328165040?embedable=true “Roll out digital public infrastructure to boost financial inclusion and healthcare access” B20 India Communique, August 2023 Speaking at the B20 India Summit in August, India’s digital identity architect Nandan Nilekani boasted how India had adopted Digital Public Infrastructure at scale and how other nations could follow suit and use DPI for everything from vaccine passports, tax collection, and toll payments to climate adaptation and the move towards a circular economy. “We reiterate our commitment to achieve global net zero GHG emissions/carbon neutrality by or around mid-century, while taking into account the latest scientific developments and in line with different national circumstances, taking into account different approaches including the Circular Carbon Economy, socioeconomic, technological, and market development, and promoting the most efficient solutions” G20 India Leaders’ Declaration, September 2023 Earlier this year, Nilekani spoke about DPI to the IMF, saying that digital ID, smartphones, and bank accounts were the three “tools of the New World” that everybody should have. https://twitter.com/TimHinchliffe/status/1648733976687673362?embedable=true “If you think, ‘what are the tools of the New World?‘ — Everybody should have a digital ID; everybody should have a bank account; everybody should have a smartphone. Then, anything can be done. Everything else is built on that” Nandan Nilekani, IMF Spring Meetings, April 2023 “ “ This digital identity determines what products, services and information we can access – or, conversely, what is closed off to us World Economic Forum, 2018 “People-centered smart cities need accessible, secure, and fair digital public infrastructure that powers digital services, and ensures everyone has equal opportunity to fully participate in civic life” United Nations, Building & Securing Digital Public Infrastructure Playbook, June 2022 For the unelected globalists, there can be no Digital Public Infrastructure, including CBDC, without digital ID, and there can be no “people-centered smart cities” without DPI. The World Economic Forum (WEF) envisions digital identity being linked to everything from financial services and healthcare records to travel, mobility, and digital governance — all of which are components of DPI. Likewise, the on Digital Public Infrastructure is pushing digital ID as being inseparable from DPI, stating, “ . United Nations playbook Broadly, there are three major types of protocols that facilitate digital public infrastructure: digital identity, digital payments, and data exchange “ .” These three protocols are typically required for most digital service transactions such as permitting, issuing licenses, or providing records that often require validating a user’s identity, enabling exchange of data across agencies and users, and finally authorizing payments online “ ,” the UN says that “ .” By prioritizing these three protocols local governments can set the stage for the successful development of an entire ecosystem of digital services in alignment with their community’s unique needs https://twitter.com/TimHinchliffe/status/1674105699897311253?embedable=true With G20 nations committing to net-zero carbon emissions policies by around 2050, many DPI initiatives are geared towards reaching that goal, meaning restrictions will be placed on what we can consume, what we can purchase, and where we can go thanks to the widespread implementation of digital ID and CBDC to track, trace, and control our every move in our people-centered, 15-minute smart cities. Think individual carbon footprint trackers, Ultra Low Emission Zones ( ), and to restrict “less desirable” purchases — all of which have been pushed by the unelected globalists at the WEF and elsewhere. ULEZ CBDC programmed by governments https://twitter.com/AndrewLawton/status/1529045188764921856?embedable=true Digital Public Infrastructure is like the digital ID to rule them all — over people, places & purchases in order to power the coming system, along with where health and/or climate lockdowns can be mandated whenever governments decide to grant themselves emergency powers for any given “crisis” — real or perceived. social credit 15-minute, people-centered smart cities This article was originally published by on Tim Hinchliffe The Sociable.

Digital Public Infrastructure: Transforming 15-Minute Smart City Governance 

ডিজিটাল পাবলিক ইনফ্রাস্ট্রাকচার: 15 মিনিটের স্মার্ট সিটি গভর্নেন্সের রূপান্তর

Digitale öffentliche Infrastruktur: Transformation der Smart City Governance in 15 Minuten

Infraestructura pública digital: transformando la gobernanza de las ciudades inteligentes en 15 minutos

Infrastructure publique numérique : transformer la gouvernance des villes intelligentes en 15 minutes

डिजिटल सार्वजनिक अवसंरचना: 15 मिनट के स्मार्ट सिटी प्रशासन में परिवर्तन

デジタル公共インフラストラクチャ: 15 分間のスマート シティ ガバナンスの変革

디지털 공공 인프라: 15분 안에 스마트 시티 거버넌스 혁신

Infraestrutura pública digital: transformando a governança de cidades inteligentes em 15 minutos

Цифровая общественная инфраструктура: трансформация управления умным городом за 15 минут

Dijital Kamu Altyapısı: 15 Dakikalık Akıllı Şehir Yönetişimini Dönüştürüyor

Cơ sở hạ tầng công cộng kỹ thuật số: Chuyển đổi quản trị thành phố thông minh trong 15 phút

数字公共基础设施：改变 15 分钟智慧城市治理

Network architecture patterns impact player interactions but they come with their own pros and cons. The choice depends on the genre and interaction scenarios. We'll discuss the lockstep, rollback, snapshot interpolation, and lag compensation patterns and share the best choice for particular genres. Hello everyone, my name is Dmitrii Ivashchenko and I'm a Lead Software Engineer at MY.GAMES. We're continuing our series of articles examining the landscape of the in 2023", and this time, we'll be discussing different network architectures in various game genres. Unity Realtime Multiplayer Network architecture patterns Game developers use various network architecture patterns to ensure reliable and fast interactions between players in multiplayer games. Each pattern has its own advantages and disadvantages, and selecting the appropriate pattern depends on the specific game genre and interaction scenarios you're working with. In this section, we'll discuss the following patterns: lockstep, rollback, snapshot interpolation, and lag compensation. Additionally, we'll discuss the best-suited patterns for different genres or games. Lockstep is one of the oldest methods of synchronizing network gameplay, and it's still frequently used today. Although this architecture can take a variety of forms, we'll focus on the most common implementation, and then discuss the necessary conditions, limitations, and possible configurations. Lockstep In lockstep architecture, each player sends their input to everyone else, and then advances their simulation as soon as they receive input from all players — that's it! The game is fully synchronized over the internet, uses very little bandwidth, and each player always sees events unfolding exactly the same as everyone else. To make a game compatible with lockstep architecture, several conditions must be met. Let's discuss those. Determinism The main requirement for the lockstep architecture is that the game simulation must support strict, bit-level . Since network code only synchronizes input, the game simulation must compute identical results on each machine in each frame with identical input data. Otherwise, game simulations will desynchronize, diverge, and deviate from each other — ultimately leading to games that look completely different. determinism This is usually a difficult condition to satisfy, and games must be carefully designed to maintain determinism. Developers often check game state checksums on each frame or at multiple points during a frame and compare these checksums between participants to help track down and fix sources of non-determinism where desynchronization occurs during game testing. Further, any game that uses floating-point arithmetic (i.e., most modern games) will require additional consideration. If your game runs on multiple platforms, floating-point determinism can be particularly challenging to achieve due to differences between platforms and compilers. Each compiler can use different sets of instructions, reorder instructions, or automatically vectorize. Each system can implement transcendental functions such as cosine, sine, and tangent differently. All of this can lead to desynchronization between platforms, or even between builds. Some developers implement their game simulation exclusively using fixed-point arithmetic or software-emulated floating-point arithmetic to bypass the non-determinism that arises from using floating-point numbers. Other sources of non-determinism include generating random numbers with different seeds or processing objects in different orders, such as contacts in physics. All of these considerations also greatly limit the third-party libraries that can be used as part of the game simulation, such as collision detection libraries, physics engines, etc. As a result, this may be impractical for many genres of games and engines. Fixed frame rate Lockstep also requires all players to synchronize the unit of time represented by each input and tick. In other words, the game will progress at a fixed frame rate. Some games lock the rendering frame rate to match the fixed simulation frame rate. Others allow rendering at an arbitrary frame rate and show interpolation between fixed tick results. Issues and limitations Although the lockstep architecture is one of the simplest to implement and comes with no visual artifacts, there are a number of issues and limitations. In some cases, these limitations can be overcome by making certain compromises in the implementation of the game. In other cases, these limitations may simply limit the applicability of this architecture for a given game. Input delay Lockstep prevents any delay-induced visual artifacts by waiting to progress until all relevant information has been received. This waiting has a downside: input delay. “Input delay” usually refers to the time between the user pressing a button and seeing a response on the screen. From a user's perspective, they perceive a response to their input when they see the result of that input on the screen. If the user has to wait until all other players receive and process their input before they see this response, this can lead to a significant input delay. And in games with high reactivity requirements, such as shooters, input delay can be a critical factor, as it can make the game less responsive and more difficult to play. Rollback Moving on, is a popular network code architecture that is widely used in modern competitive games, especially in fighting games. rollback Rollback can be seen as an extension of the classic lockstep architecture. In the Rollback architecture, players send their commands each frame and continue their game without waiting for commands from other players. The game progresses without waiting for data from remote players — this is called "client prediction" because remote input data is not yet known, and the client must make assumptions about the future actions of other players. Compared to the lockstep architecture (which provides perfect sequence from frame to frame but with input delay) the rollback architecture provides instant command response, but at the expense of sequence. In the rollback process, the game progresses and this is immediately displayed as soon as commands from a local player have been sent. However, since commands from the remote player have not yet been received, the displayed information is a prediction. Once data from the remote player is received, this new information must be reconciled with the prediction. If the information does not match the prediction, the game is rolled back to "correct" the error. What is a "rollback"? Let's assume that new data from a remote player has arrived for frame 5 while the client was preparing to render frame 7. In this case, the client needs to take the following steps: Load/restore the state of the entire game as it was on frame 4, i.e. the last unpredictable frame where all commands were known. Move the game to frame 5 using the original local commands for frame 5 along with the newly received commands from the remote player. Continue advancing forward by applying local commands for each frame until we reach the current frame (in our example, this is frame 7). After completing these steps, we may notice that the actions of the remote player in frame 5 could have led to different outcomes than predicted; it is these corrected outcomes that will now be displayed in the game. Thus, "rollback" in this context means that the game "rolls back" to the last frame when everything was known, and then "replays" forward with new information. This provides the ability to correct prediction errors. It should be noted that for this to work, the game must be able to quickly save and restore its full state, as well as move forward any number of frames with arbitrary commands. Depending on the complexity of the game, this may require significant computational resources. Snapshot interpolation is a technique that was popularized by the game Quake, and it has since been widely used in games and game engines derived from Quake. Indeed, this model is particularly well-suited for shooters. Snapshot interpolation The snapshot interpolation approach is based on the concept of two separate temporal streams for game objects: one reflects the state of objects in the past, the other the expected state of objects in the future. The client (player) sends their commands to the server, which processes these commands, changes the state of the game, and then sends back a "snapshot" of the current state of the game to the clients. This snapshot contains information about all objects in the game at the time of its creation. To keep the game responsive, clients apply part of the commands immediately to some objects, predicting their behavior. This results in objects being simultaneously present on the player's screen in different states: are represented as they were at a certain point in the past. Interpolated objects are represented as they are expected to be at a certain point in the future. Predicted objects This creates an interesting dynamic, for example, when your predicted character tries to dodge an incoming projectile that is interpolated. This can be more difficult than it seems, because your character is moving in the future, while the projectile is in the past. Nevertheless, this model has a number of advantages: The game instantly responds to player commands; unlike the lockstep model, no input delay is required. The client side requires significantly less processing time compared to full rollback architecture. Objects are interpolated between known states obtained from the server, so objects only pass through states they have already been in. It's important to note that for effective use of this model, the game must be able to quickly process and transmit snapshots of the game state, as well to instantly respond to player commands. Best architectures for different gaming genres Each gaming genre imposes its own limitations on network latency, stability, and throughput. For example, MMO games require high throughput, low latency, and high stability. Meanwhile, first-person shooter (FPS) games require low latency and high throughput. In both genres, it's recommended to use a server-client topology with an authoritative game server and data exchange based on state and input. The following table below provides a comparative overview of network requirements, data exchange formats, recommended network topologies, and network patterns for different video game genres. Genre Network requirements Data exchange format Recommended network topology Recommended network patterns Real-time strategies (RTS) Low latency, high stability Input Peer-to-peer, Server-client with authoritative client-host Lockstep MMO High bandwidth, low latency, high stability State Server-client with authoritative dedicated game server Predictive, Snapshot Interpolation First-person shooters (FPS) Low latency, high bandwidth State and Input Server-client with authoritative dedicated game server, Peer-to-peer Predictive, Rollback MOBA Low latency, high bandwidth State and Input Server-client with authoritative dedicated game server Predictive, Rollback Cooperative games Medium bandwidth, low latency State and Input Server-client with authoritative client-host, Peer-to-peer Predictive, Snapshot Interpolation Racing games Low latency, medium bandwidth State and Input Peer-to-peer, Server-client with authoritative client-host Predictive, Rollback Fighting games Very low latency, low bandwidth Input Peer-to-peer Rollback Battle Royale High bandwidth, low latency State and Input Server-client with authoritative dedicated game server Predictive, Snapshot Interpolation Tower Defense Low bandwidth, medium latency State Server-client with authoritative dedicated game server, Relay/matchmaking server Predictive Match-3 games Low bandwidth and latency requirements State Server-client with authoritative dedicated game server, Relay/matchmaking server Predictive We can draw some conclusions by analyzing this table: Network requirements depend heavily on genre: for example RTS and FPS games require low latency to ensure smooth and realistic gameplay. The data exchange format also varies depending on the genre: most games use state and input exchange, but some, such as RTS and action games, use only input. Network patterns and topology are chosen based on the specific requirements of each genre: action and sports games usually use prediction and rollback, while MMO and RPG games usually use snapshot prediction and interpolation. Rolling on Each game has its own set of requirements and genre intersections, which imposes its own limitations on network interaction. In many cases, choosing a proven solution will be a better alternative to independent development. In this post, we reviewed the main patterns of network architecture in realtime multiplayer games. We also compiled a general table of recommended solutions for different genres. Of course, these recommendations are not final and universal, but they can serve as a starting point when choosing your architectural solutions. In the next article, we will review ready-made solutions for realtime multiplayer games.

Unity Realtime Multiplayer, Part 7: Architectures in Different Genres

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