platform.sh

platformsh

Choosing the right platform for chatbot development: UX/UI perspective

At the moment, there are many options for describing dependencies in projects using Gradle. However, there is no recommended standard. So, a variety of approaches are used - and for new developers, it is not always obvious what, where, and how is used in a large and multi-module project. The 7th version of Gradle introduces a new feature that allows you to describe all dependencies in a centralized way. A version catalog is a replacement for the previous patterns, supported by Gradle, without the drawbacks of the previous approaches. To add support for version catalogs, you need to enable the feature by adding the following  in your settings file settings.gradle.kts enableFeaturePreview("VERSION_CATALOGS") Declaring a version catalog Gradle provides an API to declare catalogs. This API is designed for type safety. Version catalogs can be declared in the settings.gradle.kts dependencyResolutionManagement {
	...
    versionCatalogs {
        create("libs") {
            version("core", "1.6.0")
            version("activity", "1.3.1")
            
            alias("core-ktx").to("androidx.core", "core-ktx").versionRef("core")
            alias("activity-ktx").to("androidx.activity", "activity-ktx").versionRef("activity")
            alias("activity-compose").to("androidx.activity", "activity-compose").versionRef("activity")
            
            bundle("activity", listOf("activity-ktx", "activity-compose"))
        }
    }
} This will generate an extension in your Gradle build named libs app/build.gradle.kts dependencies {
    implementation(libs.core.ktx)
    implementation(libs.bundles.activity)
} If you are using an external directory, you must use this API. This allows users can get recommendations. The Version Catalog Can be Specified Using a File TOML To do that, create a file deps.toml [versions]
koin = "3.1.2"
kotlin = "1.5.31"
[libraries]

# Koin
koin-android = { module = "io.insert-koin:koin-android", version.ref = "koin" }
koin-android-compat = { module = "io.insert-koin:koin-android-compat", version.ref = "koin" }
koin-androidx-workmanager = { module = "io.insert-koin:koin-androidx-workmanager", version.ref = "koin" }
koin-androidx-compose = { module = "io.insert-koin:koin-androidx-compose", version.ref = "koin" }

[bundles]
koin = ["koin-android", "koin-android-compat", "koin-androidx-workmanager", "koin-androidx-compose"]

[plugins]
kotlin = { id = "org.jetbrains.kotlin.android", version.ref = "kotlin" } The file consists of 4 major sections: TOML the section is used to declare versions that can be referenced by dependencies [versions] the section is used to declare the aliases to coordinates [libraries] the section is used to declare dependency bundles [bundles] the section is used to declare plugins [plugins] Version catalogs can be declared settings.gradle(.kts) dependencyResolutionManagement {
	...
    versionCatalogs {
          create("dependencies") {
            from(files("deps.toml"))
        }
    }
} This will generate an extension in your Gradle build named deps app/build.gradle.kts dependencies {
    implementation(deps.bundles.koin)
} Gradle Versions Plugin This plugin provides a task to determine which dependencies have updates. Additionally, the plugin checks for updates to Gradle itself. You can add this plugin to your top-level build script using the following configuration. settings.gradle.kts pluginManagement {
	...
    plugins {
        id("com.github.ben-manes.versions") version "0.39.0"
    }
} build.gradle.kts plugins {
	...
    id ("com.github.ben-manes.versions")
}

android {
	...
}

dependencies {
	...
}

tasks {
    named<com.github.benmanes.gradle.versions.updates.DependencyUpdatesTask>("dependencyUpdates") {
        checkForGradleUpdate = true
        outputFormatter = "html"
        outputDir = "build/dependencyUpdates"
        reportfileName = "report"
    }
} Displays a report of the project dependencies that are up-to-date exceed the latest version found, have upgrades, or failed to be resolved. After you added the above code snippet to the file, you can simply type gradle dependencyUpdates It will check if there are newer versions for your dependencies, and this displays a report to the console Text Report ------------------------------------------------------------
:app Project Dependency Updates (report to plain text file)
------------------------------------------------------------

The following dependencies are using the latest milestone version:
 - androidx.constraintlayout:constraintlayout:2.1.0
 - com.android.application:com.android.application.gradle.plugin:7.1.0-alpha12
 - com.github.ben-manes.versions:com.github.ben-manes.versions.gradle.plugin:0.39.0
 - io.insert-koin:koin-android:3.1.2
 - io.insert-koin:koin-android-compat:3.1.2
 - io.insert-koin:koin-androidx-compose:3.1.2
 - io.insert-koin:koin-androidx-workmanager:3.1.2

The following dependencies have later milestone versions:
 - androidx.activity:activity-compose [1.3.1 -> 1.4.0-alpha02]
     https://developer.android.com/jetpack/androidx/releases/activity#1.4.0-alpha02
 - androidx.activity:activity-ktx [1.3.1 -> 1.4.0-alpha02]
     https://developer.android.com/jetpack/androidx/releases/activity#1.4.0-alpha02
 - androidx.appcompat:appcompat [1.3.1 -> 1.4.0-alpha03]
     https://developer.android.com/jetpack/androidx/releases/appcompat#1.4.0-alpha03
 - androidx.core:core-ktx [1.6.0 -> 1.7.0-beta01]
     https://developer.android.com/jetpack/androidx/releases/core#1.7.0-beta01
 - com.google.android.material:material [1.4.0 -> 1.5.0-alpha03]
     https://github.com/material-components/material-components-android
 - org.jacoco:org.jacoco.ant [0.8.3 -> 0.8.7]
     http://jacoco.org
 - org.jetbrains.kotlin.android:org.jetbrains.kotlin.android.gradle.plugin [1.5.31 -> 1.6.0-M1]
 - org.jetbrains.kotlinx:kotlinx-coroutines-android [1.3.9 -> 1.5.2]
     https://github.com/Kotlin/kotlinx.coroutines

Gradle release-candidate updates:
 - Gradle: [7.2: UP-TO-DATE]

Generated report file build\dependencyUpdates\report.html HTML Report Resources: sample (full code) https://gist.github.com/tkrest/98410f8b7ac4490e82df10838f14f4e3 Sharing dependency versions between projects https://docs.gradle.org/current/userguide/platforms.html Gradle version plugin https://github.com/ben-manes/gradle-versions-plugin Declaring Rich Versions https://docs.gradle.org/current/userguide/rich_versions.html#rich-version-constraints

The Central Declaration of Dependencies for Android Developers

Is it possible to create a useful API in the course of a day or two? That’s exactly what the authors of the new framework called API Platform promise to us. However, you probably wonder if it’s really worthwhile to spend time getting to know yet another tool. Well, it depends. One of the most important challenges in the software development is to “not reinvent the wheel”. That’s why, from the beginning of the history of coding, developers have tried to create solutions that’d speed up their work by automating the most repetitive processes. However, selecting tools that’ll really meet your requirements is almost always a painful procedure. Among the most promising frameworks/tools that have shown up in the PHP world recently is API Platform. In my work, I often consult solutions with my clients — who usually are CTOs as myself. And now, more and more often, I get questions about API Platform. Is it really a useful framework or just some temporary hype? We’ve used API Platform in The Software House in a dozen of projects now, therefore, I feel like I already have enough experience to share with you some of our thoughts. OK, what’s that? API Platform was created in 2015 by a Symfony expert, Kévin Dunglas. It was claimed to be one of the most important development events on SymfonyCon in Paris, alongside Platform.sh and Blackfire.io. Among those three, I think API Platform was the most successful one. While the first version had indeed some annoying mechanisms, the framework was greatly enhanced in version 2. But let’s start from the beginning: what does it actually do? In short, it’s a tool based on the PHP Symfony framework which helps you speed up building the backend part of your project. Especially in the beginning of the development process (but sometimes on later stages as well), we have to put the same code over and over again without any or with some little changes, for example when implementing the paging mechanism or an authorisation system. It doesn’t matter if you’re creating an app for a bookshop or for a car renting company — for sure there’ll be a list of some items, a place where you can add an item, edit it and, of course, delete it. Mechanisms which allow you to speed up the development of those parts are called CRUD or boilerplate. And there are lots of them on the market. You’ve probably already used Admin Generator for Symfony 1 or Scaffolding for Ruby on Rails. What’s new in API Platform is that times have changed and now we’re facing a completely different approach to architecture. All the previous solutions were creating monolith codes where backend and frontend were created on the server side. Now, we tend to implement more service-oriented architecture with a backend API that serves the data and a couple of clients consuming it — for example frontend SPA applications or mobile apps. API Platform focuses only on API, leaving the presentation layer to others. What exactly does API Platform give us from the beginning? Here’s the list of my favourites: — JSON, XML, CSV, whatever you need. You can also change the format with one simple alteration in a configuration file. All methods serve POST, PUT, GET and DELETE options. A ready-to-use API based on REST with a variety of formats to choose , using one of the most popular PHP frameworks now — Symfony. The API is built securely on the best programming practices and shipped with a caching mechanism (Varnish). It’s already optimised (we all know how much developers hate to write any docs, leave alone supporting them in a long term). Plus a fancy mini-console to run simple queries and play around with the examples. There’s an automatically-generated documentation . This stuff can be enabled with just a few lines of code. You don’t have to worry about things like filters, paging mechanisms or error messages If I was about to present estimation for building from scratch an API which, for example, handles about 3 database entities (let’s say, we have a school with classes, teachers and students and need to build a really simple CRM) with some authorisation, filters, paging and documentation, I’d say it’d took about 2–3 weeks to implement. With API Platform we can prepare it in one day. OK, I know what you’re thinking at this moment: RAD is only good at the beginning, as when you go deeper, there are always problems. Isn’t it the same here? Yes — that’s also true in this situation. After the basic setup, things tend to be a little more complicated than in the tutorial on the API Platform’s website (BTW: a very good tutorial). After you create a database as you like and configure all the relations, you’ll probably need some custom fields or some custom business logic for your API. You start to look for the code and it occurs that there isn’t any! You have to do everything via the special filters or create customised controllers. And both solutions require pretty good knowledge of Symfony — the framework on which everything’s built here (I’ll get back to it later). API Platform’s documentation used to be one of its biggest flaws but now it’s updated and very useful Is this for basic projects or rather for advanced ones? Actually, it’s for both — the main problem is the gap in between. If you’re a beginner and just want to create a very simple API, you can for sure use this tool but please do not expect that you’ll be able to customise it exactly as you wish. I’ve also seen frontend developers who wanted to specialise their app on frontend side using one of the modern SPA frameworks (like Angular or React for example) but still needed a simple API that’ll deliver them some backend data — API Platform worked very well for them. The same situation is with mobile apps. I know that right now there are similar solutions just delivering you simple backend service in the cloud, like Firebase, but for me it’s still easier and faster to run API Platform on my server than to configure it all using some cloud panel. It also gives me more flexibility. If you just follow the tutorial and change the sample database names to your own, you should really get your app within one day tops. It’s also available as a Docker container, but my advice is to not use it if you haven’t had anything in common with Docker before — you’ll just get frustrated at the beginning and lose lots of precious time and nerves. Installing it via composer (the second installation method in the tutorial) is, in my opinion, way more intuitive. But what if I want to create a more complicated app? Yes, you can create almost everything you want. But only under one condition — before you begin, you have to really know Symfony first. This is the biggest advantage and the biggest flaw of this solution — it’s all standing on top of another framework. Furthermore, as I’ve mentioned above, API Platform isn’t generating a CRUD code that you can just modify as you wish. I’ve used such code-generating solutions and they were very good at the beginning (you can just get some code and modify it easily without reading any further instructions) but there were certain issues with that: . Sometimes even despite following very good programming practices. If you’re not an experienced developer, it becomes very messy soon . You cannot generate it again for some database changes. There are some solutions which allow that but you need to cope with pretty confusing overriding mechanisms. After modifying any part of the pre-generated code, there’s no turning back API Platform isn’t generating any code ­–­­­ it creates the content “on the fly”. It’s not really a new solution, as I remember similar tools being available years ago (even API Platform’s predecessor, Admin Panel from Symfony 1) but the problem with these older mechanisms was that usually you had very low flexibility on what you could create — limited to what the author allowed you to do. Here the things are a little different, as API Platform is built following the “Symfony way” of how things should work, meaning it’s based on event-driven mechanisms. How deeply should I know Symfony before using API Platform? As I wrote above, if you really need to do advanced things in API Platform, you need to know Symfony first. If you’ve created a couple of Symfony-based apps before and you love it, API Platform will be a perfect enhancement for you. If not, you should first focus on understanding how Symfony works. Here are the three most important things to learn: — That’s basically the first and most important part of Symfony you really need to get to know. You can find the documentation here: . The only way you can modify anything within the API is if you hook into one of the events. To do it properly and avoid situations when you’re asking yourself “Why this @#$ isn’t doing what it’s supposed to do?”, you really need to know how events are working in Symfony and which events API Platform is using. EventStack https://symfony.com/doc/current/components/http_kernel.html — All the data that’s transferred via API goes through a serialisation component built in Symfony. You can find the documentation here: . If you’re wondering why the given field or structure isn’t displayed in the GET method, the issue probably lays in the serialisation. Serializer https://symfony.com/doc/current/components/serializer.html — You may have met this pattern in other modern frameworks. The general idea is that all the things should be independent of each other and then just bonded together at the final stage by “injecting one to another” — it improves the sustainability of the code and is a must-have in modern apps right now. Here’s the description of how Symfony handles that: . As you might suspect, API Platform is also injecting its code to other services and can also benefit from services that you wish to inject. Dependency Injection http://symfony.com/doc/current/components/dependency_injection.html There’s also one more thing that some PHP developers might not be familiar with — annotations. Annotations first started to be a popular solution in Java and soon migrated to other platforms, including PHP. The basic concept is that you define how the class or method should behave by commenting it in a proper way on top of it. Some frameworks use annotations so much, that in many cases the comment before the method name contains more data and instructions than the code itself — API Platform is definitely one of them. I call such approach “annotation programming”. It can be very useful and descriptive but if you haven’t been familiar with such things before, you’ll need to get used to it now. Here’ a link for further reading: . Of course, you can still use XML or YAML but I strongly recommend to get to know annotations, as it seems that they’re the future of programming. http://php-annotations.readthedocs.io/en/latest/UsingAnnotations.html This is a pretty big threshold to cross. Is it worth it? If you know Symfony very well, along with API Platform you have a very powerful tool in hand. Creating APIs will be much more simple and cost-effective, everything based on a foundation of a very solid framework which is Symfony. But what if you’re not so familiar with the solutions I’ve mentioned above? Learning all of this stuff requires a lot of time and practice. In return, you just receive an ability to speed up the process of creating the most common element of the API. However, in my opinion, it’s worth it. You’ll have to learn most of those components anyway if you’re planning to create a long-term project — if not in this framework, they’ll still appear in other. Although at first this solution might be very hard to implement (you might be frustrated that the things aren’t working as you expected), there’s a good reason why it works this way, as it prevents developer from making a mess in the code which was one of the biggest problems in PHP some time ago. API Platform’s pet spider showing his love for other frameworks — especially Symfony It seems like you’re defending API Platform. Does it have any flaws? Like all frameworks, API Platform also has some annoying issues that you just have to cope with. For me, the most important one is that you often cannot directly find help for the issue you’re dealing with on the internet. If you just type “API Platform <the problem you have>” in Google, you probably won’t find any solution and might be under impression that the community is very small. That’s because 90% of questions regarding API Platform are in fact questions about Symfony itself. Those questions are answered by the Symfony community — which, lucky for us, is a pretty big one. Therefore, in order to find your answer, you should rather search Symfony forums than the API Platform ones. This, of course, means that you have to ask the right questions and (again) know Symfony quite well. You can also get help by asking on GitHub — very often from Kévin Dunglas (the creator of API Platform) himself. Second thing is the naming convention. Like any other CRUD solution, API Platform has to generate some names of classes and methods on its own. Usually, those names reflect the names used in the database but you still have to guess how the particular method or filter has been named. For example, when working with API Platform in The Software House, we had a table named “Language Spoken” and we got a method for retrieving all languages that users speak. There was a lot of confusion about the auto-generated name: will it be “getLanguagesSpoken” or “getLanguageSpokens” or maybe something else? We finally got a debugger tool from Symfony (which I also suggest you should do) to see the call stack and found out what the generated name was in fact. As other CRUD solutions which I’ve seen on the market, API Platform also has issues with virtual fields. Basically, the concept of such automated tools is that we have some database and that’s the main source of all the data and structure. However, sometimes we need to return values that are not directly stored in the database but just calculated based on some business logic (or, for example, fetched from some 3rd party source). In these cases, you can still add such fields to the API but they won’t be 100% supported and you may expect some issues with that. For example, sorting is by default executed as the database query, so for a virtual field, you cannot add sorting easily (even if you implement some functions providing sorting rules). It’s also worth mentioning that although you can use any output provided by API Platform in your other Symfony bundles, it is very complicated to make API Platform use any other bundles and make API out of them. For example, if you’re using SonataAdminBundle or Sylius (which are very popular solutions in Symfony) you cannot count on API Platform to create API from the code provided there. Summary I grew up in the gaming community where there’s the saying that good games are “easy to learn and hard to master” — I think this sentence also describes API Platform. If you just want to quickly create a very simple API, this tool will be very suitable for you. If you want something more — it’s also possible but, unless you’re a Symfony expert, you’ll need some time and practice to achieve it. Although I always approach fresh projects with reserve (API Platform is just 2 years old now, which for me is a very short period of time), I think this framework is a very promising solution. By releasing the second version about half a year ago, the guys from the core team have really shown that they were able to fix their own mistakes and were listening to the community. Most of the annoying problems from the first version were solved, for example, now there’s a possibility to use subresources from parent resource instead of creating two separate API endpoints for that. It’s a sign that the framework is going in a good direction. To be honest, I’ve also wanted to write something about gaps in the documentation. We were even planning to release our internal subsidiary document explaining differences between documentation on their site and the real code situation. But, meanwhile, the documentation has been updated — which also shows that the framework is still evolving and we might expect even better versions in the future. The article was written by Marek Gajda and was first published on The Software House Blog . Visit the blog for more articles on best development practices and software outsourcing tips.

Practical guide to API Platform: How to tell if it’s the right framework for you

Hardware acceleration for data processing has a long history. It’s not new. When I was a child, my dad outfitted our 12 MHz 286 system with the optional 80287 Floating Point Unit (FPU) coprocessor, which ran at a whopping 4.7MHz. The thing was a beast. It did wonders for Lotus 1–2–3. Not long after, this specific FPU was integrated into the CPU, reducing the need for the external coprocessor for math operations. However, coprocessor actually stuck around for longer. You know that GPU you’re using to read this article? DSPs in your mobile phone? Sound card? They’re all coprocessors, and there’s a reason they’re still around. Your CPU can’t do everything No, really. It can’t. The actual silicon for the Intel x86 (the heir to the 8086 and my beloved 80286) architecture is already crazy complex. Adding additional operations to the x86 architecture has diminishing returns, according to Rick Merrit of the EETimes. to the point where it may not be worth it In fact, GlobalFoundries, one of the largest silicon chip manufacturers has announced it’s . It’s just not worth it anymore. abandoning plans for 7nm chips Intel Haswell-EP Xeon E5–2699 V3 Die on wccftech Why is that a problem? Well, each processor core in the x86 architecture does so many different things, that it has actually made programmers complacent. Don’t worry about writing bad software — the processor will make sure it runs fast! Of course, Intel already knew this back in the early 2000s. They tried to kill off the x86 architecture with Itanium, a processor series designed for 64-bit computing. However, AMD had a different plan, and released 64-bit capable x86 processors, obstructing Intel’s plans. And that’s how we ended up with this mess. Going multicore The only way to get more oomph out of CPUs was to scale them out. That’s why today’s CPUs are almost always multi-core. They have anywhere from 2 to 28 cores for higher end server chips like the Intel Xeon series. The clever pipelining and vectorization, caching, and some other complex systems found in the x86 architecture allow for pretty good use of the miniature electronics. Some tasks can run in groups, and the processor may even correctly predict what the next actions are, before they happen. However, as I previously mentioned — these processors are already crazy complex. Other architectures do exist. ARM, which you can find in a huge variety of devices — from battery chargers to your phone, and even your car — has a reduced instruction set, also known as RISC. IBM has similarly gone the RISC route. RISC processors have a much simpler core, which can’t do as much as more elaborate CPU cores. Software has to be specifically written to be multi-core to benefit from these kinds of processors. For now, x86 has a stay of execution. It could be another 10, 15 years, but x86 will be going away. If you’re interested in finding out more about what Intel was doing to enable parallel processing, read “The Story of ISPC”, about the Intel SPMD Program Compiler: https://pharr.org/matt/blog/2018/04/18/ispc-origins.html Here come the GPUs and GPGPUs Graphical Processing Units (GPUs) are also not really new. The term has , but tended to focus strictly on graphics. NVIDIA’s first card marketed as a “GPU” was the GeForce 256, from 2009. However, General Purpose GPUs ( s) actually started appearing around 2007, when NVIDIA and ATI (now AMD) started equipping their 3D graphics cards with more and more capabilities, like Unified Pixel Shaders. These capabilities could be repurposed to perform operations like matrix multiplications, Fast Fourier Transform, wavelet transform, and more. been in use since 1986 at least GPGPU NVIDIA Tesla C870 with 128 CUDA cores ( ) NVIDIA Accessing these features meant that new programming constructs had to be invented. So, in 2008, Apple started developing OpenCL. By late 2009 it was already adopted by AMD, IBM, Qualcom, Intel, and even NVIDIA. AMD decided to support OpenCL extensively, instead of their “Close to Metal” framework. AMD’s Radeon Pro V340, with 512GB/s of memory bandwidth. Source: AMD While NVIDIA supported and still supports OpenCL, they didn’t abandon their own framework, known as CUDA. Today, CUDA is the de-facto standard for high-speed, high-throughput GPU computing. It’s essentially a software layer that gives direct access to the GPU’s virtual instruction set and parallel computational elements, for the execution of compute kernels. Newer NVIDIA GPUs have up to 5,760 CUDA cores With 256 cores in 2009, and with up to 5,760 cores in a single modern GPU, I think it’s easy to see why GPUs are useful for operations that require a high degree of parallelism. But it’s not just parallelism — GPUs have extremely large memory bandwidth, which makes it suitable for high-throughput applications. Why don’t we just replace CPUs with GPUs? Not all applications are suitable for GPUs. GPUs are mostly useful if: The actions are repetitive The actions are mostly independant (do not rely on eachother) The actions are computationally intensive GPUs have found some interesting uses outside of graphics (and cryptomining). Today, GPUs are found in a variety of data processing scenarios. They can be used as part of a data processing pipeline, for running machine learning models on, as part of a hardware accelerated relational database, or just render a result nicely. Let’s take a look at three of the main areas where GPUs can help in the data processing pipeline: GPUs for Stream Processing New stream processing solutions, like ’s Plasma Engine can take advantage of GPUs for stream processing data coming in and out of databases (GPU or not). This tool can be used to perform analysis and/or transformation of streaming data on the GPU. FASTDATA.io The main competitor to FASTDATA’s engine is , which is available as an open-source add-on from IBM. GPU-enabled Spark GPU Databases for Analytics Practically all GPU databases are ground-up, purpose built for analytics. GPU Databases. Source: The 2018 Big Data Landscape The reason for building a GPU database from scratch seems to be the difficulty in retrofitting an old, row-based database with a very different technologies. There are several players in the GPU database field, each with their own benefits: OmniSci (formerly MapD) — In-memory GPU database for geospatial use-cases SQream DB — GPU data warehouse for larger-than-RAM data-sets BlazingDB — GPU database based on Parquet Kinetica — In-memory GPU database HeteroDB — Postgres plugin, based on UDFs for fast NVMe access Brytlyt — Postgres plugin for in-memory compute Blazegraph — Graph database GPUs for machine learning Machine learning tends to be highly suitable for GPU architecture. It’s not just about the high level of parallelism, and the multitude of matrix operations. A big part of the reason is actually the aforementioned memory bandwidth. NVIDIA Tesla V100 core-arrangement. Source: ServeTheHome CPUs are what we call latency-oriented processors. Latency-oriented means that they favor lower latency of operations with clock cycles in the nanoseconds (~3 billion operations per second on one piece of data). In contrast, GPUs are throughput oriented. They will perform a single operation over lots of data at once, at a lower clock speed (~1 billion operations per second on multiple chunks of data). GPUs sacrifice latency with a slower clock speed, in order to get a higher throughput on every clock cycle. There is no shortage in different GPU-enabled machine learning frameworks: TensorFlow cuBLAS Caffe Theano Torch7 cuDNN MATLAB cxxnet/mxnet Deeplearning4j Keras Mathematica and many more. Not just GPUs — ASICs, FPGAs and other exotic hardware While CPUs and GPUs are general purpose, we have some other alternatives in hardware, but there are trade-offs. We’ve already discussed CPUs and GPUs, so let’s talk about FPGAs and ASICs specifically. FPGAs Field Programmable Gate Arrays (FPGA) are a high-speed, programmable chip. They’re extremely popular for prototyping and specialist devices. They contain an array of programmable logic blocks, and a hierarchy of reconfigurable interconnects. Because FPGAs are programmable, they can be altered after they’ve been manufactured, but not by anyone. They provide good performance, but can cost quite a bit — which is why they remain mostly in low-yield devices. You can find FPGAs in medical equipment, cars, specialist equipment, etc. FPGAs provide higher memory bandwidth, lower power consumption than CPUs (and GPUs), but they may struggle with floating point computations, and are hard to work with. In fact, they’re quite difficult to program for, even though recent tooling like OpenCL for FPGA exists. In data analytics, FPGAs are suited for simple repetitive tasks. They’re found in modern platforms like , the , the , and devices like . One of the most well-known usage of FPGAs in analytics is , although the FPGA aspect of Netezza seems to have been mostly abandoned. Microsoft’s Project Brainwave Swarm64 Database Accelerator for Postgres Xilinx Alveo Data Center Accelerator Ryft IBM’s Netezza Eventually, Netezza ended up as part of and the FPGA components were used primarily for specific operations that would benefit from FPGAs like decompression and transformations. Other operations still used the CPU. IBM PureData Source: IBM Data Retrieval Technologies RDBMS, BLU, IBM Netezza and Hadoop ASICs Custom designed Application-Specific Integrated Circuits (ASIC) are the fastest option for high-speed operations, in the silicon level. Because these chips are highly customized, they can deliver the best performance with minimal overhead. ASICs are commonly found as digital signal processors (DSPs) — in audio processing, video encoding, and networking among others. These applications are primarily in mass-produced devices like cameras, mobile phones, wireless routers, etc. Because of the high cost and time investment in creating custom ASICs, they are also the most costly option. They are actually the cheaper option if you plan on manufacturing millions of them, but at smaller volumes they are hard to justify. For that reason, they’re not commonly found in analytics or data processing proper. Comparing the tradeoffs Here’s a summary of the different processors and their tradeoffs: Each of the processor types has its own tradeoffs The future of hardware accelerated data processing The future is in hardware diversity, but not all of the coprocessors are suitable for every task. : Keep standard, sequential programs and code running The CPU’s job Run high-throughput parallel code The Coprocessor’s job: Increase the throughput of parallel programs CPU + Coprocessor: It’s clear that the x86 CPU architecture will not last forever. It simply is not sustainable for the long-run. It’s not advancing at the same rate that modern data workloads are. Because they’re not bound to the same backwards compatibility, GPUs are advancing much faster. Modern data workloads are varied, varying, growing, and fast-moving. GPU and other hardware accelerated data processing applications are growing in popularity, with some notable successes in the machine learning space and data warehousing space. Tesla V100 with NVLink GPU-to-GPU and GPU-to-CPU (Power9) connections. Source: NVIDIA FPGA and GPU-enabled hardware, like IBM Power9 with NVLINK and OpenCAPI, as well as Azure and AWS GPU and FPGA instances are just a sign of things to come. Conclusion I believe hardware accelerated data processing is still in it’s infancy, but is likely to become more widespread, as the accelerating technologies continue to make strides over today’s CPU architecture. Thanks for reading this to the end! I know it isn’t easy — there’s a lot of information (and acronyms) to digest. This article is the first in a series of hardware accelerated data articles. In my next article, I will deep dive into how different hardware accelerated databases utilize their respective hardware accelerators. If you enjoyed this article, leave a comment below, or share with your friends and colleagues.

A gentle introduction to hardware accelerated data processing

How Blockchain Technology Can Redefine Health Data

Articles this week demonstrate how our past — legal corporate dominance — is undermining and stifling our future and potential. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Citizens United was only one in a long sequence of corporate legal victories — “[2010] ruling…[said] corporations have a First Amendment right to spend their money to influence elections [and]…seemed to extinguish any hope of serious campaign-finance reform…. Citizens United [While] rights of corporations were not debated in the state conventions that ratified the constitution, nor were companies mentioned in the Federalist Papers…corporations have repeatedly been able to exploit laws meant to benefit others… ….[ [For example] the 14th Amendment — designed to give equal citizenship to freed slaves — persuaded the Supreme Court that it had also meant to protect companies So] between 1868…and 1912, the justices decided 28 cases asserting the rights of African-Americans, almost all [lost]…[while] 312 cases on the rights of companies…succeeded in striking down minimum-wage and child-labor laws. …. ‘For most of American history…the Supreme Court failed to protect the dispossessed and the marginalized, with the justices claiming to be powerless in the face of hostile public sentiment.’ By contrast ‘the court has insisted that broad public sentiment favoring business regulation must bend to the demands of the constitution’ …. A focus on the rights of the listener, not the speaker, was at the heart of the Citizens United case…followed, in 2014, by the Hobby Lobby case, which gave the company an exemption from a federal rule requiring large employers to include birth control in their employees’ health plans…[as] religious freedom [ But] in aggregate, court rulings on corporate rights do not make sense. .” Companies are obliged to prioritize the profits they make for shareholders, rather than seeking to benefit employees or the wider community; that sits oddly with the notion that businesses also have political and religious freedom https://www.economist.com/news/books-and-arts/21738343-citizens-united-was-only-one-long-sequence-corporate-legal-victories-triumph _So, here we are again nostalgically celebrating an event that occurred 240 years ago — quarter millennia in the past —…_medium.com Doc Says –We’re Still Somnambulant Strangers in a Strange Land YouTube, the Great Radicalizer — “[During] 2016 presidential election campaign…[Trump] YouTube started to recommend and ‘autoplay’ videos for me that featured white supremacist rants, Holocaust denials and other disturbing content…. I created another YouTube account and started watching videos of Hillary Clinton and Bernie Sanders…[then] directed to videos of a leftish conspiratorial cast [like]…secret government agencies and…[U.S.]government was behind the attacks of Sept. 11. …. As with the Trump videos, YouTube was recommending content that was more and more extreme than the mainstream political fare I had started with ….I experimented with nonpolitical topics…[and] same basic pattern emerged…[that] promotes, recommends and disseminates videos…to constantly up the stakes [ So] YouTube may be one of the most powerful radicalizing instruments…[with] nexus of artificial intelligence and Google’s business model…[where] longer people stay…the more money Google makes…. Its algorithm seems to have concluded that people are drawn to content that is more extreme than what they started with — or to incendiary content in general …. …. [A] Wall Street Journal [study]…found that YouTube often ‘fed far-right or far-left videos to users who watched relatively mainstream news sources’ …. What we are witnessing is the computational exploitation of a natural human desire…carried along by the exciting sensation of uncovering more secrets [leading] viewers down a rabbit hole of extremism, while Google racks up the ad sales…[saying] they are merely serving us what we want …. .” This situation is especially dangerous given how many people — especially young people — turn to YouTube for information There is no reason to let a company make so much money while potentially helping to radicalize billions of people…while asking society to bear so many of the costs https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html?rref=collection%2Fsectioncollection%2Fopinion&action=click&contentCollection=opinion&region=rank&module=package&version=highlights&contentPlacement=1&pgtype=sectionfront Tim Berners-Lee: We need a ‘legal or regulatory framework’ to save the Web from dominant tech platforms — “[H]e thinks we need to do to save the Web from the concentration of power of a ‘few dominant platforms’ that has ‘made it possible to weaponize the Web at scale’…[by] as for to manipulate public opinion…. serving easy vehicles third parties, including foreign governments, . ‘ What was once a rich selection of blogs and websites has been compressed under the powerful weight of a few dominant platforms…[as] a new set of gatekeepers…controlling which ideas and opinions are seen and shared’…[creating] barriers to competition by acquiring emerging startups and new innovations and hiring the top technology talent Add to this the competitive advantage…user data gives them, and we can expect the next 20 years to be far less innovative than the last’ …. .” [These] companies have been charged with fixing these problems when their ultimate goal has more to do with appeasing shareholders than ensuring the greater good of society… which is why some level of regulation may be required https://venturebeat.com/2018/03/12/tim-berners-lee-we-need-a-legal-or-regulatory-framework-to-save-the-web-from-dominant-tech-platforms/ _As you read this you are aware of the fact that you are reading. At the same time, you are also aware of many other…_medium.com Ghost of Christmas Past? Three Cheers for Financial Repression — “[It’s] bondholders…losing money because…[of] government manipulation…forcing them into buying ‘bonds at unfavorable rates, i.e., below the prevailing level of inflation…[so] ‘savers’ subsidize ‘investors’ …[Now] it’s market forces more than government policy…. . [Still] policymakers have concentrated on ensuring the profitability of the bond market more than just about anything else…[and] the financial sector has quadrupled in size relative to the rest of the economy… What is good for the bankers has not been good for the economy….[So] sympathy for bondholders seems misplaced, considering median male real wages are lower [than]…were in 1973, that median real income is lower than…when Jimmy Carter was President, and household income is lower than it was during the last millennium …. [Still] financial repression has historically proven benefits. By limiting the advantages of existing wealth, it lessens inequality and increases social mobility [and]…subsidizes investment, which inevitably stimulates growth …. [Clearly] cheap capital, much more than cheap labor, has been China’s primary comparative advantage in the global economy …. [U.S.] 1945 to 1980, inflation generally exceeded the interest rate on treasuries . Bondholders lost money, but corporate profits boomed, and real wages more than doubled…[so] repayment of war debt essentially painless. Between 1947 and 1980, government ‘debt…fell from 120% to 35% of GDP. …. Since 1982, bondholders [with]…a 30-year treasury [and]…15% coupon, has been one of the great money makers of all time…[and] shifted vast wealth from workers to the already rich. The economy as a whole has not done so well. Growth in the developed world since 1982 has been miniscule…. Wages have been stagnant, increasing inequality rampant …. Financiers, who lend money, want high interest rates, low inflation [because]…greatest fear is to be repaid in depreciated currency. Everybody else, however, including entrepreneurs, prefers low interest rates, moderate inflation, easy money…so they can repay those debts more easily Persistently low interest rates demonstrate capital is no longer a scarce resource. Let’s stop pretending it is, allowing weathholders to demand high returns. If interest rates are a dialogue between the past and the future, savings come from the past and investment leads to the future. . ” Financial repression is the secret sauce that makes economies grow and makes us all richer… Probably the easiest way to stimulate financial repression is to raise the inflation target. evonomics.com/three-cheers-for-financial-repression/ Find more of my ideas on Medium at, . A Passion to Evolve Or click the Follow button below to add me to your feed. Prefer a weekly email newsletter — no ads, no spam, & never sell the list — email me with dochuston1@gmail.com “add me” in the subject line. May you live long and prosper!Doc Huston

News — At The Edge — 3/17

Originally published at www.cloudways.com on December 6, 2016. Not many people realize that their choice of hosting solution could determine the success or failure of their project. Even the best code base fails to perform to its maximum potential because of a hosting solution that fails to provide appropriate support. Hosting a website or a web application in the cloud could be a technically challenging endeavour. The server size needs to be determined, server has to be setup and then the appropriate application/platform is installed. Not many people, even in the ICT industry, are able to set up and manage servers. Since a number of entrepreneurs and online business owners do not possess the required technical skills to manage the servers that host their applications and/or store, they either need to hire system administrators (not a cost effective choice for startups) or deal with regular server and application crashes. Given this, it is no wonder that managed cloud hosting is a rapidly growing sector of the overall hosting industry. More and more people now opt for managed cloud hosting for their projects because of the many benefits of the hosting solution. Why Industry Leaders Recommend Managed Cloud Hosting? In order to understand why many industry leaders recommend managed cloud hosting for PHP projects, it is important to understand the benefits of these hosting solutions. In effect, managed cloud hosting offers three important benefits. The first and the most important benefit is the fact that these solution providers handle all the hassles of server management. All the user has to do is to decide the basic parameters including RAM, bandwidth and server location. The hosting solution provider takes care of everything else. The second benefit is the improved security of the web application. Server and application level security is an ongoing task and i particularly important for PHP applications. Even the most dedicated of the user could not keep up with the routine of applying security patches. The managed hosting solution provider takes care of all security related issues from patch application to threat detection and management. Finally, managed cloud hosting providers offer scalable solutions that are ideal for ecommerce websites. Scalability is an important business process requirement that comes in handy during crunch times. At these moments, having the capacity (and capability) of increasing server resources prevents the web store from crashing. Let’s see what industry leaders have to say about the uses and benefits of managed cloud hosting: Anna Filina | Twitter LinkedIn It makes a lot of sense to offer managed hosting solutions, since not every developer even knows how to manage servers. It took me many years to learn how to do it, then many more years to learn how to do it properly. Even for those who can do it, it can be too much of a hassle. Time is precious. Nicolas Grekas | Twitter LinkedIn Managed hosting solutions provide great value. They allow devs to focus on their core issue: solving business problems. I can’t wait until infrastructure as a code is really ready for the masses. Adam Wathan | Twitter LinkedIn I haven’t used Cloudways myself, but I’m a huge fan of any product that tries to help developers focus on launching their next idea instead of wasting time on server configuration. Benjamin Eberlei | Twitter LinkedIn Managed Hosting or Platform as a Service is very valuable to get projects started, when you don’t have enough time or experience with the operations part of web development. The requirements for a good platform has changed drastically in the last 15 years with the arrival of AWS (IaaS), Heroku (PaaS) or Docker (FaaS). I think the separation of companies providing hosting resources and management and software platforms on top is a very welcome occurrence because it introduces much needed specialization. AWS and Digital Ocean provide very powerful hosting platforms but will never perfectly support PHP stack in every detail. Thus, managed hosting companies like Cloudways that build on top of the modern clouds functionality are very useful. Josh Lockhart | Twitter I believe hosted solutions like Cloudways, Heroku, or Platform.sh are *amazing* tools to quickly stage and launch PHP applications without having to worry about infrastructure. I strongly encourage exploring these hosted platforms because they abstract away most infrastructure concerns. Sometimes you just want to write and deploy code without worrying about how to setup, provision, and monitor servers. If the price is right, use a hosted platform. Managing and monitoring servers isn’t fun. Let others do that for you. Tessa Mero | Twitter LinkedIn With 1 click installations, make sure you always offer the latest version of Joomla. When I see a hosting platform offer an outdated version of Joomla, instantly I’ll never ever recommend anyone to use them for life. Also, the default PHP version is pretty important too. Too many hosts fail at providing this. Lorna Mitchell | Twitter LinkedIn For the 99% of people, I really think that if you’re not using a Platform as a Service (PAAS), you’re wasting your time. Yes, you often need to learn a few things to get setup, but from personal experience, this is a *massive* investment for the future. I might spend 8 hours today setting up some complex things in a PAAS, and enjoy one-click (one second) deploys for the next 12+ months. That’s a huge time and money saver. If I never have to manually spin up a server on Amazon, I’ll be happy. I’ve never tried Cloudways specifically, but the space is still wide open. One of the biggest opportunities as a consumer of PAAS is the ability to have a micro-service architecture. If my app is 6 mini-apps, it’s almost a non-starter to try to do this with traditional hosting. But if a ‘git push’ deploys them all and they’re able to talk with each other, now we’re in business. Yitzchok Willroth (@coderrabbi) has a great presentation on micro-service architecture, and automatic deploying is one of his key points. Matt Stauffer | Twitter LinkedIn I love the idea of managed hosting solutions in general. I have managed my own servers in the past, and it’s one of those things I put in the stack of “better to pay people other to do for me.” I’m glad I have an accountant I can pay to know about taxes for me. Glad I have a lawyer. Glad I have a plumber. And I’m glad I have folks who run SaaSes that make my development life easier–including hosting and dev-ops. Manuel Lemos | Twitter LinkedIn Managed hosting can be wonderful when you need to scale your applications when they do not fit a single server. There is plenty of knowledge out there on the Web you can use to learn how to assemble all the required parts to implement a scalable solution but having it done by somebody that is in the field for many years is often worth the investment of outsourcing that effort. Gabriela Davila | Twitter LinkedIn I am always interested to see how companies manage to enable developers to learn and explore the new options and technologies available in the market. I think you do a great job with one-click install apps, because everything is contained inside the same server and easily manageable. When you talk directly about database hosting, it implies having the application in the same network too because of latency and such. One idea I would be interested in seeing in action would be getting a big client with high traffic and database needs and remove the DB from that one-click install and seeing how that goes. There is a lot going into tuning, specially for Magento, I think you could do good things with that experience. As you can see, PHP experts and Industry leaders strongly favor managed hosting. If you wish to try out managed hosting for your existing or upcoming projects, I recommend Cloudways, which comes with a trial period.

Why PHP Influencers Endorse Managed Cloud Hosting

Fabien Potencier, CEO of SensioLabs announcing SensioCloud, a Platform-as-a-Service for Symfony During the keynote address at SymfonyCon Berlin 2016, Fabien Potencier, CEO and founder of SensioLabs, announced . The offer combines the services of , , , and into one comprehensive package. SensioCloud, a new complete cloud offering focused on the development and runtime of Symfony applications Platform.sh Blackfire.io Fastly.com SensioLabs’ own support services A fully maintained stack at your fingertips Enable any service via a few configuration lines in a YAML file. SensioCloud supports PHP 5 and 7. Each service is deployed in its own custom, lightweight LXC container for optimal performance, stability and flexibility. Don’t see your service on the list? Ask for it, we’ll make it happen. Some of the key advantages include a diverse set of managed services, a Composer-based build process, Symfony optimized runtimes, massive scalability (top tiers can provision >300 CPUs and nearly 6TB of RAM for one application), and a 99.99% uptime guarantee for the entire stack — from CDN to PHP to Database/Cache/Search services). Fabien pointed out that other PaaS offerings can only provide individual SLAs on the component parts, not the whole offer. Availability of SensioCloud is imminent — early 2017 was the guidance given during the keynote. The SensioCloud team then proceeded to give demos on the expo floor to hundreds of interested Symfony developers. The title of the Keynote: “The State of Symfony in the Cloud” Fabien Potencier introduced SensioCloud by showing the history of Symfony’s efforts to be prepared for Cloud-first development and deployment. SensioCloud supports the provisioning, orchestration, configuration of rich services, such as PostgreSQL (example shown). SensioCloud will be available in early 2017 Showtime! 1,200 Symfony developers in attendance. A seriously hard-core technical crowd. Frédéric Plais, CEO of Platform.sh, tweeting the good news Anne-Sophie Bachelard of SensioLabs greeting the crowd and introducing Fabien. The Sulu team, long friends of Platform.sh, in attendance. The SensioCloud team sweetened up visitors to the booth by serving cotton candy.

A Cloud for Symfony

In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold. In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold.

Can The Government, The Technology Platforms, or Anyone Else Control the Spread of Deepfakes?

Können die Regierung, die Technologieplattformen oder sonst jemand die Verbreitung von Deepfakes kontrollieren?

¿Pueden el gobierno, las plataformas tecnológicas o cualquier otra persona controlar la propagación de deepfakes?

آیا دولت، پلتفرم‌های فناوری یا هر کس دیگری می‌توانند گسترش دیپ‌فیک را کنترل کنند؟

Poden o goberno, as plataformas tecnolóxicas ou calquera outra persoa controlar a propagación dos Deepfakes?

क्या सरकार, प्रौद्योगिकी प्लेटफॉर्म या कोई अन्य डीपफेक के प्रसार को नियंत्रित कर सकता है?

政府、テクノロジープラットフォーム、あるいは他の誰かがディープフェイクの拡散を制御できるのでしょうか?

Afaka mifehy ny fiparitahan'ny Deepfakes ve ny governemanta, ny sehatra teknolojia, na olon-kafa?

Дали владата, технолошките платформи или некој друг може да го контролира ширењето на Deepfakes?

के सरकार, टेक्नोलोजी प्लेटफर्म, वा अरू कसैले Deepfakes को फैलावट नियन्त्रण गर्न सक्छ?

Czy rząd, platformy technologiczne lub ktokolwiek inny może kontrolować rozprzestrzenianie się deepfake'ów?

Dawladdu, Platform-yada Tignoolajiyada, ama cid kale ma xakamayn kartaa fiditaanka been-abuurka?

Чи може уряд, технологічні платформи чи будь-хто інший контролювати поширення Deepfakes?

Authors:
(1) Hamid Bateni, Nobitex Labs (hamid@europe.com);
(2) Keyvan Kambakhsh, Nobitex Labs (keyvankambakhsh@gmail.com). Table of Links Abstract and 1 Introduction 2 Proof of Liability and 2.1 Commitment 2.2 Merkle Tree 2.3 Leaves Structure 2.4 Proof Statement 3 Proof of Reserve 3.1 Ethereum 3.2 Bitcoin 4 Proof of Solvency 5 Future Works and References 4 Proof of Solvency Proof of Solvency is a method by which a cryptocurrency platform can demonstrate that it has sufficient funds (assets) to cover its obligations (liabilities), without revealing sensitive details about its customers or its own operations. This provides a level of transparency and trust for the platform’s users, while maintaining privacy. [3] Proof of solvency constitutes a crucial aspect of trust in digital asset platforms. It consists of two main components: Proof of Liabilities and Proof of Reserves. Proof of Liabilities asserts the total obligations a business has to its customers, while Proof of Reserves confirms the total assets the business holds. We have discussed various approaches to achieve private proofs for both components, and now, we aim to combine these to achieve a Private Proof of Solvency. Let’s explore the whole picture for achieving private Proof of Solvency: Initially, a business commits to its liabilities. This involves creating a Merkle tree of all customer balances and publishing the root of this tree. the business creates a Zero-Knowledge Proof (ZKP) that attests to the correctness of the liabilities tree. The business also provides an infrastructure that allows users to verify that their liabilities have been correctly included in the tree. In the next step the business proves its reserves using one of the methods we introduced earlier. This involves creating a proof for each of the business’s addresses. Lastly, the business integrates these proofs in a ZKP circuit to show that the total reserves are at least equal to the total liabilities. This involves comparing the total balance of specific addresses with the balance of the last right leaf in the liabilities tree. These inputs are kept private in the circuit, with the only public data being the block root and the root of the liabilities tree. By successfully completing this process, the business achieves a private Proof of Solvency. This demonstrates that the business has sufficient reserves to cover all its liabilities without revealing any sensitive information, such as individual customer balances or business addresses. This approach not only enhances the privacy of customers and the business but also bolsters trust in the platform by providing a verifiable proof of solvency. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Hamid Bateni, Nobitex Labs (hamid@europe.com); (2) Keyvan Kambakhsh, Nobitex Labs (keyvankambakhsh@gmail.com). Authors: Authors: (1) Hamid Bateni, Nobitex Labs (hamid@europe.com); (2) Keyvan Kambakhsh, Nobitex Labs (keyvankambakhsh@gmail.com). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Proof of Liability and 2.1 Commitment 2 Proof of Liability and 2.1 Commitment 2.2 Merkle Tree 2.2 Merkle Tree 2.3 Leaves Structure 2.3 Leaves Structure 2.4 Proof Statement 2.4 Proof Statement 3 Proof of Reserve 3 Proof of Reserve 3.1 Ethereum 3.1 Ethereum 3.2 Bitcoin 3.2 Bitcoin 4 Proof of Solvency 4 Proof of Solvency 5 Future Works and References 5 Future Works and References 4 Proof of Solvency Proof of Solvency is a method by which a cryptocurrency platform can demonstrate that it has sufficient funds (assets) to cover its obligations (liabilities), without revealing sensitive details about its customers or its own operations. This provides a level of transparency and trust for the platform’s users, while maintaining privacy. [3] Proof of solvency constitutes a crucial aspect of trust in digital asset platforms. It consists of two main components: Proof of Liabilities and Proof of Reserves. Proof of Liabilities asserts the total obligations a business has to its customers, while Proof of Reserves confirms the total assets the business holds. We have discussed various approaches to achieve private proofs for both components, and now, we aim to combine these to achieve a Private Proof of Solvency. Let’s explore the whole picture for achieving private Proof of Solvency: Initially, a business commits to its liabilities. This involves creating a Merkle tree of all customer balances and publishing the root of this tree. the business creates a Zero-Knowledge Proof (ZKP) that attests to the correctness of the liabilities tree. The business also provides an infrastructure that allows users to verify that their liabilities have been correctly included in the tree. In the next step the business proves its reserves using one of the methods we introduced earlier. This involves creating a proof for each of the business’s addresses. Lastly, the business integrates these proofs in a ZKP circuit to show that the total reserves are at least equal to the total liabilities. This involves comparing the total balance of specific addresses with the balance of the last right leaf in the liabilities tree. These inputs are kept private in the circuit, with the only public data being the block root and the root of the liabilities tree. By successfully completing this process, the business achieves a private Proof of Solvency. This demonstrates that the business has sufficient reserves to cover all its liabilities without revealing any sensitive information, such as individual customer balances or business addresses. This approach not only enhances the privacy of customers and the business but also bolsters trust in the platform by providing a verifiable proof of solvency. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

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