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(NASDAQ: IPA) ("ImmunoPrecise" or "IPA" or the "Company"), an AI-driven biotherapeutic research and technology company, today announced that its subsidiary, BioStrand®, has successfully solved the Information Integration Dilemma (IID) by developing a unique technology design that enables their patented HYFT Technology to encapsulate and unify diverse data modalities - including syntactical (sequence) data,

IPA's Subsidiary, BioStrand, Solves the Information Integration Dilemma (IID) for Systems Biology

https://www.bing.com/th?id=ODF.hLbdnOSYBGcroTJHjRvHLA&pid=news

TMCnet

Technology

NetEase

There’s a lot to be optimistic about in the Technology sector as 2 analysts just weighed in on GDS Holdings (GDS – Research Report) and

Analysts Offer Insights on Technology Companies: GDS Holdings (GDS) and NetEase (NTES)

https://www.bing.com/th?id=ODF.LrIq_5vxtu4ONs5zIW54FA&pid=news

Business Insider

China

Bridge

Censorship

POLITICO's weekly transatlantic tech newsletter uncovers the digital relationship between critical power-centers through exclusive insights and breaking news for global technology elites and political influencers.

https://www.bing.com/th?id=OVFT.SLS9tcv-JBEspxA5VfL6YS&pid=News

Digital Bridge: GDPR turns 5 — Censorship vs accountability — TTC4’s China paradox

https://www.bing.com/th?id=ODF.3CNAC5ADv8S6W-YvRcGzxw&pid=news

Politico Europe

— Are you looking for the complete & verified bond data? — We have everything you need: full data on over 700 000 bonds, stocks & ETFs; powerful bond screener; over 350 pricing sources among stock exchanges & OTC market; ratings & financial reports ...

Information Services Group: Results Of Operations And Financial Condition, Financial Statements And Exhibits

Cbonds

Sam Altman

Here are some important quotes that give us a look at how OpenAI CEO Sam Altman aims to work with regulation as generative AI continues to progress

Key quotes: Use ChatGPT less, says OpenAI CEO Sam Altman at first Congress hearing

https://www.bing.com/th?id=ODF.Gh6VHAvmrF96V8YX8RYJcg&pid=news

Rappler

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Information Technology

MedPage Today

Facctum has announced that FacctViewTM, a new screening platform, has been chosen by NTT Data Payment Services to expand its anti-financial crime defenses. This new technology will support NTT ...

NTT Data Payment Services implements Facctum anti-financial crime technology

Finextra

Information technology

In this article, we will take a look at the 12 best information technology services stocks to buy. To see more such companies, go directly to 5 Best Information Technology Services Stocks to Buy.

12 Best Information Technology Services Stocks to Buy

Yahoo Finance

Get the most recent info and news about Analytica on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Analytica is an award-winning consulting firm that supports federal government health, civilian, and national security missions. Analytica is an SBA Certified 8(a), HUBZone small business specializing in data-driven consulting and technology solutions that improve citizen services.

Analytica

Analytical Therapy

provides a notable advantage over — data becomes available to consumers faster. In the traditional ETL, you would not be able to analyze events from today until tomorrow’s nightly jobs would finish. These days, within minutes, seconds, or even . With streaming technologies, we no longer need to wait for scheduled batch jobs to see new data events. Real-time processing batch processing many businesses rely on data being available milliseconds Live dashboards are . updated automatically as new data comes in Despite all the benefits, to the overall data processes, tooling, and even data format. Therefore, it’s crucial to carefully weigh out the pros and cons of switching to real-time data pipelines. In this article, we’ll look at several options to with the and maintenance effort. real-time streaming adds a lot of additional complexity reap the benefits of a real-time paradigm least amount of architectural changes Traditional approach When you hear about real-time data pipelines, you may immediately start thinking about Apache Kafka, Flink, Spark Streaming, and similar frameworks which to operate a distributed event streaming platform. Those open-source platforms are best suited to scenarios: require a lot of knowledge - when you need to continuously ingest and process reasonably of real-time data, large amounts - when you anticipate and and you want to decouple their communication, multiple producers consumers - or when you want to own the underlying infrastructure, possibly ( ). on-prem e.g. compliance While many companies and services attempt to facilitate the management of underlying distributed clusters, the . Therefore, you need to consider: architecture still remains fairly complex - whether you have the , resources to operate those clusters - do you plan to process by using this platform, how much data - whether the added complexity is . worth the effort In the next sections, we’ll look at if your real-time needs don’t justify the and of a . alternative options added complexity costs self-managed distributed streaming platform Amazon Kinesis AWS realized the customer’s difficulties in managing . As a result, they came up with — a family of services that attempt to . By leveraging serverless , you can create a data stream with a few clicks in the AWS management console. Once you configured your estimated throughput and the number of shards, you can start implementing data producers and consumers. Even though Kinesis is serverless, you still need to monitor the message size and the number of shards to ensure that you don’t encounter any unexpected write throttles. message-bus architectures a long time ago (2013) Kinesis make real-time analytics easier Kinesis Data Streams In , you can find an example of a Kinesis producer ( ) sending data to a Kinesis data stream using a Python client, and how to continuously send micro-batches of data records to S3 ( ) by leveraging a delivery stream. my previous article source consumer/destination Kinesis Data Firehose Alternatively, to consume data from Kinesis Data Stream, we could: - aggregate and analyze data with , Kinesis Data Analytics - use Apache Flink to . send this data into Amazon Timestream The main benefits of using Kinesis Data Streams as compared to sending data directly to your desired application are and . Kinesis allows you to within the stream for up to and have that would receive data at the same time. This means that if a new application would need to collect the same data, you could add a new consumer to the process. This new consumer would not affect other data consumers or producers thanks to decoupling on the Kinesis architecture level. latency decoupling store data seven days multiple consumers Amazon Timestream As mentioned in the previous section, the . If you don’t need multiple applications that would regularly consume data from the stream, you could considerably streamline the process by using Amazon Timestream — a allowing you to analyze data in ( ) real-time. The underlying architecture is smart enough to for fast retrieval of real-time data, and then it automatically moves “old” data to cheaper long-term storage according to the specified retention period. major advantage of Kinesis is decoupling serverless time-series data store near ingest data first into an in-memory store Why would you use a time-series database for real-time data? Any new data record comes into the at a particular time. You may be tracking price changes over time, sensor measurements, logs, CPU utilization — practically . Therefore, it makes sense to consider using a time series database such as Timestream. The simplicity of the service makes it very appealing, especially if you would like to use SQL to retrieve data for analytics. stream any real-time streaming data is some sort of a time series When comparing the SQL interface of Timestream against the one available in Kinesis Data Analytics, Timestream is a clear winner. Kinesis SQL is quite obscure and introduces a lot of specific vocabulary. In contrast, Timestream provides an intuitive SQL interface with many useful built-in time-series functions, making time-based aggregation ( ) much easier. ex. minutely or hourly time buckets Side note: don’t use semicolons at the end of your queries in Timestream. If you do, you’ll get an error. Demo: Real-time ingestion into Timestream using Python To demonstrate how Timestream works, we’ll be sending cryptocurrency price changes into a Timestream table. Let’s start by creating a Timestream database and table. We can do all that either from the AWS management console or from AWS CLI: The above code should create a in your AWS region. Make sure that you use one of the in which Timestream is available. database regions Side note: The easiest way to find available regions for any AWS service is to check the pricing page: https://aws.amazon.com/timestream/pricing/ . Now we can create a . You need to specify your in-memory and magnetic store retention period. table Our database and table are created. Now we can get the latest price data from the . This AP provides many useful endpoints to get the latest information about a cryptocurrency market. We will focus on getting real-time price data for selected cryptocurrencies. Cryptocompare API We’ll get data in the following format: {‘BTC’: {‘USD’: 34406.27}, ‘DASH’: {‘USD’: 178.1}, ‘ETH’: {‘USD’: 2263.64}, ‘REP’: {‘USD’: 26.6}} Additionally, we need to convert this data to the proper Timestream format with a column, , and . Here is the full script that we can use to ingest new data every 10 seconds: . time measures dimensions https://gist.github.com/d00b8173d7dbaba08ba785d1cdb880c8 That’s it! The most time-consuming part is the definition of your dimensions and measures (lines 21–44). You should be about the : with Timestream you can only query data from a single table. No JOINS between tables are allowed. Therefore, it’s important to think ahead about your access patterns before you start ingesting data into Timestream. careful design of your measures and dimensions Here is how the data looks like in the end. Note that the ingestion time is presented in UTC: AWS Timestream: exploring the results in the query console — image by author We could now easily connect Timestream to Grafana for near real-time visualization. But that’s a story for another article. Never-ending script In the Timestream example above, running in a single process, we used a defined using while True. This is a common approach for a simple service ingesting data all the time, typically running as a background process or a service in a container orchestration platform. never-ending loop Minutely scheduled jobs An alternative to a continuously running script is a . The benefit of this approach is that it allows you to , which simplifies your architecture. You can think of it as a reversed Kappa architecture: while Kappa processes batch in the same way as real-time data ( ), this approach “batchifies” real-time data streams ( ) into micro-batches. service that is scheduled to run every minute treat this near real-time process as a batch job streaming-first approach batch-first approach Instead of while True, we now still ingest data roughly every 10 seconds but the actual process is executed once per minute, allowing us to track which runs were successful, and does not depend on the health of a single job run: There is no “right” or “wrong” approach. The main purpose of this method is to treat near real-time ingestion as a batch job. Here is a full Gist: . https://gist.github.com/d953cdbc6edbf8b224815cc5d8b53f73 Which option should you choose? The following questions may help you to make the right decision for your use case: - Which do you want to solve using real-time streaming: is it anomaly detection, alerting, product recommendation, dynamic pricing algorithm, tracking current market prices, understanding user behavior? Having a specific use case in mind can help you determine the right tool for the job, especially because there are a lot of specialized tools on the market. problem(s) - Which is acceptable in your use case? Is it OK if your data is available for analytics 1 minute after the event or stream has been received? Or on the contrary, you need a millisecond latency because otherwise, this data will no longer be actionable? latency - How many ( ) do you have to keep your platform operational? Does it make sense to spin up your own Kafka cluster, use some managed service, or maybe a serverless option such as Amazon Kinesis or Amazon Timestream can address your needs? resources employees and budget - How do you plan to and the health of your data streams? monitor observe - How much would be needed to teach your team how to use this specific platform? training - Which would need to be ingested in real-time, i.e. ? data sources data producers - What is the ( ) from which you would want to retrieve this data, i.e. ? And how do you want to retrieve this data — via SQL, Python, or perhaps only via analytical dashboards? target datastore data lake, data warehouse, specific database data consumers - In which way ( ) would you want to process this data? Is Kappa, , or other architecture worth considering to ? architecture-wise Lambda distinguish between real-time and batch Conclusion Ultimately, it depends on the problem that you try to solve using real-time processing technologies, the scale of your problem, and available resources. In many scenarios, a simple minutely batch job may be sufficient. It allows having a single architecture for all data processing needs, and data available within few minutes or even seconds after its generation. For other scenarios, Kinesis Data Streams or Amazon Timestream may provide simple yet effective means to add (near) real-time capabilities with very little maintenance effort. Lastly, if you do have employees who know how to operate Kafka, Flink, or Spark Streams, those can be helpful if you want to own your infrastructure and not being reliant on cloud providers. As always, thinking about the problem at hand will help assess the trade-offs and make the best decision for your use case. Also published on: https://dashbird.io/blog/real-time-processing-analytical/

Real-Time Data Processing for  Analytical Use Cases: Is it Worth it?

Emerging low-code development platforms enable Data Science teams to derive analytical insights from Big Data quickly. As organizations become proficient in capturing, storing, and analyzing data from multiple sources, they are discovering previously untapped business opportunities. This has been possible with the help of which has been enabling the companies to make smarter, data-driven decisions, as well as build & deploy Big Data solutions faster. The challenge, however, is that the same services are not yet available at the mid-sized or smaller companies often due to the lack of Data Science professionals. Data Science Enter Low-code development With graphical user interfaces and configuration, the Low-Code technology allows non-tech professionals to enter the world of development. They can build applications with no prerequisite knowledge of coding or other database management services. Gartner the global Low-Code Tech market to burgeon by 23% in the year 2021. forecasts How does this work for Data Scientists? platforms enable Data Science teams to derive analytical insights from Big Data quickly. With the co-existence of an array of features like Visual Modelling, Real-time monitoring & reporting, and Cross-platform accessibility among others, the low-code creates templates that replace any repetitive code structure, reducing the load from the algorithms. Low-code development This adds value to the work of developers and data scientists & accelerates the decision-making process. They can then focus on constructing information perceptions, structuring big data projects, or creating new products. Leveraging Low Code for Big Data Analytics: The data is still the data, but the ways of getting insights are continuing to improve. The use of Artificial Neural Networks like Machine learning in automating Big Data solutions has augmented exponential growth in the Digital economy. However, with a long & expensive deployment process, organizations are moving towards Low-Code programming for . Big Data Analytics With this, they needn’t rely on stressed data scientists for their acumen to extract insights from structured & unstructured data— to enhance business deftness. This synergistic relationship between the data scientists & those who aren’t programmatically inclined in the organization can foster more data work in a quicker amount of time without any compromise on data analysis. Furthermore, as much as Low-Code programming is ideal for non-tech professionals, it can also be used by data scientists to build data prototyping efficiently. Lastly, while low-code techniques may not replace traditional methods instantaneously, they can help less experienced as well as professional data scientists, in uncovering analytical solutions.

Low-Code Development Helps Data Scientists Uncover Analytical Insights

Preventing fraudulent transactions online is a cumbersome process for all the parties involved. With the digital payment ecosystem expanding each year, new technologies and companies develop innovative ways to detect fraudulent transactions in real-time. In an online ecosystem where trust between the parties is deficient - new-age technologies are waiting to disrupt the ecosystem. Artificial intelligence-based training algorithms are successfully flagging and resolving fraudulent transactions in real-time. I sat down with Nimrod Lehavi, Co-founder and CEO at Simplex, to discuss how new-age technologies are changing the digital payments ecosystem. Hi Nimrod, welcome to our “Behind the Startup” series. Tell us about yourself and the story behind Simplex? Ishan Pandey: I began my entrepreneurial career in the 90’s as a self-taught software developer. I’m a hacker at my core and I enjoy finding solutions to new and challenging problems. After 15 years of running my own software development business, solving complex business problems for many companies in various sizes and verticals, I stumbled upon The Bitcoin Whitepaper (the manifesto of Bitcoin, for any readers not familiar) and was charmed by it. No solution existed that allowed me to buy bitcoin with my credit card, and in general, it was constrained. I knew I wanted to do something new in this exciting young ecosystem, So I decided to make bitcoin accessible, in 2014 Simplex was born. Today I lead over a hundred of Simplex employees throughout the world as we work alongside our vast partner network to drive enablement and advance the ecosystem, helping anyone, anywhere, securely on-ramp, anyway. Nimrod Lehavi: How is fraud prevention different in crypto compared to processing payments for traditional credit card companies? Ishan Pandey: Fraud prevention differs between verticals, but it’s incredibly complex when it comes to crypto. The crypto space is unique in being truly global, 24/7/365 and that many who fall prey to fraud don’t even know it. It’s as much about identifying that users are aware of what they’re doing as it is weeding out real fraudsters. Fraud prevention warrants a lot of creative and analytical thinking always to stay two steps ahead of scammers, and it’s incredibly dynamic. We leverage a combination of skilled analysts and AI to detect all types of fraud at their earliest stages and annihilate them. Nimrod Lehavi: For new users, on-ramping is a confusing and complicated process. How is crypto on-ramping being democratized? Ishan Pandey: Excellent question. Crypto on-ramping has not traditionally been easy and I believe that the simpler we can make the process of on-ramping, the more the ecosystem will grow. On-ramping has been making steps toward democratization since we first pioneered methods for entering the ecosystem using credit or debit cards. We’ve now gone beyond credit or debit cards to enable uniquely large and flexible purchases across our partner network with our Banking solution. Leveraging Simplex Banking, our partners can allow users to on-ramp with any fiat anywhere globally, making giant leaps towards mass crypto adoption. Nimrod Lehavi: What problems do you see that still need to be tackled when it comes to Cryptocurrency On-Ramps and Off-Ramps? Ishan Pandey: Given the recent Bitcoin Boom, more users will be looking for a seamless way to off-ramp using rails that they are already comfortable with. The knowledge that it is just as easy to off-ramp, or utilize crypto earnings, as it is to on-ramp has been proven to increase the number of on-ramps in a given exchange. As a community, we need to continue working to ensure that crypto funds are easily accessible to the mass market. Nimrod Lehavi: Fraud prevention and management is a massive problem for exchanges, brokers, wallets, etc. What technologies and protocols are you deploying for preventing fraudulent transactions? Ishan Pandey: We are always vigilant to new fraud trends and strategies to protect our partners and their users. As fraud is an incredibly dynamic threat, we are continually expanding our fraud prevention technologies and protocols. An example of a protocol we employ is identifying user patterns and building a user story to see if the patterns of behaviour fit the user profile. By triangulating data and leveraging both advanced AI and seasoned analysts, we can build a profile of each user to make a timely, yet informed, judgment call, protecting our users and partners from fraud. Nimrod Lehavi: What new fraudulent and sophisticated techniques are you seeing being deployed by scammers? Ishan Pandey: Unfortunately, the panic surrounding COVID-19 launched a wave of fraud schemes. For example, some scammers encouraged the transfer of crypto funds in exchange for the sale of masks (which they never received), others included fraudulent CDC emails that facilitated the transfer of funds to invest in a COVID cure, among others. Ponzi schemes involving cryptocurrency amounted to billions last year, and scammers are moving beyond email to use social media platforms, such as YouTube, Instagram, and Facebook, to promote their scams. When faced with an email or request, I encourage users to always look into a company before working with it and do their due diligence to make sure they are familiar with crypto concepts and what is being asked of them. Nimrod Lehavi: What advice would you give to a startup that wants to put measures for fraud detection and prevention in place? Ishan Pandey: It’s essential to have a well-prepared and trained team who are well-versed in all there is to know about fraud types and prevention. It’s also crucial to have up-to-date and sophisticated protective measures and checks in place to ensure that you are always one step ahead of the scammers. But what makes the difference is developing a profound understanding of your market, and putting yourselves in the shoes of the other side, trying to “think like the bad guys” and preempt the scams before they occur. Nimrod Lehavi: Do you think that being based in Israel, which is a global leader in cybersecurity, gives you a competitive edge as compared to others? Ishan Pandey: Being based in Israel is part of our DNA. As a company born out of the need to make crypto accessible, that mission continues to underscore and lead the company. In true, StartUp Nation fashion, we pride ourselves on being a talented and passionate group that works together to create unique solutions for difficult problems. Our team comes with a vast background in cybersecurity, finance, and defence, and brings years of quick and decisive problem-solving to join together and create something meaningful. However, the diversity of our team and values of problem-solving are not limited to our Israel office, and this culture permeates throughout our office in Europe as well. Nimrod Lehavi: What technology and trends are you looking at, which can disrupt our society? Ishan Pandey: I believe crypto has a lot to offer to the world: a lasting impact on our financial systems hence on society, a real change in the infrastructure of the economy and financial services. Our goal always has and will always be to democratize crypto accessibility and adoption for everyone. Nimrod Lehavi: I see the crypto ecosystem as a potential alternative financial ecosystem; fast, innovative, secure, and transparent. If indeed globally accessible, think about how that can impact the world. No matter the regime one may find themself in, they will be able to take hold of their finances and not be subject to others’ whims. From our Banking solution, all of our products to our Principal Membership with Visa are geared to enable our partners to offer anyone, anywhere access to purchase, anyway. As we enter into 2021, we will be focusing our efforts on making crypto even more accessible to anyone, anywhere, throughout our trusted partner network. The purpose of this article is to remove informational asymmetry existing today in our digital markets by performing due diligence by asking the right questions and equipping readers with better opinions to make informed decisions. The material does not constitute any investment, financial, or legal advice. Please do your research before investing in any digital assets or tokens, etc. The writer does not have any vested interest in the company. Interviewer - Ishan Pandey.

Fraud Prevention Requires Creative & Analytical Thinking To Stay Two Steps Ahead - Nimrod Lehavi

This is a hands-on course on how to deploy a fully Serverless web app using the . You will learn how to: AWS CDK Structure **CDK Stacks **to deploy an application from end-to-end Deploy a integrated with for dynamic requests processing Store data in a fast and cost-effective way with REST API AWS Lambda DynamoDB Use as a source for in an event-driven architecture DynamoDB streams Lambda Ingest and manipulate loads of data streams with Kinesis Firehose Deploy and query a with , and your entire application health in a single place using Data Lake Athena S3 GlueMonitor Dashbird You can use the resources declared in this demo application as a starting point to mix and adapt to your own architectures later, which should save you quite some time. The App and Architecture The demo app is a public blog where anyone can read, publish and like posts. It’s available . Go ahead and publish something in the top-left corner (yellow button) and also “like” articles already published. Check out the codebase on . on this link this repo Frontend Backend Data Lake and Analytical Querying What is the AWS CDK? CDK stands for Cloud Development Kit. Think of it as CloudFormation (CF) in your preferred language (Python, Typescript, C#, etc). Roughly speaking, it works like this: 1. You declare cloud resources using classes provided by the CDK libraries. Example: aws_cdk aws_s3
my_bucket = aws_s3.Bucket(self, ) from import 'MyBucket' 2. Run CDK deploy 3. CDK translates this to a CloudFormation template and deploy it on AWS for you. In case you would like to dig deeper, that will get your basics started. I also strongly recommend reading the . AWS also has a workshop official CDK documentation Advantages using the AWS CDK Use languages that are more expressive than YAML or JSON, for instance. Less - much less! - verbose than CloudFormation templates. Easier to apply reusability and inheritance principles to infrastructure code. Better integration with IDEs for code completion, IntelliSense, etc. Possible to test your infra code, just as any other software Portable: since it’s just a wrapper around CF, we can easily port it to JSON or YAML Disadvantages of using the AWS CDK Although released as a stable project by the AWS team, many parts (a lot of the good ones) are still experimental and APIs may change in backwards incompatible ways. It’s under constant development. During the preparation of this course, I had to upgrade my libraries three times. Documentation is still lacking at some parts and you will need to look at the CDK code, occasionally, to understand how to declare certain things. Deploy it yourself Although we have provided an online demo, you can also deploy this app in your own AWS account: Clone the repo: git clone git@github.com:byrro/serverless-website-demo.git sls-demo; cd sls-demo Setup your virtual environment: virtualenv -p /urs/bin/python3.8 .env; source .env/bin/activate; pip install -r requirements Specify an AWS account ID: ** export AWS_ACCOUNT_ID=1234567890 Deploy all three stacks: cdk deploy sls-blog; cdk deploy sls-blog-api; cdk deploy sls-blog-analytical ** You can also hard code your Account ID in the CDK project, as I’ll show in a minute; When starting a new project from scratch, you would run . This is not necessary for this demo, since the project is already created. cdk init --language [python|typescript|...] Monitoring Deploying this architecture in the cloud and blindly believing it will work flawlessly is not reasonable. We want to be the first one to know when something is not right to act upon it as quickly as possible. In this project, I used for its ease of use and seamless integration. Instead of having to deploy an agent inside my code, Dashbird plugs into my Stacks that I can deploy with the effort of one click. It not only monitors Lambda function errors, but also other resources that we’re using, such as DynamoDB tables. They even suggest cross-referenced against industry best practices. Dashbird through a CloudFormation template insights for architectural improvements Finally, Dashbird offers a free-forever plan. It’s a no-brainer to try it out by . registering for free How a CDK project is structured A CDK project creates an “Application”. This app may have one or more “Stacks”. A Stack is a group of cloud resources (Lambda functions, S3 buckets, etc) that are instantiated using CDK classes. It’s also possible to have multiple applications in a single CDK project. App Object Creating a CDK app is as simple as: app = core.App() When you run , an initial application with basic boilerplate code is created for you in the project root, under . cdk init --language [language] app.py The next thing we need is an environment, which is composed of an AWS Account ID and Region: env = env = core.Environment(
    account= ,
    region= ,
) 1234567890 'us-east-1' Declaring an environment is not required (CDK can infer from your AWS credentials), but is a good practice. Most of us work with multiple AWS accounts. It’s easy to mess around with several projects, accounts, credentials. When we explicitly set the environment in the CDK app, it’s locked and prevents mistaken deployments. Now we declare our stacks: my_project.my_project_stack MyStack

my_stack = MyStack(
    app, ,
    env=env,
) from import 'my-stack' This is how we our stacks for deployment. In the next section we’ll see how to those stacks. instantiate declare Stack Object The Stack object is where we our AWS resources. It inherits from the core.Stack CDK class and accepts a scope - which is our app object - a string identifier and an environment. declare ( . ): ( , : . , : , : . ,
            ** ,
            ) -> : (). ( , , ** )

        # class MyStack core Stack def __init__ self scope core Construct id str env core Environment kwargs None super __init__ scope id kwargs Declare AWS resources here Declaring AWS resources To declare AWS resources, we need a specific library for each service. Here’s a list of all and their counterparts. Other flavors are and . Python libraries Typescript Java .NET Let’s see how a basic REST API would be declared (typing expressions were removed for readability purposes): aws_cdk aws_apigateway, aws_lambda = aws_lambda.Function(
            self, ,
            runtime=aws_lambda.Runtime.PYTHON_3_8,
            code=aws_lambda.Code.asset( my_lambda.handler sls-blog-rest-api-gateway from import ( . ): ( , , , ): (). ( , , ** ) class MyStack core Stack def __init__ self scope id env super __init__ scope id kwargs my_lambda 'MyLambda' 'my_lambda_folder),
            handler=' ',
        )

        aws_apigateway.LambdaRestApi(
            self,
            ' ',
            handler=my_lambda,
        ) We first declare a Lambda function . We point its code to the . Inside this folder, there should be a file, containing a function called . This handler function should accept Lambda invocations normally (an and objects). my_lambda my_lambda_folder my_lambda.py handler event context Next, a is declared, using as the handler (not to confuse with the Lambda’s handler function). This will create a new API Gateway REST API integrated with using an . All HTTP requests will be routed to the Lambda function. LambdaRestApi my_lambda my_lambda AWS_PROXY integration type Our Project App & Stacks comprises one application with three Stacks. They’re all declared in the and files. This project app.py sls_website_stack.py Below we’ll walk through all Stacks in a high level. I encourage you to inspect the to learn how these resources are declared and also integrated. For example: a Kinesis Firehose is created in one Stack and referenced in another to include its name as an environment variable for the Lambda function that will interact with it. stacks file Except for the frontend static Stack - which is small - you will notice that resources are initialized with a (null) value in the beginning. The reason is that, even though the CDK is generally more succinct than CloudFormation, it can still be lengthy enough to clutter the view of the entire Stack. Having each resource declared first in one line, I can provide a short summary of everything that’s in the Stack and then instantiate the CDK classes in other methods. None = static_stack

        # SQS Queues
        self.queue_ddb_streams_dlq = None  # Dead-letter-queue DDB streams

        # DynamoDB Tables
        self.ddb_table_blog = None  # Single-table all blog content

        # DynamoDB Event Sources
        self.ddb_source_blog = None  # Blog table streams source

        # DynamoDB Indexes
        self.ddb_gsi_latest = None  # GSI ordering articles by timestamp

        # Lambda Functions
        self.lambda_blog = None  # Serves requests to the blog public API
        self.lambda_stream_reader = None  # Processes DynamoDB streams

        # Continues other resources... ( . ): ( , , , , ): (). ( , , ** ) . class SlsBlogApiStack core Stack def __init__ self scope id env static_stack super __init__ scope id kwargs self static_stack for for with Notice it takes another Stack object ( ) as an argument to its initialization. In the file, you can see that the is initialized passing the as an argument. static_stack app.py SlsBlogApiStack SlsBlogStack We use it to reference the CloudFront distribution domain ( ) in the Lambda environment variables. This variable can be used to customize the HTTP response header to comply with . This illustrates one way to easily integrate and reference information from one Stack into another within a CDK project. d1qmte5oc6ndq5.cloudfront.net Access-Control-Allow-Origin CORS standards At the end of the initialization, another method is called to instantiate the CDK classes for each resource and configure their parameters. self.create_cdk_resources() Next we’ll walk through each of our project’s Stacks. Stack 1: static website Our focus is on the Serverless backend, so the frontend here is terribly rough and simple. It’s stored in an S3 Bucket and distributed through a CloudFront CDN. CDK has a helpful class called BucketDeployment. It takes the contents of a directory and sync to an S3 bucket. In this case, we stored the frontend code in the folder. website_static aws_s3_deployment.BucketDeployment(
    self, ,
    sources=[aws_s3_deployment.Source.asset( )],
    destination_bucket=static_bucket,
    distribution=cdn,
) 'SlsBlogStaticS3Deployment' 'website_static' Stack 2: API/backend Our backend consists of an API Gateway (REST) connected to a . Microservices receives a lot of press, but you probably shouldn’t always break your applications in several functions. A Monolith is just fine - -, really. monolithic Lambda function and sometimes recommended This API & Lambda support a single endpoint (with GET and POST methods) with a queryString “action”, which takes three parameters: : populates the latest blog posts get-latest-articles : triggered when someone likes an article like-article : post a new blog article publish-article Here’s the power of the CDK model. We can create a REST API with 10 lines of code: rest_api_blog = aws_apigateway.LambdaRestApi(
    self, ,
    handler=lambda_blog,  # Previously declared Lambda 'sls-blog-rest-api-gateway' = . ( ),
    ),
) function deploy_options aws_apigateway StageOptions stage_name= ,
        throttling_rate_limit=lambda_param_max_concurrency,
        logging_level=aws_apigateway.MethodLoggingLevel( 'api' 'INFO' One nice little thing is that Lambda memory is used as a cache for the latest articles. We load the cache container outside the Lambda handler function. It remains in memory even after an invocation ends and is available for subsequent requests. Learn more here about . how to use Lambda as a cache mechanism MAX_CACHE_AGE: int = # In seconds
CACHE_LATEST_ARTICLES: Dict[str, Union[int, list]] = { : time.time(), : [],
} 120 'last_update' 'articles' All the data is stored in DynamoDB (DDB) using a , in . The site only shows the latest blog articles and items get after a few days by setting a attribute. single-table design on-demand mode auto-deleted by DDB time-to-live ddb_attr_time_to_live = ddb_param_max_parallel_streams = ddb_table_blog = aws_dynamodb.Table(
    self, ,
    partition_key=aws_dynamodb.Attribute(
        name= ,
        type=aws_dynamodb.AttributeType.STRING,
    ),
    billing_mode=aws_dynamodb.BillingMode.PAY_PER_REQUEST,
    point_in_time_recovery=True,
    removal_policy=core.RemovalPolicy.DESTROY,
    time_to_live_attribute=self.ddb_attr_time_to_live,
    stream=aws_dynamodb.StreamViewType.NEW_AND_OLD_IMAGES,
) 'time-to-live' 5 'sls-blog-dynamo-table' 'id' The DDB table also has a that makes it easier to retrieve articles ordered by date for the site: GSI (Global Secondary Index) self.ddb_table_blog.add_global_secondary_index(
    index_name= ,
    partition_key=aws_dynamodb.Attribute(
        name= ,
        type=aws_dynamodb.AttributeType.STRING,
    ),
    sort_key=aws_dynamodb.Attribute(
        name= ,
        type=aws_dynamodb.AttributeType.NUMBER,
    ),
    projection_type=aws_dynamodb.ProjectionType.ALL,
) 'latest-blogs' 'item-type' 'publish-timestamp' Modifications to DDB items generate streams that are processed by a . These streams are then repackaged and sent to a Kinesis Firehose stream processor. second Lambda function DDB doesn’t provide the flexibility that SQL databases offer and many choose , for example. Although Aurora is a great service, personally I prefer DDB for its simplicity and reliable, consistent performance. But sometimes we do need to run analytical queries, those with aggregations and on-the-fly filters. For that, we’ll be using Athena (more in the next Stack). Aurora Serverless Stack 3: analytical querying A Kinesis Firehose Stream is responsible for batching data inserted/modified in DDB, converting them to Apache Parquet format and storing in dedicated S3 buckets. From S3, we create a Data Lake with AWS Glue (used to declare our data schemas) and Athena (used to query the data). Athena is extremely powerful. We can statements ( ) to query terabytes of data and pay on-demand ($0.005 per GB of data scanned). Using Parquet not only improves query speed, but also reduces cost by minimizing the amount of data Athena needs to scan for each query. use SQL SELECT with some limitations Queries that are impossible or expensive/slow in DynamoDB, such as aggregations and JOINs, are fast and cheap in Athena. The two services combine each other in a perfect way, so that your application has optimized transactional storage and flexible analytical querying capabilities. We can use Athena to query all articles ever published and cross-reference with likes and HTTP metadata (source IP address, country, device type, etc). Even articles that were already expired by DynamoDB TTL (time-to-live) would continue to be available in the Data Lake. For example, which countries are liking the most articles? In the AWS Console, we get something like this: Queries can also be executed programmatically with or (e.g. ) to integrate anywhere we need this data. Athena API AWS SDKs Python’s boto3 Athena also supports JOINs. Here’s an example joining articles and HTTP metadata to analyze the most popular authors among readers of a particular country: Deployment CDK can deploy one Stack at a time. Since we have three, it’s necessary to specify which one when running the command. We do that by passing in the Stack ID as a CLI argument. For example, the following command will deploy the SlsBlogApiStack (id: ): CDK deploy sls-blog-api cdk deploy sls-blog-api Since all Stacks involve some type of permission granting, CDK asks for confirmation before deploying those resources. You can review the permissions requested and hit y when it’s good to go. Wrapping up We’ve covered how to structure CDK apps and add a bunch of AWS Resources to deploy with a simple command. CDK deploy If you’re new to the CDK - and as suggested early in the article -, it’s strongly recommended to follow AWS and . CDK workshop documentation Keep an eye on future publications as well, as is releasing other examples and tutorials to reap the most out of AWS serverless services with the power of infra automation with CDK or else. Dashbird Previously published at https://dashbird.io/blog/crash-course-aws-cdk-serverless-rest-api-data-lake-analytical-querying/

Quick Guide to AWS CDK and Serverless with REST API and Data Lake Analytical Querying

To start with, this is a tricky topic, where I might find people in conflict with my perception but after all when isn’t conflict a promising act. Conflict by its meaning is a disagreement where one holds an entirely opposite perspective and surely does one defend it as if it was their very own life at stake. I think that is a beauty of it, perhaps lasting for a long time that is unexpected or usual. I would put it as my experience to suggest that I have seen people get excited and enthusiastic about doing something. It can be an idea or a project or anything that they either want to achieve in life or a common goal to which one puts an effort. The phenomenon that can be instantly noticed is that the excitement or the enthusiasm does not last very long, it is as if a bubble started gulping with air and before it reached its potential where it should have held, it just bursts into nothingness. The pattern repeats very often and has been repeating for much longer than I really thought it would. This is what triggered me to write for it in the hope that maybe someone other than myself is experiencing a similar act of time. The current COVID-19 pandemic is a great setback, as we go through it, seeing and feeling what a pandemic is. I think, unknowingly our society had another pandemic embedded into it, which to this day, hasn’t been much noticed and no cure developed for it.  I would call that pandemic, the society’s algorithm for literally categorizing everything possible. This attempt of categorization is backed by the idea of understanding, the happening but I would like to add that such attempts of categorization have crept into our skin by fueling concepts such as racism, radicalism, and hierarchies. There is a category, I myself would specifically want to talk about, it is the category of the doers and the thinkers where one does and the other does not. In my opinion, the word the thinkers are totally out of context to the word doers, therefore, I would like to replace the word with doers and the no doers because thinking is a requirement for enabling one to do something, imagine doing something without thinking, it would simply not fit but somehow the comparison has been accepted by the majority that those that think simply are unequipped to do anything. Let’s talk about the no doers a little. The question at pace would be, how does one do something to become a no doer? A no doer would seem like someone moving through a river but ending nowhere. Someone who seems to pass by something but still remains there. In a more general perspective, an illusion of movement yet it remains there. Now with reference to such a definition, there is a lot around me and perhaps you, people, places, ideas, abilities, and life that would seem fitting to it. Yes, it is fitting, like those people might have met 3 years back, and they were at a position in a company, and today they still are there but when you ask, they just seem to complain that they are unable to do something about it or perhaps they are doing but the result cannot be seen. It is as if they are stuck in an endless loop of time that just repeats and repeats. The repetition is what can be termed as a pattern. A process that leads to the same result no matter what changes. I think some might term that insanity, trying to do the same thing again and again and expecting a different result. Let’s try defining, what a doer is. A doer should be the opposite of a no doer. The opposite would say that a doer would seem like someone not moving through the river and yet ending somewhere. Now to me this river really makes context, it is the river where people want you to just go with the flow, not oppose it whereas my question is if you want to be a doer, you shouldn’t be in a river where everything is about to go with the flow. From a general perspective, a doer would have no illusion of movement, therefore it doesn’t remain at a single point. A doer is someone constantly in motion, either at one point or another but not the same point where it as reached does it reach again. A doer is like what we say about lightning, that which does not strike the same place twice. Therefore, doers are unpredictable, and this unpredictability in anything is what I call power. There is no pattern to it, the doership of one’s struggle. I have a little dilemma that I have observed, it is the idea of predestination. I would say it is a theory, that all one does and will is fixed and no matter what he or she does, it cannot change. Think of an industrialist, a businessman of the dark ages. One who is smart, cunning like a fox, and a great observant. One who is dramatic and has touch of dictatorship to him. One who is power-hungry and has discovered how much man is inclined to find answers in the abyss of the unknown element of life. Now, these characteristics boiled together will make one wary of competition and because of this fear of competition one needs to stay on top of the food chain. He will surely use the idea of predestination to socially restrict his peers and society because that is the only way, he can ensure that the power struggle stays within his lineage. The idea of predestination fixates one in what one cannot be and what one should be i.e defined by systems and people around him. Once fixated, he or she repeats becoming a pattern, and patterns produce the same result over and over again while the human nature in this pattern expects something different. To find a different place in life, one that is better than what you think you currently have, one needs to break the pattern, and to break the pattern, one needs to adapt to doership and leave behind the idealogy of no doers that prisons people to restrictions. Earlier, I did mention that the word thinkers was out of context, you are not born or predestined to fixation, you can always think out of it, and change the current result. Man’s ability to learn out of situations is what makes him truly unpredictable, unpredictable as lightning does not strike the same place twice. So by violating, the law of predestination set by some fanatic person from the dark ages, you become the embodiment of free will, a  will that is powerful beyond measure, to give you whatever you desire. Reference https://www.dictionary.com/ https://unsplash.com/ https://www.pexels.com/

Doers Vs No Doers: An Analytical Approach

Towards an Analytical Discipline of Forkonomy (Summer 2018)

Image Credit: Pawel Kucynsky Last month was a strange one. We saw mainstream media questioning and Cambridge Analytica practices after recent concerns over the US election. sums it up in one frightening sentence: Facebook The Guardian They had manipulated the voters of democracies across the globe, notably in the US, with unsourced propaganda that was not necessarily true; and boasting of sting operations and honey traps Cambridge Analytica took part in the yes for the campaign, the election of Donald Trump, Ted Cruz’s campaign, and in (44 apparently in 2014, just for the US). They are now a massive Brexit dozens of local election political force, engaged all across the globe with a strong preference for conservative candidates. While Cambridge Analytica was already , they weren’t very secretive about their intent; here’s what they boast about in their promotional videos: advertising its services on Youtube since August 2015 On the American market, they claim to have “up to 5,000 data points on 220 million Americans” “We go beyond big data, we add an extra layer of personality data to get down to an individual level” The activity of Cambridge Analytica and its impact over elections have been well covered, or the . Now, even Google will tell you: such as in The Guardian’s Cambridge Analytica files NY Times What’s less discussed is how could we even get to this point? Considering the company’s goals and practices, abuses should have been expected and prevented. Let’s broaden the discussion and address the tradeoff made with “free” services instead and the consequences of this data theft: a loss of political and personal power. THE ADVERTISING MODEL: THE NECESSARY PAIN? It’s actually a quote from Mark Zuckerberg that got me started on this article. In the when asked about the , he didn’t exactly answer the question yet provided a fascinating response: Ezra Klein Show limits of Facebook’s current business model You know, I find that argument, that if you’re not paying that somehow we can’t care about you, to be extremely glib and not at all aligned with the truth. The reality here is that if you want to build a service that helps connect everyone in the world, then there are a lot of people who can’t afford to pay. And therefore, as with a lot of media, having an advertising-supported model is the . only rational model that can support building this service to reach people He presents the advertising-supported model . I would argue instead . Essentially, advertising allows generating revenue out of each/most users of the service. Because of the monetary incentive tied to ads, the provider tends to focus on collecting more data and increasing the user base even further instead of actual development of the service. It can’t be sustainable. as the only rational one able to bring free services to the world it’s only the easiest way As they restlessly harness data, . Facebook is now the kingmaker, or is it already the king? If Facebook is king, it’s a sneaky one: he’s being secretive about its regal nature. They didn’t come public about the Cambridge Analytica scandal, they waited for the public to notice and patiently prepared. online services gain actual power and influence on individuals and societies , he develops a grandiose and frightening vision for the social network, as What we’re seeing today is only stage 1. In a manifesto written in 2017 about the future of Facebook “the social infrastructure for the global community”. Yet, Facebook isn’t a lone wolf. There is a whole industry built on similar concepts: online advertising. While Cambridge Analytica and Facebook are probably at fault, many other companies such as Google, Yahoo, or Baidu have similar practices. A whole industry is at fault. A CROOKED INDUSTRY The advertising industry has always been ripe for abuse and scams, but . Abuses are common, both for advertisers and other actors. digital advertising brought it to another level Taking a closer look at the industry, you uncover several battles being lead all at once. First, . On the advertiser side, botting and click-fraud are massive. To top this all up, users, annoyed by the evermore intrusive ads, are now fighting back. shady practices are rampant in the industry ABUSES AND SHADY PRACTICES Abuses and misleading tactics are spread all around the industry on all kind of ads and devices. On mobile, apps and games may have , requiring unnecessary permissions (contacts, pictures, files…), which allow them to collect unnecessary data. Unsurprisingly, the Facebook app is among one of the most demanding in terms of permissions. aggressive data collection practices On desktop, there are intrusive banner ads and pop-ups all around deteriorating the browsing experience on the web. Worst, there are also much more intrusive practices such as (your beloved grandma’s toolbar) . spywares bundled with free softwares A glimpse in toolbars’ hell If scammy tactics are common for advertisers, brands pay the price for it as automated clicks are hard to distinguish for real ones. PRACTICALLY IN A CIVIL WAR Bots or automated activity is indeed massive in the online advertising world. It’s hard for brands to recognize these practices, and it allows the ad seller to increase their profits. AKA, why are they so many bots on Twitter? About 20% of all accounts are bots (fully automated), and . So, if Twitter was to take actual measure against automated activity, it would risk losing up to 40% of the impressions it sells to advertisers. at least 20% others use automated methods The industry itself is well aware of the issue, the estimates that . The total loss is estimated at $50bn a year, roughly a third of the current online advertising industry yearly revenues. World Federation of Advertiser between 10 to 30% of the slots are never seen by consumers because of fraud USERS ARE FIGHTING BACK TOO On top of the click fraud, you must add another stream of lost revenues for advertisers: users using an ad blocker. To protect themselves, surfers took matters in their own hand and developed Ad-Blockers extension, such as or . NanoBlock uBlock origin Considering how intrusive and annoying ads have become, it was bound to happen. Because less users now see the ads they host, media advertising revenues dwindle putting their very subsistence at risk. While adblockers minimize annoyance on the user side, it doesn’t solve the problem at all at the industry level. Image Credits On one side, installing a Firefox/Chrome extension come easy for some, many don’t know about them or even about ad blockers. Only the most tech-savvy users tend to use ad blockers. However, , some websites are now blocking access to users with an ad blocker enabled. Luckily, blockers currently have the final world with anti-ad blocker defusers like . It’s an ongoing back and forth. the ad industry is fighting back Nano Defender The advertising industry is mined by vicious circles of abuse that lead users to fight back. Since it lowers their revenues, media and advertisers are pushed towards even more intrusive advertising and data collection practices to try to break even. The whole advertising model is unsustainable. The actual consequences of the development of free services spread all the way down to the individual level. The tributes we pay is already massive. These include the abuses within the industry, the ongoing war with the users but most importantly the political clout claimed by online service which now makes them able to coerce public opinion. It’s not only about Cambridge Analytica, it’s much bigger than this. I share on Zuckberg’s hearing: Max Evans views “Words will be said, apologies noted, laws passed, but the problems will grow. Companies will not moderate their behaviour totally because they only have to accede to individual nations”. If the state is unfit to deal with this, we have but one option left: do something about it. The advertising model is not only tendentious but perverse. Is it really the only option Zuck, or just the one that happened to be proposed first and quietly established as a standard? Other models that allow a fairer distribution of the values between all the actors of the ecosystem exist. Let me know if you liked this article and you can keep pressing 👏up to 50 claps. check out my wiki for more content like this. Don’t forget to clap it to help it spread:

Facebook X Cambridge Analytica : the hidden cost of free services

Communities have always been positively and negatively affected by the actions of individual members. So why do people behave differently in the digital world? If you haven’t already, check to find out if your Facebook data was shared with Cambridge Analytica. this link Here are my results: Yep. I was 1 of the 87 million people whose data was shared with , as a result of a controversial Facebook App called . Interestingly, I personally never used this app. As you can see in the screenshot, my friend’s participation authorized the sharing of my public profile, page likes, birthday and current city. Cambridge Analytica “This is your Digital Life” My only error? Being friends on Facebook with someone who did. Debating the implications, consequences, and whether or not this controversy actually caused me any harm is best for another article. For now, I’m interested in digging into some potential factors that contributed to my friend thinking it was socially acceptable to share my birthday and zip code with a random developer. Wouldn’t this same transaction of information have seemed strange in real life? Photo by Brian Solis Would you give away a friend’s birthday or location when chatting face-to-face with a stranger? Probably not. So what’s different? Here are 5 potential factors: 1) Digital Consequences aren’t obvious My friend who used this app certainly wasn’t doing anything malicious. When using an application like this, Facebook alerts you to the items you will be sharing, but lacks the possible consequences of what could be done with this information. Without proper eduction, how is an average person expected to weigh the pros and cons, all while battling the strategic UI luring them to click “continue”? Whose role is it to educate? Facebook certainly has no reason to slow you down. Facebook did not include the names of my friends who resulted in my data being shared. But what if they did? This would certainly be embarrassing, but added transparency, and potential public repercussions would have certainly incentivized a second glance before clicking “authorize”. 2) Digital Consequences are deferred We’ve convinced ourselves that if consequences do occur from our online actions, they most certainly will happen far off in the future — a month, a year, decades from now . It’s an ignorant justification and why we’re also to blame for . From my experience, consequences that do not quickly follow the initial decision are less likely to deter that behavior in the future. Scolding my dog days after I’ve discovered she has chewed something up rarely leads to improved behavior. @CamAnalytica 3) Consumers undervalue data Mark Zuckerberg has been very successful at building a data-gathering machine. Every keystroke and behavior is analyzed and converted to a marketable and monetizable asset. Facebook’s states that you own your data, but who owns these new insights and observations obtained from your data? Your clicks, scrolls and content are worth something. That number is much higher than $0. How much? does a great job of trying to quantify the value of your data . Terms of Service Robin Bloor in this article 4) Consumers underestimate the scalable nature of the internet When we offer up information in the physical world, we first run some simple math in our heads. “What’s the worst that could happen if I give this salesman my phone number?” We calculate the potential damage that could be caused by this person (based on realistic human capabilities), weigh the pros and cons, and make a decision that is in line with our risk tolerance. However, To put it in perspective, readily available can process 1,000+ records per second. I’m sure some of you have even more powerful examples. Authorizing an app to access my friends list seems harmless independently. Yet, when mixed, matched, and overlaid with other data sets, a list of page likes can yield scary results. Check out ’s article “ ”. when computers are involved, the average consumer vastly underestimates the speed, scale and reach of automation-assisted systems. cloud resources Georges Abi-Heila Your Facebook Data is Scary as Hell 5) Limitless number of “friends” Digital communities aren’t a “fixed pie”. For example, I’ve found that as I move to a new town or job, making new friends almost always leads to me to drop older friends out of my community. After all, it’s only humanly possible for me to manage a certain number of friendships. “Dunbar’s Number” states that “ ”. It seems we implement some version of a FIFO method of keeping our casual acquaintances to a manageable size. Furthermore, physical relationships require two-sided attention. If one party stops participating in a friendship, the relationship fades. Yet this doesn’t seem to exist in the digital world. I don’t actively contribute to 1000+ friends in my Facebook friends list, yet keeping them as friends apparently represents some level of risk. Smaller, physical communities function well because there is a set of norms, expectations, and a tangible understanding of how individual decisions may negatively affect all members of the group. Yet, unwieldy social communities seem impenetrable. Surely, poor decisions couldn’t affect all these people? LinkedIn apparently enforces a . I wonder their reason for this specific number? In the physical world, personal circles are dictated by the number of relationships a person can juggle, whereas in the digital world, personal circles are only limited by database capacity. people can only manage relationships with 150 friends at a time 30,000 connection limit Would a fixed community limit on social platforms that more closely reflected our real-world help? What do you think? Do we share the blame with Facebook, or should they have prevented this in the first place? How can we create better incentives or regulations to equip people to make better online decisions? If you enjoyed this, feel free to clap 👏Follow me on Twitter , or check out what I’m working on .

5 Insights About Digital Social Responsibility in light of Facebook & Cambridge Analytica

The sun rises behind the entrance sign to Facebook headquarters in Menlo Park before the company’s IPO launch, May 18, 2012. (Credits: REUTERS/Beck Diefenbach/File Photo) Now even tech executives are calling for more regulation. “Tech is definitely about to get regulated. And probably for the best.” This was from , cofounder, CEO, and self-defined at Box. It was his first comment after the news that Cambridge Analytica, the data analytics firm that worked with the Trump campaign and the pro-Brexit campaign, illegally harvested 50 millions of Facebook profiles of US voters. a tweet Aaron Levie Lead Magician A few days later, Levie commented again on Twitter: “The responsibility of tech companies grows exponentially,” he pointed out while admitting that saying that they are “merely platforms and pipes” cannot be a sustainable argument any longer. He elaborated a bit more with CNBC’s Deirdre Bosa. “In the past 10–20 years we had a tremendous amount of technology innovation that is now starting to impact every single element of our life,” he said. “This is what we have seen from the digital revolution: it‘s changing our cars and transportation; it’s changing life sciences and healthcare; it’s changing voting and democracy.” in an interview Levie explained that “for quite sometimes we treated these technology platforms as utilities and all the users are the ones who are liable on these platforms.” “But ultimately, as you see growing use cases from machine learning and AI [artificial intelligence], the platforms themselves are going to be making more decisions on our behalf. That means that we have to make sure that their decisions are those that protect consumers, keep us safer, keep our information private.” He mentioned how regulation right now is almost non-existent. “We don’t have a lot of either self-regulation or government-imposed regulation that really understand what this future world will look like,” he said. “We’re in the very early stages of a shift in terms of how these utilities and tech companies are seen.” with ’s , Sam Lester of the Electronic Privacy Information Center (EPIC) said that asking what Facebook user can do to protect themselves as akin to asking what drivers could do to protect themselves in a car before seat belts became standard. In an interview WIRED Brian Barrett Lester mentioned, however, that new regulation in Europe might be a way of dealing with this. “The good news is, some version of a data privacy seat belt may be in the offing,” he said. “The European Union’s General Data Protection Regulation (GDPR), about what kind of data they collect, and how it will be used. And while no such law seems imminent stateside, the Attorney General of Massachusetts announced an investigation into Facebook and Cambridge Analytica that could at least shed more light on what took place. Senator Monday followed up with a for Facebook to answer.” will require transparency from companies Ron Wyden detailed series of questions Even before the Facebook and Cambridge Analytica scandal broke late last week, Sir , the inventor of the world wide web and founder of the , called for large technology firms to be regulated to prevent the web from being “weaponized at scale”. Tim Berners-Lee The Web Foundation Berners-Lee’s statement was part of an open letter to mark the 29th anniversary of the world wide web. Prophetic? Or just common sense? “In recent years, we’ve seen conspiracy theories trend on social media platforms, fake Twitter and Facebook accounts stoke social tensions, external actors interfere in elections, and criminals steal troves of personal data,” he wrote, pointing out that the current response of lawmakers has been to look “to the platforms themselves for answers” — which he argues is neither fair nor likely to be effective. “Companies are aware of the problems and are making efforts to fix them — with each change they make affecting millions of people,” he continued. “The responsibility — — of making these decisions falls on companies that have been built to maximise profit more than to maximise social good.” and sometimes burden A legal or regulatory framework that accounts for social objectives may help ease those tensions. “I want the web to reflect our hopes and fulfill our dreams, rather than magnify our fears and deepen our divisions,” Berners-Lee said. of , currently on a book leave, commented on Twitter that “one thing I keep hearing is ‘no matter how bad tech cos have screwed up, inviting regulation will make this worse’. Please, explain to me, though, how tech companies should be allowed to continue self-policing.” Mike Isaac The New York Times “Believe me, I am not one to praise the efficacy of bureaucratic oversight — especially at this particular political moment in time — but self-restraint seems to have largely failed,” he continued. As of now Mark Zuckerberg or Sheryl Sandberg haven’t talked publicly about the scandal or plans for the future. of that Facebook’s CEO “plans to speak out in the next 24 hours on the data-harvesting revelations that have hammered his stock price, inflamed lawmakers in D.C. and Europe, and trapped his social network in a crisis of trust.” Mike Allen Axios reports today and he knows it,” a source close to Zuckerberg told Axios. “We’re told that Zuckerberg’s remarks will be aimed at rebuilding trust, and that he wanted to say something meaningful rather than just rushing out.” “ , It’s a big deal And it’s interesting how Marc Benioff of , who commented the news from Facebook and Cambridge Analytica on Twitter with words like “Wow” (twice) and “Getting stranger,” is now focusing the attention on trust. Salesforce

Big Tech after the Facebook-Cambridge Analytica scandal

Why the Facebook/Cambridge Analytica Data Scandal is Awesome News

What is worse then AI robots taking over the planet? Well for one thing, Facebook let Trump use AI to win the 2016 election. Make Facebook Great Again Just over a year ago I wrote, “ ”, which was an article about . Did Donald Trump Use Artificial Intelligence To Win The Election? Cambridge Analytica If you haven’t been paying attention lately, , including the on how Cambridge Analytica was potentially misusing data. Meanwhile, the mainstream media has gone into a frenzy, , , and calling for social media reform. Facebook just banned Cambridge Analytica from their platform whistleblower that blew the whistle crashing Facebook’s stock forcing executives out of the company This story is seriously a year old, and although I was not the first journalist to write about this, I did bring it to the attention of about 6,300+ readers. While it is new that we have a whistleblower, and the number “50 million accounts”, what the media isn’t telling you is how Cambridge Analytica actually pulled this off. The fact of the matter is the data that Cambridge Analytica acquired from Facebook, much of which they obtained legally and as Facebook intended them to, was entirely useless without machine learning. Machine learning is one of the baby steps required for building artificial intelligence, it is a field of computer science that gives computer systems the ability to “learn” with data without being programmed by a person. It was this technology that Cambridge Analytica used to analyze tens of millions of users profiles using data they acquired from Facebook, and put together psychological profiles of users. They then used Facebook’s targeted advertising system to display ads and content at those users geared towards their own psychological profile. Think of it as persuasive arguing on steroids hyper targeted by your own beliefs about the world. For example, I let Cambridge Analytica analyze my data and they pegged me as a liberal. I know this may be a shocking revelation to all of my haters who believe I’m a conservative Russian agent conspiracy theorist who sells tinfoil hats for a living, but according to my Facebook data I’m a liberal. They also determined I’m more intelligent than 96% of Facebook users, and I somehow lost inteligence points because I like the Beatles (mostly a John Lennon fan), but that is a subject for an entirely different article. What this means is Cambridge Analytica knows I am probably not going to vote for Trump based on my psychological profile. However, that is actually more valuable to them than not. There is little point to spending advertising dollars on people who you know will vote for your candidate, the person you want to target is actually the person who will not vote for your candidate and instead convince them to not vote. This is what I believe Cambridge Analytica did, I believe they targeted liberals in swing states and promoted anti-Hillary content in an attempt to convince people not to vote. It means they had to target fewer people, spend less money, and ultimately accomplish their goal of winning the election. All Cambridge Analytica had to do was figure out who was a liberal, and convince them not to show up on election day. By doing so they could ignore everyone who couldn’t vote for whatever reason, and only spend a small portion of their budget on making sure Trump supporters showed up to vote. A no-vote was actually more powerful in swing states, where guess what? . Granted, I still think Hillary blew it and didn’t campaign properly. I am not excusing her ineptitude, she was a horrible candidate and I did not vote for her (Yes, I voted for the female doctor instead, and no I don’t regret it, lets just not talk about it, okay?). Liberal voter turnout was lower for Hillary than it was for Obama Perhaps this is a just a giant coincidence, but I don’t think it is. Given Donald Trump’s ad spend, I’m fairly certain this is exactly what happened. The reason I believe this is because if I were hired to help win an election and had the tools and data Cambridge Analytica had, it is exactly what I would do. I don’t have anything to prove my theory unfortunately, but given what is being reported I am fairly certain I am on to something, and first came up with this theory a year ago before the media went into freak out mode. This is why psychological profiling, privacy, machine learning, and artificial intelligence is so frightening when put in the wrong hands. It isn’t the machines we need to fear, it is the people who are in control of those machines and our data that we need to worry about. Machines are just tools, but people are fickle and with the right incentive easily corrupted. Facebook, Not Russia, Helped Trump Win The Election We know from Wikileaks that John Podesta was in touch with Sheryl Sandberg, COO of Facebook, and even thanked her for helping them “elect the first woman President of the United States.” However, Hillary didn’t win despite the fact Facebook was trying to get her elected alongside Google who was censoring search results, possibly running their own bots, and Twitter who was censoring the truth and purposely hiding Wikileaks tweets which are 100% factual ( ). as they later admitted to congress The reason Hillary Clinton did not win despite the media and social media companies doing everything they could to rig the election in her favor is because Facebook double dipped and allowed Cambridge Analytica to use their surveying tools to collect user data on tens of millions of users. This data was then used to target tens of millions of users with political advertising using Facebook’s ad platform based on psycholgoical profiles from data they bought or acquired from Facebook. Facebook is basically responsible for feeding the analytics system that enabled Cambridge Analytica and the Trump campaign to be so targeted and effective with a minimal budget. They ultimately won Donald Trump the swing states and the election. As well as subverted democracy, and likely made Facebook a bunch of money. That’s what happened, that’s how Trump won. It wasn’t the Russians, it was our own social media companies who sold our data to the Trump campaign which they then likely used to convince liberals not to vote in swing states. It’s both horrifying, and cleverly brilliant at the same time. The funny thing is, and liberals celebrated. Not so funny when the other team takes your trick and executes it more effectively now is it? Obama did something similar in 2012 P.S. Fun fact, Mark Zuckerberg is only 5'8, here’s some tips on how you can you can take pictures like Mark Zuckerberg to make yourself look taller. I didn’t get paid to publish this, so if you like this article feel free to send some Bitcoin . BTC Or ETH: 0xB7A25b8541943e6852A28CF35d1cdf53c1f33f05 I can be reached personally at trent — at — mysticlabs — dot — com

Cambridge Analytica: What The Media Won’t Tell You

We are launching the second sprint for our community — this time it’s dedicated to our analytical products. We have been collecting feedback from members of our community via Discord regarding improvements, new features, crashes, etc. and now we’re ready to begin! Our goal is essentially to create valuable and — more importantly — desirable products. Together, we are all a part of Cindicator, and so we want to create new features and products together with our community. What do we want? We’ve gathered suggestions for features from our community covering all our existing channels; Now it’s time for you to choose the ones you want the most by voting; Before the sprint starts, we are allocating a time period for all participants (observers and team members) to vote for the most important tasks; Each participant will have . We have added the feature-related tasks listed in the Discord application forms (these are given in a different format). three votes Sprint length: 30 days. Start date: end of January (active members of the community can offer their ideas for the sprint until this date). Starting date for voting on tasks: voting commences today with this announcement (all new ideas will be considered for the second sprint and will be added to the backlog). Description of boards For anyone who forgot and for new members: : team members report on the problems and features found during communications in various Cindicator Discord channels on an open format. The author/source must be listed. We will base our next sprints on these reports. You don’t need to vote for the features/ideas in this column. Backlog/ideas : all the feature-related tasks from the backlog are listed here in a strict format (as tasks for team members, the guru, or very active token holders), according to best practice. Features : all tasks for the new sprint will be moved here, based on number of votes and the deadlines we set before launching the sprint. All the tasks that were not included in the sprint will remain in the backlog and may be included in the next sprint if the community votes for them. Planning : tasks that are already being worked on in the sprint are to be moved here. Doing : this board is for features/bugs that are in the process of being uploaded to production. Done Quickfire communication on various tasks/questions will be conducted on the relevant Discord channels. If you’re interested in joining our Discord channel and taking part in the sprints, please fill out the form at . https://cindicator.typeform.com/to/OCtoNW By the way, Vlad Kazakov, who leads development of our analytical products, will be answering your questions next Thursday, 18 January, during our first Q&A session. Vlad will be able to address any questions you might have about Cindicator’s analytical products and its community sprint, as well as Cindicator’s approach to teamwork.Please submit your questions to qa@cindicator.com by Tuesday, 16 January. The Q&A session will live streamed via Youtube. The link to the live stream will be published on our official channels on Discord, Telegram, Twitter, and Facebook. Let’s make the new economy more intelligent, The Cindicator team.

Our first analytical products sprint for active community members and Q&A session

Love because it is so vast and versatile: whether you love numbers or pictures, numbers or words – you will find anything. The other side is that after eight years in the industry, many tasks have become tedious: writing ads and figuring out brand messaging doesn't excite me so much, but "a man’s gotta do what a man’s gotta do". I love and hate marketing. Thankfully, some clever guys invented ChatGPT, which can do most of that tedious work if we use it correctly. However, that raises another question: will I be able to still have my bread and butter, or is it the time to start looking for some fancy spot under a bridge and design a sign "Give me some food, AI has taken my job". That's why I decided to analyse the perspectives of AI overtaking marketing jobs and what skills marketers should keep improving to keep our jobs. Let's go! Dom Perignon and caviar How AI Influences the Marketing Job Market As you could have guessed, I am quite a lazy person, so I will let numbers do the talking: According to , . Ouch, that sounds tough! Goldman Sachs ~300 million jobs could be affected by generative AI, meaning 18% of work globally could be automated Let's see how things look in marketing. A funny and dystopian website WillRobotsTakeMyJob gives an 18% probability that . Sounds a bit bad, but it's not a big deal compared to other professions. robots will replace the marketing manager's job states that 25% of marketing work will be automated in the following years. That doesn't mean "a quarter of marketers will be fired", but some specialists could meet less workload. Marketing AI Institute's poll The numbers sound apocalyptic but wait to start looking for a new job. According to the same Marketing AI Institute report, – such naive people, mua-ha-ha (joke). 56% of marketers think that more jobs will be created by AI in the long-term Also, consider that AI will transform the industry but won't eradicate it – marketers need to adapt to new realities and learn new tools and approaches. Let's cover which marketing competencies can't be replaced with AI, so we can catch up and ensure our jobs are safe. experts 5 Essential Skills for Marketers in the Era of AI Automation The main area of AI application is repetitive tasks, including routine creative and analytical ones, like writing email copies and social media posts. AI will threaten data-processing jobs, such are marketing analysts and those who work with ad targeting and optimisation. To avoid firing, marketers need to work on human skills AI can't copy yet to stay competitive. These include: The art of asking the right questions. Connecting dots and departments. Creativity and creativity management. Marketing AI tools management. Emotional intelligence. Let's cover them in detail. Skill #1: The Art of Asking the Right Questions AI is good at reproducing billions of combinations of existing data, but it's not great at generating something entirely new or getting data from "the analogue world". And what brings us new data? Questions! That's why human marketers are still "on a horse". Speaking to the target audience, sniffing on competitors, and conducting newsjacking have become more critical than ever. To ask the right questions, it's good to have a checklist of questions for different marketing goals: finding different audience needs, positioning ideas, etc. To think out of the box and come up with unusual questions, it's good to read books from different fields and do creativity exercises, like finding 101 unique applications for an avocado. Another critical question is who to ask questions. Except for the target audience, marketers also need to talk to sales, customer support, IT, and lawyers – AI can't build relationships with them. That leads us to skill #2. Skill #2: Connecting dots and departments AI in marketing can offer invaluable insights and data. Still, it falls upon us, the human marketers, to comprehend how to leverage that information across the diverse tapestry of our organisations. Marketing doesn't exist in a vacuum; it intersects with sales, product development, customer support, and other departments. To excel, we must comprehend the entire customer journey and collaborate seamlessly with various stakeholders to craft cohesive and integrated strategies. By synthesising and unifying, marketers ensure their campaigns harmoniously align with overarching business objectives and deliver a seamless customer experience across all touchpoints. While AI may provide recommendations and suggestions, we must bridge the gaps and assemble the puzzle pieces into a unified whole. Skill #3: Creativity and creativity management Regarding creativity, AI still struggles to match the prowess of human marketers. While AI can generate content based on existing data, it often lacks the spark of originality and the audacious thinking that human creativity ignites. That's why we must foster and elevate our creative skills to conceive innovative campaigns, compelling brand messages, and captivating content that deeply resonates with their target audience. Creativity is the defining factor that sets brands apart and empowers them to shine amidst a sea of competitors. Furthermore, as AI tools increasingly permeate the marketing landscape, we need to cultivate proficiency in managing AI-generated content. They should be able to curate and refine AI-generated outputs, ensuring they align with the brand's voice and objectives. It's a delicate dance, striking the right balance between harnessing AI's capabilities and infusing that crucial human touch to forge impactful marketing materials. Skill #4: Marketing AI tools management With the surge of AI tools in marketing, we must become adept at managing and leveraging these technologies. This encompasses comprehending the capabilities and limitations of various AI applications, handpicking the most suitable tools for specific tasks, and seamlessly integrating AI into marketing workflows. We must learn how to harness AI tools to automate repetitive tasks, analyse voluminous datasets, and extract valuable insights that inform decision-making. These tools help me the most in the work I do: – it generates ad texts and strategy ideas with a click. It helps with routine tasks and is a starting point for idea generation. Aiter.io creates a website in seconds. I use it to generate secondary landing pages quickly; after this, it just needs some editing. Durable – the same as Durable, but for presentations. Beautiful.ai – uses AI to optimise ad campaigns. Albert AI Of course, now there are thousands of them, so you need to keep an eye on the industry. I use directories like Futurepedia to track new tools. BTW, remember that managing AI tools also entails safeguarding data privacy, considering ethical implications, and complying with regulations. We need to possess a comprehensive understanding of the ethical utilisation of AI and be well-versed in the potential risks and challenges associated with AI-driven marketing. Skill #5: Emotional intelligence While AI can analyse data and predict consumer behaviour, it falls short when understanding and empathising with human emotions. Emotional intelligence is a skill that we need to develop to create meaningful connections with our target audiences. Understanding consumer motivations, desires, and pain points requires emotional intelligence. We can compete with robots by empathising with our audience, anticipating their needs, and tailoring messaging to resonate emotionally. This skill becomes even more critical as customers seek authentic and personalised experiences from brands. Additionally, emotional intelligence is vital in building and maintaining relationships with colleagues, stakeholders, and clients. Successful marketers know how to navigate different personalities, resolve conflicts, and collaborate effectively to achieve shared goals. Conclusion: I Will Survive, You Will Survive In conclusion, while AI tools are transforming the marketing landscape, there are specific skills that marketers can continue to cultivate to remain relevant and valuable in the age of automation. We can position ourselves as indispensable assets in the AI-driven marketing world by honing our ability to ask the right questions, connecting dots and departments, nurturing creativity, managing AI tools effectively, and developing emotional intelligence. So, AI is not a threat to our existence but an opportunity to eliminate all that tedious marketing work nobody loves and concentrate on the fun stuff. Hooray!

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Th-th-th-that's right, folks! We've got ourselves a real doozy here! Two of the most titillating, tantalizing, and downright terrific terms in the tech world - AI and cryptocurrencies - sharing the same sentence! It's like a tech enthusiast's dream come true, I say, it's a dream come true! But hold your horses, this ain't just a figment of your imagination, no siree! It's as real as the snout on my face! And let me tell you, it's as exciting as a barn dance on a Saturday night! So, strap yourselves in, folks, 'cause we're about to embark on a rip-roaring ride through the thrilling intersection of artificial intelligence and digital currencies. It's a wild world out there, full of possibilities as vast as a cornfield in July. So, let's dive in, shall we? After all, as they say in the world of high finance (and I should know, being a pig and all), you've got to bring home the bacon! Okay, let’s get back to ‘serious mode’ - Just had to get this out of my system. The Role of AI in Cryptocurrencies Prepare for a deep dive into the intricate world of AI and cryptocurrencies. At first glance, they might seem like an unlikely duo. However, they're more intertwined than a pair of headphones left in your pocket. Artificial Intelligence, our digital Einstein, excels at deciphering complex patterns and crunching colossal amounts of data. In the volatile landscape of cryptocurrencies, this ability is as valuable as a diamond in the rough. Picture a vast ocean of data - numbers, trends, patterns, all swirling in a complex choreography. To the human eye, it's a whirlwind of chaos. But for AI, it's a playground. With its sophisticated algorithms and rapid processing power, AI sifts through this data with the precision of a master watchmaker, identifying trends and making informed decisions with the speed of a cheetah on the chase. And it doesn't stop there. AI also serves as a vigilant sentinel, tirelessly working to detect and deter cryptocurrency fraud. It's akin to having a highly intelligent, ultra-efficient assistant who never takes a coffee break, always keeping a watchful eye on your valuable digital assets. In essence, AI and cryptocurrencies, though an unconventional pair, are together reshaping the way we trade, invest, and navigate the digital economy. This is just the tip of the iceberg. As we delve deeper, you'll discover how the future of AI and cryptocurrencies is set to revolutionize the digital world. So, let's forge ahead! The Advantages of Integrating AI in Cryptocurrencies Now, let's cut to the chase. What's the real deal? What's in it for you? Well, integrating AI in cryptocurrencies has several advantages that are as clear as a bell: Increased Efficiency AI can automate trading and transactions, making them faster and more efficient. It's like upgrading from a bicycle to a rocket ship! For instance, a platform called leverages AI to create a hedge fund that allows thousands of data scientists to openly collaborate and compete to create the best predictive models. It's a clear example of how AI can increase efficiency in cryptocurrency trading. Numerai Enhanced Security AI can detect unusual activity and potential security threats, keeping your precious digital coins safe and sound. A study published on discusses how AI can enhance cryptocurrency security using a risk management model. The model uses social media indicators to mine threats based on investors' perceptions, thereby enhancing the security of cryptocurrency. IEEE Xplore Improved Accuracy AI can analyze market trends and make accurate predictions, helping you make informed investment decisions. It's like having a crystal ball, but way cooler! A platform called uses AI to predict Bitcoin prices. It's a prime example of how AI can improve accuracy in cryptocurrency predictions. BitcoinPredict In a nutshell, the integration of AI in cryptocurrencies is like adding a turbocharger to your car's engine. It boosts efficiency, ramps up security, and enhances accuracy. So, whether you're a seasoned crypto trader or a newbie, AI can give you a leg up in the crypto game. Now, isn't that something to write home about? The Challenges at the Intersection of AI and Cryptocurrencies Now, don't get me wrong, it's not all a bed of roses. Like any trailblazing technology, the crossroads of AI and cryptocurrencies has its fair share of speed bumps: Data Privacy AI is like a data vacuum, sucking up information faster than a kid with a milkshake. But who owns this data? And how is it used? These are big questions that need answering. For instance, the Cambridge Analytica scandal [ ] threw a spotlight on the potential misuse of data, raising concerns about how AI might use our personal information in the future. 1 Regulatory Issues Cryptocurrencies are still a bit of a wild west, and throwing AI into the mix is like adding dynamite to a bonfire. As KPMG points out, regulatory activity around crypto and digital assets is intensifying as usage by investors, companies, and even some central banks, shows widespread interest and adoption at retail and institutional levels Technological Hurdles Developing and implementing AI algorithms is no walk in the park. It's more like a trek up Mount Everest. It requires significant resources and expertise. For example, the concept of AI-complete problems, which are the most difficult problems in artificial intelligence and computation, poses a significant challenge in the integration of AI and cryptocurrencies Case Studies: Successful Integration of AI and Cryptocurrencies Alright, enough with the theory, let's get down to the nitty-gritty. Here are a few examples of projects that have successfully integrated AI and cryptocurrencies: Numerai This innovative platform uses AI to automate cryptocurrency trading, resulting in increased efficiency and reduced risk SingularityNET This groundbreaking initiative uses AI to detect and prevent cryptocurrency fraud, enhancing security for users Coinbase This pioneering venture uses AI to analyze market trends and make accurate predictions, helping users make informed investment decisions The Future of AI and Cryptocurrencies So, where do we go from here? Well, the sky's the limit! As AI and cryptocurrencies continue to evolve, we can expect to see even more exciting developments at their intersection. From smarter trading algorithms to more secure transactions, the future looks bright for this dynamic duo. Quick and Dirty: Alright, folks, let's cut to the chase. Here are the answers to some of the most burning questions about AI and cryptocurrencies: Q: Can AI predict cryptocurrency prices? A: Well, AI isn't a crystal ball, but it's the next best thing. It can analyze market trends and make informed predictions. But remember, cryptocurrencies are as unpredictable as a bull in a china shop. So, while AI can certainly help to make more informed predictions based on historical data and market trends, it's not foolproof. Q: How does AI enhance the security of cryptocurrencies? A: Think of AI as a digital watchdog, always on the lookout for unusual activity and potential security threats. It can help to prevent fraud and other forms of cybercrime, keeping your digital assets safe and sound. Q: What are some examples of projects that have successfully integrated AI and cryptocurrencies? A: There are several projects that have hit the bullseye. For instance, Numerai uses AI to automate cryptocurrency trading, and SingularityNET uses AI to detect and prevent cryptocurrency fraud. These are just a couple of examples of how AI and cryptocurrencies are joining forces to revolutionize the digital world. Jin Park Signing Off Well, folks, it looks like we've reached the end of the line. We've journeyed through the exhilarating crossroads of AI and cryptocurrencies, delved into the challenges and opportunities, and even dared to gaze into the crystal ball of the future. It's clear as day that the fusion of these two trailblazing technologies is set to shake up the digital world like a snow globe. But let's not forget, this is just the opening act. As AI and cryptocurrencies continue to evolve, so too will their intersection. So keep your peepers peeled and your noggin nimble, because the future of AI and cryptocurrencies is sure to be a rollercoaster ride that would make even the most seasoned thrill-seeker's head spin! We've taken a deep dive into the captivating world where AI rubs shoulders with crypto. It's been a hoot, and I hope you've enjoyed the ride as much as I have. We've laughed, we've learned, and most importantly, we've taken a leap into the future. So, as we part ways, remember to keep exploring, keep learning, and keep pushing the boundaries of what's possible in the digital world. After all, in the world of tech, the only constant is change. This is Jin Park, your guide in the digital wilderness, signing off. Until next time, stay curious, stay bold, and stay tuned for more adventures at the intersection of technology and imagination! Join the 'Jinius' movement with Jin Park. Dive into the digital realm now! Support the adventure at ❤️. Your contribution is the fuel for our journey!" ko-fi.com/jiniusme

When AI Meets Crypto: A Deep Dive into the Confluence of Machine Learning and Digital Currencies

Sales reps are trying to sell their products or services to prospective leads consistently. To establish a connection with these leads, sales representatives can either rely on marketing teams to deliver inbound leads or they can find prospects through cold calling, , and texting (SMS). cold emails Add multi-channel outreach, and you can maximize the reach to your target audience by showing up on different platforms. Multi-channel outreaches usually lead to an increase in ROI by an average of 25%. is one of the cold outreach software, which helps you not only send cold emails but also connect with your target audience via LinkedIn. Lemlist How can a multi-channel outbound sequence facilitate your SaaS growth? Fundamentally, using multiple communication channels to reach out to prospects increases your chances of connecting with them. Here are some further reasons why you should not ignore multichannel outreaches. Using multiple channels enables us to communicate with decision-makers in their desired way and gives us more opportunities. Broader, more accurate reach – Multi-channel sales outreach gives data on where prospects are most engaged and suggest the channels that need an upgrade or change. Actionable analytics (and customer insights) – Utilizing the multichannel marketing approach, you may take advantage of different channels’ capabilities while identifying those circumstances where it falls short. Take advantage of the capabilities diverse channels have to offer – One of the major advantages is how each channel works together throughout a single campaign. For instance, you can send emails to leads you connect with on LinkedIn and establish the context of the conversation without much hassle. Utilize multiple channels to engage with a single lead – What is ? Lemlist Lemlist is an AI-powered tool that allows you to automate outreach campaigns and create highly personalised email campaigns. You may segment your audience, customise your emails, and monitor your performance with Lemlist to see what’s working. It functions as a sales engagement platform that automates follow-ups for you. As a result, you can concentrate on your primary sales activities while leaving the follow-ups to Lemlist. email marketing Lemlist not only helps you to create a relationship with your target audience on various platforms, but also guides you to generate leads across several channels, including LinkedIn, cold email, and cold calls. In the process, it enables you to grow your LinkedIn network and transform your profile into a lead-generation tool as well. How to build a multi-channel outbound sequence using Lemlist? In this article, we are going to make your life a bit easier by demonstrating how you can create an outbound sequence in multiple channels by using Lemlist and reach a wider range of your target audience! 1. Defining Your Target Audience The first step towards defining your target audience is to create an Idea Customer Profile (ICP). ICP is a hypothetical description of the kind of organisation that would benefit most from your products or services. Giving your consumers greater value will increase the value you receive in return. Here are some ideas for how to make your company’s ICP as relatable as possible: Try to include customers who contribute the most revenue, receive the greatest value from you, and have been loyal to you for the longest. These are the kinds of customers you should search for more frequently. A. Make a list of your top customers – Attempt to learn the fundamentals of their business, such as its industry, size, and revenue. Then try to dive deep by understanding their social characteristics and if they have added any new features to their solutions. You can also conduct a call or interview to learn more about them if you require more in-depth information. B. Explore their attributes – Look for patterns among your best clients that are repeated from one account to another. These qualities are the ones you should look for in potential new clients to make sure they turn out to be your best customers as well. C. Identify the common characteristics – The above steps were overall research on what kind of company you want to acquire as your customer. However, that is not enough. You need to dive deep into the persona of the individual or buyer in the company who is going to opt for your solution. A buyer persona is a thorough profile of a member of your target audience. While fictional, it is based on extensive study and can help to enhance your outreach personalization. Let’s see some quick steps on how you can create this! undefined Try to pinpoint the professional background (job title, job role, etc.), demographics (location, age, etc.) and interests of the buyer that you are trying to target. You can easily get such information from the LinkedIn Profile of your ideal buyers, or the information from your website’s google analytics account. [ ] Buyer profile – How to find the professional background of your ideal buyer? undefined After you know the precise objectives of your target, you can provide them with a unique value by explaining how your product will help them reach those objectives. This may be determined by looking at their LinkedIn comments and reviews, the queries they post in the social media groups they are a part of, etc. [ ] Know the objectives and goals of your buyer – undefined Once you know the type of content, social media platform, or outbound technique your target customer is most responsive to, you may reach out to them there, increasing the likelihood that they will respond. On your website, you can monitor their behaviour to learn what drove them to make a purchase. [ ] How did they learn about you and choose to acquire your solution? – [ ] undefined It’s crucial to know what might prevent your target market from buying your product. This will enable you to anticipate their objections and persuade them to set aside their concerns in order to try your product. What might prevent them from buying your solution? – Creating tiers for your buyer personas is the final stage in defining your target audience. This stage is essential because it enables you to determine which objectives will yield the greatest profits and where you should concentrate your efforts. These tiers can be used to determine where to focus your personalization and lead-generating efforts. 2. How to Build a Lead List that You will Target in Your Outbound Sequence? After you have defined your target audience, you can make a list of the people whom you would like to reach out to through your outbound sequence. This will not only make you more efficient at work, but a smart and targeted lead list is essential to your outbound sales process. Your sales pipeline is built on this lead list. A lot of your valuable time will be used up manually creating this lead list. Hence, in order to assist you, we will cover the top 3 tools that will enable you to create these lead lists in just a few minutes! Apollo is a comprehensive sales intelligence and engagement tool for SaaS companies that equip firms with all they need to prospect, enrich leads, and connect with potential clients effectively. Apollo makes it simple to centralise your sales efforts and prevent tool exhaustion. A good value for money is also obtained with this tool. With 250 million (and increasing) contacts, it also boasts the largest lead database among all sales lead creation solutions. You may also add a lot more information to your existing lead data (from CRM, marketing platforms, etc.) as well as generate new lead lists here with just one click. Apollo usually provides valid work emails for ~75% of the leads that you identify in their filters. – Apollo With information from over 100 million business contacts and 15 million enterprises, Lusha is a powerful lead-generation tool. The platform also has tools that can help you convert leads into customers, such as enriched data for more precise sales outreach.  Lusha is a fantastic option for mid-market enterprises wanting to build their operations by increasing their sales pipeline because of its extensive set of features and dedication to 85% – 90% accurate data gathering. Additionally, the Lusha platform was created with simplicity of use in mind, making it easy for sales teams to pick it up in just a few clicks. – Lusha Any professional marketer looking for new potential clients will find ZoomInfo to be a great resource. The email search engine on the platform offers extensive data on more than 150 million professionals. Additionally, marketers can be over 95% precise in their targeting of key demographics with ZoomInfo’s data enrichment technologies. The ZoomInfo platform is an advanced software that has made a name for itself as a leader in the field of sales intelligence. However, because it is a relatively complex platform, customizing and integrating it into your current processes might be somewhat challenging. – ZoomInfo 3. How to Create Your Multi-Channel Sequence using and Cold Emails? LinkedIn With Lemlist, you can integrate multiple platforms to reach out to your target audience. In this article, we will discuss how you can create a multi-channel sequence by using LinkedIn and Outbound Cold Emails. You may connect to your leads on LinkedIn directly from your Lemlist Dashboard after you’ve connected LinkedIn with your Lemlist account. You should start by going to the ‘Sequence Stage’ of the campaign you are creating or any existing campaign you wish to alter. From here, you may access your lead’s LinkedIn page and send them an invite. Step 1 - Visit and invite your leads on LinkedIn – This step gives your future messages some context, makes individuals interested in your profile if you haven’t already connected, and demonstrates that you did some preparation. But be mindful of your daily LinkedIn invite restriction of 20. Aim to get at least 20% of the connection requests accepted. If you’re falling short of it, you should upgrade your LinkedIn profile and audit the audience you are reaching out to. You can also send customised messages to your leads on LinkedIn within the “Sequence Stage” exclusively. Since you don’t really need to give much information when engaging with people on a networking platform, you can start your communication with a LinkedIn message. But if you want to share information about yourself with them, it’s advisable to leave a LinkedIn message as a follow-up to any email that you have already given. Step 2 – Send a message on LinkedIn – Also, be sure to wait 5–6 days before entering a message after sending an invite. So your potential customers have time to accept your invitation! They won’t receive your message otherwise. You can start cold emailing your leads once you have connected with them on LinkedIn. The biggest benefit of this is that you are no longer absolutely unknown to them, increasing the likelihood that they will open your emails. Step 3 – Set up cold email sequences – Another benefit of using cold emails in multichannel sequences is that you may use immense personalization, but the entire process is automated. Therefore, you’re working quickly while still attracting leads. In order for your leads to remember you, it is crucial to follow up with them using cold emails. For this reason, you should at the very least create two cold email steps. Don’t forget to keep some details in mind to prevent your emails from ending up in spam – a. Do not send more than 100 – 200 emails per week. Open rate should be more than 50%. Reply rate should at least be 10%. Lemlist will alert you when it’s time to engage with someone’s LinkedIn or Twitter post if you add a reminder to your campaign. This will enable you to establish a strong bond with your leads and win their confidence. To simplify the procedure, you could provide the link to the post, the suggestion for a remark, or the actual comment in the “Instruction” area. Step 4 – Reminders – 4. Things You Should Keep in Mind while Writing Copies for Your Cold Emails. Every cold email starts with some basic principles. But, the best writers and cold emailers master the art of catching a reader’s attention and providing value. You must conduct in-depth market research on your target audience and have a firm grasp of the essential elements of email copy in order to write a cold email that converts well. To generate interest in your solutions, you must not only make your email content interesting but also informative. With these emails, you need to demonstrate to your target audience how you are qualified to address their issues and simplify their lives. Let’s look at some crucial points that you need to bear in mind when composing these cold emails. One of the first things a receiver observes about your email is the subject line. ~50% of recipients only consider the subject line while deciding whether to open an email. As a result, your email subject line should grab the reader’s attention right away. Personalize the subject line by using the name of your recipient or the name of their company, a connection you have, a fascinating fact about them, or a particular problem that they face. And, keep it short. A. Subject Line – What persuades a reader to continue reading are the opening lines. Your introductory paragraphs need to grab the reader’s attention and facilitate interaction. More importantly, it should provide context for your main email body and explain to readers why they should care about your cold sales email/why you have reached out. Do not send cold emails without any context or observations. B. Opening Lines – The primary body of your cold email should fulfil all of your prior promises and assertions. Tell them how the observation is tied to the problem you are solving, how your plan will help them, and how it has worked in the past for other companies. Try not to include any images in the first email that you send. Showcase your value proposition, or what you can accomplish for the recipient. C. Main Body – As soon as a lead has finished reading the email’s content, you need to make it clear what they should do next: book a meeting with you, go to your landing page, read a case study, or watch a brief video. A crystal-clear call to action encourages readers to interact with your material more and drives them towards conversion. Keep in mind that each email should only contain one CTA. CTAs that are overused might confuse readers and reduce engagement. D. Call to Action – You can add some personalizations to your emails to make your potential leads feel more connected or make them more interested in opening your email. For eg: Adding their first names, company names, adding the sender’s name, etc. when you are sending the email it will automatically take the first name of your Potential Leads as mentioned in your uploaded lead list. E. Use as much Personalization as you can – You cannot cover every topic in one simple email. This is why attaching links is important so that any lead who is interested in your solution can get to know more from these links. Another purpose that these links solve is the fact that you can track the leads who are clicking on these links. This would not only help you to see who is interested in your solution but also measure which email copy is working for your leads. But make sure you are not overwhelming recipients with links. Adding too many links also lands your emails in spam. F. Add minimal links to your email body – How to Launch Campaigns on Lemlist? You can start a campaign on Lemlist in just 10 minutes. Let me guide you a bit to make this process extremely simple for you. A. Click on and upload the lead list under that you have created and added to your Lemlsit account. Remember, two campaigns cannot have the same leads. ‘Create Campaign’ ‘Leads’ B. Then set up a name for your new campaign under and check out all the other features. You can alter these according to the goals and objectives of your campaign. ‘Settings’ C. Under create your multi-channel sequence using LinkedIn and cold emails steps as explained above. ‘Sequence’ D. Under select the timezone for which you want to launch your campaign. ‘Schedule’ E. Lastly, all the emails, and check if all leads are added, sequences are in place, and personalisations and links are working. ‘Review’ F. And there you go, you are all ready to your campaign! launch How to Measure Your Performance on Lemlist? With Lemlist, you can not only create a multi-channel outbound outreach but also get feedback on how your outreach is working and if you need to make any changes or improvements.  You can also see exactly how your campaign performed and figure out what was most effective. Let’s see how this is done! Get data for each of your Email steps, LinkedIn steps, Call steps, Manual tasks, and API Call steps for your multichannel sequences. A. Per Channel Statistics – This section contains a breakdown of the stats for your sequence, allowing you to see how well each step performed. You may assess the effectiveness of your A/B testing here as well. Here, you may also keep tabs on your campaign activities, lead analysis, etc. B. Per Step Statistics – Here, you may see the negative aspects of your statistics, such as failed, bounced, and unsubscribed attempts, among others. C. Review your campaign’s negative metrics – Depending on the timeframe you select, you’ll get an overview of the activities of your various campaigns each day or each week. It lists all the actions that your leads have taken, arranged by stat (sent, opened, clicked, replied, interested). D. Receive an overview of your team’s performance – E. The best method for measuring the performance of the Lemlist campaigns is to use its . Setup webhook for events like emailsSent, emailsOpened, emailsReplied, emailsClicked, linkedinReplied, linkedinSent etc., and pipe those events to a data warehouse like Google BigQuery via pipedream or n8n. You can connect your data warehouse to a BI tool like Google Data Studio and bam! You can build all potential analytical cuts you want. webhook Interested in empowering your SDRs with a robust outbound infrastructure? Speak to to Get a More Clear Picture of Building a Multi-Channel Sequence on Lemlist! Our Outbound Expert This article was originally posted . here

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