Find the latest AGCO Corporation (AGCO) stock forecast based on top analyst's estimates, plus more investing and trading data from Yahoo Finance

AGCO Corporation: Agco Earnings: Ag Demand Remains Solid but Is Starting to Moderate From High Levels

AGCO Power, part of the AGCO Corporation, unveils a wide range of solutions from alternative fuels to electric systems

More Than One Way to Reduce Agricultural Emissions – AGCO Power Reveals Results of Years of Research and Development

Good day, and welcome to the AGCO Third Quarter 2023 Earnings Call. (Operator Instructions) Please note this event is being recorded. I would now like to turn the conference over to Greg Peterson, AGCO Head of Investor Relations. Please go ahead.

Q3 2023 AGCO Corp Earnings Call

AGCO Chairman, President and CEO Eric Hansotia joins 'Mad Money' host Jim Cramer to talk quarterly results, growth in Brazil, growing demand in the agriculture space and more.

AGCO CEO Eric Hansotia goes one-on-one with Jim Cramer

DULUTH, Ga. (AP) — DULUTH, Ga. (AP) — Agco Corp. (AGCO) on Tuesday reported third-quarter profit of $280.6 million. On a per-share basis, the Duluth, Georgia-based company said it had net income of $3.74. Earnings, adjusted for non-recurring costs and restructuring costs, were $3.97 per share.

Agco: Q3 Earnings Snapshot

These improvements are expected to fund increases in engineering and other technology investments to support AGCO’s precision agriculture and digital initiatives. Based on these assumptions, 2023 reported earnings per share is targeted at approximately $ ...

AGCO Reports Record Third Quarter Net Sales

AGCO Corp (NYSE: AGCO) reported third-quarter FY23 net sales growth of 10.7% Y/Y to $3.456 billion, slightly missing the

AGCO Q3 Results Tops On Strong Demand, Raised FY23 EPS Guidance For This Agricultural Machinery Maker

The average estimate of seven analysts surveyed by Zacks Investment Research was for earnings of $3.27 per share. The farm equipment maker posted revenue of $3.46 billion in the period, falling short of Street forecasts.

Farm-equipment maker AGCO saw "robust demand" as third-quarter numbers beat estimates. It also raised guidance.

AGCO Stock Is Rising. Earnings Crushed Estimates.

DULUTH, GEORGIA, US — Robust demand, healthy crop production, favorable farm economics and an improving supply chain generated record third-quarter results for AGCO, a global leader in design, manufacture and distribution of agricultural machinery and ...

Robust demand, favorable economics boost AGCO results

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It was Friday evening and I needed to make myself lunch after work. I found myself staring into my fridge wondering what to cook. I had leftovers from last night, however, I needed to make something fresh and exciting. I thought to myself, why not build a personal AI kitchen assistant to analyze the ingredients I have and suggests delicious dishes tailored precisely to my pantry? This is where Large Language Models (LLMs) stepped into the kitchen with me. LLMs are a type of artificial intelligence that have been trained on a massive dataset of text (and code), including a vast trove of recipes; meaning that they can do so much more than just finding a suitable recipe. They can become your culinary genius on demand. literally So, how does it all come together? In this article, I'll detail how I: Developed an LLM app utilizing GPT-3.5 turbo LLM to assess available ingredients and recommend recipes Deploy the LLM app on for production, enabling integration into a user interface for seamless communication via my mobile device Agenta Developing The LLM App I’ll be using OpenAI gpt-3.5-turbo in this application, however, I’d also be including various directives of the turbo model and also gpt-4. Use whichever model you think is appropriate for the task. Additionally, I will make use of to deploy my LLM application, as it is my preferred open-source platform for creating robust AI applications with LLMs. Agenta provides the necessary tools for managing and evaluating prompts. Agenta Here are the capabilities of Agenta: Quickly experiment with and compare prompts in any LLM workflow (chain-of-prompts, Retrieval Augmented Generation (RAG), LLM agents...) Swiftly generate test sets and golden datasets for evaluation Evaluate your application using pre-existing or custom evaluators Annotate and A/B test your applications with human feedback Collaborate with product teams on prompt engineering and evaluation Deploy your application with a single click through the UI, CLI, or github workflows By using Agenta, I am able to concentrate on constructing my LLM app and streamline the entire development cycle of the application, thereby enhancing the speed of experimentation. I’d be using Python to build the kitchen assistant. Creating LLM app directory and dependency file Create a directory named and a file named and add the following packages inside the file: and kitchen_assistant requirements.txt agenta openai If you have something like the image attached below, you’re good to go. Environment variable & LLM app development Now that you have the assistant folder and have included the packages inside the file, proceed to create a new file named and include your OPENAI secret key to make the LLM app work. requirements.txt .env Ensure that the file is inside the folder. You should have something like this: .env kitchen_assistant OPENAI_API_KEY=sk-vxxxxxxxxxxxxx # replace with your secret key Next would be to create a file named , and copy the following code into it. I’ll explain what the code is doing in a minute. app.py # Stdlib Imports
from typing import Dict, Any

# Third Party Imports
import agenta as ag # type: ignore
from openai import AsyncOpenAI


SYSTEM_PROMPT = "You are my AI kitchen assistant. Help me find creative recipes based on my ingredients, provide step-by-step cooking instructions, and advise me on food safety practices."
CHAT_LLM_GPT = [
    "gpt-3.5-turbo-16k",
    "gpt-3.5-turbo-0301",
    "gpt-3.5-turbo-0613",
    "gpt-3.5-turbo-16k-0613",
    "gpt-4",
]

# Initialize agenta sdk and update the configuration parameters
ag.init()
ag.config.default(
    temperature=ag.FloatParam(0.2),
    max_tokens=ag.IntParam(-1, -1, 4000),
    prompt_system=ag.TextParam(SYSTEM_PROMPT),
    model=ag.MultipleChoiceParam("gpt-3.5-turbo", CHAT_LLM_GPT),
)

# Initialize async openai sdk
client = AsyncOpenAI()


@ag.entrypoint
async def suggest(inputs: ag.MessagesInput = ag.MessagesInput()) -> Dict[str, Any]:
    """
    Suggest  delicious dish tailored precisely based on the ingredients (inputs).

    Args:
        inputs (ag.MessagesInput): messages input for chat completion

    Returns:
        dict: The llm response (along with additional information).
    """

    messages = [{"role": "system", "content": ag.config.prompt_system}] + inputs
    max_tokens = ag.config.max_tokens if ag.config.max_tokens != -1 else None
    chat_completion = await client.chat.completions.create(
        model=ag.config.model,
        messages=messages,
        temperature=ag.config.temperature,
        max_tokens=max_tokens,
    )
    token_usage = chat_completion.usage.dict() # type: ignore
    return {
        "message": chat_completion.choices[0].message.content,
        **{"usage": token_usage},
        "cost": ag.calculate_token_usage(ag.config.model, token_usage),
    } The imports are essentially importing the required modules that are needed to ensure the code works correctly. System prompt sets the context for every conversation that happens between the user (you and I) and the LLM application. You’d be pissed at your friend who in the middle of a finance conversation with you starts talking about something unrelated, right? Imagine a Large Language Model (LLM) as a super-powered reader. To really grasp what it's reading, it needs context – the words and sentences surrounding whatever it's focused on. This 'context window' is like a spotlight: the bigger the window, the more information the LLM can use to understand and generate meaningful responses. Next to the system prompt is a list of defined models that can be used when testing the user prompt for the LLM app in Agenta. You get to choose whichever model to see how it performs. The command is used to initialize the Agenta SDK. Subsequently, is where you define crucial parameters that dictate the behavior of the kitchen AI assistant. These parameters include: ag.init() ag.config.default(...) Regulates the creativity of responses (lower temperature = more predictable, higher = more unexpected). temperature: Determines the maximum length of the AI's response (measured in tokens, which roughly correspond to words). max_tokens: Sets the initial instructions or context provided to the LLM, specifying it as your kitchen assistant. prompt_system: Specifies the LLM to be used (with a range of GPT model versions as options). model: The line initializes the OpenAI Async SDK, enabling your code to establish a connection and interact with OpenAI's language models. client = AsyncOpenAI() To register your LLM app with Agenta and use it in their playground, you need to define an entry point. I used the decorator on my function. This tells Agenta that this function is where the action starts. Agenta then automatically converts this function into an API endpoint (using FastAPI), making it accessible through their web interface. @ag.entrypoint suggest Publishing The LLM App Now that your AI kitchen assistant is ready, we should publish it to Agenta for experimentation. To do this, you can install the Agenta CLI in a virtual environment using your preferred Python package installer or globally on your system. However, I advise against the latter to avoid potential conflicts with your system packages. Once the installation is complete, make sure to activate the virtual environment. I will proceed with publishing the LLM app to Agenta Cloud, enabling me to deploy the LLM application to a production environment and later, create a UI application for mobile use. If you wish to publish to the cloud version of Agenta as well, I suggest creating an first and then returning to follow the steps below. account If you prefer not to deploy the app to the cloud version of agenta, you can the open-source version on your local machine by following these steps. It will be a slightly different process, but not overly complex. run Create a new project for the LLM app To publish the application in Agenta, we must first create an empty project. Execute the following command in the directory where your application code is located: (.venv) [kitchen_assistant]$ agenta init This will prompt you to enter the project name, Agenta host, API key (if using the cloud or enterprise version), organization, and how you want to initialize the app. Once the app initialization is successfully done, a blank project will be generated in Agenta along with a config.toml file in the same directory, containing all project information. The config.toml file and the empty project in Agenta Cloud will look like the following. Publishing/Serving the first app variant With the project created in Agenta cloud, we need to add the first app variant to it. This can be done by running the following command: agenta variant serve app.py This will create a new app variant in Agenta under the name . Here, is the name of the codebase containing the LLM app logic, while is a default configuration created for that codebase. Each new app variant created from the web interface or from the CLI will always have the name format . app.default app default <codebase_name>.<configuration_name> An is a distinct version of an LLM app, featuring different parameters or code but keeping the same inputs and outputs. This feature enables you to experiment with alternative methods within a single app. For instance, you could have two variants of the kitchen assistant app, each using a different system prompt. app variant Executing the serve command above will create a container for the application featuring a REST API endpoint. This endpoint will be used in Agenta when interacting with the application. The CLI will also display the URL of the endpoint, which can be used to navigate to the application API documentation or playground in Agenta. Copy the playground URI and paste it into your browser. After doing so, you will see the following user interface. It may take 10-20 seconds to load as it sets up the playground for you. You have successfully published your LLM app to Agenta and would need to experiment with it to ensure that it functions as intended. In the chat section right after “User”, write the ingredients and click on the “Run” button. You’ll see that the AI kitchen assistant is considering what you can make with the ingredients: When it’s done processing the ingredients that you inputted, you’ll get suggestion on what you can make. And so, you own a Kitchen AI assistant that suggests recipes based on your ingredients. Did you observe something after each use? You view the total tokens used, the run's cost, and the time taken! Don't hold back now, keep experimenting with the LLM app. Customize the system prompt to match a particular culture that you want, adjust the temperature, and switch the model if needed. Deploying the LLM App To An Environment After using the playground to find a good configuration for your AI Kitchen assistant, it’s time to deploy the application. By deploying the application, you can integrate it to a UI (web and/or mobile) application or anywhere since the deployed app will be an endpoint. You can also later change the configuration from the playground without having to update the code. Clicking on the “Publish” button will bring up a modal with environments you want to deploy the LLM application to. Agenta provides a way to deploy an application to multiple environments: development, staging, and production. Each environment having its own unique configuration. Since the LLM app works and it’s the weekend, we can deploy to production :-). Click on the "production" checkbox and then the "Publish" button. You will receive a success notification informing you that the LLM app variant has been deployed. Look at the side navbar, you’re going to see different sections. I’ll keep what each of them do: The playground is a user-friendly environment in Agenta where you can create and test new app variants. Here, you can adjust parameters, input data, and observe the outputs from your app. Playground: The test set is another user-friendly environment where you can create high-quality datasets. A golden test set, also known as a ground truth test set, consists of various inputs and their expected correct answers. Test Sets: Another environment that enables you to generate automated assessments using various evaluators: , , , , , , and . The purpose of conducting evaluations is to methodically evaluate the outcomes of the LLM application and compare various versions to determine the most optimal one. Another objective is to set a benchmark for the application and evaluate any potential risks. Evaluations: exact match similarity match regex test JSON field match AI critique code evaluation webhook test At times, you might require assessing your models' performance through human judgement. This is where the Human Evaluation environment becomes valuable. It enables you to carry out and to gauge your models' performance using human judgement. Annotations: A/B tests single model tests Endpoints are where you can access the deployed application code. You can view the code only after configuring your application successfully and deploying it to a specific environment. Currently, Agenta supports just three languages to integrate the deployed LLM app: Python, cURL, and TypeScript. If you’d like to add more languages or a specific language, feel free to contribute by raising an and tackling it. ;-) issue To ensure the deployed LLM app works in production, choose cURL in the languages drop-down, click "Copy," update the content value with the ingredients you have, and run it in your terminal. You should receive a result similar to the one below. Conclusion How does it feel to have a Personal Kitchen AI assistant? My partner feels like she has a sous chef that handles recipe making for her. Quite amusing, isn't it? XD In this article, you learnt what LLMs are, how to create a kitchen assistant app using the GPT-3.5-turbo model to analyze your available ingredients and offer recipe suggestions. You also learned how to deploy the LLM app on Agenta for practical use. As an extra tip, I will be a user interface using Svelte and deploy it on Github Pages. I will design it to allow you to input the URI of your LLM app, enabling personal interaction. In the upcoming article, I will demonstrate how to generate test sets for conducting human evaluations to ensure that the LLM app's output aligns with your expectations. building

Your AI-Powered Kitchen Assistant: Building a Recipe Genius With GPT3.5-turbo and Agenta

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您的人工智能厨房助手：使用 GPT3.5-turbo 和 Agenta 打造食谱天才

I dare say there are two types of people in this world: people who love Toy Story and people who have not watched it. Well, this is just the opinion of a huge fan of the animation film. When I was a child, I always dreamed of having toys that could move and play with me, like my own Buzz Lightyear. Thanks to a fancy technique called rigging, I can now bring my toys to life, albeit I'm probably too old for them now. What Is Rigging in 3D Animation and Why Do We Need It? Put simply, rigging is a process whereby a skeleton is created for a 3D model to make it move. In other words, rigging creates a set of connected virtual bones that are used to control a 3D model. It paves the way for animation because it enables a model to be deformed, making it moveable, which is the very reason that rigging is necessary for 3D animation. What Is Auto Rigging 3D animation has been adopted by mobile apps in a number of fields (gaming, e-commerce, video, and more), to achieve more realistic animations than 2D. However, this graphic technique has daunted many developers (like me) because rigging, one of its major prerequisites, is difficult and time-consuming for people who are unfamiliar with modeling. Specifically speaking, most high-performing rigging solutions have many requirements. An example of this is that the input model should be in a standard position, seven or eight key skeletal points should be added, as well as inverse kinematics which must be added to the bones, and more. Luckily, there are solutions that can automatically complete rigging, such as the solution from HMS Core 3D Modeling Kit. auto rigging This capability delivers a wholly automated rigging process, requiring just a biped humanoid model that is generated using images taken from a mobile phone camera. After the model is input, auto rigging uses AI algorithms for limb rigging and generates the model skeleton and skin weights (which determine the degree to which a bone can influence a part of the mesh). Then, the capability changes the orientation and position of the skeleton so that the model can perform a range of preset actions, like walking, running, and jumping. Besides, the rigged model can also be moved according to an action generated by using motion capture technology or be imported into major 3D engines for animation. Lower requirements do not compromise rigging accuracy. Auto rigging is built upon hundreds of thousands of 3D model rigging data records. Thanks to some fine-tuned data records, the capability delivers ideal algorithm accuracy and generalization. I know that words alone are no proof, so check out the animated model I've created using the capability. The movement is smooth, making the cute panda move almost like a real one. Now I'd like to show you how I created this model and how I integrated auto rigging into my demo app. Integration Procedure Preparations Before moving on to the real integration work, make necessary preparations, which include: configuring app information in AppGallery Connect, integrating the HMS Core SDK with the app project, which includes Maven repository address configuration, configuring obfuscation scripts, and declaring necessary permissions. Capability Integration Set an access token or API key — which can be found in — during app initialization for app authentication. agconnect-services.json Using the access token: Call to set an access token. This task is required only once during app initialization. setAccessToken ReconstructApplication.getInstance().setAccessToken("your AccessToken"); Using the API key: Call to set an API key. This key does not need to be set again. setApiKey ReconstructApplication.getInstance().setApiKey("your api_key"); The access token is recommended. And if you prefer the API key, it is assigned to the app when it is created in AppGallery Connect. Create a 3D object reconstruction engine and initialize it. Then, create an auto rigging configurator. // Create a 3D object reconstruction engine.
Modeling3dReconstructEngine modeling3dReconstructEngine = Modeling3dReconstructEngine.getInstance(context); 
// Create an auto rigging configurator.
Modeling3dReconstructSetting setting = new Modeling3dReconstructSetting.Factory() 
    // Set the working mode of the engine to PICTURE.
    .setReconstructMode(Modeling3dReconstructConstants.ReconstructMode.PICTURE) 
    // Set the task type to auto rigging.
    .setTaskType(Modeling3dReconstructConstants.TaskType.AUTO_RIGGING) 
    .create(); Create a listener for the result of uploading images of an object. private Modeling3dReconstructUploadListener uploadListener = new Modeling3dReconstructUploadListener() { 
    @Override 
    public void onUploadProgress(String taskId, double progress, Object ext) { 
         // Callback when the upload progress is received.
    } 
    @Override 
    public void onResult(String taskId, Modeling3dReconstructUploadResult result, Object ext) { 
         // Callback when the upload is successful. 
    } 
    @Override 
    public void onError(String taskId, int errorCode, String message) { 
        // Callback when the upload failed.
    } 
}; Use a 3D object reconstruction configurator to initialize the task, set an upload listener for the engine created in step 1, and upload images. // Use the configurator to initialize the task, which should be done in a sub-thread.
Modeling3dReconstructInitResult modeling3dReconstructInitResult = modeling3dReconstructEngine.initTask(setting); 
String taskId = modeling3dReconstructInitResult.getTaskId(); 
// Set an upload listener.
modeling3dReconstructEngine.setReconstructUploadListener(uploadListener); 
// Call the uploadFile API of the 3D object reconstruction engine to upload images.
modeling3dReconstructEngine.uploadFile(taskId, filePath); Query the status of the auto rigging task. // Initialize the task processing class.
Modeling3dReconstructTaskUtils modeling3dReconstructTaskUtils = Modeling3dReconstructTaskUtils.getInstance(context); 
// Call queryTask in a sub-thread to query the task status.
Modeling3dReconstructQueryResult queryResult = modeling3dReconstructTaskUtils.queryTask(taskId); 
// Obtain the task status.
int status = queryResult.getStatus(); Create a listener for the result of the model file download. private Modeling3dReconstructDownloadListener modeling3dReconstructDownloadListener = new Modeling3dReconstructDownloadListener() { 
    @Override 
    public void onDownloadProgress(String taskId, double progress, Object ext) { 
        // Callback when download progress is received.
    }     
    @Override 
    public void onResult(String taskId, Modeling3dReconstructDownloadResult result, Object ext) { 
        // Callback when download is successful.
    } 
    @Override 
    public void onError(String taskId, int errorCode, String message) { 
        // Callback when download failed.
    } 
}; Pass the download listener to the 3D object reconstruction engine, to download the rigged model. // Set download configurations.
Modeling3dReconstructDownloadConfig downloadConfig = new Modeling3dReconstructDownloadConfig.Factory() 
    // Set the model file format to OBJ or glTF.
    .setModelFormat(Modeling3dReconstructConstants.ModelFormat.OBJ) 
    // Set the texture map mode to normal mode or PBR mode.
    .setTextureMode(Modeling3dReconstructConstants.TextureMode.PBR) 
    .create(); 
// Set the download listener.
modeling3dReconstructEngine.setReconstructDownloadListener(modeling3dReconstructDownloadListener); 
// Call downloadModelWithConfig, passing the task ID, path to which the downloaded file will be saved, and download configurations, to download the rigged model.
modeling3dReconstructEngine.downloadModelWithConfig(taskId, fileSavePath, downloadConfig); Where to Use Auto rigging is used in many scenarios, for example: The most direct way of using auto rigging is to create moveable characters in a 3D game. Or, I think we can combine it with AR to create animated models that can appear on the camera display of a mobile device, which will interact with users. Gaming: We can use auto rigging to animate 3D models of popular toys and liven them up with dance moves, voice-overs, and nursery rhymes to create educational videos. These models can be used in educational videos to appeal to kids more. Online education: Anime figurines look rather plain compared to how they behave in animes. To spice up the figurines, we can use auto rigging to animate 3D models that will look more engaging and dynamic. E-commerce: Conclusion 3D animation is widely used in mobile apps because it presents objects in a more fun and interactive way. A key technique for creating great 3D animations is rigging. Conventional rigging requires modeling know-how and expertise, which puts off many amateur modelers. Auto rigging is the perfect solution to this challenge because its full-automated rigging process can produce highly accurate rigged models that can be easily animated using major engines available on the market. Auto rigging not only lowers the costs and requirements of 3D model generation and animation but also helps 3D models look more appealing.

Streamlining 3D Animation Creation via Rigging

The integration of into the physical and industrial worlds is close at hand. There are real-life applications already in use in several sectors. augmented reality (AR) and virtual reality (VR) Going from science fiction to the real world at a rapid pace, AR and VR are already redefining our digital world. Virtual Reality VR is closely related to the idea of telepresence which “enables people to feel as if they are present in a different place or time” as well as to the notion of immersion — deep mental involvement. VR technologies will allow greater emotional intensity and provide a new layer of experience. Technological advances in coding language have spurred the creation of a coding language called Virtual Reality Modeling Language (VRML). This is used to create a wide range of 3D images and new functionalities. This coding language is the backbone of Virtual Reality. The potential of virtual reality technology is being harnessed for more productive applications in the real and industrial worlds. Case studies have shown that VR technology can offer new opportunities for training, maintenance, and problem-solving scenarios without the physical event needing to take place. VR in Marketing Thanks to the constantly changing digital landscape, advertisers have been forced to get more and more creative with their advertisements, all while looking for the next great advertising idea. As such, VR has been one of the most discussed new consumer technologies over the last couple of years, especially as production costs have decreased. Companies, for example, have shown how VR technology can be used to achieve a company’s goals. Using Google Cardboard technology, companies created a 360° experience allowing customers to remotely "visit" resorts in the world. The experience allows customers to see the destinations and the activities they have to offer, such as swimming with the dolphins and walking on cliffs. The main goal of the campaign was to tempt people into planning their next trip with Virgin Holidays. As a result, sales of the trips showcased in the VR video rose significantly. VR can also provide a great way for companies to showcase their products to customers without them stepping into a brick-and-mortar store. A great example of this is the IKEA Virtual Store, which gives customers an opportunity to explore IKEA household furniture departments at any time of the day. This enables customers to “walk” the store, browse, and even buy products — all without leaving their couch. Several brands such as , , and already use VR to enhance engagement and improve the customer experience. Moreover, it’s estimated that VR will continue to disrupt business models in the future, becoming the next major platform shift in online shopping. Samsung Google Facebook VR in Agriculture Farming is changing rapidly. The intersection of virtual reality, commercial autonomous flying vehicles, i.e., drones, and data-driven agriculture is creating exciting opportunities for software developers to support the growth of digital resources in agriculture. Everybody knows that both too much rain and too much sun are equally harmful to crops. Weather forecasts help farmers understand what weather they should expect. But virtual reality can go one step further, simulating various weather conditions and their potential consequences on crops. A real-life example of VR input in agriculture is the AGCO’s Jackson plant in the USA. There, assembly workers wear digital glasses that allow them to access instructions, pictures, and video to ensure the correct assembly of farming tractor components. This VR equipment, called Glass, has increased the plant's productivity. In a not-so-far future, VR technology will not only be useful in the production of mechanized farming equipment but farmers themselves will also benefit from this new technology. VR in the Automobile Industry Technology is transforming the way we experience and buy cars. Dealerships have been changing in format, size, and concept for years, but now we can buy cars on our lunch breaks from city center retail stores or virtually test drive the latest models . The London Audi City showroom in Green Park, for example, is the smallest Audi dealership in the UK. Interestingly, 50 percent of its customers in the first half of 2017 ordered vehicles at the store without a physical test drive, having "experienced" their future car in an entirely virtual environment. Also, Volvo, a leading automobile brand manufacturer came up with its own version of virtual reality called the Volvo Reality, built on the virtual platform Google Cardboard. During the pre-release of the Volvo XC 90 Luxury SUV, the automobile maker ran a massive campaign for the model. In this campaign, users were immersed in a breathtaking mountain via VR technology. The possibilities for VR applications are endless, and it’s just a matter of time before creating and ordering our virtual dream car over lunch and having it delivered to our doorstep is the norm. But the question remains: Will VR negate the need for physical showrooms completely? Augmented Reality AR is a technology that overlays information and virtual objects onto real-world scenes in real-time. Technically, AR modifies a real environment by adding digital information to create a hybrid real/digital experience. Developers are churning out cool augmented reality apps every day, and this has given the technology a broader commercial appeal. Get ready to see the integration of augmented reality in the physical and industrial spaces: AR in Aviation Innovations in the aviation industry will be in the form of augmented reality technology. Using AR, real-time information in the form of text, images, and audio enhancements can be integrated with actual, physical objects. The primary utility of AR in aviation is its ability to overlay relevant information on demand. Today’s AR systems, such as the Aero Glass, allow users to visualize terrain, air-traffic, instrument readings, weather, and airspace information in a 360-degree, 3D overlay that is easy to use. Since 2011, has used AR applications on tablets to help technicians who are charged with inspecting brackets on Airbus A380 aircrafts. More recently, we've seen augmented reality being used by workers (not trainees) to reduce errors and increase efficiency. GE, for example, adopted Google’s Enterprise Glass to allow workers to see instructions or references while working on a part and also helping to document the work done. Airbus AR in Medicine AR will also continue to advance medicine. Before the introduction of augmented reality into medicine, surgeons only had 2-dimensional anatomical images to review before or during surgery. AR has the potential to provide an overlay display with detailed information to surgeons without requiring them to step away from the patient. We already struggle with a shortage of physicians all over the world. Training new physicians takes time, money, and other resources that aren't always practical or readily available. AR is already having a profound impact on medical training, with applications ranging from to bring anatomical learning to life to helping train nurses to master techniques for checking vital signs. This could help speed up the process of creating new healthcare workers for underserved areas. 3D visualizations AR also has a key role to play in improving the quality of treatment, especially in surgery. For example, the app uses AR to allow surgical procedures to be carried out long-distance, with a specialist using AR tools to guide and collaborate with a colleague in real time. Proximie Conclusion When it comes to augmented reality and virtual reality application in all sphere of life, the potential benefits and possibilities are nearly endless. Thankfully, we’re already past the point of speculation and entering a phase where practical uses are being utilized to save both lives and money.

Integrating AR and VR into the Physical and Industrial Worlds

Augmented Reality (AR) is transforming the way we work. The rapid growth of computing power and the maturation of 5G capabilities are feeding the development of AR technologies, and making AR systems more flexible and accessible for use in the workplace. These use cases include boosting worker productivity, enhancing collaboration, and increasing workplace safety. Consider a scenario in which you want to digitize an image of an object from your physical space and place it into your PowerPoint or Keynote presentation. First, you would take a picture using your camera phone or another digital camera. Next, you would probably connect the phone or camera to your computer via either a USB cable, memory card, or a cloud drive and transfer the photo. At this point, you would need to crop or edit the image to remove any unnecessary background. Finally, you would drag the image from the correct folder and drop it into your document. This is a process we have become accustomed to, so we may not realize the number of steps, devices, and applications it takes to accomplish an otherwise straightforward task. © Cyril Diagne Now, imagine you can use a single app on your mobile device to capture a perfectly-cropped digital image of the object and drop it into a document that lives on your computer. With the release of the app (currently in beta), you will be able to do just that. AR Copy Paste This app presents a time-saving value by allowing you to consolidate steps in the typical image capture and upload process, so you can work faster, spend more energy on other tasks, and be more productive overall. With AR, apps like AR Copy Paste are able to bridge the gap between the real world and the digital world, allowing graphic designers, editors, marketers, and other professionals to interact more efficiently between the two realms. Scan this QR code with your phone to see something awesome! or go directly to https://go.echoar.xyz/LVQc with your phone browser. You can to the echoAR platform for free to create this yourself! sign up In addition to increasing personal productivity, AR also has the ability to enhance the productiveness of teams by supporting remote collaboration. Now more than ever, there is a growing demand for creating an effective remote workplace. is one tool that leverages AR to allow people to work together virtually and seamlessly. Put on a set of AR goggles to see your virtual workspace. You can hold meetings with coworkers who appear as digital avatars of themselves in your environment, without any travel required. You can write, share, and manipulate virtual sticky notes with your hands for brainstorming sessions with teammates. Spatial The information you share through Spatial is dynamic and not limited by the size of your computer screen or your physical proximity to your colleagues. Applications like Spatial highlight the ways in which AR can be used to bring people and ideas together digitally, thereby creating a more connected work environment. © Spatial Systems, Inc. Finally, AR is being applied in industrial settings and changing the way we train workers, ensure high-quality output, and enhance workers’ safety. BMW’s production group, for example, employs AR-based applications in order to virtually train their employees in areas such as engine assembly and lean processes, with trainees viewing digital visuals and instructions through AR goggles for step-by-step instruction. Along these lines, AR can also be used to help with quality control by equipping inspectors with wearable glasses that show assistive information during inspections. , an agricultural equipment manufacturer, uses such glasses when manufacturing glass to replace computers and tablets, which are not easily accessible or are hard to manage during the time of inspection. AGCO Lastly, in other applications, AR combined with geographic information system (GIS) technology can improve safety by making it possible for industrial workers to virtually see what is beneath their work surface, whether it be pipes, cables, or other utilities they need to avoid. © BMW Group AR is making our workplaces smarter, more productive, and safer. Helping to further these advancements and make AR even more accessible, echoAR is a 3D-ready cloud platform for AR and virtual reality (VR) that provides tools and server-side infrastructure to help developers and companies quickly build and deploy AR/VR apps and experiences. The echoAR platform allows you to build your backend in minutes, easily manage 3D content, and deliver that content anywhere. echoAR ( http://www.echoAR.xyz ; Techstars ’19) is a cloud platform for augmented reality (AR) and virtual reality (VR) that provides tools and server-side infrastructure to help developers & companies quickly build and deploy AR/VR apps and experiences.

Why Augmented Reality is the Future of the Work Environment

2019 is the year of adoption. We have weathered the storm of early 2018, lived through the ensuing drought and we can see glimpses of the promised land on the horizon. At , we are strongly passionate about overall adoption of blockchain technology as we believe it can benefit many people and industries. As such, we are always on the look-out for news of increasing adoption and collaboration. aelf Last year I wrote an article with a as I found it very difficult to find a single source for this information online. Comprehensive List of Banks using Blockchain tech Although there was a strong interest in blockchain tech over the last 24 months, due to a mixture of issues, including the volatile ‘unproven’ market and rampant ICO scams, many companies avoided public exposure linking them to the blockchain industry. Many would agree we have seen a shift in this mindset as more high profile individuals and companies are now being more open about their association with blockchain. Take for example, the #1 YouTuber, . Pewdiepie, who has recently partnered with Dlive In fact, according to a by Gemalto, adoption of blockchain to help secure IoT doubled in 2018. report Deloitte recently released survey results in regards to . The companies represented by these executives all have an annual revenue of over $100M USD. One point to note, was that over 75% of respondents believe they will lose a competitive advantage if they do not adopt blockchain. Did someone say FOMO? executive’s views on blockchain adoption Also, according to the survey, over 90% of respondents are already participating in or planning to within 12 months, a blockchain consortium or alliance. According to the , global spending on blockchain development will almost double to $2.9 Billion this year, and increase exponentially to reach $12.4 billion by 2022. International Data Corporation The financial sector will lead the charge with an estimated of $1.1 Billion to be spent in this sector alone. The Manufacturing and resources sector will see the fastest growth this year with a growth rate of 77.6%. Notably, of (Statisca.com), over 50% are included in this list; and in particular, 9 out of the top 10 with a combined market value of over $5.6 trillion USD in 2018. the 100 largest companies in the world by market value Surprisingly, the largest industry in this list is Transport, Shipping & Logistics. There could be a few reasons due to this, firstly, there were a few large alliances that have been created specifically to aid in blockchain adoption with members in the hundreds. Secondly, this is one of the biggest industries in which there is a high degree of interdependence and cross company reliance. The finance industry was always going to be in the top of this list, but another exciting development is the high rank that government received with many countries showing a clear positive attitude towards the technology. In light of all this, it is safe to say, blockchain is here to stay and 2019 is the Year of Adoption. So let’s take a look at the enterprises who have already taken the leap. This list is focusing on active enterprises in 2019. For those that are wondering, this list has almost 1,000 Enterprises in it. *There may be early adopters that are not in this list due to having little activity in the first quarter of this year. If there are ones that are active and not on this list, comment below with the links and I’ll aim to add them in **Accomodation** Cheval Blanc The Dolder Grand **Agriculture** Bayer Crop Science Bumble Bee FoodsCargill **Auto Industry** Hyundai BMW Ford General Motors Genuine Parts CompanyGreat DaneHummer Jaguar Karamba Security Volkswagen Waymo Data **Automated Retail Companies**AAEONAR SystemsFastcorp VendingGlobal Vending GroupGreenbox RoboticsInvendaIVMIVSRetail Automated ConceptsSandenVendoThe-Venders and Wemp **Communication** Deutsche Telekom NEC Europe Smart Communications Solace Corporation Swisscomm Broadridge China Mobile Comcast FarEasTone Kakao OrbcommOrange SoftBank Sprint Telefónica **Consultancy**21 Consulting GmbHAccentureAdaptive Financial Consulting Bain & CompanyBCS ConsultingBerkeley Research Group CH & CoCOMPELLIO DNV GL Business Assurance Group Everis InfosysJDX Post Oak LabsProtivitiRaoof Consultancy Limited Scrypt.MediaSynechronSynpulse XLNTEC Thomas ReutersThoughtWorksTigerRiskTomorrow Labs AizoonAONAsset Balance Solutions Cambridge Associates Deloitte enVista EY J.J. Keller & Associates Inc KPMG RevUnit ThreatAdvice Tata Consultancy Services **Data Managemen**Accuity CommodDT EverisInterxion RoyaltyRange Ansonia Credit DataApplya Connected Holdings Docusign Nielsen Ocean Protocol **Digital Security**CrossVerifyGemaltoGuardtimeObjectTechSLIBSovrinUtimacoYoti **Education** RMIT Duke University Harvard University Swinburne University The Air Force Institute of TechnologyThe University of TennesseeUniversity of Central ArkansasUniversity of Denver University of Colorado Boulder University of Memphis University of Michigan University of Nevada Yale University **Finance**ABN AMROAIBAl Rajhi Banking & Investment CorporationAlawwal BankAlfa Bank Allied Irish Bank ANZ Banking GroupATB FinancialB3Banca Intesa SanpaoloBanca MediolanumBanca MifelBanco BICEBanco BisaBanco Bradesco Banco de Crédito del PerúBanco de Investment GlobalBanco de SabadellBanco de Galicia y Buenos AiresBanco IndustrialBanco Mercantil del NorteBanco Mercantil Santa CruzBanco Nacional de BoliviaBanco Popular DominicanoBanco RegionalBanco SantanderBangkok BankBank ABCBank Al Habib LimitedBank BusanBank Frick & CoBank of America | Merrill LynchBank of CyprusBank of Montreal BankinterBanorteBanreservasBanque Internationale de Commerce — BRED (Suisse)Banque ThalerBarclays BCIBladex BNY Mellon Bolsa de MadridBPCE BRAC Bank LimitedBradescoCBH Compagnie Bancaire HelvétiqueChina Banking AssociationChina Merchant BankChina CITIC Bank International LimitedChina Guangfa BankChong Hing Bank LimitedCIBCCIM Banque S.A GeneveCitiBankCLS GroupCommercial Bank of Africa LimitedCommerzBankCommonwealth Bank of AustraliaCooperatieve RabobankCredicorp BankCredit Agricole S.A.Credit Libanais SAL CTBC BankDaiwa Securities GroupDanske BankDavivienda DenizBank A.S. Deutsche BörseDigiledgeDianrongDNB Ernst & Young European Central Securities Depositories Association First America BankFulton Financial CorporationGazprombank Habib Metropolitan Bank LimitedHo Chi Minh City Development JS CommercialHQLA ICICI Bank LimitedIndovina Bank Ltd Intesa SanpaoloItaú Joint Stock Commercial Bank for Investment and Development of VietnamJoint-Stock Company BCS-Investment BankJPMorgan ChaseJS Bank Limited KASIKORNBANK Public Company LimitedKB Financial GroupKEB Hana BankKuveyt Turk Katilim Bankasi Macquarie Bank MauBank LimitedMizuho Bank National Bank of CanadaNational Bank of EgyptNational Bank of Kenya LimitedNational Bank of KuwaitNatixis Norddeutsche Landesbank GirozentraleNordeaNorthern TrustOmidyar NetworkOP Financial Group Paris Europlace PKO Bank PolskiPortal FinancePrime Bank Ltd.Privatbank IHAG Zurich PT Bank Central Asia TbkPT Bank CIMBNiaga TbkQiwiRaiffeisen BankRBCRBSRCBCRCI Bank & ServicesResona Bank, Ltd. Reyl & Cie SA Satoshi Systems ScopusSEB Group Shinhan BankShuttle HoldingsSIA SMBCSnapCheck SRI Capital Stock Exchange of ThailandSuMi TRUSTSunTrust Synchrony Bank Temasek Holdings The First International Bank of Israel Ltd The Toronto-Dominion Bank TradeCloudTradeIXTradeWindTurkiye Finans Katilim BankasiUBSUniCreditUnion Bank Of The PhilippinesUSBancorpVietnam Joint Stock Commercial Bank for Industry and Trade Volksbank Mittweida Wells FargoWestpac Woori Bank Alipay Ant Financial Banco BTG Pactual Bank MellatBank Melli IranBank Pasargad BBVA Bloomberg BNP Paribas Boerse Stuttgart Digital Ventures BP PLC Credit Suisse DBS Deutsche Bank DTCC Eastern Caribbean Central Bank Employees’ Retirement System eToro Fidelity Investments Goldman Sachs HSBC ING International Chamber of Commerce International Securities Services Association Jamica Stock Exchange Julius Baer Linx Partners London Metal Exchange London Stock Exchange Luxembourg Stock Exchange Mastercard Morgan Creek MUFG NASDAQ Nomura Pakistani Central Bank Paypal Proof of Capital Revolut Santander SBI Holdings SGX Signature Bank Silvergate Bank SIX Group Société Générale SoFi Square Standard Chartered Bank Swissquote SWIFT Telenor Microfinance Bank Thai Stock Exchange The National Payments Corporation of India Thomson Reuters Eikon Tora Virginia’s Police Officer’s Retirement System Visa WEG Bank Western Union **Government** Caisse des dépôts et consignations CSIRO United NationsU.S. Department of Agriculture Australian Federal Government AUSTRAC Brazil’s Ministry of Planning, Budget & Management Canadian Municipality of Innisfil City of Denver Colorado State Government Colorado Water Institute Commerce Committee of Connecticut General Assembly County Auditors’ Association of Ohio Department of Homeland Security Government of Kyrgyzstan Government of Rwanda Illinois Department of Financial & Professional Regulation Illinois Department of Transportation Luxembourg Government Malta Financial Services Authority Ministry of Land & Natural Resources, ZambiaMunicipal Government of Tulum, Mexico Municipal Government of Tsukuba, Japan New York City Economic Development Corporation Pennsylvania’s Economic Development Office (Venture Capital Arm) Seoul Digital Foundation Singapore Government South Korea’s Defence Acquisition Program Administration South Korea Internet & Security Agency Thailand’s Ministry of Science & Technology UK Parliament U.S. Department of Energy U.S. Department of State U.S. House of Representatives West Virginia State Government Wyoming State Government **Insurance** MetLife Ping An Insurance Aegon AIA Allianz AXA Generali Health Care Service Corporation MAPFRE RE Munich Re State Farm Swiss ReTokia Marine TrueNorth XL Catlin ZhongAn Zurich **Internet** Google Twitter Baidu Digital Garage Facebook Opera **IT Solutions**7CommAKonceptsAmazon Web ServicesAOS SASAppwayAt.cashaXpireBillon Group LtdBlanc LabsBoleroBRQ Digital SolutionsCalypso TechnologyCanonical Česká fintech asociace, z.s. CIeNETCMACOCUS AGCodeITCognizantcoIB Eclipse FoundationEPAMEvernym FinastraFinFabrik GFT TechnologiesGiant MachinesGoogle CloudGuildOne Hewlett Packard EnterpriseIBM Inacta AGInfosys Finacle iSiDLG CNSMagiaDigitalMicrosoftNec NTT Data PCCW SolutionsRedhat Vyoma SoftwareWiproZirconTech Capgemini Cass Information Systems Inc. CISCO Collabrian EXL Fujitsu Technology Solutions HCL Imaginnovate Intel Neuro Red Oracle Salesforce SAP SE VMWare **Legal**ACCORD ProjectCOALA National Council of Clerks (France) Linklaters Orrick Taylor Associates **Medical** COCIR European Federation of Nurses Associations Abbvie AnthemBig Blue Cardinal Health Ciox Health CVS Health Genentech HumanaMultiplan NMC Healthcare Optum Pfizer Quest DiagnosticsUnited Healthcare **Media**Les Échos _CBS_Fox Business Network **Mining** BHP Billiton GoldcorpKutcho CopperLG CorpOcean Partners USAWheaton Precious Metals **Non-Profit** Oxfam Freshwater Trust UNICEF **Online Marketplace** Moonlighting **Pharmaceuticals**Amgen MerckPfizer Sanofi Boehringer Ingelheim Roche **Real Estate** Convexity Properties Emaar Group Greenbrier Management Growth Capital ServicesICP Medici Land Governance St. Regis Aspen Resort **Retail & Manufacturing** Acqua di Parma Ardbeg BelvedereBenefit CosmeticsBerlutiBodega Numanthia BulgariCaffe-Pasticceria CovaCape Mentelle CelineChandonCharles & KeithChâteau Cheval BlancChâteau d’YquemChaumetCloudy BayDFSDiorDom PerignonEmilio PucciFendiFenty Beauty by Rihanna FREDFresh (Cosmetics & FragrancesGivenchyGlenmorangie GuerlainHennessyHouse of BijanHublotKalaKat Von D BeautyKenzo KrugLa Grande Epicerie LoeweLoro pianaL’Oreal Maison Francis KurkdjianMarc JacobsMercierMoet & ChandonMoynat Naja Newton VineyardNicholas Kirkwood RimowaRuinartSephoraStarboard Cruise ServicesTAG Heuer Terrazas de los AndesThomas Pink Veuve ClicquotVolcan di mi tierra WenjunZenith A. Favre & Fils AB InBev Anheuser-Busch InBev Albertsons Companies BASF Bridgestone Carrefour Foxconn Golden State Foods Kroger Levi Strauss Louis Vuitton Naked Brand Group Nestle Nike Overstock Parfums Christian Dior Rakuten Target Unilever Walmart **Sports Teams** U.S. LA Kings (Ice Hockey) **Technology** Dell HuaweiLenovo T-Mobile Aidant Technologies AMD Apple Avnet Bosch Foxconn Fujitsu Hewlett PackardHitachi HTC LG NASA Nvidia PitneyBowes RCS Global Samsung Sapphire Technology Seagate TechnologySiemens Sony SweetSense Tencent TSMC **Transport, Logistics & Shipping** CMA CGM ERTICO Feadship Penske 10–4360dataABAQ.USAble FreightAdventAGForceAITAgistixAltriaAmaltoAmerican Global LogisticsAmerican Short Line & Regional Railroad AssociationAMTRAMXAnhueser-BuschApex APL ArcBest CorporationAriaLogisticsAriesArrive LogisticsArrowATSAxle LogisticsBCGBest TransportBlueGraceBlue HorseshoeBourque LogisticsBP BNSF Railway BoyleBoxLockBreakthrough FuelBucklandBurris LogisticsC.H. RobinsonC & K TruckingC.R. EnglandCargoChainCarrier DirectCDL MarketingCenturyCerasisChopTankCitizens ReserveClearDestinationCJ Logistics CNColumbia TransportConvoyCovenant TransportCowan SystemsCoyoteCRST InternationalCSXCTSI GlobalCustoms DirectDaimlerDAMCODasekeDATDDCDecker Truck Line Inc.DeringerDescartesDexFreightDicomDleveredDLT LabsDMTBDreamOrbitDriverReachEagle TransportationEast Coast TransporteCapitalEchoEdge LogisticsEnergy Transportation GroupEnvio360Eroad eShipGlobaleVansEXPAK Logistics FedExFirstFleetFleetCompleteFleetPalFLSFMIFontinalis PartnersForward AirFourKitesFR8StarFraightFreight RoverFreight SourceFullbayGateHouse LogisticsGebruder WeissGE TransportationGlobalTranzGT SolutionsHill Bros Hyundai Merchant Marine IMCInnovativosIntelligent AuditIrvingITLIts LogisticsJ.B. HuntJD LogisticsKamionKansas City Southern LinesKencoKingsgate LogisticsKion GroupKleinschmidtKochKonexial Kuehne & Nagel LabelmasterLandStarLanehubLazer SpotLDILiquid FreightLivingstonLoad OneLoadblockLoadDeliveredLogiflexLogistics+Logistyx TechnologiesLotte Global LogisticsLyncAmericaMach1 Maersk Manhattan AssociatesMatch FactorsMaven MachinesMaverickMcLeod SoftwareMedallion Transport & LogisticsMercerMercuryGateMMM ExpressMode TransportationMorpheus.NetworkMSTSNascentNavistarNBTGNFINGLNorfolk SouthernNT LogisticsNTGNVision GlobalOAROmnitracsOpenPortOOIDAOrigintrailP & GP & S TransportationP.B. IndustriesPackygePAMPanalpinaPantos LogisticsPaschall Truck LinesPavestone CapitalPegasus TransTechPeir Phillips Connect TechnologiesPhoenix Capital GroupPilot Freight ServicesPitt OhioPlatform SciencePLMPolarisPrecisionPrime3Project44ProTransPTIPurolatorPWCQITR & R ExpressRaider ExpressRail IncRay-Mont LogisticsRedwoodReliance PartnersRIMRoadrunner Transportation SystemsRoadSyncRouteiqueRSI LogisticsRTWAirRUANRyderSAIASaint John Capital Schneider Trucking SCLogicScopelitisSeaboard MarineSharelogisShawSheerShipwellSimpleLogisticsSinglePoint CapitalSkyBitzSmartHopSMC3SolarisSouthEastern Freight LinesSource Alliance NetworkSpireonSuddathSunsetSupplyOnSyfan LogisticsTadmoreTAFSTeam WorldwideThe Home DepotThird Coast LogisticsTideworks TechnologyTMCTMW SystemsTRAC IntermodalTraffixTransCardTransfixTransfloTransLytixTransPlaceTransportationInsightTransProTranzlogixTranzportTrinity LogisticsTrinium TechnologiesTriumphPayTrucker PathTruckstopTruck ToolsUber FreightUNISUnion Pacific UPS U.S. XpressUSA TruckVelocity TransportVHI TransportVitesseVnomicsWataco Supply Chain ServicesWELWernerWhirlpool CorporationWhite ArrowWiseTech GlobalWolf DirectWOLTWorldwide ExpressWWS TradingXLNTECYRC WorldwideZoof-It **Utilities** ENGIE Repsol WASSHA (DIGITAL GRID Corporation) Concentric Gazprom Petroteq TEPCO Wien Energie **Noteworthy Individuals** — Russian Opposition Leader — U.S. Presidential Candidate — South Korean Actor — Inventor of World Wide Web — Founder SpaceX & Tesla — French President — CEO Twitter & Square — Retired NBA Basketball Player — №1 Youtube Channel — LinkedIn co-founder — Professional Tennis Player — CEO Berkshire Hathaway & Billionaire investor Alexei Navalny Andrew Yang Bae Yong-joon Tim Berners-Lee Elon Musk Emmanuel Macron Jack Dorsey Kobe Bryant Pewdiepie Reid Hoffman Serena Williams Warren Buffet <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

A Comprehensive List of Enterprises Partnering with blockchain in 2019

Written together with Tiffine Wang of Singtel Innov8, Ryan Morgan of DFJ Growth, and Frederick Dopfel of Samsung Ventures. The short version of this post was featured in VentureBeat Many have dreamed of robots taking over monotonous functions of our daily lives, freeing us humans to work on highly-skilled or creative tasks. Today, robots are increasingly making their way into our homes, offices, and public spaces, but this integration is not occurring in the way that most futurists and popular science fiction writers have anticipated. Here’s our top picks for tasks that were surprisingly easy to automate, and tasks that remain surprisingly hard. Tasks Surprisingly Easy to Automate: Art Prevailing wisdom claimed that computer systems would be incapable of creating art or music, because they lack the natural creativity of humans. However, automated systems have been surprisingly successful at generating works of art. Since the early 2000s, the University of London’s The Painting Fool program has been generating artwork, much of which has been featured in prominent galleries alongside human-created art. Google’s DeepDream software uses a recursive convolutional neural network to iteratively adjust images to morph them into objects in its database (usually dogs, buildings, and eyeballs). Other neural networks such as DeepStyle or Prisma use convolutional neural networks to stylize photos after the work of a specific artist. Google is now experimenting with automated curation of art exhibits through machine understanding of the style and topic of artists’ work. Logo generation systems like Withoomph, Tailor Brands, and Logojoy, use partially- or fully-automated systems to generate logos for a business based on keywords. Researchers have also demonstrated systems that can generate music: Stephen Wolfram has made an automated music generator available to the public since 2005, the University of Malaga has created Melomics, a system that autonomously composes and plays music to match your lifestyle and activities, and IBM has partnered with artists to help compose music with Watson by combining massive musical datasets and their lyrics with sentiment analysis. Although these automated art and music systems are arguably derivative on the work of others, they are undeniably interesting and beautiful in their own right. Many scholars may argue that, like artificial art, all human art is derivative, although that is a discussion best left to philosophers. It appears that natural human creativity is not, in fact, necessary to create beauty. Science/Research Historically, scientific research has been conducted by highly educated researchers and lab assistants, and as such was not an obvious place for a blossoming of applications. However, a core basis of science is reproducibility, and many aspects of science and research are extremely repetitive, requiring neither continuous thought nor the high level of education attained by the individuals who perform those tasks today. robotics Companies such as OpenTrons are working to save scientists both time and money by helping them automate pipetting, a precise but monotonous and labor intensive task used in many biological and chemical laboratories. Startups Arcturus and BioRealize enable scientists to remotely run many genetic engineering experiments in parallel, greatly reducing mistakes and lab time. Other startups, such as Emerald Therapeutics and Transcriptic, are hoping to move research to the cloud, using remote robotic systems to perform the experiment itself. In addition to vastly increased lab efficiency, these systems could greatly improve reproducibility, as researchers need only share their experiment’s source code. Not content with automating manual lab work, machines are now automating scientific discovery and understanding. Scientists at Cornell’s AI lab created a program called Eureqa, that uses an evolutionary approach to model creation and is able to create models on data without being given any prior assumptions. The team has since commercialized the technology through a spinout, Nutonian. Researchers at Cambridge and Aberystwyth have created a similar autonomous science algorithm called Adam, which they claim was the first machine to independently discover new scientific knowledge. That same team moved on to the University of Manchester, where they created Eve, which is a robot designed to make the drug discovery process faster and cheaper. As the pace of scientific research increases, scientists may rely more and more on automated systems. Legal The practice of law requires many years of studying and understanding laws, cases, and other legal precedence. Law firms often employ large teams of paralegals and interns to perform the majority of initial research; current AI technologies can augment and eventually automate the bulk of that research, leaving the more difficult aspects of practicing law such as synthesizing the information and advising to trained legal professionals. To date, case law, contract law, and advocacy law have seen the most automation. Startup DoNotPay helps users appeal traffic tickets; the bot started in the UK and has expanded to cover several US cities. Over a quarter million tickets have been successfully challenged, and several cities have banned the bot. ROSS Intelligence augments legal research by using AI to surface relevant legal passages and cases to increase the efficiency and quality of legal research. eBrevia uses AI to extract data from contracts to accelerate diligence, contract analysis, and other related applications. Legalist automates the qualification process for litigation financing and backs cases they identify as having a high success rate. There is a ceiling, imposed by liability concerns, to how much law can be automated; a startup might expose itself if it advised a client on legal matters, so most companies likely can only research and synthesize information. Because much of the legal industry charges by the hour, it will be interesting to see to what degree lawyers will accept automation and other tools to increase the efficiency of their practice. Policing and Security Unlike warfighting robots, which have clearance to hurt or even kill humans, robots that work as a police force or physical premises security, particularly in areas far from any war zone, are expected to more closely follow Asimov’s three laws of robotics: avoiding injury to humans while still keeping an area secure. For this reason, many would expect criminals to ignore robotic security guards, knowing full well that the robots could not harm them. However, the primary function of human physical security teams is to observe and report security incidents; due to the risk of liability to their employers, many human guards are not allowed to interfere, making their work surprisingly easy to automate. Security automation to date has focused on augmenting existing security forces, inexpensively amplifying the eyes and ears of security guards without increasing their numbers. Traditionally, fixed camera systems could perform this task, but constantly monitoring a large campus by camera is difficult. Knightscope and Gamma 2 Robotics build ground-based security robots that act as “force multipliers” to provide deterrence through physical presence; these robots are being deployed at an increasing number of corporate campuses and shopping malls. Another startup, Nightingale Security, is using flying drone robots to help their customers carry out continuous surveillance. In time, robots may replace substantial portions of police and security forces, requiring humans only for resolving violent incidents. security Tasks Shockingly Hard to Automate Cleaning Cleaning is perhaps one of the most obvious and logical applications for robots, yet with the exception of the occasional household Roomba, robotic cleaning is not widespread. Not only is cleaning an extremely repetitive task that requires very little technical skill, but it is also a task that almost everyone has to do in their personal lives and despises. What makes the dearth of cleaning robots more perplexing is that the cleaning robot Roomba was one of the first commercially available robots to everyday consumers in 2002. Almost fifteen years later, there has not been any real innovation in terms of cleaning robots that has seen commercial success. While Roomba, which is made by iRobot is the best known of these robots, competing products are made by a healthy list of competitors, including Dyson and LG. On the commercial side of the market, work on an automated janitor / cleaner has gone back at least as far as the 1980s, when Electrolux’s subsidiary, The Kent Company was working on a solution. Yet somehow, to this day, cleaning on a commercial scale is still done by hand. Several employees of Kent went on to create a company called Intellibot that creates a large scale automated floor cleaner called TASKI. Another large scale floor cleaner, the RS26, comes from a collaboration of Brain Corp and International Cleaning Equipment. With that said, there are some very interesting applications on the horizon outside of floor cleaning robots. The startup Ecoppia is using robots to clean solar panels, and Kurion has a division that makes robots that are used to clean up nuclear facilities, including Fukushima. Clothing and Textiles It is surprising that textile manufacturing, one of the first industries to flourish in the industrial revolution through automation remains an incredibly hard to automate completely. Robots work best when manipulating solid objects. Textiles, however, shear, stretch, and compress, making them difficult for robots to manipulate, even though such manipulation comes naturally to humans. Machines today can dye and cut fabrics into the right shape for assembly and embroider intricate designs in an instant, but nimble human fingers are still required for assembling that fabric, sewing on buttons, pockets, and adding on delicate finishing touches such as lace and pleats. Even today, few companies have made any headway in sewing. Softwear Automation is one such company, which uses high speed cameras to instantaneously map the position and orientation of a textile as it flexes and bends, allowing the robot to make micro-adjustments as it sews pieces of fabric together. Nike is using automation to circumvent the traditional shoe assembly process altogether with its FLYKNIT line of shoes, which uses robotics to custom-knit the top portion of a shoe out of one continuous thread, doing away with the need to assemble the shoe from multiple separate pieces of material. Alternative textile and clothing manufacturing techniques such as 3D printing are been heralded as poised to revolutionize the fashion industry, but the items created by these processes and new materials remain niche outside of the athletic wear market. Automation has struggled with clothing care almost as much as it has with sewing. Although machines can wash and dry clothing (sometimes in the same machine), the process of loading, unloading, and folding remains repetitive and laborious with few reasonable automated alternatives. Startups Laundroid and Foldimate have developed laundry folding robots, although their solutions are large desktop or closet-sized machines with limited capacity. Robotic clothes care systems similar to those imagined in science fiction are likely many years away. Farming Automating the harvesting of crops that are today picked by hand has so far been hard because many of these crops can be damaged easily and computers have had trouble with visual recognition of the fruit or produce they are trying to pick. Despite these challenges, there are many companies working towards fully automating a variety of agricultural tasks. Harvest Automation has created a robot called the HV-100 that can do a series of tasks in nurseries and greenhouses. According to one of their customers at McCorkle Nurseries, “We have many plants in the field that have been growing for as many as 24-months; they’ve been spaced, collected and spaced again and they haven’t been touched by a person since the day they were potted.” In the fruit picking space, companies such as FFRobotics, Abundant Robotics, and Vision Robotics have worked on solutions for fruit picking. While none of their products are widespread yet, their efforts are helping pave the way towards a more automated world. Abundant Robotics, for instance, is avoiding many of the difficulties of robotic grippers and instead using a suction device to pick fruit. There are also a series of startups working on weeding and thinning robots. Among these are Naio Technologies, which has created a robot called Oz for small scale weeding. It is also working on a robot called Dino for larger scale weeding, and a robot named Ted for vineyard weeding. It should also be noted that many of the the traditional agricultural players are also working towards automating the farm. John Deere has offered some form of kit for autonomy for years, the current version being AutoTrac. While not in farming, Deere also offers a robot called the Tango E5, which is essentially a Roomba for mowing a lawn. Other competitors in the autonomous tractor space include AGCO and Kinze. AGCO, through its subsidiary Kendt, is working on a project they call MARS, or Mobile Agricultural Robot Swarms. These swarms of small farming robots will be dragged into fields in logistics units (which also hold seed supply as well as batteries for the robots), and then split up to do their work. Looking ahead Several themes emerge from this overview that enable us to predict what’s next for the robotics industry. Today, robotics have made significant headway into automating repetitive tasks requiring little finesse and tolerant of blunt solutions, like washing laundry and dishes or creating large quantities of textiles. Tasks that require processing large amounts of data and information can also be augmented by automation. On the other hand, tasks that require complicated manipulation of materials are still difficult to automate and need more skilled assistance, as does the deeper decision making and synthesis of information that robotics help us surface. As much as robotics have transformed many major industries, the need for humans to complete the last finishing steps, as diminishing as they may be, makes it abundantly clear that total robotic world domination remains out of reach.

Top Unexpectedly Easy and Surprisingly Difficult Use Cases for Automation

HackerNoon Reporter: Please tell us briefly about your background. I'm Charley, cofounder/CTO of Remotion.  I'm currently located in Chicago, Illinois, but I used to work in SF at Google. What's your startup called? And in a sentence or two, what does it do? is a virtual office that puts your hybrid team right on your desktop. It helps you connect with your team like everyone's together. Remotion What is the origin story? Back in 2019, I was in the process of moving to Chicago for my partner's career. At the time, I was working on a different startup idea with my cofounder, Alexander. We were used to working in person in SF, and we immediately noticed how much harder it was to work creatively and collaboratively when not colocated. We looked through a variety of tools, and realized that no one had made the right product to solve our problems yet - so we built it ourselves! What do you love about your team, and why are you the ones to solve this problem? Our team is highly distributed and motivated to solve the problems of Remote work. Everyone on the team is constantly thinking about how we can improve our product to help ourselves become even more collaborative. If you weren’t building your startup, what would you be doing? Probably building some other kind of software tool! At the moment, how do you measure success? What are your core metrics? Call minutes, number of calls, average call lengths. We get excited to see lots of short <5 min calls where people are getting answers to quick questions, and lots of long >1 hr calls where people are hanging out and coworking. What’s most exciting about your traction to date? Our recent launch on Product Hunt ( ) - most importantly, it was incredible seeing all the reviews by our passionate users! https://www.producthunt.com/posts/remotion-2-0 What technologies are you currently most excited about, and most worried about? And why? Hybrid work tools are really exciting - I'm looking forward to being able to go back into office, but it won't look the same. I want to be able to have meetings while in person without feeling like the remote teammates are left out. There are a lot of technologies being developed to help create that kind of inclusive meeting right now, like hybrid audio echo cancellation, or VR meeting tools, etc. an What drew you to get published on HackerNoon? What do you like most about our platform? The focus on small, early-stage startup engineering - I love reading about how other startups approach solving the same technical problems we do, especially compared to larger tech company building - the way we approach solving problems is very different, and HackerNoon is a consistently great resource for this. What advice would you give to the 21-year-old version of yourself? Keep building, and as long as what you make is useful everything else will be okay. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Meetings aren't the answer to creating collaboration - they're too transactional. is currently available for free for macOS. Remotion Vote for Remotion for Startup of the Year, San Francisco, California

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Authors:
(1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch);
(2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch);
(3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Table of Links Abstract and 1 Introduction 2 Background 2.1 Data Augmentation 2.2 Anchor Regression 3 Anchor Data Augmentation 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.3 Algorithm 4 Experiments and 4.1 Linear synthetic data 4.2 Housing nonlinear regression 4.3 In-distribution Generalization 4.4 Out-of-distribution Robustness 5 Conclusion, Broader Impact, and References A Additional information for Anchor Data Augmentation B Experiments 3 Anchor Data Augmentation In this section, we introduce Anchor Data Augmentation (ADA), a domain-independent data augmentation method inspired by AR. ADA does not require previous knowledge about the data invariances nor manually engineered transformations. As opposed to existing domain-agnostic data augmentation methods [10, 45, 46], we do not require training of an expensive generative model, and the augmentation only adds marginally to the computation complexity of the training. In addition, since ADA originates from a causal regression problem, it can be readily applied to regression problems. Even when ADA does not improve performance, its effect on performance remains minimal. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch); (2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch); (3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Authors: Authors: (1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch); (2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch); (3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2.1 Data Augmentation 2.1 Data Augmentation 2.2 Anchor Regression 2.2 Anchor Regression 3 Anchor Data Augmentation 3 Anchor Data Augmentation 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.3 Algorithm 3.3 Algorithm 4 Experiments and 4.1 Linear synthetic data 4 Experiments and 4.1 Linear synthetic data 4.2 Housing nonlinear regression 4.2 Housing nonlinear regression 4.3 In-distribution Generalization 4.3 In-distribution Generalization 4.4 Out-of-distribution Robustness 4.4 Out-of-distribution Robustness 5 Conclusion, Broader Impact, and References 5 Conclusion, Broader Impact, and References A Additional information for Anchor Data Augmentation A Additional information for Anchor Data Augmentation B Experiments B Experiments 3 Anchor Data Augmentation In this section, we introduce Anchor Data Augmentation (ADA), a domain-independent data augmentation method inspired by AR. ADA does not require previous knowledge about the data invariances nor manually engineered transformations. As opposed to existing domain-agnostic data augmentation methods [10, 45, 46], we do not require training of an expensive generative model, and the augmentation only adds marginally to the computation complexity of the training. In addition, since ADA originates from a causal regression problem, it can be readily applied to regression problems. Even when ADA does not improve performance, its effect on performance remains minimal. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Anchor Data Augmentation (ADA): A Domain-Agnostic Method for Enhancing Regression Models

Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. Please tell us your name, what you currently do, and where you work: My name is Maria Carolina. I'm a data scientist with 4.5 years of experience, currently working at a tech company in Brazil. How did you get into Data Science? My journey to data science is unique, I guess. I began my career as an agronomist, completing advanced degrees including a master's and doctorate, and worked as both a postdoctoral researcher and university professor (did not like teaching undergrad very much though). When I first learned about data science through international websites and blogs, it was still an emerging field in Brazil, with very few local job opportunities. So that seemed pretty impossible to me. But somehow, despite living far from the country's major financial centers where most tech jobs were concentrated (and I could not move out of state for a job), I managed to break into the field. My agricultural background was the main reason, I joined a company focused on agribusiness. What does your day-to-day role involve? My role varies depending on current projects, but research is a constant and significant component. Some tasks require several days of research before any coding begins (which I really enjoy). What technology or programming languages do you frequently use to get your work done? I primarily use Python, which is standard across our team. Given our focus on agribusiness, I also work extensively with GIS software, particularly QGIS (a big fan of this tool), and SQL. What do you like the most about your job? The research-intensive nature of my work is what I enjoy most. It feels like an extension of my academic research background but with the added satisfaction of developing practical products. I have to stay current with the latest research, state-of-the-art developments in the field, statistical analysis, machine learning techniques, and efficient programming practices. What is something you think people do not know about data science or data scientists? I understand that there is a wide variety of data roles. Even though data scientist is just one of them, there are many different profiles that a data scientist can embody, depending on the company, client, and projects. I don't know if everyone realizes the diversity of possibilities that this role provides - and of course, how much it evolves and seems to change by the year. But I believe that any data scientist should be very interested in research and learning, and I say this in addition to knowing how to run models for example (which of course also requires a lot of study). While technical skills are crucial, a fundamental curiosity and passion for research and continuous learning are essential traits for any data scientist. What are some trends in data science that you’re particularly excited about? I'm particularly excited about AI's evolving role in data science (although at the same time, I feel the need (daily) to continue learning much more about what we already call "traditional"). While it began by streamlining basic tasks, I believe we're moving toward more sophisticated and complex applications of AI. If you weren't a Data Scientist, what would you be doing? I would probably be in a research position (if I managed to, of course), as I'm deeply passionate about the scientific process - forming hypotheses, developing methodologies, testing assumptions, and documenting findings. What are 3 quick ways for someone to get into Data Science and land a job within a year? For aspiring data scientists, my advice is to: Develop a genuine interest in continuous learning and problem-solving (NEVER ends….and maybe that's very good)
Focus on understanding underlying concepts and their connections (understanding the reasons behind the things!)
Balance your studies between statistics, programming, and business domains (no one knows all…) Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. Welcome to HackerNoon ’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE . HackerNoon HERE Please tell us your name, what you currently do, and where you work: My name is Maria Carolina. I'm a data scientist with 4.5 years of experience, currently working at a tech company in Brazil. How did you get into Data Science? My journey to data science is unique, I guess. I began my career as an agronomist, completing advanced degrees including a master's and doctorate, and worked as both a postdoctoral researcher and university professor (did not like teaching undergrad very much though). When I first learned about data science through international websites and blogs, it was still an emerging field in Brazil, with very few local job opportunities. So that seemed pretty impossible to me. But somehow, despite living far from the country's major financial centers where most tech jobs were concentrated (and I could not move out of state for a job), I managed to break into the field. My agricultural background was the main reason, I joined a company focused on agribusiness. What does your day-to-day role involve? My role varies depending on current projects, but research is a constant and significant component. Some tasks require several days of research before any coding begins (which I really enjoy). What technology or programming languages do you frequently use to get your work done? I primarily use Python , which is standard across our team. Given our focus on agribusiness, I also work extensively with GIS software, particularly QGIS (a big fan of this tool), and SQL. Python What do you like the most about your job? The research-intensive nature of my work is what I enjoy most. It feels like an extension of my academic research background but with the added satisfaction of developing practical products. I have to stay current with the latest research, state-of-the-art developments in the field, statistical analysis, machine learning techniques, and efficient programming practices. What is something you think people do not know about data science or data scientists? I understand that there is a wide variety of data roles. Even though data scientist is just one of them, there are many different profiles that a data scientist can embody, depending on the company, client, and projects. I don't know if everyone realizes the diversity of possibilities that this role provides - and of course, how much it evolves and seems to change by the year. But I believe that any data scientist should be very interested in research and learning, and I say this in addition to knowing how to run models for example (which of course also requires a lot of study). data scientist While technical skills are crucial, a fundamental curiosity and passion for research and continuous learning are essential traits for any data scientist. What are some trends in data science that you’re particularly excited about? I'm particularly excited about AI's evolving role in data science (although at the same time, I feel the need (daily) to continue learning much more about what we already call "traditional"). While it began by streamlining basic tasks, I believe we're moving toward more sophisticated and complex applications of AI. If you weren't a Data Scientist, what would you be doing? I would probably be in a research position (if I managed to, of course), as I'm deeply passionate about the scientific process - forming hypotheses, developing methodologies, testing assumptions, and documenting findings. What are 3 quick ways for someone to get into Data Science and land a job within a year? For aspiring data scientists, my advice is to: Develop a genuine interest in continuous learning and problem-solving (NEVER ends….and maybe that's very good) Focus on understanding underlying concepts and their connections (understanding the reasons behind the things!) Balance your studies between statistics, programming, and business domains (no one knows all…) Develop a genuine interest in continuous learning and problem-solving (NEVER ends….and maybe that's very good) Focus on understanding underlying concepts and their connections (understanding the reasons behind the things!) Balance your studies between statistics, programming, and business domains (no one knows all…)

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