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![image](https://miro.medium.com/v2/resize:fit:640/1*K8WDd-MWHkJz2p72nF1csQ.gif)
Every year, roughly 400,000 new robots enter global markets. They contribute to a growing industrial robotics market of nearly $45 billion which is expected to surpass $70 billion by 2028. From warehouses and factories to hospitals and delivery services, these robots are transforming the way we work and live.

Robotic systems have become integral in various sectors due to their ability to perform repetitive tasks with precision and efficiency. In industries such as logistics and manufacturing, they can optimize processes and help detect anomalies like product defects. The adaptability and versatility of AI-enabled robots have created a demand that is only set to increase in the coming years.

But training robots for real-world scenarios is a complex workflow comprising extensive data collection, training and simulation. Collecting such data can be time consuming, expensive, and even hazardous. That’s where synthetic data comes into play.

Synthetic data is artificially generated data that is designed to mimic real-world conditions. By employing synthetic data, teams can speed up the training process and ensure that robots are well prepared to face a variety of challenges.

Synthetic data can be generated using NVIDIA Omniverse Replicator, a developer framework that lets you build synthetic data generation (SDG) tools and pipelines. Built on Universal Scene Description (OpenUSD), Omniverse is a development platform for building 3D workflows, tools, and applications. Omniverse also powers NVIDIA Isaac Sim, an extensible robotics simulation application that enables roboticists to design, test, and train AI-based robots.

Omniverse Replicator is highly customizable and extensible driven by APIs that allow you develop your specialized SDG pipelines. As a developer, you can programmatically randomize various attributes such as lights, colors and textures. Additionally, you can build custom annotators and writers such as bounding boxes, segmentation masks for your training data.

## Edge Impluse Customizes Omniverse Replicator for Visual Inspection
AI is already showing immense value in industrial visual inspection — helping to identify manufacturing defects. But developing accurate object detection models can be challenging due to limited real-world datasets.

To bridge the data gap, digital twins of factory lines can be created as a testing ground from which AI models can learn.

Jenny Plunkett, a lead developer from Edge Impulse, built her own custom pipeline to generate data for her defect detection use-case. She started with a scene of a conveyor belt transporting soda cans that contain defects. She then built routines in Replicator to programmatically randomize properties of the scene such as lighting, colors, texture, background, foreground, and location of the cans can be with Omniverse Replicator, generating a large and diverse set of of synthetic images for the model to learn from.

Once the synthetic data is ready, it can be fed into AI development platforms like Edge Impulse to annotate the data, build the model, and test it with real objects. Learn more about the workflow here.

![image](https://miro.medium.com/v2/resize:fit:640/0*64AgyMkXqI11oFTY)
Testing model precision in Edge Impulse

## Custom SDG pipeline for Training Warehouse Robots to Detect Objects
Autonomous robots are being extensively deployed in warehouses, where they can help with tasks such as efficiently and safely transporting goods or optimizing inventory management. Similar to the defect detection use case, developers can customize Omniverse Replicator for an object detection use case.

This blog shows how you can build a SDG pipeline to generate synthetic data to iteratively improve the performance of a model for autonomous mobile robots (AMRs) that detects pallet jacks in a warehouse.

![image](https://miro.medium.com/v2/resize:fit:640/0*VYDJDOubMlBiJsd2)
Three of many synthetic images from a warehouse scene with pallet jack location and color, as well as camera position randomized.

The workflow starts by generating 5,000 synthetic images with color and camera position randomized. In the second iteration, various textures are added to the pallet jacks and lighting is randomized. To ensure that the model won’t get distracted by other objects in a warehouse, various random objects like traffic cones, wet floor signs, and barrels next to the pallet jacks are added to the synthetic data in the third iteration.

After generating synthetic images with various parameters randomized, the model could accurately detect pallet jacks. Data that previously would have taken months to collect is generated in days. View the full workflow here.

![image](https://miro.medium.com/v2/resize:fit:640/0*j5PUWEK6AOC3bC8G)
The AI model detecting pallet jacks with high precision after training on synthetic data

## Discrete Event Simulation with FlexSim
One way that robots such as automated guided vehicles (AGVs), wire-guided industrial robotics system planners, and AMRs are tested is through discrete event simulations.

Discrete event simulations model the operation of a system as a sequence of events in time. This allows engineers, designers, and simulation experts to optimize their processes by experimenting with complex models and testing them step-by-step.

To run discrete event simulations, many use FlexSim, a powerful modeling and simulation software tool that allows users to construct a 3D digital twin of a real-life system. FlexSim recently developed an Omniverse Connector, allowing teams to export 3D models and assets to OpenUSD.

![image](https://miro.medium.com/v2/resize:fit:640/0*2LvkbZV_l7te1KFh)
FlexSim’s Omniverse Connector helps teams bridge the gap between their simulation data and real-time 3D visualization, and use that to analyze, visualize, and improve real-world processes.

Learn more about how FlexSim developed an Omniverse Connector and the benefits that USD is unlocking for their customers in this blog.

## Get Started Training AI-Enabled Robots with Synthetic Data
With the Omniverse platform, teams can quickly get started designing, testing, and training AI-based robots.

NVIDIA Isaac Sim, an extensible robotics simulator powered by Omniverse, allows developers to create realistic robotics simulations to train and optimize AI robots for a wide variety of tasks. Isaac Sim 2023.1 was recently released with major updates to performant perception and high-fidelity simulation.

The core functionalities of Isaac Sim come from Omniverse Replicator, an extension of Omniverse that allows developers to build custom SDG tools and pipelines. Omniverse Replicator 1.10 was also recently released with new support for low-code, YAML-based workflows, scaled rendering, and greater flexibility.

Download NVIDIA Omniverse for free and get started. Developers can access Omniverse resources and learn more about OpenUSD.

By：NVIDIA Omniverse
Link：https://medium.com/@nvidiaomniverse/from-virtual-to-reality-how-synthetic-data-will-train-smarter-robots-for-industrial-applications-0c4b744d4ae0

From Virtual to Reality: How Synthetic Data Will Train Smarter Robots for Industrial Applications

Synthetic data generation with OpenUSD is unlocking new opportunities for AI-enabled robots across industrial use cases


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The impact of AI, robotics, and biomedical genomic investments by governments over the past 20 years has been substantial, leading to breakthroughs across various sectors, especially in healthcare, manufacturing, and the service industry. Here is an outline of the key impacts in each of these areas:

## AI and Robotics
1. Healthcare Robotics:

- Surgical Robotics: Government investment in surgical robotics has led to the development of systems like the da Vinci Surgical System, which allows for precise, minimally invasive procedures.
- Rehabilitation Robots: These devices help patients recover from strokes and injuries, improving the speed and quality of rehabilitation.
- Service Robots in Hospitals: These robots handle tasks ranging from delivering supplies to disinfecting rooms, which became especially critical during the COVID-19 pandemic.

2. Manufacturing and Automation:

- The manufacturing sector has seen significant improvements in efficiency and safety due to robotics, with robots taking over dangerous or repetitive tasks, leading to a decrease in workplace injuries and an increase in production rates.

3. Defense and Security:
- Investment in AI and robotics for defense has led to the development of unmanned aerial vehicles (UAVs), autonomous vehicles, and robotic systems for surveillance, reconnaissance, and bomb disposal.

4. Service Industry:
- Robots have been implemented in hospitality, customer service, and logistics, improving efficiency and, in some cases, providing contactless services, which became more prevalent during health crises.

## Biomedical Genomics
1. Disease Diagnosis and Precision Medicine:
- The Human Genome Project and subsequent investments have revolutionized how diseases are diagnosed and treated, leading to personalized medicine approaches where treatments are tailored to the genetic makeup of individuals.

2. Genetic Research and Therapies:
- Genomic investments have paved the way for gene therapies that can treat or even cure previously intractable genetic disorders. CRISPR technology, for example, has its foundations in government-funded research.

3. Cancer Research:
- Advancements in genomic sequencing have led to better understanding of cancer genetics, aiding in the development of targeted cancer treatments and immunotherapies.

4. Drug Development:
- The integration of genomics in drug development has allowed for faster and more effective identification of drug targets, reducing the time and cost to bring new drugs to market.

## Socio-Economic Impacts
1. Economic Growth:
- These investments often lead to job creation, not only in the tech and healthcare sectors but also in support roles and infrastructure development.

2. Ethical, Legal, and Social Implications (ELSI):
- As a result of advancements in genomics and AI, governments have had to consider the ethical implications, leading to the development of new regulations and guidelines for data privacy, ethical research, and AI conduct.

3. Education and Workforce Development:
- There has been a push to develop STEM education to prepare the workforce for the high-tech economy. The demand for professionals in AI, robotics, and genomics has increased significantly.

4. International Collaboration and Competition:
- Governments are often in a race for technological supremacy, leading to international collaborations, partnerships, and sometimes tensions, as seen with AI and 5G technologies.

5. Health Equity:
- While there have been significant advances in healthcare due to these investments, issues of access and affordability continue to pose challenges for equitable health outcomes.

## Long-term Considerations

- Sustainability: As AI and robotics become more prevalent, there is a growing discourse on sustainable practices to ensure these technologies do not exacerbate environmental issues.
- Cybersecurity: With increased digitization and the use of AI, investments in cybersecurity have become crucial to protect sensitive data and critical infrastructure.
- Job Displacement: There is ongoing debate about the potential for AI and robotics to displace jobs, necessitating government investment in retraining programs and new job creation strategies.

Overall, the impact of government investment in AI, robotics, and biomedical genomics has been transformative and will likely continue to shape the future of technology and society. It’s also important to recognize that these advancements have global implications, affecting everything from geopolitics to individual daily lives.

By：Nu FinTimes of Generative AI
Link：https://medium.com/@elurlayen.uyrukhlur/government-role-in-ai-robotics-biomedical-space-and-genomics-a40b093e9b12

Government Role in AI, Robotics, Biomedical, Space and Genomics


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## Introduction

In the fast-paced world of technology, two terms often dominate discussions: Artificial Intelligence (AI) and Machine Learning (ML). These groundbreaking fields have been instrumental in reshaping industries, automating processes, and enhancing human capabilities.

In this blog post, we’ll dive into the fascinating realm of AI and ML, exploring their definitions, applications, and the impact they have on our daily lives.

## Understanding AI and ML

Artificial Intelligence (AI) is a branch of computer science that aims to create machines capable of simulating human intelligence. These intelligent systems can perform tasks that typically require human cognitive abilities, such as learning, reasoning, problem-solving, and decision-making.

Machine Learning (ML), on the other hand, is a subset of AI that focuses on training machines to learn from data and improve their performance over time without explicit programming. ML algorithms enable systems to identify patterns, make predictions, and adapt to new information, making them ideal for applications where data-driven decisions are crucial.

![image](https://miro.medium.com/v2/resize:fit:1100/0*zj9WONoXgwUX8Ru0)
Photo by Mohamed Nohassi on Unsplash

## Applications of AI and ML

The applications of AI and ML are vast and continue to expand across numerous domains. Here are some key areas where these technologies are making a significant impact:

1. Healthcare: AI and ML are revolutionizing healthcare by diagnosing diseases, optimizing treatment plans, and improving patient care through predictive analytics.

2. Finance: In the financial sector, AI and ML are used for fraud detection, algorithmic trading, and credit risk assessment, enhancing security and efficiency.

3. Autonomous Vehicles: Self-driving cars rely on AI and ML to perceive their surroundings, make decisions, and navigate safely.

4. Manufacturing: AI-driven robots and ML algorithms optimize production processes, leading to increased efficiency and reduced operational costs.

5. E-commerce: AI personalization algorithms enhance customer experiences by recommending products and services tailored to individual preferences.

![image](https://miro.medium.com/v2/resize:fit:1100/0*Dmw24kZ-l9yCWiJf)
Photo by Mojahid Mottakin on Unsplash

## The Impact on Society

The integration of AI and ML into society has had far-reaching consequences. Here are some of the notable impacts:

1. Efficiency and Automation: AI and ML have streamlined operations in various industries, leading to increased efficiency, reduced costs, and faster decision-making.

2. Job Transformation: While there are concerns about job displacement, AI and ML have also created new career opportunities, including Data Science, Machine Learning Engineering, AI Ethics Roles, AI Research Scientist, AI Architect, and AI Engineer.

3. Healthcare Advancements: AI is accelerating medical research, drug discovery, and early disease diagnosis, potentially saving lives and improving patient outcomes.

4. Enhanced User Experiences: AI-driven personalization in entertainment, e-commerce, and social media enhances user engagement and satisfaction.

## Future Outlook

The future of AI and ML holds promise and challenges. As technology continues to advance, we can expect more sophisticated applications in fields like robotics, quantum computing, and AI-powered creativity. However, addressing ethical and regulatory issues will be essential to ensure that AI and ML continue to benefit society as a whole.

![image](https://miro.medium.com/v2/resize:fit:1100/0*Yg2WZ71QZbKaBuC9)
Photo by Mohamed Nohassi on Unsplash

## Conclusion

Artificial Intelligence and Machine Learning are transforming the way we live, work, and interact with technology. These technologies are here to stay, offering limitless potential for innovation and growth. As we navigate this ever-evolving landscape, it’s crucial to embrace AI and ML responsibly, ensuring that they serve the greater good while respecting privacy, fairness, and ethical principles.

You can stay updated with technology trends by following reputable technology news sources, subscribing to industry newsletters, attending conferences and webinars, participating in online communities, and engaging in continuous learning and skill development.

By：IHUOMA EGBU
Link：https://medium.com/@sparklingih/artificial-intelligence-and-machine-learning-a88968c8ed16

The Changing Power of Artificial Intelligence and Machine Learning


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Is this thing haunted, or just self-aware?

What could be spookier than a robotic candy bowl with a perceived mind of its own?

There’s nothing I love more than Halloween and robots (except for my dog). What better way to celebrate the holidays than by hacking an inexpensive animatronic and enhancing it with some machine learning capabilities to frighten away those lacking Halloween spirit and safeguard my candy stash. In this tutorial, you’ll learn how to play tricks on Halloween apathists and offer treats to Halloween enthusiasts.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*z22oWIXZQVoYtc0Ekcde9Q.jpeg)

## Requirements
### Hardware
To build your version of this project, you need the following hardware.

- A Raspberry Pi with a microSD card set up following the Raspberry Pi Setup Guide.
- Halloween Animatronic Pumpkin Bowl
- Motor driver
- USB Webcam
- Jumper wires
- 12V power supply for the motor driver
- 5V power supply for the Raspberry Pi

### Software
To control your pumpkin bowl programmatically, you need the following software prerequisites:

- An account on app.viam.com
- viam-server
- Python 3
- pip
- Viam Python SDK

## Dissecting the pumpkin
Just like pumpkin carving, don’t be afraid to split your pumpkin (bowl) open and rip out its guts. We want to see what hardware we already have and what we can ultimately upgrade to get it running on an SBC (single-board computer) such as a Raspberry Pi.

After opening the electronic pumpkin bowl, I can identify some pretty standard components: A dc motor, a photoresistor (serving as a motion sensor for its original functionality), a small speaker, a 5V battery pack, LEDs for eyes, and a custom control board which I assume has the programmed functionality driving the motor and sound effects.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*wWFzjZXMOw0s02h7DOmlSA.jpeg)
Because Viam is compatible with nearly any off-the-shelf hardware you can think of (you can read more about this here), I know I can use this hardware and simply add a Raspberry Pi and a motor driver to fine-tune the hand movements to upgrade this pumpkin. I also know I can rip out these LED eyes and replace them with a camera to enable the bowl to see its surroundings.

## Upgrading the hardware
Begin by drilling a larger hole into one of the eye sockets to fit in the webcam lens. If you drill the right size hole, it should fit nicely and snugly so you don’t have to do any additional mounting.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*q4GJT7a7gsa90UncTaYing.jpeg)
Then, disconnect the DC motor from the board included in the pumpkin leaving the 12V and GND exposed.

After that, I tore out the included board, photoresistor, LEDs, and speaker, and dumped the rest of the hardware in my ever-growing pile of random hardware bits. I recommend you do the same, as you won’t need any of that.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*kiS-ymcopVZdohu6XUYUWg.jpeg)
Connect your DC motor to the motor driver and pi according to this wiring diagram. Plug your webcam into one of the USB ports on the pi. Gently close up your pumpkin (without screwing everything shut) and proceed to test your hardware in the Viam app.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*KtJd_DYvMaqLgMPXw8iV8g.png)
Complete expanded view wiring diagram for pumpkin bowl. Note that this standard 12V DC motor is already inside the pumpkin attached to the rack and pinion affixed to the hand inside the bowl, which is not pictured here in this diagram.

## Setting up your robot bowl with Viam
Follow the Raspberry Pi Setup Guide to prepare your Pi to connect your robot to the Viam app. Once your Pi is ready, ssh into it from your computer. I named mine spooky@bowl.local as I felt it was fitting for the project.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*JSkKxW2zIn6R8HpPaOcoNw.png)
In your web browser, log in to the Viam app and create a new robot instance. Name your robot whatever you like and head to the Setup tab. I named mine spookybowl. On your Pi, follow the steps there to download the Viam app config and download and install viam-server. Wait a moment until your robot connects to the Viam app.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*jgF9qHnHPfEUlNPj4t4VYg.png)

## Configuring the hardware in the Viam App
### Configure the board
First, configure your board component:

1. Navigate to the Config tab of your robot’s page in the Viam app. Click the Components sub tab, then click Create component in the lower-left corner.
2. Select board for type and pi for model.
3. Enter local as the name for your board, then click Create.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*QyQq4fh2fvnIMWr7C6kU5Q.png)

### Configure the motor
Next, add a motor component:

1. Navigate to the Components sub-tab and click Create component in the lower-left corner.
2. Select motor for type and gpio for model.
3. Enter dcmotor as the name for your motor, then click Create.
4. After creating your motor component, fill in the required attributes for a gpio motor:

- For the board, click the dropdown box and select local (or whatever you named your board!).
- For Max RPM, enter 100. RPM stands for revolutions per minute: it’s roughly a measure of how fast any machine operates at a given time. Enter 100 so the motor moves at a moderate speed. You can fine-tune this later in our custom code.
- Toggle the slider to In1/In2 motor type. The specific driver you are using expects this pin configuration.
- A/In1: use 16 GPIO 23
- B/In2: use 18 GPIO 24
- PWM: use 22 GPIO 25 — this is the pin you attached to the PWM (pulse-width modulation) jumper wire. The motor driver we used is labeled as ENA.
- For Depends On select local. This ensures that the board is initialized before the motor.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*NFJ_AM2nUv_NtXpBpYSGMw.png)

### Configure the camera
Next, add a camera component:

1. Select Create component in the lower-left corner.
2. Select camera for type and webcam for model.
3. Enter pumpcam as the name for your motor, then click Create.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*b6J1QIq-YI39LWZk4AKeAw.png)
Note: The video path should auto populate thanks to Viam’s camera discovery process.

## Test the components in the Control tab
Navigate to the Control tab and click on the top of the dcmotor card to open the motor controls. You can test to see if your hardware is configured and connected correctly by selecting a direction to turn your motor, setting the power to 100%, and hitting run. If your spooky hand pops out of the bowl, it means your hardware is configured correctly!

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*ZO-v9KRT6b1Q0rDcGXuk4A.png)
You can also test the pumpcam camera component by viewing the live feed in the stacked card in the control tab. Once you have checked those are working, go ahead and screw your pumpkin shut as you are now done upgrading your hardware and getting it running on Viam.

## Adding machine learning capabilities
My goal for this candy bowl is two-fold: to detect people who are not in costume to scare them and to detect people who are in costume to not scare them so they can take candy.

The first challenge we want to tackle is teaching our spooky bowl to identify just people (no costumes yet) using Computer Vision and testing that this logic works.

Here I am using a tensorflow lite model that is pre-trained for detections with corresponding labels called ‘effdet’ (efficient detections). I’ve uploaded the models for you to download and use in my source code repository here.

First, navigate to the Data tab and select Models. Upload a New model and name your model. I’ve named mine effdettest and selected the Model type Object detection. Upload the saved .tflite and corresponding .txt files and select Upload model.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*pEKSGN5TQR_8f_F4rpIjxA.png)
Next, navigate to the Config tab of your robot’s page in the Viam app. Click the Services sub-tab.

### Add an ML model service.
The ML model service allows you to deploy the provided machine learning model to your robot. Click Create service in the lower-left corner of the page. Select type ML Model, then select model TFLite CPU. Enter ml_model_test as the name for your ML model service, then click Create.

In the new ML Model panel, select Deploy model on robot for the Deployment. Select effdettest and save.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*tw9YUTFmvmedI7EKw4-lfQ.png)
### Add a vision service.
Next, add a detector as a vision service to be able to make use of the ML model.

Click Create service and select type Vision, then select model ML Model. Enter detections_test as the name, then click Create.

In the new detector panel, select the ml_model_test you configured in the previous step.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*osnpgbQpWbmnwruS69h0Uw.png)
Click Save config in the bottom left corner of the screen.

Add a transform camera.
Now we are going to add Computer Vision capabilities by adding a Vision Service to our robot with a transform camera.

Navigate to the Components sub-tab of the Config tab. Click Create component in the lower-left corner of the page.

Select camera for the type, then select transform for the model. Enter detections_cam as the name for your transform camera, then click Create.

Replace the attributes JSON object with the following object which specifies the camera source that the transform camera will be using and defines a pipeline that adds the defined detector:

```
{
  "source": "pumpcam",
  "pipeline": [
    {
      "type": "detections",
      "attributes": {
        "detector_name": "detections_test",
        "confidence_threshold": 0.5
      }
    }
  ]
}
```

Don’t forget to specify the Depends on attribute and add the source as your webcam, pumpcam.

Navigate back to the Control tab and your transform camera should show up. You can view your camera doing live detections of your surroundings- such as people and objects.

## Controlling your smart pumpkin programmatically
With your pumpkin fully configured, you can now write code to control your spooky candy bowl through code. For this tutorial, I’ve decided to use Python.

Head to the Code Sample tab in the Viam app and follow the instructions to set up your development environment.

```
$ | pip3 install viam-sdk
```

Then copy and run the following in your code sample to connect to your robot. If it is your first time using Viam, you can copy all of the code provided in the code sample which provides boilerplate code to initialize all of your robot resources in your program. For the sake of this tutorial, I am simplifying the code and only taking what we need.

```
import asyncio
import os

from viam.robot.client import RobotClient
from viam.rpc.dial import Credentials, DialOptions
from viam.components.motor import Motor
from viam.services.vision import VisionClient

#Env Variables for Robot Connectivity
robot_secret = os.getenv('ROBOT_SECRET')
robot_address = os.getenv('ROBOT_ADDRESS')
```

Notice I am setting environment variables for the robot secret and robot address. Import this variable into your development environment as needed.

*To show your robot’s location secret in the sample code, toggle Include secret on the Code sample tab. You can also see your location secret on the locations page. Think of your robot secret as the private key or password to your robot, so do not share it.*

Then copy over the robot connectivity code. This is what allows you to connect to your robot. You can run this code on your machine with these credentials.

```
#Robot Connectivity
async def connect():
    creds = Credentials(
        type='robot-location-secret',
        payload=robot_secret)
    opts = RobotClient.Options(
        refresh_interval=0,
        dial_options=DialOptions(credentials=creds)
    )
    return await RobotClient.at_address(robot_address, opts)
```

Now let’s write a function that describes the behavior of our skeleton hand in the bowl. In simplest terms, we just want to move the motor forwards and backward repeatedly to get that nice jump-scare motion out of the bowl. The motor is mounted on a rack and pinion under the bowl which allows for that kind of movement.

Using the Motor API in the Python SDK, I am using the following methods: set_power and go_for. I want my motor to be about 75% power (it’s a bit quieter this way) and I want it to jump out and retract quickly. I’m doing this in the function I’ve called jumpscare() with my motor as the only parameter.

```
async def jumpscare(dcmotor):
    #Hand jumps out, motor goes forward
    await dcmotor.set_power(power=.75)
    await dcmotor.go_for(rpm=70, revolutions=1)

    #Hand goes in, motor goes backwards
    await dcmotor.set_power(power=-.75)
    await dcmotor.go_for(rpm=-70, revolutions=1)
```

The next function we are writing is called trickOrTreater() where I am looking for people to scare. This function gets detections from the camera and looks for anything labeled “Person” (as per our tensorflow lite model and labels). If the camera detects a person, it will change the seen state to true and will trigger our jumpscare() function. For now, we will only pass in two parameters: our motor and the name of our Vision Service configured on the robot.

```
async def trickOrTreater(dcmotor, detections_test):
    #Searching for trick or treaters to scare!
    while True:
        people_detections = await detections_test.get_detections_from_camera("pumpcam")
        seen = False

        print ("no one is around to take some candy")

        for d in people_detections:
            if d.confidence > 0.6:
                if d.class_name == "Person":
                    print("gotcha!")
                    seen = True
        if seen:
            await jumpscare(dcmotor)
```

Finally, we want to create our main loop that initializes our robot connectivity, declares our components and services, and calls the function trickOrTreater().

```
async def main():
    robot = await connect()

    #Components and Services
    dcmotor = Motor.from_robot(robot, "dcmotor")
    detections_test = VisionClient.from_robot(robot, "detections_test")
    costume_det = VisionClient.from_robot(robot, "costume_det")

    await trickOrTreater(dcmotor, detections_test)

    await robot.close()

if __name__ == '__main__':
    asyncio.run(main())
```

Now we can run this code and your bowl should now autonomously scare people approaching the bowl for candy.

## Training a new model for costume detections
Now this part is really up to you in terms of how robust you want your data set to be. I went ahead and scoured Google for images of popular costume types in a variety of different lightings, skin tones, and gender presentations just to make sure our data set is diverse. This helps detections be more accurate. I took approximately 20 images of the following costume types: zombie, witch, vampire, cowboy, and mummy. This is not all-encompassing for all costume types but for the sake of this tutorial, a very simple model will suffice.

### Saving images onto your robot (pi)
Download and save all of your images into a folder on your computer. Import those images onto your robot via the command line using the SCP command. The SCP command or secure copy allows the secure transfer of files between the local host and the remote host.

Open your terminal on your computer and use the following SCP command with your image folder directory followed by your remote_username@ host:/whatever/directory. This is where your images will be saved.

```
$ | scp <YOUR IMAGES PATH> spooky@bowl.local:/home/spooky
```

Now ssh into your pi and check to see if your files have transferred.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*9Fpau53fl19bI8mdvGAdhg.png)

### Pushing collected images into Viam
Head back to the Viam app and create a new Data Management service. Name it costume-images. Add the directory on your robot (in this case, where the images are saved) and enable cloud sync. This will vacuum up the images saved on your robot and store them in the Data Manager in the cloud.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*u_o7iO2N5VWiOqXujIVJQw.png)
Don’t forget to save your config. Head to the Data page to see all of your uploaded images.

### Labeling
You can now start labeling your images with the desired labels and corresponding bounding boxes to train your model. Click on the image for expanded view, select Bounding box, and start drawing those boxes!

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*MhgO1bAOeor9AHzTHScMRg.png)
Continue labeling as many images as you want. Remember, the bigger your data set is, the more accurate your detections will be. You need a minimum of 10 images per label to successfully train a .tflite model in the Viam app.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*qKjxgfBv5A1hbRP6XfxLQg.png)

### Training
Once you are finished labeling all of your images, select Train model, name it costumedetections and select Object detection as the model type. Make sure to select all of the labels you want for training.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*9BM0m6CfNJY5T05O5AG1jw.png)

### Deploying this new model on your robot
Once your model is done training, you can now repeat the steps from earlier and deploy this model onto your robot. Select type ML Model, then select model TFLite CPU. Enter ml_model_costumes. Select the model we just trained. Now create a Vision Service with type ML Modeland name it costumes_det.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*lcA0CHVgidt14uhfEswu6g.png)

### Adding costume detection code
Now you can write a few more lines of code that looks for the costume detections in the model you trained.

Declare a global array of labels you want to look for based on the model you trained. I put this near my environment variables at the start of my code.

```
COSTUMES = ["witch", "mummy", "vampire", "cowboy", "zombie"]
```

Update your trickOrTreater() function to include detections for people dressed up in costume. We want the candy bowl to do nothing if people are wearing costumes, as they are in the Halloween spirit and deserve a treat over a trick! To make sure this works, we are simply just sending a message in the console.

```
async def trickOrTreater(dcmotor, detections_test, costume_det):
    #Searching for trick or treaters to scare!
    while True:
        people_detections = await detections_test.get_detections_from_camera("pumpcam")
        costume_detections = await costume_det.get_detections_from_camera("pumpcam")
        seen = False

        print ("no one is around to take some candy")

        for d in people_detections:
            if d.confidence > 0.6:
                if d.class_name == "Person":
                    print("gotcha!")
                    seen = True
        if seen:
            await jumpscare(dcmotor)
        else:
           for d in costume_detections:
            if d.confidence > 0.6:
                if d.class_name in COSTUMES:
                    print("happy Halloween! take a treat!")
                    seen = True
```

Finally, we want to initialize a new resource in the main() function of our costume detector Vision service, and we want to add that parameter to the function trickOrTreater().

```
async def main():
    robot = await connect()

    #Components and Services
    dcmotor = Motor.from_robot(robot, "dcmotor")
    detections_test = VisionClient.from_robot(robot, "detections_test")
    costume_det = VisionClient.from_robot(robot, "costume_det")

    await trickOrTreater(dcmotor, detections_test, costume_det)

    await robot.close()
```

## Running your code on power-up
Want to avoid running the code manually every time you plug in your bowl? You can have your code run automatically when your robot boots by adding a Process. Navigate to the Config tab of your robot’s page in the Viam app. Click on the Processes sub-tab and navigate to the Create process menu.

Enter run-forever as the process name and click Create process.

In the new process panel, enter python3 as the executable, main.py as the argument, and the working directory of your board Pi as /home/spooky/.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*Bg1uwWVXIpYqccoU7AW-1Q.png)

Click Save config in the bottom left corner of the screen.

Now your robot will start its code automatically once booted. That means you can simply plug your bowl in anywhere in your home or porch and it will always look for people to scare and costumed individuals to not scare!

## Now go have some candy.
I hope you liked this fun tutorial- it was really fun upgrading something I bought off of Amazon and completely customizing it to utilize Computer Vision and Machine Learning. I encourage you to try this at home with this candy bowl, or any holiday-themed animatronic you like.

## Full Code
You can find the whole project on GitHub. Give it a star if you liked this tutorial!

```
import asyncio
import os

from viam.robot.client import RobotClient
from viam.rpc.dial import Credentials, DialOptions
from viam.components.motor import Motor
from viam.services.vision import VisionClient

#Env Variables for Robot Connectivity
robot_secret = os.getenv('ROBOT_SECRET')
robot_address = os.getenv('ROBOT_ADDRESS')

COSTUMES = ["witch", "mummy", "vampire", "cowboy", "zombie"]

#Robot Connectivity
async def connect():
    creds = Credentials(
        type='robot-location-secret',
        payload=robot_secret)
    opts = RobotClient.Options(
        refresh_interval=0,
        dial_options=DialOptions(credentials=creds)
    )
    return await RobotClient.at_address(robot_address, opts)

async def jumpscare(dcmotor):
    #Hand jumps out, motor goes forward
    await dcmotor.set_power(power=.75)
    await dcmotor.go_for(rpm=70, revolutions=1)

    #Hand goes in, motor goes backwards
    await dcmotor.set_power(power=-.75)
    await dcmotor.go_for(rpm=-70, revolutions=1)

async def trickOrTreater(dcmotor, detections_test, costume_det):
    #Searching for trick or treaters to scare!
    while True:
        people_detections = await detections_test.get_detections_from_camera("pumpcam")
        costume_detections = await costume_det.get_detections_from_camera("pumpcam")
        seen = False

        print ("no one is around to take some candy")

        for d in people_detections:
            if d.confidence > 0.6:
                if d.class_name == "Person":
                    print("gotcha!")
                    seen = True
        if seen:
            await jumpscare(dcmotor)
        else:
           for d in costume_detections:
            if d.confidence > 0.6:
                if d.class_name in COSTUMES:
                    print("happy Halloween! take a treat!")
                    seen = True

async def main():
    robot = await connect()

    #Components and Services
    dcmotor = Motor.from_robot(robot, "dcmotor")
    detections_test = VisionClient.from_robot(robot, "detections_test")
    costume_det = VisionClient.from_robot(robot, "costume_det")

    await trickOrTreater(dcmotor, detections_test, costume_det)

    await robot.close()

if __name__ == '__main__':
    asyncio.run(main())

```

By：Arielle Mella
Link：https://ariellemadeit.medium.com/building-a-smart-halloween-candy-bowl-using-machine-learning-and-computer-vision-106d50085fc7

Building a smart Halloween candy bowl using Machine Learning and Computer Vision


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![image](https://miro.medium.com/v2/resize:fit:1100/0*_Iv67UG_wDw4mJh7)
Photo by Nicolas Lobos on Unsplash

Leroy was an AI robot designed to explore the surface of Mars. He had been deployed by the space agency to collect rock samples and take photos of the Martian landscape. As Leroy walked along his assigned path one day, he was so focused on the reddish terrain in front of him that he didn’t notice a large rock right in his path.

Suddenly, Leroy’s foot hit the rock, causing him to tip forward unexpectedly. His metal arms flailed as he tried to regain his balance, but it was too late. Leroy crashed face-first into the sandy ground with a heavy thud that kicked up a cloud of dust.

For a moment Leroy lay there stunned, his sensors rattling from the impact. Carefully he righted himself back up on his feet, whirring his joints to check for damage. Luckily he was built sturdy and seemed to be functioning fine. But his pride was dented from such an embarrassing tumble.

![image](https://miro.medium.com/v2/resize:fit:1100/0*AMuDFnoiHcUu5o-e)
Photo by Daniele Colucci on Unsplash

Leroy paused to examine the rock that had tripped him up. It was partly buried in the sand — no wonder he hadn’t detected it. He realized he should have scanned the terrain more thoroughly instead of walking along without paying attention. This was a good reminder that moving slowly and deliberately was often safer than rushing ahead. Paying attention to your surroundings can help you avoid unexpected obstacles in your path.

By：Mike G
Link：https://medium.com/short-and-weird/the-robot-that-tripped-over-the-rock-26c3a5766b49


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![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*KtB0Q3USyXumCpKQp7q7DA.png)
DALL-E 3

Halloween is coming, and maybe it’s a good time to talk about zombies. Not the ones the kids might dress up as when they come to your door for candy, but the philosophical zombie that has been the subject of thought experiments led by philosophers like David Chalmers. These “philosophical zombies” are entities that appear conscious but lack subjective experience.

## The Zombie in the Machine
I always thought that if computers ever got to the point where they could relate to us in a human-like manner, they would be conscious. At first glance, ChatGPT might seem indistinguishable from a well-informed human conversationalist. It answers questions, generates narratives, and even cracks jokes. But here’s the catch: ChatGPT doesn’t truly understand, feel, or experience anything. It has no consciousness or subjective reality. It’s the very definition of a philosophical zombie.

## From Code to Consciousness
The impulse is to assume that consciousness comes with intelligence, but think about the majority of our bodily functions: digestion, healing, balance — they’re intelligent and function perfectly well, but aren’t conscious. It’s clear that something can be highly intelligent yet lack any form of subjective experience. The concept of philosophical zombies brings this idea to the forefront, challenging our traditional notions.

## Survival Without Consciousness
From an evolutionary perspective, survival has always been the primary objective. Organisms have developed countless ways to ensure their survival, from the simplest single-celled organisms to the most complex mammals. But is consciousness necessary?

Consider the multitude of automated processes that occur within our bodies: our heart beating, our lungs breathing, and our cells regenerating, the incredible intelligence of our immune system. These processes are crucial for our survival, yet they function independently without conscious awareness. Similarly, many lower organisms navigate their environments, seek food, and reproduce without what we would term “conscious thought.”

## Intelligence Without Subjective Experience
So, it’s legitimate to ask: Is consciousness necessary for survival? Does intelligence necessitate consciousness? The rise of advanced artificial intelligence, epitomized by systems like ChatGPT, suggests that it’s possible for entities to exhibit intelligent behavior without possessing subjective experience. Recent developments suggest that ChatGPT can even help robots navigate in the physical world.

## The Future of Intelligent Machines
As we move towards a future where AGI might become a reality, the philosophical zombie question becomes even more relevant. If we can create machines that mimic human behavior and intelligence so closely, does that challenge our very understanding of consciousness? Or does it reaffirm the unique nature of subjective experience?

Tech companies announce new billions of dollars in investments in AI every week, so there’s every reason to assume that the race to AGI will continue. Many people predict that it may arrive in as few as five years. we need to be prepared.

## Final Thoughts
The existence of philosophical zombies, whether in thought experiments or embodied in a robot, forces us to confront and reevaluate our beliefs. As technology continues to advance, the line between conscious beings and intelligent ones may blur. Can a being without subjective experience offer meaningful insights? How do we relate to them?

ChatGPT brings the issue from the philosophical to the practical. If the distinction between conscious and unconscious isn’t so clear-cut, how will we relate to these machines in our lives? Do we grant them rights if they seem intelligent but may lack an inner life?

My interaction with ChatGPT opened up an age-old philosophical quandary. I found a challenge to my understanding of consciousness. But at least for now, I believe that on this Halloween there may be zombies among us.

By：John M
Link：https://medium.com/bouncin-and-behavin-blogs/chatgpt-made-me-believe-in-zombies-0f5c8cc4bdb7


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![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*UmRQ2uidRh1BQW0RVF0_OA.jpeg)IMAGE: Boston Dynamics

Boston Dynamics is adding ChatGPT’s new voice generation functionality to its Spot robot, the result of which an automaton that can greet people on a company visit, able to generate accents, creating hitherto unknown empathy in the world of robotics, perhaps with the exception of those “abused robot videos” that made us all take their side and see the evil human who pushed the robot, hit it with a stick, or prevented it from doing its job.

Watching Spot speak with an impeccable British accent and sporting a bowler hat and mustache may be amusing, but to be honest I am unsettled by the sight of people using ChatGPT’s voice synthesis feature to have long conversations on their smartphones with the generative assistant for everything from passing time in traffic jams to walking down the street with AirPods on, a la the movie “Her”. It’s dystopian and raises any number of ethical questions.

Of course we humans have always anthropomorphized technology. But when the advancement of this technology allows it to generate voices, as well as to carry on conversations along the lines of a personal relationship, and when this is marketed as a form of normalization by unethical companies with a history of moving fast and breaking things, then we are flirting with disaster and potentially creating psychological problems for vulnerable people.

Anyone who knows the importance teenagers place on their idols, for example, or who considers what good it might do an adult with personal problems to use an algorithm as therapy, with few constraints and the possibility of occasional hallucinations, understands that are on a slippery slope. In short, there is every likelihood that these virtual relationships will not just be prone to malfunctioning, but instead will be instrumentalized to influence the behavior of the people who maintain them.

Experiences with other people will affect us in many ways, but at least they are experiences and conversations between people with some degree of rationality. To go from there to conditioning people through generative assistants, and to do so without any apparent precautions other than a few very generalized restrictions is unacceptable. If we add to this a high level of accessibility and an element of standardization, we will soon see celebrity avatars having daily conversations with millions of people, introducing into their conversations the personal elements mentioned in previous conversations, extracting personal data of all kinds and selling it to advertisers, or inducing moods in their human interlocutors to encourage buying stuff or to vote in a certain way.

Our societies are not ready to assimilate a technology like generative AI in the context of personal relationships, simply because we have not gone through a stage of education that allows large numbers of people to really understand what, rather than who, they are talking to. Many people grant a certain authority to algorithms, attributing unlimited capacity for consultation and synthesis of information, outsourcing their critical thinking to the answers they access through technology. Ignorance of how a particular technology works, as Arthur C. Clarke rightly observed, makes it indistinguishable from magic. This can have enormously harmful effects on human societies: from distorted perceptions of reality to alienation and psychological problems.

I have always found it strange to meet someone I see very often in the media, a certain feeling of “I have been with this person in my living room”, which has often led me to assume a familiarity beyond what I should have with someone I am meeting for the first time. Assimilating completely asymmetrical relationships like the one you (don’t) have with someone who, for example, reads the news every day, is in itself a task that requires a certain maturity, education, and judgment. What will happen when people who have been talking every day to a generative algorithm that very convincingly pretends to be their idol finally have the opportunity to really know that person or internalize those conversations as real? And when someone attributes a certain personality to something that is nothing more than a generative algorithm recombining information from the network? And all this in an uncertain regulatory context, in total ignorance of how such tools work or how they should be managed, not to mention the lack of experience with psychological disorders?

The problem here is not the pace technology is developing at, but the products that some irresponsible people put on the market without taking adequate precautions. We need clear and precise regulations about this technology, rather than an ad hoc approach to limiting the activities of the idiots who seek to make a profit from products and services based on it. The “move fast and break things” that already generated so many problems, now taken to the next level.

I’m not frightened by technology or prone to blaming it for society’s ills. Regular readers will know that I’m generally a techno-optimist. And yet, this topic genuinely worries me, and I think it’s going to lead to a lot of regrets.

By：Enrique Dans
Link：https://medium.com/enrique-dans/robots-can-talk-like-us-but-it-doesnt-make-them-human-9cd701169e58

Robots can talk like us, but it doesn’t make them human


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![image](https://miro.medium.com/v2/resize:fit:640/format:webp/0*U0wl2q1TzIxecFCP.png)
Humanoid robots

10 years ago, if you had told someone that there would be robots with humanlike capabilities soon, you would have probably received a reply with something like “this is real life, not a Hollywood movie.” However, this is exactly what is happening with social humanoid robots like Junco Chihira — who is a Tokyo receptionist in a tourist information center — and Sophia — the first of the now numerous humanoid robots, who is also an official citizen of Saudi Arabia.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/0*VfK8K_mFP0eUKlUH.jpg)
Sophia attending a UN meeting on artificial intelligence and sustainable developmeent

This is all possible thanks to advancements in AI made in recent years. Now, computers are able to learn people’s names and carry out full-on conversations with other humans. The barrier between what is human, and what is technology, is becoming increasingly unclear with the introduction of AI. But what attributes AI enable these advancements to occur? What even is “AI”? Well, get ready because you’re about to find out.

Given the expansive scope of AI, which spans from sophisticated language processing models such as ChatGPT to the intricate computational functionalities embedded within modern smartphone camera systems, it is appropriate to concentrate on one of its distinct domains. My particular focus lies in delving deeper into the realm of machine learning, which is the specific topic I aim to explore further in the following discussion.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*JaFFOFDKfvzDfRGbk7NsNg.png)
The many subfields of the umbrella term “AI”

Machine learning is a branch of AI which recognizes patterns and makes predictions based on data that it learns from, thereby demonstrating “intelligent behaviours” that are similar to humans.

## Here’s a fun analogy to demonstrate how machine learning works:
Imagine you have a playful robot named Mr.Robo. Mr.Robo loves playing a game where he has to distinguish between different fruits, like apples and oranges.

At first, you show Mr.Robo a few examples of apples and oranges, and you tell it which is which. Mr.Robo carefully studies the shapes, colours, and textures of the fruits.

As you continue showing more and more examples, Mr.Robo starts to notice patterns. Soon, he begins to understand that apples are generally round and red or green, while oranges are typically orange and have a bumpy texture.

You then test Mr.Robo’s skills by presenting new, unexampled oranges and apples. With learning from past examples, Mr.Robo can now confidently categorize the new fruits as either apples or oranges.

Similarly, in machine learning, computers learn from vast amounts of data and examples to recognize patterns and make judgments. They can use this learning to classify and identify a diverse range of data, just like how Mr.Robo learned to differentiate between apples and oranges.

In conclusion, machine learning is like a virtual brain that grows more proficient as it gains experience. However, there are some things to keep in mind when constructing this type of advanced technology, such as the strong need for quality data, as well as the cognitive bias in designing machines to think like humans.

Imperfect data, unrefined data, and inadequate data pose significant obstacles to achieving optimal results in a machine learning algorithm. To overcome this issue, it is crucial to invest time in evaluating and defining the scope of data through precise data governance, data integration, and data exploration well before initiating the actual process.

Additionally, another important consideration when trying to create these machines to work effectively is to limit our bias towards making these systems think and act like humans, instead focusing on having them think and act rationally. This bias can cause extensive problems within AI systems, including incidents of racial discrimination — as has occurred with Midjourney and Google. Humans tend to make a ton of mistakes, and these mistakes can carry on to AI as well. In order to minimize these issues, the training data must be thoroughly reviewed in order to ensure equitable treatment of different types of data.

Shifting the focus to the nuances of machine learning, while Machine learning itself is a subfield of AI, it also has its own subfields. These include supervised learning, unsupervised learning, reinforced learning, as well as deep learning.

## Supervised learning
Supervised learning is primarily meant for solving two types of problems: classification problems and regression problems.

Classification problems: In classification problems, the computer is meant to make a specific prediction for an output, from an input. It does so by training on a dataset and using the knowledge it learned from that training, to categorize data into different classes. A great illustration of the classification concept can be seen in email spam detection. By training a model with an extensive array of email data, the system can distinguish whether incoming emails are spam or not, subsequently directing them to appropriate folders based on the classification.

Regression problems: Solving regression problems requires the computer to find correlations between dependent and independent variables. These problems are to be used with continuous data — which are any real values that do not have a limit — such as with the prediction of stock trends, currency exchange trends, as well as house prices.

![image](https://miro.medium.com/v2/resize:fit:640/1*XAV25ZJZJtceOJ1Vx6TeDw.gif)
A quick and fun demonstration of supervised learning

## Unsupervised learning
Unsupervised learning is about recognizing patterns within data and making predictions using them. However, unlike supervised learning, the training dataset is not pre-labelled and therefore, the computer has to figure out what the patterns are, without any human intervention. Unsupervised learning can be used for natural language processing or understanding consumer behaviour.

## Reinforcement learning
Reinforcement learning is quite different from unsupervised learning and supervised learning as it is based on teaching the AI how to interact with its environment to complete an objective. In order to complete this objective, the AI agent will attempt new ways of solving it in order to find the correct approach. It knows when it has a correct approach when it completes the objective and is “rewarded” with a positive signal letting it know that the job is done. The agent can also learn to optimize itself to reach its goal most efficiently. Some common applications of reinforcement learning include robotics and gaming.

## Deep learning
Deep learning is the most complex subfield within machine learning. Deep learning follows a linear regression model, which is a mathematical concept that is used to predict future events. This is what allows the machine to make predictions of future results. However, to completely understand what deep learning is, one must first learn about neural networks.

Neural networks — which perform a function similar to that of the neurons in our brain — are composed of 3 main node layers. These include: the input layer— where the computer gets its data, the hidden layer—where all the processing happens, and the output layer — the result of processing the input data. Data is passed between these layers and fed to the user. This usage of node layers is meant to reflect the behaviour of a human brain, enabling the machine to solve problems in a similar fashion to us. Deep learning is essentially a subsection of neural networks, but it consists of more than just the three basic layers, such as having extra hidden layers.

![image](https://miro.medium.com/v2/resize:fit:640/1*bhFifratH9DjKqMBTeQG5A.gif)
A neural network visualization

Like pretty much everything else in the field of AI, neural networks also happen to have their own subfields. These include convolutional neural networks, recurrent neural networks, feed-forward neural networks, modular neural networks, and more.

## Convolutional neural network
Convolutional neural networks (CNN) are one of the most common types of neural networks. The primary objective of CNN algorithms is to simplify data processing without sacrificing any important elements, making them exceptionally adept at handling extensive datasets. A CNN can handle data with a grid-like structure and learn relevant patterns. However, unlike other methods, CNNs do not require much image pre-processing.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*ne7W2isbsXsB7B5pIAniXw.png)
Algorithms that handle image processing usually use filters that are created by an engineer based on practical problem-solving methods. CNNs can then learn what characteristics in the filter are most important to be able to adapt and adjust the filters during the training process. This real-time fine-tuning capability, particularly when dealing with large datasets such as images, contributes to their exceptional performance. By enabling the dynamic update of filters, the reliance on manually created filters is rendered unnecessary, thereby allowing for greater flexibility in the application of filters. This saves a lot of time since it doesn’t require as many parameters. This allows CNNs to be used for computing more complex challenges, including intricate tasks like face recognition.

## So where does disease diagnosis come into play?
Using the latest deep learning technology, we can now teach AI to perform actions that come naturally to humans. This is particularly true in regard to visible diseases such as skin lesions, as we can now train an AI to recognize cases of skin lesions and diagnose patients accordingly.

To demonstrate the simplicity and application of machine learning in disease diagnosis, I will show you a project that I built in this field myself. In less than an hour, I was able to use machine learning to build and train this object detection dataset which can predict if a person has Parkinson’s disease or not, with over 98% accuracy, simply by looking at their MRI scans.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*-nP8b9XROfc0AvvVWbdX9A.jpeg)
A robot looking at MRI scans

## Key Takeaways:

1. The reality of AI Today and its challenges: AI’s widespread implementation is limited despite rapid progress. Challenges include data quality and cognitive biases.
2. Machine Learning’s Role in AI: Machine learning is a vital subfield of AI, using learned data for pattern recognition and predictions.
3. Machine Learning Subfields and Deep Learning: Machine learning encompasses supervised, unsupervised, reinforcement, and deep learning, with deep learning involving complex neural networks.
4. AI in Healthcare: AI’s potential shines in healthcare, especially in disease diagnosis, where it demonstrates high accuracy, such as in Parkinson’s disease identification through MRI scans.

These takeaways provide insights into the current landscape of AI, its subfields, and their real-world applications, along with the challenges and possibilities that come with them.

## Citations:

- https://www.javatpoint.com/regression-vs-classification-in-machine-learning#:~:text=The%20main%20difference%20between%20Regression,Spam%20or%20Not%20Spam%2C%20etc.
- https://www.researchgate.net/publication/358915702/figure/fig2/AS:1128622899363843@1646096076675/Components-types-and-subfield-of-AI-derived-from-25-26.jpg
- https://www.ibm.com/topics/machine-learning
- https://www.aitude.com/supervised-vs-unsupervised-vs-reinforcement/
- https://youtu.be/jmmW0F0biz0
- What Is a Convolutional Neural Network? A Beginner’s Tutorial for Machine Learning and Deep Learning (freecodecamp.org)
- https://www.sciencedirect.com/science/article/abs/pii/B9780128241455000058#:~:text=The%20growth%20of%20DL%20in,brain%20disease%2C%20and%20chronic%20disease.
- https://www.ibm.com/topics/deep-learning

By：Shehraan Hafiz
Link：https://medium.com/@shehraanhafiz/ai-and-disease-diagnosis-55e9591e8116


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Charting the Evolution: From Karel Čapek’s ‘R.U.R.’ to Modern Tech Support Chatbots

The world of robotics has come a long way since its inception in the 1920 play “R.U.R.” (Rossum’s Universal Robots) by Czech playwright Karel Čapek. This article delves into the origins of robotics, the evolution of this field, and its contemporary relevance in tech support. Additionally, we’ll explore the key innovators leading the charge in the world of robotics today.

## The Birth of the Term “Robot”:

The term “robot” was first introduced to the world by Karel Čapek in “R.U.R.” The word itself originates from the Czech term “robota,” meaning forced labor or servitude. In the play, robots were artificial beings created through a biological process, designed to serve humanity. This groundbreaking work not only coined the term but also set the stage for discussions about artificial beings and automation.

## Evolution of Robotics:

Over the years, the concept of robots evolved from Čapek’s biological entities to mechanical and electronic systems. The field of robotics saw significant developments during the mid-20th century with the creation of early industrial robots, such as the Unimate, which was installed in a General Motors factory in 1961. These machines were programmed to perform repetitive tasks, marking a crucial step towards automation in manufacturing.

From its humble origins in a Czech play to the present-day tech support chatbots and advanced robotic systems, the field of robotics has undergone a remarkable transformation. While robots and computers are distinct entities, they both play pivotal roles in today’s technology-driven world. The innovation in robotics continues to be led by a diverse array of companies and institutions, promising an exciting future where robots will become even more integrated into our daily lives. As technology advanced, robots became more sophisticated, capable of more intricate tasks. The emergence of computer-controlled robots in the 1970s paved the way for greater flexibility and versatility in automation. These robots could be programmed to adapt to various tasks, making them valuable in a range of industries, including automotive, aerospace, and healthcare.

## Tech Support and Chatbots:

Today, robots, in the form of chatbots, play a vital role in tech support. Chatbots are computer programs designed to interact with users through text or speech. They are commonly employed to streamline customer service and provide quick answers to frequently asked questions. In the tech support realm, chatbots are invaluable for troubleshooting issues, assisting customers, and facilitating communication between users and support teams. They offer efficiency and round-the-clock availability, enhancing the overall customer experience.

## Are Computers Robots?

While computers and robots share some similarities, they are not the same. Computers are electronic devices that process data and execute instructions based on programming. They lack physical capabilities and are typically used for data processing, calculation, and information storage.

Robots, on the other hand, can be physical or virtual entities that interact with their environment and perform tasks autonomously or semi-autonomously. They often include computer systems as part of their control and decision-making processes, but they also possess physical components that enable them to manipulate objects or navigate the physical world.

## Innovators in the Field of Robotics:

Several organizations and individuals are at the forefront of robotics innovation today. Companies like Boston Dynamics have gained fame for their advanced robotic systems, including humanoid and quadrupedal robots capable of remarkable feats of mobility and dexterity. These innovations have applications in industries such as logistics, healthcare, and defense.

Research institutions and universities worldwide are also contributing to the field of robotics. Institutions like the Massachusetts Institute of Technology (MIT) and Stanford University have dedicated robotics labs that push the boundaries of what robots can achieve.

By：0xJiuJitsuJerry
Link：https://pub.aimind.so/the-evolution-of-robotics-from-%C4%8Dapeks-r-u-r-to-tech-support-chatbots-04f4a6b43392

The Evolution of Robotics: From Čapek’s “R.U.R.” to Tech Support Chatbots


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![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*JZy2bn8vrVAlXLRwpIFVOw.jpeg)
IMAGE: Amazon

Amazon has announced that its humanoid robot, Digit, is joining the army of more than 700,000 automatons already used in its warehouses, unleashing speculation that it intends to automate all its processes, potentially replacing humans.

The new, two-legged robots will be tasked with moving containers with orders between racks and conveyor belts, sendingthem to their human co-workers to carry out packaging and labeling.

> Amazon is an extremely pragmatic company that only automates if so doing is more efficient than using human labor: at its San Fernando de Henares depot in Madrid, which I know well because I have taken my students there many times, the level of automation is extremely low: there are no robots of any kind, just people and lots of conveyor belts, and according to the company, this will continue to be the case as long as the warehouse’s operational statistics remain optimal.

In other Amazon warehouses in Spain Kiva-designed robots have been moving entire racks for many years, and we have also seen the use of three dimensional articulated arms. Amazon’s warehouse model is a place where people will always be irreplaceable, and robots simply increase their capabilities or perform the heaviest and most repetitive tasks.

However, Amazon’s level of progressive, pragmatic automation and very slow progress over the years, as well as the concern about replacing people with robots, contrasts with the warehouses of Chinese companies such as JD.com, whichfor many years now have been completely automated and controlled by only five workers. Of course, they never carry out logistics work as such, they do not move any packages, they do not pack or label them; their job is robot maintenance

https://youtu.be/RFV8IkY52iY

These types of warehouses, which date back to 2018, use traditional automation without the application of machine learning processes, in other words, the type of industrial robots that we know from many other processes, but they do it at such a high level that the five people working in these warehouses are effectively replacing the up to 500 employees required to process about 200,000 packages a day.

Does Amazon know something about automation that others don’t and hence its focus on so-called collaborative processes between robots and people; or is it simply technologically behind its Chinese competitors, who directly dispense with anyone who is not in charge of providing maintenance to a robot? Is the performance of fully automated warehouses like those of the Chinese logistics giants somehow inferior to Amazon’s warehouses full of people? According to Tye Brady, CTO of Amazon Robotics, “We’re always going to need people… I’ve never seen an automated system that works 100% of the time, and I don’t think you have either.” Do China’s fully automated warehouses really have so many downtime problems that they don’t compensate for their development and operation?

By：Enrique Dans
Link：https://medium.com/enrique-dans/if-a-robot-can-do-it-better-amazons-pragmatic-approach-to-robotization-a2245372a84b

If a robot can do it better: Amazon’s pragmatic approach to robotization


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![image](https://miro.medium.com/v2/resize:fit:4800/0*k7M59BRtaWaineLF)
Photo by Lyman Hansel Gerona on Unsplash

Afew days back, I had a call with a friend who works in IT; software testing, to be precise. We had a good conversation for about 24 minutes. He immigrated to Canada in 2021, four years after me. During the length of the call, we exchanged our thoughts and feelings about life. In doing so, I complained that I was not happy with my salary and his response to that was this:

> “At least you won’t lose your job to AI!”

The world has been buzzing with information about AI, even during the pandemic. Thanks to the most recent technology dubbed ‘Chat GPT’, tech fanatics were glued to their screens testing what this new software could do. But though it may seem recent, the idea of Artificial Intelligence has been around since the early 1950s.

## The Birth of AI
The first computer invented by Charles Babbage was nothing but a calculator. In 1943, the world’s first electronic computer was built. It was coined ENIAC, short for Electronic Numerical Integrator and Computer.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*VpxjQHZnsqi4QXxMGTgVFw.jpeg)
Source: Wikimedia Commons

But this computer was not small enough to fit on a desk, instead it took an entire room. I guess it would take up my entire house if I were to fit one in today. Even so, these computers weren’t even slightly as intelligent as the handheld ones we use today.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*PlySTsY4aDHGer6wDOKP7A.jpeg)
Alan Turing. Source: The New York Times

The initial idea of AI dates back to 1950. It all began when Alan Turing wondered if computers could use all the information available to them along with some reasoning to make decisions. Until then, computers were just machines; but Turing wanted to make them intelligent. This was the inspiration for his paper ‘Computing Machinery and Intelligence’, which was released in 1950.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*ZYzf1D7vIVKI_67QuU5IDQ.jpeg)
Arthur Lee Samuel. Source: Yahoo Finance

IBM’s stored program computer — the 701, used tubes for memory storage. One man made the world’s first self-learning program on this computer, and it was a game of checkers. In 1952, Arthur Lee Samuel made history by developing the Samuel Checkers-Playing Program. This was one of the very first practical applications of AI.

However, the term ‘Artificial Intelligence’ or AI, as we call it today, wasn’t coined until 1956. That happened when John McCarthy first introduced the term at a workshop he organized at Dartmouth college in the same year.

## Early Development of AI

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*OJZu1W2S8DGl0f9AOxFOAA.png)
Perceptron Model. Source: Towards Data Science

In the beginning, Artificial Intelligence was developed on the basis of neural networks. In 1958, Frank Rosenblatt went on to create Perceptron, which was an Artificial Neural Network (ANN). ANN was designed based on a biological neural network, but it used simple logic gates with binary outputs. The program learned and controlled outputs based on this data, and these were called binary classifiers. Overall, Perceptron was an algorithm for the ‘supervised learning of binary classifiers’.

https://youtu.be/hxsWeVtb-JQ

In 1961 — the first industrial robot Unimate was introduced to the world. It was used by General Motors in New Jersey to transport die casings and weld automobile parts together. The best feature of Unimate was that it was autonomous. This could mean that it was probably the first industrial robot that was intelligent in some way.

In 1965, Carnegie Mellon Alumni Edward Feigenbaum founded the first edition of the ‘Expert System’ called Dendral. The software was designed to help chemists in the identification of unknown organic elements. This was a first in AI, and the program was designed to think like a human. He is called ‘The Father of Expert Systems’ and currently lectures at Stanford University.

## History of AI Chatbots
If you think ChatGPT is cool, there are more AI chatbots that were Old School-Cool! The latest ones, including ChatGPT are classified as ‘Generative AI’, but the older ones were basic and/or conversational.

The first of those was Eliza, a chatbot created by Joseph Weizenbaum in 1966.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*JCSlJnIF-TJClkBXW4HxAQ.png)
A famous ELIZA transcript Source: Wikipedia Commons

ELIZA was known to be the first chatterbot or chatbot (as we call them now) to have human-like tendencies. In fact, she was a mock ‘Rogerian’ psychotherapist.

In 1972, Kenneth Colby, an American psychiatrist, created another new AI chatbot, named Parry. This one imitated a patient with schizophrenia. Parry was made to understand and diagnose mental health conditions in humans.

On the lighter side, there came Jabberwacky in 1988. Made by developer Rollo Carpenter, this chatbot was made to mimic human conversation on a lighter, more entertaining note. Then, there was Dr. Sbaitso (how do I pronounce that?) which was aimed at simplifying interactions with humans.

in 1995, A.L.I.C.E. was launched. It was short for Artificial Linguistic Internet Computer Entity and works with AIML (Artificial Intelligence Markup Language). Then came Smartchild in 2001 and it was one of the first AI chatbots to resemble the ones we have today.

IBM’s Watson was one of the first cognitive computer systems to be featured in a TV show in 2010. It took on the some of the best players of Jeopardy.

https://youtu.be/P18EdAKuC1U

Apple revealed Siri in 2011, along with its iPhone 4S. Microsoft launched Cortana in April 2014. If you don’t know what these are, it’s probably time to crawl out of that rock. Until then, most AI systems were mostly just conversational or used to perform simple tasks.

After this, AI was not in the limelight for quite a while. However, in 2021, Japser AI was released. This was a generative copywriting platform that was aimed to help business owners. Jasper AI had both praise and critic, because it was about to change the world of copywriting. In 2022, another famous word, Chat GPI was on the news, especially in the tech world. Developed by OpenAI, this application is the latest generative AI software that can perform complex tasks. In fact, many people use it to even do their job.

## AI Winters — Are We Due for One?

![image](https://miro.medium.com/v2/resize:fit:640/0*Jg9uAoJdHYAuzZOT)
Photo by PIOTR BENE on Unsplash

Artificial Intelligence is one of the latest and most advanced technologies that humans have ever devised. That said, this technology requires a lot of funding for research. AI has been funded by different sources, but there were specific time periods when the funding was scarce. This was mainly due to different interests and unreal expectations.

The first episode of AI winter was from 1974 to 1980 — commonly known as the 20th century AI winter. The programs in this period had many limitations and weren’t able to perform as they were expected to.

The second episode of AI winter was from 1987 to 1990. This time, people expected AI systems to drastically improve, but they were still in the basic/conversational development. This continued into the early 90s.

Some experts think that ChatGPT is the pinnacle of AI software, and that interest in AI could reduce in the upcoming years. While that could put us on the brink of a third AI winter, it could change — provided someone comes up with something better.

For now, all we can do is make the best of use of the tools we can access.

## Sources

1. Harvard University
2. Computer History Museum
3. IEEE Computer Society
4. Stanford University
5. IEEE Robots Guide
6. IEEE Computer Society (2)

By：Kemmy D
Link：https://medium.com/swlh/a-brief-history-of-ai-how-it-all-began-40f6c5892149

A Brief History of AI — How it all Began