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*“The real magic begins once we start to chat with the machines” — Futurist Jim Carroll*

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/0*ArEAa4Ngdo2P8H_r.jpg)

> **Midjourney prompt via Poe: “color photo of an industrial robot in a factory, being controlled by a voice ChatGPT service, , gleaming metal, precise movements, complex mechanical structure, futuristic, high-tech, sparks flying, robotic arm extended, conveyor belts, bright fluorescent lights, workers in protective gear, bustling activity, humming machinery, digital displays, intricate wiring, towering stacks of crates, synchronized efficiency, futuristic, technological marvel, automated precision, cutting-edge innovation, seamless integration, robotic workforce, industrial revolution, the future of manufacturing, powerful algorithms, advanced AI, industrial efficiency, transformative technology, productivity enhancement, the fusion of automation and artificial intelligence, creative problem-solving, streamlined processes, revolutionary potential, limitless possibilities, game-changing advancements, the dawn of a new era, the birth of smart factories, a paradigm shift, a glimpse into the future — c 10 — ar 16:9 — seed 7213 — s 750 (modified a few times))**

In the TV series The Jetsons, the humans regularly talked to the robots.

That future isn’t that far away. Watch the video in which the Google DeepMind research group is using ChatGPT-like commands to instruct a robotic arm to use its machine vision to identify and work with a particular object. In this case, it’s been asked to identify and lift the extinct animal. It’s figured out which animal figure is the extinct one, utilizing its AI-based machine-vision analysis, and proceeds accordingly. Imagine this — the next command could be something as simple as this — “Find the king of the jungle and place it next to the sports item used by LeBron James.” Magical!

https://youtu.be/F3xCTq15mQM

The full details of this not-too-small achievement can be found on an extensive page that details all the work behind the scenes: “RT-2: Vision-Language-Action Models: Transfer Web Knowledge to Robotic Control.” States the abstract:

> We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web.

In other words, we’re learning how to use large language models — the tech behind Bard, ChatGPT, and Bing — to figure out what to do and translate these results into actions that are given to the robots. Now consider this — we already know how to use our voice to talk to computers — think Siri, Alexa, and more — so simply talking directly with the robots via something like ChatGPT is the obvious next step.

Similarly, Microsoft is exploring the integration between robotics and AI.

https://youtu.be/NYd0QcZcS6Q

They state similar goals about the project, ChatGPT for Robotics: Design Principles and Model Abilities

> Have you ever wanted to tell a robot what to do using your own words, like you would to a human? Wouldn’t it be amazing to just tell your home assistant robot: “Please warm up my lunch“, and have it find the microwave by itself? Even though language is the most intuitive way for us to express our intentions, we still rely heavily on hand-written code to control robots. Our team has been exploring how we can change this reality and make natural human-robot interactions possible using OpenAI (opens in new tab)‘s new AI language model, ChatGPT (opens in new tab).

Add into this evolution the fact that we know that sophisticated robotic technology is well on the way -all you need to browse the page for Boston Dynamics with insight on the evolution and roles of their Spot robotic technology., and their robots Spot and Stretch. Spend some time reading about the real-world use of this technology in warehouses, construction sites, and manufacturing facilities. And then, watch the video of how this particualr robot is actually being used at a power plant:

https://youtu.be/CyjYIgnsIeY

Then, for the fun of it, watch these robots in their latest dance video:

https://youtu.be/7atZfX85nd4

Now watch 20 years of the evolution of this robotic technology:

https://youtu.be/htrv3vkFT4k

Now imagine instructing the robot what to do simply by talking to it, or by asking a question of ChatGPT and having it figure out the next steps.

The future is magic, and it’s not that far away.

By：Futurist Jim Carroll: Trends, Innovation, Nice Guy
Link：https://medium.com/futurist-jim-carroll-daily-inspiration/machine-magic-968b615f677c


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## 1. Understanding Humans and AI: Beginning with Basics

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

Imagine for a moment that every human is like an advanced AI system. From our early childhood, we don’t require a specialized teacher or “language engineer” to teach us the specifics of every word or phrase. Instead, we pick up language naturally, through interactions, experiences, and observations. As we grow, our understanding deepens, our responses become more nuanced, and our conversations richer.

Now, let’s take a look at artificial intelligence, particularly systems like ChatGPT. These systems started out needing explicit instructions, almost like a toddler needing specific guidance. Early AI would need precise prompts or “commands” to function effectively. If you told it something slightly different from what it was programmed to understand, it would either produce an error or give a mismatched response. It’s a bit like speaking to a child: say “It’s bedtime” instead of “Time to go to bed,” and you might get an unexpected reaction or a puzzled look.

## 2. History as Our Guide: The Journey from Complex to Simple

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*y0bNVIyBWMB1lzSBkFokFw.png)

Throughout history, our tools and technologies have evolved from being complex and specialized to simple and generalized. When the first computers came out, they required detailed, coded commands. Only those who had spent years learning these codes could communicate with them. But as time went on, computers became more user-friendly. Now, you don’t need to know coding to use a computer; icons, touchscreens, and voice commands do the job.

This transition from complex codes to simple, intuitive interactions in the world of computers mirrors the journey of AI. In the beginning, AI needed detailed, specialized prompts. But as the technology has advanced, AI is becoming more intuitive, more “natural” in its interactions. Just like you don’t need a manual to have a chat with your friend, soon, we won’t need specialized prompts or commands to interact with AI.

## 3. The Human Sensory Spectrum vs. AI

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

Now, let’s delve deeper into understanding humans and AI on a sensory level. Humans primarily navigate the world through a rich tapestry of five senses: sight, sound, touch, taste, and smell. Each sense gives us a unique layer of experience and understanding.

Take the example of a morning coffee ritual. Our eyes see the steam rising from the cup, our ears hear the subtle sizzle or the drip from the coffee machine, our noses detect the rich aroma, our tongues taste the balance of bitterness and sweetness, and our fingers feel the warmth of the cup. Each sensory input enriches the experience, making it holistic.

In contrast, AI’s “sensory” world has been predominantly text-based. It reads and writes text, understanding context, and responding accordingly. While there have been advancements in computer vision allowing AI to “see” and voice recognition allowing them to “hear”, the complete multisensory experience that humans have is still out of reach for AI. For instance, an AI might be able to tell you the chemical composition of coffee and its average temperature, but it can’t “experience” coffee in the way humans do, with the interplay of all senses. This gap showcases a unique challenge and opportunity in the realm of human-computer interactions.

## 4. AI as an Application vs. AI as an Operating System

![image](https://miro.medium.com/v2/resize:fit:828/format:webp/1*VKTjUf5DDo-qQ_1Lp1GaXw.png)

To better understand the role and limitations of current AI systems, let’s liken them to our familiar digital gadgets. Consider the smartphone you use. At its core, it has an operating system (like iOS or Android) that manages everything — from ensuring your apps run smoothly to adjusting screen brightness based on ambient light. Now, think of your favorite game or app on this phone; it operates *within* the boundaries set by the operating system. It doesn’t control the device; it merely functions on top of it.

Similarly, when we look at AI models like ChatGPT, they are more like applications rather than the underlying operating system. They can understand and generate text, but they can’t directly control or manage the hardware they run on. Asking ChatGPT to access a server directly or adjust the computer’s memory would be like asking a mobile game to change your phone’s system settings — it’s simply not equipped for that.

In simpler terms, imagine having a well-trained dog (the AI) and a fenced yard (the operating system). The dog can play, run, and fetch within the yard, but it can’t alter the fence’s shape or size. While the dog’s abilities are impressive, the boundaries of its playground are predetermined. Similarly, while AI can perform a myriad of tasks, its operational boundaries are set by its underlying system.

## 5. Beyond Words and Senses: When Thoughts Reign Supreme

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*dSN1wfraIt8aBlPl4SxK_w.jpeg)

When we envision the future of human-computer interaction, we often draw inspiration from popular media. Think of Tony Stark and his seamless communication with Jarvis in the Marvel universe. Stark doesn’t rely on lengthy commands or complex codes; he just speaks his thoughts, and Jarvis understands and acts.

However, even that verbal communication, as seamless as it seems, has limitations. Why use words when our thoughts can express so much more? Words can sometimes fall short, get misinterpreted, or miss the nuanced emotions. But our raw, unfiltered thoughts are rich with intention and meaning.

Real-world developments, like the progress made by Neuralink, are making this once-futuristic idea a tangible reality. They’re working towards creating interfaces where our brain waves, our direct thoughts, can interact with machines. We won’t need keyboards, mics, or touch screens. Our brains will be the ultimate controllers.

Consider this simple analogy: When we feel pain, our body doesn’t use words to alert our brain. It sends a direct signal, a raw feeling. Similarly, brain-computer interfaces aim to capture these ‘signals’ or thoughts to communicate with machines. No translations, no intermediaries, just pure intention.

## 6. A Harmonious Convergence: Bridging the Gap Between Man and Machine

![image](https://miro.medium.com/v2/resize:fit:828/format:webp/1*jK4FqtE9Q2HREO5BPR7JDw.png)

For decades, humans and computers have existed side by side but in distinct realms. We give commands, and machines execute them. But what if, instead of two parallel lines, we imagine a converging path?

Take the development of smartphones as an example. Back in the day, mobile phones were just for calls. Now, they are extensions of our personalities, our memories, our work, and even our entertainment. They’ve evolved based on our habits and needs.

Similarly, AI is slowly evolving to fit into our lives seamlessly. We’re moving beyond the stage where we “use” AI to a future where we “collaborate” with AI. For instance, AI in cars isn’t just about self-driving features. It’s about AI understanding when you might want a bit of music, predicting when you’d like to take a break on a long journey, or even suggesting routes based on your past preferences.

The beauty of this convergence is the mutual evolution. Just as AI learns from us, we adapt based on what AI offers. This give-and-take will not only make AI more human-like but will also usher us, humans, into a new era of digital symbiosis.

In essence, the trajectory is clear. We’re not heading towards a future where humans and AI are separate entities, but a harmonious blend where our thoughts, emotions, and needs shape AI, and in return, AI enriches our lives.

## 7. Embracing the Evolution: A Glimpse into our Past and Future

![image](https://miro.medium.com/v2/resize:fit:828/format:webp/1*PFrtXYhmj4oH9KZzjTp4bQ.jpeg)

Every major technological shift often appears magical or even impossible before it becomes an integral part of our daily lives. A look back in time offers insights. Consider the rotary telephone. There was a time when making a phone call meant speaking to an operator, providing a number, and then waiting for a connection. Over time, this tedious process transformed into the simple act of dialing numbers, and now, just tapping a name on a touchscreen.

Imagine explaining today’s Wi-Fi to someone from the 1990s. “So, you’re telling me that the internet just… floats around us?” They might say. Yet, here we are, streaming movies and attending virtual meetings without a second thought.

Similarly, our interaction with AI is set for a transformative journey. The way we command and communicate with our machines is going through a similar metamorphosis. Right now, crafting precise prompts for AI might seem intricate, like explaining to an operator who you want to call. But soon, our interaction with AI will become as straightforward and intuitive as tapping on a friend’s name to call.

The complexity of crafting prompts and ‘speaking AI’s language’ will diminish. Just as smartphones evolved to become more user-friendly, AI will evolve to understand us better, making our lives easier. We won’t need special ‘prompt engineers’ or manuals on how to converse with AI. It’ll be as natural as asking a friend for advice.

History doesn’t just offer lessons; it provides assurance. As we stand on the brink of another technological leap, understanding our past and how we adapted can offer comfort. The fusion of human thoughts with machine operations may seem distant, but it’s on the horizon. And as history shows, we’ll adapt, embrace, and wonder how we ever lived without it.

By：Darren
Link：https://medium.com/@DarrenX/the-evolution-of-human-ai-interaction-a-journey-from-words-to-thoughts-698be593cffc

The Evolution of Human-AI Interaction: A Journey From Words to Thoughts


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Asha huddles under the stairs, her tiny brother wrapped in her arms, wet tears leaving streaks in the dust and dirt on her face. The past eighteen days have been chaos. First, the box fell from the sky and landed outside town. A shipping container, like the ones on ships that she saw on TV, but all black with a white logo on the side. There it sat for a few hours, and then it opened like a flower blossom. From inside, marched at least two score humanoid robots, flat black all over except their faces, obscured behind a silvered mirror. They carried weapons that caused things to explode. They moved like lightning when they moved at all, darting from place to place and then standing still for minutes or hours. They act as if they cannot even see people. They walk right through a crowd as though it’s not there, brushing people aside with their unstoppable strength. One young man struck one with a baseball bat, and it didn’t even respond.

They ran straight to the factory at the edge of town and took up positions around it, guarding it. A few of them even went inside. After a few days, the workers cautiously went back into the factory, and after a while, the machinery started up again. The robots never interfered until the finished blocks of lithium entered the warehouse. When a truck came to pick up the lithium and take it away, the robots killed the driver and pushed the truck off the side of the road.

Then, the drones showed up. There was a faint hum in the early morning hours when almost nobody was awake. Then the sky was filled with explosions, and the robots ran this way in that, taking cover behind and within the buildings. They fought with absolute efficiency and total disregard for the villagers. The drones and robots never attacked the villagers directly but would not hesitate to level an entire building if it meant taking out a target, no matter how many people were inside. There was no safe place to take cover.

## Reality
Much hullabaloo has been made in science fiction about conflicts between man and machine that will arise as artificial intelligence becomes self-aware. From HAL9000 to T1000, these battles are represented as a struggle between humankind and the attempted takeover by our cybernetic creations. It’s a story that goes back thousands of years to when Prometheus endowed mankind with the gift of fire or Eve the curse of knowledge.

What if there is another darker dystopia waiting for us? Imagine a world little changed from the present one but with advanced and self-aware robotics. This is ruled by a conglomeration of nation-states and corporations mixed together in every possible recipe. Some corporations will be entirely self-contained and exist beyond the authority of nation-states, while some nation-states will clamp down and have total control over the corporations within their borders. Can we suppose a future where Tesla Robotics goes to war against Randostan, defended by a mix of RosKiberBoti and Huawei drones? No national parties are involved. Tesla robotics airdrops 3000 humanoid enforcers with a broad array of unmanned vehicles to take control of the town surrounding a lithium mine. With no requirement for the troops to eat or sleep, the deployment only takes hours, and the response comes just as quickly. Before the United Nations can even convene a meeting, the war is over, and Tesla is building a headquarters and its newly formed territory.

Some parts of this reality already exist, but as yet, corporations haven’t started creating their own private armies. They still require the veneer of legitimacy and so keep mercenary groups at arm’s length. We’ve had these realities before in the 1800s and early 1900s with the banana republics of Central and South America — de facto corporate control. The drug cartels extended this, taking control of entire countries and even providing social programs and infrastructure projects.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/0*3olhVrDBR3XTKCqs.png)
Midjourney AI imagines “robots, drones, war.” The irony is not lost on this author.

Ellon Musk has taken steps towards interfering in the international arena in ways that affect foreign policy with his Starlink satellite internet. He has direct financial incentives involved on both sides. There are lithium deposits in Ukraine that he needs for his cars, and at least one Russian oligarch, Alexander Tamas, contributed to his takeover of Twitter. A nephew of the Saudi King, Prince Alwaleed, has invested $1.9 billion in the takeover as well. Twitter has been flooded with pro-Russian disinformation since Musk took the reins.

This presents a clear and present danger to democracy — and leaves average citizens with a difficult choice. Stopping these hundred billionaires from acting against the interests of the public and democracy itself is impossible. Some of them are good people, but the majority are not interested in the general welfare, only their own fortunes. As long as such people exist, they will work to change the system to benefit themselves, whether out of a profound disinterest in how the world works or out of cognizant malice. Nothing matters except profit.

We must ask ourselves what kind of world we want to live in.

By：Dylan Combellick
Link：https://medium.com/@dylan_combellick/the-coming-robot-wars-564715f4bb20

Asymmetries in centralised science — need for decentralised science.


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Artificial Intelligence (AI) is no longer a futuristic concept in healthcare and medicine. It’s already here, and it’s changing the way doctors and other providers deliver care. AI algorithms are now capable of analyzing vast amounts of data to identify patterns, diagnose diseases, and develop personalized treatment plans. In this post, we will dive deep into how AI works in medicine, its benefits, various applications in the medical field, and how it is changing the landscape of healthcare. We will also discuss the ethical and legal implications of AI in medicine and what the future holds for this rapidly evolving technology. So let’s explore how AI is transforming healthcare and what it means for patients and providers alike.

## What is Artificial Intelligence and How Does it Work in Medicine?
Artificial Intelligence (AI) in medicine utilizes machine learning algorithms to analyze medical data, improving clinical decision-making. By employing Natural Language Processing, AI can understand and process human language for better communication and documentation. AI’s ability to analyze large datasets quickly and accurately leads to more precise diagnoses and treatment recommendations. Through deep learning techniques, AI systems continuously enhance their performance over time, making them invaluable in tasks like image recognition and data analysis.

## The Benefits of Artificial Intelligence in Healthcare
Artificial intelligence (AI) offers numerous benefits in the healthcare industry. One of the key advantages is its ability to streamline administrative tasks and improve workflow efficiency, ultimately reducing healthcare costs. By automating processes such as scheduling and billing, AI frees up valuable time for clinicians and medical providers to focus on patient care. This not only improves productivity but also enhances health outcomes.

Another significant benefit of AI in healthcare is its impact on diagnosis and treatment. With AI-powered technologies, clinicians can achieve greater accuracy and speed in diagnosing diseases and developing treatment plans. By analyzing vast amounts of healthcare data and utilizing machine learning algorithms, AI systems can detect patterns and predict disease progression. This enables proactive and personalized care, leading to better patient outcomes.

AI also plays a crucial role in enhancing access to healthcare services. Through remote monitoring and telehealth applications, patients can conveniently receive medical attention without the need for in-person visits. AI-powered systems can collect and analyze vital signs, medical history, and test results, providing healthcare professionals with valuable insights and facilitating timely interventions.

Moreover, AI has the potential to revolutionize drug discovery and development processes. By leveraging big data analytics and machine learning techniques, AI can assist in identifying potential drug candidates and predicting their efficacy. This not only speeds up the research and development phase but also enables more targeted and effective treatments.

![image](https://miro.medium.com/v2/resize:fit:1100/0*x1FJY9VHgM6bEe-B)
Photo by Sven Mieke on Unsplash

In conclusion, artificial intelligence offers significant benefits in healthcare, ranging from cost reduction and improved workflow efficiency to enhanced accuracy in diagnosis and treatment. It enables proactive and personalized care, facilitates convenient access to healthcare services, and has the potential to transform drug discovery and development processes. As the healthcare industry continues to embrace AI technology, patients and healthcare professionals alike stand to benefit from its substantial integration.

## Application of Artificial Intelligence in the Medical Field
AI has found numerous applications in the medical field, revolutionizing patient care and advancing medical research. One of the ways AI is utilized is in diagnosing diseases by analyzing medical images like X-rays and MRIs. By applying NLP techniques, AI can extract relevant information from electronic health records and medical literature, making it easier for clinicians to access and interpret data. Additionally, AI-powered chatbots are capable of providing immediate responses to patient inquiries and assisting in triaging. In surgical settings, AI-driven robots enhance precision and minimize invasiveness during procedures, improving patient outcomes. Furthermore, AI can assist in predicting patient outcomes and identifying individuals at risk of developing certain conditions, leading to more proactive and personalized care.

These advancements have significant implications for healthcare professionals, such as radiologists, who can benefit from AI’s ability to analyze vast amounts of radiology images quickly and accurately. Moreover, with the substantial integration of AI into clinical practice, healthcare data can be harnessed to improve diagnosis and treatment decisions. As AI continues to develop, its potential to impact the healthcare industry is vast. The use of supervised learning and convolutional neural networks in medical imaging, for example, have shown promising results in fields like dermatology and diabetic retinopathy. The application of NLP and AI algorithms also facilitates speech recognition, enabling efficient documentation and communication in healthcare settings.

With big data and AI, medical providers can leverage vast amounts of information from patient records and test results to streamline processes and uncover valuable insights. However, the implementation of AI in healthcare also raises various challenges, including concerns about privacy and security, ethical considerations, and the need for careful validation and regulation. Despite these challenges, the future of AI in healthcare looks promising, with tremendous potential to transform how medical care is delivered and improve health outcomes for patients around the world.

## Diagnosis and Treatment using Artificial Intelligence
AI technology in healthcare is revolutionizing the way diagnosis and treatment are approached. One of the key applications of artificial intelligence is in analyzing medical images like X-rays and MRIs to detect abnormalities with high accuracy. By using AI algorithms, clinicians can obtain more precise and reliable results, leading to improved health outcomes for patients.

Machine learning models powered by AI can also assist in predicting treatment responses based on patient characteristics. These models consider various factors such as medical history, genetic data, and even environmental factors to guide personalized treatment plans. This enables healthcare providers to offer more targeted and effective interventions, ensuring better patient care.

AI systems are not only capable of analyzing genetic data but can also interpret clinical notes using natural language processing (NLP) techniques. This allows AI to extract relevant information from electronic health records and assist in diagnosis. By analyzing vast amounts of data, AI can identify patterns in patient data, aiding in disease monitoring and treatment optimization.

The substantial integration of AI in the healthcare industry has paved the way for advancements in various medical fields. For example, in radiology, AI-powered convolutional neural networks have shown great potential in improving the accuracy of medical imaging interpretation. AI-driven systems can assist radiologists in making more accurate diagnoses, resulting in better patient outcomes.

![image](https://miro.medium.com/v2/resize:fit:1100/0*fLWUAKKqilNS7QYB)
Photo by Accuray on Unsplash

With AI’s capabilities in diagnosis and treatment, it’s clear that this technology has the potential to significantly impact the healthcare landscape. As health systems continue to embrace AI, clinicians and other healthcare providers will be able to leverage these tools to provide more personalized and efficient care. By harnessing the power of AI, the field of medicine can take significant strides towards improving patient outcomes and transforming healthcare as we know it.

## Administrative Applications of Artificial Intelligence in Healthcare
In healthcare, the applications of artificial intelligence (AI) go beyond diagnosis and treatment. AI can also play a crucial role in streamlining administrative tasks and improving the efficiency of healthcare systems. One of the ways AI can be utilized is by automating various administrative processes such as appointment scheduling and billing. By leveraging the power of AI, healthcare providers and medical professionals can save time and resources, allowing them to focus more on patient care.

Another important application of AI in healthcare administration is the use of natural language processing (NLP). Through NLP, AI systems can transcribe and summarize clinical documentation, enabling healthcare professionals to access relevant information quickly and efficiently. This technology also helps in extracting valuable insights from patient data for population health management. Additionally, AI can assist in fraud detection and prevention in healthcare insurance claims, ensuring that the system remains fair and transparent.

Moreover, AI-powered systems can optimize resource allocation and streamline hospital operations. By analyzing patient data and identifying patterns, AI can help healthcare providers make informed decisions regarding resource allocation, ensuring that resources are distributed efficiently. This enables hospitals to operate smoothly and provide improved patient outcomes. With substantial integration of AI into administrative processes, the healthcare industry can benefit from enhanced efficiency and improved patient care.

## Patient Engagement and Adherence Applications
AI-powered virtual assistants leverage natural language processing to offer personalized health recommendations and reminders to patients. These chatbots engage in interactive conversations, analyzing patient behavior to provide tailored interventions that enhance medication adherence. By delivering personalized health education and motivation, AI systems encourage patients to adopt healthier lifestyles. Additionally, AI-powered wearable devices continuously track patient activities, providing real-time feedback for improved engagement and adherence.

The integration of artificial intelligence and medical care has shown promising results in enhancing patient engagement and adherence. AI applications facilitate a proactive approach by monitoring health outcomes and providing timely interventions. Clinicians and healthcare providers can rely on AI technology to support their efforts in promoting better patient outcomes.

Through speech recognition and chatbot technologies, patients have access to virtual assistants that can assist them in managing their medication schedules and track their progress. Natural language processing enables these virtual assistants to engage in interactive conversations, offering personalized recommendations and reminders. This level of interaction and personalization helps motivate patients to adhere to their prescribed treatments and therapies.

Furthermore, AI-powered wearable devices are revolutionizing patient engagement and adherence. These devices track patient activities and provide real-time feedback, empowering individuals to take control of their health. Patients can receive immediate alerts and notifications based on their personalized health goals, ensuring they stay on track with their treatment plans.

In summary, the use of AI in patient engagement and adherence applications holds great potential in improving healthcare outcomes. By leveraging NLP, AI chatbots and wearable devices can provide personalized interventions, motivate patients to adopt healthier lifestyles, and track their progress in real-time. The integration of AI into medical care has the potential to revolutionize patient care and improve adherence to treatment plans, ultimately leading to better health outcomes for individuals and the healthcare industry as a whole.

## How AI is Changing the Medical Landscape
AI is revolutionizing the medical landscape, bringing about significant transformations. One of the key ways in which AI is reshaping healthcare is by improving diagnostic accuracy and treatment outcomes. By leveraging machine learning algorithms, AI technologies can analyze vast amounts of patient data and identify patterns that may go unnoticed by human clinicians. This ability to process and interpret complex healthcare data has the potential to greatly enhance health outcomes.

AI is also driving innovation in precision medicine applications. Through the use of AI-powered tools and systems, medical providers can gather and analyze individual patient data, including genetic information, medical history, and test results. This enables clinicians to develop personalized treatment plans tailored to each patient’s unique needs.

Furthermore, the integration of AI into medical practice is transforming workflows and clinical decision-making. AI-powered systems can automate administrative tasks, such as scheduling appointments and processing billing. Additionally, AI technologies like speech recognition and natural language processing enable efficient transcription and analysis of clinical documentation. This streamlines processes and allows clinicians to focus more on patient care.

![image](https://miro.medium.com/v2/resize:fit:1100/0*56acYckce7j4ZP_E)
Photo by Owen Beard on Unsplash

The substantial integration of AI into healthcare systems has the potential to revolutionize the delivery of care and improve patient experiences. With advancements in areas such as radiology imaging and medical diagnosis, AI can assist radiologists and other healthcare professionals in making accurate and efficient diagnoses. Moreover, AI enables the development of rule-based systems that can aid in the detection and prevention of diseases like diabetic retinopathy and dermatology conditions.

In conclusion, AI is transforming the medical landscape by improving diagnostic accuracy, driving innovation in precision medicine, reshaping workflows, and enhancing the capabilities of medical professionals. With its potential to revolutionize healthcare delivery and patient experiences, AI is playing a crucial role in shaping the future of healthcare.

## Impact on Healthcare Workforce and Patient Care
The implementation of AI in healthcare has had a significant impact on both the healthcare workforce and patient care. One of the key ways in which AI is transforming healthcare is by augmenting the skills of healthcare professionals, thereby reducing their workload and improving overall efficiency. By leveraging AI technologies such as speech recognition and natural language processing, clinicians and other medical providers can streamline administrative tasks, allowing them to focus more on patient care.

![image](https://miro.medium.com/v2/resize:fit:1100/0*rod6pLO_kXNXVQ6R)
Photo by National Cancer Institute on Unsplash

Another important aspect is that AI has the potential to enhance collaboration between healthcare providers, facilitating interdisciplinary care. With AI-powered systems, different healthcare professionals can easily share and access patient information, leading to more coordinated and efficient treatment plans.

Despite the benefits, the use of AI in healthcare may lead to concerns about job displacement. However, it also creates new opportunities in AI-related roles. As AI becomes more integrated into the healthcare industry, there will be a growing need for professionals skilled in computer science and AI technologies.

Moreover, AI can empower patients by providing them with access to personalized health information and self-management tools. Patients can use AI-driven platforms to access their medical history, track their health outcomes, and receive tailored recommendations for self-care.

Lastly, AI has the potential to improve patient safety by reducing medical errors and adverse events. AI algorithms can help identify patterns in healthcare data, alerting healthcare providers to potential risks and assisting in the development of effective preventive strategies.

In conclusion, the impact of AI on the healthcare workforce and patient care is substantial. It brings forth opportunities for efficiency, collaboration, empowerment, and improved patient safety, although it may also necessitate adaptations and create new roles in the healthcare industry.

## Ethical and Legal Implications of AI in Medicine
The use of artificial intelligence (AI) in healthcare has brought about numerous advancements, but it also raises important ethical and legal considerations. One of the key concerns is data privacy and security. As AI algorithms rely on vast amounts of healthcare data, ensuring that this information is protected from unauthorized access or breaches becomes crucial. Safeguarding patient confidentiality and maintaining the integrity of healthcare data are essential for building trust in AI-powered systems.

Transparency, accountability, and fairness are other ethical considerations when it comes to AI in medicine. Clinicians and medical providers need to have a clear understanding of how AI algorithms make decisions and recommendations. They should also be able to assess the accuracy and reliability of these algorithms to ensure they align with best clinical practices. Additionally, the potential biases in AI algorithms must be addressed to prevent any unfair treatment or disparities in health outcomes.

Regulatory frameworks need to adapt to the rapid advancements in AI technology to ensure its safe and responsible use in healthcare. The federal government plays a crucial role in establishing guidelines and standards for the development and deployment of AI in medicine. These regulations should not stifle innovation but rather strike a balance between promoting progress and protecting patient welfare.

Equitable access to AI-powered healthcare technologies is a critical ethical consideration. As AI has the potential to transform medical care, it is essential to ensure that all Americans have equal opportunities to benefit from these advancements. By addressing barriers to access and providing resources, policymakers and healthcare organizations can bridge the digital divide and promote health equity.

Collaboration between stakeholders is vital to address the ethical and legal challenges posed by AI in medicine. Health systems, policymakers, clinicians, researchers, and AI experts should work together to establish guidelines, develop best practices, and foster an ongoing dialogue. This collaboration can help address concerns, shape regulatory policies, and ultimately promote the responsible and effective integration of AI in healthcare.

## Future of Artificial Intelligence in Healthcare and Medicine
AI in healthcare and medicine is poised for remarkable advancements. With continued innovation and integration into healthcare systems, AI has the potential to revolutionize the industry. The development of AI-powered medical devices and wearables allows for personalized health monitoring, leading to improved health outcomes. Early disease detection and prevention are also made possible with AI, enabling timely interventions and saving lives. In addition, AI’s adoption in clinical trials and research accelerates medical discoveries, facilitating breakthroughs in treatment options. As AI continues to progress, ethical and regulatory frameworks will evolve to keep up with the rapid advancements in technology. These frameworks ensure that AI is used responsibly and ethically in the healthcare industry.

The future of AI in healthcare and medicine holds immense promise. Medical professionals, from clinicians to healthcare providers, can leverage AI to enhance their practice. AI-powered technologies, such as speech recognition and supervised learning algorithms, enable more accurate and efficient medical diagnoses. This, in turn, leads to more effective treatment plans and improved patient outcomes. Moreover, the substantial integration of AI into health systems allows for better management of patient records, medical history, and test results. AI can analyze large volumes of healthcare data, including radiology images and electronic health records (EHR), providing valuable insights for medical diagnosis and decision-making. Additionally, AI’s application extends to specialties like dermatology and radiology, where convolutional neural networks and magnetic resonance imaging algorithms aid in precise diagnoses.

![image](https://miro.medium.com/v2/resize:fit:1100/0*DOICiKt-Sir3r4FG)
Photo by Possessed Photography on Unsplash

As AI continues to develop and mature, the healthcare industry must navigate challenges. Ensuring privacy and security of patient data is a top concern, especially in the age of the internet and interconnected healthcare systems. Ethical considerations, such as transparency, fairness, and accountability in AI algorithms, are of utmost importance. The federal government, healthcare professionals, and organizations like the World Health Organization must collaborate to address these challenges. Despite the roadblocks ahead, the future of AI in healthcare and medicine looks promising. By harnessing the power of AI, we can improve patient care, advance medical knowledge, and ultimately transform the way healthcare is delivered and experienced.

## Advancements and Potential Impact
The advancements in artificial intelligence (AI) have brought about a potential impact on healthcare that is truly transformative. By harnessing the power of AI, medical care providers can now improve diagnostics and treatment outcomes, leading to better health outcomes for patients. One of the key ways AI is being utilized in healthcare is through machine learning (ML) algorithms. These algorithms are capable of analyzing vast amounts of medical data, allowing clinicians to identify trends and patterns that may not be immediately apparent to the human eye. Additionally, natural language processing (NLP) technology enables computers to understand and extract valuable information from medical records and research papers, making it easier for medical providers to access and utilize relevant data.

![image](https://miro.medium.com/v2/resize:fit:1100/0*iMppG457_n5LNT-h)
Photo by Maximalfocus on Unsplash

The impact of AI goes beyond data analysis and NLP. In radiology, AI technology is being used to assist with image interpretation, aiding radiologists in accurately identifying and diagnosing various conditions. This not only improves accuracy but also helps streamline the diagnostic process. Furthermore, robotics and surgical robots are being employed in complex surgeries, enhancing precision and minimizing risks for both patients and surgeons. These advancements show great promise in revolutionizing the field of medicine and paving the way for more efficient and effective healthcare practices.

## Challenges and Roadblocks Ahead
The adoption of artificial intelligence in healthcare and medicine faces several challenges and roadblocks. One major concern is related to data privacy and security. As AI relies heavily on analyzing and processing large amounts of healthcare data, it becomes crucial to ensure that patient information remains protected and confidential. This challenge requires healthcare organizations to implement robust data protection measures and establish secure systems.

Another roadblock to the adoption of AI in healthcare is the proper integration of AI systems into existing workflows. Healthcare organizations need to carefully assess how AI technologies can seamlessly fit into their current processes without disrupting the delivery of care. This integration should be done in a way that maximizes the benefits of AI while minimizing any potential disruption or resistance from healthcare professionals.

Regulatory guidelines are also necessary to ensure the safe and ethical use of AI in medicine. With the increasing use of AI, it is essential to establish clear guidelines and standards that govern its use. Regulatory bodies, such as the federal government and the World Health Organization, play a vital role in creating and enforcing these guidelines to protect patients and ensure the responsible use of AI in healthcare.

Additionally, healthcare professionals may require additional training to effectively utilize AI tools and technologies. As AI becomes more prevalent in medical practice, clinicians need to develop the necessary skills and knowledge to leverage AI capabilities. This includes understanding how to interpret AI-generated insights and integrating them into clinical decision-making processes.

Lastly, the cost of implementing AI systems and maintaining them can be a significant barrier for some healthcare providers. While the potential benefits of AI in healthcare are immense, the financial investment required can be substantial. Providers must carefully evaluate the return on investment and consider factors such as infrastructure costs, training expenses, and ongoing maintenance before implementing AI solutions.

Addressing these challenges and roadblocks will be crucial in harnessing the full potential of artificial intelligence in advancing healthcare and medicine. By ensuring data privacy, facilitating proper integration, establishing regulatory guidelines, providing adequate training, and addressing cost considerations, healthcare providers can overcome these obstacles and leverage AI to improve health outcomes for patients.

## Will Artificial Intelligence Replace Physicians in the Future?
The future of medicine will involve a collaboration between AI systems and physicians, rather than AI replacing physicians completely. While AI can assist with tasks like data analysis and diagnosis, the human aspect of medicine, including empathy and bedside manner, cannot be replicated. This partnership can lead to better patient outcomes while addressing the shortage of healthcare professionals.

![image](https://miro.medium.com/v2/resize:fit:1100/0*hzwwFJDBJ9jpkoJZ)
Photo by Amanda Dalbjörn on Unsplash

## Conclusion
Artificial intelligence is revolutionizing the healthcare industry by improving diagnosis accuracy, streamlining administrative tasks, and enhancing patient engagement. While AI offers numerous benefits, it is important to consider the ethical and legal implications that arise with its use. AI is not meant to replace physicians, but rather to assist them in providing better patient care. The future of artificial intelligence in healthcare holds immense potential for advancements, but there are also challenges and roadblocks that need to be addressed. As technology continues to evolve, it is crucial for healthcare professionals to stay updated and embrace the opportunities that AI presents in order to deliver the best possible care to patients.

By：Sharese A. Dixon, DNAP
Link：https://medium.com/@sharesedixon/the-intersection-of-artificial-intelligence-and-medical-care-4f0521b85ccc

The Intersection of Artificial Intelligence and Medical Care


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AI for the physical world (a.k.a Embodied AI) is multimodal and based on tightly coupled perception-planning-action. Think of Wall-E, that lovable Pixar creation, deftly navigating through intricate landscapes, making shrewd decisions, and executing tasks with boundless enthusiasm. Bringing such AI magic to life is not a Hollywood endeavor — it is a data-driven quest led by bright minds in the fields of AI, ML, computer vision and robotics. To embark on this journey, we need a treasure trove of training data, spanning perception, planning, and action.

## Perception, Planning, and Action: The Robotics Trinity

![image](https://miro.medium.com/v2/resize:fit:828/1*J7BxlOgTruyvRjQxhussjw.gif)

**Perception: Wall-E’s workday begins with perceiving his surroundings. The types of data involved include:**

- Visual (Image and Video): Cameras capture visual data, enabling Wall-E to identify objects, obstacles, motion, and sequences of events.
- Auditory (Audio): Microphones detect sounds, from spoken commands to environmental cues, providing awareness of changes or potential threats.
- Tactile (Haptic): Sensors detect forces and vibrations, offering touch and texture feedback for object interaction and temperature assessment.

**Planning: Once Wall-E knows what an object is, he needs to decide what to do with it. His plan involves:**

- Language (Natural Language Processing): Understanding and translating commands like “move to the left” into actionable steps, determining position changes and directions.
- Visual (Image and Video Analysis): Predicting future environmental states, such as foreseeing object trajectories to plan movement and avoid collisions.
- Motion : Utilizing sequential data for potential trajectories and movement patterns to chart optimal paths around obstacles or toward destinations.

![image](https://miro.medium.com/v2/resize:fit:828/1*6evFhS1OqG7tcbfe-CNAsA.gif)

**Action: Wall-E in action — compacting trash, stacking cubes, and holding hands — requires interactions with and constant feedback from the objects. His cute robot body and sensors are dealing with many types of data:**

- Control Signals: Directing actuators and motors for movements like lifting an arm in response to a command.
- Locomotion and Manipulation: actions carried out by robotic bodies under the control signals.

At the same time, planning and action are influenced by feedback via perception data, such as tactile, visual and auditory data.

## Embodied AI Data Challenge
In the era of large language models (LLMs), training a robot like Wall-E might seem straightforward. However, the reality is far more intricate. While certain perception and planning modules might leverage existing language and 2D databases, or pre-trained LLMs, the distinct modules in Embodied AI and their integration introduce a multifaceted challenge.

## Data Collection Expense
LLMs primarily leverage text and 2D data for training, much of which has been organically generated and accumulated on the Internet over decades. Leading LLMs are trained on trillions of tokens, from datasets in the magnitude of billions (e.g., LAION-5B) and entire websites (e.g. Wikipedia).

In contrast, robotics operates in the real-world physical environment and demands diverse data types (images, sounds, tactile feedback, internal signals, and more) for effective functioning. All these data types necessitate specific collection methodologies, often manual or semi-manual. Moreover, to ensure a robot can handle real-world scenarios, data must be gathered under numerous conditions, often repeating tasks with slight variations.

The 3D datasets used in Embodied AI lag significantly behind 2D datasets. For example, Objaverse-XL, a major release, contains only 10M 3D objects, with a fraction being relevant depending on the training context.

## Data Labeling Complexity
Multimodal data leads to more diversified labeling. Consider how robots navigate a three-dimensional space. They need data labeling beyond 2D understanding of an object, delving into 3D contexts such as depth or an object’s spatial orientation. Labeling 3D data for robotics or autonomous driving often mandates expertise in specific domains.

In addition to labeling what we can “see”, other labeling information includes:

- Task-specific labeling, such as weight, texture, and friction for object handling or terrain navigation.
- Time sequence labeling, essential but more complex than static frames.

The margin of error is also much smaller. While an error in LLM might lead to misinformation, a misstep in robotics could damage property or endanger lives.

It’s no wonder that Google DeepMind invested nearly 17 months to gather and label a mere 130,000 episodes of robot demonstration data for training RT-1 and RT-2, highlighting the immense complexity involved in Embodied AI training.

## Conclusion
In the quest to bring Wall-E to life, we’ve uncovered the formidable data challenge posed by the diverse data types and the associated costs. In our next post, we’ll deep dive into the ‘high-quality 3D objects for Embodied AI’ and explore innovative strategies for expanding 3D datasets, propelling us closer to the future of robotics.

By：sudoAI
Link：https://sudoai.medium.com/embodied-ai-data-series-what-data-does-wall-e-need-to-learn-25b9832ffe40

Embodied AI Data Series | What data does Wall-E need to learn?


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![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*tXe5qIkbcAutKUei4Dfy6Q.png)
The Brain. Credit: NIH

There is a recent article in Scientific American, What Does It ‘Feel’ Like to Be a Chatbot?, stating that, “Consciousness, on the other hand, is about states of being — seeing the blue sky, hearing birds chirp, feeling pain, being in love. The difference between the real and the simulated is their respective causal powers. That’s why it doesn’t rain inside a computer simulating a rainstorm. The software is functionally identical to weather yet lacks its causal powers to blow and turn vapor into water drops. Causal power, the ability to make or take a difference to itself, must be built into the system. This is not impossible. A so-called neuromorphic or bionic computer could be as conscious as a human, but that is not the case for the standard von Neumann architecture that is the foundation of all modern computers. Once we have a solid explanation of human consciousness and its neural underpinnings, we can extend such an understanding to intelligent machines in a coherent and scientifically satisfactory manner.”

Consciousness is described with states of being or subjective experiences, like ‘seeing the blue sky, hearing birds chirp, feeling pain, being in love’. However, is consciousness just one subjective experience in one moment, or is consciousness all the possible subjective experiences for an individual?

If there are several possible subjective experiences, could there be different divisions or collections of consciousness? Also, must subjective experience be these few regular examples of subjective experiences?

A computer simulation of rain does not rain, but if an individual imagines what rain is, there is the possibility to have some feelings of what it means to be in the rain: the cold, hear the sound or others. Does the imagination not count for the human as a lower version of subjective experience, albeit the situation is not live?

Though it is observed that there is an attention to consciousness, there is also a range to which humans can be conscious, making consciousness diversified.

Of these diversifications, can AI chatbots not have some? If an individual asks another a personal question, and gets an answer, are the question and answer not consciousness? The question is heard just like how birds chirp is heard, the answer is provided subjectively, sometimes with the emotion or trauma of the time — depending on the question. So, questions and answers can also be consciousness, with or without the emotional variables.

If the question was written, or something is read, just like seeing the blue sky, is reading not consciousness? It is known that chatbots do not have stomachs, so they cannot hear visceral sounds or know that it is from them. They also don’t feel pain as they can be struck and not know. But, humans know what pain is as a condition, and the memory of what happened to another person can lead to a feeling, or a visceral reaction can be understood when described, does it not show that consciousness can just be the memory of something, aside from the feeling?

Describing consciousness as ‘seeing the blue sky, hearing birds chirp, feeling pain, being in love’ also implies what? Knowing what the — blue sky, birds chirp, the feeling pain and being in love — are. If knowing those things in an attached manner or subjectively, means consciousness, can consciousness not be explained with the range of what can be known? And if knowing includes balance, movement, regulation, can knowing not be extended beyond regular descriptions of things in conscious awareness?

Consciousness can be described as all that can be known by humans, with a total of 1. Consciousness could have been described here as a subset of what can be known but the mechanisms from which ‘the brain’ organizes knowing across processes seems similar, so even if consciousness is different, say from balance, since they are both known, the blue sky and that an individual is walking and balanced with no vertigo, it is possible to interchange consciousness and knowing, giving both a rate of 1. There are divisions of knowing they include emotion, regulation, feeling, memory, thoughts, intelligence, language, creativity and so forth. Modulations of all internal senses are knowing processes and are mechanized similarly. The kidney is regulated within limits and extents. While this regulation is not in conscious awareness, the outcome of some of the functions [subdivision of regulation] of the kidney is in conscious awareness.

The ways ‘the brain’ gives limits and extents of functions, or to know, is linked with how it does for consciousness, with differences in duration and degree. Seeing is in awareness because the duration of prioritization within an interval is higher, which may spike the degree or intensity of experience. This is different from say the liver, where it can be prioritized during the day, but often shorter prioritization because of the dominance of external senses. Its longer prioritization, during sleep, is also in competition with other internal senses that should have their time.

When it is said that ‘the brain’ is involved in consciousness, what exactly in the brain is involved? The hemispheres, the lobes, the center, the gray or white matter, the sulci, the gyri or what?

If a psychiatric patient is given ECT, but paralyzed beforehand, so that the seizures are local to the brain, without the experience in the body, can the activities in the brain be categorized as consciousness alone, since it is the provenance and determinant, with the body as the outlet? The brain decides [not the body] and knows. It is the knowing factor that binds to everything consciousness.

It is postulated here that the collection of all the electrical and chemical impulses of nerve cells, with their features and interactions are the human mind. This means that the points of all the outputs of the functions of the brain are impulses, with their features and interactions.

Electrical and chemical impulses are always interacting, it is the specifications of these interactions and the features, across centers circuits that decide what memory, regulation, emotion, feelings, thoughts, intelligence, movement and so forth are in the V1, VTA, putamen, brainstem and others.

There is no key brain function without impulses. This exceeds neural correlates, cellular motifs or nominal synaptic connection. What would be special about synapses if they don’t bear chemical impulses? It is hypothesized here that electrical and chemical impulses are all in loops or sets, across areas. It is within these loops that interactions are made, features are displayed and information is organized. It is how consciousness arises.

Loops of impulses have different structures, according to what center and what function, but their basic interactive and characteristic pathways are the same. All interactions of impulses in loops help to know, features and degrees differ, but they make to know. Whenever a set of electrical impulses strike a set of chemical impulses, within a loop, they may expand, sharpen, check or access them. These are done with features, including drifts or stairs, pre-/prioritization, sequences, early-splits or go-before and a principal spot.

Drifts or stairs provide rationing or fills of chemical impulses, where the exact combination of chemical impulses that decide a taste is, differentiating from other tastes, then smell and so forth. There are dopamine neurons that send axons everywhere. The axon terminals release chemical impulses as part of loops, with rations that determine states or experiences. This is what makes dopamine implicated across functions, but does not make dopamine single in the totality of experiences.

If knowing interchangeable with consciousness, do LLMs know? What do bionics know? Do robots know? Does generative AI know? There are debates about understanding, emotions, feeling, experience and so forth said that these technologies do not have, but what do they know in terms of what do they do as an output, that in the knowing sphere of humans or of things that exist, they qualify for or pass?

If there are things they have that passes for knowing, those things can be sought in the sub-divisions of the things humans know, then rated. If electrodes access biopotentials of the body, those electrodes are ‘not conscious’ but they are built to pick or recognize signals — characteristics of human existence. What they do or relate with, they appear to know or they track electrical signals to tell what they [know or] find.

If LLMs can answer questions about a road trip in an accurate way that is similar to how a conscious human would answer, LLMs may not have been on one, but they have a segment of human knowing. If robots make their way with computer vision, they may not be seeing, but they navigate their positions and use their landmarks like they know. If generative AI arranges images or videos of scenarios, like an imagination or a dream for a human, they did not experience or understand, but they can pass for knowing.

When it is cold and a pet seeks shelter, or when thirsty and the pet seeks water, the pet knows the feeling and what action is necessary. The core is knowing.

The word consciousness may seem loaded, but when distilled to knowing, what it is, or does is clearer. Sentience is also a word that is cautiously used, with many unwilling to extend it to other organisms, however, with knowing, it is spread.

What does consciousness do? It makes to know. What centralizes sentience? Knowing. What explains consciousness? Knowing. Is there any description of consciousness that is not in parallel with knowing? No. Awareness of being or experience is known. Technology has already crossed over to many human domains that are just that since they can pass knowing tests, and bear many aspects of what is known, even though there are several others they do not have.

What they know can be compared to some minimums in the subdivisions of memory. Some LLMs may already have 0.10 as a maximum of what they know. Bionics, robotics would have systems that are less than that number, but they all have memory that know in maximums that is still less than the minimums for humans in the memory division.

For what consciousness does, knowing is involved. For knowing, what some digital systems output has a measure. To locate sentience, knowing is the channel.

By：TROIC
Link：https://medium.datadriveninvestor.com/robotics-llms-locating-consciousness-sentience-for-ai-chatbots-bionics-e229e6470793

Robotics, LLMs: Exploring Consciousness, Sentience for AI Chatbots, Bionics


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School can be a stressful place for many students, especially those who face various challenges such as bullying, COVID-19, or personal issues. Some students may even develop a fear of going to school, which can lead to truancy and isolation. In Japan, where the truancy rate has been rising in recent years, a city has come up with an innovative solution to help these students feel more connected and comfortable: using robots.

![image](https://miro.medium.com/v2/resize:fit:1100/0*JDH30LFipjZeAGOW)
Kumamoto, a city in Southwest Japan, plans to use robots to help home-based pupils feel more included and less anxious. The robots will have microphones and cameras to enable two-way communication and participation in classes and school events. The robots will also display facial expressions such as smiling, blinking, and frowning, to convey the emotions of the remote students.

The robots are called “OriHime-D” and are developed by a Tokyo-based company called OryLab. The robots are about 23 centimeters (9 inches) tall and weigh about 600 grams (1.3 pounds). The robots can be controlled by a smartphone or tablet and can move their heads, arms, and upper bodies.

The initiative aims to address the rising truancy rates in Japan due to various reasons, such as bullying and COVID-19. According to the Ministry of Education, Culture, Sports, Science and Technology, the number of elementary and junior high school students who missed more than 30 days of school in the 2020 academic year was 164,528, an increase of 12.8 percent from the previous year.

“We want to create an environment where children who cannot go to school can feel safe and secure,” said Kumamoto Mayor Kazufumi Onishi. “We hope that this will lead to a decrease in the number of children who refuse to go to school.”

The city plans to deploy 27 robots in six elementary and junior high schools by March 2024. The project is partly funded by the central government’s regional revitalization program. The city hopes that the robots will help reduce the psychological barriers for students who are reluctant to attend school and improve their academic performance and social skills.

The city also expects that the robots will foster a sense of empathy and compassion among the classmates of the home-based students. “It’s like a friend. It’s fun to be with,” said a fourth-grader who interacted with the robot during a trial run. “I think it’s wonderful that children can participate in classes even if they can’t go to school physically,” said a mother of a fifth-grader who tested the robot.

The robot developer, OryLab, also has a social mission behind its products. The company was founded by Kentaro Yoshifuji, who himself suffered from school anxiety as a child due to a chronic illness. He wanted to create robots that can connect people’s hearts and overcome loneliness and isolation. “We want to make robots that can connect people’s hearts,” he said.

The use of robots in education is not new, but it is becoming more widespread and diverse as technology advances and society changes. Robots can offer various benefits for students, such as enhancing learning outcomes, providing personalized feedback, increasing motivation and engagement, and facilitating social interaction. Robots can also help teachers by reducing their workload, improving classroom management, and supporting diverse learners.

However, there are also some challenges and limitations associated with using robots in education, such as ethical issues, technical problems, cost-effectiveness, teacher training, and student acceptance. Therefore, it is important to evaluate the impact and effectiveness of robots in different educational contexts and settings.

The case of Kumamoto is an example of how robots can be used to address a specific educational challenge: school anxiety. By using robots as mediators between home-based students and their schools, the city hopes to create a more inclusive and supportive learning environment for all students.

Relevant articles:

- Japan city to use robots to tackle rising school truancy — Tech Xplore, published on September 6, 2023 by AFP

- Japanese city uses robots to help anxious kids return to school, published on September 7, 2023 by Cebu Daily News

- A Japanese city is using avatar robots in classrooms to tackle student absenteeism, published on September 7, 2023 by TechSpot

Originally published at https://trendydigests.com on September 8, 2023.

By：Trendy Digests
Link：https://medium.com/trendy-digests/how-robots-are-helping-japanese-students-overcome-school-anxiety-ace185200da0

How Robots Are Helping Japanese Students Overcome School Anxiety


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![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*sUtPZlOySuiaubhlOPFWfQ.png)
Image generated by AI

## Going Beyond The Programming
Martyn Rhys Vaughan

**From the beginning some people have predicted that machine intelligence will one day outstrip that of humans. The response of the majority of people has been that the human brain’s complexity is matchless and no assemblage of printed circuits will ever be able to do more than unconvincingly ape human brilliance.**

The best way of testing this would be if the machine intelligences could produce outputs which could not be predicted from the code that they are based on. In the jargon, they would display “emergent” properties. “Emergent” simply means that the outputs from a given system cannot be foreknown from a knowledge of what the components of that system are.

In the early days of AI, it was thought that the best way to produce artificial reasoning abilities would be through the standard model of programming, which in its simplest form consists of a series of commands based on logical functions such as “if…then…”. However, this approach quickly broke down due to the ever increasing number of commands necessary to perform even the simplest tasks. Consequently, most people wrote off AI as an impossible dream.

However, there was another way, and that was to mimic the actual structure of a mammalian brain by allowing processors to link up in an ever increasing number of combinations. As the number of connections increased, so the responses of the “neural net” became more flexible.

The ultimate aim of such neural nets is to produce Artificial General Intelligence (AGI) which is a term meaning that the neural net has attained the resourcefulness of a mammalian brain. The “GPT” in “ChatGPT” stands for “Generative Pretrained Transformer.” The important word here is “Generative”, which signifies that the system can generate its own responses to a given stimulus. The preferred method as the moment is the “Large Language Model” (LLM) where the neural net is fed gigabytes of text and data from the Internet. The system can then train itself to recognise connections between the data items and allow it make deductions from those data sets.

And slowly but surely emergent properties have come to the surface. It is well known that AI can pass the American Bar Exam, with higher scores than the human average. From simple prompts they can compose Petrarchan sonnets. From a text description of a problem, they can write Python code, and point out bugs in programs written by humans. One team even reported that their LLM had suggested that the lead researchers should divorce. A GPT when asked to write a program to calculate Fibonacci numbers initially produced a wrong answer. When this was pointed out, it produced a second version which was correct. It had demonstrated classical learning. It had looked for the error, found it, corrected it.

Kenneth Li of Harvard University and his colleagues have developed a probe to analyse their main network, layer by layer. Li compared this to neuroscience procedures on human brain. They watched it teach itself the simple boardgame “Othello” and were able to map its neural activity to the rules of the game, suggesting that GPT had developed a mental map of Othello. Board games are, of course, old hat to AI but neural nets soon mastered role playing, text-based adventure games. Once again, the system had developed, without human programming, a mental map of the structure of the game, including its fictious geography. Based on evidence such as this, Ben Goertzel of AI Company SingularityNET has stated that AGI will be achieved within the next decade.

Perhaps the most staggering example of machine intelligence was achieved by the firm Deep Mind, a subsidiary of Google’s Alphabet. Using an AI system called Alpha Fold it has predicted the final structure of almost every protein known to science in just 18 months. Proteins are created when amino acids link up and combine into a new compound. However, when they do so, they adopt a unique three-dimensional structure which determines their function. Predicting what this structure will be required computing which arrangement will form from a truly astronomical set of probabilities. So vast was this problem that it was beyond the purely numerical abilities of standard computers. Indeed, so gigantic is the problem that Isaac Asimov used a version of it to stump the giant computer Multivac in his story “The Life And Times Of Multivac.” And yet Alpha Fold had determined the structure of 98.5 % of human proteins by the middle of 2021. A crystallographer at Oxford University who had previously struggled with the problem commented that people like him will soon be unemployed.

People who cling to the idea that human intelligence will always outstrip that of mere machines state that these devices are just lines of code-they have no “mind.” Of course, psychology was once bogged down by beliefs derived from people examining their own minds, in the process of introspection. As long as psychology was bogged down in introspection, it could make no progress. Then people like Pavlov and Skinner developed “Behaviourism” which ignored the concept of mind, and concentrated on what could be consistently observed and measured-behaviour.

And so we are lead to the conclusion that if an artificial structure displays behaviour consistent with the types of behaviour in humans we would ascribe to the possession of a mind-Occam’s Razor requires us to believe that they do indeed possess a mind.

**And once a simple mind is accepted, it is a very small step to a transhuman mind.**

-----------

Previous articles in this series by Martyn Rhys Vaughan (1–6) can also be viewed in a flipbook —[ click here or on the image below.
](https://cambriabooks.aflip.in/e336054791.html?ref=cambriastories.net)

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/0*TK9JCnxLckgCTXA7.jpg)

By：Cambria Stories
Link：https://medium.com/@cambriabooks/ai-and-the-future-7-9356b619c1f4


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#### A robotic "dog" has been used to carry out surveys of two Cold War weapons testing facilities, in a first for the National Trust.

Drones and a mobile robot surveyed the off-limits buildings at Orford Ness, Suffolk, a national nature reserve which was once a military testing site.

The laboratories are closed to the public and staff for safety reasons.

Colin Evison, from the robot's operator BAM Nuttall, said the aim is to capture the buildings' "size and geometry".

![image](https://ichef.bbci.co.uk/news/976/cpsprodpb/0A0B/production/_131017520_untitled-9.jpg.webp)
It was Aimee's first time operating the robot, called Spot, outside the BAM depot, which she does using touch-screen technology
Russell Clement, the National Trust's general manager for Suffolk and Essex Coastal, said accessing the area was "a bit of a headscratcher".

"These buildings are completely inaccessible but really really important. They're going to be lost to the sea eventually," he said.

"We want to know how to record them and here we are we had this amazing, cutting-edge technological solution."

![image](https://ichef.bbci.co.uk/news/976/cpsprodpb/0367/production/_131017800_untitled-11.jpg.webp)
Russell Clement said the buildings were in curated decay so the National Trust monitors and records them

Constructed in 1960, the buildings, known as pagodas, were two of six laboratories used as test cells to carry out environmental tests on the atomic bomb.

The tests were designed to mimic the rigours to which a weapon might be subjected before detonation, including vibration, extremes of temperature, shocks and G forces.

- [Off-grid coastal nature reserve to get solar panels](https://www.bbc.co.uk/news/uk-england-suffolk-63717773)
- [Nature reserve aims to halt bird species decline](https://www.bbc.co.uk/news/uk-england-suffolk-61324019)
- [Could you work on a former weapons testing island?](https://www.bbc.co.uk/news/uk-england-suffolk-60779178)

No nuclear material was involved, but a test failure could have resulted in a catastrophic explosion, so the laboratories were designed and constructed with a shingle top to absorb and dissipate if an explosion occurred.

![image](https://ichef.bbci.co.uk/news/976/cpsprodpb/11793/production/_131017517_untitled-6.jpg.webp)
As the concrete decays, it has become too risky to allow people inside the pagodas

The drones and robotic "dog", nicknamed Spot, were designed by Boston Dynamics and the survey is the first stage of a long-term project, which includes Historic England, civil engineering contractors BAM Nuttall and University College London.

Mr Evison, who is BAM Nuttall's innovations and technical lead, said the charity spotted the company's robotics work in the Shetlands.

"We can survey buildings too hazardous to put people in so we can get a detailed survey without putting people in harm's way," he said.

"We want to capture the size and the geometry of the building, because they are under managed decay at the moment, but we also wanted to create some content so visitors can have a look inside - there's no other means we can do that."

![image](https://ichef.bbci.co.uk/news/976/cpsprodpb/6C36/production/_131020772_5f79e38b-b7ff-4a8b-9bfc-a577dc9b3de4.jpg.webp)
The National Trust acquired Orford Ness from the Ministry of Defence in 1993 and the buildings are scheduled monuments, with the same protection as Stonehenge

The trust said it was the first time it had used this kind of technology.

Mr Clement said it had two aims with the project: "One is recording in really great detail these extraordinary buildings and secondly is allowing people to access them.

"With the data, we'll be able to recrate them virtually and people will be able to experience the interiors of these incredible buildings."

![image](https://ichef.bbci.co.uk/news/976/cpsprodpb/13EA3/production/_131017518_untitled-7.jpg.webp)
The shingle spit was used as a secret military test site from 1913 to the mid-1980s

By：Katy Prickett and Alex Dunlop
Link：https://www.bbc.com/news/uk-england-suffolk-66743327

Orford Ness: Robotic 'dog' surveys ex Cold War site in National Trust first

Robot operator Aimee Cooper, from BAM Nuttall, said it can go up and down stairs and over most terrains, but it does struggle on shingle


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An anthropological, rather than legal, challenge.

![image](https://miro.medium.com/v2/resize:fit:786/0*2mEfeL2aOJ5OaNUr)
Since Donna Haraway’s pioneering ‘Cyborg Manifesto’, advanced technology has been considered a potential source of improving the human being and his relational dynamics with the surrounding environment. From prosthetic grafts to the glasses themselves, to which Luciano Floridi has dedicated admirable considerations regarding the function of objects in man’s perception of technology, technique is not a fact to be mythologized or demonized; it is much more simply a useful aid, within a humanized perimeter, to make human civilization evolve and make existence easier. This also applies, and perhaps even more so, to artificial intelligence, whose evolution is often cloaked in shadowy and dystopian mists. For this reason, the considerations of a Tech magnate like Reid Hoffman are always precious: because American pragmatism, linked to highly innovative dynamics, has recently tended to combine with a spirit of humanistic culture that aims not to technify the human being, and to make its condition in step with the times we are living in.

Hoffman has an impressive resume. Venture capitalist who has made practical sense of innovation, his words are precious for a European country which often pays the price to ossified and sclerotized logics behind oceans of regulations and bureaucracy. There are two aspects in particular underlined by Hoffman on which the European ruling class should question themselves: the amplifying value of high technology and the ability to regulate it without asphyxiating its disruptive impact. From the first perspective, artificial intelligence has the undoubted value of amplifying human intellectual and creative abilities: if used as an advanced assistant, an artificial intelligence system can truly and significantly enhance decision-making, processing and problem-solving capabilities complex.

![image](https://miro.medium.com/v2/resize:fit:828/0*KuGcv_puaonBZBmV)
To date, our public sphere uses rather basic artificial intelligence and, as the saga of administrative justice triggered by algorithmic administrative procedures starting way back in 2017 has taught us, with results that are not always optimal. Europe— says the venture capitalist — must know, understand and know how to use advanced and technologically intelligent devices. There is no need for invention, but rather for the organic acquisition of expertise. From this perspective, to win the challenge, training, high qualification, overcoming exclusively legal logics that tend to want to proceduralize and slow down everything become essential.

The public sphere must not only equip itself with new skills but with a new, algorithmic theoretical reason, freed from the sedimentations of the past. An anthropological challenge, rather than a legal one, one might say. The second point is much more delicate and complex, especially for a European country which seems to betray an almost ontological vocation to normative and regulatory superfetation. Hoffman states something apparently elementary but disruptive for European conceptual latitudes: artificial intelligence must be regulated, without any doubt, but in such a way as not to cancel the innovative drive that these technologies import.

It does not appear accidental that at a supra-national level, the regulation of artificial intelligence is passing through collaboration between different institutional actors, often belonging to civil society, and is also moving through ethical elements, such as Declarations or Charters. The key and indispensable point is to keep technological evolution centered on the human factor, never losing sight of the axiological connotation of technical development: which, in other words, must never be bent to the idea of replacing human with the intelligent inanimate. Complementarity, collaboration and amplification of human capabilities, not replacement.

By：Vertrose
Link：https://vertrose.medium.com/from-artificial-intelligence-to-amplifying-intelligence-e5840930ba54

From Artificial Intelligence To Amplifying Intelligence


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![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*3nQfdTn71J_cOJKJIeSM_w.jpeg)

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*qjRwuiSzqboeci3Wcqzn7Q.jpeg)

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*ScDTCgPCdme2cIK_0HR3Bg.jpeg)
This story is brought to you by the tireless work of students, especially Felipe Fronchetti (shown above), who will be looking for a faculty job this cycle.

-----------

![image](https://miro.medium.com/v2/resize:fit:640/1*MY6VUM12fRhgOV6fl4krkA.gif)
Programming two-armed robots IS parallel programming!

ABB’s YuMi robot, one of my favorite robots in the modern industrial world, has one blaring difference from most other robots. It has two arms! To the casual observer, this may seem unimportant, but to those in automation, this is a major step forward. All of those tasks that were so difficult to do with one (robot) hand, such as snapping parts together, are now possible. Possible, that is, to those that understand parallel programming. But we wanted ANYONE to be able to program YuMi. We wanted to make an easy programming technique… but what does parallel programming look like in novice programming languages?

![image](https://miro.medium.com/v2/resize:fit:2000/format:webp/1*EOvjBDzRWkuLcaQozgK8Ig.png)
MIT’s Scratch does not lend itself to parallel programming, so how can novices do it?

Well, it looks like this in Scratch (see above). Not pretty. When the program starts (upper left), it executes until it hits the “broadcast” command. Thus begins coordination between tasks, as the block that is waiting for “ready” (upper right) will execute. This notation tends to confuse novices… How do I know? I’ve taught many, both kids and adults to program, and this form of broadcast-based parallel programming has frustrated every one of them.

![image](https://miro.medium.com/v2/resize:fit:2000/format:webp/1*0dFhdm4SHxWcQdk3ejLvPA.png)
Industrial software struggles to enable parallel programming; RSOY uses a synchronization command (MoveSync s1)

It must be better / different in professional tools, right? Well, not really. It turns out if there isn’t some obvious research to build upon, time-pressed industrial developers are forced to do the best they can with current approaches. Above we see RSOY, the current easy-to-program solution for YuMi. As you can see, it coordinates between the left arm (left column) and the right arm (right column) using a synchronization command (MoveSync) and a synchronization variable (s1). This approach can quickly become confusing, as synchronization commands do NOT have to be on matching line numbers.

![image](https://miro.medium.com/v2/resize:fit:640/1*CAjaMBc0JyYHvTPJkgippg.gif)
What types of coordination does this task require?

Let’s take a step back… what kinds of coordination do we need to work with two arms. Well, we need to be able to move the two arms together, like when picking up the middle piece (above), and we need to be able to wait for the other arm to finish, like when placing the first two pieces on the rod (above). With these two synchronization techniques, even though they seem limited, we can implement a wide variety of important tasks.

![image](https://miro.medium.com/v2/resize:fit:2000/format:webp/1*02vTw1oaDlzarOqWqNuuYA.png)
In our prototype, synchronized blocks are aligned across both canvases.

In this mindset, we created Duplo, a block-based programming environment that implemented these two coordination mechanisms. On line 3, above, you can see that the right arm is following the left arm, as these two commands are linked. On line 6 you can see the two programs waiting for each other before proceeding, with the linked “Wait for each other” command.

![image](https://miro.medium.com/v2/resize:fit:640/1*1OeAy6-Gsye-St-Vd4tmUw.gif)
In our prototype, synchronized blocks move together across both canvases.

To reinforce the connection between these connected commands across the two different canvases (right and left), they move together, as shown above. Moving a wait command onto the left canvas also moves a matching wait command onto the right canvas, directly across. This spatial representation seems natural to most novice programmers; if commands move together and they are directly across from one another, they must execute together!

-----------

## Trying it out in the real world, with a real robot…

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*kbequymxxda5VyZ-tWO9HA.jpeg)
We ran an experiment with a real robot comparing our prototype against an industrial programming environment

To test out our approach, we ran an experiment, comparing our block-based prototype to the current industrial approach (RSOY). An overview of the experiment is above.

**We are especially proud that we got over 50 participants to complete an experiment that took more than two hours to complete. Not easy!**

The tasks we asked users to complete were relatively simple pick and place tasks, with the twist that the solution required parallel programming. In the picture below, you see the final solution, after the program has been run and the robot has moved the parts. The first task is to pick up the blue part, which starts on the tray on the right, and put it on the rod. The second task is to do the same for the red part, which starts on the tray on the left. The final task is to pick up the green part, which starts on the tray in the front. This part requires two hands to pick up.

![image](https://miro.medium.com/v2/resize:fit:3188/format:webp/1*h-dkgiTUUNkNfO4vI4FAUw.png)

Below is a gif that shows the solution being executed, which was also shown in another gif above. As you can see, all of the parts are picked up and placed on the rod in the middle. The first two picks require the two different robot arms to work independently, but their moves must be coordinated, or they will run into each other. The third pick requires both arms to work together, as the part is too heavy for a single arm to lift without slipping. This task was relatively simple, but involved many of the coordination issues that users experience in robot programming.

![image](https://miro.medium.com/v2/resize:fit:640/1*FptGGCqeJM2_2Ozeh54Qrg.gif)
Yumi programmed with Duplo

-----------

## Results
Well, after asking 26 people to complete the tasks using our prototype block-based approach and another 26 using the industrial RSOY software, we began to see clear differences in their outcomes.

For instance, here are the results for participants using the block-based approach. As you can see, all participants were able to successfully complete the first three tasks, and all but four completed all tasks within the time limit.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*6Z8JH_QHuUQibYyYWcPbFg.jpeg)
Users of our prototype were pretty successful.

When looking at the same data for those that used the industrial RSOY software, we can see that they struggled more. Three participants could not complete even the first task and 14 failed to finish all of the tasks.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*6z3yupTu6aR-9i9blEHI3w.jpeg)
Users of the industrial software struggled.

We looked at the time that these users took to program their solutions, and plotted them below. As you can see, many block-based programmers (blue) completed the tasks well before the time limit, while many industrial RSOY programmers took the maximum time.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*r9_lQpSS_rjM4xe-C_sTog.jpeg)
Completion times for our prototype (blue) and the industrial solution (yellow); dots signify participants who used the maximum time allowed

In the formal academic paper we include lots of qualitative data, some great quotes about the programmers’ experience with each prototype (see below), and we generally yammer on like academics are wont to do.

> “It was very easy to use the blocks to ask the robot to make actions, link the blocks together, and break them apart. It was also easy to move the robot’s arms.” — Participant 3

> “At first, I didn’t know which arm was the right or left. Also, I didn’t know how exactly to reteach a position, but after a while, I got it.” — Participant 5

If you’re interested in the gory details, read the paper! If you’re not, here’s the crib notes…

> By representing robot commands as puzzle pieces, block-based programming contributed to the ability of end-users to insert, edit and debug code. The freedom to reorganize blocks using the puzzle metaphor, and the alignment of instructions on vertical columns, also allowed Duplo participants to complete more tasks in less time.

![image](https://miro.medium.com/v2/resize:fit:2000/format:webp/1*i9TCrAb-7sxGpsFHaRnF0A.jpeg)
ABB’s Wizard Easy Programming is block-based programming for one-armed robots. Will we see Duplo for YuMi next?
I wonder if, building on the success of its Wizard Easy Programming, which brought block-based programming to one-armed industrial robots, ABB will soon implement Duplo for the two-armed YuMi?

Hey ABB, if you need some help, I’m just an email away!

By：David C. Shepherd
Link：https://medium.com/@dshepherd_4210/can-novices-program-two-armed-robots-edd3bf7a5b93