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Let’s have a small talk about visualizing images with interactive graphical representation!

Scene graph generation is the process of generating scene graphs and a scene graph contains the visual understanding of an image in the form of a graph. It has nodes and edges representing the objects and their relationships, respectively. Contextual information about the scenes can help in semantic scene understanding. Although there are certain challenges such as uncertainty of real-world scenarios or unavailability of a standard dataset, researchers are trying to apply the scene graphs in the field of robotics. This write-up contains two major parts: one is about how scene graphs can be obtained by having an in-depth discussion about two state-of-the-art approaches for scene graph generation based on region-based convolutional neural networks, and the other part deals with the application of scene graphs in robot planning. In its former part, this article further explains that only having a numerical metric for model evaluation is insufficient for proper performance analysis. The latter part includes a thorough explanation of a method that uses the concept of scene graphs for visual context-aware robot planning.

![image](https://miro.medium.com/v2/resize:fit:2000/1*JO4sBahgRRXxtulQALChGQ.gif)
Figure 1a: Scene Graph Generation Example Introduction

## 1. Introduction
When considering the description of image elements in words, well-known active techniques such as image segmentation, object classification, activity identification, etc. play a significant role in the process. Scene Graph Generation (SGG) is a method to generate scene graphs (SGs), by depicting objects (e.g., humans, animals, etc.) and their attributes (e.g., color, clothes, vehicles, etc.) as nodes, and the relationships among objects as the edges. Fig. 1a and 1b shows how a scene graph generated from an RGB image usually looks like. It can be seen that the distinct colored nodes depict different major objects in the image (a male, a female, and a vehicle), the information about these objects is shown by the nodes having the variant of the same node color, and the edges are labeled with verbs showing a connection, or we can say relation among the nodes. The research has been going on for past years to make the SGs possible even for small details in the image.

![image](https://miro.medium.com/v2/resize:fit:2000/1*OS-kBG6q3LJbKGTWw4Zyjg.gif)
Figure 1b: Process and Major Challenges in Scene Graph Generation

This article extensively discusses two approaches in the upcoming sections for a better understanding of generating scene graphs. The write-up structure comprises six sections. The brief start of SGG and its prior applications including image and video captioning is explained in section 2. Section 3 describes the state-of-the-art methods with their model description. The experimental analysis of these methods is discussed and compared in section 4. Section 5 discusses some possible applications of SGs in the field of robotics with a main focus on robot planning, and also how having accurate SGs will make the applications like telepresence robots, human-robot collaboration, etc. much easier to implement. Finally, section 6 concludes the entire literature.

## 2. Related Work
The SGs have a history of distinct applications, for instance, image and video captioning, visual question answering, 3D scene understanding, many applications in robotics, etc. [10] [3]. Most of the recent approaches include machine learning methods like Region-based Convolutional Neural Networks (R-CNN) [8], faster R-CNN [9], Recurrent Neural Networks (RNN) [7], etc. [7] proposed a structure that takes the image as input, then passes messages containing contextual information, and refines the prediction using RNN. Another method [6] uses the energy model of the image to generate comprehensive scene graphs. The datasets for SGG are also obtained using different real-world scenarios, images, and videos. The dataset mentioned here is a subset [7] of Visual Genome [4] containing 108,073 human-annotated images.

-----------

## 3. State-of-the-Art Approaches
3.1 Graph R-CNN Approach
J. Yang et. al. [8] proposes a model that first considers all the objects connected by an edge, relationship, then nullifies the unlikely relationship using a parameter called relatedness. For instance, it is more likely to have a relation between car and wheel, than a building and a wheel.

The paper shows a novel framework, namely Graph R-CNN with the introduction of a new evaluation metric, SGGen+. The model consists of three blocks, first to extract object nodes, second to remove unlikely edges, and finally to propagate graph context throughout the remaining graph so that the final scene graph can be produced. The novelty resides in the second and third block of the model, having a new relation proposal network (RePN) which computes relatedness that further helps in removing unlikely relationships, also called relation edge pruning and an attentional graph convolutional network (aGCN) that constitutes the propagation of higher-order context that leads to an update in scene graph giving it a final touch, respectively. Basically, the final block labeled the generated scene graph. The probability distribution of the whole idea can be depicted by the equation below, where for image I, O is the set of objects, R is the relation among objects, and Rl and Ol are relation and object labels respectively.

Pr(G|I) = Pr(O|I) * Pr(R|O, I) * Pr(Rl, Ol|O, R, I)

Fig. 2 shows the entire algorithm in pictorial form, and how all three stages work consecutively. The pipeline can be described as: using faster R-CNN for object detection, then generating an initial graph with all defined relationships, then pruning unlikely edges based on relatedness score, and then finally using aGCN for refining.

![image](https://miro.medium.com/v2/resize:fit:2000/1*vG3mHIBzi8BW5WXzqOqiUw.gif)
Figure 2: Graph R-CNN Structure

### 3.2 Stacked Motif Network (MotifNet)
R. Zellers et. al. [9] proposes a network that performs bounding box collection, object identification, and relation identification consecutively. The method shows the intense use of faster R-CNN and Long Short-term Memory (LSTM) networks. In contrast with Graph R-CNN, this method is based on detecting the relationships among objects and adding the relationship edges.

The idea here can be seen as a conditional probability equation, as shown in the equation below, which states the probability to find graph G of image I is given as the product of the probability of finding bounding box array B given I, probability of obtaining object O given B and I, and the probability of getting relations R among O given B, O and I.

Pr(G|I) = Pr(B|I) * Pr(O|B,I) * Pr(R|B,O,I)

Firstly, to find the set of the bounding box, they also use faster R-CNN, as in [8], shown on the left side of Fig. 3. It uses the VGG backbone structure for object identification while determining bounding boxes in I. Then, the model proceeds further with bidirectional LSTM layers depicted by the object context layer in Fig. 3. This block of layers generates the object labels for each bounding box region, bi ∈ B. Another bidirectional LSTM layers block is again used for determining relations as edge context, portrayed in the same figure. The output is computed as determining the edge labels which clearly depict the outer product with objects and relations among them.

![image](https://miro.medium.com/v2/resize:fit:2000/1*P_YGTUhIxm-XBEEno1C0IQ.gif)
Figure 3: MotifNet Structure

## 4. Experiments in State-of-the-Art Approaches
### 4.1 Experimental Setup
The setup for both methods is described below:

Graph R-CNN: The use of faster R-CNN with VGG16 backbone to ensure object detection is implemented via PyTorch. For the RePN implementation, a multi-layer perceptron structure is used to analyze the relatedness score using two projection functions, each for subject and object relation. Two aGCN layers are used, one for the feature level, the result of which is sent to the other one at the semantic level. The training is done in two stages, first only the object detector is trained, and then the whole model is trained jointly.

MotifNet: The images that are fed into the bounding box detector are made to be of size 592x592, by using the zero-padding method. All the LSTM layers undergo highway connections. Two and four alternating highway LSTM layers are used for object and edge context respectively. The ordering of the bounding box regions can be done in several ways using central x-coordinate, maximum non-background prediction, size of the bounding box, or just random shuffling.

The main challenge is to analyze the model with a common dataset framework, as different approaches use different data preprocessing, split, and evaluation. However, the discussed approaches, Graph R-CNN and MotifNet uses the publicly available data processing scheme and split from [7]. There are 150 object classes and 50 classes for relations in this Visual Genome dataset [4].

Visual Genome Dataset [4] in a nutshell:

Human Annotated Images

More than 100,000 images

150 Object Classes

50 Relation Classes

Each image has around 11.5 objects and 6.2 relationships in scene graph

### 4.2 Experimental Results
![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*0XD0W_fgaCiLfYFquNDTiQ.png)
Table 1: Performance Comparison
Quantitative Comparison: Both methods evaluated their model using the recall metric. Table 1 shows the comparison of both methods via different quantitative indicators. (1) Predicate Classification (PredCls) denotes the performance to recognize the relation between objects, (2) Phrase Classification (PhrCls) or scene graph classification in [9] depicts the ability to observe the categories of both objects and relations, (3) Scene Graph Generation (SGGen) or scene graph detection in [9] represents the performance to combine the objects with detected relations among them. In [8], they enhance the latter metric with a comprehensive SGGen (SGGen+) that includes the possibility of having a certain scenario like detecting a man as boy, technically it is a failed detection, but qualitatively if all the relations to this object is detected successfully then it should be considered as a successful result, hence increasing the SGGen metric value.

According to table 1, MotifNet [9] performs comparatively better when analyzing objects, edges, and relation labels separately. However, the generation of the entire graph of a given image is more accurate using the second approach, Graph R-CNN [8]. It also shows that having the comprehensive output metric shows a better analysis of the scene graph model.

Qualitative Comparison: In neural motifs structure [9], they consider the qualitative results separately. For instance, the detection of relation edge wearing as wears falls under the category of failed detection. It shows that the model [9] performs better than what the output metric number shows. On the other hand, [8] includes this understanding of result in their comprehensive SGGen (SGGen+) metric which already takes possible not-so-failed detections into consideration.

-----------

## 5. Applications in Robotics
This article will overview the understanding of scenes that helps in robotic applications, especially robot planning. The wide area of robotics including automatic indoor mapping, teleoperation robots, human-robot control for purposes like telemedicine, and many more results in a very deep area of SGs applications.

When both humans and robots collaborated in a similar workspace to complete a specified task, then it is called Human-Robot Collaboration (HRC), and the collaborative robots are called cobots. Having semantic information along with object detection makes the tasks even easier to do. [5] shows a method that uses SGG for safety analysis in HRC. Another application involves teleoperation robots that include social interaction skills like a meeting chat room on a mobile stick controlled by a human from a remote location. The applications include elderly care, attending events even if a person is disabled, etc. [2] discusses a method where scene understanding helps the user to analyze and control remote environment clearly.

### 5.1 Robot Planning
Assigning certain motion tasks to a robot, such as moving one object from one place to another, and expecting smooth service from a machine requires a highly accurate level of planning and development. SGG plays an important role in producing service robots as it gives the robot an in-depth abstracted picture of the scene which further helps it to locate objects precisely. [1] uses local scene graphs to perceive the global view to reach its target. It describes robot planning for a common task that includes searching for an object, such as a fruit, in an indoor environment.

Scene analysis for robot planning (SARP) [1] is an algorithm designed for robots to use visual contextual information to complete the planned task. This method takes advantage of scene graphs to model the global scene understanding using MotifNet [9], the algorithm discussed in section 3.2. The block diagram of the model is shown in Fig. 4. The model uses MotifNet to generate local scene graphs, then the contextual information to analyze uncertainty between observations and actions, and update the robot’s belief. The latter process is done using the Partially Observable Markov Decision Process (PO-MDP) framework. The MDPs are known for sequential decision-making. In the block diagram, the offline trained scene graph network generates the global scene graphs and feeds the result into the Markov process.

![image](https://miro.medium.com/v2/resize:fit:2000/1*EEQOu3OsvC5wq6JiU-FH9Q.gif)
Figure 4: Scene Analysis for Robot Planning — Block Diagram

The method is experimented for locating objects scattered through the area with precision and in minimal time. The robot is fed with a scene graph dataset, an already trained scene graph network, and a domain map for directive mobility. As a result, the robot navigates through the area for target search, and to do so, it creates scene graphs for contextual information every step of the way, as clearly shown in fig. 5. It is the demonstration where the robot is assigned the task of locating a banana. It increments the global scene graph at every time instant except at T=4 because there are no new objects’ instances in that frame. It can be seen that having local scene graphs helped the robot to determine the global context for the target search. According to the performance comparison in [1], SARP outperforms the baseline methods both in terms of success rate and action cost. However, the model can be further expanded with facial recognition of humans, and also analyzed when changing or adding objects during the test process.

![image](https://miro.medium.com/v2/resize:fit:2000/1*Rk5Mghos2SsiQpBpgPg7Qg.gif)
Figure 5: Scene graphs generated by robot during navigation for target search

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## 6. Conclusion
The article discusses the different methods for SGG, the generation of graphs having semantic information about scenes, and some of its robotic applications. The object and relation contextual information gives us a very descriptive understanding of a scene. MotifNet detects and adds the relation among objects as edge context using the LSTM network, while Graph R-CNN forms the scene graphs by eliminating the unlikely relation edges using relatedness score. There are several versions of the Visual Genome dataset used in recent approaches, however, MotifNet and Graph R-CNN evaluate their model on the same dataset model, which makes their quantitative comparison reasonable. Overall, both the quantitative and qualitative measurements are analyzed and it can be seen that numbers don’t tell the whole story, the not-so-failed scenarios, such as man instead of boy, should also be considered as successful outcome, if all of its relation edges are detected correctly, while evaluating a method for SGG. The applications in robotics is growing rapidly over the past decades and having an in-depth semantic description of the image, or scene will help in several image and video-related applications. One method is extensively discussed for the robot planning application that uses scene graphs for better visual scene understanding so that the robot can perform tasks (e.g., locating objects in indoor environment) in the long run.

Well, this is so much for a small research talk about converting images into interactive graphical texts. I hope you guys had fun (maybe a little!). I’ll be back with another research article summing up recent cool researches (probably on any topic from communication networking, Network Simulator (NS3), or maybe Wi-Fi 7😉).

Have fun researching!!

Regards,

Ritanshi

-----------

## REFERENCES
S. Amiri, K. Chandan, and S. Zhang. Reasoning with scene graphs for robot planning under partial observability. IEEE Robotics and Automation Letters, 7(2):5560–5567, 2022.
F. Amodeo, F. Caballero, N. Díaz-Rodríguez, and L. Merino. Og-sgg: Ontology-guided scene graph generation. a case study in transfer learning for telepresence robotics. IEEE Access, pages 1–1, 2022.
X. Chang, P. Ren, P. Xu, Z. Li, X. Chen, and A. Hauptmann. A comprehensive survey of scene graphs: Generation and application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1):1–26, 2023.
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International journal of computer vision, 123(1):32–73, 2017.
H. Riaz, A. Terra, K. Raizer, R. Inam, and A. Hata. Scene understanding for safety analysis in human-robot collaborative operations. In 2020 6th International Conference on Control, Automation and Robotics (ICCAR), pages 722–731, 2020.
M. Suhail, A. Mittal, B. Siddiquie, C. Broaddus, J. Eledath, G. Medioni, and L. Sigal. Energy-based learning for scene graph generation. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13931–13940, 2021.
D. Xu, Y. Zhu, C. B. Choy, and L. Fei-Fei. Scene graph generation by iterative message passing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5410–5419, 2017.
J. Yang, J. Lu, S. Lee, D. Batra, and D. Parikh. Graph r-cnn for scene graph generation. In Proceedings of the European conference on computer vision (ECCV), pages 670–685, 2018.
R. Zellers, M. Yatskar, S. Thomson, and Y. Choi. Neural motifs: Scene graph parsing with global context. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5831–5840, 2018.
G. Zhu, L. Zhang, Y. Jiang, Y. Dang, H. Hou, P. Shen, M. Feng, X. Zhao, Q. Miao, S. A. A. Shah, et al. Scene graph generation: A comprehensive survey. arXiv preprint arXiv:2201.00443, 2022.

By：Ritanshi Agarwal
Link：https://medium.com/@ritanshiagarwal/scene-graph-generation-and-its-application-in-robotics-f9ba864aa572

Scene Graph Generation and its Application in Robotics


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![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*K_3H4ynLDuzALsIi0VG6JQ.jpeg)
Photo by Steve Johnson on Unsplash

I recall several years ago writing a couple of articles on LinkedIn about how most entrepreneurs operate like robots and can't’ seem to come out of their defaults of patterns.

I remember getting messages from new connections and they would all have the same canned introduction that would make me wonder if they even read my profile. This would also happen when people were trying to get you interested in their products and services or tell you about what they do. Nothing organic or truly authentic. It just all seemed robotic like some having to quote something at gunpoint or a script that just won’t flip.

One of my articles was called Another Day Amongst the Robots. I swear it was like living the same day over and over again with entrepreneurs as if they had no other programming or defaults but to share who they are and what they do or how they reach out to people in the same ole tired format.

I kept thinking to myself, these entrepreneurs are going to be in some serious trouble if some sudden disruption occurs that takes them out of their frame.

Because this type of activity has gained so much momentum in their life and business, it’s hard to just shift from being a robot in human format to actually being a human being again.

Unfortunately, this is over 80% of our world. Globally and collectively, people have been creating the portal for AI to come in and take over because they became so robotic themselves in their life and careers.

Our education systems, lifestyle and job structures and not to mention the digital domination of how we do mostly everything has played a huge part in this conditioning; therefore, people have become more and more tech and machine reliant even though there have been other options to not become so consumed with being creatures of habit/default.

Again, most have chosen to be robots instead of operating out of their true human potential of divine innate intelligence, now they’ve paved the way for artificial intelligence to be their umpire and new life governing system. This brings a whole new meaning to “I ROBOT”.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*esLOKbFFi7IR7IonFlnlUw.jpeg)
Photo by Possessed Photography on Unsplash

Though most are excited about AI and figure, if we can’t beat them, we might as well join them and leverage this new wave for maximum benefits. However, most will find that in the upcoming months and years, they will be paying the price for surrendering their greater human potential and evolutionary expansion so many years ago.

Indeed, there are benefits to using AI, but its usage should never be to substitute for what only the human experience is designed to bring forth. But this is what we’ve come to in this era so this will be interesting in how things playout over the next few years.

So, there’s no need to complain about this AI takeover over how many jobs will be replaced by artificial intelligence because people wanted this experience whether they knew it or not. They wanted/chose to operate as robots; therefore, a new coexisting alliance begins.

I’m not here to knock AI, I’m just glad to understand how we’ve arrived at this point. AI can certainly be a wonderful component to your experience as long as it doesn’t become your entire experience. I talk more about this in my New Divine Development for New Human Potential works.

So, now when most people have to answer this question in the image below, they might have to rethink their answer. LOL!

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*i3e51IXGdBl427tdQCRaXQ.jpeg)
I hope this article was informative and thought provoking. I would love to hear your thoughts and also how/if you are using AI to your greater advantage.

I invite you to follow me here on Medium, on Gumroad at MAJESTY AliNICOLE WOW! and join me on other platforms in my multimedia multiverse at https://linktr.ee/majestyalinicolewow.

By：MAJESTY AliNICOLE WOW!
Link：https://medium.com/@wowshedoesitallmedia/one-of-the-main-reason-ai-exists-taking-over-is-because-people-became-robotic-themselves-in-life-82dabe92a8f5

One of the Main Reason AI Exists & Taking Over Is Because People Became Robotic Themselves in Life & Careers


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*“Centuries-old relationships are changing. Are you?” — Futurist Jim Carroll*

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/0*3FJXUtOOqOI8Lc29.jpg)

I’m often asked about the impact of technologies like AI and robotics on careers going forward. You can best get a sense of my response and mindset by watching this video from an event in the UK:

https://youtu.be/yvFTmRHlK34

I often say you’ll define your future by your ability to redefine your reality. Here’s the transcript of the Q&A, where I was talking about the potential for robotic dentistry driven by AI.

It’s interesting you talk about, you know, robotic denstry. How is that going to square out with the emotional aspect of the dental patient relationship?

You know, I I would put that in the context of the visit with my with my family doctor. My my family doctor relationship changed 25 years ago. I was in the home office one morning and I’m feeling my mouth little funny as I’m doing a radio interview and it got worse and worse and, “My God, I’m having a stroke. I’m only 30 something,”

And I went to a walk in clinic and they said you have Bell’s palsy, and they said you need to go see your family physician. So I called him, got an appointment six hours later. What do you think? It’s 1996. What do you think I did the next six hours? Early morning, Doctor Google on Bell’s palsy.

So by the time I went in to see him, he said “you’ve got Bell’s palsy.” I said “I know.” He said “I’m going to put you on Prednisone steroid treatment.” And I said, “Well, I don’t know, you know, a study out of the University of Southern California, 297 out of 300 patients, you know, it was found that it was not a……”

What has happened here, good or bad: Our knowledge empowerment or disempowerment as it were, has changed the nature of our relationship between doctor and people. And when I go in with my doctor on Friday, I mean I’ve got more statistics on glucose and blood pressure and heart rate and aerobics and everything on here. It’s changed the nature of the relationship. I don’t think that relationship will go away. The relationship has evolved.

And I think as we get into more of a virtual world, more of a robotic world, more of a world driven by crazy science fiction like trends, I don’t think the interpersonal relationship disappears. I think it evolves, it changes, it transforms.

The impact of AI, robotics, and technology? It’s not just that your future changes — it’s that the essence of your relationships changes forever! Relationships don’t get replaced, but they fundamentally change.

We already know that mass information and misinformation at scale have forever changed doctor-patient relationships, both good and bad. The same thing is happening with every other industry and profession, from insurance brokers to architects; auto dealers to customer service representatives. People empower themselves with information — and whether it is real or not, they use that in their relationships with those they interact with throughout their lives. It makes things far more challenging; shifts the balance of power; and in essence, changes the relationship forever.

Virtual technologies involving robotics and the impact of AI are only going to accelerate this trend; it’s almost as if intelligent third parties are entering into the relationship, with an AI and virtual assistant now becoming a part of your overall doctor-patient or doctor-dentist relationship. That was the first version of today’s quote, which I abandoned because I just thought it was too difficult to try to explain myself!

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/0*VQwDR8mg9xM0xp1c.jpg)
Going from a one-to-one relationship with your doctor to a one-to-one-with-many (the ‘many’ being AI), is a really small BIG deal.

The art of change is working with it, accepting it, molding it, and shaping it. I wrote this issue into trend #3 of my 23 Trends for 2023 series: Inevitable Inevitability! It’s worth a. read.

-----------

This talk took a look at the key disruptive trends sweeping the world of property and casualty, as well as life insurance. The theme tied into the same type of theme for the Purina dealers — when your business model is disrupted and some consumers are choosing to go ahead without you, you only survive by focusing on your role and the value of the unique services you provide to ensure that others don’t.

For as long as there has been business model disruption, there has been the idea of ‘disintermediation.’ We wrote that into our book 22 years ago:

Businesses Will Compete With Their Channel Partners

With online retail sales in North America expected to top $75 billion by the year 2003, no one wants to be left behind. And no group is more eager to cash in on the growth in online shopping than manufacturers, who have traditionally had to rely on retail partners to sell their products to consumers.

Lured by the ease of online selling and the prospect of higher profit margins, many manufacturers are bypassing their retail channel partners and are choosing to sell directly to consumers over the Web.

….

Such efforts often cause rather tumultuous relationships within an industry, as established dealers struggle to deal with the situation. A good example of an industry that faces the biggest challenge in this regard is the auto-industry. Ford, GM, Chrysler and every other manufacturer knows that they can easily sell cars on their own Web sites. They are aware that there are probably sufficient numbers of consumers to make such an effort very worthwhile.

Yet they also know that undertaking this type of activity is going to cause some rather extreme disruption with the auto dealers who represent them. Hence, they are moving carefully to assess how to best go about doing it.

It isn’t an easy issue to deal with, given that a business can damage itself if it moves too quickly to compete against its dealers. Take the insurance industry: there are many insurance agents and brokers who represent a variety of insurance companies. They are in a situation in which they can pick and choose which companies they will represent. CEOs of insurance companies are rightfully worried that if they start selling insurance through the Internet, they will upset a number of their representative agents, who might then choose to place their policies elsewhere. A company can decide to start selling online, and watch its traditional business evaporate almost overnight.

Hence, suppliers of product in every industry are faced with a conundrum : they must get involved with e-commerce in order to deal with empowered consumers. Yet, they must do so in a way that doesn’t cause upset and upheaval within their current distribution base.

Everywhere you turn, you can see situations in which suppliers or manufacturers of products are wrestling with the issue. Some have bit the bullet and have clearly chosen to compete directly with their channel partners

Twenty-two years in, it’s pretty clear that the automotive industry is still struggling with this issue. And yet, it seems pretty clear that the CEO of Ford, Jim Farley, recognizes that the time is finally ripe for this type of change.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/0*5L0qoSMUlTP7e074.jpg)

Ford is doing several things: requiring auto dealers to invest in adequate electric charging infrastructure, committing to significant training in this new automotive and truck platform, and going forward with a no-haggling set price for electric vehicles. Not surprisingly, there is tremendous pushback by various automotive dealer groups, and political pressure by various state legislative bodies brought about as a result of pressure by those groups.

And yet, certain things are certain, and it seems pretty clear that he (and other automotive manufacturers) recognize the inevitability of where the automotive industry is headed.

-----------

Centuries-old relationships are changing.

Relationships matter.

So too does your ability to accept how they are changing.

Futurist Jim Carroll wrote the story of his 1996 visit with his doctor into a book a year later. Good Health Online, now long out of print, defined most of the medical and healthcare industry changes that would occur through the next 25 years, including the virtualization of healthcare that happened through the global pandemic.

By：Futurist Jim Carroll: Trends, Innovation, Nice Guy
Link：https://medium.com/futurist-jim-carroll-daily-inspiration/one-to-one-with-many-e7dc3f893361


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So I had a chat with a friend just recently, and we discussed at length about the possibility of Artificial Intelligence making its way into our world on a FULL scale within a very short period of time.

And one thing I was able to extract from the whole discussion is the fact that what we see as “Normal” right now will gradually cease to exist.

AI is amongst us already, and like it or not, tons of jobs will be replaced by this entity.

So rather than feeding off a glory that’ll gradually wane with time, we should instead prepare and equip ourselves accordingly so we will be in a better position to cooperate, integrate and work with the new development.

We should be building companies of the future.

There’s no point shying away from the inevitable.

AI will replace many. It’s happening on a subtle scale for now.

The Great Reset is upon us.

So let’s prepare accordingly.

By：Obaloluwa Ola-Joseph Isaiah
Link：https://medium.com/@kinglyjoe/the-robots-are-coming-1ac5947e33d6


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In the busy streets of New York City, the police department has introduced an exciting and groundbreaking new member to their team the Robot Force. These remarkable robots are designed to assist our human officers in making our city safer and more secure.

## What Are These Robots?
The Robot Force is a group of special machines created to support the NYPD. They look like big, sturdy computers on wheels. Imagine a friendly, helpful robot you might see in a sci-fi movie, but right here in New York City

The Robot Force is a team of special machines made to help the police in New York City. They look like big computers with wheels, like friendly robots from a movie, but real

## What Can They Do?

![image](https://miro.medium.com/v2/resize:fit:1100/0*Eu3D9GJ2NSOZLCli)
Photo by Rock'n Roll Monkey on Unsplash

These robots have a lot of cool skills. They can monitor public places, like parks and subways, to help the police spot trouble. They have cameras that can record what’s happening and send it back to the police officers in real time. This helps the police track what’s happening and respond faster when needed.

These robots have many useful abilities. They can watch over parks and subways to help the police see if anything is wrong. They have cameras that can record what’s happening and share it with the police immediately. This makes it easier for the police to know what’s happening and react quickly.

## How Do They Help?
One of the Robot Force’s main jobs is to keep our city safe. They can help out in situations that might be too dangerous for humans. For example, if there’s a suspicious package, a Robot Force member can check it out without putting a human officer at risk. This keeps everyone safer.

The Robot Force’s main job is to make our city safe. They can assist in situations that could be unsafe for people. For instance, if a strange package is dangerous, a robot can check it without risking a human officer’s safety. This keeps everyone out of harm’s way.

## Communication is Key
These robots are smart talkers they can talk to people and answer questions. If someone needs directions or has an emergency, the Robot Force can step in and provide help. They’re like a friendly guide, ready to assist anyone in need.

These robots are good at talking! They can chat with people and give answers. If someone needs help finding their way or has an urgent problem, the Robot Force can assist. Think of them as friendly helpers, always ready to lend a hand.

## Always on Duty

![image](https://miro.medium.com/v2/resize:fit:1100/0*nMUcR3_25_KY2AE8)
Photo by Adlemi Mar on Unsplash

Unlike humans, these robots don’t get tired. They work around the clock, 24/7, ensuring our city is secure day and night. This means that even when the rest of us are sleeping, the Robot Force is watching things. These robots never get tired. They work all day and night, every day of the week. They make sure our city stays safe even when we’re asleep. So, they’re like guardians who are always awake.

## Friendly Faces
The Robot Force isn’t here to take over the job of our human police officers. They are here to lend a hand and make their jobs easier. Think of them as trusty sidekicks for our brave officers. The Robot Force isn’t trying to replace our human police officers. They’re here to help and make their work easier. Imagine them as helpful partners for our brave police officers.

If you would like to read more interesting and insightful stories, why not join subscribe and read my latest content

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## Sources
[Are Police Robots the Future of Law Enforcement?
](https://builtin.com/robotics/police-robot-law-enforcement?source=post_page-----9671e90376a6--------------------------------)

[NYPD now has a robot police officer for its Manhattan subway
](https://interestingengineering.com/innovation/nypd-now-has-a-robot-police-officer-for-its-manhattan-subway?source=post_page-----9671e90376a6--------------------------------)

[New York City welcomes a robot to its police force, but we're a long way from the Terminator
](https://www.gearrice.com/update/new-york-city-welcomes-a-robot-to-its-police-force-but-were-a-long-way-from-the-terminator/?source=post_page-----9671e90376a6--------------------------------)

By：RoboTech
Link：https://generativeai.pub/nypd-introduces-revolutionary-new-member-the-robot-force-9671e90376a6

NYPD Introduces Revolutionary New Member: The Robot Force


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In the not-so-distant future, we might find ourselves working alongside robots that bear an uncanny resemblance to humans. These humanoid robots are no longer just the stuff of science fiction but a rapidly evolving reality. As technology advances at an unprecedented pace, humanoid robots are poised to redefine automation, transforming industries, and enhancing our daily lives. In this blog post, we’ll delve into the world of humanoid robots, exploring how they work, their diverse use cases, the industries at the forefront of their adoption, their current capabilities, and what the future holds for these remarkable machines in the next 1–2 years.

## How Do Humanoid Robots Work?
Humanoid robots are complex machines designed to mimic human movements and behaviors. While the specifics of their design and functionality can vary, they share some fundamental principles in their operation:

1. Sensors: Humanoid robots are equipped with an array of sensors, including cameras, microphones, touch sensors, and more. These sensors enable them to perceive and interpret their environment.
2. Processing Unit: The “brain” of a humanoid robot typically consists of a powerful computer equipped with specialized software for tasks such as image recognition, speech processing, and decision-making.
3. Actuators and Joints: To mimic human motion, humanoid robots have joints and actuators that allow them to move their limbs and body. These components are often a combination of electric motors and pneumatic systems.
4. Learning Algorithms: Many humanoid robots incorporate machine learning algorithms to improve their ability to perform tasks and adapt to new situations over time.
5. Human-Interface Systems: Some robots are equipped with advanced human-interface systems that allow them to interact with humans through natural language processing and facial recognition.

The synergy of these components enables humanoid robots to navigate their surroundings, interact with objects, and perform tasks with remarkable dexterity.

## Where are they being used?
Humanoid robots are still in their early stages of development, but they are already being used in a variety of industries. For example,

1. Healthcare: Humanoid robots are becoming increasingly important in healthcare. They can assist in patient care by monitoring vital signs, delivering medications, and helping with rehabilitation exercises. Robots like “Pepper” have been used to provide companionship and mental support to patients.
2. Manufacturing: In manufacturing, humanoid robots can collaborate with human workers to improve efficiency. They can handle repetitive tasks, assist in assembly processes, and even operate machinery in hazardous environments. Companies like Toyota have deployed humanoid robots to assist with car production.
3. Customer Service: Retailers and service providers are using humanoid robots to enhance customer experiences. These robots can greet customers, provide information, and assist with shopping. Softbank’s “Pepper” has been employed in this role at various locations.
4. Education: In educational settings, humanoid robots serve as valuable teaching assistants. They can engage with students, answer questions, and facilitate interactive learning experiences. Some robots have even been employed to assist children with special needs in schools.
5. Research and Development: Humanoid robots are utilized in scientific research, particularly in fields like robotics and artificial intelligence. They serve as testbeds for developing new algorithms and control systems.

In the next 1–2 years, we can expect to see humanoid robots become more sophisticated and more capable. We can also expect to see them being used in a wider range of applications.

- Military: Humanoid robots can be used in military operations to perform dangerous tasks such as bomb disposal, reconnaissance, and combat. This can help to reduce the risk of casualties to human soldiers.
- Disaster relief: Humanoid robots can be used in disaster relief operations to search for survivors, deliver supplies, and clear debris. This can help to save lives and to reduce the risk of injuries to human aid workers.
- Nuclear power plants: Humanoid robots can be used in nuclear power plants to perform tasks such as maintenance, inspection, and repair. This can help to reduce the risk of exposure to radiation to human workers.
- Space exploration: Humanoid robots can be used in space exploration to perform tasks

In the long term, humanoid robots could become so sophisticated that they are indistinguishable from humans. This could lead to a new era of human-robot interaction, where humans and robots work together to solve problems and to build a better future.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*lvpwg1cpSZ8E2wtj29CpSw.jpeg)

## Challenges with Humanoid Robots
It’s important to note that while humanoid robots have gained attention and investment, they are not yet ubiquitous. Some key hurdles yet to be cleared include:

### 1. Cost and Accessibility:
Humanoid robots remain relatively expensive, limiting their widespread adoption in smaller businesses and households. However, advancements in technology and manufacturing processes are gradually driving down costs.

### 2. Technical Challenges:
Achieving seamless human-like movement and interaction remains a challenge. Although robots like Boston Dynamics’ “Atlas” can perform acrobatic feats, achieving natural and graceful movements akin to humans is a complex engineering task.

### 3. Regulatory Hurdles:
As humanoid robots are used in various industries, regulatory frameworks are evolving to address safety, privacy, and ethical concerns. Ensuring robots adhere to strict standards is crucial for their continued integration into society.

## From a technology perspective, what can we expect from Humanoid Robots?
The future of humanoid robots holds exciting possibilities. Over the next 1–2 years, we can anticipate several developments and trends in this field:

### 1. Advances in AI and Machine Learning:
As AI and machine learning technologies advance, humanoid robots will become smarter and more capable of understanding and responding to human interactions. This will enhance their roles in customer service, education, and research.

### 2. Enhanced Mobility and Dexterity:
Robotics companies will work on improving the mobility and dexterity of humanoid robots, making them more adept at navigating complex environments and performing delicate tasks.

### 3. Collaborative Work Environments:
The manufacturing industry will see increased collaboration between human workers and humanoid robots. These robots will assist in tasks requiring precision and strength, ultimately improving productivity.

### 4. Ethical Considerations:
As humanoid robots become more integrated into society, discussions around their ethical implications will intensify. Issues such as privacy, job displacement, and robot rights will be subjects of debate and legislation.

Humanoid robots represent a significant leap in automation technology, with the potential to revolutionize multiple industries. While they have made impressive strides, there are still challenges to overcome, including cost, technical limitations, and regulatory concerns. However, with ongoing advancements in AI, machine learning, and robotics, we can expect to see humanoid robots playing increasingly prominent roles in our lives in the coming years. As they become more affordable, capable, and integrated into our daily routines, the line between science fiction and reality will continue to blur, ushering in a new era of automation and human-robot collaboration.

By：Levintutu Automation
Link：https://medium.com/@LevintutuAutomation/the-rise-of-humanoid-robots-revolutionizing-automation-3d2c4fbe029f

The Rise of Humanoid Robots: Revolutionizing Automation


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Every year brings exciting developments in robotics that enhance our quality of life and throws millions of people out of work — and it’s just getting started!

Fast food companies are using robots to cut labor costs. Many of these robots are not only preparing meals but serving them.

- Of course, there are technical glitches — robots are still not able to spit on the food.
- Though there has been progress in programming the robots to screw up simple orders.
- Experts say it will be at least a decade before robots are able to sneak behind the dumpster to smoke weed.

More than 12 million robotic surgery procedures have been performed around the world.

- Doctors overwhelmingly prefer this method of surgery because if the patient dies, they can blame the robot.
-
The DaVinci robotic surgeon has just celebrated its 10,000th procedure after nearly 20 years of helping patients.

- And patients say the robot’s bedside manner is far more empathetic than the way they’re treated on Medi-Cal.

A group of researchers from Princeton, Stanford, and Google have created a robot called the TidyBot that follows spoken commands to clean your house.

- This works fine for right now, but sooner or later, lazy robots will figure out how to make other robots clean your house.

The TidyBot uses AI to respond to personalized commands such as, “Put the books in the bookcase” or “Sort the laundry”.

- Princeton and Stanford students are hoping TidyBot 2.0 will be able to respond to the command, “Write my dissertation.”

Two hundred million dollars worth of sex robots are sold globally each year.

- Approximately 156 sex robots are sold every day — and none of their buyers are missed in the dating pool.
- Humans having sexual relations with robots has profoundly sobering cultural implications, no matter how hilarious it looks.

In a recent survey of 13,000 respondents, 15% report having had sex with a robot, or currently owning one.

- That’s almost 2,000 people who are incapable of feeling shame.
- Anything that keeps them from rubbing against you on the subway.

Forty-eight percent of respondents were interested in visiting a sex robot brothel.

- These are people who don’t even want to pretend the hooker likes them.

Fifty percent of respondents believed they could form a genuine romantic connection with a sex robot.

- In fact, 39% of them were in love with a household appliance.

Forty-one percent think sex with a robot is cheating.

- Even if you’re cheating on your regular robot.

Forty-seven percent believe sex robots will replace humans in the future.

- And half of them are waiting for the Amazon truck to pull up.

Proponents of sex robots say it will cure loneliness, boredom and frustration.

- Plus, you don’t have to call the next day.
- Experts predict very soon these robots will be so realistic, they can be programmed to trigger pointless and repetitious arguments every two to three days.
- And eventually they’ll figure out how to avoid having sex altogether.

But observers say beware of sex robots manufactured in hostile nations that could contain smart devices for spying.

- So be extremely cautious, if you don’t want the Russians to see your O face.

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Brand art by David Todd McCarty

By：Steve Rosenfield
Link：https://muddyum.net/this-week-in-the-robot-apocalypse-354d7ea6e0b4


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In the ever-evolving landscape of robotics and artificial intelligence, bionic hands represent a remarkable milestone in achieving human-like interaction with the environment. Since the inception of robotics, the replication of the human hand’s intricate functionality has captivated the attention of researchers and industries alike. Beyond its awe-inspiring technological allure, bionic hand technology offers practical applications, enabling the execution of teleoperated tasks that demand complex dexterity– feats that are challenging to attain using conventional grippers, such as jaws or bellows. In this article, we explore the practical applications of bionic hand technology currently in use.

The emerging research and development around bionic hands has not only resulted in the creation of advanced haptic systems, AI-driven control, and innovative materials but has also paved the way for tangible solutions that exhibit human-like dexterity.

The initial insights into bionic hand development emerged as a response to the need for robots capable of performing tasks requiring fine motor skills and adaptability. The Stanford/JPL Hand, developed in the early 1980s, marked a significant milestone as the first dexterous robotic hand, equipped with three independently controlled fingers and force feedback capabilities for natural object interaction. Subsequently, the Utah/MIT Dextrous Hand broke new ground in human-like articulation, robotic dexterity, and sensing, laying the foundation for future advancements in bionic hand design.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*2EP9VzfcRJj6kfMgNPmsXg.jpeg)
Credit: Yantr Robotics

## Technologies Used in Bionic Hands
The engineering of bionic hands has benefited from and contributed to the advancement of various key technologies, including actuators, sensors, haptic systems, artificial intelligence, machine learning, and materials science. Each of these domains plays a pivotal role in shaping the capabilities of bionic hands.

### Actuators
Actuators serve as the essential components responsible for imparting movement to the fingers, wrist, and other joints of a bionic hand. These movements are directed by a teleoperated controller and sensors integrated into the hand itself. The choice of actuators depends on factors such as power, size, precision, and application. Several types of actuators are employed in robotic hands, including:

- Pneumatic actuators: These systems utilize air pressure to control the bending of fingers and joints, typically constructed from metals and hard plastics.
- Soft actuators: Comprising soft and flexible materials, these actuators employ various mechanisms to induce movement, including the inflation and deflation of elastomeric materials or shape alteration through electric fields or temperature changes.
- Fluidic actuators: These actuators employ fluid pressure to manipulate both rigid hydraulic systems and soft actuators.

An important stride in bionic hand technology was the introduction of underactuated mechanisms, where the number of actuators is fewer than the degrees of freedom. This innovation involves breaking down finger movements into modular units controlled by a single actuator, simplifying the complexity of controls and enabling agile movements [1].

### Sensors
Sensors are crucial components that integrate with actuators, enabling a bionic hand to interact with its environment effectively. They collect data regarding object properties, hand state, and position. This data is then integrated into the actuator movement, facilitating human-like responses to an object’s weight, fragility, temperature, and other characteristics.

Advanced sensor technology in bionic hands includes:

- Soft capacitive sensors: Embedded in the silicone material covering the robot hand, these sensors detect object conductivity, providing information about an object’s composition.
- Neuromorphic sensors: Designed to mimic the human nervous system, these sensors adapt to the data they receive, incorporating both vision and touch sensors.
- Piezoresistive sensors: These soft sensors measure changing resistance as force is applied, improving grasping and manipulation [2].

### Haptics and Touch
Sensor data can be translated into haptic feedback, allowing human operators to perceive the properties of objects the robotic hand interacts with remotely. Haptic feedback can be conveyed through handheld controllers, gloves, or other wearable devices. Advancements in haptic capabilities can enhance object recognition and handling. Notable haptic feedback methods include:

- Vibration Feedback: Controllers or gloves generate vibrations, mimicking touch sensations.
- Force Feedback: The system applies force to simulate effort, such as lifting or pushing objects.
- Tactile Feedback: Actuators simulate the feel of different objects, materials, and surfaces.
- Visual and Audio Feedback: Instead of direct touch sensations, visual cues or sounds convey object data.
- Neural Feedback: Emerging research explores neural interfaces, linking the operator’s brain to robotic devices through electrical signals, potentially enabling more precise and intuitive control and haptic feedback.

### Artificial Intelligence and Machine Learning
Artificial intelligence and machine learning algorithms play pivotal roles in enhancing the capabilities of bionic hands. Algorithms are applied to sensor data to improve perception and object recognition, enhancing grasping and manipulation. Additionally, these technologies enable robotic hands to learn from data, improving their handling of various objects and materials across different environments. AI and machine learning also enhance human-robot interaction, allowing the robotic hand to recognize gestures, spoken commands, and other inputs, ultimately providing more effective haptic feedback. These advancements contribute to the development of versatile and intuitive robotic hands.

### Materials Science
Materials science is a critical research area that profoundly influences the design, functionality, and performance of bionic hands. The choice of materials significantly impacts the hand’s durability, performance, and adaptability. Key considerations in materials science for robotic hands include:

- Surface Coatings and Tactile Materials: These affect gripping capabilities and the accuracy of sensor data and haptic feedback.
- Material Tolerances: Researchers must understand material properties, limitations, and how different materials work together.
- Soft Robotics: Combining movement and materials is pivotal, with elastomer materials influencing bending angles and applied forces in actuators.
- Bio-Inspired Materials: Materials inspired by biological touch mechanisms enhance robotic hand sensitivity, approaching human capabilities.

![image](https://miro.medium.com/v2/resize:fit:2000/format:webp/1*sDKFd--8PqYI8QkevPrR6w.jpeg)
Credit: Yantr Robotics

Applications of Bionic Hands
The versatility and potential of bionic hands have significant implications across diverse industries, including:

- Industrial Automation: Bionic hands can automate various aspects of industrial manufacturing, from safe placement of goods to pick-and-place systems that enhance production speed and the handling of complex objects [3].
- Medical and Healthcare: In the medical field, bionic hands assist in delicate surgical procedures requiring precision and accuracy. Additionally, advanced prosthetics powered by bionic hand technology offer patients tactile sensation and freedom of movement, significantly improving their quality of life.

![image](https://miro.medium.com/v2/resize:fit:3072/format:webp/1*K1Ouaes5HK1S-QgiUPUffg.png)
Prosthetic Hand development by Yantr Robotics

- Space Exploration and Hazardous Environments: Bionic robotic limbs are deployed in space exploration and hazardous environments for teleoperated object manipulation. For instance, the Mars Curiosity Rover’s robotic arm analyzes soil composition, while the European Robotic Arm (ERA) on the International Space Station handles various tasks, including transferring loads and supporting crew members [4][5].
- Entertainment and Gaming: Bionic hand technology finds applications in gaming and entertainment, particularly through haptic gloves that enable users to control and receive feedback from telepresence robots or immersive virtual environments [6].

## Conclusion
The realm of bionic hands continues to inspire innovation and imagination. From delicate surgical procedures to harsh and perilous environments, bionic hands open new horizons for industries and scientific fields alike. This evolution has led to the development of underactuated technology, sensors capable of detecting increasingly subtle properties, haptic systems with human-like responses, and the exploration of artificial intelligence as it relates to dexterity. As bionic hand technology advances, it promises to reshape industries, enhance human capabilities, and contribute to the ongoing progress of robotics and automation.

## References
[1] Zey Ren, et al. “HERI II: A Robust and Flexible Robotic Hand based on Modular Finger design and Under Actuation Principles”. IEEE (2018). https://eprints.whiterose.ac.uk/144459/1/IROS_2018_errata.pdf

[2] Ilker S. Bayer. “MEMS-Based Tactile Sensors: Materials, Processes and Applications in Robotics”. Micromachines (2022).

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782357/

[3] Intel. “Industrial Robotic Arms: Changing How Work Gets Done”. https://www.intel.com/content/www/us/en/robotics/robotic-arm.html

[4] NASA. “Mars Curiosity Rover: Robotic Arm”.

https://mars.nasa.gov/msl/spacecraft/rover/arm/

[5] The European Space Agency. “European Robotic Arm”. https://www.esa.int/Science_Exploration/Human_and_Robotic_Exploration/International_Space_Station/European_Robotic_Arm

[6] Adam Savage’s Tested. “Using Haptic Gloves to Control an Amazing Telepresence Robot!” YouTube (2021). https://www.youtube.com/watch?v=rWHk4ht-boM

By：Vighnesh Ganage
Link：https://vighneshganage.medium.com/advancements-in-bionic-hand-technology-f8cf9a0d08a4


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> “”An entire generation of university students are growing up in a world where they expect to receive a delivery from a robot after a few taps on their smartphone,” Starship Technologies CEO Lex Bayer said in a press release.

During a recent trip to a college campus, I had the incredible opportunity to see Starship Technologies’ delivery robots in action, and it was truly a glimpse into the future.

I was completely amazed by these little self-driving robots, and it got me thinking about how they are transforming the way things work on campus and beyond.

![image](https://www.pnglog.com/d6lM6J.png)
Credit: X

As I wandered through the lively campus, my attention was drawn to these adorable robots that seemed to effortlessly glide around, going about their tasks with a clear sense of purpose.

What really captured my fascination was their incredible adaptability in meeting the needs of both students and staff. They served as a seamless delivery service, handling everything from delicious meals and tempting snacks to essential supplies, making life on campus hassle-free.

> Now, let me tell you about the captivating mechanics that power these robots. They are equipped with a sophisticated array of sensors, cameras, and incredibly intelligent algorithms, which grant them an almost human-like ability to perceive and gracefully navigate around obstacles with remarkable precision.

It’s like they perform a technological ballet, ensuring not only the safety of their journeys but also the timely delivery of our orders. This, in turn, elevates the campus experience for those who cross paths with them.

-----------

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*Zwjsw3D_i-qaIaL4SleaiA.jpeg)
Image by B.R. Shenoy

## Starship Technologies Expands Autonomous Robot Fleet to Cater to Educational Institutions

- Widespread Deployment: Starship Technologies, a pioneer in emission-free robot technology, has substantially grown its fleet of self-driving delivery robots. With a presence in multiple countries, these robots are actively facilitating over 100,000 road crossings every day as part of their daily commercial operations.
- Transforming Campus Experiences: The impact of Starship’s autonomous delivery robots extends far beyond roads. The company has taken a pioneering step by providing Level 4 on-demand meal services to an impressive 1.1 million students across the United States. This service is now accessible in 50 schools, and the company is further expanding its reach to encompass 50 college campuses.
- Distinctive Campus Ambiance: Starship’s innovative approach to campus services includes introducing new “characters” among their robot fleet, each with its own unique voice and personality. As part of this distinct experience, students now have the entertaining option of requesting songs for delivery.
- Seamless Process: Utilizing the Starship Deliveries app, users initiate the delivery process by selecting their desired food or beverages. They conveniently pinpoint the delivery location on a map, with the robot’s real-time movement being trackable through an interactive map feature.
- Efficiency and Convenience: The autonomous robots are geared to carry items weighing up to 20 pounds, equivalent to around three shopping bags. Upon the robot’s timely arrival, users are notified through the app, which also enables them to effortlessly unlock and collect their orders.
- Swift and Varied Deliveries: The duration of delivery varies depending on the ordered items and the distance involved. However, thanks to their cutting-edge technology, the robots manage to complete deliveries within minutes, enhancing both speed and efficiency.

-----------

## According to Electrek:

> “Starship Technologies was founded in 2014 by Skype co-founders Ahti Heinla and Janus Friis based on the idea that autonomy can help many of the challenges present in last-mile deliveries. To date, the company’s L4 autonomous delivery robots have completed over five million trips, transporting people’s meals, packages, and groceries in thirty different locations around the globe..”

## Per Robotics 247:.

> “The robots use a combination of machine learning, artificial intelligence, and sensors to travel on sidewalks and navigate around obstacles. Computer vision-based navigation helps the robots map their environments to the nearest inch, said Starship. The robots can cross streets, climb curbs, travel at night, and operate in both rain and snow, it claimed.”

Following their launch, all these campuses have significantly expanded their fleet of robots, diversified dining choices, and extended operating hours. These adjustments were made in response to the substantial demand for the service, showcasing its popularity and effectiveness.

-----------

## Closing Thoughts
Myexperience with Starship Technologies’ delivery robots was a remarkable glimpse into the advancement of autonomous technology.

The way these robots effortlessly blend innovation and convenience paints an exciting picture of the future, where they may become essential companions in our daily lives.

This evolving relationship between humans and technology promises to redefine efficiency and convenience in a profoundly informative way.

-----------

## Sources:

https://starshipdeliveries.com/campus/

https://roboticsandautomationnews.com/2023/09/26/starship-technologies-now-offering-autonomous-robot-delivery-on-50-us-college-campuses/72392/

https://electrek.co/2023/08/24/these-autonomous-robots-deliver-students-meals-while-popping-wheelies-and-using-unique-voices/

https://www.robotics247.com/article/starship_technologies_adds_smu_campuses_robot_deliveries_serve

https://www.theverge.com/2021/8/10/22617175/starship-technologies-delivery-robots-college-campuses

https://www.newsweek.com/starship-technologies-delivery-robots-college-1455348

By：B.R. Shenoy
Link：https://medium.com/predict/starship-technologies-delivery-robots-at-a-college-campus-1f620930c82f

Starship Technologies Now Providing Autonomous Robot Delivery on 50 US College Campuses

The self-driving six-wheeled delivery robots are roughly the size of a large cooler and exhibit the smooth movements of a remote-controlled car


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A lot has happened in the scientific field since the epic Covid-19 pandemic. Globally, scientists are now being recognized a lot more especially in general social gatherings and otherwise. Once a largely marginalized community is now being put up on a higher pedestal. And rightly so. Ok not at par with soccer stars and movies stars but we will get there. ;)

The speed at which we developed vaccines, diagnostics and drugs to combat the pandemic and save lives is unprecedented to say the least. A dear author friend of mind was astonished when one of my own scientific venture was progressing rapidly as to how can science move so fast. He must have been surprised to see how the vaccines that usually took 10 years to make could be made and delivered in under a year!! Science and technology is progressing much more rapidly than you can grasp was my response. Of course, it also depends on the will and tenacity of the researchers behind the translational project.

Academic and fundamental research takes tremendous effort, time and numerous failures and successes to prove a certain hypothesis, sometimes decades. However, translational research that works on the back of proven scientific hypothesis requires careful planning of experiments that result in the end product that becomes a drug, diagnostic or a medical device. Data shows FDA has significantly increased its approval times of all these scientific products since 2011. The notoriously slow and behemoth organization has realized the importance of bringing inventions that serve public, quicker, as the delays will only result in worsening health or even worse increased death count which puts significant burden on healthcare systems.

In science and technology we have now reached a point where basic understanding of how a lot of human biology and engineering methodologies work. Of course there is a lot to dig in still, but we have now all tools to really try and find that cure for cancer, achieve a constant state of homeostasis, perform minimally invasive surgeries using robots, crunch through big diagnostic data to detect life threatening diseases at the earliest stages and a lot more. Science and technology is at the forefront of an era of breakthroughs that do not happen every decade but perhaps every year.

I am covering some of my favorite and path breaking topics here and ending with my favorite pet project, achieving perfect homeostasis using wearable single molecule detection diagnostic and drug delivery patches.

I have also tried to write it in as simple a manner to be understood by most. This is the best i can do :)

1. Drug Discovery and Development using AI

When i worked with supercomputers and on particle accelerators back in 2012, it used to take months to elucidate protein structures to help develop drugs against diseases such as cancer, MS and Alzheimer's. Now, with the advent of quantum computing, faster processing chips (GPUs included), and cloud computing the rate at which we are able to solve disease causing protein structures has increased by 50x and more. Google Deep Mind did a good job of cracking the age old problem of protein structure prediction by mining all of PDB (Protein Data Bank). This is a commendable feat although as an ex pure wet lab scientist who understands the dynamics of biological entities in human cells i am sceptical on the success rate which will be evaluated in a year or so. Happy to be surprised here. Biology is like crypto market…unpredictable ;)

In the last 5 years more than 50 biotech companies are using AI and ML to accelerate the drug discovery process. It used to take us 4- 5 years to get a drug from lab bench to clinic and now very early and few examples are showing this can be reduced to less than a year with massive reductions in time and investment. More successes needed here. AI and ML still has to crack the drug development and clinical trial success. For example, only 2 drugs out of 50–100 really succeed to making it to the market. I am proud to be a co-inventor of one such cancer drug that took 11 years to be approved by FDA as a breakthrough drug for lymphomas. This is my bragging right.

2. Non/Semi-Invasive Diagnostics

Many of you must have seen 30mL or more of your blood drawn out to tell you how your antigens are doing and to paint your current health status. A band of scientists are working on using saliva, stool, urine and sweat to give you the same data. I will not go into details of the science behind it but all i can say here is that the advent of non and semi invasive diagnostics are really helping patients/humans avoid the painful prick. I foresee these methodologies replacing blood diagnostics in the coming decade. Those of you who are diabetics or know who are, might have heard of Abbot’s wearable non-invasive continuous glucose monitoring device to know the general trend of your blood glucose levels and to avoid certain foods, lifestyles etc. Flow Neuroscience has invented a Brain Computing Headset that reads the brain waves to quantify the depression status of an individual. They have also gone a step ahead to use trans cranial brain stimulation (tCS) to stimulate the pre frontal cortex (PFC) to treat it! Israeli upstart Olive Diagnostics have a device now that sticks to your toilet and can give you an indication on more than 100 disease causing biomarkers. The pace is hot and is seeing tremendous development.

3. Gene editing and CRISPR

Perhaps, this decade’s biggest invention has been gene editing and CRISPR. Nobel prize was awarded to Jenifer Doudna and colleagues. What this means is that we now can edit the DNA of our, bacterial and animal genomes to treat diseases and for vanity sake make designer babies who will be just perfect in life a they grow. This is huge, i am a huge proponent of adopting gene editing and CRISPR not just to treat genetic diseases but also to design better humans. What is the problem here?. Critics will always look at the negative side and come up with ludicrous possibilities of designer baby killers or bad humans who will never die. We always have the tendency to use our fight-flight mindset to think of the worse. Get some therapy critics. ;)

Gene editing has become so common place that one bio hacker designed a DNA that would give him bigger muscles and injected it in his veins live in front of the audience. This may be funny to many but this just shows how a breakthrough invention of just 5 years has become common place in scientific and broader community. Data shows that there are more than 1500 CRISPR and Gene editing projects in pipelines to treat rare diseases. Rare diseases are those that affect under 1000 patients globally, and Pharma with their eyes on profits will never develop a drug for just 1000 patients. So, enter CRISPR and gene therapy. A promise to the unlucky rare to have their genes fixed and to alleviate them of their disease.

4. Longevity Science

In the last 5 years, there is a big movement building (mostly in west) to help us live till 100 and beyond. The current median age of survival is 72 years and this has increased from 65 since the 1970s. Thanks to the adevnt of life saving drugs, better care and diagnostics tech. However, Living till 100 should not be for lucky few but our healthcare systems need an overhaul wherein 50% of their efforts have to be proactively to enhance life and not just treat the sick. This is the main argument of this group of dreamers who are using latest science and technology to enhance human healthspan.

A combination of deep diagnostics that include imaging, microbiome analysis, epigenetics and neuroscience need to be harnessed to truly understand the biological age of an individual. This coupled with high end treatments such as hyperbaric oxygen therapy (HBOT), stem cell rejuvenation, plasma transplantation and a suite of mitochondria boosting supplements can truly enhance the cellular age of indivduals 45 and above. Numerous clinics such as Fountain Life are cropping up in US and EU to offer all of the above to enhance human healthspan.

5. Robotic surgeries

When Isaac Asimov wrote about robots performing surgeries in The Bicentennial Man, everyone must have laughed. Fast forward to today, numerous hospitals in Israel, UK and Finland have now performed more than 1000 simple and complicated surgeries using robots where surgeons have to just monitor and technicians ensure that robot is doing its job right and to firefight any issue. Robots have tremendous potential in every area of industrial technology but more so in medical science. Surgeons are humans and small errors can have devastating impact on the patient and on the medic’s career. So Robots, when programmed seamlessly can not only perform but reduce the time energy and investment.

6. High resolution medical imaging and AI

In 2015, When Amanda, who was otherwise a healthy 36 year old woman felt nauseous went to the doctor for scans, the doctors at Beth Israel Hospital missed on the early sign of her pancreatic cancer. When she went to MGH, the newly installed Radiomics AI platform was able to detect small lesion and globules using AI on her scan. Radiologists who are humans with biological limitations with respect to their eye sight and the resolution of the scanner can miss early signs of debilatiting diseases such as cancer, Alzheimers. However, now a plethora of 50+ approved biotech companies are crunching vast amounts of radiology and pathology scan data to train their neural nets to get sharper in detecting micrometer size lesions that could indicate onset of a chronic disease. This revolution is only getting hotter and as these systems get trained on multi million sets of data they will only get smarter in detecting and helping the human radiologists and pathologists. There is a lot of hype still in systems being very robust but they wont achieve this level of sophistication for at least another 5 years or so. Until then AI and human docs have to collaborate and work smartly together.

Quest for Homeostasis- The Holy Grail in Human Biology

Imagine a patch that you strap on to your skin and that detects single disease causing biomolecule in the blood and uses on-patch electronics to trigger subcutaneous single or multi molecule drug release. What this will do is that it will keep your body in constant disease free state. Science fiction? No. Its real and its my pet project that i plan to execute on at some point this decade. I plan to write a detailed blog post with my current designs and hypothesis shortly.

By：Aman Iqbal
Link：https://amaniqbal.medium.com/the-future-of-science-and-biotech-bd2d7532fc4a


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The invention of Zero, the Charkha, etc. have their own histories and all of them have made significant contributions to the world. Without them, maybe, we would still be in —

All wheeled robots move about the Instantaneous Center of Curvature. Each wheel of the robot has an axis perpendicular to the wheel. They create the Instantaneous center of curvature at the point where they intersect. Any wheeled robot moves about this point.

![image](https://miro.medium.com/v2/resize:fit:720/format:webp/1*i3_VuwiNncpusobos4XJYQ.png)
Just look at the red dot and the axis perpendicular to the wheel

The world of robots has seen various different inventions and each brainchild has its own significance. Starting off with -

1) Differential drive -
There are two parameters namely velocity of the left wheel and right wheel (vL and vR) respectively that can be controlled.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*ma_uorBqunLxh0rgQCOwuQ.png)
Theta is the orientation angle that the vehicle makes with respect to the perpendicular axes passing through the wheels of the robot.

Omega is the angular velocity of the vehicle about the instantaneous center of curvature.

(x,y) is the midpoint of the line joining the two wheels.

Once the ICC is determined, the velocity of the vehicle is determined by the dot product of the angular velocity of ICC (Omega) and the Radius(R) of the circle the vehicle makes.
The velocity of the vehicle can be determined by the average sum of the velocities of the left and right wheels of the robot.

2.) Car Drive (Ackermann Steering)-

As the name suggests, and definitely the GIF, most cars today follow this drive.

There are three control parameters, viz. —

Velocity of the left wheel, the right wheel, and the orientation angle of the front wheel.

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*7RUY1dcIlBxFVVJ3FraehA.png)
Just your regular car

Angular velocity and other findings for a differential drive remain the same for Ackermann steering as well.
The only difference here is that the velocity of the left wheel, right wheel, and the orientation of the front wheel cannot be chosen as per your own choice.

3.) Synchronous Drive -

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*CR_RZv-g4NOqzWRyfmnw9Q.png)
Don’t go by the looks of it. Let me make it straight and simple for you —

- All wheels are connected by chains.
- All wheels are driven.
- Every motor has its own controller.
- Such bots can follow every circular trajectory but cannot go sideways.

Now, we move on to one of the sick — est — -looking wheels I have ever come across.

4.) Mecanum Wheels —

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*SxV6ebDpPVjX314iAz-YPg.jpeg)
Mecanum Wheeled bot

Drift, move, go straight, go back—a bot with such wheel looks like they are hovering.
Not only does it look so different but the rolls have it’s own significance.

The roll on the wheels are oriented at certain angles so that the bot can move in every direction.

As we move past the different drives, we are yet to uncover an important point of discussion. Holonomic and Non-holonomic constraints.

No time! Please be quick, mate!

I will wrap this up in a jiffy.
Non—holonomic constraints limit possible incremental movements within the configuration space of the robot.
Differential drive and synchronous drive move on circular trajectory and cannot move sideways.
Since, mecanum wheeled robots can move sideways, they have no non-holonomic constraints.

So, I end this up but before that!!! — —

By：Abhishek Mishra
Link：https://medium.com/@abhimishra.cetb/rolling-towards-tomorrow-the-epic-saga-of-the-wheel-5bde5d57f17a

Rolling Towards Tomorrow: The Epic Saga of the Wheel! 🌟🤖


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The ability to create bots is one of the main features of Telegram. By interacting with the messenger API, bots allow users to automate many DeFi functions, such as trading, managing wallets, searching for tokens, farming, and much more.

After receiving a command, the Telegram bot generates a main menu, offering choices for creating a new wallet or linking an existing one. Once a wallet is configured, users can deposit funds and choose various transaction options to cater to their needs.

Among the most popular projects, UniBot, Moonbot, and AIMBOT are worth noting. All of these cryptocurrencies saw significant growth in Q2 2023.

Despite the fact that Telegram bots have different use cases, their price dynamics in recent weeks closely correlate with the dynamics of TON, a native coin of The Open Network, created by the Telegram team.

You might also like:
[Bot on Telegram releases crypto payment solution
](https://crypto.news/bot-on-telegram-releases-crypto-payment-solution/)

## Why are crypto bots needed on Telegram?
Cryptocurrency bots on Telegram are like personal crypto assistants, ready to help buy, sell, and manage digital assets. Here are some main reasons users might consider using a Telegram bot for their cryptocurrency needs.

With the Telegram bot, users can quickly and easily buy and sell various cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), and others. This is especially useful for those who want to stay on top of the latest market trends and make quick trades when needed.

Another key feature of TG bots is the ability to send and receive cryptocurrencies. Telegram bots also offer cryptocurrency acquiring, which means vendors can accept payments from their clients in cryptocurrency and automatically convert them to fiat currency.

## The most popular Telegram bot tokens
According to CoinMarketCap, at the time of writing, the market capitalization of Telegram bot tokens was $91 million, and the trading volume in 24 hours was more than $9.5 million.

![image](https://crypto.news/app/uploads/2023/10/Telegrambots-1024x405.png.webp)
Top Telegram bot tokens by market capitalization | Source: CoinMarketCap

The excitement in the Telegram bot token market was largely due to Unibot, a service that allows traders of the Uniswap crypto exchange to make transactions in the messenger. After the successful launch of Unibot, several more bots with similar functionality appeared in Telegram.

According to CoinMarketCap, UniBot (UNIBOT) currently ranks first among Telegram bot tokens in terms of trading volume and market capitalization. Among other features, it allows users to trade on Uniswap V3 without leaving Telegram. Since its launch on June 16, Unibot saw a rapid price increase from $18.18 to an all-time high of $236.43 in August 2023.

The ChainGPT (CGPT) trading bot has also gained significant popularity in the crypto community. From April 10, its price soared from $0.008 to $0.046.

## How the official Telegram bot works
The official bot from Telegram, @wallet, allows users to buy, exchange, and transfer cryptocurrencies using bank cards.

![image](https://crypto.news/app/uploads/2023/10/Screenshot-2023-10-04-at-14.24.41.png.webp)
Source: @wallet/Telegram

To get started with the service, users need to link their phone numbers and create a virtual wallet. Replenishing funds is not only possible through P2P but also by transferring funds via a cryptocurrency exchange to the address provided by the service (make sure to specify the correct network when transferring).

Buying and selling Bitcoins can be accomplished through the ‘Market’ section of the web interface using a bank card or via P2P. Furthermore, users have the option to exchange TON, USDT, and BTC with one another, with transactions processed instantly and at favorable rates.

Recently, Telegram has also introduced support for USDT transfers for making purchases in USDT. Users can now send and receive stablecoins in chats with zero commissions.

## Are Telegram bots dangerous for virtual assets?
Bobby Ong, co-founder and COO of CoinGecko, also highlighted the growing presence of tokens in the sector while underscoring certain risks. He pointed out that users are required to transfer tokens to a third-party wallet or share private keys to link existing wallets, thereby exposing their funds to potential exploits or rug pulls.

Ong added that he wouldn’t be surprised “if one of the bots ends up intercepting or storing users’ private keys.”

![image](https://pfp.nostr.build/f83f049591d47ac5bd92b57c5c4fd460bfe5fd3d89db5f69fd7f8d42d1101c59.jpg)

## Should you use Telegram crypto bots?
Creating Telegram bots for cryptocurrency can indeed be an attractive avenue for simplifying digital asset investments. They offer a user-friendly and accessible interface that can be appealing to many crypto enthusiasts. However, it’s crucial to weigh the benefits against the inherent risks associated with these bots, especially considering the relative novelty of the sector.

When using Telegram bots for cryptocurrency, security emerges as a paramount concern. Interactions with these bots often involve the creation of a dedicated wallet or connection to an existing one, granting the bot access to your private keys. This accessibility raises the potential for exposing your digital assets to risks if the bot lacks robust security measures.

Additionally, Telegram bots necessitate engagement with smart contracts, which aren’t immune to vulnerabilities despite rigorous audits. Users must exercise caution when dealing with bots without thorough vetting.

To mitigate risks, you can adopt strategies like trading smaller sums, maintaining minimal funds in the bot-linked wallet, or establishing a new wallet exclusively for bot interactions.

By：Anna Kharitonova
Link：https://crypto.news/what-are-telegram-bots-and-why-are-they-popular-in-crypto-community/

What are Telegram bots, and why are they popular in crypto community?

Telegram crypto bots offer convenience, but they come with risks. Explore how they can simplify crypto tasks while staying vigilant against potential pitfalls.


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*Navigating the Nuanced Relationship Between Automation and Employment*

![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*lpTCfa0cKrBZjodfvpBkJw.jpeg)

In the evolving landscape of automation and artificial intelligence, a crucial question echoes through boardrooms, classrooms, and communities: Will robots replace people, or will they create more jobs and enhance productivity? This debate, often presented as a stark choice, is far more intricate than a binary decision.

## The Changing Nature of Work:

Automation excels at repetitive, mundane tasks, leading to the automation of specific job roles, particularly in manufacturing and logistics. However, as machines handle routine tasks, humans are liberated to focus on areas where their unique qualities shine — creativity, empathy, complex problem-solving, and innovation. New roles are emerging in fields like AI development, data analysis, and robotics maintenance, highlighting the adaptability of the workforce.

## Human-Robot Collaboration:

The future is not about replacement but collaboration. Robots can augment human capabilities, leading to increased productivity and safety. In healthcare, they assist surgeons in delicate procedures, while in agriculture, they optimize planting and harvesting. This synergy between human intuition and robotic precision marks a new era of collaboration, emphasizing the potential for coexistence.

## Addressing Socioeconomic Disparities:

One of the key challenges in this evolution is addressing socioeconomic disparities. Accessible education and robust training programs are pivotal. Upskilling and reskilling initiatives empower individuals, enabling them to align their skills with emerging job opportunities. These programs are the cornerstone of ensuring an inclusive transition.

## Ethical Considerations and Societal Well-being:

Beyond economics, the debate delves into ethics, job quality, income inequality, and societal well-being. Policymakers, businesses, and communities must collaboratively develop policies that prioritize equitable access to opportunities. Ethical considerations drive discussions about job quality, worker rights, and the overall impact on communities, guiding the responsible integration of technology into our lives.

## Evolving Roles in Manufacturing:

Take the manufacturing sector, where robots have revolutionized production lines. In car manufacturing plants, robots handle intricate welding tasks, freeing human workers to focus on quality control and process optimization. This synergy has not only improved efficiency but also elevated the role of human expertise.

## Healthcare’s Technological Renaissance:

In healthcare, robotic assistants are transforming patient care. Robots like the da Vinci Surgical System enable surgeons to perform minimally invasive surgeries with unparalleled precision. This collaboration ensures delicate procedures are executed flawlessly, demonstrating the potential of technology in enhancing medical outcomes.

## Precision Agriculture at Work:

Agriculture, too, embraces collaboration. Autonomous drones equipped with AI algorithms monitor crop health and optimize irrigation, enabling farmers to make data-driven decisions. By automating repetitive tasks, farmers can concentrate on sustainable practices and crop innovation, ensuring a vibrant future for agriculture.

## Education and Personalized Learning:

In education, AI-powered platforms adapt to students’ learning styles, providing personalized lessons. For instance, Duolingo’s language learning app uses AI to tailor exercises to individual learners, enhancing comprehension. This tailored approach empowers students, making education more accessible and effective.

## Empowering Customer Support:

Customer support exemplifies collaboration. Chatbots equipped with natural language processing handle routine inquiries, allowing human agents to focus on complex customer issues. This streamlined process ensures efficient service, showcasing the harmonious blend of human empathy and AI efficiency.

These real-life examples paint a vivid picture of the human-robot alliance. Automation, rather than replacing jobs, refines them. It’s about humans leveraging technology to enhance expertise, achieve precision, and improve lives. As we progress, let’s learn from these stories, appreciating the intricate dance between humans and machines. Embracing this synergy, we pave the way for a future where innovation and collaboration redefine work, ensuring a world where humans and robots flourish together.

By：Lera Pazniak
Link：https://medium.com/@lera.poznyak111/humans-robots-and-the-workforce-revolution-no-job-in-the-future-68a37759e4a1

Humans, Robots, and the Workforce Revolution: “No Job” in the Future?


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![image](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*9Uz08XoHDn72L9Fs5wa5vw@2x.jpeg)
The future is here, and it comes with a steaming cup of coffee served by AI robots. London, known for its innovation and bustling coffee culture, has taken a leap forward by introducing AI robots into its coffee shops, revolutionizing the way we experience our daily caffeine fix.

In these cutting-edge establishments, you’ll find humanoid robots seamlessly working alongside baristas to deliver exceptional service. These AI marvels are equipped with advanced artificial intelligence and machine learning capabilities, enabling them to interact with customers, take orders, and even prepare customized beverages with precision.

The introduction of AI robots in London coffee shops not only adds an element of novelty but also enhances efficiency and accuracy. These robots are programmed to work tirelessly, ensuring prompt service and reducing waiting times for customers. With their robotic precision, they can consistently deliver the perfect cup of coffee, customized according to each individual’s preferences.

But it’s not just about the efficiency and consistency these AI robots offer. They also provide entertainment and a futuristic ambiance. As you sip your latte, you can watch in amazement as these humanoid servers glide effortlessly between tables, engaging customers in friendly conversations and even showcasing their dance moves during quieter moments.

Beyond the novelty factor, AI robots have the potential to streamline operations, minimize errors, and elevate customer experience in coffee shops. By automating routine tasks, human baristas can focus more on ensuring quality and providing a personal touch that only human interaction can achieve.

London’s coffee shops have always been at the forefront of embracing innovation, and the introduction of AI robots is another testament to this. By melding the traditional warmth of coffee shop culture with cutting-edge technology, these establishments create a truly unique and memorable experience for their patrons.

As AI continues to advance, we can expect to see more AI robots integrated into various industries, transforming the way we interact with services and products. In London’s coffee shops, these AI robots are not only bringing a touch of the future but also reshaping the way we enjoy our daily coffee ritual.

So, the next time you’re in London and in need of a caffeine boost, step into one of the coffee shops embracing this AI revolution. Prepare to be amazed as AI robots seamlessly blend technology and hospitality, serving up a memorable experience with each cup of coffee.

By：NFT Planetarium
Link：https://medium.com/@sportnft2022/ai-robots-serving-up-a-futuristic-experience-in-london-coffee-shops-708acaf83a39

AI Robots: Serving Up a Futuristic Experience in London Coffee Shops