Archive 15.05.2025

The ability to remotely control robots in real-time, also known as teleoperation, could be useful for a broad range of real-world applications. In recent years, some engineers have been trying to develop teleoperation systems that allow users to guide the actions of humanoid robots, which have a body structure resembling that of humans, getting the robots to precisely imitate their whole-body movements.

AI vs Automation: Understanding the Key Differences and Their Impact

In our high-speed era of a fast and furious digital lifestyle, the terms “automation” and “Artificial Intelligence (AI)” are drivers. While at first glance they appear to speak of the same things robots doing things with little human intervention, they are actually distinct technologies and have different jobs and impacts.

Knowing the main differences between automation and AI is vital, particularly with businesses and society becoming more reliant on them. This article discusses the difference between automation and artificial intelligence, challenges, and applications on industries and employees.

What is Automation? 

Automation means applying technology to perform tasks with little or no human intervention. The overall goal of automation is to create efficiency, consistency, and speed. Through automation, we can definite procedures, rules, or processes, which are performed by equipment without having to “think” or “learn.”

Types of Automation 

1. Fixed or Hard Automation: Applied in manufacturing, it is extremely structured, repetitive work with minimal variation.
2. Programmable Automation: Applied to batch production, the machines are reprogrammed to perform many different tasks.
3. Flexible or Soft Automation: Provides more flexibility, usually in robots or machines switched from task to task with little setup.
4. Business Process Automation (BPA): Used in the cyber world to perform repetitive tasks such as data entry, scheduling, and system monitoring.  

What is Artificial Intelligence?

Artificial intelligence, however, is the simulation of human intelligence on machines. AI allows systems to learn through experience, adapt, and make decisions based on sophisticated algorithms instead of pre-programmed rules.

Core Capabilities of AI 

1. Machine Learning (ML): Allows systems to learn over time from experience.
2. Natural Language Processing (NLP): Allows machines to read and write natural languages.
3. Computer Vision: Allows machines to read and react to visual input.
4. RPA (Robotic Process Automation): Allows rule-based autonomous operations and choice in the physical world.

While automation only gets to do things according to the rule, AI gets to handle uncertainty, solve issues, and even mimic such high-level thinking as learning and solving problems.

Real-World Applications of AI and Automation 

Automation in Practice 

• Manufacturing: Robot arms, automated conveyor belts, and quality checks.
• Finance: Automated fraud detection and transaction processing.
• Retail: Automatic restocking and checkout software.
• IT Operations: Server monitoring, backup infrastructure, and software deployment. 

AI in Practice 

• Healthcare: Predictive patient care insights, AI-based diagnostic tools.
• Finance: Customer sentiment analysis, credit risk models, algorithmic trading.
• Marketing: Recommendations, advertisement targeting, customer segmentation.
• Transportation: Autonomous cars and AI-based logistical planning. 

Automation Vs AI: Impact on Industries

Manufacturing 

• Automation Impact: Increased productivity and reduced labor costs because of optimized production lines.
• AI Impact: Predictive maintenance, computer vision-based quality control, and optimized supply chains. 

Healthcare 

• Automation Impact: Automated scheduling of appointments, billing, and automatic updating of patient records.
• AI Impact: Diagnostic imaging, virtual health assistants, personalized treatment plans. 

Retail

• Automation Impact: Inventory, checkout.
• AI Impact: Dynamic pricing, customer behavior analysis, virtual shopping assistants.

Challenges of AI and Automation Adoption 

1. Fear Of Employment Replacement

With automation and AI doing the repetitive jobs, many of the jobs, especially those in sectors like manufacturing and retail, are disappearing. This is supporting more stress on low-skilled workers and can widen the gap between the poor and rich.

2. Surveillance and Data Privacy

AI needs large amounts of data to operate optimally, but getting all that data is a direct threat to privacy. Tools like facial recognition can track people without their permission, overstepping on basic rights and freedoms if unregulated.

3. Transparency and Accountability

AI decides on black processes, but even to those who create it. However, when something goes wrong, like an incorrect medical diagnosis, it is unclear who is responsible.

4. Security and Safety Risks

As deals with data, AI systems can be hacked with disastrous effects. For instance, autonomous vehicles might be tricked by bogus information, or AI might be employed in cyberattacks. Strong defenses must be constructed to make these systems safe and secure.

5. Overdependence and Loss of Skills

As we increasingly depend on AI to make routine decisions, there’s a chance we’ll begin losing our own capabilities. If we let the machines do all the thinking for us, we’ll be forgetting how to make decisions, solve problems, or even perform our work efficiently without them.

The Future: Synergy, Not Substitution 

True potential is not either-or, automation vs. AI, but mastering how to use them together. Used correctly:

• Automation can handle repetitive, routine work.
• AI can bring in intelligence and responsiveness.
• Human beings can focus on strategy, creativity, and empathy work.

These companies that capitalize on this synergy will be able to innovate, compete, and build strong futures. 

The Cost of AI Development 

The expense of building AI can be prohibitive, here are some reasons why it is so costly:

1. Research and Development

It is expensive to recruit skilled AI researchers, data scientists, and engineers. They are in-demand individuals and get compensated well. The finest AI talent usually comes from academia or leading tech companies, so it is competitive and usually pricey to recruit them.

2. Data Collection and Labeling

AI models need huge amounts of high-quality data to learn from, especially for healthcare applications, where data must be carefully curated and anonymized. Collecting, cleaning, and labeling such data is labor-intensive, which reduces costs.

3. Computational Resources

 

Training advanced AI models like large language models or computer vision requires enormous computational resources. That entails high-end GPUs or TPUs, which are extremely costly to buy or rent from cloud providers. The power consumption also commands a significant portion of ongoing operational costs.

4. Infrastructure and Maintenance

Building and maintaining AI infrastructure, including servers, storage, networking, and monitoring software, requires long-term investment.

5. Testing and Safety Measures

AI development involves a lot of testing, including model verification, bias identification, and safety checks. For self-driving cars or medical diagnostics, this testing must be highly specific, sometimes to the extent of requiring real-world tests and regulatory approval, and both are expensive.

6. Legal and Compliance Costs

AI development must meet regulatory requirements and adherence to law in data protection (e.g., GDPR) saves costs significantly.

7. Deployment and Scaling

Migrating an AI model means adaptation and interfacing with other systems. Scaling AI to numerous regions, languages, or platforms adds additional expense.

Also Read: How Much Does Artificial Intelligence Cost?

Conclusion

AI and automation are change drivers with inherent strengths and potential. Where automation works by speed through inflexible, fixed principles, AI is gifted with learning, growth, and decision abilities. Rather than setting the two against each other as new technologies, they are better placed to be put side by side as complementary technologies. They revolutionize the way of living, working, and existing with the world entirely together.

Connect with USM Business Systems, the best AI development company, to bring your dreams into reality.

 

[contact-form-7]

Researchers found that vision-language models, widely used to analyze medical images, do not understand negation words like 'no' and 'not.' This could cause them to fail unexpectedly when asked to retrieve medical images that contain certain objects but not others.

Listen to the first notes of an old, beloved song. Can you name that tune? If you can, congratulations -- it's a triumph of your associative memory, in which one piece of information (the first few notes) triggers the memory of the entire pattern (the song), without you actually having to hear the rest of the song again. We use this handy neural mechanism to learn, remember, solve problems and generally navigate our reality.

A new study outlines how artificial intelligence-powered handwriting analysis may serve as an early detection tool for dyslexia and dysgraphia among young children.

Scientists inspired by the octopus's nervous system have developed a robot that can decide how to move or grip objects by sensing its environment.

Scientists inspired by the octopus's nervous system have developed a robot that can decide how to move or grip objects by sensing its environment.

Researchers have developed a digital laboratory (dLab) system that fully automates the material synthesis and structural, physical property evaluation of thin-film samples. With dLab, the team can autonomously synthesize thin-film samples and measure their material properties. The team's dLab system demonstrates advanced automatic and autonomous material synthesis for data- and robot-driven materials science.

New AI agent evolves algorithms for math and practical applications in computing by combining the creativity of large language models with automated evaluators

Amazon has just unveiled its newest warehouse robot called Vulcan, which has a "sense of touch." Designed to gently stow items using pressure-sensitive gripping and artificial intelligence (AI), Vulcan is now being tested in two Amazon facilities, in Spokane, Washington state, US, and Hamburg, Germany.

With major tech players investing heavily in this next generation of robotics, construction leaders are beginning to ask: could humanoid robots soon find a place in the industry. And, if so, what would that mean for workers, safety, and the economics of the job site?

Kushal Kedia (left) and Prithwish Dan (right) are members of the development team behind RHyME, a system that allows robots to learn tasks by watching a single how-to video.

By Louis DiPietro

Cornell researchers have developed a new robotic framework powered by artificial intelligence – called RHyME (Retrieval for Hybrid Imitation under Mismatched Execution) – that allows robots to learn tasks by watching a single how-to video. RHyME could fast-track the development and deployment of robotic systems by significantly reducing the time, energy and money needed to train them, the researchers said.

“One of the annoying things about working with robots is collecting so much data on the robot doing different tasks,” said Kushal Kedia, a doctoral student in the field of computer science and lead author of a corresponding paper on RHyME. “That’s not how humans do tasks. We look at other people as inspiration.”

Kedia will present the paper, One-Shot Imitation under Mismatched Execution, in May at the Institute of Electrical and Electronics Engineers’ International Conference on Robotics and Automation, in Atlanta.

Home robot assistants are still a long way off – it is a very difficult task to train robots to deal with all the potential scenarios that they could encounter in the real world. To get robots up to speed, researchers like Kedia are training them with what amounts to how-to videos – human demonstrations of various tasks in a lab setting. The hope with this approach, a branch of machine learning called “imitation learning,” is that robots will learn a sequence of tasks faster and be able to adapt to real-world environments.

“Our work is like translating French to English – we’re translating any given task from human to robot,” said senior author Sanjiban Choudhury, assistant professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science.

This translation task still faces a broader challenge, however: Humans move too fluidly for a robot to track and mimic, and training robots with video requires gobs of it. Further, video demonstrations – of, say, picking up a napkin or stacking dinner plates – must be performed slowly and flawlessly, since any mismatch in actions between the video and the robot has historically spelled doom for robot learning, the researchers said.

“If a human moves in a way that’s any different from how a robot moves, the method immediately falls apart,” Choudhury said. “Our thinking was, ‘Can we find a principled way to deal with this mismatch between how humans and robots do tasks?’”

RHyME is the team’s answer – a scalable approach that makes robots less finicky and more adaptive. It trains a robotic system to store previous examples in its memory bank and connect the dots when performing tasks it has viewed only once by drawing on videos it has seen. For example, a RHyME-equipped robot shown a video of a human fetching a mug from the counter and placing it in a nearby sink will comb its bank of videos and draw inspiration from similar actions – like grasping a cup and lowering a utensil.

RHyME paves the way for robots to learn multiple-step sequences while significantly lowering the amount of robot data needed for training, the researchers said. They claim that RHyME requires just 30 minutes of robot data; in a lab setting, robots trained using the system achieved a more than 50% increase in task success compared to previous methods.

“This work is a departure from how robots are programmed today. The status quo of programming robots is thousands of hours of tele-operation to teach the robot how to do tasks. That’s just impossible,” Choudhury said. “With RHyME, we’re moving away from that and learning to train robots in a more scalable way.”

This research was supported by Google, OpenAI, the U.S. Office of Naval Research and the National Science Foundation.

Read the work in full

One-Shot Imitation under Mismatched Execution, Kushal Kedia, Prithwish Dan, Angela Chao, Maximus Adrian Pace, Sanjiban Choudhury.

Engineers built E-BAR, a mobile robot designed to physically support the elderly and prevent them from falling as they move around their homes. E-BAR acts as a set of robotic handlebars that follows a person from behind, allowing them to walk independently or lean on the robot's arms for support.

As demand for robotic delivery grows worldwide, an Australian-made design is leading the charge into a new era of temperature-controlled food delivery.

At less than one and a half inches tall, roughly the same height as a LEGO minifigure, the world's smallest self-contained bipedal robot can self-start from standstill, walk faster than a half mile per hour, turn, skip, and ascend small steps with just the power of its on-board battery, actuator, and control system.