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Artificial Intelligence in Phishing: Which Benefits Attackers or Defenders More?

As cybercrime grows, the cybersecurity industry has had to adopt the latest technology to keep up.

 Artificial intelligence (AI) has quickly become one of the most helpful tools in stopping cyberattacks, but attackers can use it too. Recent phishing trends are a perfect example of both sides of the issue.

Phishing is by far the most common type of cybercrime today . As more companies become aware of this growing threat, they have implemented AI tools to stop it.

However, cybercriminals are also increasing their use of artificial intelligence in phishing.

 Here’s a closer look at how both parties are using this technology and who benefits more from it.

How Does AI Help Fight Phishing?

cyber-security

Phishing attacks take advantage of people’s natural tendencies for curiosity and fear.

Because this social engineering is so effective, one of the best ways to guard against it is to make sure you don’t see it. This is where artificial intelligence comes into play.

Anti-phishing AI tools often come in the form of advanced email filters. These programs scan your incoming messages for phishing attempts and automatically send suspicious emails to your junk folder.

Some new solutions can: Detect phishing emails with 99.9% accuracy by creating different versions of scam messages based on real examples to train themselves to detect variations.

As security researchers detect more phishing emails, they can feed more data into these models, making them even more accurate.

 AI’s continuous learning capabilities also help improve models to reduce false positives.

AI can also help you stop phishing attacks when you click on a malicious link. Automated monitoring software can create a baseline for normal behavior and detect anomalies that are likely to occur when someone else uses your account.

They can then lock the profile and alert security teams before the intruder does too much damage.

How Do Attackers Use Artificial Intelligence in Phishing?

Attackers

AI’s potential to stop phishing attacks is impressive, but it is also a powerful tool for creating phishing emails.

 As generative AI like ChatGPT becomes more accessible, it makes phishing attacks more effective.

Spear phishing, which uses personal details to create user-specific messages, is one of the most effective types of phishing.

 An email that gets all your personal information correctly will naturally be much more convincing.

However, creating these messages has traditionally been difficult and time-consuming, especially on a large scale. With generative AI, this is no longer the case.

Artificial intelligence can generate massive amounts of tailored phishing messages in a fraction of the time a human can.

He’s also better than humans at writing convincing fakes. In a 2021 study, AI-generated phishing emails saw significantly higher click-through rates than human-written ones — and this was before the release of ChatGPT.

Just as marketers use AI to customize customer outreach campaigns, cybercriminals can use it to create effective, user-specific phishing messages.

 As generative AI improves, these hoaxes will only become more convincing.

Attackers Stay Ahead Thanks to Human Weaknesses

Attackers Stay Ahead Thanks to Human Weaknesses

As attackers and defenders benefit from AI, which side has seen the most significant benefits? If you look at recent cybercrime trends, you will see that cybercriminals are evolving despite more advanced protections.

Business email compromise attacks rose 81% in the second half of 2022, and employees opened 28% of those messages.

 This is part of a longer-term increase of 175% over the past two years, which shows phishing is growing faster than ever.

These attacks are also effective. Stealing $17,700 per minute , which is probably why they’re behind 91% of cyberattacks.

Why has phishing increased so much despite artificial intelligence improving anti-phishing protections? It probably comes down to the human element. Employees must actually use these tools to be effective.

Beyond that, employees may engage in other unsafe activities that make them susceptible to phishing attempts, such as logging into work accounts from unauthorized, unprotected personal devices.

The aforementioned survey also found that employees reported only 2.1% of attacks. This lack of communication can make it difficult to see where and how security measures need to be improved.

How to Protect Against Increasing Phishing Attacks?

Given this alarming trend, businesses and individual users must take steps to stay safe.

Implementing AI anti-phishing tools is a good start, but it can’t be your only measure. Not only are 7% of Security teams not using or planning to use AI, but phishing continues to dominate, so companies need to address the human element as well.

Since humans are the most vulnerable link to phishing attacks, they should be the focus of mitigation steps. Organizations should make security best practices a more prominent part of employee onboarding and ongoing training.

 These programs should include how to detect phishing attacks, why this is a problem, and simulations to test the retention of information after training.

It’s also important to use stronger identity and access management tools, as they help stop successful breaches once you get into an account.

Even experienced employees can make mistakes, so you must be able to detect and stop breached accounts before they cause major damage.

AI is a Powerful Tool for Both Good and Bad

AI is one of the most disruptive technologies in recent history. Whether this is good or bad depends on its use.

It is crucial to recognize that AI can help cybercriminals as much as – or even more than – cybersecurity experts.

 When organizations acknowledge these risks, they can take more effective steps to address increasing phishing attacks.

Robotics: The Future of Automation and Manufacturing

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Robotics technology has led to a revolutionary change in industrial production processes in recent years.

This technology is widely used to increase efficiency in production, reduce costs and perform more complex tasks.

In this article, we will discuss the impact of robotics in the future of automation and production.

Robotics technology in automation and production

Automation and robotic technology in production have provided a significant transformation in industrial production.

Robots have the potential to reduce the workforce and increase efficiency by automating production processes in industrial facilities.

Robots, used in many areas from assembly lines to material handling operations, perform repeatable tasks without error.

Robots capable of performing complex tasks; They can increase productivity in production by using them effectively in areas such as precise assembly processes, high-speed production and continuous workflow.

Robotics technology can also increase flexibility in production. For example, the programs of robots can be easily changed and they can perform different tasks for the production of various products. This allows production processes to be quickly adapted and flexible.

Industry 4.0 and smart production systems

Industry 4.0 represents an important step in digital transformation in the production sector. This concept includes the integration of technologies such as smart manufacturing systems, data analytics, artificial intelligence (AI) and the internet of things (IoT).

One of the main goals of Industry 4.0 is to adopt a more flexible, efficient and sustainable approach to production. This concept enables digital transformation in production processes, encouraging the industrial sector to be more competitive and the adoption of smarter production systems.

Smart production systems include digitalization and automation in production processes. These systems have the ability to collect and analyze real-time data through sensors and technology and optimize production processes using this data.

For example; The internet of things (IoT) enables physical devices in production to communicate with each other. Devices on production lines collect data through sensors and transfer this data to cloud-based systems, providing real-time monitoring and control.

Artificial intelligence (AI) and machine learning are used in areas such as predictive maintenance, inventory management and production efficiency by analyzing large data sets in production processes. These technologies increase operational efficiency in production while also enabling more flexible and personalized production models.

Human-machine collaboration and industrial robots

While industrial robots are in the same workspaces as humans, they are actually designed to work together safely. This has introduced the concept of human-machine collaboration and allowed industrial robots to take on a more flexible and collaborative role.

Human-machine collaboration refers to the ability of industrial robots to work together with humans to perform certain tasks. This approach enables robots to work alongside humans, especially on tasks that require complex assembly operations or fine workmanship.

So-called collaborative robots can interact with humans safely. They perceive people around them through sensors and cameras and react according to their movements.

Such robots offer ergonomic advantages in industry. For example, they can assist people with physically demanding tasks, such as lifting heavy loads or performing repetitive tasks. This increases the productivity of employees and also ensures occupational safety.

Robotics and its impact on the workforce: Retraining and job change

The spread of robotic technology has had impacts on the workforce. Automating some jobs may lead to the disappearance or replacement of certain occupations. This situation reveals the need for employees to be retrained and adapt to changes in the workforce.

The development of robotic technology has revolutionized production processes and transformed the industrial sector. However, the effects of this technology also lead to profound effects in areas such as workforce, education and industrial transformation

Is It Possible to Colonize Space?

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The possibility of colonizing space is a frequently discussed and researched topic among scientists, engineers and space exploration enthusiasts.

Although it is not yet possible to establish permanent human settlements in space, advances in space exploration make humanity’s dream of establishing permanent settlements on the Moon, Mars, and even more distant planets an increasingly realistic possibility.

In this article, we discuss the challenges of establishing a community life in space and potential ways to overcome these challenges.

Challenges to colonizing space

-Radiation: While Earth has a magnetic field and atmosphere that protect us from harmful cosmic rays and solar radiation, there is no such protection in space.

Therefore, space radiation can pose serious health risks to humans. Even long Mars and Moon missions that can be realized with current technology will expose astronauts to much higher radiation than normal.

Although spacecraft and special clothing protect in this regard, the technological competence that can make humanity’s life in space permanent does not yet exist.

-Microgravity Effects: Gravity in space is so low as to be negligible. The gravitational effect is much less on some planets than on Earth, and on others it is many times higher than on Earth.

Prolonged exposure to microgravity causes health problems such as muscle atrophy and loss of bone density. For a permanent settlement in space, it is imperative to design large artificial gravity fields.

-Sustainability: Extraterrestrial planets do not have the habitable conditions that the earth offers to humans. For example, temperatures at night on Mars drop to -73 degrees.

To create a habitable area on a planet, the presence of liquid water is essential. Therefore, establishing a self-sufficient habitat in space brings with it major logistical challenges, such as producing food and water and providing breathable air, as well as designing protective habitats against harsh space conditions.

These challenges are among the obstacles that must be overcome on the way to realizing sustainable life in space.

-Psychological and Social Factors: Life in space, whether on a spacecraft or in a manufactured sheltered artificial space on the surface of a planet, will be quite different compared to Earth.

Far from Earth’s natural light cycle, biological clock and sleep pattern disruptions are common.

In addition, the stress of staying in a closed environment all the time; It can lead to feelings of isolation and loneliness. Current space programs subject astronauts to special training to overcome these challenges.

-Political and Economic Conflicts: While achieving space colonization requires a large amount of financial resources, it also requires international cooperation.

Issues such as the management and legal regulations of space colonies are complex and important aspects of this process and need to be handled carefully.

Current efforts

Currently, the International Space Station (ISS) provides a reference for long-term human settlements in space with its permanent human presence.

The experiences of the astronauts here may shed light on the effects of extraterrestrial life on human mental health and studies on eliminating the negative effects.

Apart from this, the Mars mission project jointly carried out by NASA and SpaceX and the idea of ​​establishing a base on the Moon can be seen as small steps and testing stages at the beginning of the path to space colonization.

Theories on colonization routes

It is essential to create earth-like conditions for the colonization of space. In this context, studies and theories aimed at creating closed ecological systems form the backbone of supporting human life in space.

Self-sustaining life support systems; It is planned to recycle air, water and waste, and may also include growing plants for food and oxygen production. The BIOS-3 project in Siberia and the Biosphere 2 experiment in the USA are examples of attempts to create closed ecosystems.

Additionally, concepts such as the Stanford Torus and the O’Neill Cylinder have provided comprehensive theoretical suggestions on establishing habitats in space.

One of these examples, the Stanford Bagel, developed by NASA, is a design that can accommodate between 10 thousand and 140 thousand permanent residents.

These structures are giant man-made closed ecosystems powered by the sun, designed to rotate to create artificial gravity.

In conclusion; Colonization of space does not seem possible in the near future, considering the cost of the difficulties that must be overcome.

However, considering the speed of advancement in technology and human curiosity about the secrets of space, it is possible for life in space to occur in the long term.

2024 Technology Trends

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We are in the age of technology and information.

Therefore, encountering new technological developments that transform our lives every year has become something we are used to.

2023 witnessed the beginning of groundbreaking developments in many other fields, especially artificial intelligence and quantum computing.

We started to feel the effects of these developments on our lives. So, what breakthroughs does 2024 bring? In this blog post, we take a look at some of the technology trends that are expected to dominate in 2024.

Generative AI

Generative Artificial Intelligence (AI), one of the latest technologies, has created a major transformation in various industries by enabling machines to create content that resembles human-produced works.

This technology can provide effective solutions in a wide range of applications, from text generation to image synthesis and even music composition.

The ever-expanding applications of generative AI promise a bright future for those who specialize in this field, offering opportunities to shape the way we interact and create content in the digital age.

extended reality

Extended reality includes the combined use of all technologies that simulate reality with Virtual Reality, Augmented Reality, Mixed Reality and similar applications.

It also stands out as an important rising technology trend in this period when we are eager to expand the borders of the world.

This technology, which creates a reality that has no tangible existence, is hugely popular among gamers, medical professionals, retail and modeling.

digital trust

As people became intertwined with devices and technologies, digital technologies were adopted in a very short time and a certain trust in them was formed.

Digital trust is another vital trend leading to greater innovation. Thanks to digital faith, people believe that technology can create a safe, secure and reliable digital world and help companies invent and innovate without worrying about maintaining public trust.

For this reason, it is expected that more serious work will be carried out in the coming period to create more robust practices and regulations on this issue.

New energy solutions

The world tends to switch to greener practices to protect nature and the energy we use. As a result, efforts continue to make greener options such as electric or battery-powered cars, solar and other renewable energy sources feasible and integrate them into our lives.

In this context; Improving the performance and reducing the costs of batteries has become a major focus for businesses and governments alike.

The aim is to support electric mobility and accelerate the energy transition to renewable energy and increasing the capacity of smart grids. While LFP (lithium ferro-phosphate) and NMC (nickel manganese cobalt) are becoming the standard for electric vehicle applications, various technologies are being investigated regarding the chemistry of batteries, such as cobalt-free (sodium ion) or solid-state batteries.

Significant developments are expected in this field in 2024. Additionally, new energy solutions represent a major shift in battery technology, primarily for electric vehicles, as they have higher energy densities (i.e.

storage capacity) at lower cost than traditional batteries.

In addition; It promises longer lifespan and more robust security while reducing reliance on materials such as certain lithium, nickel, cobalt, rare earth minerals and graphite.

edge computing

With the amount of data constantly increasing, many organizations have realized the limitations of cloud computing.

Edge computing is a system developed to overcome the delays caused by cloud computing and to process data in the area closest to the user or objects in need without transmitting it to a data center.

Edge computing is important because of its ability to reduce latency by providing faster response times to applications.

It meets the needs of time-sensitive operations by processing data closer to the source, which confirms that it can be an ideal solution for applications in areas such as automation and healthcare.

Additionally, edge computing offers a more powerful solution, especially in regions with limited connectivity, by increasing data security and privacy.

Internet of things (IoT)

Another promising new technology trend is the Internet of Things, or IoT. Many “things” are now manufactured with WiFi connectivity so they can connect to the internet and each other.

Therefore, IoT is the future and has already begun to enable devices, home appliances, cars and many other objects to connect to the internet and exchange data over the internet.

All of the things we have listed are just some of the technologies that are thought to be trending in the coming period.

These important technology trends are expected to shape the transformation that 2024 will bring and offer new opportunities to our lives.

As we rapidly approach the target dates for reducing and stopping the effects of the climate crisis, these trends have the potential to interactively reinforce each other and make our lives and business world more efficient, productive and sustainable.

Human-Guided AI Framework Promises Faster Robotics Learning in New Environments

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In the future era of smart homes, it will not be uncommon to get a robot to make household chores easier.

However, frustration can set in when these automated assistants fail to perform simple tasks. Andi Peng, an academic from MIT’s Department of Electrical Engineering and Computer Science, and her team are developing a way to improve the learning curve of robots.

Peng and his interdisciplinary research team developed a human-robot interactive framework .

 The salient feature of this system is its ability to generate counterfactual narratives that identify the changes required for the robot to successfully perform a task.

For example, when a robot has trouble recognizing a specially painted mug, the system presents alternative situations in which the robot would be successful, perhaps if the mug were a more common color.

These counterfactual explanations, combined with human feedback, facilitate the process of generating new data for fine-tuning the robot.

“Fine-tuning is the process of optimizing an existing machine learning model that is already proficient at one task to enable it to perform a second, similar task,” Peng explains.

A Leap in Efficiency and Performance

Efficiency

When put to the test, the system showed impressive results. Robots trained with this method demonstrated rapid learning abilities while reducing the time commitment of human teachers.

If successfully implemented on a larger scale, this innovative framework could help robots quickly adapt to new environments and minimize the need for users to have advanced technical knowledge.

This technology could be the key to unlocking general-purpose robots that can efficiently assist elderly or disabled individuals.

“The ultimate goal is to empower a robot to learn and operate at a human-like abstract level,” says Peng.

Revolutionary Robot Training

Robot Training

The primary obstacle in robotic learning is ‘deployment drift’, a term used to describe the situation where a robot encounters objects or spaces to which it has not been exposed during the training period.

To solve this problem, researchers applied a method known as ‘imitation learning’. But it had its limitations.

“Imagine having to demonstrate with 30,000 cups for a robot to pick up any cup. Instead, I prefer to demonstrate with just one cup and teach the robot to understand that it can pick up a cup of any color,” says Peng.

In response, the team’s system determines which features of the object are essential to the task (like the shape of a cup) and which are not (like the color of the cup).

Armed with this information, synthetic data optimizes the robot’s learning process by replacing “non-essential” visual elements.

Connecting Human Reasoning to Robotic Logic

Robotic Logic

To measure the effectiveness of this framework, the researchers conducted a test involving human users.

Participants were asked whether counterfactual descriptions of the system improved their understanding of the robot’s task performance.

“We found that humans are innately skilled at this type of counterfactual reasoning. It is this counterfactual element that allows us to seamlessly translate human reasoning into robotic logic,” Peng says.

During multiple simulations, the robot consistently learned faster with its approach, outperformed other techniques, and required fewer demonstrations from users.

Going forward, the team plans to apply this framework to real robots and reduce the time to generate data through manufacturing.

 machine learning models. This groundbreaking approach has the potential to transform the robot learning trajectory, paving the way for a future where robots coexist harmoniously in our daily lives.

Researchers Find Learning Can Be Mimicked in Synthetic Substance

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Researchers at Rutgers University have discovered that learning can be mimicked in synthetic matter.

Learning is a fundamental feature of intelligence, and the new finding could have major implications for algorithm development in artificial intelligence (AI).

The new study was published in the journal PNAS .

Basic Characteristics of Man 

The fundamental feature of learning in humans has inspired the development of many artificial intelligence technologies and enables them to adapt to changing conditions and environments.

 However, AI often focuses on imitating human logic. The researchers’ new discovery offers a way to mimic human cognition in devices that can learn, remember, and make decisions in ways similar to humans and our brains.

By creating this in solid form, new algorithms in artificial intelligence and neuromorphic computing can be developed with the flexibility to address uncertainties, contradictions, and other similar aspects present in our daily lives.

 Neuromorphic computing creates artificial nervous systems to transmit electrical signals that mimic brain signals and does so to mimic the general neural structure and functioning of the human brain.

Researchers at Rutgers were joined by colleagues at Purdue, the University of Georgia and Argonne National Laboratory.

The Role of Nickel Oxide

Together, the researchers examined how the electrical conductivity of a special type of insulating material, nickel oxide, responded after its environment was changed many times over various periods.

Subhashish Mandal is a postdoctoral associate in the Department of Physics and Astronomy at Rutgers-New Brunswick.

“The goal was to find a material whose electrical conductivity could be tuned by modulating the concentration of atomic defects with external stimuli such as oxygen, ozone and light,” Mandal said. “We studied how this material behaves when we mix the system with oxygen or hydrogen and, most importantly, how external stimulation changes the electronic properties of the material.”

One of the researchers’ findings was that the material was unable to respond fully when gas stimuli changed rapidly.

 Instead, he remained in an uncertain state in both environments as his response began to wane.

The researchers then introduced a noxium stimulus, such as ozone, and the material responded more strongly before decreasing again.

“The most interesting aspect of our results is that they show universal learning features such as habit and sensitivity that we generally find in living species,” Mandal said. Said.

“These material properties could inspire new algorithms for artificial intelligence. Just as the collective motion of birds or fish has inspired artificial intelligence, we believe the collective behavior of electrons in a quantum solid could do the same in the future.”

“The growing field of artificial intelligence requires hardware that can accommodate adaptive memory capabilities beyond those used in today’s computers,” he continued. “We found that nickel oxide insulators, which have historically been limited to academic pursuits, may be interesting candidates to test in the future for brain-inspired computers and robotics.”

UBC and Honda Unveil Revolutionary Soft Sensor for Precision Robotics

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Researchers at the University of British Columbia (UBC), in collaboration with Honda, have pioneered a smart, stretchable and remarkably sensitive soft sensor in a breakthrough development that promises to reshape the world of robotics and prosthetics.

This advanced technology will unlock countless applications, heralding a new era in which robots and prosthetic devices not only move but also feel, enhancing their interactions with the world and people.

The combination of sensitivity and durability in the new sensor mimics the touch of human skin, giving machines the unprecedented ability to perform tasks that require delicate touch, such as processing soft fruit without damaging it.

The implications of this advancement are vast and diverse, from improved safety in human-robot interactions to improved functionality in automated tasks.

The development of the sensor, Dr. It is a testament to the creativity and forward-thinking approach of the UBC team, guided by Mirza Saquib Sarwar’s innovative research in electrical and computer engineering.

However, Honda’s Frontier Robotics brings a storied history of robotics innovation to the table, making collaboration a powerhouse of technological synergy.

As the world stands on the cusp of this robotic renaissance, the introduction of the soft sensor marks an important milestone in our journey to create machines that not only mimic human actions but also have human sensitivity.

This breakthrough is a sign of the extraordinary achievements we can achieve when science, engineering and vision come together.

The Innovation of Touch

Innovation of Touch

The new soft sensor developed by UBC and Honda researchers is not just an incremental update of existing technology; It represents a significant leap forward in robotics and prosthetic functionality.

With the ability to provide tactile sensitivity and dexterity to robotic limbs and prosthetic arms, this sensor addresses one of the most challenging aspects of robotics: the precise handling of objects.

The sensor enables delicate tasks previously out of reach of machines, such as picking up and holding fragile items such as eggs or full glasses without the risk of applying excessive force.

The importance of this technology lies in its capacity to mimic the complex sensory feedback of human touch.

It allows machines to measure the amount of force needed to grasp without causing damage, making them more capable of integrating into environments that require a light touch.

This advancement is not only a step forward for robotics, but also a step towards humanizing interactions between machines and the living world.

The sensor’s human skin-like softness further strengthens this bridge, making human interactions with machines safer and more natural than ever before.

 

The Science Behind the Sensor

Behind the Sensor

The basis for this innovative sensor is a composition of silicone rubber, a practical yet versatile material widely used for realistic skin effects in cinema productions.

What sets the UBC-Honda sensor apart is its unique ability to mimic the bending and wrinkling properties of human skin, giving it the edge in realistic haptic feedback.

The sensor works on the principle of weak electric fields to detect objects, drawing parallels with touch screens familiar in everyday life, but surpassing them with its flexible form that can detect not only touch but also the direction and magnitude of forces.

This sensitivity is made possible by a complex design that allows the sensor to be compressed and contoured, providing a level of responsiveness that is incomparable to current standards.

One of the leading names in the development of this technology, Dr. John Madden emphasizes the importance of the sensor’s ability to detect interactions on its surface.

His leadership at UBC’s Advanced Materials and Process Engineering Laboratory (AMPEL) has been instrumental in pushing the boundaries of what is possible in flexible sensor technology.

The design of the sensor, which facilitates wrinkling similar to human skin, is a very important invention that allows the detection of various stimuli that a robotic limb or prosthesis may encounter.

As this technology moves from the laboratory to real-world applications, it stands as a shining example of innovation inspired by the natural world and designed to enhance the artificial.

The sensor promises to revolutionize the way robots not only perceive their environment but also change the way they interact with the environment by blending the line between organic touch and synthetic sensation.

From Laboratory to Life

The creativity of the sensor combines with its practicality in production. The researchers emphasize the simple manufacturing process, which is crucial for scalability and widespread application.

The simplicity of the sensor design allows it to be easily manufactured, making it a suitable option for covering large surface areas or being produced in significant quantities without exorbitant costs.

This practical approach to design and manufacturing means this technology can move seamlessly into everyday use in a variety of environments, from the research laboratory and industrial automation to personal assistive devices.

The future of the UBC-Honda sensor shines brightly, with the potential for scalability opening the door to a multitude of applications in robotics and beyond.

As technology continues to evolve, there is a clear path to cover broader areas of robots and prosthetics, improving their functionality and user experience.

The sensor’s ability to be produced in large quantities also points to a future in which this technology could become a standard component in robotics, and precision touch could become a common feature rather than a luxury.

With the continued development of sensors and artificial intelligence, the next frontier is to create robots that can not only perceive with the acuity of human skin but also intelligently interpret and respond to a multitude of sensory information.

This advancement in sensor technology lays the foundation for a future in which robots are not just tools but partners who can interact with the world around them with more subtlety and precision.

How Do Asimov’s Three Laws of Robotics Affect Artificial Intelligence?

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The Three Laws of Robotics are iconic in the world of science fiction and have become a symbol in the artificial intelligence and robotics community of how difficult it is to properly design a perfect system.

To fully understand the importance of these three laws, we must first learn about the brilliant mind who designed these laws, the late science fiction writer Isaac Asimov.

Then we must understand how to adapt these laws and ensure that they evolve to protect humanity.

Isaac Asimov – The Rise of a Genius

Isaac Asimov was born in Russia. He immigrated to the United States on January 2, 1920, and at the age of three. 

He grew up in Brooklyn, New York, and graduated from Columbia University in 1939. He became known as a talented and prolific writer focusing on science and science fiction.

He wrote and/or edited more than 500 books throughout his career.

Asimov was heavily inspired by some of the most iconic writers in science fiction. He began his work at the Philadelphia Navy Yard, where he met two co-workers who would soon emerge as two of the most successful science fiction writers in the history of speculative fiction: L. Sprague de Camp and Robert A. Heinlein.

L. Sprague de Camp is an award-winning author who wrote more than 100 books and was a major figure in science fiction in the 1930s and 1940s. Some of his most popular works include “Darkness Fall” (1939), “The Wheels of If” (1940), “A Gun for Dinosaur” (1956), “Aristotle and the Gun” (1958), and “The Glory That Happened” (1956). 1960).

At the height of his career, Robert A. Heinlein was arguably the world’s most popular science fiction writer.

Along with Isaac Asimov and Arthur C. Clarke, he was considered the “Big Three” of science fiction writers. Some of Robert A.

Heinlein’s most popular works are “Farnham’s Freehold” (1964) and “ Sail Beyond the Sunset” (1987). The current generation probably knows him best for the film adaptation of his novel “Starship Troopers” (1959).

Being surrounded by these giants of futurism inspired Issac Asimov to begin his prolific writing career.

Asimov was also highly respected in the scientific community and was frequently commissioned as a speaker to give talks about science.

Three Laws of Robotics

Issac Asimov was the first person to use the term ‘Robotics’. ‘Liar!’ It was published in 1941.

Shortly thereafter, his 1942 short story “Runaround” introduced the world to the three laws of robotics. The laws are:

1. A robot cannot injure a human being or allow a human being to come to harm by remaining inactive.

2. A robot must obey orders given to it by humans, unless they conflict with the First Law.

3. A robot must protect its own existence as long as it does not violate the First and Second Laws.

These laws were designed to present interesting plot points, and Asimov went on to create a series of 37 science fiction short stories and six novels featuring positronic robots.

One of these short story collections, called “I, Robot”, was later adapted into film in 2004. The movie “I, Robot” starring Will Smith is set in a dystopian 2035 and features highly intelligent public servant robots who operate according to three laws.

of robotics. Much like the stories, the film quickly became a parable of how programming can go wrong and how programming any type of advanced AI involves a high level of risk.

The world has now caught up with what was previously science fiction, we are now designing artificial intelligence that is in some ways far more advanced and also far more limited than anything Issac Asimov could have imagined.

The three laws of Robotics are referenced quite frequently in discussions of Artificial General Intelligence (AGI).

We will quickly explore what AGI is and how the three laws of Robotics should evolve to avoid possible problems in the future.

Artificial General Intelligence (AGI)

Most types of AI we currently encounter on a daily basis are measured as “narrow AI.” This is a type of AI that is very specific and narrow in its utility function.

For example, an autonomous vehicle can navigate the streets, but due to its “narrow” limitations, the AI ​​cannot easily complete other tasks.

Another example of narrow AI would be an image recognition system that can easily identify and tag images in a database, but cannot be easily adapted to another task.

Artificial General Intelligence, commonly referred to as “AGI,” is an artificial intelligence that can quickly learn, adapt, pivot, and function in the real world, similar to humans.

It is a type of intelligence that is not narrow in scope, can adapt to any situation, and learns how to deal with real-world problems.

It is worth noting that while Artificial Intelligence is advancing exponentially, we still have not achieved AGI.

 When we will reach AGI is up for debate, and everyone has a different answer as to the timeline. I personally agree with the views of Ray Kurzweil, inventor, futurist and author of ‘The Singularity is Near’. Achieved AGI by 2029 .

It’s this 2029 timeline that’s a ticking clock, we have to learn to code some kind of rulebook into AI that is not only similar to the three laws, but also more advanced and able to actually avoid the real world. Conflict between humans and robots.

Today’s Robotics Laws

While the three laws of robotics are outstanding for the literature, they significantly lack the complexity to seriously program a robot. That was the plot point behind the short stories and novels, after all.

Contradictions between the three laws, or at least interpretations of the three laws, have resulted in robots melting down, retaliating against humans, or other major plot points.

The main problem with current laws is that the ethical programming of always following human instructions and always self-preservation may conflict.

After all, is the robot allowed to defend itself against an owner who abuses it?

What type of fail-safe mechanism needs to be programmed? How do we tell a robot that it must shut down, regardless of the consequences?

If a robot is in the process of rescuing a housewife from abuse, should the robot automatically shut down if the abusive husband instructs it to do so?

Who should give instructions to robots? With autonomous weapons capable of identifying and targeting enemies from around the world, could the robot refuse a command to eliminate a target if it identifies the target as a child?

In other words, if the robot is owned and controlled by a psychopath, can the robot refuse immoral orders? The questions are many and the answers are too difficult for any individual to answer.

 That is why such organizations Future of Life Institutes are so important that the time to discuss these moral dilemmas is before a true AGI emerges.

A New Dawn in Robotics: Touch-Based Object Rotation

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In a groundbreaking development, a team of engineers at the University of California San Diego (UCSD) has designed a robotic hand that can rotate objects simply by touch, without the need for visual input.

 This innovative approach is inspired by the way people can effortlessly manipulate objects without having to see them.

A Touch-Sensitive Approach to Object Manipulation

Touch-Sensitive

The team equipped a four-fingered robotic hand with 16 touch sensors spread across the palm and fingers. Each sensor, which costs about $12, performs a simple function: it detects whether an object is touching it.

This approach is unique because it relies on a large number of low-cost, low-resolution touch sensors that use simple binary signals (touch or non-touch) to perform robotic hand rotation.

In contrast, other methods rely on several high-cost, high-resolution touch sensors affixed to a small area of ​​the robotic hand, specifically the fingertips.

Xiaolong Wang, a professor of electrical and computer engineering at UC San Diego who led the study, explained that these approaches have several limitations.

They limit the system’s detection ability by minimizing the possibility of sensors coming into contact with the object.

High-resolution touch sensors that provide information about texture are extremely difficult to simulate and prohibitively expensive, making them difficult to use in real-world experiments.

The Power of Binary Signals

Binary Signals

“We show that we don’t need details about the texture of an object to do this task. We just need simple binary signals of whether the sensors are touching the object, and these are much easier to simulate and transfer to the real world,” he said.

The team trained their system using simulations of a virtual robotic hand rotating various objects, including those with irregular shapes.

The system evaluates which sensors in the hand the object touches at any point in time during rotation.

It also evaluates previous movements as well as current positions of the hand joints. Using this information, the system tells the robot hand which joint should go where at the next time point.

The Future of Robotic Manipulation

Future of Robotic

The researchers tested their system on a real-life robotic hand with objects the system had not yet encountered.

The robotic hand was able to rotate various objects without stopping or losing its grip. The objects included a tomato, a pepper, a can of peanut butter, and a toy rubber ducky, which was the most difficult object due to its shape.

Objects with more complex shapes took longer to rotate. The robotic hand can also rotate objects around different axes.

The team is now working to extend their approach to more complex manipulation tasks.

They are currently developing techniques that will enable robotic hands to catch, throw and juggle, for example. “In-hand manipulation is a very common skill that we humans have, but it is very complex for robots to master,” Wang said. “If we can give robots this ability, it will open the door to the types of tasks they can perform.”

This development marks a significant step forward in the field of robotics, potentially paving the way for robots that can manipulate objects in the dark or visually challenging environments.

China’s PLA Explores Artificial Intelligence Integration to Improve Military Combat Proficiency

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China is leading the way in military artificial intelligence (AI) development with ChatGPT-like technology included into an experimental project.

A group of scientists working with the People’s Liberation Army (PLA) in China is trying to make the country’s military AI better at dealing with unexpected situations involving humans.

Many stories in Chinese media suggest that they want the AI to be more intelligent when dealing with situations involving human opponents.

China has officially acknowledged the use of commercial large language models (LLMs) in military applications for the first time with the unveiling of this AI project.

Moreover, this move raises questions about the potential risks and ethical considerations associated with unleashing sophisticated AI, which has been praised for its capabilities but also criticized for its lack of control and potential to lead to unintended consequences has been lauded for its capabilities but also criticized for the lack of control and potential for unintended consequences.

The research team has established a physical link between their AI system and commercially developed language models such as Baidu’s Ernie and iFlyTek’s Spark. and commercially developed language models, namely Baidu’s Ernie and iFlytek’s Spark.

The military AI can translate frontline reports and a wealth of sensor data into text or images.

It shares this with other commercial models. Once collected, the military AI can communicate with them without the need for human intervention.

It even creates suggestions for more detailed discussions, like practicing for battles, all on its own.

A peer-reviewed report detailing the project was made accessible in the Chinese academic journal Command Control & Simulation in December 2023.

In a research paper, scientist Sun Yifeng and his team from the PLA’s Information Engineering University stated that both people and machines could benefit from the project.

One scientist has warned that if the issue is not handled carefully, it could turn into a story like the ones in popular culture, and comparisons to those scenarios have been made. Terminator films, where AI becomes uncontrollable.

The project’s objectives are clarified in the released document, which highlights the aim of making military AI more “human-like.”

This entails having a deeper comprehension of the goals of commanders at all levels and improving communication with human counterparts.

The integration of commercial large language models is seen to deepen the military AI’s understanding of human behavior.

Predicting the Next Move on the Battlefield

Battlefield

In a fictitious experiment described in the paper, Ernie received intelligence from the military AI regarding a fictitious US military attack of Libya in 2011.

Following multiple conversations, Ernie was able to accurately identify the US military’s next course of action.

According to the research team, these predictive capacities could make up for human shortcomings by addressing problems like cognitive biases that could cause people to overestimate or underestimate dangers on the battlefield.

However, it is accepted that the information disclosed in the published article is only the tip of the iceberg.

The research team deliberately kept certain aspects of the project secret, including how military and commercial models could learn from past failures and mutually gain new knowledge and skills.

It is worth noting that China is not alone in exploring military applications of artificial intelligence .

 Various branches of the U.S. Similar technologies have attracted the interest of the military, which is investigating uses such as decrypting communications codes, controlling drones, and conducting psychological warfare.

As the global race for AI supremacy intensifies, cautionary voices from scientists underscore the need for responsible and ethical development and highlight the potential risks posed by unrestricted access to powerful AI systems, military networks, and classified information