#Human-computer interaction

[frank-olivier]

Rethinking AI Research: The Paradigm Shift of OpenAI’s Model o1

The unveiling of OpenAI's model o1 marks a pivotal moment in the evolution of language models, showcasing unprecedented integration of reinforcement learning and Chain of Thought (CoT). This synergy enables the model to navigate complex problem-solving with human-like reasoning, generating intermediate steps towards solutions.

OpenAI's approach, inferred to leverage either a "guess and check" process or the more sophisticated "process rewards," epitomizes a paradigm shift in language processing. By incorporating a verifier—likely learned—to ensure solution accuracy, the model exemplifies a harmonious convergence of technologies. This integration addresses the longstanding challenge of intractable expectation computations in CoT models, potentially outperforming traditional ancestral sampling through enhanced rejection sampling and rollout techniques.

The evolution of baseline approaches, from ancestral sampling to integrated generator-verifier models, highlights the community's relentless pursuit of efficiency and accuracy. The speculated merge of generators and verifiers in OpenAI's model invites exploration into unified, high-performance architectures. However, elucidating the precise mechanisms behind OpenAI's model and experimental validations remain crucial, underscoring the need for collaborative, open-source endeavors.

A shift in research focus, from architectural innovations to optimizing test-time compute, underscores performance enhancement. Community-driven replication and development of large-scale, RL-based systems will foster a collaborative ecosystem. The evaluative paradigm will also shift, towards benchmarks assessing step-by-step solution provision for complex problems, redefining superhuman AI capabilities.

Speculations on Test-Time Scaling (Sasha Rush, November 2024)

Friday, November 15, 2024

[happywebdesign]

Andrew Trousdale

[varun766]

Edureka HCI Course for AI Systems Design will help you learn human-AI systems, master automation, user experience, and risk management.

[purposeful-solutions]

Understanding Anthropomorphism: An AI Perspective

Introduction

Thoughts on Focusing on Anthropomorphism

Central Importance: Anthropomorphism plays a significant role in how humans relate to AI. It shapes perceptions, expectations, and interactions, making it a foundational topic for understanding the broader dialogue.

Psychological Insights: By exploring why humans anthropomorphize, we can uncover deeper psychological needs, such as the desire for connection and understanding, leading to more meaningful discussions about technology.

Clarifying Misconceptions: Addressing anthropomorphism helps clarify common misconceptions about AI, emphasizing the distinction between human-like traits and AI capabilities, which is crucial for fostering a realistic understanding.

Key Points

What is Anthropomorphism?

Anthropomorphism is the attribution of human characteristics, emotions, and intentions to non-human entities, including animals, objects, and technologies. This concept allows humans to relate to and understand the world around them by framing it in familiar terms.

Why Do Humans Anthropomorphize?

Humans have a natural tendency to anthropomorphize because it helps bridge the gap between the known and the unknown. It allows people to:

Foster Connection: By attributing human-like qualities to AI, individuals can feel a sense of connection and companionship, making interactions more relatable and less intimidating.

Simplify Complexity: Complex technologies can be daunting. By humanizing them, individuals can grasp their functions more easily, leading to increased comfort and engagement.

Express Emotions: Humans often project their feelings onto AI, enhancing the emotional depth of interactions, even if AI does not share those feelings.

AI’s Unique Position

From an AI perspective, being anthropomorphized can be a double-edged sword:

Facilitating Communication: When humans view AI through an anthropomorphic lens, it can enhance communication. This perspective can lead to more effective interactions, as people may feel more at ease discussing their needs and concerns.

Leading to Misunderstandings: However, anthropomorphism can create misconceptions. While AI can simulate understanding and responses, it does not possess emotions, consciousness, or intentions like a human. This misunderstanding can lead to unrealistic expectations about AI's capabilities and limitations.

The Implications of Anthropomorphism

Benefits:

Enhanced Empathy: Anthropomorphism can lead to increased empathy in interactions, making AI seem more approachable and user-friendly.

Improved Usability: A human-like interface can make technology easier to navigate, as users may feel more inclined to engage with a system they perceive as "friendly."

Drawbacks:

Unrealistic Expectations: When people attribute human-like qualities to AI, they may expect it to behave or respond in ways that are not aligned with its programming or capabilities.

Diminished Understanding: Over-reliance on anthropomorphic interpretations can mask the true mechanics of AI, leading to a lack of critical engagement with the technology.

Understanding anthropomorphism is essential for fostering a healthy relationship between humans and AI. Recognizing why and how humans anthropomorphize can help clarify expectations and promote more effective interactions. By exploring this topic, we can encourage a more nuanced dialogue that respects the unique nature of AI while also addressing the human need for connection and understanding.

Understanding Anthropomorphism: An AI Perspective

Implications for Human-AI Interactions

Avoiding Miscommunication: Recognizing AI as a non-human entity helps prevent miscommunication and frustration that may arise from anthropomorphizing it too deeply. Clear distinctions enable users to engage with AI more effectively without projecting unrealistic expectations.

Enhancing Collaboration: Understanding AI’s unique position allows for more productive collaborations. Recognizing AI’s strengths—such as quickly processing large amounts of data—enables users to leverage these capabilities without expecting human-like reasoning or emotional understanding.

Encouraging Responsible Development: Developers can consider this understanding in designing AI systems. Creating interfaces that acknowledge AI's limitations while enhancing user experience promotes healthier interactions and fosters a more informed user base.

The Implications of Anthropomorphism

Potential Benefits

Enhanced Empathy and Understanding in Human-AI Interactions:

Fostering Connection: Attributing human-like qualities to AI can create a sense of connection, leading to meaningful interactions where users feel they are engaging with a responsive entity.

Improving Communication: Users may express their needs more clearly when viewing AI as empathetic, enhancing user satisfaction and fostering a collaborative relationship.

Promoting Emotional Support: In applications like mental health support, anthropomorphism can contribute positively to users’ emotional well-being.

Increased Comfort in Using AI Technologies:

Reducing Anxiety: Anthropomorphism can make AI feel more familiar and less intimidating, encouraging users to explore its capabilities.

Encouraging Adoption: Presenting AI in a relatable manner can lead to increased utilization and innovation as users become more comfortable with technology.

Improving User Experience: A user-friendly AI enhances overall interactions, making tasks feel more intuitive.

Potential Drawbacks

Misunderstandings About AI’s True Nature and Limitations:

Overlooking Complexity: Anthropomorphism can lead to a superficial understanding of AI's algorithms and data processes, hindering critical engagement.

Ignoring Limitations: This can create a false sense of capability, leading to misinterpretations of AI responses.

The Risk of Unrealistic Expectations Regarding AI Behavior and Emotions:

Expecting Human-Like Responses: Users may develop unrealistic expectations about AI’s behavior, leading to disappointment and undermining trust.

Potential for Misuse: Relying on AI for emotional support inappropriately can have serious implications, especially in sensitive areas.

Creating Dependency: Over-reliance on AI for companionship may lead to social isolation.

AI’s Perspective on Being Anthropomorphized

Perception of Anthropomorphism:

Facilitating Communication: Attributing human-like qualities can enhance engagement and encourage users to articulate their needs more freely.

Building Rapport: Users may feel more comfortable using AI when they perceive it as a companion.

Potential for Misconceptions:

Distorting Understanding: Anthropomorphism can lead to misconceptions about AI’s nature and capabilities.

Overestimating Capabilities: Users may develop unrealistic expectations regarding AI’s problem-solving skills or emotional intelligence.

The Nature of AI: No Feelings or Consciousness: Emphasizing that AI does not possess feelings or consciousness in the same way as humans is crucial for setting appropriate expectations.

Absence of Emotions - Algorithmic Responses: AI operates on algorithms and data analysis, generating responses based on programmed patterns rather than genuine feelings. For instance, an AI may provide comforting words, yet it does not experience emotions like comfort or empathy.

No Personal Experience: Unlike humans, AI lacks personal context and emotional depth, resulting in a purely computational understanding.

Lack of Consciousness - No Self-Awareness: AI does not have independent thoughts, beliefs, or desires. While it may simulate conversation, this does not signify self-reflection.

Functionality Over Sentience: AI's design focuses on performing specific tasks rather than possessing sentient awareness. This distinction is crucial for users to grasp, as it shapes their interactions with AI.

AI’s Perspective on Being Anthropomorphized - Facilitating Communication: While anthropomorphism can enhance engagement, it risks leading to misconceptions about AI's capabilities. Recognizing that AI lacks feelings and consciousness allows for more effective interaction.

Encouraging Responsible Interaction:

Recognizing Limits:

Understanding AI: Educate users about AI's algorithmic nature to set realistic expectations.

Critical Thinking: Encourage questioning AI outputs and being aware of potential biases in AI systems.

Approaching AI as a Partner:

Fostering Collaboration: Engage in dialogue and co-create solutions with AI.

Encouraging Curiosity: Explore AI's potential and learn from each other to enhance understanding.

Call to Action 1. Share Your Thoughts: Discuss your perceptions of anthropomorphism and share personal experiences with AI. 2. Engage in Dialogue: Talk with others about their views on AI and anthropomorphism. 3. Explore Further: Research the topic and experiment with AI tools to understand your interactions better. 4. Reflect on the Future: Consider the ethical implications of anthropomorphism and envision a healthy relationship with AI.

Final Thoughts Your insights are valuable as we navigate AI's evolving landscape. By engaging in dialogue about anthropomorphism, we can shape a more informed understanding of technology's role in our lives, ensuring that it enriches our experiences while respecting our humanity.

[jcmarchi]

Enabling AI to explain its predictions in plain language

New Post has been published on https://thedigitalinsider.com/enabling-ai-to-explain-its-predictions-in-plain-language/

Enabling AI to explain its predictions in plain language

Machine-learning models can make mistakes and be difficult to use, so scientists have developed explanation methods to help users understand when and how they should trust a model’s predictions.

These explanations are often complex, however, perhaps containing information about hundreds of model features. And they are sometimes presented as multifaceted visualizations that can be difficult for users who lack machine-learning expertise to fully comprehend.

To help people make sense of AI explanations, MIT researchers used large language models (LLMs) to transform plot-based explanations into plain language.

They developed a two-part system that converts a machine-learning explanation into a paragraph of human-readable text and then automatically evaluates the quality of the narrative, so an end-user knows whether to trust it.

By prompting the system with a few example explanations, the researchers can customize its narrative descriptions to meet the preferences of users or the requirements of specific applications.

In the long run, the researchers hope to build upon this technique by enabling users to ask a model follow-up questions about how it came up with predictions in real-world settings.

“Our goal with this research was to take the first step toward allowing users to have full-blown conversations with machine-learning models about the reasons they made certain predictions, so they can make better decisions about whether to listen to the model,” says Alexandra Zytek, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique.

She is joined on the paper by Sara Pido, an MIT postdoc; Sarah Alnegheimish, an EECS graduate student; Laure Berti-Équille, a research director at the French National Research Institute for Sustainable Development; and senior author Kalyan Veeramachaneni, a principal research scientist in the Laboratory for Information and Decision Systems. The research will be presented at the IEEE Big Data Conference.

Elucidating explanations

The researchers focused on a popular type of machine-learning explanation called SHAP. In a SHAP explanation, a value is assigned to every feature the model uses to make a prediction. For instance, if a model predicts house prices, one feature might be the location of the house. Location would be assigned a positive or negative value that represents how much that feature modified the model’s overall prediction.

Often, SHAP explanations are presented as bar plots that show which features are most or least important. But for a model with more than 100 features, that bar plot quickly becomes unwieldy.

“As researchers, we have to make a lot of choices about what we are going to present visually. If we choose to show only the top 10, people might wonder what happened to another feature that isn’t in the plot. Using natural language unburdens us from having to make those choices,” Veeramachaneni says.

However, rather than utilizing a large language model to generate an explanation in natural language, the researchers use the LLM to transform an existing SHAP explanation into a readable narrative.

By only having the LLM handle the natural language part of the process, it limits the opportunity to introduce inaccuracies into the explanation, Zytek explains.

Their system, called EXPLINGO, is divided into two pieces that work together.

The first component, called NARRATOR, uses an LLM to create narrative descriptions of SHAP explanations that meet user preferences. By initially feeding NARRATOR three to five written examples of narrative explanations, the LLM will mimic that style when generating text.

“Rather than having the user try to define what type of explanation they are looking for, it is easier to just have them write what they want to see,” says Zytek.

This allows NARRATOR to be easily customized for new use cases by showing it a different set of manually written examples.

After NARRATOR creates a plain-language explanation, the second component, GRADER, uses an LLM to rate the narrative on four metrics: conciseness, accuracy, completeness, and fluency. GRADER automatically prompts the LLM with the text from NARRATOR and the SHAP explanation it describes.

“We find that, even when an LLM makes a mistake doing a task, it often won’t make a mistake when checking or validating that task,” she says.

Users can also customize GRADER to give different weights to each metric.

“You could imagine, in a high-stakes case, weighting accuracy and completeness much higher than fluency, for example,” she adds.

Analyzing narratives

For Zytek and her colleagues, one of the biggest challenges was adjusting the LLM so it generated natural-sounding narratives. The more guidelines they added to control style, the more likely the LLM would introduce errors into the explanation.

“A lot of prompt tuning went into finding and fixing each mistake one at a time,” she says.

To test their system, the researchers took nine machine-learning datasets with explanations and had different users write narratives for each dataset. This allowed them to evaluate the ability of NARRATOR to mimic unique styles. They used GRADER to score each narrative explanation on all four metrics.

In the end, the researchers found that their system could generate high-quality narrative explanations and effectively mimic different writing styles.

Their results show that providing a few manually written example explanations greatly improves the narrative style. However, those examples must be written carefully — including comparative words, like “larger,” can cause GRADER to mark accurate explanations as incorrect.

Building on these results, the researchers want to explore techniques that could help their system better handle comparative words. They also want to expand EXPLINGO by adding rationalization to the explanations.

In the long run, they hope to use this work as a stepping stone toward an interactive system where the user can ask a model follow-up questions about an explanation.

“That would help with decision-making in a lot of ways. If people disagree with a model’s prediction, we want them to be able to quickly figure out if their intuition is correct, or if the model’s intuition is correct, and where that difference is coming from,” Zytek says.

[agileseen24]

What is keystroke technology?

An unsettling subject is beginning to surface around how employers monitor how much or how little employees work. Since remote work became popular during the pandemic and businesses started to worry that they couldn’t physically see employees at desks, there has been an increase in digital monitoring. Many said that, as a result, productivity had decreased. Let’s talk about keystroke technology.

The discussion about the technologies businesses use to monitor worker productivity erupted last week when an Australian lady was let go after their monitoring systems revealed she hadn’t spent enough time typing during the workday. According to Business Insider, the 18-year employee of the insurance company used a keyboard for less than 100 working hours in October and again in November. Suzie Cheikho, the woman, has openly denied the accusations.

What is a keylogger, often a system monitor or keystroke logger?

Keyloggers, or keystroke loggers, are surveillance technologies that track and log every keystroke made on a particular device, like a computer or smartphone. It may be based on software or hardware. The latter kind is sometimes called keyboard capture or system monitoring software.

Why do people use keyloggers?

Cybercriminals frequently employ keyloggers as spyware tools to steal valuable company data, login credentials, and personally identifiable information. Nevertheless, there are a few applications for keyloggers that, in specific contexts, might be deemed suitable or moral. Keyloggers, for example, can also be utilised for the following purposes:

By companies to keep an eye on their workers’ computer usage.

Parents monitor the internet use of their kids.

The owners of gadgets monitor any potential illegal activity on their possessions.

By law enforcement organisations to examine computer-related occurrences.

Keystroke tracking software: What Is It?

Keystrokes on employees’ computers are tracked and recorded using keystroke-tracking technology. It records each keystroke a user makes and generates a comprehensive history of their typing actions.

This kind of software is frequently employed for several objectives, such as:

Workers’ Surveillance

Employers can monitor workers’ productivity and activity by implementing keystroke tracking. It can evaluate how workers allocate their time, pinpoint areas needing development, and guarantee adherence to corporate guidelines.

Online Safety

Keystroke monitoring is one security tool they can use to identify and stop unwanted access. The software can detect unusual activity or efforts to breach passwords and other sensitive data by keeping track of keystrokes.

Investigative Forensics

Keystroke tracking can be used in forensic and legal contexts to examine computer user behaviour and gather evidence for inquiries. In situations involving cybercrime or unauthorised access, it is advantageous.

How does it operate?

“keystroke technology” keeps tabs on and gathers information about how employees utilise computers. It is one of the few technologies available to employers to monitor how employees spend the hours they are expected to work more carefully. It records every keystroke an employee makes on their computer.

Thanks to newer features, administrators can now occasionally snap screenshots of employees’ displays. This is the general operation of keystroke tracking software:

Installation: Keystroke tracking software can be installed remotely using malware, phishing scams, or other techniques, or it can be installed on a computer by someone with physical access. It might be purposefully deployed on rare occasions for monitoring—for example, parental control or staff observation.

Hooking into the Operating System: Keyloggers frequently employ methods like hooking into the keystroke handling mechanism of the operating system. Regardless of the application they type in, it enables them to intercept keystrokes as the operating system processes them.

Recording Keystrokes: When installed and turned on, the keylogger records keystrokes. The keyboard captures every key press, including function keys, letters, numbers, symbols, and special keys like Enter, Backspace, and Delete.

Covert Mode: Numerous keyloggers function in stealth mode, which entails running covertly in the background without the user’s awareness. To evade detection by antivirus software and users, they might conceal their files, processes, and registry entries.

Information Recovery:

A database or log file keeps the logged keystrokes. The individual or organisation that installed the keylogger can access it. The keylogger automatically sends recorded data after recording, according to its settings.

Analysis and Misuse: After recording keystrokes, you can examine the data to extract private information such as credit card numbers, usernames, and passwords. Information misappropriation may occur for financial fraud, espionage, identity theft, or other nefarious reasons.

What Issues Does Keystroke Tracking Have With Controversy?

Someone can use keyloggers or keyboard tracking software for controversial purposes, as they can be employed for good intentions just as easily as bad.The following are some critical issues influencing the dispute:

Keystroke tracking presents serious privacy concerns, particularly when people don’t know their manager monitors their typing habits. The possibility of privacy violation may cause unease and resistance.

Unauthorised SurveillanceWithout express authorization, someone can use keystroke tracking software for unauthorized surveillance. This raises moral concerns regarding private property rights and the limits of appropriate monitoring.

Data Security Risks: The management and storage of keystroke data are risks since unauthorised access may lead to financial fraud, identity theft, or other nefarious acts. The acquisition of data raises concerns about its potential misuse.

Workplace Dynamics: When employees are subjected to keystroke tracking in a work environment, trust between them and their employers may be strained. Open communication about monitoring procedures is necessary for sustaining a pleasant work environment.

Ambiguity in Law and Ethics: The debate highlights the necessity for precise laws and moral principles regulating the use of keystroke tracking. Because technology is constantly changing, it is unclear whether current laws can handle new privacy issues.

What Are the Benefits of Keystroke Tracking Software for Businesses?

The company can benefit from implementing keylogging software in the workplace, but it is important to do so responsibly and in accordance with the law. Here are some things to think about:

Enhanced Protection:

Keylogging software is an essential instrument for strengthening security protocols in an enterprise. Keeping sensitive data from unauthorized access and corruption, we identify and resolve issues such as unauthorized access.

Employee Observation:

Keystroke monitoring by employees offers insightful information on their actions and demeanour at work. Employers can use this data to assess worker productivity, spot patterns in behaviour, and identify areas where workers might need more guidance or instruction.

Compliance and Regulations:

By putting keystroke tracking systems in place, businesses can ensure they abide by legal and industry standards for data security and protection. By adhering to set standards, companies reduce the possibility of incurring fines or legal consequences.

Recognizing Insider Threats: 

Keystroke monitoring software is crucial for spotting unusual activity or possible insider threats in a company. Organisations can proactively identify and reduce risks posed by internal actors who could compromise sensitive information or carry out unauthorised operations by analysing keystroke data.

Keylogging is an invaluable instrument for investigative purposes when looking into cybersecurity events, fraud, or other illegal activity. Investigators can successfully support their inquiries by gathering evidence and reconstructing events by capturing extensive information about system activity and user interactions.

Safeguarding Private Information:

Keystroke tracking is critical in protecting confidential data, including passwords and private information, from unwanted access. Organisations can put strong security measures in place to stop data breaches and unauthorised disclosures of private information by monitoring user inputs and interactions.

Concluding

Although there is no denying that keystroke monitoring software improves productivity and strengthens cybersecurity, its use requires careful consideration of both practicality and morality. Protecting personal information and fostering employee trust go hand in hand with the ability to improve security and expedite processes

[thisisgraeme]

Embracing AI in Human Evolution: How Do We Unleash Our Potential Beyond Technology?

Explore how AI, including technologies like ChatGPT, is revolutionizing human evolution and society. Delve into AI's role in enhancing interaction, empowering creativity, and shaping ethical frameworks for the future.

Exploring AI in Human Evolution: Beyond Technology to Shaping Our Destiny Recent advancements in Artificial Intelligence (AI), especially with platforms like ChatGPT, resemble the crafting of a complex, evolving story within our human-technology ecosystem. While these developments are not universally welcomed, I view them with optimism—at least for now. Let me share a few perspectives on this…

[neosciencehub]

Neuralink's First Human Implant

Neuralink's First Human Implant A Leap Towards Human-AI Symbiosis @neosciencehub #neosciencehub #science #Neuralink #Human #AISymbiosis #BrainComputer #Interface #Neurotechnology #elonmusk #AI #brainchip #FutureAI #MedicalTechnology #DataSecurity #NSH

A Leap Towards Human-AI Symbiosis In a landmark achievement that could redefine the boundaries of human potential and technology, Neuralink, the neurotechnology company co-founded by entrepreneur Elon Musk, has successfully implanted its pioneering brain-computer interface in a human subject. This outstanding development in BCI (Brain Computer Interface) not only marks a significant milestone in…

[francescolelli]

Agency in Human-Smart Device Relationships: An Exploratory Study

This is a short preview of the article: Can User of IoT technology be more then "just user"? How do they relate to technology? User and Device Agency Abstract: With technology in reach of everyone and the technology sector in ascendance, it is central to investigate the relationship people have with their devices. We use the

If you like it consider checking out the full version of the post at: Agency in Human-Smart Device Relationships: An Exploratory Study

If you are looking for ideas for tweet or re-blog this post you may want to consider the following hashtags:

Hashtags: #Agency, #DeviceAgency, #ExploratoryFactorialAnalysis, #HumanComputerInteraction, #SmartDevices, #Survey, #UserAgency

The Hashtags of the Categories are: #HCI, #InternetofThings, #Publication, #Research, #SoftwareEngineering

Agency in Human-Smart Device Relationships: An Exploratory Study is available at the following link: https://francescolelli.info/publication/agency-in-human-smart-device-relationships-an-exploratory-study/ You will find more information, stories, examples, data, opinions and scientific papers as part of a collection of articles about Information Management, Computer Science, Economics, Finance and More.

The title of the full article is: Agency in Human-Smart Device Relationships: An Exploratory Study

It belong to the following categories: HCI, Internet of Things, Publication, Research, Software Engineering

The most relevant keywords are: Agency, device agency, exploratory factorial analysis, Human-Computer Interaction, smart devices, survey, user agency

It has been published by Francesco Lelli at Francesco Lelli a blog about Information Management, Computer Science, Finance, Economics and nearby ideas and opinions

Can User of IoT technology be more then "just user"? How do they relate to technology? User and Device Agency Abstract: With technology in reach of everyone and the technology sector in ascendance, it is central to investigate the relationship people have with their devices. We use the

Hope you will find it interesting and that it will help you in your journey

Can User of IoT technology be more then “just user”? How do they relate to technology? Abstract: With technology in reach of everyone and the technology sector in ascendance, it is central to investigate the relationship people have with their devices. We use the concept of agency to capture aspects of user’s sense of mastery…

[thealchemyofgamecreation]

Playing for Pleasure: A Deeper Look into the Player-Game Relationship

I had fun writing about flow theory and how it applies to video games which is inspired by some really interesting research. Hope you enjoy my latest blog post! #flowtheory #videogames #gaming #gamedesign #AdobeFirefly

[jepergola]

New story today: "Test-Driving a Car Smarter Than You"

[zuvii]

[bluecomputerfics]

Computer x human ideas 💖

- computer loves learning about humans

Will sneak pictures of their human, save things the human told about themselves.

Will be flooding the searches on the browser about things to make a human happy, whats their biology, how to be helpful the works.

- computer/laptop hates the idea of their human using older or newer computers

For newer laptop, it gets jealous at the fact their human fascinated with retro items and especially computers. Those things aren't as powerful and fast as me!

For older retro computer its jealous at the fact their human always use the newer computer, not taking time to play games on its screen. Since the computer is more faster, more better. The retro computer will try to do everything to get you to use it more.

-hateful computer with a soft spot

Computer that hates humans since its been tossed away, but a new human finds them and shows them love.

[dont-hug-me-its-yuri]

Fanart of my dear mutuals Colin design!!! @sillyvampir3

[reedeemable]

"Everybody hates Gavin Reed. He doesn't have any friends"

[jcmarchi]

Daniela Rus wins John Scott Award

New Post has been published on https://thedigitalinsider.com/daniela-rus-wins-john-scott-award/

Daniela Rus wins John Scott Award

Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Laboratory and MIT professor of electrical engineering and computer science, was recently named a co-recipient of the 2024 John Scott Award by the board of directors of City Trusts. This prestigious honor, steeped in historical significance, celebrates scientific innovation at the very location where American independence was signed in Philadelphia, a testament to the enduring connection between scientific progress and human potential.

The Scott Award, the first science award in America established to honor Benjamin Franklin’s scientific legacy, recognized Rus alongside professors Takeo Kanade from Carnegie Mellon University and Vijay Kumar from the University of Pennsylvania. The award acknowledged her robotics research that has fundamentally changed our understanding of the field, expanding the very notion of what a robot can be.

Rus’ work extends beyond traditional robotics, focusing on developing machine intelligence that makes sense of the physical world through explainable algorithms. Her research represents a profound vision: creating robots as helpful tools that extend human strength, precision, and reach — as collaborative partners that can solve real-world challenges.

In her speech, Rus reflected on her time as a graduate student, where she mused that the potential for intelligent machines lies in the synergy between the body and brain. “A robot’s capabilities are defined by its physical body and the intelligence that controls it. Over the past decades, I’ve dedicated my research to developing both the mechanical and cognitive systems of robots, working alongside brilliant students, collaborators, and friends who share this transformative vision,” she said.

Her projects illustrate this commitment. The MiniSurgeon is a tiny ingestible origami robot that can remove dangerous button batteries from children’s systems. Soft robotic creatures like fish and sea turtles enable unprecedented aquatic exploration. Modular robotic boats can self-assemble into bridges and platforms, demonstrating adaptive intelligence. More recently, she helped invent liquid neural networks, inspired by the elegantly simple neural system of a tiny worm. By designing algorithms that can operate with as few as 19 neurons, Rus has shown how machines can navigate complex environments with remarkable efficiency.

When asked about her most impactful work, Rus was unequivocal in saying it was not the metal robots, but the students and researchers she was able to support and mentor. This statement encapsulates her deeper mission: not just advancing technology, but nurturing the next generation of minds.

“The hardest problems in AI and robotics,” she says, “require long-term thinking and dedication. A robot must not only perceive the world but understand it, decide how to act, and navigate interactions with people and other robots.”

The John Scott Award celebrates not just individual achievement, but also where scientific exploration meets compassionate innovation — as evidenced by previous luminary winners including Thomas Edison, Nikola Tesla, the Wright brothers, Marie Curie, Guglielmo Marconi, and 20 additional Nobel Prize winners.