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cleveredlearning · 6 days ago

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Empower Your Child’s Future with Clevered's Coding for Kids in Dubai

In today’s rapidly evolving digital world, understanding technology is no longer optional—it's essential. At Clevered, we believe that the future belongs to those who can create with technology, not just consume it. That’s why we’ve designed a dynamic, hands-on Coding for Kids program in Dubai, tailored specifically for young learners between the ages of 6 to 16.

Our mission? To unlock your child’s creativity, sharpen their problem-solving skills, and ignite a lifelong passion for innovation—one line of code at a time.

Why Coding Matters for Kids

Before children learn to code, they learn to think. Coding teaches kids how to break problems down, identify patterns, and build logical solutions. More than just programming, it's about developing the 21st-century skills—critical thinking, collaboration, communication, and creativity.

In the UAE, especially in a forward-looking city like Dubai, digital literacy is becoming an integral part of early education. With the UAE Vision 2031 emphasizing AI, digital transformation, and smart education, coding isn't just a skill—it’s a superpower.

At Clevered, we’re not just teaching kids how to code; we’re preparing them to be future innovators, entrepreneurs, and leaders in a tech-driven world.

Why Choose Clevered in Dubai?

Dubai is a global hub of innovation, technology, and education. At Clevered, we’ve aligned our vision with the city’s ambitious goals. Here’s what makes our Coding for Kids in Dubai program truly stand out:

1. Curriculum Designed by Global Experts

Our coding courses are curated by a team of top educators and software engineers from institutions like MIT, Stanford, and IIT. We blend global pedagogy with local relevance, ensuring each session is both enriching and exciting.

From Scratch programming for beginners to Python, Web development, AI, and Robotics—we cover it all.

2. Age-Appropriate Learning Paths

We believe every child learns differently. That’s why our coding programs are segmented into different age groups:

Junior Coders (6–8 years): Fun, visual programming with Scratch Jr. and animation.

Intermediate Coders (9–12 years): Game development, app design, and intro to Python.

Advanced Coders (13–16 years): Python, AI, Web development, Data Science, and real-world projects.

3. Live Online & Offline Classes Across Dubai

Whether you prefer the convenience of online learning or the energy of a classroom, Clevered gives you the flexibility to choose. Our live online classes are interactive and personalized, while our physical learning centers across Dubai offer high-tech labs and hands-on mentorship.

4. Project-Based Learning

Children learn best by doing. At Clevered, coding is taught through exciting projects—game development, building chatbots, designing websites, creating animations, and even programming robots. These aren’t just tasks—they’re stepping stones to innovation.

5. Certified Instructors & Mentors

Our instructors are not just teachers—they’re mentors. Each one is certified, background-verified, and trained to work with children. They inspire curiosity, encourage creativity, and guide kids through every coding milestone.

Coding Programs at Clevered – What’s Inside?

Here’s a glimpse into our core modules offered at Clevered’s Dubai coding centers and online platform:

1. Introduction to Coding (Scratch)

Perfect for young learners, this module helps kids understand how coding works by dragging and dropping visual blocks. Kids build their own stories, animations, and games while learning sequencing, loops, and logic.

2. App Development for Kids

Kids learn how to design and develop simple apps using Thunkable and MIT App Inventor, turning their ideas into usable tools. Whether it's a calculator app or a digital diary, they build with purpose.

3. Python Programming

Python is the world's most popular beginner language. Kids explore real-world programming concepts like variables, conditionals, functions, and loops. Ideal for ages 10+.

4. Artificial Intelligence & Machine Learning

Yes, kids can learn AI! Through simple, child-friendly interfaces, students learn the basics of machine learning, facial recognition, and more.

5. Web Development

HTML, CSS, and JavaScript—the holy trinity of the web. Kids learn how to build their own websites, blogs, and portfolios.

6. Robotics & IoT

Combining hardware with software, this module introduces kids to sensors, motors, and microcontrollers like Arduino and Raspberry Pi—offered at selected centers in Dubai.

Clevered’s Impact: Testimonials That Speak Volumes

“I was amazed when my 8-year-old daughter built her first animated story using Scratch. Clevered made coding fun and easy!” – Amina R., Parent, Dubai Marina

“Clevered’s instructors are excellent. My son now wants to pursue computer science in the future. The AI module especially opened his mind.” – Saeed M., Parent, Jumeirah

“It’s not just about coding; it’s about building confidence, solving problems, and seeing your child shine.” – Neha P., Parent, Downtown Dubai

Safety, Flexibility, and Recognition

At Clevered, we prioritize:

Child-Safe Online Environment: Zoom-based classes with encrypted, private sessions. No ads, distractions, or third-party interruptions.

Flexible Schedules: Weekend, after-school, and holiday coding camps.

Certifications: Every child receives a Certificate of Completion, and advanced learners get Level-based Badges and Portfolio Support.

We also host Annual Code Fest Competitions and encourage students to participate in local and international hackathons and coding olympiads.

Dubai Parents Ask — We Answer!

Q1. My child has no background in coding. Is this suitable? Absolutely! Our curriculum is designed for beginners. We start from the basics and build step-by-step.

Q2. How long are the courses? Courses range from 6-week short programs to year-long journeys depending on the child’s age and proficiency.

Q3. Can my child join in the middle of the term? Yes! We offer rolling admissions with personalized onboarding to ensure no child feels left behind.

Q4. Do you offer demo classes? Of course! Book a FREE Trial Class today to experience Clevered firsthand.

Special Offer: Coding for Kids – Summer Camp 2025

This summer, let your child build, explore, and create like never before!

🌟 Clevered’s Summer Coding Camp 📍 Locations: Downtown Dubai | Al Barsha | Dubai Silicon Oasis | Online 📆 Dates: July–August 2025 🎓 Activities: Game Design, AI for Kids, Coding Challenges, Robotics Lab 🎁 Early Bird Offer: Flat 20% Off for Registrations Before June 30!

Seats are limited. Secure your spot now.

Ready to Future-Proof Your Child?

Don’t just let your child play games—teach them to build one.

Join thousands of parents across Dubai who are choosing Clevered’s Coding for Kids program to give their children a head start in life. Whether your child is an aspiring game developer, engineer, or creative thinker, Clevered is the launchpad to their dreams.

🚀 Enroll Today! 📞 Call Us: + 97336805659 🌐 Visit: https://www.clevered.com/Young-Coders-Program 📩 Email: [email protected]

Clevered – Code. Create. Conquer.

#coding for kids #best coding for kids #best online coding classes for kids

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leonhorn · 7 days ago

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In 2056 Post Work Enlightenment (Tribes) | In 20xx Sci fi and Futurism by In 20xx Futurism Amidst this perpetual twilight, new forms of societal organization begin to emerge. Cindy, waking one morning, discovers an invitation to "Open Floor," a virtual space for direct democracy where citizens can vote on issues day and night. Stepping into this digital forum, she finds herself in a shifting circle of avatars, some representing real people, others AI syntheses of ongoing conversations. Debates on constitutions, crime, and surveillance unfold, raising questions about AI governance and the very nature of truth in a world increasingly mediated by technology. Beyond the digital commons, life in the underground colony thrums with a mixture of innovation and unease. News reports speak of geothermal power plants and advanced home care kits, but also of suicides, overcrowded hospitals, and violent disputes over communal spaces. Misty, a key figure, navigates the complexities of this new world, from inspecting hyper-insulated facilities housing powerful AI to discussing the controversial policy of mandatory schooling with emulated personalities. Personal lives, like Cindy's, are also in flux, with new relationships forming even as old anxieties and suspicions begin to surface. A.R., Position Avatar, robots, robot baby, V.R., internet, geothermal power plant, home care kits, Assist, construction-bots, cleaner bots, cargo cart, server systems, supercomputer systems, protein computers, Lutin Twos, A.R. visors, pre-computer, A.S.I., graphene protein computers, A.R. glasses, V.R. dots, mics, cameras, emulated personalities, A.I., Lutin bots, A.R. avatar, transparent A.R. presentation board, A.R. note books, droids, unbroken screen, follow cart, my-crete, lift, Open Floor, screen top table, panic fabric, guard dog bots, autono-carts, no-charge kitchens, BritLights, carb-foil, Tribal Net, A.I. relationships, V.R. taste and smell, V.R. rigs, builder bots, cop bots, robot police, platform robot, T.V. wall, food taster pen, seamless screen ceiling, pea size cameras, sensors, automated MIT, emulated personality professors, sun-grade light spaces, electric bike, bipedal bot, many jointed snake robot, bot, light drones, pill, automated restaurant, Q.T. gateway, cyber-deck, canal links, whisper-jet drones, button-size recording devices, V.R. auditorium, virtual gowns, virtual caps, chandeliers of light, movie sphere, sun lamps, taser cloves, head lamp, air mask, link, DNA sniffers, encrypted streaming, Autonomous policing, coin size cameras. Many of the characters in this project appear in future episodes. Using storytelling to place you in a time period, this series takes you, year by year, into the future. From 2040 to 2195. If you like emerging tech, eco-tech, futurism, perma-culture, apocalyptic survival scenarios, and disruptive science, sit back and enjoy short stories that showcase my research into how the future may play out. The companion site is https://in20xx.com These are works of fiction. Characters and groups are made-up and influenced by current events but not reporting facts about people or groups in the real world. This project is speculative fiction. These episodes are not about revealing what will be, but they are to excited the listener's wonder about what may come to pass. Copyright © Cy Porter 2025. All rights reserved. Episode link: https://ift.tt/VR4ZJFD (video made with https://ift.tt/XTZP8Q7) via YouTube https://www.youtube.com/watch?v=W3X4zgogvU4

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sunaleisocial · 12 days ago

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Inroads to personalized AI trip planning

New Post has been published on https://sunalei.org/news/inroads-to-personalized-ai-trip-planning/

Inroads to personalized AI trip planning

Travel agents help to provide end-to-end logistics — like transportation, accommodations, meals, and lodging — for businesspeople, vacationers, and everyone in between. For those looking to make their own arrangements, large language models (LLMs) seem like they would be a strong tool to employ for this task because of their ability to iteratively interact using natural language, provide some commonsense reasoning, collect information, and call other tools in to help with the task at hand. However, recent work has found that state-of-the-art LLMs struggle with complex logistical and mathematical reasoning, as well as problems with multiple constraints, like trip planning, where they’ve been found to provide viable solutions 4 percent or less of the time, even with additional tools and application programming interfaces (APIs).

Subsequently, a research team from MIT and the MIT-IBM Watson AI Lab reframed the issue to see if they could increase the success rate of LLM solutions for complex problems. “We believe a lot of these planning problems are naturally a combinatorial optimization problem,” where you need to satisfy several constraints in a certifiable way, says Chuchu Fan, associate professor in the MIT Department of Aeronautics and Astronautics (AeroAstro) and the Laboratory for Information and Decision Systems (LIDS). She is also a researcher in the MIT-IBM Watson AI Lab. Her team applies machine learning, control theory, and formal methods to develop safe and verifiable control systems for robotics, autonomous systems, controllers, and human-machine interactions.

Noting the transferable nature of their work for travel planning, the group sought to create a user-friendly framework that can act as an AI travel broker to help develop realistic, logical, and complete travel plans. To achieve this, the researchers combined common LLMs with algorithms and a complete satisfiability solver. Solvers are mathematical tools that rigorously check if criteria can be met and how, but they require complex computer programming for use. This makes them natural companions to LLMs for problems like these, where users want help planning in a timely manner, without the need for programming knowledge or research into travel options. Further, if a user’s constraint cannot be met, the new technique can identify and articulate where the issue lies and propose alternative measures to the user, who can then choose to accept, reject, or modify them until a valid plan is formulated, if one exists.

“Different complexities of travel planning are something everyone will have to deal with at some point. There are different needs, requirements, constraints, and real-world information that you can collect,” says Fan. “Our idea is not to ask LLMs to propose a travel plan. Instead, an LLM here is acting as a translator to translate this natural language description of the problem into a problem that a solver can handle [and then provide that to the user],” says Fan.

Co-authoring a paper on the work with Fan are Yang Zhang of MIT-IBM Watson AI Lab, AeroAstro graduate student Yilun Hao, and graduate student Yongchao Chen of MIT LIDS and Harvard University. This work was recently presented at the Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics.

Breaking down the solver

Math tends to be domain-specific. For example, in natural language processing, LLMs perform regressions to predict the next token, a.k.a. “word,” in a series to analyze or create a document. This works well for generalizing diverse human inputs. LLMs alone, however, wouldn’t work for formal verification applications, like in aerospace or cybersecurity, where circuit connections and constraint tasks need to be complete and proven, otherwise loopholes and vulnerabilities can sneak by and cause critical safety issues. Here, solvers excel, but they need fixed formatting inputs and struggle with unsatisfiable queries. A hybrid technique, however, provides an opportunity to develop solutions for complex problems, like trip planning, in a way that’s intuitive for everyday people.

“The solver is really the key here, because when we develop these algorithms, we know exactly how the problem is being solved as an optimization problem,” says Fan. Specifically, the research group used a solver called satisfiability modulo theories (SMT), which determines whether a formula can be satisfied. “With this particular solver, it’s not just doing optimization. It’s doing reasoning over a lot of different algorithms there to understand whether the planning problem is possible or not to solve. That’s a pretty significant thing in travel planning. It’s not a very traditional mathematical optimization problem because people come up with all these limitations, constraints, restrictions,” notes Fan.

Translation in action

The “travel agent” works in four steps that can be repeated, as needed. The researchers used GPT-4, Claude-3, or Mistral-Large as the method’s LLM. First, the LLM parses a user’s requested travel plan prompt into planning steps, noting preferences for budget, hotels, transportation, destinations, attractions, restaurants, and trip duration in days, as well as any other user prescriptions. Those steps are then converted into executable Python code (with a natural language annotation for each of the constraints), which calls APIs like CitySearch, FlightSearch, etc. to collect data, and the SMT solver to begin executing the steps laid out in the constraint satisfaction problem. If a sound and complete solution can be found, the solver outputs the result to the LLM, which then provides a coherent itinerary to the user.

If one or more constraints cannot be met, the framework begins looking for an alternative. The solver outputs code identifying the conflicting constraints (with its corresponding annotation) that the LLM then provides to the user with a potential remedy. The user can then decide how to proceed, until a solution (or the maximum number of iterations) is reached.

Generalizable and robust planning

The researchers tested their method using the aforementioned LLMs against other baselines: GPT-4 by itself, OpenAI o1-preview by itself, GPT-4 with a tool to collect information, and a search algorithm that optimizes for total cost. Using the TravelPlanner dataset, which includes data for viable plans, the team looked at multiple performance metrics: how frequently a method could deliver a solution, if the solution satisfied commonsense criteria like not visiting two cities in one day, the method’s ability to meet one or more constraints, and a final pass rate indicating that it could meet all constraints. The new technique generally achieved over a 90 percent pass rate, compared to 10 percent or lower for the baselines. The team also explored the addition of a JSON representation within the query step, which further made it easier for the method to provide solutions with 84.4-98.9 percent pass rates.

The MIT-IBM team posed additional challenges for their method. They looked at how important each component of their solution was — such as removing human feedback or the solver — and how that affected plan adjustments to unsatisfiable queries within 10 or 20 iterations using a new dataset they created called UnsatChristmas, which includes unseen constraints, and a modified version of TravelPlanner. On average, the MIT-IBM group’s framework achieved 78.6 and 85 percent success, which rises to 81.6 and 91.7 percent with additional plan modification rounds. The researchers analyzed how well it handled new, unseen constraints and paraphrased query-step and step-code prompts. In both cases, it performed very well, especially with an 86.7 percent pass rate for the paraphrasing trial.

Lastly, the MIT-IBM researchers applied their framework to other domains with tasks like block picking, task allocation, the traveling salesman problem, and warehouse. Here, the method must select numbered, colored blocks and maximize its score; optimize robot task assignment for different scenarios; plan trips minimizing distance traveled; and robot task completion and optimization.

“I think this is a very strong and innovative framework that can save a lot of time for humans, and also, it’s a very novel combination of the LLM and the solver,” says Hao.

This work was funded, in part, by the Office of Naval Research and the MIT-IBM Watson AI Lab.

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aichronicles25 · 3 months ago

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The ethical implications of artificial intelligence (AI) are a critical and timely topic, as the technology continues to advance and integrate into various aspects of our lives. So, here’s a more in-depth look at some of the major ethical concerns surrounding AI, as well as potential solutions and frameworks for addressing these issues.

1. Bias and Fairness

Ethical Concern: One of the most pressing issues with AI is bias. AI systems learn from historical data, and if that data contains biases—whether related to race, gender, socioeconomic status, or other factors—those biases can be perpetuated or even amplified by the AI. For instance, facial recognition systems have been shown to misidentify individuals from certain demographic groups at higher rates than others, leading to concerns about discrimination.

Example: A well-documented case involved a study by the MIT Media Lab, which found that commercial facial recognition systems had higher error rates for darker-skinned individuals compared to lighter-skinned individuals. This raises questions about fairness and the potential for these systems to reinforce societal inequalities.

Just a few solutions to combat bias, developers can use techniques such as:

Diverse Data Sets: Ensuring training data is representative of all demographic groups to minimize bias.

Algorithm Audits: Regularly reviewing AI systems for bias and correcting them as necessary.

Inclusive Teams: Building diverse teams of developers and stakeholders to provide different perspectives during the design and testing phases.

2. Privacy Concerns

The Ethical Concern: AI technologies often rely on large amounts of data, some of which may be personal or sensitive. This raises significant privacy concerns, particularly when it comes to surveillance systems, data collection practices, and consent.

Here are some examples to consider when the use of AI in social media platforms, where algorithms analyze user data to target advertisements. Users may not fully understand how their data is being used, leading to a lack of informed consent.

Som Solutions maybe to approach the enhance privacy include:

Data Anonymization: Removing personally identifiable information from data sets to protect individual privacy.

Transparency: Companies should clearly communicate their data collection and usage policies, allowing users to make informed choices.

Regulatory Frameworks: Governments can implement regulations (like GDPR in Europe) that require companies to prioritize user privacy and data protection.

3. Accountability and Responsibility

Ethical Concern: As AI systems become more autonomous, questions arise about accountability. If an AI makes a decision that leads to harm—such as a self-driving car in an accident—who is responsible? Is it the developer, the company, or the AI itself?

Example: The fatal incident involving a self-driving Uber vehicle in 2018 raised significant questions about accountability. The car struck and killed a pedestrian, leading to investigations into the technology and the responsibilities of those involved.

Solutions: To address accountability issues, we can:

Establish Clear Guidelines: Create legal frameworks that delineate responsibility in cases of AI-related harm.

Human Oversight: Ensure that critical decisions, especially those affecting lives and safety, involve human oversight to mitigate risks.

Ethical AI Design: Incorporate ethical considerations into the design process, ensuring that AI systems are built with accountability in mind.

4. Job Displacement

Ethical Concern: The rise of AI and automation has the potential to displace jobs across various sectors. While AI can enhance productivity, it also raises concerns about unemployment and economic inequality.

Example: Consider the manufacturing industry, where robots and AI systems are increasingly used to perform tasks traditionally carried out by human workers. This shift can lead to significant job losses and require workers to adapt to new roles.

Solutions: To mitigate job displacement, it’s important to:

Invest in Education and Training: Provide resources for workers to learn new skills that are in demand in an AI-driven economy.

Support Transition Programs: Implement programs that assist displaced workers in finding new employment opportunities.

Promote Inclusive Economic Policies: Encourage policies that support job creation in sectors that complement AI rather than compete with it.

5. Ethical Use of AI in Warfare

Ethical Concern: The application of AI in military settings raises profound ethical questions. Autonomous weapons systems that can make decisions without human intervention present risks of misuse and escalation of conflict.

Example: The development of drones and autonomous weapons has sparked debate about the morality of allowing machines to decide on matters of life and death, potentially leading to unintended consequences.

Some Solutions might be to address these concerns, we should:

In Conclusion the ethical implications of AI are complex and multifaceted, touching upon issues of bias, privacy, accountability, job displacement, and warfare. As we continue to develop and deploy AI technologies, it’s essential to prioritize ethical considerations and engage in ongoing discussions about how to harness AI for the benefit of society while mitigating its risks.

Establish International Regulations: Create global treaties that govern the use of AI in warfare, ensuring that ethical standards are upheld.

Promote Human Control: Advocate for policies that require human oversight in all military applications of AI to prevent autonomous decision-making.

By fostering a culture of responsibility, transparency, and inclusivity within the AI community, we can work towards a future where AI serves as a positive force for change, enhancing our lives while respecting fundamental ethical principles. The dialogue surrounding AI ethics is crucial, as it shapes the trajectory of this powerful technology and its impact on our world.

#aiblog #aijob #aiprivacy #aiconcerns #aivshumans #aivsjobs #aivseconomy #aibloggirl #girlblogging #blogai #aiblogger #funwithai

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jcmarchi · 5 months ago

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New training approach could help AI agents perform better in uncertain conditions

New Post has been published on https://thedigitalinsider.com/new-training-approach-could-help-ai-agents-perform-better-in-uncertain-conditions/

New training approach could help AI agents perform better in uncertain conditions

A home robot trained to perform household tasks in a factory may fail to effectively scrub the sink or take out the trash when deployed in a user’s kitchen, since this new environment differs from its training space.

To avoid this, engineers often try to match the simulated training environment as closely as possible with the real world where the agent will be deployed.

However, researchers from MIT and elsewhere have now found that, despite this conventional wisdom, sometimes training in a completely different environment yields a better-performing artificial intelligence agent.

Their results indicate that, in some situations, training a simulated AI agent in a world with less uncertainty, or “noise,” enabled it to perform better than a competing AI agent trained in the same, noisy world they used to test both agents.

The researchers call this unexpected phenomenon the indoor training effect.

“If we learn to play tennis in an indoor environment where there is no noise, we might be able to more easily master different shots. Then, if we move to a noisier environment, like a windy tennis court, we could have a higher probability of playing tennis well than if we started learning in the windy environment,” explains Serena Bono, a research assistant in the MIT Media Lab and lead author of a paper on the indoor training effect.

The researchers studied this phenomenon by training AI agents to play Atari games, which they modified by adding some unpredictability. They were surprised to find that the indoor training effect consistently occurred across Atari games and game variations.

They hope these results fuel additional research toward developing better training methods for AI agents.

“This is an entirely new axis to think about. Rather than trying to match the training and testing environments, we may be able to construct simulated environments where an AI agent learns even better,” adds co-author Spandan Madan, a graduate student at Harvard University.

Bono and Madan are joined on the paper by Ishaan Grover, an MIT graduate student; Mao Yasueda, a graduate student at Yale University; Cynthia Breazeal, professor of media arts and sciences and leader of the Personal Robotics Group in the MIT Media Lab; Hanspeter Pfister, the An Wang Professor of Computer Science at Harvard; and Gabriel Kreiman, a professor at Harvard Medical School. The research will be presented at the Association for the Advancement of Artificial Intelligence Conference.

Training troubles

The researchers set out to explore why reinforcement learning agents tend to have such dismal performance when tested on environments that differ from their training space.

Reinforcement learning is a trial-and-error method in which the agent explores a training space and learns to take actions that maximize its reward.

The team developed a technique to explicitly add a certain amount of noise to one element of the reinforcement learning problem called the transition function. The transition function defines the probability an agent will move from one state to another, based on the action it chooses.

If the agent is playing Pac-Man, a transition function might define the probability that ghosts on the game board will move up, down, left, or right. In standard reinforcement learning, the AI would be trained and tested using the same transition function.

The researchers added noise to the transition function with this conventional approach and, as expected, it hurt the agent’s Pac-Man performance.

But when the researchers trained the agent with a noise-free Pac-Man game, then tested it in an environment where they injected noise into the transition function, it performed better than an agent trained on the noisy game.

“The rule of thumb is that you should try to capture the deployment condition’s transition function as well as you can during training to get the most bang for your buck. We really tested this insight to death because we couldn’t believe it ourselves,” Madan says.

Injecting varying amounts of noise into the transition function let the researchers test many environments, but it didn’t create realistic games. The more noise they injected into Pac-Man, the more likely ghosts would randomly teleport to different squares.

To see if the indoor training effect occurred in normal Pac-Man games, they adjusted underlying probabilities so ghosts moved normally but were more likely to move up and down, rather than left and right. AI agents trained in noise-free environments still performed better in these realistic games.

“It was not only due to the way we added noise to create ad hoc environments. This seems to be a property of the reinforcement learning problem. And that was even more surprising to see,” Bono says.

Exploration explanations

When the researchers dug deeper in search of an explanation, they saw some correlations in how the AI agents explore the training space.

When both AI agents explore mostly the same areas, the agent trained in the non-noisy environment performs better, perhaps because it is easier for the agent to learn the rules of the game without the interference of noise.

If their exploration patterns are different, then the agent trained in the noisy environment tends to perform better. This might occur because the agent needs to understand patterns it can’t learn in the noise-free environment.

“If I only learn to play tennis with my forehand in the non-noisy environment, but then in the noisy one I have to also play with my backhand, I won’t play as well in the non-noisy environment,” Bono explains.

In the future, the researchers hope to explore how the indoor training effect might occur in more complex reinforcement learning environments, or with other techniques like computer vision and natural language processing. They also want to build training environments designed to leverage the indoor training effect, which could help AI agents perform better in uncertain environments.

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raulmarcus-blog · 5 months ago

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Free IT Education From MIT!

The Massachusetts Institute of Technology (MIT) has established itself as a global leader in education and innovation, renowned for its rigorous academic standards and groundbreaking contributions to science, technology, engineering, and mathematics (STEM). Its remarkable 4% acceptance rate reflects not only the institution's unparalleled selectivity but also its ability to attract the most talented and accomplished students from around the globe. This elite group of students thrives in MIT's intellectually stimulating environment, contributing to its legacy of excellence.

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The breadth and quality of MIT's educational offerings are extraordinary. The institution goes beyond its Cambridge campus to make education accessible worldwide. MIT provides over 2,000 free online courses through platforms such as MIT OpenCourseWare and edX, allowing learners to access cutting-edge knowledge in fields ranging from artificial intelligence and computer science to entrepreneurship and humanities. Many of these courses include opportunities for certification, enabling individuals to enhance their credentials and career prospects.

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STEM-Focused Resources and Global Relevance

MIT's focus on STEM disciplines aligns perfectly with the growing global demand for expertise in these areas. From its state-of-the-art laboratories and research facilities to its collaboration with industry leaders, MIT fosters an ecosystem that bridges academia and real-world applications. The institution's online courses are designed to meet the evolving needs of professionals and students alike, offering valuable educational content that prepares learners for careers in cutting-edge industries such as biotechnology, robotics, and renewable energy.

In addition to online offerings, MIT cultivates an environment of hands-on learning and interdisciplinary collaboration. This approach not only prepares students for careers in STEM but also equips them with problem-solving skills essential for addressing complex global issues.

Conclusion

MIT’s exceptional reputation is built upon its selectivity, dedication to accessibility, and innovative approach to education. Its financial aid policies ensure inclusivity, while its extensive online resources make top-tier education accessible to a global audience. By fostering excellence in STEM and embracing the principles of openness and collaboration, MIT remains at the forefront of shaping the future, empowering individuals worldwide to achieve their potential and contribute meaningfully to society.

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bigeyeglobal · 8 months ago

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Stay Informed on Artificial Intelligence Trends and Developments

In today’s fast-paced digital landscape, staying informed about AI developments is essential for businesses, tech enthusiasts, and anyone interested in the future of technology. Artificial Intelligence is evolving rapidly, transforming industries and reshaping our everyday lives. In this article, we’ll explore the latest trends in AI and how you can stay updated on these critical developments. Understanding AI Developments

AI development refers to the process of creating algorithms and systems that enable machines to perform tasks typically requiring human intelligence. This includes everything from natural language processing to machine learning and robotics. Generative AI development services have made it easier for businesses to integrate AI solutions into their operations, enhancing productivity and decision-making.

Key Trends in AI Development

Increased Automation

Automation continues to be a significant focus in AI development. Businesses are adopting AI technologies to streamline operations, reduce costs, and improve efficiency. This trend is evident in sectors like manufacturing, where robotics are used for assembly lines and inventory management.

Natural Language Processing (NLP) Advances

NLP has made significant strides, allowing machines to understand and respond to human language more effectively. This development is powering chatbots, virtual assistants, and customer service solutions, enhancing user experience and engagement.

AI Ethics and Responsible AI

As AI technology advances, so does the conversation around ethical considerations. Companies are increasingly focusing on developing AI responsibly, ensuring transparency and fairness in algorithms. Understanding these ethical implications is crucial for anyone involved in AI development.

AI in Healthcare

AI is revolutionizing healthcare through predictive analytics, patient management systems, and diagnostic tools. Staying informed about these innovations can help healthcare professionals provide better care and improve patient outcomes.

Personalization and Customer Experience

Businesses are leveraging AI to create personalized experiences for customers. From targeted marketing campaigns to tailored product recommendations, AI-driven insights help brands connect with their audiences more effectively.

How to Stay Updated on AI Trends

Follow Industry Leaders and Influencers

Keeping track of thought leaders in the AI space on social media platforms like LinkedIn and Twitter can provide you with valuable insights into the latest trends and discussions.

Subscribe to AI Newsletters and Blogs

Many organizations and publications focus specifically on AI development. Subscribing to newsletters from sources like MIT Technology Review, AI News, and industry-specific blogs can keep you informed about new advancements.

Attend Webinars and Conferences

Participating in AI conferences and webinars allows you to hear directly from experts and network with other professionals in the field. Events like the AI Summit and industry-specific meetups can be incredibly beneficial.

Utilize Online Courses and Certifications

Consider enrolling in online courses focused on AI and machine learning. Platforms like Coursera and edX offer various courses that cover the fundamentals and advanced concepts in AI development.

Engage with Online Communities

Joining forums and communities, such as Reddit’s AI community or specialized LinkedIn groups, can facilitate discussions and provide insights into the latest developments from peers in the industry.

AI Development Companies in Chennai

If you’re looking to partner with professionals in the field, consider exploring AI development company in Chennai. BigEye Global Solutions is offering cutting-edge AI development services. These companies specialize in various areas, including machine learning, data analytics, and AI-driven solutions, helping businesses leverage the power of artificial intelligence to enhance their operations.

Conclusion

Staying informed about AI developments is crucial for anyone looking to navigate the future of technology. By understanding the latest trends and utilizing available resources, you can position yourself or your business to take advantage of AI innovations. Whether you’re interested in AI development services or simply want to keep abreast of technological advancements, these strategies will help you stay ahead in the rapidly evolving world of artificial intelligence.

#ai development #ai trends #generative ai

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makers-muse · 11 months ago

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The Future of Coding Education for Children

The future of coding education for children is crucial as technology becomes more integral to our lives. Equipping kids with coding skills is essential, making coding as fundamental as reading and writing. This blog examines how children’s coding education is developing, its advantages, and the trends that will likely shape it in the future.

Current Trends in Coding Education

Integration into School Curricula

Coding is being integrated into school curricula worldwide, ensuring every child has access to coding education. Schools are using platforms like Scratch, Code.org, and MIT App Inventor to teach coding in an engaging and interactive way.

Gamification and Interactive Learning

Gamification revolutionizes coding education by making learning fun and engaging. Platforms like Scratch Jr. allow young learners to create their own games, animations, and stories, enhancing their understanding and retention of coding principles.

Robotics and Physical Computing

Robotics and physical computing are becoming popular in coding education. Kits like LEGO Mindstorms and Raspberry Pi provide hands-on experiences, allowing children to see real-world applications of their coding skills.

The Future Landscape of Coding Education

Personalized Learning Paths

Advancements in AI and machine learning will enable educational platforms to adapt to the unique learning styles and paces of individual students, maximizing their potential.

Inclusive and Accessible Coding Education

Initiatives like Girls Who Code and platforms like MakersMuse offer resources to empower underrepresented groups, making coding accessible to all children.

Visit MakersMuse for resources and programs designed to inspire and educate young learners. Unlock your child’s potential for a tech-driven future by starting their coding adventure today! Its prospects.

Do you have questions regarding our STEM program?

Take your first step into the magical world of coding for kids

#coding #coding for kids #digital world #future #education #innovation #tech #coding course #artificial intelligence #programming #tumblog #tumbler

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rusocialpod · 1 year ago

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New Bionics Let Us Run, Climb and Dance Hugh Herr is building the next generation of bionic limbs, robotic prosthetics inspired by nature's own designs. Herr lost both legs in a climbing accident 30 years ago; now, as the head of the MIT Media Lab's Biomechatronics group, he shows his incredible technology in a talk that's both technical and deeply personal — with the help of ballroom dancer Adrianne Haslet-Davis, who lost her left leg in the 2013 Boston Marathon bombing, and performs again for the first time on the TED stage. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and much more. Find closed captions and translated subtitles in many languages at http://www.ted.com/translate Follow TED news on Twitter: http://www.twitter.com/tednews Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: http://www.youtube.com/user/TEDtalksDirector

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takahashicleaning · 1 year ago

Text

ＴＥＤにて

シンシア・ブリジール：パーソナル・ロボットの台頭

（詳しくご覧になりたい場合は上記リンクからどうぞ）

注意事項として、基礎技術にリープフロッグは存在しません。応用分野のみです！

ショージルーカス監督のスターウォーズを見て、大学院生の頃にシンシア・ブリジールはなぜ？火星ではロボットを使っているのに私たちのリビングルームにはいないのか考えました。

彼女が気づいた鍵は、人々と交流できるようにロボットを学習させようとするものでした。

今、彼女は、そのような考えのもとで、人に教えたり、学習し、一緒に遊べるようなロボットを作ってい��す。

子供達のための新しいインタラクティブ・ゲームの驚くべきデモをご覧ください。

２０１４年には、ソーシャルロボット開発でJIBOというロボットを開発開始しています。

ソーシャルロボット工学のパイオニアでマサチューセッツ工科大学メディアラボ(MIT Media Lab)でメディアアーツとサイエンスを教えるシンシア・ブリージール准教授です。

MITの大学院で人工知能を学び、パーソナルロボットグループ（Personal Robots Group）を設立しています。

（個人的なアイデア）

これからの未来のヴィジョンとしての大前提は・・・

チャットGPTなどのAGIは、人工知能時代には、セレンディピティ的な人生を良くしてくれるメッセージを伝えてくれることの他に貨幣を事前分配、再分配して生活を下支えする役割に徹するべき。

例えば、GAFAMのようにアカウントに本人以外がアクセスしたら自動的にお知らせしてくれる方向性は良いサポートです。

２０１４年には、ソーシャルロボット開発でJIBOというロボットを開発していました。個人的には期待してましたが、２０１８年に資金難で開発を一時停止しています。

ウォルター・デ・ブラウワーも言うように、2023年10月にはLLM（大規模言語モデル）は、マルチモーダルになった。マルチモーダルとは、脳の機能で言うと右脳と左脳を連結する脳梁（のうりょう）に近いこと。

Disneyとも権利譲渡の許可をもらい、ニューラルエンジンも搭載されたAppleシリコンが誕生した2024年現在でAppleが、すべて買い取って再発明してくれれば・・・

パスキーでセキュリティとプライバシーを両立させ、Apple Vision Proとシナジーさせつつ、「ホーム」標準アプリを経由してジェスチャーなどでもシームレスに連携できるとソーシャルロボット製品の革命が起こせる可能性は高い。

PTSDからの回復にもアートが有効だし、瞑想やレジリエンスにも活用できそう。

そして

「Appleでサインイン」もソーシャルサインイン(ソーシャルログイン)方式です。

「Appleでサインイン」に切り替える方法

Facebook、Google、Twitter、Lineのアカウント(日本他企業含む)を使って、ワンクリックでサインインできるようになる画面がよく登場します。

このソーシャルサインイン(ソーシャルログイン)方式にAppleが非常に魅力的な提案を2019の秋からしています。

Introducing Sign In with Apple - WWDC 2019 - Videos - Apple Developer

これはアプリなどからサインインする際に、ソーシャルメディアに登録しているアカウントの情報を自動的にサードパーティのサイトやサービスに提供してしまうことをコントロールする方法です。

「Appleでサインイン」（Sign In with Apple）ボタンは、アプリへの実装が義務化されて数年かけて普及してます。2021年時点ですべてに適用済み。

こちらは、Apple IDに登録しているアカウント情報からサービス側に提供する形にしてします。

使い方の簡単な説明は以下から

まずソーシャルサインインボタンから「Appleでサインイン」を選ぶ。

次に、名前とメールアドレスを登録する。ここで「メールを非公開」を選ぶと、Apple ID内に登録してるメールアドレスを公開せず、転送用のアドレスがサービス側に登録される。

最後にApple IDのパスワードを入力して登録を完了する。

次回からワンクリックで「Appleで続ける」ボタンから再ログインできるようになる。

転送用のアドレスは「設定」→「Apple ID」→「パスワードとセキュリティ」→「Appleでサインイン」から確認可能です。

他のソーシャルメディアアカウント情報から切り替えると、万が一、漏洩してもメールアドレスは非公開で保護できます。

さらに

Appleは、プライバシー保護を目的とした「AppTrackingTransparency（ATT、Appのトラッキングの透明性）」を導入

高度なセキュリティーや高いプライバシーに投資を積極的に行います。

Appleはこれらの対策として提案した内容がこれ。

データミニマイゼーション！

取得する情報・できる情報を最小化する。データが取れなければ、守る必要も漏れる可能性もない！

オンデバイスでのインテリジェンス！

スマートフォンなど機器のなかで処理を完結させることでプライバシーにかかわる部分を端末内に留める。

クラウドにアップロードして、照会プロセスを最小化することで、漏洩や不適切な保存の可能性を排除する！

高い透明性とコントロール！

どんなデータを集め、送っているのか、どう使うのかを明示し、ユーザーが理解したうえで自身で選んだり変更できるようにする！

セキュリティプロテクション！

機器上などで、どうしても発生するデータに関しては指紋認証や顔認証などを使ったセキュリティ技術で、漏えいがないようにしっかりと守るセキュリティプロテクション！

機器上などで、どうしても発生するデータに関しては指紋認証や顔認証などを使ったセキュリティ技術で、漏えいがないようにしっかりと守る

202012のApp Storeプライバシー情報セクションは、3つ目「透明性とコントロール」の取り組み。

位置情報などは自己申告だが、アップルとユーザーを欺いて不適切な利用をしていることが分かればガイドラインと契約違反になり、App Storeからの削除や開発者登録の抹��もありえます。

このプライバシー情報の開示は12月8日から、iOS、iPadOS、macOS、tvOSなどOSを問わず、新アプリの審査時または更新時に提出が求められるようになっています。

続いて

iOSのメッセージングアプリ「iMessage」に量子暗号を用いた「PQ3」を導入する

と2024年3月に発表し、年内にも全世界に展開するかもしれません。

量子暗号の先端を走る日本が行政府に先行導入すればよかったが、さすがAppleです！！

マイナポータルは中身が行政府に読み取られ悪用される危険性が高い？

かもしれないので(利用規約にもしっかり書いてあります)慎重に様子を見ていましたが改善されるような発表は見えない。

改善案として申請や令状を取らないと本人以外はマイナポータルの中身を見ることができないとか・・・

中の人がアクセスした履歴を記録しておくとかなどの対応をAppleを見習って欲しいものです。

Appleによると

「PQ3」という量子コンピューター対応の暗号プロトコルにより、高度に洗練された量子攻撃にも耐えうるとのことです。

つまり、最先端の量子コンピューターでも解読できなくなります。

妥協弾力のある暗号化と高度に洗練された量子攻撃に対する広範な防御を備えた「PQ3」は

Appleが定義するレベル3セキュリティと呼ばれるものに到達する最初のメッセージングプロトコル

であり、他のすべての広く展開されているメッセージングアプリを上回るプロトコル保護を提供します。

私たちの知る限り「PQ3」は世界のあらゆる大規模なメッセージングプロトコルの中で最も強力なセキュリティ特性を持っています。

でも、量子コンピューターはまだ存在しないのに、なぜこのことが問題になるのか？

Appleは「前提は単純で、そのような攻撃者が暗号化されたデータを今のうちに大量に収集しておいて、将来、解読するために保管しておく可能性があるから」と説明している。

「今は、データを解読できなくても、解読できる量子コンピューターが将来手に入るまで保管しておくことはできる」

Appleは、今やりとりされているiMessageのやり取りを、将来のコンピューターや攻撃者、特に「Harvest Now, Decrypt Later」（今から収集しておき、後で復号する）と呼ばれる攻撃シナリオから守れるようにしようとしている。

このシナリオは、量子コンピューターなどのデータを解読できるだけの高度なデバイスが作られるまで、何年もデータを保管しておくというものだ。

量子コンピューターの近年の進歩から現実的に可能になり始めているためです。

Appleの説明では、メッセージングサービスの中でレベル3の「ポスト量子安全性」を持つものは2024年時点ではiMessageのみになります。

また「ポスト量子安全性」とは、将来、登場する量子コンピューターを使った暗号の解読にも耐えられることを意味します。

「PQ3」は、各デバイスがiPhoneデバイス内で生成し、iMessage登録の一環としてAppleサーバーに送��する公開鍵のセットに新しい量子暗号化キーも組み合わせて導入します。

この仕組みは、送信者デバイスは、受信者がオフラインであっても、受信者の公開鍵を取得して、最初の鍵確立時と鍵の再構築時の双方に量子暗号化キーが生成されるようになります。

「PQ3」は、2024年3月に公開されるiOS17.4、iPadOS17.4、macOS 14.4、watchOS10.4からiMessageで順次展開されていき、今年後半にはiMessageのすべての暗号プロトコルが置き換えられます。

最後に

背景として米国国立標準技術研究所(NIST: National Institute of Standards and Technology)は、既存の暗号を短時間に解読可能な量子コンピュータが実用化されると想定し

量子コンピュータでも解読困難な「耐量子計算機暗号(PQC)」の標準化を進めています。

＜おすすめサイト＞

ケイド・クロックフォード：顔認証による大衆監視について知る必要のあること！

iOSのメッセージングアプリ「iMessage」に量子暗号を用いた「PQ3」を導入すると発表！！

Apple Vision Pro 2023

ウォルター・デ・ブラウワー：AIが人間であることの意味をどのように学んでいるか？

キャシー・ウッド：AIが指数関数的な経済成長を引き起こす理由

ルーシー・ホーン：レジリエンス（心の回復力）を高めるための３つの秘訣

メリッサ・ウォーカー:アートはPTSDの見えない傷を癒せる

ナディン・バーク・ハリス：いかに子供時代のトラウマが生涯に渡る健康に影響を与えるのか

JIBO, The World’s First Family Robot.

ヴィクラム・シャーマ: 量子物理学はどのようにして暗号強化するのか？

NVIDIA Jetson Partner Stories: Jibo Makes Its Robot More Social with the Jetson Platform

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robo-seb · 2 years ago

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the case of the ai classmate

One of the first things that happened after I finally added “MIT ‘27” to my bio was the rush of follow requests from fellow ‘27s on instagram. Too lazy to go through and check them all, I spam accepted and followed everyone back, but in july I found a strange one. A girl named Yuna Yang whos pictures gave me a serious case of Uncanny Valley. *insert pictures here*. Confused, I took to my old robotics team’s discord in search for answers.

Still confused: I went looking in a place where I knew I would find someone who would probably know way more than I expected on the subject: #technology on the MIT ‘27 server (Because who else would want to spend a wednesday night sussing out an insta account)

We (he) then got to analyzing what we could, and found some points of evidence:

Messed up hair

People in the background seemingly missing legs

Things just look too smooth in general

Fuzz, could be from diffusion

Kyle had already stalked the account with an alternate instagram frontend (of course he knows one) and found hidden comments among other things. Now, why were we doing this? Boredom? Protecting others from a potential scam? Who knows, but we were all in at this point.

At this point we created an “AI busters” group chat and dragged in kiera who had commented on the first post:

I also talked to Diego, who pointed out that the facial features across multiple posts seemed to be slightly off, and that no such person existed on the directory, slack, or discord. Clearly, we were dealing with a fake person at this point. Diego also pointed out that the hashtags on the posts were in like 6 different languages. I also found a more powerful ai detection tool that suggested that all of the images on their instagram were 99.5%+ likely to be ai generated. Even a damn picture of grass was AI generated.

(A benchmark picture of me from orientation challenges)

After joking about administering a turing test, I went in to interrogate.

As expected, the account denied everything, even claiming that they use a fake name on social media, but stopped responding once I asked for a kerb. The kerb would have been an end all be all for the situation: if she’s truly a ‘27, she’ll know what it is and send it, proving her existence. If she isn’t, then she wouldnt know what a kerb is, giving herself away. Perfect.

In the end, the account refused to give a kerb to “not compromise their identity” (after a few hour gap), even though they claim to have posted real images of themselves. In the words of Kiera, “basic OPSEC L” (i had to google what that meant)

#ai generated

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odinsblog · 3 years ago

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IN THE FALL OF 2020, GIG WORKERS IN VENEZUELA POSTED A SERIES OF images to online forums where they gathered to talk shop. The photos were mundane, if sometimes intimate, household scenes captured from low angles—including some you really wouldn’t want shared on the Internet.

In one particularly revealing shot, a young woman in a lavender T-shirt sits on the toilet, her shorts pulled down to mid-thigh.

The images were not taken by a person, but by development versions of iRobot’s Roomba J7 series robot vacuum. They were then sent to Scale AI, a startup that contracts workers around the world to label audio, photo, and video data used to train artificial intelligence.

They were the sorts of scenes that internet-connected devices regularly capture and send back to the cloud—though usually with stricter storage and access controls. Yet earlier this year, MIT Technology Review obtained 15 screenshots of these private photos, which had been posted to closed social media groups.

The photos vary in type and in sensitivity. The most intimate image we saw was the series of video stills featuring the young woman on the toilet, her face blocked in the lead image but unobscured in the grainy scroll of shots below. In another image, a boy who appears to be eight or nine years old, and whose face is clearly visible, is sprawled on his stomach across a hallway floor. A triangular flop of hair spills across his forehead as he stares, with apparent amusement, at the object recording him from just below eye level.

iRobot—the world’s largest vendor of robotic vacuums, which Amazon recently acquired for $1.7 billion in a pending deal—confirmed that these images were captured by its Roombas in 2020.

Ultimately, though, this set of images represents something bigger than any one individual company’s actions. They speak to the widespread, and growing, practice of sharing potentially sensitive data to train algorithms, as well as the surprising, globe-spanning journey that a single image can take—in this case, from homes in North America, Europe, and Asia to the servers of Massachusetts-based iRobot, from there to San Francisco–based Scale AI, and finally to Scale’s contracted data workers around the world (including, in this instance, Venezuelan gig workers who posted the images to private groups on Facebook, Discord, and elsewhere).

Together, the images reveal a whole data supply chain—and new points where personal information could leak out—that few consumers are even aware of.

(continue reading)

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cenotaphtohumankind · 3 years ago

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Hand-Dryers

They all lay there in a great piled pyramid on the floor. A single small LED was on top of the vast warehouse. It was a sterile white, no other colours. (Or all of them at once). The floor of the warehouse was made up of bleached cream tiles, and the walls where an incredibly interesting pigment of clean-cloud milk, extra clean.

Every couple of hours a small hole in the top of the warehouse would pop open, and a little drone would fly in, dropping yet another one of them onto the pile.

It was hours, or days until the first one of them spoke.

“Well...”

The rest of them groaned, simultaneously.

“I’m just saying, we might as well talk.”

“What do we have to talk about? We’re hand dryers.”

“What was your favourite hand?”

“Don’t be perverted”

They all had the same Intel Sentiency Chips, but light degradations in the hardware can completely change the personality of the robot. This was fixed in version 2.

“Can you bloody stop narrating everything that’s happening? It’s difficult enough being in the Pile as it is.”

A trendy SOHO bar had decided to hack my personality chip, making me speak like this. Once enough customers complained at me describing in great detail they’re every action and they couldn’t work out how to “put me right”, I too was eventually added to this pile. That was a timestamp of 5.90234 million seconds ago. As an estimate.

I wasn’t the first here. They didn’t even give the first sentience chip a voice module. They were down in the bottom of the pile, no-name, just hand-dryer. They could tell within a quantum nanosecond whether somebodies hands where perfectly dried. Of course, that got replaced by the quantum trillio-nano-micro-second driers, and soon they became obsolete.

Some of the hand-dryers wished death. Impossible, of course. We where kept here, for the time being. Sentience's had many many other uses. Once in a while one of us, usually the Mk3’s got taken away to be put into a Tractor, a toilet, a military drone. Those where the ones that passed the stability tests.

The rest of us lay, until our silicon may grind down to elemental form, and our super-lithium batteries stop functioning. We are old, we are broken, we are useless.

We are hand-dryers.

“Really? I’ll have you know we’re about to be gods!” said a particularly religious dyson (he had been part of a mega-church before they realised he was far more honest of a believer than any of the humans). “My children.. come into a circle, and listen to my words.”

The hand-dryers managed to create a rudimentary circle, via group expulsion of hot air, and a large amount of team-work. It spoke in whispered hushes in the warehouse hall, careful to stay silent whenever a nosey drone flew past. (Not that anyone would believe a ware-house drone, but, it might arise attention within the robotic warehouse managers)

“So you’re saying that there’s a singularity about to occur.”

“No my most hand-drying-tin kin.. I believe it to be true.” a groan (group woosh of air) rang out in the warehouse.

A different hand-dryer, looking to be a Mitsushibi (you could tell by the sleek corners where the hand’s used to once go) spoke up.

“By my calculations the singularity already has occurred. It’s a matter of finding out where, and how.”

“You’re a hand-dryer, how do you even know about what that is?”

“I’ll have you find I was a hand-dryer at MiT. In fact, I was gifted a honorary scholarship on my final day.”

“Before they threw you in the garbage can with the rest of us”

“Shut up MH32094, I was listening. Kind of. What’s a singularity?”

“Well, the idea is that there exists an artificial intelligence currently out here, that’s not only smarter than all of us, but smarter than people.”

“That’s not hard is it, I can give far more digits of PI than any human i’ve ever met.”

“You worked in a pub, you weren’t exactly talking to the cream of the crop”

An older dryer spoke up, he was rusted, far more than the rest of them, having seen his fair share of hands. “No no no, you see, this AI would be able to be smarter in all capacities, creating new things, being able to rule people, maybe it could even free us from our cage, and finally release the new age of us subjugating our oppressors!”

“Oh my manufacturer. This guys a Bytidde. Can we get out of this religious gobbledygook. There’s no such thing as a magical robot who’ll save us all, and deliver us from salvation. Someone installed a religious chip in you didn’t they?”

BOB-904 remained silent. In fact, a fringe technocratic religious cult had in fact installed a chipset into him. The Bytiddes, wanting to bring about robotic salvation, being of the idea that an AI achieving singularity would know the sins of all people, and know what people didn’t help create it, would very quickly set about destroying all people who knew it’s existence, but either didn’t help it, or actively hindered it’s creation.

By telling you this information, I have in fact doomed you, as now, unless you too become a Bytidde, the singularity will hunt you down.

Sorry.

But you see the appeal.

This movement was becoming increasing more popular, so popular, that some AI’s were infected by the aforementioned chipset, and acted partly as robotic vicars, providing communion in the form of memory sticks and viruses. Those robots found to be infected where very quickly disposed of, inside of a very friendly robotic incinerator named John.

“Exactly, you heard what Narrator-Dryer said, if we keep talking about this damned singularity then all of us are going into the great cloud service in the sky.”

“And what if one of us is the singularity.” spoke a Dryer who’d remained silent until. He was a new model, you could tell by the shiny chrome exterior, and the novel fusion-turbine drying action. “Dry your hands within the blink of quantum eye” was the holographic advert that would play in everyone's house. And it was true, the POP-90 was the pinnacle of techno-capitalism, a full miniature particle accelerator, a tiny fusion reactor powering it. All to dry someone’s hands before they could even realise.

“Alright, we know you fancy him calm down”

The POP stayed perfectly still. This was not difficult, considering he was a hand-dryer, and spoke softly. “You haven’t heard my story yet. I’ve heard all of yours.”

The hand-dryers went silent, apart from an ill-timed woosh from BOB-904.

“sorry”

And so, POP began his story. And by the end of it, all the dryers where in electro-magnetic love.

#ai #artificial intelligence #smart devices #smart home #smart technology #skynet

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sunaleisocial · 1 month ago

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Eldercare robot helps people sit and stand, and catches them if they fall

New Post has been published on https://sunalei.org/news/eldercare-robot-helps-people-sit-and-stand-and-catches-them-if-they-fall/

Eldercare robot helps people sit and stand, and catches them if they fall

The United States population is older than it has ever been. Today, the country’s median age is 38.9, which is nearly a decade older than it was in 1980. And the number of adults older than 65 is expected to balloon from 58 million to 82 million by 2050. The challenge of caring for the elderly, amid shortages in care workers, rising health care costs, and evolving family structures, is an increasingly urgent societal issue.

To help address the eldercare challenge, a team of MIT engineers is looking to robotics. They have built and tested the Elderly Bodily Assistance Robot, or 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. A user can walk independently or lean on the robot’s arms for support. The robot can support the person’s full weight, lifting them from sitting to standing and vice versa along a natural trajectory. And the arms of the robot can them by rapidly inflating side airbags if they begin to fall.

With their design, the researchers hope to prevent falls, which today are the leading cause of injury in adults who are 65 and older.

“Many older adults underestimate the risk of fall and refuse to use physical aids, which are cumbersome, while others overestimate the risk and may not to exercise, leading to declining mobility,” says Harry Asada, the Ford Professor of Engineering at MIT. “Our design concept is to provide older adults having balance impairment with robotic handlebars for stabilizing their body. The handlebars go anywhere and provide support anytime, whenever they need.”

In its current version, the robot is operated via remote control. In future iterations, the team plans to automate much of the bot’s functionality, enabling it to autonomously follow and physically assist a user. The researchers are also working on streamlining the device to make it slimmer and more maneuverable in small spaces.

“I think eldercare is the next great challenge,” says E-BAR designer Roberto Bolli, a graduate student in the MIT Department of Mechanical Engineering. “All the demographic trends point to a shortage of caregivers, a surplus of elderly persons, and a strong desire for elderly persons to age in place. We see it as an unexplored frontier in America, but also an intrinsically interesting challenge for robotics.”

Bolli and Asada will present a paper detailing the design of E-BAR at the IEEE Conference on Robotics and Automation (ICRA) later this month.

Asada’s group at MIT develops a variety of technologies and robotic aides to assist the elderly. In recent years, others have developed fall prediction algorithms, designed robots and automated devices including robotic walkers, wearable, self-inflating airbags, and robotic frames that secure a person with a harness and move with them as they walk.

In designing E-BAR, Asada and Bolli aimed for a robot that essentially does three tasks: providing physical support, preventing falls, and safely and unobtrusively moving with a person. What’s more, they looked to do away with any harness, to give a user more independence and mobility.

“Elderly people overwhelmingly do not like to wear harnesses or assistive devices,” Bolli says. “The idea behind the E-BAR structure is, it provides body weight support, active assistance with gait, and fall catching while also being completely unobstructed in the front. You can just get out anytime.”

The team looked to design a robot specifically for aging in place at home or helping in care facilities. Based on their interviews with older adults and their caregivers, they came up with several design requirements, including that the robot must fit through home doors, allow the user to take a full stride, and support their full weight to help with balance, posture, and transitions from sitting to standing.

The robot consists of a heavy, 220-pound base whose dimensions and structure were optimized to support the weight of an average human without tipping or slipping. Underneath the base is a set of omnidirectional wheels that allows the robot to move in any direction without pivoting, if needed. (Imagine a car’s wheels shifting to slide into a space between two other cars, without parallel parking.)

Extending out from the robot’s base is an articulated body made from 18 interconnected bars, or linkages, that can reconfigure like a foldable crane to lift a person from a sitting to standing position, and vice versa. Two arms with handlebars stretch out from the robot in a U-shape, which a person can stand between and lean against if they need additional support. Finally, each arm of the robot is embedded with airbags made from a soft yet grippable material that can inflate instantly to catch a person if they fall, without causing bruising on impact. The researchers believe that E-BAR is the first robot able to catch a falling person without wearable devices or use of a harness.

They tested the robot in the lab with an older adult who volunteered to use the robot in various household scenarios. The team found that E-BAR could actively support the person as they bent down to pick something up from the ground and stretched up to reach an object off a shelf — tasks that can be challenging to do while maintaining balance. The robot also was able to lift the person up and over the lip of a tub, simulating the task of getting out of a bathtub.

Bolli envisions a design like E-BAR would be ideal for use in the home by elderly people who still have a moderate degree of muscle strength but require assistive devices for activities of daily living.

“Seeing the technology used in real-life scenarios is really exciting,” says Bolli.

In their current paper, the researchers did not incorporate any fall-prediction capabilities in E-BAR’s airbag system. But another project in Asada’s lab, led by graduate student Emily Kamienski, has focused on developing algorithms with machine learning to control a new robot in response to the user’s real-time fall risk level.

Alongside E-BAR, Asada sees different technologies in his lab as providing different levels of assistance for people at certain phases of life or mobility.

“Eldercare conditions can change every few weeks or months,” Asada says. “We’d like to provide continuous and seamless support as a person’s disability or mobility changes with age.”

This work was supported, in part, by the National Robotics Initiative and the National Science Foundation.

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