Connect with us

AI Research

Trump: US leading China in artificial intelligence – breakingthenews.net

Published

on

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

AI Research

Clanker! This slur against robots is all over the internet – but is it offensive? | Artificial intelligence (AI)

Published

on


Name: Clanker.

Age: 20 years old.

Appearance: Everywhere, but mostly on social media.

It sounds a bit insulting. It is, in fact, a slur.

What kind of slur? A slur against robots.

Because they’re metal? While it’s sometimes used to denigrate actual robots – including delivery bots and self-driving cars – it’s increasingly used to insult AI chatbots and platforms such as ChatGPT.

I’m new to this – why would I want to insult AI? For making up information, peddling outright falsehoods, generating “slop” (lame or obviously fake content) or simply not being human enough.

Does the AI care that you’re insulting it? That’s a complex and hotly debated philosophical question, to which the answer is “no”.

Then why bother? People are taking out their frustrations on a technology that is becoming pervasive, intrusive and may well threaten their future employment.

Clankers, coming over here, taking our jobs! That’s the idea.

Where did this slur originate? First used to refer pejoratively to battle androids in a Star Wars game in 2005, clanker was later popularised in the Clone Wars TV series. From there, it progressed to Reddit, memes and TikTok.

And is it really the best we can do, insult-wise? Popular culture has spawned other anti-robot slurs – there’s “toaster” from Battlestar Galactica, and “skin-job” from Blade Runner – but “clanker” seems to have won out for now.

It seems like a stupid waste of time, but I guess it’s harmless enough. You say that, but many suggest using “clanker” could help to normalise actual bigotry.

Oh, come on now. Popular memes and spoof videos tend to treat “clanker” as being directly analogous to a racial slur – suggesting a future where we all harass robots as if they were an oppressed minority.

So what? They’re just clankers. “Naturally, when we trend in that direction, it does play into those tropes of how people have treated marginalised communities before,” says linguist Adam Aleksic.

I’m not anti-robot; I just wouldn’t want my daughter to marry one. Can you hear how that sounds?

I have a feeling we’re going to be very embarrassed about all this in 10 years. Probably. Some people argue that, by insulting AI, we’re crediting it with a level of humanity it doesn’t warrant.

That would certainly be my assessment. However, the “Roko’s basilisk” thought experiment posits that a future artificial superintelligence might punish all those who failed to help it flourish in the first place.

I guess calling it a clanker would count. We may end up apologising to our robot overlords for past hate crimes.

Or perhaps they’ll see the funny side of all this? Assuming the clankers develop a sense of humour some day.

Do say: “The impulse to coin this slur says more about our anxieties than it does about the technology itself.”

Don’t say: “Some of my best friends are clankers.”



Source link

Continue Reading

AI Research

Most ninth-graders use AI: survey

Published

on


  • By Rachel Lin and Lery Hiciano / Staff reporter, with staff writer

About 69 percent of ninth-graders use artificial intelligence (AI), most commonly for homework, creating images or videos, and chatting, a survey found yesterday.

The poll was conducted by the National Academy of Educational Research (NAER) and Academia Sinica as part of the Taiwan Assessment of Student Achievement Longitudinal Study.

Asked about AI, 94.2 percent of the students knew what generative AI was, although 31 percent said that they had never used it, the survey showed.

Photo: Rachel Lin, Taipei Times

Among those who said that they used generative AI, 6.8 percent used it daily, 3.9 percent used it five to six times per week, 12.2 percent three to four times per week and 46 percent used it once or twice a week, the survey found.

About 53.2 percent of ninth-graders said that teachers had taught them how to use generative AI, while 46.8 percent said their teachers did not, suggesting that the adoption rate of the technology could continue to improve.

Students said they used AI for homework, translation, research and content creation, indicating that the technology has already become a part of their studies and daily habits across academic and creative interests.

This could reflect how younger, digitally native groups are more amenable to new technologies, and points to a growing trend of using AI in a balanced way, the NAER said.

The gap between those who are aware of AI and those who use it suggests that most are not becoming advanced or heavy users, it said.

As about half of schools are teaching students how to use AI, it suggests that teachers recognize how important the technology is becoming, the NAER said, adding that the wide array of ways in which students use AI tools also shows its wide-ranging capability and potential.



Source link

Continue Reading

AI Research

Brain–computer interface control with artificial intelligence copilots

Published

on


  • Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006).


    Google Scholar
     

  • Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 1142–1145 (2015).


    Google Scholar
     

  • Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain-computer interface. eLife 6, e18554 (2017).


    Google Scholar
     

  • Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012).


    Google Scholar
     

  • Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557–564 (2013).


    Google Scholar
     

  • Wodlinger, B. et al. Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations. J. Neural Eng. 12, 016011 (2015).


    Google Scholar
     

  • Aflalo, T. et al. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348, 906–910 (2015).


    Google Scholar
     

  • Edelman, B. J. et al. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci. Robot. 4, eaaw6844 (2019).


    Google Scholar
     

  • Reddy, S., Dragan, A. D. & Levine, S. Shared autonomy via deep reinforcement learning. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2018.XIV.005 (RSS, 2018).

  • Laghi, M., Magnanini, M., Zanchettin, A. & Mastrogiovanni, F. Shared-autonomy control for intuitive bimanual tele-manipulation. In 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids) 1–9 (IEEE, 2018).

  • Tan, W. et al. On optimizing interventions in shared autonomy. In Proc. AAAI Conference on Artificial Intelligence 5341–5349 (AAAI, 2022).

  • Yoneda, T., Sun, L., Yang, G., Stadie, B. & Walter, M. To the noise and back: diffusion for shared autonomy. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2023.XIX.014 (RSS, 2023).

  • Peng, Z., Mo, W., Duan, C., Li, Q. & Zhou, B. Learning from active human involvement through proxy value propagation. Adv. Neural Inf. Process. Syst. 36, 20552–20563 (2023).


    Google Scholar
     

  • McMahan, B. J., Peng, Z., Zhou, B. & Kao, J. C. Shared autonomy with IDA: interventional diffusion assistance. Adv. Neural Inf. Process. Syst. 37, 27412–27425 (2024).

  • Shannon, C. E. Prediction and entropy of printed English. Bell Syst. Tech. J. 30, 50–64 (1951).

  • Karpathy, A., Johnson, J. & Fei-Fei, L. Visualizing and understanding recurrent networks. In International Conference on Learning Representations https://openreview.net/pdf/71BmK0m6qfAE8VvKUQWB.pdf (ICLR, 2016).

  • Radford, A. et al. Language models are unsupervised multitask learners. OpenAI https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf (2019).

  • Gilja, V. et al. A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 15, 1752–1757 (2012).


    Google Scholar
     

  • Dangi, S., Orsborn, A. L., Moorman, H. G. & Carmena, J. M. Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces. Neural Comput. 25, 1693–1731 (2013).

    MathSciNet 

    Google Scholar
     

  • Orsborn, A. L. et al. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 82, 1380–1393 (2014).


    Google Scholar
     

  • Silversmith, D. B. et al. Plug-and-play control of a brain–computer interface through neural map stabilization. Nat. Biotechnol. 39, 326–335 (2021).


    Google Scholar
     

  • Kim, S.-P., Simeral, J. D., Hochberg, L. R., Donoghue, J. P. & Black, M. J. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J. Neural Eng. 5, 455 (2008).


    Google Scholar
     

  • Sussillo, D. et al. A recurrent neural network for closed-loop intracortical brain–machine interface decoders. J. Neural Eng. 9, 026027 (2012).


    Google Scholar
     

  • Sussillo, D., Stavisky, S. D., Kao, J. C., Ryu, S. I. & Shenoy, K. V. Making brain–machine interfaces robust to future neural variability. Nat. Commun. 7, 13749 (2016).


    Google Scholar
     

  • Kao, J. C. et al. Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nat. Commun. 6, 7759 (2015).


    Google Scholar
     

  • Kao, J. C., Nuyujukian, P., Ryu, S. I. & Shenoy, K. V. A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models. IEEE Trans. Biomed. Eng. 64, 935–945 (2016).


    Google Scholar
     

  • Shenoy, K. V. & Carmena, J. M. Combining decoder design and neural adaptation in brain-machine interfaces. Neuron 84, 665–680 (2014).


    Google Scholar
     

  • Lawhern, V. J. et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J. Neural Eng. 15, 056013 (2018).


    Google Scholar
     

  • Forenzo, D., Zhu, H., Shanahan, J., Lim, J. & He, B. Continuous tracking using deep learning-based decoding for noninvasive brain–computer interface. PNAS Nexus 3, pgae145 (2024).


    Google Scholar
     

  • Pfurtscheller, G. & Da Silva, F. L. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110, 1842–1857 (1999).


    Google Scholar
     

  • Olsen, S. et al. An artificial intelligence that increases simulated brain–computer interface performance. J. Neural Eng. 18, 046053 (2021).


    Google Scholar
     

  • Schulman, J., Wolski, F., Dhariwal, P., Radford, A. & Klimov, O. Proximal policy optimization algorithms. Preprint at https://arxiv.org/abs/1707.06347 (2017).

  • Liu, S. et al. Grounding DINO: marrying DINO with grounded pre-training for open-set object detection. In 18th European Conference 38–55 (ACM, 2024).

  • Golub, M. D., Yu, B. M., Schwartz, A. B. & Chase, S. M. Motor cortical control of movement speed with implications for brain-machine interface control. J. Neurophysiol. 112, 411–429 (2014).


    Google Scholar
     

  • Sachs, N. A., Ruiz-Torres, R., Perreault, E. J. & Miller, L. E. Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface. J. Neural Eng. 13, 016009 (2016).


    Google Scholar
     

  • Kao, J. C., Nuyujukian, P., Ryu, S. I. & Shenoy, K. V. A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models. IEEE Trans. Biomed. Eng. 64, 935–945 (2017).


    Google Scholar
     

  • Stieger, J. R. et al. Mindfulness improves brain–computer interface performance by increasing control over neural activity in the alpha band. Cereb. Cortex 31, 426–438 (2021).


    Google Scholar
     

  • Stieger, J. R., Engel, S. A. & He, B. Continuous sensorimotor rhythm based brain computer interface learning in a large population. Sci. Data 8, 98 (2021).


    Google Scholar
     

  • Edelman, B. J., Baxter, B. & He, B. EEG source imaging enhances the decoding of complex right-hand motor imagery tasks. IEEE Trans. Biomed. Eng. 63, 4–14 (2016).


    Google Scholar
     

  • Scherer, R. et al. Individually adapted imagery improves brain-computer interface performance in end-users with disability. PLoS ONE 10, e0123727 (2015).


    Google Scholar
     

  • Millan, J. d. R. et al. A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Trans. Neural Netw. 13, 678–686 (2002).


    Google Scholar
     

  • Huang, D. et al. Decoding subject-driven cognitive states from EEG signals for cognitive brain–computer interface. Brain Sci. 14, 498 (2024).


    Google Scholar
     

  • Meng, J. et al. Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks. Sci. Rep. 6, 38565 (2016).


    Google Scholar
     

  • Jeong, J.-H., Shim, K.-H., Kim, D.-J. & Lee, S.-W. Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 1226–1238 (2020).


    Google Scholar
     

  • Zhang, R. et al. NOIR: neural signal operated intelligent robots for everyday activities. In Proc. 7th Conference on Robot Learning 1737–1760 (PMLR, 2023).

  • Jeon, H. J., Losey, D. P. & Sadigh, D. Shared autonomy with learned latent actions. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2020.XVI.011 (RSS, 2020).

  • Javdani, S., Bagnell, J. A. & Srinivasa, S. S. Shared autonomy via hindsight optimization. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2015.XI.032 (RSS, 2015).

  • Newman, B. A. et al. HARMONIC: a multimodal dataset of assistive human-robot collaboration. Int. J. Robot. Res. 41, 3–11 (2022).

  • Jain, S. & Argall, B. Probabilistic human intent recognition for shared autonomy in assistive robotics. ACM Trans. Hum. Robot Interact. 9, 2 (2019).


    Google Scholar
     

  • Losey, D. P., Srinivasan, K., Mandlekar, A., Garg, A. & Sadigh, D. Controlling assistive robots with learned latent actions. In 2020 IEEE International Conference on Robotics and Automation (ICRA) 378–384 (IEEE, 2020).

  • Cui, Y. et al. No, to the right: online language corrections for robotic manipulation via shared autonomy. In Proc. 2023 ACM/IEEE International Conference on Human-Robot Interaction 93–101 (ACM, 2023).

  • Karamcheti, S. et al. Learning visually guided latent actions for assistive teleoperation. In Proc. 3rd Conference on Learning for Dynamics and Control 1230–1241 (PMLR, 2021).

  • Chi, C. et al. Diffusion policy: visuomotor policy learning via action diffusion. Int. J. Rob. Res. https://doi.org/10.1177/02783649241273668 (2024).

  • Brohan, A. et al. RT-1: robotics transformer for real-world control at scale. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2023.XIX.025 (RSS, 2023).

  • Brohan, A. et al. RT-2: vision-language-action models transfer web knowledge to robotic control. In Proc. 7th Conference on Robot Learning 2165–2183 (PMLR, 2023).

  • Nair, S., Rajeswaran, A., Kumar, V., Finn, C. & Gupta, A. R3M: a universal visual representation for robot manipulation. In Proc. 6th Conference on Robot Learning 892–909 (PMLR, 2023).

  • Ma, Y. J. et al. VIP: towards universal visual reward and representation via value-implicit pre-training. In 11th International Conference on Learning Representations https://openreview.net/pdf?id=YJ7o2wetJ2 (ICLR, 2023).

  • Khazatsky, A. et al. DROID: a large-scale in-the-wild robot manipulation dataset. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2024.XX.120 (RSS, 2024).

  • Open X-Embodiment Collaboration. Open X-Embodiment: robotic learning datasets and RT-X models. In 2024 IEEE International Conference on Robotics and Automation (ICRA) 6892–6903 (IEEE, 2024).

  • Willett, F. R. et al. A high-performance speech neuroprosthesis. Nature 620, 1031–1036 (2023).


    Google Scholar
     

  • Leonard, M. K. et al. Large-scale single-neuron speech sound encoding across the depth of human cortex. Nature 626, 593–602 (2024).


    Google Scholar
     

  • Card, N. S. et al. An accurate and rapidly calibrating speech neuroprosthesis. N. Engl. J. Med. 391, 609–618 (2024).


    Google Scholar
     

  • Sato, M. et al. Scaling law in neural data: non-invasive speech decoding with 175 hours of EEG data. Preprint at https://arxiv.org/abs/2407.07595 (2024).

  • Kaifosh, P., Reardon, T. R. & CTRL-labs at Reality Labs. A generic non-invasive neuromotor interface for human–computer interaction. Nature https://doi.org/10.1038/s41586-025-09255-w (2025).

  • Zeng, H. et al. Semi-autonomous robotic arm reaching with hybrid gaze-brain machine interface. Front. Neurorobot. 13, 111 (2019).


    Google Scholar
     

  • Shafti, A., Orlov, P. & Faisal, A. A. Gaze-based, context-aware robotic system for assisted reaching and grasping. In 2019 International Conference on Robotics and Automation 863–869 (IEEE, 2019).

  • Argall, B. D. Autonomy in rehabilitation robotics: an intersection. Annu. Rev. Control Robot. Auton. Syst. 1, 441–463 (2018).


    Google Scholar
     

  • Nuyujukian, P. et al. Monkey models for brain-machine interfaces: the need for maintaining diversity. In Proc. 33rd Annual Conference of the IEEE EMBS 1301–1305 (IEEE, 2011).

  • Suminski, A. J., Tkach, D. C., Fagg, A. H. & Hatsopoulos, N. G. Incorporating feedback from multiple sensory modalities enhances brain-machine interface control. J. Neurosci. 30, 16777–16787 (2010).


    Google Scholar
     

  • Kaufman, M. T. et al. The largest response component in motor cortex reflects movement timing but not movement type. eNeuro 3, ENEURO.0085–16.2016 (2016).


    Google Scholar
     

  • Dangi, S. et al. Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces. Neural Comput. 26, 1811–1839 (2014).


    Google Scholar
     

  • Fitts, P. M. The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47, 381 (1954).


    Google Scholar
     

  • Gramfort, A. et al. MNE software for processing MEG and EEG data. NeuroImage 86, 446–460 (2014).


    Google Scholar
     

  • Lee, J. Y. et al. Data: brain–computer interface control with artificial intelligence copilots. Zenodo https://doi.org/10.5281/zenodo.15165133 (2025).

  • Lee, J. Y. et al. kaolab-research/bci_raspy. Zenodo https://doi.org/10.5281/zenodo.15164641 (2025).

  • Lee, J. Y. et al. kaolab-research/bci_plot. Zenodo https://doi.org/10.5281/zenodo.15164643 (2025).



  • Source link

    Continue Reading

    Trending