Machine learning and AI experts have been challenged to join astronomers in the hunt for planets outside the solar system as part of the Ariel Data Challenge 2023, launched on 14 April.
Another rat plasma experiment, and it’s all good.
Key points summary of Live Forever Club article. 2 month life extension is equivalent of giving a 60 year-old human an extra 6 years of healthy life.
ChatGPT’s immense popularity and power make it eye-wateringly expensive to maintain, The Information reports, with OpenAI paying up to $700,000 a day to keep its beefy infrastructure running, based on figures from the research firm SemiAnalysis.
“Most of this cost is based around the expensive servers they require,” Dylan Patel, chief analyst at the firm, told the publication.
The costs could be even higher now, Patel told Insider in a follow-up interview, because these estimates were based on GPT-3, the previous model that powers the older and now free version of ChatGPT.
What if we could just skip the workout part and take the results in supplement form? Researchers did it… On mice and flies.
Hidden Markov model (HMM) [ 1, 2 ] is a powerful model to describe sequential data and has been widely used in speech signal processing [ 3-5 ], computer vision [ 6-8 ], longitudinal data analysis [ 9 ], social networks [ 10-12 ] and so on. An HMM typically assumes the system has K internal states, and the transition of states forms a Markov chain. The system state cannot be observed directly, thus we need to infer the hidden states and system parameters based on observations. Due to the existence of latent variables, the Expectation-Maximisation (EM) algorithm [ 13, 14 ] is often used to learn an HMM. The main difficulty is to calculate site marginal distributions and pairwise marginal distributions based on the posterior distribution of latent variables. The forward-backward algorithm was specifically designed to tackle this problem. The derivation of the forward-backward algorithm heavily relies on HMM assumptions and probabilistic relationships between quantities, thus requiring the parameters in the posterior distribution to have explicit probabilistic meanings.
Bayesian HMM [ 15-22 ] further imposes priors on the parameters of HMM, and the resulting model is more robust. It has been demonstrated that Bayesian HMM often outperforms HMM in applications. However, the learning process of a Bayesian HMM is more challenging since the posterior distribution of latent variables is intractable. Mean-field theory-based variational inference is often utilised in the E-step of the EM algorithm, which tries to find an optimal approximation of the posterior distribution in a factorised family. The variational inference iteration also involves computing site marginal distributions and pairwise marginal distributions given the joint distribution of system state indicator variables. Existing works [ 15-23 ] directly apply the forward-backward algorithm to obtain these values without justification. This is not theoretically sound and the result is not guaranteed to be correct, since the requirements of the forward-backward algorithm are not met in this case.
In this paper, we prove that the forward-backward algorithm can be applied in more general cases where the parameters have no probabilistic meanings. The first proof converts the general case to an HMM and uses the correctness of the forward-backward algorithm on HMM to prove the claim. The second proof is model-free, which derives the forward-backward algorithm in a totally different way. The new derivation does not rely on HMM assumptions and merely utilises matrix techniques to rewrite the desired quantities. Therefore, this derivation naturally proves that it is unnecessary to make probabilistic requirements on the parameters of the forward-backward algorithm. Specifically, we justify that heuristically applying the forward-backward algorithm in the variational learning of Bayesian HMM is theoretically sound and guaranteed to return the correct result.
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It’s a step that could one day lead to advances for humans that boost quality of life for many by: giving amputees and those with spinal injuries control of advanced prosthetics, stimulating the sacral nerve to restore bladder control, stimulating the cervical vagus nerve to treat epilepsy and providing deep brain stimulation as a possible treatment for Parkinson’s.
How far would you go to keep your mind from failing? Would you go so far as to let a doctor drill a hole in your skull and stick a microchip in your brain?
It’s not an idle question. In recent years neuroscientists have made major advances in cracking the code of memory, figuring out exactly how the human brain stores information and learning to reverse-engineer the process. Now they’ve reached the stage where they’re starting to put all of that theory into practice.
Last month two research teams reported success at using electrical signals, carried into the brain via implanted wires, to boost memory in small groups of test patients. “It’s a major milestone in demonstrating the ability to restore memory function in humans,” says Dr. Robert Hampson, a neuroscientist at Wake Forest School of Medicine and the leader of one of the teams.
As fun as brain-computer interfaces (BCI) are, for the best results they tend to come with the major asterisk of requiring the cutting and lifting of a section of the skull in order to implant a Utah array or similar electrode system. A non-invasive alternative consists out of electrodes which are placed on the skin, yet at a reduced resolution. These electrodes are the subject of a recent experiment by [Shaikh Nayeem Faisal] and colleagues in ACS Applied NanoMaterials employing graphene-coated electrodes in an attempt to optimize their performance.
Although external electrodes can be acceptable for basic tasks, such as registering a response to a specific (visual) impulse or for EEG recordings, they can be impractical in general use. Much of this is due to the disadvantages of the ‘wet’ and ‘dry’ varieties, which as the name suggests involve an electrically conductive gel with the former.
This gel ensures solid contact and a resistance of no more than 5 – 30 kΩ at 50 Hz, whereas dry sensors perform rather poorly at 200 kΩ at 50 Hz with worse signal-to-noise characteristics, even before adding in issues such as using the sensor on a hairy scalp, as tends to be the case for most human subjects.
Cameras for machine vision and robotics are essentially bionic devices mimicking human eyes. These applications require advanced color imaging systems to possess a number of attributes such as high resolution, large FoV, compact design, light-weight and low energy consumption, etc1. Conventional imaging systems based on CCD/CMOS image sensors suffer from relatively low FoV, bulkiness, high complexity, and power consumption issues, especially with mechanically tunable optics. Recently, spherical bionic eyes with curved image sensor retinas have triggered enormous research interest1,2,3,4,5,6,7. This type of devices possess several appealing features such as simplified lens design, low image aberration, wide FoV, and appearance similar to that of the biological eyes rendering them suitable for humanoid robots8,9,10,11,12,13. However, the existing spherical bionic eyes with curved retinas typically only have fixed lens and can only acquire mono color images. Fixed lenses cannot image objects with varying distances. On the other hand, conventional color imaging function of CCD/CMOS image sensors are achieved by using color filter arrays, which add complexity to the device fabrication and cause optical loss14,15,16,17,18,19. Typical absorptive organic dye filters suffer from poor UV and high-temperature stabilities, and plasmonic color filters suffer from low transmission20,21,22. And it is even more challenging to fabricate color filter arrays on hemispherical geometry where most traditional microelectronic fabrication methods are not applicable.
Herein, we demonstrate a novel bionic eye design that possesses adaptive optics and a hemispherical nanowire array retina with filter-free color imaging and neuromorphic preprocessing abilities. The primary optical sensing function of the artificial retina is realized by using a hemispherical all-inorganic CsPbI3 nanowire array that can produce photocurrent without external bias leading to a self-powered working mode. Intriguingly, an electrolyte-assisted color-dependent bidirectional synaptic photo-response is discovered in a well-engineered hybrid nanostructure. Inspired by the vertical alignment of a color-sensitive cone cell and following neurons, the device structure vertically integrates a SnO2/NiO double-shell nanotube filled with ionic liquid in the core on top of a CsPbI3/NiO core-shell nanowire. It is found that the positive surrounding gate effect of NiO due to photo hole injection can be partially or fully balanced by electrolyte under shorter (blue) or longer (green and red) wavelength illuminations, respectively. Thus, the device can yield either positive or negative photocurrent under shorter or longer wavelength illumination, respectively. The carriers can be accumulated in SnO2/NiO structure, giving rise to the bidirectional synaptic photo-response. This color-sensitive bidirectional photo-response instills a unique filter-free color imaging function to the retina. The synaptic behavior-based neuromorphic preprocessing ability, along with the self-powered feature, effectively reduce the energy consumption of the system23,24,25,26,27,28. Moreover, the color selectivity of each pixel can be tuned by a small external bias to detect more accurate color information. We demonstrate that the device can reconstruct color images with high fidelity for convolutional neural network (CNN) classifications. In addition, our bionic eye integrates adaptive optics in the device, by integrating an artificial crystalline lens and an electronic iris based on liquid crystals. The artificial crystalline lens can switch focal length to detect objects from different distances, and the electronic iris can control the amount of light reaching the retina which enhances the dynamic range. Both of the optical components can be easily tuned by the electric field, which are fast, compact, and much more energy efficient compared to the conventional mechanically controlled optics reported hitherto. (Supplementary Table 1 compares our system with some commercial zoom lenses.) The combination of all these unique features makes the bionic eye structurally and functionally equivalent to its biological counterpart.
Researchers show how Stable Diffusion can read minds. The method reconstructs images from fMRI scans with amazing accuracy.
Researchers have been using AI models to decode information from the human brain for years. At their core, most methods involve using pre-recorded fMRI images as input to a generative AI model for text or images.
In early 2018, for example, a group of researchers from Japan demonstrated how a neural network reconstructed images from fMRI recordings. In 2019, a group reconstructed images from monkey neurons, and Meta’s research group, led by Jean-Remi King, has published new work that derives text from fMRI data, for example.
