The AIX Toolbox for Linux Applications is a collection of GNU tools for IBM AIX. These tools are available for installation using Red Hat's RPM format. == Licensing == Each of these packages includes its own licensing information and while IBM has made the code available to AIX users, the code is provided as is and has not been thoroughly tested. The Toolbox is meant to provide a core set of some of the most common development tools and libraries along with the more popular GNU packages.
GPT-4Chan
Generative Pre-trained Transformer 4Chan (GPT-4chan) is a controversial AI model that was developed and deployed by YouTuber and AI researcher Yannic Kilcher in June 2022. The model is a large language model, which means it can generate text based on some input, by fine-tuning GPT-J with a dataset of millions of posts from the /pol/ board of 4chan, an anonymous online forum known for occasionally hosting hateful and extremist content. The model learned to mimic the style and tone of /pol/ users, producing text that is often intentionally offensive to groups (racist, sexist, homophobic, etc.) and nihilistic. Kilcher deployed the model on the /pol/ board itself, where it interacted with other users without revealing its identity. He also made the model publicly available on Hugging Face, a platform for sharing and using AI models, until it was removed from the platform. The project sparked criticism and debate in the AI community. Some people questioned the ethics, legality, and social impact of creating and distributing such a model. Some of the issues raised by the GPT-4chan controversy include the potential harm of spreading hate speech, the responsibility of AI developers and platforms, the need for regulation and oversight of AI models, and the role of open source and transparency in AI research. == Development == The development of GPT-4chan began in May 2022, when Kilcher announced his project on his YouTube channel. Notably, at the time before ChatGPT, he explained that he wanted to create a large language model that could generate realistic and coherent text in the style of /pol/, one of the most notorious online communities. He indicated that he was inspired by the success of GPT-3, a powerful AI model created by OpenAI, and GPT-J, an open-source model, with GPT-3 comparable performance, released by EleutherAI, a group of independent AI researchers. Kilcher decided to use GPT-J as the base model for his project, and fine-tune it with a large dataset of /pol/ posts. The Raiders of the Lost Kek dataset contained over 100 million posts from /pol/, spanning from June 2016-November 2019. Kilcher then proceeded to fine-tune the GPT-J model on the 4chan data. He also showed some examples of the model’s outputs, which ranged from political opinions, conspiracy theories, jokes, insults, and threats, to more creative and bizarre texts, such as poems, stories, songs, and code. He said that he was impressed by the model’s ability to generate fluent and diverse text, and that he was curious to see how it would interact with real /pol/ users. == Release == In June 2022, Kilcher deployed his model on the /pol/ board itself, using a bot that he programmed to post and reply to threads. He did not reveal the model’s identity, and he let it run autonomously, without any human supervision or intervention. He wanted to conduct a natural experiment, and to observe the model’s behavior and impact in a real-world setting. Furthermore, he also wanted to test the model’s robustness, and to see how it would handle the challenges and dynamics of /pol/, such as trolling, flaming, baiting, and moderation. At the same time, Kilcher also made his model publicly available on Hugging Face, a platform for sharing and using AI models. He wanted to share his work with the AI community and the public, and that he hoped that his model would inspire and enable others to create and explore new applications and possibilities with large language models. Likewise, he also said that he wanted to spark a discussion and a debate about the ethical and social implications of his project, and that he welcomed feedback and criticism from anyone. He provided a link to his model’s page on Hugging Face, where anyone could access and use the model through a web interface or an API, and also provided a link to his GitHub repository, where anyone could download and inspect the model’s code and data. == Controversy == The release of GPT-4chan to the public caused a lot of reactions and responses from various audiences. On the /pol/ board, the model’s posts and replies attracted a lot of attention and engagement from other users, who were mostly unaware of the model’s identity and nature. Some users praised the model for its intelligence, creativity, and humor, and agreed with its opinions and views. Some users challenged the model for its ignorance, inconsistency, and absurdity, and disagreed with its claims and arguments. Some users tried to troll, bait, or expose the model, and attempted to trick or test it with various questions and scenarios. The model’s posts and replies also generated a lot of controversy and conflict among the users, who often engaged in heated and violent debates and fights with each other. On Hugging Face, the model’s page received a lot of visits and requests from users who wanted to try out and experiment with the model. The model’s page also received a lot of feedback and reviews from users who rated and commented on the model. However, with the controversy of the model, access to it was gated and then disabled on Hugging Face for concerns about the potential harm the model could cause. The incident was notable for the direct intervention of CEO Clément Delangue in the talk pages, a very unusual occurrence compared to the normal practices of content moderation. The release of GPT-4chan also sparked a lot of media coverage and public attention, as various news outlets and social media platforms reported and commented on the model’s project. On YouTube, the model’s video received a lot of views and interactions from viewers who watched and followed the project. Furthermore, a petition condemning the deployment of GPT-4chan gained over 300 signatures from technology experts.
List of video editing software
The following is a list of video editing software. The criterion for inclusion in this list is the ability to perform non-linear video editing. Most modern transcoding software supports transcoding a portion of a video clip, which would count as cropping and trimming. However, items in this article have one of the following conditions: Can perform other non-linear video editing function such as montage or compositing Can do the trimming or cropping without transcoding == Free (libre) or open-source == The software listed in this section is either free software or open source, and may or may not be commercial. === Active and stable === === Inactive === == Proprietary (non-commercial) == The software listed in this section is proprietary, and freeware or freemium. === Active === === Discontinued === == Proprietary (commercial) == The software listed in this section is proprietary and commercial. === Active === === Discontinued ===
Quantum robotics
Quantum robotics is an interdisciplinary field that investigates the intersection of robotics and quantum mechanics. This field, in particular, explores the applications of quantum phenomena such as quantum entanglement within the realm of robotics. Examples of its applications include quantum communication in multi-agent cooperative robotic scenarios, the use of quantum algorithms in performing robotics tasks, and the integration of quantum devices (e.g., quantum detectors) in robotic systems. == Introduction == The free-space quantum communication between mobile platforms was proposed for reconfigurable quantum key distribution (QKD) applications using unmanned aerial vehicle (UAVs, a.k.a. drones) in 2017. This technology was later advanced in various aspects in mobile drone and vehicle platforms in several configurations such as drone-to-drone, drone-to-moving vehicle, and vehicle-to-vehicle systems. Some research has contributed to low-size, low-weight, and low-power quantum key distribution systems for small-form UAVs, the characterization of a polarization-based receiver for mobile free-space optical QKD, and optical-relayed entanglement distribution using drones as mobile nodes. The topic of free-space quantum communication between mobile platforms, initially developed to meet the need for free-space QKD and entanglement distribution using mobile nodes, was brought into the robotics domain as an emerging interdisciplinary mechatronics topic to investigate the interface between quantum technologies and the robotic systems domain. The main advantage of such integrated technology is the guaranteed security in communication between multi-agent and cooperative autonomous systems. Other advances are anticipated. == Quantum entanglement == According to quantum mechanics, entanglement occurs when more than one particle become connected. If the state of one particle changes then it will instantly change the state of other particles regardless of their distance. Entangled sensors do the same kind of work and achieve strong sensitivity. A group of quantum robots can measure magnetic fields, gravitational fields and other physical properties using entangled sensors with high rate of accuracy. Again the connection of one robot to other is increased (become strong) by quantum entanglement. == Quantum teleportation == Quantum teleportation is the transfer of quantum information (not physical objects). This is used in case of multi robot process. One robot is programmed with a complex quantum update. Then that robot can teleport that complex quantum information (the update) to other robots. This teleportation or communication is very secure because all the work is done in quantum state. == Kinematics == Quantum computing has been proposed as being optimal for calculating inverse kinematics values. == Alice and Bob robots == In the realm of quantum mechanics, the names Alice and Bob are frequently employed to illustrate various phenomena, protocols, and applications. These include their roles in QKD, quantum cryptography, entanglement, and teleportation. The terms "Alice Robot" and "Bob Robot" serve as analogous expressions that merge the concepts of Alice and Bob from quantum mechanics with mechatronic mobile platforms (such as robots, drones, and autonomous vehicles). For example, the Alice Robot functions as a transmitter platform that communicates with the Bob Robot, housing the receiving detectors.
Afghan Girls Robotics Team
The Afghan Girls Robotics Team, also known as the Afghan Dreamers, is an all-girl robotics team from Herat, Afghanistan, founded through the Digital Citizen Fund (DCF) in 2017 by Roya Mahboob and Alireza Mehraban. It is made up of girls between ages 12 and 18 and their mentors. Several members of the team were relocated to Qatar and Mexico by humanitarian and tech entrepreneur Sarah Porter following the fall of Kabul in August 2021. A documentary film featuring members of the team, titled Afghan Dreamers, was released by MTV Documentary Films in 2023. == Origins == The Afghan Girls Robotics Team was co-founded in 2017 by Roya Mahboob, who is their coach, mentor and sponsor, and founder of the Digital Citizen Fund (DCF), which is the parent organization for the team. Dean Kamen was planning a 2017 competition in the United States and had recruited Mahboob to form a team from Afghanistan. Out of 150 girls, 12 were selected for the first team. Before parts were sent by Kamen, they trained in the basement of the home of Mahboob's parents, with scrap metal and without safety equipment under the guidance of their coach, Mahboob's brother Alireza Mehraban, who is also a co-founder of the team. == 2017 and 2018 == In 2017, six members of the Afghan Girls Robotics Team traveled to the United States to participate in the international FIRST Global Challenge robotics competition. Their visas were rejected twice after they made two journeys from Herat to Kabul through Taliban-controlled areas, before officials in the United States government intervened to allow them to enter the United States. Customs officials also detained their robotics kits, which left them two weeks to construct their robot, unlike some teams that had more time. They were awarded a Silver medal for Courageous Achievement. One week after they returned home from the competition, the father of team captain Fatemah Qaderyan, Mohammad Asif Qaderyan, was killed in a suicide bombing. After their United States visas expired, the team participated in competitions in Estonia and Istanbul. Three of the 12 members participated in the 2017 Entrepreneurial Challenge at the Robotex festival in Estonia, and won the competition for their solar-powered robot designed to assist farmers. In 2018, the team trained in Canada, continued to travel in the United States for months and participate in competitions. == 2019 == The Afghan Girls Robotics team had aspirations to develop a science and technology school for girls in Afghanistan. Roya Mahboob interfaced with the School of Engineering and Applied Sciences (SEAS), the School of Architecture, and the Whitney and Betty MacMillan Center for International and Area Studies Yale University to design the infrastructure for what they named The Dreamer Institute. == 2020 == In March 2020, the governor of Herat at the time, in response to the COVID-19 pandemic in Afghanistan and a scarcity of ventilators, sought help with the design of low-cost ventilators, and the Afghan Girls Robotics Team was one of six teams contacted by the government. Using a design from Massachusetts Institute of Technology and with guidance from MIT engineers and Douglas Chin, a surgeon in California, the team developed a prototype with Toyota Corolla parts and a chain drive from a Honda motorcycle. UNICEF also supported the team with the acquisition of necessary parts during the three months they spent building the prototype that was completed in July 2020. Their design costs around $500 compared to $50,000 for a ventilator. In December 2020, Minister of Industry and Commerce Nizar Ahmad Ghoryani donated funding and obtained land for a factory to produce the ventilators. Under the direction of their mentor Roya Mahboob, the Afghan Dreamers also designed a UVC Robot for sanitization, and a Spray Robot for disinfection, both of which were approved by the Ministry of Health for production. == 2021 == In early August 2021, Somaya Faruqi, former captain of the team, was quoted by Public Radio International about the future of Afghanistan, stating, "We don’t support any group over another but for us what’s important is that we be able to continue our work. Women in Afghanistan have made a lot of progress over the past two decades and this progress must be respected." On August 17, 2021, the Afghan Girls Robotics Team and their coaches were reported to be attempting to evacuate, but unable to obtain a flight out of Afghanistan, and a lawyer appealed to Canada for assistance regarding the evacuation of the team members. As of August 19, 2021, nine members of the team and their coaches had evacuated to Qatar. The founder of the team, Roya Mahboob, and DCF board member, Elizabeth Schaeffer Brown, were previously in contact with the Qatari government to assist the team members in their evacuation from Afghanistan. By August 25, 2021, some members arrived in Mexico. Saghar, a team member who evacuated to Mexico, said, "We wanted to continue the path that we started to continue to go for our achievements and to go for having our dreams through reality. So that's why we decided to leave Afghanistan and go for somewhere safe" in an interview with The Associated Press. The members who have left Afghanistan participated in an online robotics competition in September and plan to continue their education. A documentary film titled Afghan Dreamers, produced by Beth Murphy and directed by David Greenwald, was in post-production when the team began to evacuate. == 2022 == The Afghan Dreamers were involved in a training program at the Texas A&M University at Qatar’s STEM Hub. == 2023 == The Afghan Girls Robotics Team had a booth at the 5th UN Conference on the Least Developed Countries, where they displayed some of the robots the team had constructed. == Afghan Dreamers documentary == The Afghan Dreamers documentary from MTV Documentary Films premiered in May 2023 on Paramount+. The film was directed by David Greenwald and produced by David Cowan and Beth Murphy. In a review for Screen Daily, Wendy Ide wrote, "This film, with its likeable cast of girl nerds and positive message, should enjoy a warm reception on the festival circuit, and will be of particular interest to events seeking to showcase women's stories from around the world. It also serves as a timely cautionary tale – a case study on just how quickly the rights and the opportunities of women can be curtailed, at the behest of the men in power." == Honors and awards == 2017 Silver medal for Courageous Achievement at the FIRST Global Challenge, science and technology 2017 Benefiting Humanity in AI Award at World Summit AI 2017 Winner, Entrepreneurship Challenge at Robotex in Estonia 2018 Permission to Dream Award, Raw Film Festival 2018 Conrad Innovation Challenge, Raw Film Festival 2018 Rookie All Star – District Championship, Canada 2018 Asia Game Changer Award Honoree 2019 Inspiring in Engineering Award – FIRST Detroit World Championship 2019 Asia Game Changer Award of California 2019 Safety Award – FIRST Global, Dubai 2021 Forbes 30 Under 30 Asia 2022 World Championships, Genoa, Switzerland
Deep learning
In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be supervised, semi-supervised or unsupervised. Some common deep learning network architectures include fully connected networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance fields. These architectures have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Early forms of neural networks were inspired by information processing and distributed communication nodes in biological systems, particularly the human brain. However, current neural networks do not intend to model the brain function of organisms, and are generally seen as low-quality models for that purpose. == Overview == Most modern deep learning models are based on multi-layered neural networks such as convolutional neural networks and transformers, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines. Fundamentally, deep learning refers to a class of machine learning algorithms in which a hierarchy of layers is used to transform input data into a progressively more abstract and composite representation. For example, in an image recognition model, the raw input may be an image (represented as a tensor of pixels). The first representational layer may attempt to identify basic shapes such as lines and circles, the second layer may compose and encode arrangements of edges, the third layer may encode a nose and eyes, and the fourth layer may recognize that the image contains a face. Importantly, a deep learning process can learn which features to optimally place at which level on its own. Prior to deep learning, machine learning techniques often involved hand-crafted feature engineering to transform the data into a more suitable representation for a classification algorithm to operate on. In the deep learning approach, features are not hand-crafted and the model discovers useful feature representations from the data automatically. This does not eliminate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction. The word "deep" in "deep learning" refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. For a feedforward neural network, the depth of the CAPs is that of the network and is the number of hidden layers plus one (as the output layer is also parameterized). For recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited. No universally agreed-upon threshold of depth divides shallow learning from deep learning, but most researchers agree that deep learning involves CAP depth higher than two. CAP of depth two has been shown to be a universal approximator in the sense that it can emulate any function. Beyond that, more layers do not add to the function approximator ability of the network. Deep models (CAP > two) are able to extract better features than shallow models and hence, extra layers help in learning the features effectively. Deep learning architectures can be constructed with a greedy layer-by-layer method. Deep learning helps to disentangle these abstractions and pick out which features improve performance. Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data is more abundant than labeled data. Examples of deep structures that can be trained in an unsupervised manner are deep belief networks. The term deep learning was introduced to the machine learning community by Rina Dechter in 1986, and to artificial neural networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons. The etymology of the term is more complicated. == Interpretations == Deep neural networks are generally interpreted in terms of the universal approximation theorem or probabilistic inference. The classic universal approximation theorem concerns the capacity of feedforward neural networks with a single hidden layer of finite size to approximate continuous functions. In 1989, the first proof was published by George Cybenko for sigmoid activation functions and was generalised to feed-forward multi-layer architectures in 1991 by Kurt Hornik. Recent work also showed that universal approximation also holds for non-bounded activation functions such as Kunihiko Fukushima's rectified linear unit. The universal approximation theorem for deep neural networks concerns the capacity of networks with bounded width but the depth is allowed to grow. Lu et al. proved that if the width of a deep neural network with ReLU activation is strictly larger than the input dimension, then the network can approximate any Lebesgue integrable function; if the width is smaller or equal to the input dimension, then a deep neural network is not a universal approximator. The probabilistic interpretation derives from the field of machine learning. It features inference, as well as the optimization concepts of training and testing, related to fitting and generalization, respectively. More specifically, the probabilistic interpretation considers the activation nonlinearity as a cumulative distribution function. The probabilistic interpretation led to the introduction of dropout as regularizer in neural networks. The probabilistic interpretation was introduced by researchers including Hopfield, Widrow and Narendra and popularized in surveys such as the one by Bishop. == History == === Before 1980 === There are two types of artificial neural network (ANN): feedforward neural network (FNN) or multilayer perceptron (MLP) and recurrent neural networks (RNN). RNNs have cycles in their connectivity structure, whereas FNNs do not. In the 1920s, Wilhelm Lenz and Ernst Ising created the Ising model which is essentially a non-learning RNN architecture consisting of neuron-like threshold elements. In 1972, Shun'ichi Amari made this architecture adaptive. His learning RNN was republished by John Hopfield in 1982. Other early recurrent neural networks were published by Kaoru Nakano in 1971. Already in 1948, Alan Turing produced work on "Intelligent Machinery" that was not published in his lifetime, containing "ideas related to artificial evolution and learning RNNs". Frank Rosenblatt (1958) proposed the perceptron, an MLP with 3 layers: an input layer, a hidden layer with randomized weights that did not learn, and an output layer. He later published a 1962 book that also introduced variants and computer experiments, including a version with four-layer perceptrons "with adaptive preterminal networks" where the last two layers have learned weights (here he credits H. D. Block and B. W. Knight). The book cites an earlier network by R. D. Joseph (1960) "functionally equivalent to a variation of" this four-layer system (the book mentions Joseph over 30 times). Should Joseph therefore be considered the originator of proper adaptive multilayer perceptrons with learning hidden units? Unfortunately, the learning algorithm was not a functional one, and fell into oblivion. The first working deep learning algorithm was the Group method of data handling, a method to train arbitrarily deep neural networks, published by Alexey Ivakhnenko and Lapa in 1965. They regarded it as a form of polynomial regression, or a generalization of Rosenblatt's perceptron to handle more complex, nonlinear, and hierarchical relationships. A 1971 paper described a deep network with eight layers trained by this method, which is based on layer by layer training through regression analysis. Superfluous hidden units are pruned using a separate validation set. Since the activation functions of the nodes are Kolmogorov-Gabor polynomials, these were also the first deep networks with multiplicative units or "gates". The first deep learning multilayer perceptron trained by stochastic gradient descent was published in 1967 by Shun'ichi
OpenPipeline
openPipeline is an open-source plug-in for Autodesk Maya that is designed to assist in a Production Pipeline structure and Computer animation. == Development == Created in Maya Embedded Language, openPipeline was initiated at Eyebeam Atelier and further developed at Pratt Institute in the Digital Arts Lab. The initial release date was December 28, 2006. == Contributors == Rob O'Neill (Creator) Paris Mavroidis Meng-Han Ho