AI News, artificial intelligence
OpenAI is the for-profit corporation OpenAI LP, whose parent organization is the non-profit organization OpenAI Inc, which conducts research in the field of artificial intelligence (AI) with the stated aim to promote and develop friendly AI in such a way as to benefit humanity as a whole.
Nevertheless, Sutskever stated that he was willing to leave Google for OpenAI “partly of because of the very strong group of people and, to a very large extent, because of its mission.” Brockman stated that “the best thing that I could imagine doing was moving humanity closer to building real AI in a safe way.” OpenAI researcher Wojciech Zaremba stated that he turned down “borderline crazy” offers of two to three times his market value to join OpenAI instead.
and which sentiment has been expressed elsewhere in reference to a potentially enormous class of AI-enabled products: 'Are we really willing to let our society be infiltrated by autonomous software and hardware agents whose details of operation are known only to a select few?
Vishal Sikka, former CEO of Infosys, stated that an “openness” where the endeavor would “produce results generally in the greater interest of humanity” was a fundamental requirement for his support, and that OpenAI “aligns very nicely with our long-held values” and their “endeavor to do purposeful work”.
We could sit on the sidelines or we can encourage regulatory oversight, or we could participate with the right structure with people who care deeply about developing AI in a way that is safe and is beneficial to humanity.” Musk acknowledged that “there is always some risk that in actually trying to advance (friendly) AI we may create the thing we are concerned about”;
During a 2016 conversation about the technological singularity, Altman said that “we don’t plan to release all of our source code” and mentioned a plan to “allow wide swaths of the world to elect representatives to a new governance board”.
Gym aims to provide an easy-to-setup general-intelligence benchmark with a wide variety of different environments—somewhat akin to, but broader than, the ImageNet Large Scale Visual Recognition Challenge used in supervised learning research—and that hopes to standardize the way in which environments are defined in AI research publications, so that published research becomes more easily reproducible.
when an agent is then removed from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had learned how to balance in a generalized way.
OpenAI Five is the name of a team of five OpenAI-curated bots that are used in the competitive five-on-five video game Dota 2, who learn to play against human players at a high skill level entirely through trial-and-error algorithms.
Before becoming a team of five, the first public demonstration occurred at The International 2017, the annual premiere championship tournament for the game, where Dendi, a professional Ukrainian player of the game, lost against a bot in a live 1v1 matchup.
The system uses a form of reinforcement learning, as the bots learn over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map objectives.
Computer Science—Artificial Intelligence (MSc, online, part-time)
This module introduces learners to the different categories of machine learning task and provides in-depth coverage of important algorithms for tackling them.
Statistical Relational Learning is an area of Artificial Intelligence and Machine Learning concerned with the representation of, and reasoning and learning with, uncertain (probabilistic) and relational domain knowledge (such as graphs, web links or symbolic facts). Planned
OverviewThe topic of Agents and Multi-Agent Systems, examines environment that involve autonomous decision making software actors to interact with their surroundings with the aim of achieving some individual or overall goal.
More recently, significant global attention has focussed on the vision of autonomous vehicles, which also follows the core principle of an agent attempting to achieve a set of defined goals.
The module will examine Adaptive Learning Agents through the use of Reinforcement Learning algorithms an area of Machine Learning, which focuses on training learners to choose actions which yield the maximum reward in the absence of prior knowledge.
The module takes a hands-on, practical approach to reinforcement learning theory, beginning with Markov Decision Processes, detailing practical learning examples in discrete environments and how to formulate a reinforcement learning task.
This module will build on the basic concepts with a view to delving deeper into core computer vision, machine learning and deep learning topics.
In addition we will examine a range of deep learning architectures ranging from AlexNet upto the current state of the art in this ever expanding field.
Deep learning based computer vision forms the core of many of the recent developments in this field and has been widely adopted as a core AI tool by all the key industrial players such as Google, Facebook, IBM, Apple, Baidu ...
This module is primarily aimed at those who aim to undertake research in computer vision or require a deeper understanding of the subject to address commercial computer vision development.
Computer vision applications span a wide range of disciplines including industrial/machine vision, video data processing, biomedical engineering, healthcare, astronomy, imaging science, sensor technology, multimedia and enhanced reality systems.
module is intended for students who have completed a first course in machine learning, and already have a good grounding in supervised learning topics including: classification and regression;
This module takes a practical approach to introducing learners to the strengths and weaknesses of human perception, and the use of best practices to represent complex and large data stories using visual primitives.
Large-scale data analytics is concerned with the processing and analysis of large quantities of data, typically from distributed sources (such as data streams on the internet).
Students learn about foundational concepts, software tools and advanced programming techniques for the scalable storage, processing and predictive analysis of high- volume and high-velocity data, and how to apply them to practical problems.
Artificial neural network
Artificial neural networks (ANN) or connectionist systems are computing systems that are inspired by, but not identical to, biological neural networks that constitute animal brains.
For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as 'cat' or 'no cat' and using the results to identify cats in other images.
An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.
In ANN implementations, the 'signal' at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs.
Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.
ANNs have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.
In 1970, Linnainmaa published the general method for automatic differentiation (AD) of discrete connected networks of nested differentiable functions.
Werbos's (1975) backpropagation algorithm enabled practical training of multi-layer networks.In 1982, he applied Linnainmaa's AD method to neural networks in the way that became widely used.
who discovered that basic perceptrons were incapable of processing the exclusive-or circuit and that computers lacked sufficient power to process useful neural networks.
In 1992, max-pooling was introduced to help with least shift invariance and tolerance to deformation to aid in 3D object recognition.
Schmidhuber adopted a multi-level hierarchy of networks (1992) pre-trained one level at a time by unsupervised learning and fine-tuned by backpropagation.
(2006) proposed learning a high-level representation using successive layers of binary or real-valued latent variables with a restricted Boltzmann machine
In 2012, Ng and Dean created a network that learned to recognize higher-level concepts, such as cats, only from watching unlabeled images.
Unsupervised pre-training and increased computing power from GPUs and distributed computing allowed the use of larger networks, particularly in image and visual recognition problems, which became known as 'deep learning'.
showed that despite the vanishing gradient problem, GPUs make backpropagation feasible for many-layered feedforward neural networks.
Between 2009 and 2012, ANNs began winning prizes in ANN contests, approaching human level performance on various tasks, initially in pattern recognition and machine learning.
won three competitions in connected handwriting recognition in 2009 without any prior knowledge about the three languages to be learned.
ANNs began as an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had had little success.
They soon reoriented towards improving empirical results, mostly abandoning attempts to remain true to their biological precursors.
ANNs retained the biological concept of artificial neurons, which receive input, combine the input with their internal state (activation) and an optional threshold using an activation function, and produce output using an output function.
The propagation function computes the input to a neuron from the outputs of its predecessor neurons and their connections as a weighted sum.
They can be pooling, where a group of neurons in one layer connect to a single neuron in the next layer, thereby reducing the number of neurons in that layer.
The outputs are actually numbers, so when the error is low, the difference between the output (almost certainly a cat) and the correct answer (cat) is small.
The learning rate defines the size of the corrective steps that the model takes to adjust for errors in each observation.
A high learning rate shortens the training time, but with lower ultimate accuracy, while a lower learning rate takes longer, but with the potential for greater accuracy.
In order to avoid oscillation inside the network such as alternating connection weights, and to improve the rate of convergence, refinements use an adaptive learning rate that increases or decreases as appropriate.
The concept of momentum allows the balance between the gradient and the previous change to be weighted such that the weight adjustment depends to some degree on the previous change.
While it is possible to define a cost function ad hoc, frequently the choice is determined by the functions desirable properties (such as convexity) or because it arises from the model (e.g., in a probabilistic model the model's posterior probability can be used as an inverse cost).
Technically, backprop calculates the gradient (the derivative) of the cost function associated with a given state with respect to the weights.
A commonly used cost is the mean-squared error, which tries to minimize the average squared error between the network's output and the desired output.
Tasks suited for supervised learning are pattern recognition (also known as classification) and regression (also known as function approximation).
This can be thought of as learning with a 'teacher', in the form of a function that provides continuous feedback on the quality of solutions obtained thus far.
The cost function is dependent on the task (the model domain) and any a priori assumptions (the implicit properties of the model, its parameters and the observed variables).
whereas in statistical modeling, it could be related to the posterior probability of the model given the data (note that in both of those examples those quantities would be maximized rather than minimized).
In applications such as playing video games, an actor takes a string of actions, receiving a generally unpredictable response from the environment after each one.
In reinforcement learning, the aim is to weight the network (devise a policy) to perform actions that minimize long-term (expected cumulative) cost.
at each point in time the agent performs an action and the environment generates an observation and an instantaneous cost, according to some (usually unknown) rules.
At any juncture, the agent decides whether to explore new actions to uncover their costs or to exploit prior learning to proceed more quickly.
Because the state transitions are not known, probability distributions are used instead: the instantaneous cost distribution
because of ANNs ability to mitigate losses of accuracy even when reducing the discretization grid density for numerically approximating the solution of control problems.
Tasks that fall within the paradigm of reinforcement learning are control problems, games and other sequential decision making tasks.
However, batch learning typically yields a faster, more stable descent to a local minimum, since each update is performed in the direction of the batch's average error.
A common compromise is to use 'mini-batches', small batches with samples in each batch selected stochastically from the entire data set.
The simplest types have one or more static components, including number of units, number of layers, unit weights and topology.
Some of the main breakthroughs include: convolutional neural networks that have proven particularly successful in processing visual and other two-dimensional data;
competitive networks such as generative adversarial networks in which multiple networks (of varying structure) compete with each other, on tasks such as winning a game
The basic search algorithm is to propose a candidate model, evaluate it against a dataset and use the results as feedback to teach the NAS network.
Design issues include deciding the number, type and connectedness of network layers, as well as the size of each and the connection type (full, pooling, ...).
Hyperparameters must also be defined as part of the design (they are not learned), governing matters such as how many neurons are in each layer, learning rate, step, stride, depth, receptive field and padding (for CNNs), etc.
object recognition and more), sequence recognition (gesture, speech, handwritten and printed text recognition), medical diagnosis, finance
and to distinguish highly invasive cancer cell lines from less invasive lines using only cell shape information.
the dynamics of neural circuitry arise from interactions between individual neurons and how behavior can arise from abstract neural modules that represent complete subsystems.
Studies considered long-and short-term plasticity of neural systems and their relation to learning and memory from the individual neuron to the system level.
specific recurrent architecture with rational-valued weights (as opposed to full precision real number-valued weights) has the power of a universal Turing machine,
This concept emerges in a probabilistic (Bayesian) framework, where regularization can be performed by selecting a larger prior probability over simpler models;
but also in statistical learning theory, where the goal is to minimize over two quantities: the 'empirical risk' and the 'structural risk', which roughly corresponds to the error over the training set and the predicted error in unseen data due to overfitting.
Supervised neural networks that use a mean squared error (MSE) cost function can use formal statistical methods to determine the confidence of the trained model.
A confidence analysis made this way is statistically valid as long as the output probability distribution stays the same and the network is not modified.
By assigning a softmax activation function, a generalization of the logistic function, on the output layer of the neural network (or a softmax component in a component-based network) for categorical target variables, the outputs can be interpreted as posterior probabilities.
Potential solutions include randomly shuffling training examples, by using a numerical optimization algorithm that does not take too large steps when changing the network connections following an example, grouping examples in so-called mini-batches and/or introducing a recursive least squares algorithm for CMAC.
Sensor neurons fire action potentials more frequently with sensor activation and muscle cells pull more strongly when their associated motor neurons receive action potentials more frequently.
Other than the case of relaying information from a sensor neuron to a motor neuron, almost nothing of the principles of how information is handled by biological neural networks is known.
This allows simple statistical association (the basic function of artificial neural networks) to be described as learning or recognition.
Alexander Dewdney commented that, as a result, artificial neural networks have a 'something-for-nothing quality, one that imparts a peculiar aura of laziness and a distinct lack of curiosity about just how good these computing systems are.
Neural networks, for instance, are in the dock not only because they have been hyped to high heaven, (what hasn't?) but also because you could create a successful net without understanding how it worked: the bunch of numbers that captures its behaviour would in all probability be 'an opaque, unreadable table...valueless as a scientific resource'.
In spite of his emphatic declaration that science is not technology, Dewdney seems here to pillory neural nets as bad science when most of those devising them are just trying to be good engineers.
argued that the brain self-wires largely according to signal statistics and therefore, a serial cascade cannot catch all major statistical dependencies.
While the brain has hardware tailored to the task of processing signals through a graph of neurons, simulating even a simplified neuron on von Neumann architecture may consume vast amounts of memory and storage.
Schmidhuber noted that the resurgence of neural networks in the twenty-first century is largely attributable to advances in hardware: from 1991 to 2015, computing power, especially as delivered by GPGPUs (on GPUs), has increased around a million-fold, making the standard backpropagation algorithm feasible for training networks that are several layers deeper than before.
Neuromorphic engineering addresses the hardware difficulty directly, by constructing non-von-Neumann chips to directly implement neural networks in circuitry.
Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering general principles that allow a learning machine to be successful.
Advocates of hybrid models (combining neural networks and symbolic approaches), claim that such a mixture can better capture the mechanisms of the human mind.
Will China Overtake the U.S. in Artificial Intelligence Research?
China not only has the world’s largest population and looks set to become the largest economy —
These include making significant contributions to fundamental research, being a favoured destination for the world’s brightest talents and having an AI industry that rivals global leaders in the field.
But observers warn that there are several factors that could stymie the nation’s plans, including a lack of contribution to the theories used to develop the tools underpinning the field, and a reticence by Chinese companies to invest in the research needed to make fundamental breakthroughs.
AI technologies promise advances in health care, transport and communications, and the nations that make fundamental breakthroughs in the field are likely to shape its future directions and reap the most benefits.
The initiative unveiled in 2017, known as theNew Generation Artificial Intelligence Development Plan, has spurred myriad policies and billions of dollars of investment in research and development from ministries, provincial governments and private companies.
For instance, the open-source platforms TensorFlow and Caffe, developed by US academics and companies to design, build and train the sets of algorithms that enable computers to function more like the human brain, are widely used in industry and academia the world over.
According to the 2018China AI Development Report, jointly written by academics and industry, by the end of 2017, China was home to the second-largest pool of AI scientists and engineers, about 18,200 people, ranking behind the United States, which had roughly 29,000.
Zheng says that the centre also offers a more holistic evaluation system for staff than is found at many Chinese universities, which tend to reward high publication rates over other criteria.
He has also implemented a hiring system that bypasses centralized university procedures and allows scientists to build teams of engineers quickly, and now runs undergraduate courses in AI.
China’s plan to have globally leading AI companies by 2020 is also within reach, given the growing expertise of its three core tech companies, Tencent, Baidu and Alibaba, says Ding.
Ma says that a big advantage for China is the size of its population, which creates a large potential workforce and unique opportunities to train AI systems, including large patient data sets for training software to predict disease.
In February, Chinese researchers showed that their natural language processing system could diagnose common childhood conditions from electronic health records with comparable accuracy to experienced paediatricians1.
If China is to have global influence in the field of AI, it is also important that it has proper governance, says Ma, because this will allow researchers and companies in China to build the trust necessary to gain users across the world —
China, for instance, has attracted criticism from human-rights advocates over alleged uses of facial recognition technology to track members of the Uighur people, a predominantly Muslim community in Xinjiang.
As the U.S. moves forward as a leader in 5G technologies and deployment, it critically needs fast and efficient wireless spectrum policy creation, adoption, and management of wireless spectrum.
Potential areas to be explored in this workshop include, but are not limited to: Experts from government, private industry, and academia will discuss current use cases, effective technology, tools, and practices, while identifying gaps and issues that will require additional research to resolve.
Today's artificial intelligence (AI), specifically machine learning (ML), has the potential to touch every area of our global digital society (i.e., our lives), and wireless spectrum is no exception.
This panel will explore the AI landscape, shedding light on how types of data and different functional capabilities guide the selection of AI/ML techniques, mapping these insights to applications, and then drawing connections to the opportunities and challenges facing next generation wireless spectrum management.
Discussion Theme 1: Artificial Intelligence for Future Communications Networks Communication networks and their associated architectures are evolving to be complex, heterogenous, interconnected entities that are becoming increasingly difficult to manage using traditional, model-based approaches.
This discussion theme will focus on how AI can be used as a tool for (a) dynamic network planning and resource allocation (b) network monitoring, diagnosis and self-healing and (c) integrating heterogeneous networks end-to-end.
The goal is to address the overall potential for AI to assist in operating and securing large complex networks more efficiently, addressing emerging use cases and applications, in addition to achieving target end-to-end objectives.
Breakout Topics Discussion Theme 3: Artificial Intelligence for Spectrum Sharing Spectrum sharing among independent users presents challenging decision-making problems, whether for the users themselves (peer-to-peer sharing) or for a control system seeking to arbitrate among them (spectrum access system).
Following this, workshop participants will divide into groups to identify opportunities and prioritize critical research topics in three areas: AI-enabled peer-to-peer sharing, AI-enabled spectrum access systems, and verification/validation of AI-enabled spectrum access mechanisms.
- On 27. februar 2021
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