It is never easy to predict what society and technology will look like in the coming decades, but one futurist used the imaginations of children to come up with ideas. Futurist Brian David Johnson spoke to kids aged 8-13 as part of a study into their vision of life in the 2050s for the Institution of Engineering and Technology (IET). 'The current generation of young minds is nothing like we've seen before', Johnson explained, saying they were born and grew up constantly connected. Every child he spoke to was optimistic about the future, with many showing'jump-out-of-their-seat' levels of excitement about'what is to come' as they reach adulthood. He used the conversations he had with the children and their parents to formulate predictions about the future of smart homes, food and personal virtual assistants. Futurist Brian David Johnson spoke to children aged 8-13 as part of a study into their vision of life in the 2050s for the Institution of Engineering and Technology (IET).

The feedforward neural network is the simplest type of artificial neural network which has lots of applications in machine learning. It was the first type of neural network ever created, and a firm understanding of this network can help you understand the more complicated architectures like convolutional or recurrent neural nets. This article is inspired by the Deep Learning Specialization course of Andrew Ng in Coursera, and I have used a similar notation to describe the neural net architecture and the related mathematical equations. This course is a very good online resource to start learning about neural nets, but since it was created for a broad range of audiences, some of the mathematical details have been omitted. In this article, I will try to derive all the mathematical equations that describe the feedforward neural net. Currently Medium supports superscripts only for numbers, and it has no support for subscripts. So to write the name of the variables, I use this notation: Every character after is a superscript character and every character after _ (and before if its present) is a subscript character. A neuron is the foundational unit of our brain. The brain is estimated to have around 100 billion neurons, and this massive biological network enables us to think and perceive the world around us. Basically what a neuron does is receiving information from other neurons, processing this information and sending the result to other neurons. This process is shown in Figure 1. A single neuron has some inputs which are received throughout the dendrites. These inputs are summed together in the cell body and transformed into a signal that is sent to other neurons through the axon. The axon is connected to the dendrites of other neurons by synapses. The synapse can act as a weight and make the signal passing through it stronger or weaker based on how often that connection is used. This biological understanding of the neuron can be translated into a mathematical model as shown in Figure 1. The artificial neuron takes a vector of input features x_1, x_2, . . . The transfer function is chosen by the designer, but w_i and b are adjusted by some learning rule during the training process of the neural network. There are different activation functions that you can use in a neural net, and some of them which are used more commonly are discussed below. A binary step function is a threshold-based activation function. If the function's input (z) is less than or equal to zero, the output of the neuron is zero and if it is above zero, the output is 1 The step function is not differentiable at point z 0, and its derivative is zero at all the other points. Figure 1 shows a plot of the step function and its derivative. This function is shown in Figure 2 (left).

NEW DELHI: Information technology major IBM said on Thursday it is collaborating with the Central Board of Secondary Education (CBSE) to integrate artificial intelligence (AI) in the high school curriculum (grade 11 and 12) for the current academic year (2020-21). The curriculum is part of CBSE's Social Empowerment through Work Education and Action (SEWA) programme and will be introduced in about 200 schools across 13 states -- Delhi-NCR, Karnataka, Tamil Nadu, Orissa, Kerala, West Bengal, Andhra Pradesh, Telangana, Maharashtra, Madhya Pradesh, Uttar Pradesh, Rajasthan and Punjab. The IBM AI curriculum is structured around a course framework for students consisting of base strands of knowledge (basics, history, applications), skills (design thinking, computational thinking, data fluency, critical thinking) and values (ethical decision making, bias) in AI. It is further made robust with problem-based learning outcomes and assessment methods for teachers to build foundational skills of AI in students making them not just consumers of AI but creators as well. To meet CBSE's requirements for grades 11 and 12, the curriculum was co-developed with Australia's Macquarie University and Indian implementation partners -- Learning Links Foundation and 1M1B.

New York lawmakers have passed a moratorium that would ban the use of facial recognition in schools until 2022. Their decision comes a month after the New York Civil Liberties Union sued the State Education Department for approving Lockport City School District's facial recognition system. If you'll recall, Lockport started testing a facial and object recognition technology called "Aegis" in June 2019, and the district officially activated it in January this year. The bill mandating the moratorium still needs Governor Cuomo's approval, but once it's official, the New York education department will also be compelled to study and craft regulation around the technology's use. Education Policy Center deputy director Stephanie Coyle issued a statement for the NYCLU, explaining how facial recognition can affect Black and Brown students' experiences.

In a previous article, we have discussed about what Machine Learning is and saw some of its applications. Today, we continue this series of articles, dedicated to Data Science, Machine Learning and Artificial Intelligence (AI), by discussing what SQL Server Machine Learning Services are, and how you can use them, for efficiently implementing high-quality Data Science projects in SQL Server. SQL Server Machine Learning Services, were originally released with SQL Server 2016, known as "R Services", with support for the R language. Later on, with the release of SQL Server 2017, one of its most significant features, was the enhanced support for Machine Learning which provided support for both R and Python programming languages. In this release of SQL Server, "R Services" were renamed to "Machine Learning Services".

The New York legislature today passed a moratorium banning the use of facial recognition and other forms of biometric identification in schools until 2022. The bill, which has yet to be signed by Governor Andrew Cuomo, comes in response to the planned launch of facial recognition by the Lockport City School District and appears to be the first in the nation to explicitly regulate the use of the technology in schools. In January, Lockport Schools became one of the only U.S. school districts to adopt facial recognition in all of its K-12 buildings, which serve about 5,000 students. Proponents argued the $1.4 million system could keep students safe by enforcing watchlists and sending alerts when it detected someone dangerous (or otherwise unwanted). But critics said it could be used to surveil students and build a database of sensitive information about people's faces, which the school district then might struggle to keep secure.

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a key challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This paper presents a survey of the integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps. First, we systematically cover approaches to dynamics model learning, including challenges like dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. Second, we present a systematic categorization of planning-learning integration, including aspects like: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. After these two key sections, we also discuss the potential benefits of model-based RL, like enhanced data efficiency, targeted exploration, and improved stability. Along the survey, we also draw connections to several related RL fields, like hierarchical RL and transfer, and other research disciplines, like behavioural psychology. Altogether, the survey presents a broad conceptual overview of planning-learning combinations for MDP optimization.

The theory of discrete-time online learning has been successfully applied in many problems that involve sequential decision-making under uncertainty. However, in many applications including contractual hiring in online freelancing platforms and server allocation in cloud computing systems, the outcome of each action is observed only after a random and action-dependent time. Furthermore, as a consequence of certain ethical and economic concerns, the controller may impose deadlines on the completion of each task, and require fairness across different groups in the allocation of total time budget $B$. In order to address these applications, we consider continuous-time online learning problem with fairness considerations, and present a novel framework based on continuous-time utility maximization. We show that this formulation recovers reward-maximizing, max-min fair and proportionally fair allocation rules across different groups as special cases. We characterize the optimal offline policy, which allocates the total time between different actions in an optimally fair way (as defined by the utility function), and impose deadlines to maximize time-efficiency. In the absence of any statistical knowledge, we propose a novel online learning algorithm based on dual ascent optimization for time averages, and prove that it achieves $\tilde{O}(B^{-1/2})$ regret bound.

Most existing training algorithms for SNNs assume spiking neuron models in which a neuron outputs individual spikes as a function of the dynamics of an internal state variable known as membrane potential. This paper explores a more general model in which each spiking neuron contains multiple compartments, each tracking the dynamics of a distinct membrane potential, while sharing the same synaptic weights across compartments. It is demonstrated that learning rules based on probabilistic generalized linear neural models can leverage the presence of multiple compartments through modern variational inference based on importance weighting or generalized expectation-maximization. The key idea is to use the neural compartments to sample multiple independent spiking signals from hidden neurons so as to obtain better statistical estimates of the likelihood training criterion. The derived online learning algorithms follow three-factor rules with global learning signals. Experimental results on a structured output memorization task and classification task with a standard neuromorphic data set demonstrate significant improvements in terms of accuracy and calibration with an increasing number of compartments.

In this paper we investigate problematic practices and consequences of large scale vision datasets. We examine broad issues such as the question of consent and justice as well as specific concerns such as the inclusion of verifiably pornographic images in datasets. Taking the ImageNet-ILSVRC-2012 dataset as an example, we perform a cross-sectional model-based quantitative census covering factors such as age, gender, NSFW content scoring, class-wise accuracy, human-cardinality-analysis, and the semanticity of the image class information in order to statistically investigate the extent and subtleties of ethical transgressions. We then use the census to help hand-curate a look-up-table of images in the ImageNet-ILSVRC-2012 dataset that fall into the categories of verifiably pornographic: shot in a non-consensual setting (up-skirt), beach voyeuristic, and exposed private parts. We survey the landscape of harm and threats both society broadly and individuals face due to uncritical and ill-considered dataset curation practices. We then propose possible courses of correction and critique the pros and cons of these. We have duly open-sourced all of the code and the census meta-datasets generated in this endeavor for the computer vision community to build on. By unveiling the severity of the threats, our hope is to motivate the constitution of mandatory Institutional Review Boards (IRB) for large scale dataset curation processes.