There is little argument about whether or not machine learning models are useful for applying to social systems. But if we take seriously George Box's dictum, or indeed the even older one that "the map is not the territory' (Korzybski, 1933), then there has been comparatively less systematic attention paid within the field to how machine learning models are wrong (Selbst et al., 2019) and seeing possible harms in that light. By "wrong" I do not mean in terms of making misclassifications, or even fitting over the'wrong' class of functions, but more fundamental mathematical/statistical assumptions, philosophical (in the sense used by Abbott, 1988) commitments about how we represent the world, and sociological processes of how models interact with target phenomena. This paper takes a particular model of machine learning research or application: one that its creators and deployers think provides a reliable way of interacting with the social world (whether that is through understanding, or in making predictions) without any intent to cause harm (McQuillan, 2018) and, in fact, a desire to not cause harm and instead improve the world, 1 for example as most explicitly in the various "{Data [Science], Machine Learning, Artificial Intelligence} for [Social] Good" initiatives, and more widely in framings around "fairness" or "ethics." I focus on the almost entirely statistical modern version of machine learning, rather than eclipsed older visions (see section 3). While many of the limitations I discuss apply to the use of machine learning in any domain, I focus on applications to the social world in order to explore the domain where limitations are strongest and stickiest.

Exploration in sparse reward environments remains one of the key challenges of model-free reinforcement learning. Instead of solely relying on extrinsic rewards provided by the environment, many state-of-the-art methods use intrinsic rewards to encourage exploration. However, we show that existing methods fall short in procedurally-generated environments where an agent is unlikely to visit a state more than once. We propose a novel type of intrinsic reward which encourages the agent to take actions that lead to significant changes in its learned state representation. We evaluate our method on multiple challenging procedurally-generated tasks in MiniGrid, as well as on tasks with high-dimensional observations used in prior work. Our experiments demonstrate that this approach is more sample efficient than existing exploration methods, particularly for procedurally-generated MiniGrid environments. Furthermore, we analyze the learned behavior as well as the intrinsic reward received by our agent. In contrast to previous approaches, our intrinsic reward does not diminish during the course of training and it rewards the agent substantially more for interacting with objects that it can control.

The American Association for the Advancement of Science (AAAS) has selected Stony Brook University Professor Heather Lynch as one of 12 researchers in the area of artificial intelligence (AI) to be a 2020-2021 AAAS Alan I. Leshner Leadership Institute Public Engagement Fellow. Lynch, Institute for Advanced Computational Science Endowed Chair for Ecology & Evolution, was chosen for having demonstrated leadership and excellence in her research career and for her interest in promoting meaningful dialogue between science and society. AI focuses on developing and studying machines and algorithms that augment or mimic human abilities, learn from and adjust to new situations, and perform tasks. As a quantitative ecologist whose research focuses on the population dynamics of Antarctic wildlife, Lynch uses AI to study penguins, among other species. "Communicating with the public about penguins is pretty easy, but explaining AI? That's quite a bit harder," said Lynch.

The field of data science relies heavily on the predictive capability of Machine Learning (ML) algorithms. Python offers an opportune playground for experimenting with these algorithms due to the readability and syntactical efficiency of the language. The vast availability of ML libraries accessible to Python users makes it an even more attractive solution to interpret the immense amount of data available today. This article explores the top 10 ML packages that you need to know, as well as the advantages and disadvantages of each. A Python framework is an interface or tool that allows developers to build ML models easily, without getting into the depth of the underlying algorithms.

As a data science educator, many people interested in getting into data science have contacted me for guidance on how to get into the field of data science. This article will discuss the recommended topics that one has to study to build essential skills in data science. The topics presented here, if studied thoroughly, will provide the minimum background needed to start doing data science. This curriculum could also be used for designing an introductory college-level course in data science. Keep in mind that knowledge acquired from courses alone will not make you a data scientist.

When we talk about artificial intelligence, we often envisage a suite of robots performing tasks, transferring labour from humans to machines. Within higher education, the AI revolution has arrived in a different, inconspicuous form – but just because we do not notice it, it doesn't mean its impact will be any less. Sebastian Distefano, worldwide higher education development manager at Adobe, stressed this point during his presentation at the session "AI-ready: building digital talent in an age of automation", which took place at THE Live 2019. A passionate advocate of digital literacy and creativity in the classroom, Adobe wants to challenge the idea that creativity will be a bridge too far for AI. "What we are reading about today is that creativity is the one skill that AI is not going to be able to master – and if it does master it, it might be the very last thing it can actually do," Mr Distefano said.

Scientists have proposed a new international framework to keep ethics and human wellbeing at the forefront of our relationship with technology. From gene therapy and AI-predicted disease to self-driving cars and 3D printing, advances in technology can improve health, free up time, and boost efficiency. However despite the best intentions of its creators, technology might lead to unintended consequences for individual privacy and autonomy. There's currently no internationally agreed-upon regulation about who, for example, has access to the data recorded by black boxes in cars, smart TVs and voice enabled personal assistants - and recent findings have shown that technology can be used to influence voting behaviour. Now, Imperial College London researchers have suggested a new regulatory framework with which governments can minimise unintended consequences of our relationship with technology.

DENTON, Texas (CBSDFW.COM) – The University of North Texas is launching a new program to meet the booming demand for artificial intelligence professionals. UNT will be soon offer the only Master of Science degree in AI in Texas. "I really think everyone should be learning more about AI," said Mark Albert, a computer science professor at UNT. From Google searches to chats with Alexa, AI is all around us, and it's expected to play a significant role in nearly every industry. "It's needed for a lot of applications, so we feel like we're in the position to help educate an AI-ready workforce, which is important for the state and the nation as well," said Yan Huang, senior associate dean of the UNT College of Engineering.

Welcome this great course where you'll learn python programming and ethical hacking at the same time, the course assumes you have NO prior knowledge in any of these topics, and by the end of it you'll be at a high intermediate level being able to combine both of these skills and write python programs to hack into computer systems exactly the same way that black hat hackers do, and use the programming skills you learn to write any program even if it has nothing to do with hacking. This course is highly practical but it won't neglect the theory, we'll start with basics on ethical hacking and python programming, installing the needed software and then we'll dive and start programming straight away. From here onwards you'll learn everything by example, by writing useful hacking programs, so we'll never have any boring dry programming lectures. The course is divided into a number of sections, each aims to achieve a specific goal, the goal is usually to hack into a certain system, so we'll start by learning how this system work and its weaknesses, and then you'll lean how to write a python program to exploit these weaknesses and hack the system, as we write the program I will teach you python programming from scratch covering one topic at a time, so by the end of the course you're going to have a number of ethical hacking programs written by yourself (see below) from backdoors, keyloggers, credential harvesters, network hacking tools, website hacking tools and the list goes on. You'll also have a deep understanding on how computer systems work, how to model problems, design an algorithm to solve problems and implement the solution using python.

Bio: Pier Paolo Ippolito is a final year MSc Artificial Intelligence student at The University of Southampton. He is an AI Enthusiast, Data Scientist and RPA Developer.