Welcome to this series on neural network programming with PyTorch. In this post, we will look at the prerequisites needed to be best prepared. We'll get an overview of the series and a sneak peek at a project we'll be working on. This will give us a good idea about what we'll be learning, and what skills we'll have by the end of the series. Without further ado, let's jump right in with the details.

Powered leg prostheses can give amputees the ability to walk for long periods of time and to do so briskly. The reality is that these devices are pretty clunky and programming them to operate smoothly to produce a natural gait takes hours, and the results are rarely perfect. Scientists at North Carolina State University, the University of North Carolina, and Arizona State University have now developed an automated tuning system that relies purely on a technique called reinforcement learning to do its job. This allows the patient to simply walk with a new prosthetic on a treadmill while guided by a therapist. The system monitors the intentions of the patient and movement of the prosthetic, and adjusts in real time based on the readings.

Time Machine foresees to design and implement advanced new digitisation and Artificial Intelligence (AI) technologies to mine Europe's vast cultural heritage, providing fair and free access to information that will support future scientific and technological developments in Europe The Time Machine will create advanced AI technologies to make sense of vast amounts of information from complex historical data sets. This will enable the transformation of fragmented data – with content ranging from medieval manuscripts and historical objects to smartphone and satellite images – into useable knowledge for industry. In essence, a large-scale computing and digitisation infrastructure will map Europe's entire social, cultural and geographical evolution. Considering the unprecedented scale and complexity of the data, The Time Machine's AI even has the potential to create a strong competitive advantage for Europe in the global AI race. "Time Machine is likely to become one of the most advanced Artificial Intelligence systems ever built, trained on data from wider geographical and temporal horizons", explains Frederic Kaplan, Professor of Digital Humanities at the Ecole Polytechnique Fédérale de Lausanne (EPFL) and Coordinator of the Time Machine Project.

The Asian superpower is cementing its position as the world leader in big data, artificial intelligence (AI) and robotics, as its government announces plans to unleash a massive number of university majors in this field. Its universities will offer around 400 new majors related to these emerging technologies, as well as 612 new engineering research projects, China's Ministry of Education (MOE) announced last week, as reported by the People's Daily Online. "AI and big data are newly established majors and will be taught in some directions like computer application technology, information and communication, control science and engineering," Fan Hailin, Deputy Director of MOE's Department of Higher Education said. The new courses show China's ambitions for global leadership in two areas: AI and higher education. Analysts predict it will probably win the former, as the US shows no signs of national technology investment strategies that could compete with China's.

There is little doubt artificial intelligence (AI) will play a major role in the future of work – a future that has already begun. Think, for example, of self-driving cars, algorithmic stock market trading, or even computer-aided medical diagnosis. The rapid advances in AI have the potential to create new opportunities, higher productivity and better earnings, but there are also fears they could cause job losses and a rise in inequality, with a lucky few appropriating the benefits of AI while leaving others behind. So which way will it be? The answer is, we can be moderately optimistic, provided policy-makers and social partners adopt the right measures.

Creating a fruitful and encouraging environment for young minds is a serious business and thanks to Turkey's 2023 Education Vision, students from all over the country are bringing out the scientists and inventors that lay inside of them. Believing that they can do good for society with their inventions, two high school students from Düzce created special goggles for the hearing impaired which turns speech into text. Düzce Science and Art Center (BİLSEM) students Ömer Berkay Biçen and Dağhan Akyürek rolled up their sleeves to make life easier for the hearing impaired and came up with light-emitting diode (LED) goggles which transfer spoken language into text. Speaking to Anadolu Agency (AA), the project supervisor Adem Akkuş said the students from the Science and Art Center compete in national and international competitions with the projects they created. "I really liked the idea of helping the hearing impaired citizens in society. I helped the students while developing their project. These goggles are the product of intense work and determination. Our aim is to turn the project into a more practical device," he added.

Learning interpretable and transferable subpolicies and performing task decomposition from a single, complex task is difficult. Some traditional hierarchical reinforcement learning techniques enforce this decomposition in a top-down manner, while meta-learning techniques require a task distribution at hand to learn such decompositions. This paper presents a framework for using diverse suboptimal world models to decompose complex task solutions into simpler modular subpolicies. This framework performs automatic decomposition of a single source task in a bottom up manner, concurrently learning the required modular subpolicies as well as a controller to coordinate them. We perform a series of experiments on high dimensional continuous action control tasks to demonstrate the effectiveness of this approach at both complex single task learning and lifelong learning. Finally, we perform ablation studies to understand the importance and robustness of different elements in the framework and limitations to this approach.

Navigation is a rich and well-grounded problem domain that drives progress in many different areas of research: perception, planning, memory, exploration, and optimisation in particular. Historically these challenges have been separately considered and solutions built that rely on stationary datasets - for example, recorded trajectories through an environment. These datasets cannot be used for decision-making and reinforcement learning, however, and in general the perspective of navigation as an interactive learning task, where the actions and behaviours of a learning agent are learned simultaneously with the perception and planning, is relatively unsupported. Thus, existing navigation benchmarks generally rely on static datasets (Geiger et al., 2013; Kendall et al., 2015) or simulators (Beattie et al., 2016; Shah et al., 2018). To support and validate research in end-to-end navigation, we present StreetLearn: an interactive, first-person, partially-observed visual environment that uses Google Street View for its photographic content and broad coverage, and give performance baselines for a challenging goal-driven navigation task. The environment code, baseline agent code, and the dataset are available at http://streetlearn.cc

Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact of the algorithmic decisions today on the long-term welfare and prosperity of certain segments of the population. We take a broader perspective on algorithmic fairness. We propose an effort-based measure of fairness and present a data-driven framework for characterizing the long-term impact of algorithmic policies on reshaping the underlying population. Motivated by the psychological literature on social learning and the economic literature on equality of opportunity, we propose a micro-scale model of how individuals respond to decision making algorithms. We employ existing measures of segregation from sociology and economics to quantify the resulting macro-scale population-level change. Importantly, we observe that different models may shift the group-conditional distribution of qualifications in different directions. Our findings raise a number of important questions regarding the formalization of fairness for decision-making models.

We consider a problem of stochastic online learning with general probabilistic graph feedback. Two cases are covered. (a) The one-step case where for each edge $(i,j)$ with probability $p_{ij}$ in the probabilistic feedback graph. After playing arm $i$ the learner observes a sample reward feedback of arm $j$ with independent probability $p_{ij}$. (b) The cascade case where after playing arm $i$ the learner observes feedback of all arms $j$ in a probabilistic cascade starting from $i$ -- for each $(i,j)$ with probability $p_{ij}$, if arm $i$ is played or observed, then a reward sample of arm $j$ would be observed with independent probability $p_{ij}$. Previous works mainly focus on deterministic graphs which corresponds to one-step case with $p_{ij} \in \{0,1\}$, an adversarial sequence of graphs with certain topology guarantees or a specific type of random graphs. We analyze the asymptotic lower bounds and design algorithms in both cases. The regret upper bounds of the algorithms match the lower bounds with high probability.