Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. Tim El-Sheikh is a biomedical scientist, entrepreneur, and CEO and co-founder of Nebuli, the world's first Augmented Intelligence Studio. A self-taught coder since the age of 10, he has a real passion for designing and intelligent algorithms. After a master's degree in Computer Science and Information Technology, Tim combined his experience in design, neuroscience, and engineering to start as an entrepreneur in online multitier system architectures in the media and advertising sectors, scientific publishing, and social enterprises. From there, he founded Nebuli, an augmented Intelligence studio that focuses on building dynamic user experiences, solving complex problems and bringing positive impact into people's lives by harnessing the power of ethical AI.

Is artificial intelligence the new alchemy? That is, are the powerful algorithms that control so much of our lives -- from internet searches to social media feeds -- the modern equivalent of turning lead into gold? Moreover: Would that be such a bad thing? According to the prominent AI researcher Ali Rahimi and others, today's fashionable neural networks and deep learning techniques are based on a collection of tricks, topped with a good dash of optimism, rather than systematic analysis. Modern engineers, the thinking goes, assemble their codes with the same wishful thinking and misunderstanding that the ancient alchemists had when mixing their magic potions.

Uncertainty quantification is at the core of the reliability and robustness of machine learning. It is well-known that uncertainty consists of two different types, often referred to as aleatoric and epistemic uncertainties. In this paper, we provide a systematic study on the epistemic uncertainty in deep supervised learning. We rigorously distinguish different sources of epistemic uncertainty, including in particular procedural variability (from the training procedure) and data variability (from the training data). We use our framework to explain how deep ensemble enhances prediction by reducing procedural variability. We also propose two approaches to estimate epistemic uncertainty for a well-trained neural network in practice. One uses influence function derived from the theory of neural tangent kernel that bypasses the convexity assumption violated by modern neural networks. Another uses batching that bypasses the time-consuming Gram matrix inversion in the influence function calculation, while expending minimal re-training effort. We discuss how both approaches overcome some difficulties in applying classical statistical methods to the inference on deep learning.

In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job. We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib NumPy -- A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library. Pandas -- A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.

Bio: Antonio Bicchi is a scientist interested in robotics and intelligent machines. After graduating in Pisa and receiving a Ph.D. from the University of Bologna, he spent a few years at the MIT AI Lab of Cambridge before becoming Professor in Robotics at the University of Pisa. In 2009 he founded the Soft Robotics Laboratory at the Italian Institute of Technology in Genoa. Since 2013 he is Adjunct Professor at Arizona State University, Tempe, AZ. He has coordinated many international projects, including four grants from the European Research Council (ERC).

In this paper we explore the use of symbolic knowledge and machine teaching to reduce human data labeling efforts in building neural task bots. We propose SYNERGY, a hybrid learning framework where a task bot is developed in two steps: (i) Symbolic knowledge to neural networks: Large amounts of simulated dialog sessions are generated based on task-specific symbolic knowledge which is represented as a task schema consisting of dialog flows and task-oriented databases. Then a pre-trained neural dialog model, SOLOIST, is fine-tuned on the simulated dialogs to build a bot for the task. (ii) Neural learning: The fine-tuned neural dialog model is continually refined with a handful of real task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the task bot. We validate SYNERGY on four dialog tasks. Experimental results show that SYNERGY maps task-specific knowledge into neural dialog models achieving greater diversity and coverage of dialog flows, and continually improves model performance with machine teaching, thus demonstrating strong synergistic effects of symbolic knowledge and machine teaching.

Machine learning is relying more and more on training large models on large static datasets to reach impressive results (Kaplan et al., 2020; Lazaridou et al., 2021; Hombaiah et al., 2021). However, the real world is changing over time and new information is becoming available at an unprecedented rate (Lazaridou et al., 2021; Hombaiah et al., 2021). In such real world problems, the learning agent is exposed to a continuous stream of data, with potentially changing data distribution, and it has to absorb new information efficiently while not being able to iterate on previous data as freely as wanted due to time, sample, compute, privacy, or environmental complexity issues (Parisi et al., 2018). To overcome these inefficiencies, fields, such as Continual learning (CL) (Ring et al., 1994) or lifelong learning (Thrun, 1995) are gaining a lot of attention recently. One of the key challenges in continual learning models is the abrupt erasure of previous knowledge, referred to as Catastrophic Forgetting (CF) (McCloskey and Cohen, 1989). Alleviating catastrophic forgetting has attracted a lot of attention lately, and many interesting solutions are proposed to partly overcome the issue (e.g., Toneva et al., 2018; Nguyen et al., 2019; Hsu et al., 2018; Li et al., 2019; Wallingford et al., 2020). These solutions vary in degree of complexity from simple replay-based methods to complicated regularization or network expansion-based methods. Unfortunately, however, there is not much fundamental understanding of the intrinsic properties of neural networks that affects continual learning performance through catastrophic forgetting or forward/backward transfer (Mirzadeh et al., 2020). Work done during an internship at DeepMind.

Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen before and cannot make a safe decision. This problem first emerged in 2017 and since then has received increasing attention from the research community, leading to a plethora of methods developed, ranging from classification-based to density-based to distance-based ones. Meanwhile, several other problems are closely related to OOD detection in terms of motivation and methodology. These include anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD). Despite having different definitions and problem settings, these problems often confuse readers and practitioners, and as a result, some existing studies misuse terms. In this survey, we first present a generic framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e., AD, ND, OSR, OOD detection, and OD. Under our framework, these five problems can be seen as special cases or sub-tasks, and are easier to distinguish. Then, we conduct a thorough review of each of the five areas by summarizing their recent technical developments. We conclude this survey with open challenges and potential research directions.

End-to-end learning robotic manipulation with high data efficiency is one of the key challenges in robotics. The latest methods that utilize human demonstration data and unsupervised representation learning has proven to be a promising direction to improve RL learning efficiency. The use of demonstration data also allows "warming-up" the RL policies using offline data with imitation learning or the recently emerged offline reinforcement learning algorithms. However, existing works often treat offline policy learning and online exploration as two separate processes, which are often accompanied by severe performance drop during the offline-to-online transition. Furthermore, many robotic manipulation tasks involve complex sub-task structures, which are very challenging to be solved in RL with sparse reward. In this work, we propose a unified offline-to-online RL framework that resolves the transition performance drop issue. Additionally, we introduce goal-aware state information to the RL agent, which can greatly reduce task complexity and accelerate policy learning. Combined with an advanced unsupervised representation learning module, our framework achieves great training efficiency and performance compared with the state-of-the-art methods in multiple robotic manipulation tasks.

The fourth annual Massachusetts STEM Week kicked off on Monday, Oct. 18 at the MIT Media Lab. Organized by the Massachusetts Executive Office of Education and the STEM Advisory Council, Mass STEM Week is a statewide effort to boost awareness, interest, and access in STEM education and career opportunities for learners of all ages and backgrounds. A focus of this year's STEM Week is "see yourself in STEM," with particular emphasis on the importance of mentoring to bolster confidence in STEM subjects among students from underrepresented groups -- including girls, people of color, low-income families, people with disabilities, and first-generation students. "STEM is the toolkit of the future no matter what your interests are," said Massachusetts Governor Charlie Baker. "You can't think anymore of STEM just being about science, technology, engineering, and math because it's everywhere. There's almost no tool, no capability, no thing you need to succeed, that doesn't involve ... some element of STEM."