Google is looking into doling out AI-generated life advice


Recently, there's been plenty of anxiety around companies investing in AI to replace creative types, such as professional writers. Now, the tech could be coming for life coaches. Google's DeepMind division is internally testing generative AI's ability to perform "at least" 21 kinds of tasks, which include giving sensitive life advice to users, per a report from the New York Times. This comes, the Times notes, after Google's AI experts reportedly warned company executives about letting people become too emotionally invested in chatbots in December. As for the kinds of advice that could potentially be doled out by the chatbot, the Times report suggests that a user could present a scenario about not being able to afford airfare to a close friend's destination wedding and ask the chatbot what to do about it.

Deep Knowledge Tracing Chris Piech ∗, Jonathan Bassen ∗, Jonathan Huang ∗ ‡, Surya Ganguli

Neural Information Processing Systems

Knowledge tracing--where a machine models the knowledge of a student as they interact with coursework--is a well established problem in computer supported education. Though effectively modeling student knowledge would have high educational impact, the task has many inherent challenges. In this paper we explore the utility of using Recurrent Neural Networks (RNNs) to model student learning. The RNN family of models have important advantages over previous methods in that they do not require the explicit encoding of human domain knowledge, and can capture more complex representations of student knowledge.

Neural Attribution for Semantic Bug-Localization in Student Programs Rahul Gupta Aditya Kanade

Neural Information Processing Systems

Providing feedback is an integral part of teaching. Most open online courses on programming make use of automated grading systems to support programming assignments and give real-time feedback. These systems usually rely on test results to quantify the programs' functional correctness. They return failing tests to the students as feedback. However, students may find it difficult to debug their programs if they receive no hints about where the bug is and how to fix it. In this work, we present NeuralBugLocator, a deep learning based technique, that can localize the bugs in a faulty program with respect to a failing test, without even running the program. At the heart of our technique is a novel tree convolutional neural network which is trained to predict whether a program passes or fails a given test. To localize the bugs, we analyze the trained network using a state-of-the-art neural prediction attribution technique and see which lines of the programs make it predict the test outcomes. Our experiments show that NeuralBugLocator is generally more accurate than two state-of-the-art program-spectrum based and one syntactic difference based bug-localization baselines.

SOAT: A Scene-and Object-Aware Transformer for Vision-and-Language Navigation

Neural Information Processing Systems

Natural language instructions for visual navigation often use scene descriptions (e.g., 'bedroom') and object references (e.g., 'green chairs') to provide a breadcrumb trail to a goal location. This work presents a transformer-based vision-andlanguage navigation (VLN) agent that uses two different visual encoders - a scene classification network and an object detector - which produce features that match these two distinct types of visual cues. In our method, scene features contribute high-level contextual information that supports object-level processing. With this design, our model is able to use vision-and-language pretraining (i.e., learning the alignment between images and text from large-scale web data) to substantially improve performance on the Room-to-Room (R2R) [1] and Room-Across-Room (RxR) [2] benchmarks. Specifically, our approach leads to improvements of 1.8% absolute in SPL on R2R and 3.7% absolute in SR on RxR. Our analysis reveals even larger gains for navigation instructions that contain six or more object references, which further suggests that our approach is better able to use object features and align them to references in the instructions.

Giving Feedback on Interactive Student Programs with Meta-Exploration ⇤, Moritz Stephan

Neural Information Processing Systems

Developing interactive software, such as websites or games, is a particularly engaging way to learn computer science. However, teaching and giving feedback on such software is time-consuming -- standard approaches require instructors to manually grade student-implemented interactive programs. As a result, online platforms that serve millions, like, are unable to provide any feedback on assignments for implementing interactive programs, which critically hinders students' ability to learn. One approach toward automatic grading is to learn an agent that interacts with a student's program and explores states indicative of errors via reinforcement learning. However, existing work on this approach only provides binary feedback of whether a program is correct or not, while students require finer-grained feedback on the specific errors in their programs to understand their mistakes. In this work, we show that exploring to discover errors can be cast as a meta-exploration problem. This enables us to construct a principled objective for discovering errors and an algorithm for optimizing this objective, which provides fine-grained feedback. We evaluate our approach on a set of over 700K real anonymized student programs from a

Supplementary Materials for: Online Training Through Time for Spiking Neural Networks

Neural Information Processing Systems

A.2 A few notes for the notation of time for multi-layer networks Please note that the notation of discrete time steps for multi-layer networks may be slightly different from Eq. (2). To simplify the notations, we use 0, 1, T for each layer to represent the corresponding discrete time steps, while the actual time of different layers at time step t should consider some delay across layers. A.3 Proof of Theorem 1 In this subsection, we prove Theorem 1 with Assumption 1. () As described in Sections 4.1 and 4.2, for gradients of OTTT, we have Remark 2. The above conclusion mainly focuses on the gradients for connection weights W Remark 3. Note that the gradients based on spike representation may also include small errors since the calculation of SNN is not exactly the same as the equivalent ANN-like mappings. And a larger time step may lead to more accurate gradients. We connect the gradients of OTTT and gradients based on spike representation to demonstrate the overall descent direction, and it is tolerant to small errors, which can also be viewed as randomness for stochastic optimization.

Deep Learning and Reinforcement Learning


This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.

Machine Learning Data Lifecycle in Production


In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you're looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.

Recommender Systems and Deep Learning in Python - Udemy Free Coupons Discount - Couse Sites


Free Coupon Discount - Recommender Systems and Deep Learning in Python The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques | Created by Lazy Programmer Inc. Students also bought Artificial Intelligence: Reinforcement Learning in Python Data Science: Natural Language Processing (NLP) in Python Unsupervised Machine Learning Hidden Markov Models in Python Natural Language Processing with Deep Learning in Python Cluster Analysis and Unsupervised Machine Learning in Python Preview this Udemy Course GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Artificial Intelligence with Machine Learning, Deep Learning - Udemy Free Coupons Discount - Couse Sites


Welcome to the "Artificial Intelligence with Machine Learning, Deep Learning " course. It's hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon's Alexa and the iPhone's Siri, are all technologies that function based on machine learning algorithms and mathematical models. Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, my course on Udemy is here to help you apply machine learning to your work. Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand. Udemy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you're interested in machine learning, data mining, or data analysis, Udemy has a course for you. If you want to learn one of the employer's most requested skills?