Machine Learning: It is the process by which a machine can become intelligent and behave like a human for the assigned tasks.

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More than three years ago, this editor sat down with Sam Altman for a small event in San Francisco soon after he'd left his role as the president of Y Combinator to become CEO of the AI company he co-founded in 2015 with Elon Musk and others, OpenAI. At the time, Altman described OpenAI's potential in language that sounded outlandish to some. Altman said, for example, that the opportunity with artificial general intelligence -- machine intelligence that can solve problems as well as a human -- is so incomprehensibly enormous that if OpenAI managed to crack it, the outfit could "maybe capture the light cone of all future value in the universe." He said that the company was "going to have to not release research" because it was so powerful. Asked if OpenAI was guilty of fear-mongering -- Elon Musk, a co-founder of the outfit, has repeatedly called all organizations developing AI to be regulated -- Altman talked about dangers of not thinking about "societal consequences" when "you're building something on an exponential curve."

Anyone who's seen the show knows that this is not what happened. Instead, it's a somewhat humorous misconfiguration of the details--sorta like listening to a friend misremember a series that they haven't seen in awhile. In a different conversation, I asked ChatGPT how the TV show Gilligan's Island ended. In reality, Gilligan's Island was cancelled by its network, so there was no ending.

The course provides a general overview of the main methods in the machine learning field. Starting from a taxonomy of the different problems that can be solved through machine learning techniques, the course briefly presents some algorithmic solutions, highlighting when they can be successful, but also their limitations. These concepts will be explained through examples and case studies.

The graph represents a network of 1,758 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 09 December 2022 at 12:09 UTC. The requested start date was Friday, 09 December 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 3-day, 12-hour, 10-minute period from Monday, 05 December 2022 at 12:48 UTC to Friday, 09 December 2022 at 00:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The Structures and Artificial Intelligence Lab (SAIL) at the University of Houston invites applications from highly motivated students for graduate research in the broad area of artificial intelligence applications in structural engineering. Research will be highly interdisciplinary, at the intersection of civil engineering, computer science and robotics. Application statements should directly address the applicant's programming experience (may include links to reports or github repositories if any). Additionally, prior research experience in the aforementioned or similar topics, or any relevant research in structural engineering should be articulated in the application. Admission is highly competitive but successful PhD applicants will receive full tuition support and stipend for the duration of their study through research and teaching assistantships.

Abstract--Although considerable progress has been obtained in neural network quantization for efficient inference, existing methods are not scalable to heterogeneous devices as one dedicated model needs to be trained, transmitted, and stored for one specific hardware setting, incurring considerable costs in model training and maintenance. In this paper, we study a new vertical-layered representation of neural network weights for encapsulating all quantized models into a single one. It represents weights as a group of bits (i.e., vertical layers) organized from the most significant bit (also called the basic layer) to less significant bits (i.e., enhance layers). Hence, a neural network with an arbitrary quantization precision can be obtained by adding corresponding enhance layers to the basic layer. With this representation, we can theoretically achieve any precision network for on-demand service while only needing to train and maintain one model. However, we empirically find that models obtained with existing quantization methods suffer severe performance degradation if adapted to vertical-layered weight representation. A significant issue is that once a quantization model is well-trained for a certain precision, it is difficult to reuse the weights and transfer them to another precision since essential information lying in the full precision source model is discarded after dedicated training. To this end, we propose a simple once quantization-aware training (QAT) scheme for obtaining high-performance vertical-layered models. Our design incorporates a cascade downsampling mechanism which allows us to obtain multiple quantized networks from one full precision source model by progressively mapping the higher precision weights to their adjacent lower precision counterparts.

To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-view ambiguities. Existing solutions typically suffer from poor generalization performance to new settings, largely due to the limited diversity of image-mesh pairs in multi-view training data. To address this shortcoming, people have explored the use of synthetic images. But besides the usual impact of visual gap between rendered and target data, synthetic-data-driven multi-view estimators also suffer from overfitting to the camera viewpoint distribution sampled during training which usually differs from real-world distributions. Tackling both challenges, we propose a novel simulation-based training pipeline for multi-view human mesh recovery, which (a) relies on intermediate 2D representations which are more robust to synthetic-to-real domain gap; (b) leverages learnable calibration and triangulation to adapt to more diversified camera setups; and (c) progressively aggregates multi-view information in a canonical 3D space to remove ambiguities in 2D representations. Through extensive benchmarking, we demonstrate the superiority of the proposed solution especially for unseen in-the-wild scenarios.

A machine learning (ML) system must learn not only to match the output of a target function on a training set, but also to generalize to novel situations in order to yield accurate predictions at deployment. In most practical applications, the user cannot exhaustively enumerate every possible input to the model; strong generalization performance is therefore crucial to the development of ML systems which are performant and reliable enough to be deployed in the real world. While generalization is well-understood theoretically in a number of hypothesis classes, the impressive generalization performance of deep neural networks has stymied theoreticians. In deep reinforcement learning (RL), our understanding of generalization is further complicated by the conflict between generalization and stability in widely-used RL algorithms. This thesis will provide insight into generalization by studying the learning dynamics of deep neural networks in both supervised and reinforcement learning tasks.