Artificial intelligence in some shape or form has been a part of everyday life for years, but the meteoric rise of ChatGPT and the resulting aggressive development pace of conversational and generative AI models is, for the first time ever, putting the underlying technology into the hands of the general public. Even though current large language models are primarily able to guess the best-fitting next word in a sentence based on the corpus of content they were fed, CEOs, researchers and AI experts are now urging the industry to pump the brakes on training and developing models more capable than OpenAI's GPT-4. The company's latest large language model is currently available in a limited capacity for ChatGPT Plus subscribers and will soon be integrated into Microsoft productivity and security products. According to an open letter signed by influential figures like Elon Musk and Stability AI CEO Emad Mostaque, "powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable." The letter was released by the Future of Life Institute, a non-governmental organization founded in 2014 by MIT professor Max Tegmark and Skype co-founder Jaan Tallinn, among others.

TikTok may get Banned in 2023-24 due to A.I. Ethics and privacy rules (Dec, 2022)

Safety is a critical component of autonomous systems and remains a challenge for learning-based policies to be utilized in the real world. In particular, policies learned using reinforcement learning often fail to generalize to novel environments due to unsafe behavior. In this paper, we propose Sim-to-Lab-to-Real to bridge the reality gap with a probabilistically guaranteed safety-aware policy distribution. To improve safety, we apply a dual policy setup where a performance policy is trained using the cumulative task reward and a backup (safety) policy is trained by solving the Safety Bellman Equation based on Hamilton-Jacobi (HJ) reachability analysis. In Sim-to-Lab transfer, we apply a supervisory control scheme to shield unsafe actions during exploration; in Lab-to-Real transfer, we leverage the Probably Approximately Correct (PAC)-Bayes framework to provide lower bounds on the expected performance and safety of policies in unseen environments. Additionally, inheriting from the HJ reachability analysis, the bound accounts for the expectation over the worst-case safety in each environment. We empirically study the proposed framework for ego-vision navigation in two types of indoor environments with varying degrees of photorealism. We also demonstrate strong generalization performance through hardware experiments in real indoor spaces with a quadrupedal robot. See https://sites.google.com/princeton.edu/sim-to-lab-to-real for supplementary material.

Although large language models (LLMs) have shown exceptional performance in various natural language processing tasks, they are prone to hallucinations. State-of-the-art chatbots, such as the new Bing, attempt to mitigate this issue by gathering information directly from the internet to ground their answers. In this setting, the capacity to distinguish trustworthy sources is critical for providing appropriate accuracy contexts to users. Here we assess whether ChatGPT, a prominent LLM, can evaluate the credibility of news outlets. With appropriate instructions, ChatGPT can provide ratings for a diverse set of news outlets, including those in non-English languages and satirical sources, along with contextual explanations. Our results show that these ratings correlate with those from human experts (Spearmam's $\rho=0.54, p<0.001$). These findings suggest that LLMs could be an affordable reference for credibility ratings in fact-checking applications. Future LLMs should enhance their alignment with human expert judgments of source credibility to improve information accuracy.

The TCPD-IPD dataset is a collection of questions and answers discussed in the Lower House of the Parliament of India during the Question Hour between 1999 and 2019. Although it is difficult to analyze such a huge collection manually, modern text analysis tools can provide a powerful means to navigate it. In this paper, we perform an exploratory analysis of the dataset. In particular, we present insightful corpus-level statistics and a detailed analysis of three subsets of the dataset. In the latter analysis, the focus is on understanding the temporal evolution of topics using a dynamic topic model. We observe that the parliamentary conversation indeed mirrors the political and socio-economic tensions of each period.

Vision-and-language (V&L) models pretrained on large-scale multimodal data have demonstrated strong performance on various tasks such as image captioning and visual question answering (VQA). The quality of such models is commonly assessed by measuring their performance on unseen data that typically comes from the same distribution as the training data. However, when evaluated under out-of-distribution (out-of-dataset) settings for VQA, we observe that these models exhibit poor generalization. We comprehensively evaluate two pretrained V&L models under different settings (i.e. classification and open-ended text generation) by conducting cross-dataset evaluations. We find that these models tend to learn to solve the benchmark, rather than learning the high-level skills required by the VQA task. We also find that in most cases generative models are less susceptible to shifts in data distribution compared to discriminative ones, and that multimodal pretraining is generally helpful for OOD generalization. Finally, we revisit assumptions underlying the use of automatic VQA evaluation metrics, and empirically show that their stringent nature repeatedly penalizes models for correct responses.

Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling techniques are used to implement Bayesian inference. In the past three decades, MCMC methods have faced a number of challenges in being adapted to larger models (such as in deep learning) and big data problems. Advanced proposals that incorporate gradients, such as a Langevin proposal distribution, provide a means to address some of the limitations of MCMC sampling for Bayesian neural networks. Furthermore, MCMC methods have typically been constrained to use by statisticians and are still not prominent among deep learning researchers. We present a tutorial for MCMC methods that covers simple Bayesian linear and logistic models, and Bayesian neural networks. The aim of this tutorial is to bridge the gap between theory and implementation via coding, given a general sparsity of libraries and tutorials to this end. This tutorial provides code in Python with data and instructions that enable their use and extension. We provide results for some benchmark problems showing the strengths and weaknesses of implementing the respective Bayesian models via MCMC. We highlight the challenges in sampling multi-modal posterior distributions in particular for the case of Bayesian neural networks, and the need for further improvement of convergence diagnosis.

The task of dimensionality reduction and visualization of high-dimensional datasets remains a challenging problem since long. Modern high-throughput technologies produce newer high-dimensional datasets having multiple views with relatively new data types. Visualization of these datasets require proper methodology that can uncover hidden patterns in the data without affecting the local and global structures within the data. To this end, however, very few such methodology exist, which can realise this task. In this work, we have introduced a novel unsupervised deep neural network model, called NeuroDAVIS, for data visualization. NeuroDAVIS is capable of extracting important features from the data, without assuming any data distribution, and visualize effectively in lower dimension. It has been shown theoritically that neighbourhood relationship of the data in high dimension remains preserved in lower dimension. The performance of NeuroDAVIS has been evaluated on a wide variety of synthetic and real high-dimensional datasets including numeric, textual, image and biological data. NeuroDAVIS has been highly competitive against both t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) with respect to visualization quality, and preservation of data size, shape, and both local and global structure. It has outperformed Fast interpolation-based t-SNE (Fit-SNE), a variant of t-SNE, for most of the high-dimensional datasets as well. For the biological datasets, besides t-SNE, UMAP and Fit-SNE, NeuroDAVIS has also performed well compared to other state-of-the-art algorithms, like Potential of Heat-diffusion for Affinity-based Trajectory Embedding (PHATE) and the siamese neural network-based method, called IVIS. Downstream classification and clustering analyses have also revealed favourable results for NeuroDAVIS-generated embeddings.

In the spirit of reconciliation, InStyle Australia acknowledges the Traditional Custodians of country throughout Australia and their connections to land, sea and community. We pay our respect to their Elders past and present and extend that respect to all Aboriginal and Torres Strait Islander peoples today.

After the latest update of the infamous artificial intelligence (AI) chatbot ChatGPT-4, the technology has been a buzzword inside and outside the crypto industry. While opinions on the technology may be mixed, companies continue to integrate AI to enhance their user experience. On March 31, the cryptocurrency exchange and Web3 technology company OKX announced that it will be launching a new integration from EndoTech.io The algorithms incorporate both machine learning and "other advanced techniques" in an effort to conduct real-time analyses of data and trading opportunities. OKX also jumped on the AI bandwagon on March 30 when it posted an AI-generated poem from ChatGPT-4 about the company's wallet. The amazing features of #OKXWallet presented as Sea Shanty by #ChatGPT.