In this work, we provide a large-scale empirical study of the scaling properties of multilingual neural machine translation models. We examine how increases in the model size affect the model performance and investigate the role of the training mixture composition on the scaling behavior. We find that changing the weightings of the individual language pairs in the training mixture only affect the multiplicative factor of the scaling law. In particular, we observe that multilingual models trained using different mixing rates all exhibit the same scaling exponent. Through a novel joint scaling law formulation, we compute the effective number of parameters allocated to each language pair and examine the role of language similarity in the scaling behavior of our models. We find little evidence that language similarity has any impact. In contrast, the direction of the multilinguality plays a significant role, with models translating from multiple languages into English having a larger number of effective parameters per task than their reversed counterparts. Finally, we leverage our observations to predict the performance of multilingual models trained with any language weighting at any scale, significantly reducing efforts required for language balancing in large multilingual models. Our findings apply to both in-domain and out-of-domain test sets and to multiple evaluation metrics, such as ChrF and BLEURT.

We present AutoDOViz, an interactive user interface for automated decision optimization (AutoDO) using reinforcement learning (RL). Decision optimization (DO) has classically being practiced by dedicated DO researchers where experts need to spend long periods of time fine tuning a solution through trial-and-error. AutoML pipeline search has sought to make it easier for a data scientist to find the best machine learning pipeline by leveraging automation to search and tune the solution. More recently, these advances have been applied to the domain of AutoDO, with a similar goal to find the best reinforcement learning pipeline through algorithm selection and parameter tuning. However, Decision Optimization requires significantly more complex problem specification when compared to an ML problem. AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts. In this paper, we report our findings from semi-structured expert interviews with DO practitioners as well as business consultants, leading to design requirements for human-centered automation for DO with RL. We evaluate a system implementation with data scientists and find that they are significantly more open to engage in DO after using our proposed solution. AutoDOViz further increases trust in RL agent models and makes the automated training and evaluation process more comprehensible. As shown for other automation in ML tasks, we also conclude automation of RL for DO can benefit from user and vice-versa when the interface promotes human-in-the-loop.

Training a good deep learning model requires substantial data and computing resources, which makes the resulting neural model a valuable intellectual property. To prevent the neural network from being undesirably exploited, non-transferable learning has been proposed to reduce the model generalization ability in specific target domains. However, existing approaches require labeled data for the target domain which can be difficult to obtain. Furthermore, they do not have the mechanism to still recover the model's ability to access the target domain. In this paper, we propose a novel unsupervised non-transferable learning method for the text classification task that does not require annotated target domain data. We further introduce a secret key component in our approach for recovering the access to the target domain, where we design both an explicit and an implicit method for doing so. Extensive experiments demonstrate the effectiveness of our approach.

Unsupervised meta-learning aims to learn the meta knowledge from unlabeled data and rapidly adapt to novel tasks. However, existing approaches may be misled by the context-bias (e.g. background) from the training data. In this paper, we abstract the unsupervised meta-learning problem into a Structural Causal Model (SCM) and point out that such bias arises due to hidden confounders. To eliminate the confounders, we define the priors are \textit{conditionally} independent, learn the relationships between priors and intervene on them with casual factorization. Furthermore, we propose Causal Meta VAE (CMVAE) that encodes the priors into latent codes in the causal space and learns their relationships simultaneously to achieve the downstream few-shot image classification task. Results on toy datasets and three benchmark datasets demonstrate that our method can remove the context-bias and it outperforms other state-of-the-art unsupervised meta-learning algorithms because of bias-removal. Code is available at \url{https://github.com/GuodongQi/CMVAE}

Learning Enabled Components (LEC) have greatly assisted cyber-physical systems in achieving higher levels of autonomy. However, LEC's susceptibility to dynamic and uncertain operating conditions is a critical challenge for the safety of these systems. Redundant controller architectures have been widely adopted for safety assurance in such contexts. These architectures augment LEC "performant" controllers that are difficult to verify with "safety" controllers and the decision logic to switch between them. While these architectures ensure safety, we point out two limitations. First, they are trained offline to learn a conservative policy of always selecting a controller that maintains the system's safety, which limits the system's adaptability to dynamic and non-stationary environments. Second, they do not support reverse switching from the safety controller to the performant controller, even when the threat to safety is no longer present. To address these limitations, we propose a dynamic simplex strategy with an online controller switching logic that allows two-way switching. We consider switching as a sequential decision-making problem and model it as a semi-Markov decision process. We leverage a combination of a myopic selector using surrogate models (for the forward switch) and a non-myopic planner (for the reverse switch) to balance safety and performance. We evaluate this approach using an autonomous vehicle case study in the CARLA simulator using different driving conditions, locations, and component failures. We show that the proposed approach results in fewer collisions and higher performance than state-of-the-art alternatives.

The high profile race to enhance their search products has underscored the importance of artificial intelligence to Google and Microsoft – and the rest of the economy, too. Two of the world's largest tech companies announced plans for AI-enhanced search this month, ratcheting up a tussle for supremacy in the artificial intelligence space. However, the debut of Google's new chatbot, Bard, was scuppered when an error appeared, knocking $163bn (£137bn) off the parent company Alphabet's share price. The stock's plunge showed how crucial investors think AI could be to Google's future. However, the increasing prominence of AI has implications for every corner of the economy.

ChatGPT has gained both positive and negative publicity after reports suggesting that it is able to pass various professional and licensing examinations. This suggests that ChatGPT may pass Turing Test in the near future. However, a computer program that passing Turing Test can either mean that it is a Chinese Room or artificially conscious. Hence, the question of whether the current state of ChatGPT is more of a Chinese Room or approaching artificial consciousness remains. Here, I demonstrate that the current version of ChatGPT (Feb 13 version) is a Chinese Room. Despite potential evidence of cognitive connections, ChatGPT exhibits critical errors in causal reasoning. At the same time, I demonstrate that ChatGPT can generate all possible categorical responses to the same question and response with erroneous examples; thus, questioning its utility as a learning tool. I also show that ChatGPT is capable of artificial hallucination, which is defined as generating confidently wrong replies. It is likely that errors in causal reasoning leads to hallucinations. More critically, ChatGPT generates false references to mimic real publications. Therefore, its utility is cautioned.

We propose a distributed planning method with asynchronous execution for multi-agent pickup and delivery (MAPD) problems for environments with occasional delays in agents' activities and flexible endpoints. MAPD is a crucial problem framework with many applications; however, most existing studies assume ideal agent behaviors and environments, such as a fixed speed of agents, synchronized movements, and a well-designed environment with many short detours for multiple agents to perform tasks easily. However, such an environment is often infeasible; for example, the moving speed of agents may be affected by weather and floor conditions and is often prone to delays. The proposed method can relax some infeasible conditions to apply MAPD in more realistic environments by allowing fluctuated speed in agents' actions and flexible working locations (endpoints). Our experiments showed that our method enables agents to perform MAPD in such an environment efficiently, compared to the baseline methods. We also analyzed the behaviors of agents using our method and discuss the limitations.

Artificial intelligence (AI) offers a new way to track the insect pollinators essential to farming. In a new study, we installed miniature digital cameras and computers inside a greenhouse at a strawberry farm in Victoria, Australia, to track bees and other insects as they flew from plant to plant pollinating flowers. Using custom AI software, we analysed several days' video footage from our system to build a picture of pollination behaviour over a wide area. In the same way that monitoring roads can help traffic run smoothly, our system promises to make pollination more efficient. This will enable better use of resources and increased food production.

Lebanon, Slovenia, and the United Arab Emirates (UAE) are the top three countries that are most interested in Artificial Intelligence (AI) crypto, according to CoinGecko's recent report. Countries with major economic problems, like Nigeria, Sri Lanka, and Pakistan, have also ranked higher in the charts -- while the U.S. was placed 33rd, the CoinGecko report stated. The report measured the search popularity of 14 English search terms related to AI crypto between Nov. 30, 2022, and Feb. 16. A 100 indicates maximum popularity, while 50 indicates half -- zero would mean there was not enough data to examine. Lebanon scored 100 on almost all 14 search terms -- collecting 1,200 points and ranking first on the list.