"By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it." Today, AI is an indispensable part of our daily lives. To define it scientifically, it is the field of study of "intelligent agents"; any device that can mimic a human task, perceive its environment, and takes actions that maximize its chance of successfully achieving its goals. AI has helped numerous industries develop novel approaches to solve challenging problems. While all this is enthralling, there is a catch, the black box dilemma.

It is believed that each company must embrace the AI revolution from the smallest local businesses to the largest global players, and recognize how artificial intelligence can have the greatest impact on their business. The boundary for mistake is quickly diminishing, as global supply chains increase in complexity. With the rising rivalry in a linked digital environment, optimizing productivity by reducing uncertainties of all sorts becomes even more important. The increase of supersonic speed and efficiency standards among suppliers of all kinds further emphasizes the need for AI Solutions Company to harness Artificial Intelligence skills in both supply chains and logistics. Artificial Intelligence has experienced a long, zigzagging evolution to get to this opinion of application in logistics.

When he was a boy, Mark Solms obsessed over big existential questions. What happens when I die? What makes me who I am? He went on to study neuroscience but soon discovered that neuropsychology had no patience for such open-ended questions about the psyche. So Solms did something unheard of for a budding scientist. He reclaimed Freud as a founding father of neuroscience and launched a new field, neuropsychoanalysis. Solms had one other obstacle in his path. Born in Namibia, where his father worked for a South African diamond mining company, he grew up under apartheid in South Africa. Solms later worked at a hospital in Soweto, where a military occupation tried to clamp down on protesters. "Once you reach the end of your studies, you're required to join the very same army whose victims I was looking after," he told me.

The Biden administration has quietly imposed temporary limits on counterterrorism drone strikes and commando raids outside conventional battlefield zones like Afghanistan and Syria, and it has begun a broad review of whether to tighten Trump-era rules for such operations, according to officials. The military and the C.I.A. must now obtain White House permission to attack terrorism suspects in poorly governed places where there are scant American ground troops, like Somalia and Yemen. Under the Trump administration, they had been allowed to decide for themselves whether circumstances on the ground met certain conditions and an attack was justified. Officials characterized the tighter controls as a stopgap while the Biden administration reviewed how targeting worked -- both on paper and in practice -- under former President Donald J. Trump and developed its own policy and procedures for counterterrorism kill-or-capture operations outside war zones, including how to minimize the risk of civilian casualties. The Biden administration did not announce the new limits.

Tensor decomposition is a fundamentally challenging problem. Even the simplest case of tensor decomposition, the rank-1 approximation in terms of the Least Squares (LS) error, is known to be NP-hard. Here, we show that, if we consider the KL divergence instead of the LS error, we can analytically derive a closed form solution for the rank-1 tensor that minimizes the KL divergence from a given positive tensor. Our key insight is to treat a positive tensor as a probability distribution and formulate the process of rank-1 approximation as a projection onto the set of rank-1 tensors. This enables us to solve rank-1 approximation by convex optimization. We empirically demonstrate that our algorithm is an order of magnitude faster than the existing rank-1 approximation methods and gives better approximation of given tensors, which supports our theoretical finding.

It is important for a broad range of applications, including policy making [136], medical imaging [30], advertisement [22], the development of medical treatments [189], the evaluation of evidence within legal frameworks [183, 218], social science [82, 96, 246], biology [235], and many others. It is also a burgeoning topic in machine learning and artificial intelligence [17, 66, 76, 144, 210, 247, 255], where it has been argued that a consideration for causality is crucial for reasoning about the world. In order to discover causal relations, and thereby gain causal understanding, one may perform interventions and manipulations as part of a randomized experiment. These experiments may not only allow researchers or agents to identify causal relationships, but also to estimate the magnitude of these relationships. Unfortunately, in many cases, it may not be possible to undertake such experiments due to prohibitive cost, ethical concerns, or impracticality.

Training machines to understand natural language and interact with humans is an elusive and essential task in the field of artificial intelligence. In recent years, a diversity of dialogue systems has been designed with the rapid development of deep learning researches, especially the recent pre-trained language models. Among these studies, the fundamental yet challenging part is dialogue comprehension whose role is to teach the machines to read and comprehend the dialogue context before responding. In this paper, we review the previous methods from the perspective of dialogue modeling. We summarize the characteristics and challenges of dialogue comprehension in contrast to plain-text reading comprehension. Then, we discuss three typical patterns of dialogue modeling that are widely-used in dialogue comprehension tasks such as response selection and conversation question-answering, as well as dialogue-related language modeling techniques to enhance PrLMs in dialogue scenarios. Finally, we highlight the technical advances in recent years and point out the lessons we can learn from the empirical analysis and the prospects towards a new frontier of researches.

Two popular types of machine translation (MT) are phrase-based and neural machine translation systems. Both of these types of systems are composed of multiple complex models or layers. Each of these models and layers learns different linguistic aspects of the source language. However, for some of these models and layers, it is not clear which linguistic phenomena are learned or how this information is learned. For phrase-based MT systems, it is often clear what information is learned by each model, and the question is rather how this information is learned, especially for its phrase reordering model. For neural machine translation systems, the situation is even more complex, since for many cases it is not exactly clear what information is learned and how it is learned. To shed light on what linguistic phenomena are captured by MT systems, we analyze the behavior of important models in both phrase-based and neural MT systems. We consider phrase reordering models from phrase-based MT systems to investigate which words from inside of a phrase have the biggest impact on defining the phrase reordering behavior. Additionally, to contribute to the interpretability of neural MT systems we study the behavior of the attention model, which is a key component in neural MT systems and the closest model in functionality to phrase reordering models in phrase-based systems. The attention model together with the encoder hidden state representations form the main components to encode source side linguistic information in neural MT. To this end, we also analyze the information captured in the encoder hidden state representations of a neural MT system. We investigate the extent to which syntactic and lexical-semantic information from the source side is captured by hidden state representations of different neural MT architectures.

The next section talks about the intersection over union (IoU) which is how an object detection generates the prediction scores.

Welcome to the second of our monthly digests, designed to keep you up-to-date with the happenings in the AI world. You can catch up with any AIhub stories you may have missed, get the low-down on recent conferences, and generally immerse yourself in all things AI. You may be aware that we are running a focus series on the UN sustainable development goals (SDG). Each month we tackle a different SDG and cover some of the AI research linked to that particular goal. In February it was the turn of climate action.