The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. In many real-world applications, the agents can only acquire a partial view of the world. However, in realistic settings, one or more agents that show arbitrarily faulty or malicious behavior may suffice to let the current coordination mechanisms fail. In this paper, we study a practical scenario considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. Under these circumstances, learning an optimal policy becomes particularly challenging, even in the unrealistic case that an agent's policy can be made conditional upon all other agents' observations. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) algorithm which selects correct and relevant information for each agent at every time-step. The multi-head attention mechanism enables the agents to learn effective communication policies through experience concurrently to the action policies. Empirical results have shown that FT-Attn beats previous state-of-the-art methods in some complex environments and can adapt to various kinds of noisy environments without tuning the complexity of the algorithm. Furthermore, FT-Attn can effectively deal with the complex situation where an agent needs to reach multiple agents' correct observation at the same time.

This paper attempts to answer a central question in unsupervised learning: what does it mean to "make sense" of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that explains the sensory sequence and satisfies a set of unity conditions. This model was inspired by Kant's discussion of the synthetic unity of apperception in the Critique of Pure Reason. On our account, making sense of sensory input is a type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the Kantian unity constraints. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and "impute" (fill in the blanks of) missing sensory readings, in any combination. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction IQ tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction IQ tasks, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve IQ tasks, but a general purpose apperception system that was designed to make sense of any sensory sequence.

Meta-learning is a paradigm in machine learning which aims to develop models and training algorithms which can quickly adapt to new tasks and data. Our focus in this paper is on meta-learning in reinforcement learning (RL), where data efficiency is of paramount importance because gathering new samples often requires costly simulations or interactions with the real world. A popular technique for RL meta-learning is Model Agnostic Meta Learning (MAML) (Finn et al., 2017, 2018), a model for training an agent (the meta-policy) which can quickly adapt to new and unknown tasks by performing one (or a few) gradient updates in the new environment. We provide a formal description of MAML in Section 2. MAML has proven to be successful for many applications. However, implementing and running MAML continues to be challenging. One major complication is that the standard version of MAML requires estimating second derivatives of the RL reward function, which is difficult when using backpropagation on stochastic policies; indeed, the original implementation of MAML (Finn et al., 2017) did so incorrectly, which spurred the development of unbiased higher-order estimators (DiCE, (Foerster et al., 2018)) and further analysis of the credit assignment mechanism in MAML (Rothfuss et al., 2019).

Separation of these tasks is the medium within the current state-of- the-art navigation methods. Each task is performed by an individual module and modularity is attained easily by this way. Nevertheless, modularity comes with the cost of possible incompatibility, especially with the presence of erroneous modules. An erroneous module in the pipeline could easily cause the other modules to fail as well. Therefore, in this work, the unification of these tasks is attempted within a single, reliable module using deep reinforcement learning (RL) [13]-[16].

Dr Bayju Thakar is a former NHS doctor and founder of the digital health company Doctor Care Anywhere, who writes about the role of AI in digital health. Whether wide-eyed forecasts that it'll replace human clinicians in the foreseeable future, or alarm bells suggesting it's a danger to public health, AI's barely out of the news. I'm certainly reluctant to see chatbots as a viable alternative to clinician care – on which more below – but I'd rather we didn't make a bogeyman from these technologies either. I think a dose of patience is needed here – we mustn't rush into adoption of such revolutionary tech, but equally let's not scare ourselves witless over what it can do. I see AI as rather like a child prodigy.

Don't let your annotation project bury you. Whether you call it "content analysis," "textual data labeling," "hand-coding," or "tagging," a lot more researchers and data science teams are starting up annotation projects these days. Many want human judgment labeled onto text so they train AI (via supervised machine learning approaches). Others have tried automated text analysis and found it wanting. Now they're looking for ways to label text that aren't so hard to interpret and explain.

Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies.

After the Government's success to make Malta The Blockchain Island, we are now in a position to explore Artificial Intelligence as a new economic niche. Our vision is to replicate what we have done in the Blockchain sector and transform the potential of Artificial Intelligence into a new contributor to Malta's economic growth in digital innovation. The Government's aim is to develop a National AI Strategy and put Malta amongst the top 10 nations with a national strategy for Artificial Intelligence. Our objectives include having discussions on this subject with stakeholders to build awareness of the key topics and issues that will form a national AI Framework. We want to consult on a policy that considers for ethically aligned, transparent and socially responsible AI, identify policy, regulatory and fiscal measures to strengthen Malta's appeal as a hub for foreign investment in this sector, while identifying the underlying skill base and infrastructure needed to support AI.

In certain countries, the populace holds a deep bond with their armed forces. Reasons such as patriotism, giving back and love for freedom can often be attributed to such emotions and this holds true for the USA as well. Therefore, its own way, The U.S Chamber of Commerce hosts an online website at "hiringourheroes.org" to help armed forces veterans find jobs. However, attackers seem to have found ways to bend this to their advantage. Exploiting the impact and need of the site, recently, a pretender website with the URL of "hiremilitaryheroes[.]com" was discovered distributing malware by prompting users to download an application for computers based on the Windows OS.

Despite efforts to curb online piracy, some estimates suggest that pirate websites secured around 190 billion visits in 2018, affecting industries spanning TV, movies, music, publishing, and software. According to a recent report by the U.S. Chamber of Commerce, digital video piracy alone results in nearly $30 billion in lost revenue annually in the U.S. Against this backdrop, Smart Protection is working to help brands and rightsholders identify online hubs that host illegal streams, downloads, and other types of content infringement. Founded out of Madrid, Spain in 2015, Smart Protection uses machine learning and big data processing -- in concert with natural language processing (NLP), computer vision, keyword searches, and more -- to find the hubs hosting piracy and counterfeit content. By scanning the web, Smart Protection effectively builds a huge database of URLs, and it then applies its various machine learning algorithms that have been tailored to each content type and classifies the URLs based on the likelihood that they are hosting illicit content. "Typically, we eliminate approximately 98% [of the URLs], and it is from the remaining 2% that counterfeits, piracy, or brand abuse are hosted," Javier Perea, CEO and cofounder of Smart Protection, told VentureBeat.