Data poisoning attacks aim at manipulating model behaviors through distorting training data. Previously, an aggregation-based certified defense, Deep Partition Aggregation (DPA), was proposed to mitigate this threat. DPA predicts through an aggregation of base classifiers trained on disjoint subsets of data, thus restricting its sensitivity to dataset distortions. In this work, we propose an improved certified defense against general poisoning attacks, namely Finite Aggregation. In contrast to DPA, which directly splits the training set into disjoint subsets, our method first splits the training set into smaller disjoint subsets and then combines duplicates of them to build larger (but not disjoint) subsets for training base classifiers. This reduces the worst-case impacts of poison samples and thus improves certified robustness bounds. In addition, we offer an alternative view of our method, bridging the designs of deterministic and stochastic aggregation-based certified defenses. Empirically, our proposed Finite Aggregation consistently improves certificates on MNIST, CIFAR-10, and GTSRB, boosting certified fractions by up to 3.05%, 3.87% and 4.77%, respectively, while keeping the same clean accuracies as DPA's, effectively establishing a new state of the art in (pointwise) certified robustness against data poisoning.

The graph represents a network of 1,514 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 04 February 2022 at 13:10 UTC. The requested start date was Friday, 04 February 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 2-day, 21-hour, 31-minute period from Tuesday, 01 February 2022 at 02:59 UTC to Friday, 04 February 2022 at 00:30 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Neom Tech & Digital Company, a subsidiary of Neom, is building a 3D cognitive digital twin metaverse platform, which aims to enable a "ground-breaking, mixed-reality" model for urban living. Called XVRS, the platform was announced at the Leap22 technology event which has been taking place in Riyadh, Saudi Arabia from 1-3 February. Combining digital and physical architectures with hyper-connected technologies and artificial intelligence (AI), XVRS seeks to seamlessly integrate the virtual and real worlds. Neom is a region in northwest Saudi Arabia on the Red Sea being built from the ground up as a living laboratory. Neom Tech & Digital Company was founded in 2021 as its first subsidiary, charged with helping to create an ecosystem of cognitive technologies to co-invent the future of living.

People often ask me about the real-life potential for inhumane, merciless systems like Hal 9000 or the Terminator to destroy our society. Growing up in Belgium and away from Hollywood, my initial impressions of robots were not so violent. In retrospect, my early positive affiliations with robots likely fueled my drive to build machines to make our everyday lives more enjoyable. Robots working alongside humans to manage day-to-day mundane tasks was a world I wanted to help create. Now, many years later, after emigrating to the United States, finishing my PhD under Andrew Ng, starting the Berkeley Robot Learning Lab, and co-founding Covariant, I'm convinced that robots are becoming sophisticated enough to be the allies and helpful teammates that I hoped for as a child.

The drone attack by a little-known armed group in Iraq on the United Arab Emirates (UAE) this week has raised questions about Baghdad's involvement in regional tensions between Iran-backed Houthi rebels in Yemen and the Saudi-led coalition. Alwiyat al-Waad al-Haq (AWH), or the True Promise Brigades, claimed responsibility for the strike on the UAE on Wednesday, saying in a statement it launched "four drones targeting vital facilities in Abu Dhabi" in retaliation for the Emirates' policies in Iraq and Yemen. Several analysts linked the strikes to a shadowy militia Kataib Hezbollah (KH), a powerful Iran-backed Shia armed group in Iraq that has been listed by the United States as a "terrorist organisation". The incident brought to light that the UAE was now being targeted from its north and south, after three recent attacks launched by Houthi rebels in Yemen. Following the drone strikes, Iraqi Shia leader Muqtada al-Sadr condemned the attack in a statement, saying some "terrorist outlaws" have dragged Iraq into a "dangerous regional war" by targeting a Gulf state.

Over the last few years, neural networks have started penetrating safety critical systems to take decisions in robots, rockets, autonomous driving car, etc. A problem is that these critical systems often have limited computing resources. Often, they use the fixed-point arithmetic for its many advantages (rapidity, compatibility with small memory devices.) In this article, a new technique is introduced to tune the formats (precision) of already trained neural networks using fixed-point arithmetic, which can be implemented using integer operations only. The new optimized neural network computes the output with fixed-point numbers without modifying the accuracy up to a threshold fixed by the user. A fixed-point code is synthesized for the new optimized neural network ensuring the respect of the threshold for any input vector belonging the range [xmin, xmax] determined during the analysis. From a technical point of view, we do a preliminary analysis of our floating neural network to determine the worst cases, then we generate a system of linear constraints among integer variables that we can solve by linear programming. The solution of this system is the new fixed-point format of each neuron. The experimental results obtained show the efficiency of our method which can ensure that the new fixed-point neural network has the same behavior as the initial floating-point neural network.

The impact of Artificial Intelligence does not depend only on fundamental research and technological developments, but for a large part on how these systems are introduced into society and used in everyday situations. Even though AI is traditionally associated with rational decision making, understanding and shaping the societal impact of AI in all its facets requires a relational perspective. A rational approach to AI, where computational algorithms drive decision making independent of human intervention, insights and emotions, has shown to result in bias and exclusion, laying bare societal vulnerabilities and insecurities. A relational approach, that focus on the relational nature of things, is needed to deal with the ethical, legal, societal, cultural, and environmental implications of AI. A relational approach to AI recognises that objective and rational reasoning cannot does not always result in the 'right' way to proceed because what is 'right' depends on the dynamics of the situation in which the decision is taken, and that rather than solving ethical problems the focus of design and use of AI must be on asking the ethical question. In this position paper, I start with a general discussion of current conceptualisations of AI followed by an overview of existing approaches to governance and responsible development and use of AI. Then, I reflect over what should be the bases of a social paradigm for AI and how this should be embedded in relational, feminist and non-Western philosophies, in particular the Ubuntu philosophy.

Stock Market can be easily seen as one of the most attractive places for investors, but it is also very complex in terms of making trading decisions. Predicting the market is a risky venture because of the uncertainties and nonlinear nature of the market. Deciding on the right time to trade is key to every successful trader as it can lead to either a huge gain of money or totally a loss in investment that will be recorded as a careless trade. The aim of this research is to develop a prediction system for stock market using Fuzzy Logic Type2 which will handle these uncertainties and complexities of human behaviour in general when it comes to buy, hold or sell decision making in stock trading. The proposed system was developed using VB.NET programming language as frontend and Microsoft SQL Server as backend. A total of four different technical indicators were selected for this research. The selected indicators are the Relative Strength Index, William Average, Moving Average Convergence and Divergence, and Stochastic Oscillator. These indicators serve as input variable to the Fuzzy System. The MACD and SO are deployed as primary indicators, while the RSI and WA are used as secondary indicators. Fibonacci retracement ratio was adopted for the secondary indicators to determine their support and resistance level in terms of making trading decisions. The input variables to the Fuzzy System is fuzzified to Low, Medium, and High using the Triangular and Gaussian Membership Function. The Mamdani Type Fuzzy Inference rules were used for combining the trading rules for each input variable to the fuzzy system. The developed system was tested using sample data collected from ten different companies listed on the Nigerian Stock Exchange for a total of fifty two periods. The dataset collected are Opening, High, Low, and Closing prices of each security.

The study of generalisation properties of deep neural networks is arguably one of the topics gaining most traction in deep learning theory (see, e.g., the recent surveys Kawaguchi et al., 2020; Jiang et al., 2020b). In particular, a characterisation of out-of-sample generalisation is essential to understand where trained neural networks are likely to succeed or to fail, as evidenced by the recent NeurIPS 2020 competition "Predicting Generalization in Deep Learning" (Jiang et al., 2020a). One stream of this joint effort, which the present paper contributes to, is dedicated to the study of shallow neural networks, potentially paving the way to insights on deeper architectures.

In addition to the growing use of AI within surveillance systems across Africa, AI has also been integrated into weapon systems. Most prominently, lethal autonomous weapons systems use real-time sensor data coupled with AI and machine learning algorithms to "select and engage targets without further intervention by a human operator." Depending on how that definition is interpreted, the first use of a lethal autonomous weapon system in combat may have taken place on African soil in March 2020. That month, logistics units belonging to the armed forces of the Libyan warlord Khalifa Haftar came under attack by Turkish-made STM Kargu-2 drones as they fled Tripoli. According to a United Nations report, the Kargu-2 represented a lethal autonomous weapons system because it had been "programmed to attack targets without requiring data connectivity between the operator and munition."