Four years ago, I stood in the darkened operations center in front of a wall of blinking screens, arms crossed and squinting at video footage on one of them. The commander asked me for the second time, signaling toward the figure on the screen. I looked over and reviewed a mental checklist of the individual's pattern of life over more than a decade. I weighed this against his latest movements, reflected on the screen in real time. The commander took a step toward me and started again, "Kara. We are running out of time. I had a decision to make. Using a machine to determine the validity of the target and take action is a nonstarter. But not everyone agrees on the details. Though the machines I dealt with that day were only semi-autonomous, it is not difficult to imagine a world where fully autonomous weapons are programmed to make a lethal decision. Institutions, countries, industry, and society must choose when and how to govern this technology in today's world, where semi-autonomous ...

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A woman uses an ATM with facial recognition technology during the presentation of the new service by CaixaBank in Barcelona on February 14, 2019. So, I just got the new iPhone 11 Pro. I have to say, I pretty much love the facial recognition unlock feature. And no, Apple is not paying me to say that. Prior, I was a facial recognition skeptic.

The MobileNet model was used by applying transfer learning on the 7 skin diseases to create a skin disease classification system on Android application. The proponents gathered a total of 3,406 images and it is considered as imbalanced dataset because of the unequal number of images on its classes. Using different sampling method and preprocessing of input data was explored to further improved the accuracy of the MobileNet. Using under-sampling method and the default preprocessing of input data achieved an 84.28% accuracy. While, using imbalanced dataset and default preprocessing of input data achieved a 93.6% accuracy. Then, researchers explored oversampling the dataset and the model attained a 91.8% accuracy. Lastly, by using oversampling technique and data augmentation on preprocessing the input data provide a 94.4% accuracy and this model was deployed on the developed Android application.

This paper is organized as follows: Section 2 briefly introduces some studies related to the formalization of theoretical frameworks in the context of the Data Clustering (DC) problem; Section 3 introduces a general formulation for the DC and HC problems; Section 4 discusses the Coarse-Refinement Dilemma considering the homology group H 0; Section 5 shows that homology groups of degree greater than zero are affected by overrefined and over-coarsed topologies; Section 6 compares our proposed generalization bounds to Carlsson and M emoli [12]'s consistency; finally, conclusions and future directions are provided in Section 8. 2. Related work Data Clustering (DC) faces many challenges in defining and guaranteeing generalization from datasets, as it does not rely on labels and, consequently, it cannot take advantage of computing any evident error measurement such as risk [7]. While studying this issue, Kleinberg [8] considered that a clustering model is an application of a mapping f on top of a distance function d: I I R, given I contains indices of data points in some fixed-size set S, disregarding its ambient space though [25]. From this initial setup, Kleinberg [8] defined three properties to be respected in order to assess clustering algorithms and models: - Scale-invariance: Given a distance and a clustering function, d and f, and a scalar α, the following must hold f (d) f (αd). Thus, the similarity representation over S must be consistent with the units of measurement; - Consistency: Let Γ be a partition of S and d,d null two distance functions. Function d null is referred to as a Γ transformation of d if: (i) for all i,j S belonging to the same cluster, d null (i,j) d( i,j); and (ii) for all i,j S belonging to different clusters, d null (i,j) d( i,j). Consistency holds if f (d null) f ( d) whenever d null is a Σ transformation of d.

The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping $f: \mathcal{X} \to \mathcal{Y}$ given $f$ is selected from its search space bias $\mathcal{F}$. This formal result depends on the Shattering coefficient function $\mathcal{N}(\mathcal{F},2n)$ to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of $\mathcal{F}$, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias $\mathcal{F}$, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Experiments were performed to illustrate the advantages of our approach while studying the search space $\mathcal{F}$ on synthetic and one toy datasets and on two widely-used deep learning benchmarks (MNIST and CIFAR-10). In order to permit reproducibility and the use of our approach, our source code is made available at~\url{https://bitbucket.org/rodrigo_mello/shattering-rcode}.

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising edges. In this work, we propose MIDAS, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. MIDAS has the following properties: (a) it detects microcluster anomalies while providing theoretical guarantees about its false positive probability; (b) it is online, thus processing each edge in constant time and constant memory, and also processes the data 108-505 times faster than state-of-the-art approaches; (c) it provides 46%-52% higher accuracy (in terms of AUC) than state-of-the-art approaches.

Marketers in 2019 will find it hard to be successful without understanding the cultural transformation that's happening in this country. Between 2012 and 2017, the US multicultural population – Hispanics, African Americans, and Asian Americans – grew to 11.7 million people. Notably, these groups are younger and growing at a faster rate than their White counterparts. This makes multicultural marketing an essential component of all advertising campaigns. Yet, even the most seasoned and "culturally woke" brands can have trouble navigating this cultural transformation and shifts in consumer behavior.

The disruptive sales startup presented its solution to local investors, after it has gained successful track record working with leading clients in Europe and South America. Enerjoy, an artificial intelligence platform that uses personalized gamification to increase sales teams' motivation and performance, announced the launch of its series A fundraising round, as part of a remarkable event held by Strtupboost and SAP Next Gen at the latter's headquarters in New York city. Enerjoy's scientifically based solution was proven to increase sales reps performance by an average of 25%, catering leading clients as banks, insurance, travel and telecom companies in Europe and South America. Gabby Hasson, Managing Partner at Bseed who's mother company Besadno VC invested in enerjoy last June says; "as a venture capital firm that sees hundreds of startups each year, we always look for smart technologies that can make a real impact on different industries while presenting significant scale potential. Besadno joins enerjoy's former investors' list: Nielsen Innovate, Samurai-Incubate and Israeli Innovation Authority. Enerjoy takes pride in its customers as Orange telecom and Tatra Bank in Slovakia, Coca Cola in Israel, Fattal hotel reservation centers and other media, insurance and technology companies in different countries. The company developed a personal motivational profile algorithm based on research it has conducted with its organizational and behavioural scientists, along with other studies such as Harvard motivational model. Their conclusions were then translated into an artificial intelligence platform that uses gamification to motivate each rep individually. The platform increases each reps' sales rates and responds to changes in real time. In addition, it serves as a support tool for the teams' manager, sending relevant notifications about reps progress. Viki Glam, enerjoy's CEO and Co-Founder noted: "We have spent a lot of time and resources to validate our motivational algorithm and to prove it can bring results to serious clients in different industries and different cultures.

At the Common Good in the Digital Age tech conference recently held in Vatican City, Pope Francis urged Facebook executives, venture capitalists, and government regulators to be wary of the impact of AI and other technologies. "If mankind's so-called technological progress were to become an enemy of the common good, this would lead to an unfortunate regression to a form of barbarism dictated by the law of the strongest," he said. In a related but contextually different conversation, this summer Joy Buolamwini testified before Congress with Rep. Alexandria Ocasio-Cortez (D-NY) that multiple audits found facial recognition technology generally works best on white men and worst on women of color. What these two events have in common is their relationship to power dynamics in the AI ethics debate. Arguments about AI ethics can wage without mention of the word "power," but it's often there just under the surface. In fact, it's rarely the direct focus, but it needs to be. Power in AI is like gravity, an invisible force that influences every consideration of ethics in artificial intelligence. Power provides the means to influence which use cases are relevant; which problems are priorities; and who the tools, products, and services are made to serve. It underlies debates about how corporations and countries create policy governing use of the technology.