Importantly, they can also prevent costly defects and avoid operational inefficiencies. While COVID-19 sped up the pace of adoption for many industries, including industrial manufacturing, manufacturing companies have historically embraced new ways of working. Manufacturers were early endorsers of Kaizen, Six Sigma, and Lean, known business improvement models with direct impacts to the continuous improvement methodology critical to manufacturing processes. And now, AI is being embraced for its ability to make supply chains more flexible -- mostly to evaluate vulnerabilities identified during the COVID-19 pandemic among their suppliers and in the supply chain itself -- reduce costs, and fully leverage human talent and intelligence. According to a new KPMG report, Thriving in an AI World, 93% of industrial manufacturing respondents indicated they have moderate or fully functional AI, primarily machine learning technologies, implemented into their processes.

A scholarship programme seeking to nurture talent in technological research in Africa's public universities has been launched. The three-year programme aims to meet the rising demand for expertise in responsible artificial intelligence (AI) and machine learning (ML) in the continent. While machine learning encompasses the study of computer algorithms and use of data, artificial intelligence involves the simulation of human intelligence by machines, usually computer system. The scholarship programme will support selected scholars to undertake PhD research in AI and ML in African universities, and early career academics to strengthen their research and development capacities in the two areas. Murang'a County to give dairy firm to farmers Sacco What Matiang'i didn't reveal on deployment of police officers The initiative, dubbed the A14D Africa scholarship, is implemented by the African Centre for Technology Studies (ACTS) based in Kenya in partnership with Kwame Nkrumah University in Ghana, University of Linkoping, Sweden, University Cheikh Anta Diop de Dakar, Senegal, University of California, Human Sciences Research Council and Institute for Humanities in Africa based in South Africa and the University of Eduardo Mondlane, Mozambique.

Sooner or later, the concept of digitization will completely take over all repetitive tasks. Today, with the help of big data, advanced technologies like automation, artificial intelligence, IoT, and machine learning are leveraging unimaginable amounts and types of information to work from. It is streamlining tedious, repetitive, and difficult tasks, which tend to slow down production and also increases the cost of operation. Owing to the evolution of technology, artificial intelligence startups are mushrooming like never before. The companies are driving the world into a new phase of digitization with a mixture of disruptive statistical methods, computational intelligence, soft computing, and traditional symbolic AI. Artificial intelligence is the combination of two amazing concepts namely science and engineering. With the infusion of disruptive trends and human intelligence, intelligent machines and intelligent computing programs are emerging. Slowly, the flare of innovations moved away from IT and entered into diverse industries including healthcare, education, finance, marketing, business, telecommunication, etc. Organizations realized that by digitizing repetitive tasks, an enterprise can cut the cost of paperwork and labor which further eliminates human error, thus boosting efficiency. Automating processes involve employing artificial intelligence solutions that can support digitization and deliver data-driven insights. Artificial intelligence startups emerge as a ready-made solution provider that supports every company's individual needs. AI startups in 2021 use big data to sophisticated AI models and leverage new solutions that could better serve customers. Analytics Insight has listed the top 100 artificial intelligence startups that are driving the next-generation development in technology. It democratizes the way investments are done by bringing sophisticated elite trading technology to laymen. Accrad is a health tech company that assists radiologists to reduce their workload with the precision of artificial intelligence. Radiologists work under different circumstances and deadlines and might find diagnosis through x-rays a bit difficult. Therefore, Accrad has come up with a futuristic solution to help with accurate and fast image diagnosis. The company has made x-ray processing more convincing and simpler. Its signature product CheXRad, a deep learning algorithm that identifies locations in the chest radiograph has the capability to predict 15 different diseases including Covid-19. Affable.ai is a data-driven influencer marketing platform where customers can find relevant and authentic influencers and manage marketing operations. By using cutting-edge computer vision algorithms on social media posts, the company delivers actionable insights about micro-influencers and their audience. Similar to how Google has sophisticated its search and promote relative ads to users, Affable.ai has also built one-click marketing at a shorter scale.

In recent years, neural paraphrase generation based on Seq2Seq has achieved superior performance, however, the generated paraphrase still has the problem of lack of diversity. In this paper, we focus on improving the diversity between the generated paraphrase and the original sentence, i.e., making generated paraphrase different from the original sentence as much as possible. We propose BTmPG (Back-Translation guided multi-round Paraphrase Generation), which leverages multi-round paraphrase generation to improve diversity and employs back-translation to preserve semantic information. We evaluate BTmPG on two benchmark datasets. Both automatic and human evaluation show BTmPG can improve the diversity of paraphrase while preserving the semantics of the original sentence.

This article is brought to you thanks to the collaboration of The European Sting with the World Economic Forum. One of the popular memes in literature, movies and tech journalism is that man's creation will rise and destroy it. Lately, this has taken the form of a fear of AI becoming omnipotent, rising up and annihilating mankind. The economy has jumped on the AI bandwagon; for a certain period, if you did not have "AI" in your investor pitch, you could forget about funding. However, is there actually anything deserving of the term AI?

Lee's technical explanations sit alongside Chen's fictional short stories to produce an exploration of the perils and possibilities of AI. This story, translated by Emily Jin, revolves around a Nigerian video producer who is recruited to make an undetectable deepfake. Touching on impending breakthroughs in computer vision, biometrics, and AI security, it imagines a future world marked by cat-and-mouse games between deepfakers and detectors, and between defenders and perpetrators. As the light-rail train inched into Yaba station, Amaka pushed a button next to the door of his carriage. Even before the train came to a complete stop, the doors opened with a whoosh and Amaka hopped off.

Multi-modal program synthesis refers to the task of synthesizing programs (code) from their specification given in different forms, such as a combination of natural language and examples. Examples provide a precise but incomplete specification, and natural language provides an ambiguous but more "complete" task description. Machine-learned pre-trained models (PTMs) are adept at handling ambiguous natural language, but struggle with generating syntactically and semantically precise code. Program synthesis techniques can generate correct code, often even from incomplete but precise specifications, such as examples, but they are unable to work with the ambiguity of natural languages. We present an approach that combines PTMs with component-based synthesis (CBS): PTMs are used to generate candidates programs from the natural language description of the task, which are then used to guide the CBS procedure to find the program that matches the precise examples-based specification. We use our combination approach to instantiate multi-modal synthesis systems for two programming domains: the domain of regular expressions and the domain of CSS selectors. Our evaluation demonstrates the effectiveness of our domain-agnostic approach in comparison to a state-of-the-art specialized system, and the generality of our approach in providing multi-modal program synthesis from natural language and examples in different programming domains.

Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models. In this paper we focus on mitigating noise in augmented data. We develop a denoising training approach. Multiple models are trained with data produced by various augmented methods. Those models provide supervision signals to each other. The experimental results show that our method outperforms the existing state of the art by 3.05 and 4.24 percentage points on two benchmark datasets, respectively. The code will be made open sourced on github.

Conditionals are useful for modelling, but are not always sufficiently expressive for capturing information accurately. In this paper we make the case for a form of conditional that is situation-based. These conditionals are more expressive than classical conditionals, are general enough to be used in several application domains, and are able to distinguish, for example, between expectations and counterfactuals. Formally, they are shown to generalise the conditional setting in the style of Kraus, Lehmann, and Magidor. We show that situation-based conditionals can be described in terms of a set of rationality postulates. We then propose an intuitive semantics for these conditionals, and present a representation result which shows that our semantic construction corresponds exactly to the description in terms of postulates. With the semantics in place, we proceed to define a form of entailment for situated conditional knowledge bases, which we refer to as minimal closure. It is reminiscent of and, indeed, inspired by, the version of entailment for propositional conditional knowledge bases known as rational closure. Finally, we proceed to show that it is possible to reduce the computation of minimal closure to a series of propositional entailment and satisfiability checks. While this is also the case for rational closure, it is somewhat surprising that the result carries over to minimal closure.

Random Fourier features provide a way to tackle large-scale machine learning problems with kernel methods. Their slow Monte Carlo convergence rate has motivated the research of deterministic Fourier features whose approximation error decreases exponentially with the number of frequencies. However, due to their tensor product structure these methods suffer heavily from the curse of dimensionality, limiting their applicability to two or three-dimensional scenarios. In our approach we overcome said curse of dimensionality by exploiting the tensor product structure of deterministic Fourier features, which enables us to represent the model parameters as a low-rank tensor decomposition. We derive a monotonically converging block coordinate descent algorithm with linear complexity in both the sample size and the dimensionality of the inputs for a regularized squared loss function, allowing to learn a parsimonious model in decomposed form using deterministic Fourier features. We demonstrate by means of numerical experiments how our low-rank tensor approach obtains the same performance of the corresponding nonparametric model, consistently outperforming random Fourier features.