We present a system that utilizes machine learning for tactic proof search in the Coq Proof Assistant. In a similar vein as the TacticToe project for HOL4, our system predicts appropriate tactics and finds proofs in the form of tactic scripts. To do this, it learns from previous tactic scripts and how they are applied to proof states. The performance of the system is evaluated on the Coq Standard Library. Currently, our predictor can identify the correct tactic to be applied to a proof state 23.4% of the time. Our proof searcher can fully automatically prove 39.3% of the lemmas. When combined with the CoqHammer system, the two systems together prove 56.7% of the library's lemmas.

The global artificial intelligence and education Market is significantly driven by the integration of intelligent algorithms as well as Advanced Technologies in to e-learning platforms. Education software, machine learning, and artificial intelligence are some of the Innovative learning models and Technologies change the rules and creating tremendous shift from the teaching methods. These technologies have completely transformed with a classroom. The sophistication level has increased tremendously with the increasing adoption of artificial intelligence and machine learning algorithms. These Technologies are becoming extremely useful for developing user-friendly decision support systems and used in knowledge acquisition applications, language translation, and information retrieval.

Conferences you speak at: Data Innovation Summit; Big Data Conference; EduData, etc.

With more than 2000 spoken languages, Africa's linguistic diversity is second only to Asia. A third of the world's languages is spoken by the 1.2 billion people living within her 54 countries. But the language of artificial intelligence is yet to gain fluency. It has become hackneyed to weave AI into every conversation about technology and society. AI will take away jobs.

Recurrent neural networks are extremely appealing for sequence-to-sequence learning tasks. Despite their great success, they typically suffer from a shortcoming: they are prone to generate unbalanced targets with good prefixes but bad suffixes, and thus performance suffers when dealing with long sequences. We propose a simple yet effective approach to overcome this shortcoming. Our approach relies on the agreement between a pair of target-directional RNNs, which generates more balanced targets. In addition, we develop two efficient approximate search methods for agreement that are empirically shown to be almost optimal in terms of either sequence level or non-sequence level metrics. Extensive experiments were performed on three standard sequence-to-sequence transduction tasks: machine transliteration, grapheme-to-phoneme transformation and machine translation. The results show that the proposed approach achieves consistent and substantial improvements, compared to many state-of-the-art systems.

CAPE TOWN 17th March – AI Expo Africa is the continent's largest B2B trade-focused artificial intelligence (AI) business event. The expo, which is now in its third year, will be held at Century City Convention Centre in Cape Town on 3rd and 4th September 2020. Zindi, Learning Machines – which provides a number of services across machine learning and data science – and FinChatBot, which develops AI-powered chat bots for the financial services industry, will showcase their solutions at AI Expo Africa's Innovation Cafe. Other companies that will exhibit at AI Expo Africa's Innovation Cafe include data science specialists Ashanti AI and visitor management solutions provider WizzPass. AI Expo Africa Event Co-founder Dr Nick Bradshaw stated, "It's great to welcome back supporters like City of Cape Town, FinChatBot and Zindi, the latter launched at our 2018 event and have gone onto be a massive success. It's also great to introduce brand new supporters and companies like Learning Machines, WizzPass and Ashanti AI. Our goal is to promote the newest and best AI, RPA and Data Science companies and startups via our various media platforms to maximise brand exposure and generate new business for the growing 4IR community of suppliers that is emerging in Africa."

"We're trying to change how people run agronomic research. Instead of establishing a small field plot, running statistics and publishing the means, what we're trying to do involves the farmer far more directly. We are running experiments with farmers' machinery in their own fields. We can detect site-specific responses to different inputs. And we can see whether there's a response in different parts of the field," said Nicolas Martin, assistant professor in the U of I Department of Crop Sciences and co-author of the study.

Federated Learning enables visual models to be trained on-device, bringing advantages for user privacy (data need never leave the device), but challenges in terms of data diversity and quality. Whilst typical models in the datacenter are trained using data that are independent and identically distributed (IID), data at source are typically far from IID. Furthermore, differing quantities of data are typically available at each device (imbalance). In this work, we characterize the effect these real-world data distributions have on distributed learning, using as a benchmark the standard Federated Averaging (FedAvg) algorithm. To do so, we introduce two new large-scale datasets for species and landmark classification, with realistic per-user data splits that simulate real-world edge learning scenarios. We also develop two new algorithms (FedVC, FedIR) that intelligently resample and reweight over the client pool, bringing large improvements in accuracy and stability in training.

Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order datasets stored in tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $. $CANDECOMP/PARAFAC$ (CP) decomposition has been extensively studied and applied to approximate $\mathcal{X}$ by $N$ loading matrices $A^{(1)}, \dots, A^{(N)}$ where $N$ represents the order of the tensor. We propose a new efficient CP decomposition solver named NeCPD for non-convex problem in multi-way online data based on stochastic gradient descent (SGD) algorithm. SGD is very useful in online setting since it allows us to update $\mathcal{X}^{(t+1)}$ in one single step. In terms of global convergence, it is well known that SGD stuck in many saddle points when it deals with non-convex problems. We study the Hessian matrix to identify theses saddle points, and then try to escape them using the perturbation approach which adds little noise to the gradient update step. We further apply Nesterov's Accelerated Gradient (NAG) method in SGD algorithm to optimally accelerate the convergence rate and compensate Hessian computational delay time per epoch. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets show that our method provides more accurate results compared with existing online tensor analysis methods.

While automation and digital technologies are disrupting the workplace as we traditionally know it, it has become imperative for organisations to start reskilling their workforce. Automation and artificial intelligence are not only disrupting the assembly lines but right across the so-called blue-collar jobs. This is mainly because in most instances, artificial intelligence (AI), is actually doing a better job than humans. For example, the use of virtual assistants in the workplace is growing. By 2021, Gartner predicts that 25 percent of digital workers will use a virtual employee assistant on a daily basis.