If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email webmaster@digital.fco.gov.uk. Please tell us what format you need. It will help us if you say what assistive technology you use. The UK's Department for International Trade (DIT) is pleased to announce 12 high-growth Canadian FinTech companies from across 5 major Canadian cities chosen to participate in a trade mission to the UK – 21- 25 October 2019. These leading companies have collectively raised over half a billion in venture capital and are well positioned for global expansion.

Proximal operators are of particular interest in optimization problems dealing with non-smooth objectives because in many practical cases they lead to optimization algorithms whose updates can be computed in closed form or very efficiently. A well-known example is the proximal operator of the vector $\ell_1$ norm, which is given by the soft-thresholding operator. In this paper we study the proximal operator of the mixed $\ell_{1,\infty}$ matrix norm and show that it can be computed in closed form by applying the well-known soft-thresholding operator to each column of the matrix. However, unlike the vector $\ell_1$ norm case where the threshold is constant, in the mixed $\ell_{1,\infty}$ norm case each column of the matrix might require a different threshold and all thresholds depend on the given matrix. We propose a general iterative algorithm for computing these thresholds, as well as two efficient implementations that further exploit easy to compute lower bounds for the mixed norm of the optimal solution. Experiments on large-scale synthetic and real data indicate that the proposed methods can be orders of magnitude faster than state-of-the-art methods.

The skip-connections used in residual networks have become a standard architecture choice in deep learning due to the increased training stability and generalization performance with this architecture, although there has been limited theoretical understanding for this improvement. In this work, we analyze overparameterized deep residual networks trained by gradient descent following random initialization, and demonstrate that (i) the class of networks learned by gradient descent constitutes a small subset of the entire neural network function class, and (ii) this subclass of networks is sufficiently large to guarantee small training error. By showing (i) we are able to demonstrate that deep residual networks trained with gradient descent have a small generalization gap between training and test error, and together with (ii) this guarantees that the test error will be small. Our optimization and generalization guarantees require overparameterization that is only logarithmic in the depth of the network, while all known generalization bounds for deep non-residual networks have overparameterization requirements that are at least polynomial in the depth. This provides an explanation for why residual networks are preferable to non-residual ones.

This paper presents a method for semantic indexing and describes its application in the field of knowledge representation. Starting point of the semantic indexing is the knowledge represented by concept hierarchies. The goal is to assign keys to nodes (concepts) that are hierarchically ordered and syntactically and semantically correct. With the indexing algorithm, keys are computed such that concepts are partially unifiable with all more specific concepts and only semantically correct concepts are allowed to be added. The keys represent terminological relationships. Correctness and completeness of the underlying indexing algorithm are proven. The use of classical relational databases for the storage of instances is described. Because of the uniform representation, inference can be done using case-based reasoning and generic problem solving methods.

CALGARY, Alberta--(BUSINESS WIRE)--The Alberta Machine Intelligence Institute (Amii) and Imperial have announced a two-year agreement to collaborate on the development of Imperial's in-house machine learning capabilities, which will enable a range of applied artificial intelligence (AI) projects. Through these projects, Imperial will work to develop more effective ways to recover oil and gas resources, reduce environmental impacts and improve the safety of its workforce. "At Imperial, we are taking action to be a leader in advancing digital and AI technology across the value chain," said John Whelan, Imperial's senior vice-president, upstream. "Amii is not only a leader in the AI space globally, but based locally in Alberta. We believe the institute is a perfect partner to help us showcase Alberta's leadership in technology and digital solutions for responsibly-produced oil and gas."

This paper presents a family of backpropagation-free neural architectures, Gated Linear Networks (GLNs),that are well suited to online learning applications where sample efficiency is of paramount importance. The impressive empirical performance of these architectures has long been known within the data compression community, but a theoretically satisfying explanation as to how and why they perform so well has proven difficult. What distinguishes these architectures from other neural systems is the distributed and local nature of their credit assignment mechanism; each neuron directly predicts the target and has its own set of hard-gated weights that are locally adapted via online convex optimization. By providing an interpretation, generalization and subsequent theoretical analysis, we show that sufficiently large GLNs are universal in a strong sense: not only can they model any compactly supported, continuous density function to arbitrary accuracy, but that any choice of no-regret online convex optimization technique will provably converge to the correct solution with enough data. Empirically we show a collection of single-pass learning results on established machine learning benchmarks that are competitive with results obtained with general purpose batch learning techniques.

The AI Times is a weekly newsletter covering the biggest AI, machine learning, big data, and automation news from around the globe. If you want to read A I before anyone else, make sure to subscribe using the form at the bottom of this page. Toronto-based Daisy Intelligence, which has created an AI-powered platform for retail and insurance, has raised $10 million in Series A financing. "Microsoft and Eros are partnering to take Bollywood's $5B movie industry global by developing a new platform, creating new offerings, and delivering personalized content – all using Azure." You don't see a startup get a $50 million seed round all that often, but such was the case with Vianai, an early-stage startup launched by Vishal Sikka, former Infosys managing director and SAP executive.

We've created a conversational user interface (UI) that allows people to intuitively communicate with databases so they can get answers from their data quickly and easily. What does this look like in practice? We're focused on helping small and medium-sized businesses make their data work for them, so we've built our interface to facilitate data access and analysis through natural language processing and dynamic query building. In simpler terms, users can ask questions in their own words and get answers from the business software they use day-to-day, in real time. We believe that being able to understand and access your data can help you make strong decisions about your business.

Brisbane startup FloodMapp has raised $1.3 million, as it looks to take its flood-prediction tech to the rest of Australia, and into hurricane-prone areas of the US. The funding comes from several VC firms, including Allectus Capital, Transition Level Investments, Jelix Ventures and Mercurian, as well as from a number of individual investors. Founded by Juliette Murphy and Ryan Prosser, FloodMapp combines big data analytics and machine learning techniques with traditional hydrology and hydraulic modelling approaches. The tech measures river height and rainfall data in real-time, and uses underlying elevation and topography to predict how and where water will flow over the land, Murphy tells StartupSmart. This allows the team to "predict a map of the inundated areas" and share that data with third parties.

With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on encrypted data without ever decrypting it. However, the most critical obstacle in deploying FHE at large-scale is the enormous computation overhead. In this paper, we present HEAX, a novel hardware architecture for FHE that achieves unprecedented performance improvement. HEAX leverages multiple levels of parallelism, ranging from ciphertext-level to fine-grained modular arithmetic level. Our first contribution is a new highly-parallelizable architecture for number-theoretic transform (NTT) which can be of independent interest as NTT is frequently used in many lattice-based cryptography systems. Building on top of NTT engine, we design a novel architecture for computation on homomorphically encrypted data. We also introduce several techniques to enable an end-to-end, fully pipelined design as well as reducing on-chip memory consumption. Our implementation on reconfigurable hardware demonstrates 164-268x performance improvement for a wide range of FHE parameters.