The synthesis of string transformation programs from input-output examples utilizes various techniques, all based on an inductive bias that comprises a restricted set of basic operators to be combined. A new algorithm, Transduce, is proposed, which is founded on the construction of abstract transduction grammars and their generalization. We experimentally demonstrate that Transduce can learn positional transformations efficiently from one or two positive examples without inductive bias, achieving a success rate higher than the current state of the art.

In mapping enterprise applications, data mapping remains a fundamental part of integration development, but its time consuming. An increasing number of applications lack naming standards, and nested field structures further add complexity for the integration developers. Once the mapping is done, data transformation is the next challenge for the users since each application expects data to be in a certain format. Also, while building integration flow, developers need to understand the format of the source and target data field and come up with transformation program that can change data from source to target format. The problem of automatic generation of a transformation program through program synthesis paradigm from some specifications has been studied since the early days of Artificial Intelligence (AI). Programming by Example (PBE) is one such kind of technique that targets automatic inferencing of a computer program to accomplish a format or string conversion task from user-provided input and output samples. To learn the correct intent, a diverse set of samples from the user is required. However, there is a possibility that the user fails to provide a diverse set of samples. This can lead to multiple intents or ambiguity in the input and output samples. Hence, PBE systems can get confused in generating the correct intent program. In this paper, we propose a deep neural network based ambiguity prediction model, which analyzes the input-output strings and maps them to a different set of properties responsible for multiple intent. Users can analyze these properties and accordingly can provide new samples or modify existing samples which can help in building a better PBE system for mapping enterprise applications.

A major bottleneck in search-based program synthesis is the exponentially growing search space which makes learning large programs intractable. Humans mitigate this problem by leveraging the compositional nature of the real world: In structured domains, a logical specification can often be decomposed into smaller, complementary solution programs. We show that compositional segmentation can be applied in the programming by examples setting to divide the search for large programs across multiple smaller program synthesis problems. For each example, we search for a decomposition into smaller units which maximizes the reconstruction accuracy in the output under a latent task program. A structural alignment of the constituent parts in the input and output leads to pairwise correspondences used to guide the program synthesis search. In order to align the input/output structures, we make use of the Structure-Mapping Theory (SMT), a formal model of human analogical reasoning which originated in the cognitive sciences. We show that decomposition-driven program synthesis with structural alignment outperforms Inductive Logic Programming (ILP) baselines on string transformation tasks even with minimal knowledge priors. Unlike existing methods, the predictive accuracy of our agent monotonically increases for additional examples and achieves an average time complexity of $\mathcal{O}(m)$ in the number $m$ of partial programs for highly structured domains such as strings. We extend this method to the complex setting of visual reasoning in the Abstraction and Reasoning Corpus (ARC) for which ILP methods were previously infeasible.

Is AI driving digital transformation? Or is digital transformation driving the adoption of AI? Are they synergistic, or just getting in the way of each other? Numerous studies show that large organizations are realizing only limited success in digital transformation projects. For example, BCG and McKinsey research have put success rates in the 30% range. However, it is still unclear whether or not that acceleration is leading to the success of transformation efforts beyond the ability to work remotely. In many cases, acceleration has simply meant getting a program initiated and deployed quickly, but at the cost of increased technical debt and less attention to foundational processes and data quality.

Let's face it: AI investments are just that -- investments, where substantial costs in people, infrastructure, data (collection, hygiene), processes and tools are needed to realize the benefits of the AI deployment, be they efficiency, cost reduction, or revenue and growth. Universally, AI is recognized as a complex and often costly undertaking. For those reasons, AI is considered, but often not adopted, as a key lever in larger business transformation programs. Gartner, McKinsey and others have found that anywhere between 40% to 50% of enterprises planned to deploy or have deployed AI technologies by the end of 2020. This AI adoption curve includes companies undertaking "proofs of concept" as well as those deploying AI on use cases "at scale."

Believing that your company has become digital because it makes its content available online, use data analytics a little bit more than it did historically, or because your IT department decided to move from owning its own servers to cloud services are great steps but not enough. Putting a digital lipstick is a (quick) win but it does not confer any competitive advantage to your organization and what's more, this false sense of digitisation may unfortunately delay a required core transformation. The typical digital usages inside an SME revolve around the automation for logistics, procurement, HR, general services, and part of sales. Digitisation is about reshaping every element of your business model, including what you produce, how you produce, how you buy, how you sell, your relationship with customers, suppliers, and other partners, as well as you manage the company internally. A digital SME is not necessarily an organization using the latest Artificial Intelligence (AI) tools.

Robotic Process Automation (RP A) is a technology for automating repetitive routines consisting of sequences of user interactions with one or more applications. In order to fully exploit the opportunities opened by RP A, companies need to discover which specific routines may be automated, and how. In this setting, this paper addresses the problem of analyzing User Interaction (UI) logs in order to discover routines where a user transfers data from one spreadsheet or (Web) form to another. The paper maps this problem to that of discovering data transformations by example - a problem for which several techniques are available. The paper shows that a naive application of a state-of-the-art technique for data transformation discovery is computationally inefficient. Accordingly, the paper proposes two optimizations that take advantage of the information in the UI log and the fact that data transfers across applications typically involve copying alphabetic and numeric tokens separately. The proposed approach and its optimizations are evaluated using UI logs that replicate a real-life repetitive data transfer routine.

The Australian Security Intelligence Organisation (ASIO) has a problem, it collects too much data and might miss something. "That's the problem we are dealing with right now, given the threats are at the unprecedented level," recently installed Director-General of Security Mike Burgess said during his 38th day on the job. "There is the potential to miss something, the application of data analytics helps us to reduce the possibility of that being an event." ASIO is currently undertaking an enterprise-wide transformation that it believes will place it "at the forefront of agencies" using artificial intelligence and machine learning, according to its recent annual report. Providing an update on the project, Burgess said the organisation has so far put a new operating structure and model in place, as well as other foundational work subject to further government approvals.

You probably have your budget and plan set for 2019 and are getting ready to wind down. But before you close the books on 2018 and take off for the holidays, there are somethings I want you to think about and be ready for in 2019. I believe that CIO, IT leaders, and digital transformation leaders may hit some speedbumps in 2019. In my recent post, five digital predictions for 2019 I suggested, "While some technologies and transformation programs will see increases in 2019, there will be strong culture and financial headwinds that will challenge CIO, CDO, CEO, Boards and leaders to defend their investments." In this post, I'd like to share some recommendations on things to do next year to maximize your chance of hitting your transformational goals.

Applying machine learning to the Virgin Velocity Frequent Flyers program has already seen communication effectiveness increase by 10 per cent and given teams the ability to apply advanced analytics at 10 times the pace, its data analytics chief says. Virgin embarked on a data transformation program a little over 12 months ago, work that's running alongside a wider digital transformation program across the organisation. The emphasis is twofold: Enhance customer experiences across the Velocity customer loyalty member base by improving redemption offers that are personalised and relevant; and lift the team's ability to understand and attribute what communications and digital activities are supporting this quest. As part of this overhaul, the data analytics team adopted DataRobot's machine learning platform to bolster predictive modelling capability. Oliver Rees, general manager of Torque Data, the data analytics arm of Virgin Australia, told CMO the group's application of machine learning is about driving personalised customer experiences, and at pace, by being able to run data analytics and modelling more accurately and faster.