Overcoming the trade-off between quality, speed and cost in software development with AI

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This is usually as true for the delivery of software as it is for anything else, but mounting pressure to digitally transform and continuously deliver updates has made speed a default requirement for most organisations. This leaves a choice between quality and cost, which often comes down to a decision about testing. Testing--especially unit testing--has been an underappreciated stage in the software delivery lifecycle (SDLC) for decades. It's historically been slow, resource-intensive, and less interesting than the development of new features, which may be why the primary motivation to write unit tests for many developers is external pressures, e.g. Within organisations that enforce code coverage targets, mandated manual testing can feel a lot like being told to eat your vegetables because they're good for you.


Artificial Intelligence and Intellectual Property - CEIPI - University of Strasbourg

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CEIPI is pleased to announce the offering of the 3rd edition of the Advanced Training Program on "Artificial Intelligence and Intellectual Property" that will take place in Strasbourg from 23 to 25 April 2020. This new training follows the very successful editions of past years, gathering a high number of professionals coming from almost all the European countries, and as far as Brazil, Canada, United States, China, India, Malaysia and Japan, and including senior officials from renowned institutions. Artificial Intelligence (AI) and robots have been the subject of science fiction for some time. That fictional future is now a present reality. The regulation of AI's activities is set to become a primary policy issue.


Publications

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Inceoglu I, Thomas G, Chu C, Plans D, Gerbasi A (2018). Leadership behavior and employee well-being: an integrated review and a future research agenda. Lopez D, Brown AW, Plans D. (2019). Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multiagent system. Murphy J, Brewer R, Coll M-P, Plans D, Hall M, Shiu SS, Catmur C, Bird G. (2019).


The Big Discussion: Artificial Intelligence - Part 3

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Field Service organizations need to take a proactive approach to build a platform infrastructure that is capable of capitalizing on AI. This selection is critical to set up early and helps pave the way for the future. Field Service Management platforms that are purpose-built on the cloud and leverage a robust, open API communication structure are best poised to help organizations take advantage of the growing use cases for AI. Anticipating the future needs of the organization and investing appropriately in the current systems is critical. We are just starting to scratch the surface of the potential use cases for AI.


Levels And Limits Of AI

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I recently spoke with the innovation team of a Fortune 50 company about their 2020 initiatives, one of which was artificial intelligence. When I asked what specifically they want to use AI for, an executive replied, "Everything." I pushed a little more asking, "Are there any specific problems that you're seeking AI vendors for?" The reply was something like "We want to use AI in all of our financial services groups." This was particularly unsatisfying considering that the company is a financial services company.


An ASP semantics for Constraints involving Conditional Aggregates

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We elaborate upon the formal foundations of hybrid Answer Set Programming (ASP) and extend its underlying logical framework with aggregate functions over constraint values and variables. This is achieved by introducing the construct of conditional expressions, which allow for considering two alternatives while evaluating constraints. Which alternative is considered is interpretation-dependent and chosen according to an associated condition. We put some emphasis on logic programs with linear constraints and show how common ASP aggregates can be regarded as particular cases of so-called conditional linear constraints. Finally, we introduce a polynomial-size, modular and faithful translation from our framework into regular (condition-free) Constraint ASP, outlining an implementation of conditional aggregates on top of existing hybrid ASP solvers.


AVANTI KUMAR on LinkedIn: Skymind has big plans for AI in ASEAN

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Following the opening of its London office in January this year, artificial intelligence (#AI) technology incubator and investor Skymind Global Ventures (SGV) announced a $800m fund to boost the AI #ecosystems in #Europe and #Asia.


An Indian politician used AI to translate his speech into other languages to reach more voters

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As social media platforms move to crack down on deepfakes and misinformation in the US elections, an Indian politician has used artificial intelligence techniques to make it look like he said things he didn't say, Vice reports. In one version of a campaign video, Manoj Tiwari speaks in English; in the fabricated version, he "speaks" in Haryanvi, a dialect of Hindi. Political communications firm The Ideaz Factory told Vice it was working with Tiwari's Bharatiya Janata Party to create "positive campaigns" using the same technology used in deepfake videos, and dubbed in an actor's voice to read the script in Haryanvi. "We used a'lip-sync' deepfake algorithm and trained it with speeches of Manoj Tiwari to translate audio sounds into basic mouth shapes," Sagar Vishnoi of The Ideaz Factory said, adding that it allowed the candidate to target voters he might not have otherwise been able to reach as directly (while India has two official languages, Hindi and English, some Indian states have their own languages and there are hundreds of various dialects). The faked video reached about 15 million people in India, according to Vice.


Why algorithms can be racist and sexist

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Humans are error-prone and biased, but that doesn't mean that algorithms are necessarily better. Still, the tech is already making important decisions about your life and potentially ruling over which political advertisements you see, how your application to your dream job is screened, how police officers are deployed in your neighborhood, and even predicting your home's risk of fire. But these systems can be biased based on who builds them, how they're developed, and how they're ultimately used. This is commonly known as algorithmic bias. It's tough to figure out exactly how systems might be susceptible to algorithmic bias, especially since this technology often operates in a corporate black box.


12-Hour Machine Learning Challenge: Build & deploy an app with Streamlit and DevOps tools - KDnuggets

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TL;DR --In this article, I want to share my learnings, process, tools, and frameworks for completing a 12-hour ML challenge. I hope you can find it useful for your personal or professional projects. Disclaimer: this is not sponsored by Streamlit, any of the tools I mention, nor any of the firms I work for. Follow me on Medium, LinkedIn, or Twitter. It used to be the time of the year when I hung out with my wife and puppy on the couch and binge-watched movies and shows.