Europe Mulls New Tougher Rules for Artificial Intelligence

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The European Union is considering new legally binding requirements for developers of artificial intelligence in an effort to ensure modern technology is developed and used in an ethical way. The EU's executive arm is set to propose the new rules apply to "high-risk sectors," such as healthcare and transport, and suggest the bloc updates safety and liability laws, according to a draft of a so-called "white paper" on artificial intelligence obtained by Bloomberg. The European Commission is due to unveil the paper in mid-February and the final version is likely to change. The paper is part of the EU's broader effort to catch up to the U.S. and China on advancements in AI, but in a way that promotes European values such as user privacy. While some critics have long argued that stringent data protection laws like the EU's could hinder innovation around AI, EU officials say harmonizing rules across the region will boost development.


Birth of the Next Generation Security Analyst

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In 2017, a provocative article went viral in the cybersecurity industry -- Death of the Tier 1 SOC Analyst by Kelly Jackson Higgins of Dark Reading. In this arresting piece of analysis, Ms. Higgins argued that entry level analyst positions in security operations centers were on track for extinction, thanks to a combination of new technologies, industry skills shortages and the particular joys of a role that consisted of triaging an ever-growing flood of alerts. Instead, she predicted that SOC managers would reorganize responsibilities, apply automation to manage the level 1 work and reallocate human input for higher level investigations. A couple of years down the line, how is that analysis playing out? The industry certainly isn't operating how we used to.


IIT Kharagpur develops AI-powered tech for reading legal cases - Times of India

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KHARAGPUR: Researchers at IIT Kharagpur have evolved an Artificial Intelligence-aided method to automate the reading of legal case judgments, the premier institute said in a statement on Friday. The researchers from IIT Kharagpur's Computer Science and Engineering department have developed two deep neural models to understand the rhetorical roles of sentences in a legal case judgment, which could prove phenomenal in India where AI is yet to sufficiently penetrate the legal field. The country uses a Common Law system that prioritises the doctrine of legal precedent over statutory law, and where legal documents are often written in an unstructured way. "Taking 50 judgments from the Supreme Court of India, we segmented these by first labelling sentences with the help of three senior law students from IIT Kharagpur's Rajiv Gandhi School of Intellectual Property Law, then performing extensive analysis of the human-assigned labels and developing a high quality gold standard corpus to train the machine to carry out the task," explained research lead Professor Saptarshi Ghosh. Unlike earlier attempts which required substantial human intervention, the neural methods used by Ghosh's team enables automatic learning of the features, given sufficient amount of data, and can be used across multiple legal domains.


Top NLP Open Source Projects For Developers In 2020

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The year 2019 was an excellent year for the developers, as almost all industry leaders open-sourced their machine learning tool kits. Open-sourcing not only help the users but also helps the tool itself as developers can contribute and add customisations that serve few complex applications. The benefit is mutual and also helps in accelerating the democratisation of ML. LIGHT (Learning in Interactive Games with Humans and Text) -- a large-scale fantasy text adventure game and research platform for training agents that can both talk and act, interacting either with other models or humans. The game uses natural language that's entirely written by the people who are playing the game.


How Artificial Intelligence is Changing the Transportation Sector

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Customers are growing to expect more from transportation companies. Plans can change last minute, so 24/7 support is a must. The key to travel is saving time, and the last thing a customer wants to do is lose more time after contacting a company for help. With the proper use of customer data, Artificial Intelligence (AI) enhanced customer support can be ready to answer any and all questions before they ever need to be asked.


This Week in AI – Issue #1 Rubik's Code

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Every week we bring to you best research papers, articles and videos that we have found interesting that week. Rubik's Code is a boutique data science and software service company with more than 10 years of experience in Machine Learning, Artificial Intelligence & Software development. Check out the services we provide. Eager to learn how to build Deep Learning systems using Tensorflow 2 and Python? Get our'Deep Learning for Programmers' ebook here!


Phenom People raises $30 million for AI recruitment platform used by Microsoft, others

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Phenom People, a human resources (HR) platform that leverages artificial intelligence (AI) to help companies attract new talent, has raised $30 million in a series C round of funding led by WestBridge Capital, with participation from eBay founder Pierre Omidyar's VC firm Omidyar Ventures, AXA Venture Partners, Sierra Ventures, Sigma Prime Ventures, Karlani Capital, and a fund belonging to AllianceBernstein. Founded in 2010, Philadelphia-based Phenom People touts its "talent experience management" (TXM) platform as an all-in-one solution for companies looking to build career websites with personalized job and content recommendations, chatbots, and a content management system (CMS) for pushing fresh content to the site. Numerous startups are leveraging AI and automation to streamline the recruitment process, including New York-based Fetcher, which crunches data to proactively headhunt new candidates; San Francisco's Xor, which uses AI for recruitment and screening; and New York-based Pymetrics, which helps companies carry out candidate assessments through neuroscience games. Phenom People claims some 300 clients around the world, including big-name customers such as Microsoft, which uses the Phenom People platform to power its career portal. "Microsoft is an excellent example of a leading technology organization working to revolutionize candidate experience through AI-driven experiences," said Phenom People CEO and cofounder Mahe Bayireddi in an earlier statement.


The rapid growth in artificial intelligence in Japan

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Current rapid growth in artificial intelligence (AI) is fuelled predominantly by the rediscovery of deep learning. When it first surfaced 15 years ago, the concept was met with unfavourable conditions and largely abandoned in the aftermath. Nowadays, deep learning is back, circumstances are right and the field flourishes. The described third boom in artificial AI and subsequent tightening technological and economic competition sent ripples through various aspects of the social realm, including policymaking. Many countries began working on national AI strategies, including current leaders – China and U.S. – and Japan also followed suit.


T. Scott Clendaniel on LinkedIn: #AI #ArtificialIntelligence #MachineLearning

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In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from ML theory. The hyper-parameters are knobs that you tweak during each iteration of training a model to improve the accuracy in the predictions made by the model. The hyper-parameters are variables that govern the training process itself.


Importance of Hyper-parameters in Model development

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Machine Learning (ML) development is an iterative process in which the accuracy of predictions made by the models is continuously improved by repeating the training and evaluation phases. In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from ML theory. The hyper-parameters are knobs that you tweak during each iteration of training a model to improve the accuracy in the predictions made by the model.