More than anything, Star Wars: Episode 1 - The Phantom Menace is a fascinating cultural object. It's been 25 years since I saw the film in theaters, and over a decade since I last rewatched it (in a vain attempt to help my Trekkie wife catch up to the prequels). I've had enough time to process the initial disappointment and embarrassment of introducing my wife to Jar Jar Binks. So when Disney announced it was bringing the prequel trilogy back to theaters, I was practically giddy about revisiting them to see how George Lucas's final films compared to the onslaught of Star Wars media we've experienced over the past decade. Was The Phantom Menace as bad as I'd remembered?

SingularityNET Insider Monthly is a monthly stream in which you can learn about the latest developments behind the scenes at the foundation. Join us to learn more about how we are working towards our Phase 2 goals in order to build the world's largest decentralized AI network and drive massive platform utilization.

There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications. Meanwhile, there is still a lack of academic NLP benchmarks that are applicable for realistic CL settings, which is a major challenge for the advancement of the field. In this paper we discuss some of the unrealistic data characteristics of public datasets, study the challenges of realistic single-task continuous learning as well as the effectiveness of data rehearsal as a way to mitigate accuracy loss. We construct a CL NER dataset from an existing publicly available dataset and release it along with the code to the research community.

If you would like to understand the RL, Q-learning, and key terms please read Part 1. In this part, we will implement a simple example of Q learning using the R programming language from scratch. It is expected from you to understand the basics of R programming and complete the reading of Part 1 of this article. We are coding the algorithms using the R base package only however we would need a few libraries to plot the various matrix and visualize the output. The below function will create a plot of any R matrix using the plot.matrix

Shankar Radhakrishnan, Founder of Skedler, recently sat down with Bharat Kandanoor to discuss the use of Artificial Intelligence (AI) in cybersecurity. Bharat, who is the Technology Head for cybersecurity and cloud at Blue Ally, a managed service provider, was able to shed light on the intricacies of AI's usage in cybersecurity processes. Let's dive deep into understanding whether AI is an overhyped cybersecurity solution, how it is being used to tackle network security problems, and how AI may be able to create a better cybersecurity future for the end user. Is this level of AI adoption a response to measurable cyber threats that AI can help to remediate or is it merely an overhyped reach by firms around the world? Bharat Kandanoor tells us in our exclusive one-on-one video podcast that "Artificial Intelligence is being used as an overhyped terminology in general."

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EdgeTier, an Irish start-up using artificial intelligence (AI) to improve customer service, has raised €1.5 million in a new funding round. The investment is to be used to to increase research and development efforts in its AI assistant known as Arthur, and for further international expansion. Arthur uses AI, analytics and automation to guide call centre staff through complex customer queries, resulting in speedier and more accurate responses. Founded in 2015 by Shane Lynn, Bert Lehane and Ciarán Tobin, the company recently edged out rival start-ups Webio, Data Chemist, VRAI and UrbanFox to win Enterprise Ireland's'digital disruptor' award. Last year the company also won'best start-up' at AI Ireland's awards and'best use of data science in a start-up' at the DatSci awards.

We explore the behavior of a standard convolutional neural net in a setting that introduces classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks. This setting corresponds to that which human learners face as they acquire domain expertise, for example, as an individual reads a textbook chapter-by-chapter. Through simulations involving sequences of ten related tasks, we find reason for optimism that nets will scale well as they advance from having a single skill to becoming domain experts. We observed two key phenomena. First, _forward facilitation_---the accelerated learning of task $n+1$ having learned $n$ previous tasks---grows with $n$. Second, _backward interference_---the forgetting of the $n$ previous tasks when learning task $n+1$---diminishes with $n$. Amplifying forward facilitation is the goal of research on metalearning, and attenuating backward interference is the goal of research on catastrophic forgetting. We find that both of these goals are attained simply through broader exposure to a domain.

Building agents to interact with the web would allow for significant improvements in knowledge understanding and representation learning. However, web navigation tasks are difficult for current deep reinforcement learning (RL) models due to the large discrete action space and the varying number of actions between the states. In this work, we introduce DOM-Q-NET, a novel architecture for RL-based web navigation to address both of these problems. It parametrizes Q functions with separate networks for different action categories: clicking a DOM element and typing a string input. Our model utilizes a graph neural network to represent the tree-structured HTML of a standard web page. We demonstrate the capabilities of our model on the MiniWoB environment where we can match or outperform existing work without the use of expert demonstrations. Furthermore, we show 2x improvements in sample efficiency when training in the multi-task setting, allowing our model to transfer learned behaviours across tasks.

We're so excited to announce our first guest: Ellen Broad. Ellen is a renowned expert in the field of data ethics and AI. She has a wealth of knowledge and experience, having worked around the world advising governments and corporates on data sharing, open data, strategy and licensing. Ellen's writing has appeared in the New Scientist, the Guardian and a range of civil service and tech publications. She has spoken about AI and data to ABC Radio National's'Big Ideas' and'Future Tense' programmes, and at SXSW in the US Ellen's recently published first book Made by Humans: The AI Condition, analyses our role and responsibilities in automation and how machine learning is affecting our decisions.