Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.

Albert Einstein's favourite childhood toy that he cherished all his life will go up for sale next month for £46,000 ($60,000). The German game, titled'Perlen Mosaik Spiel' (Mosaic Pearl Game), consists of 520 coloured beads that can be inserted into a punch-hole frame to create patterns. The game shows some general signs of wear and tear, including minor chipping to the title label and'intriguing' pencil markings inside the box, which may represent his first autograph. The board game would have been cherished by the young and gifted physicist, who went on to win the 1921 Nobel Prize in Physics. Seller Bonhams New York describes the set as'a rare and joyous Einstein artefact of museum quality' and'an essential learning tool for the young genius'.

Do you know why I got into SEO? Not many people know this, but I grew up in middle-class America, and I wanted a better life for me and my parents. When I was 16 years old, I worked at a theme park called Knotts Berry Farm where I picked up trash, cleaned restrooms, and swept up vomit every single day. I didn't mind it because that's life and I needed the money. At 16, I realized I was too young to get a high paying job, so I did the next best thing… I started a business.

We see the growth of people analytics at first-hand at Insight222, where we are now working with over 60 global organisations to help them put people analytics at the centre of business. In tandem we have also created a digital learning academy with myHRfuture to upskill HR in digital and analytics. For the last six years I have collated and published a collection of the'best' articles of the preceding 12 months – see 2014, 2015, 2016 2017 and 2018, and following are my choices for the 50 best articles of 2019. Those who have read the previous annual collections may note that the number of articles that make the cut has steadily risen. This is partly down to my inability to prune down to 30 or 20 - although it was hard enough to get it down to 50! Mainly though this recognises the increased number, variety and quality of people analytics and data-driven HR material now being published, which is another indicator of progress in the field. I hope that the articles selected will act as a venerable resource library for those working, researching or interested in the people analytics space. That is certainly the intention. I have arranged the 50 articles into twelve topics: i) Driving business value, ii) the future of work, iii) the future of the HR function, iv) ethics and trust, v) employee experience, vi) strategic workforce planning, vii) ONA, viii) diversity and inclusion, ix) organisational culture, perspectives and case studies from people analytics leaders, x) retention, xi) assessment and xii) getting started, as well as highlighting a few of my own articles from 2019 at the end. I hope you enjoy the articles selected, and if you do, please subscribe to my weekly Digital HR Leaders newsletter. Ultimately, people analytics should be about creating value – for leaders, for managers and for the workforce. So, where better to start than with seven articles that collectively provide insights on how to create value and/or give examples of where organisations have created value from people analytics.

Right this moment's main minds discuss AI with host Byron Reese Hearken to this episode or learn the total transcript at www.VoicesinAI.com Byron Reese: That is Voices in AI dropped at you by GigaOm, and I'm Byron Reese. Right this moment our visitor is Steve Durbin. His principal areas of focus embody technique, info know-how, cybersecurity and the rising safety risk panorama throughout the company and private surroundings. He runs his firm because the managing director, which he has been doing for nearly a decade. Welcome to the present, Steve.

Last week I had the privilege to serve as a panelist at a Northwestern Pritzker School of Law event entitled "Symposium 2020: AI, the New Law Firm Attorney: Artificial Intelligence Entering the Legal Profession." The event's keynote speaker Seyfarth Shaw Chair Emeritus Stephen Poor and our panel explored the growing impact of Artificial Intelligence (AI) tools in the legal industry to help lawyers achieve more by getting out of the repetitive, routine and mundane tasks that lawyers have performed in the past so they can "practice at the top of their license" as Mr. Poor stated. I love the phrase "practice at the top of their license" as all lawyers will need to do more of this – especially as we see the rise of tech intensity as technology plays a bigger role in our professional and personal lives and leading technology like AI is increasingly used by lawyers, law firms and other legal organizations to deliver legal services to their clients. As AI tools seek to automate and perform certain tasks that have been traditionally performed by lawyers, I believe that the Emotional Intelligence or Emotional Quotient (EQ) skills that lawyers use everyday to deliver legal services to their clients will be more important than ever before as stronger EQ skills will help enable lawyers to truly "practice at the top of their license." Since AI, algorithms, machines and technology do not embrace EQ, the proverbial "soft skills" that are often associated with EQ can help lawyers provide even more high-impact/high-value legal counsel to their clients and differentiate their legal services from others.

Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. While many different methods have been proposed, there is a lack of a unifying framework that would lead to state-of-the-art results. Here we develop PathCon, a knowledge graph completion method that harnesses four novel insights to outperform existing methods. PathCon predicts relations between a pair of entities by: (1) Considering the Relational Context of each entity by capturing the relation types adjacent to the entity and modeled through a novel edge-based message passing scheme; (2) Considering the Relational Paths capturing all paths between the two entities; And, (3) adaptively integrating the Relational Context and Relational Path through a learnable attention mechanism. Importantly, (4) in contrast to conventional node-based representations, PathCon represents context and path only using the relation types, which makes it applicable in an inductive setting. Experimental results on knowledge graph benchmarks as well as our newly proposed dataset show that PathCon outperforms state-of-the-art knowledge graph completion methods by a large margin. Finally, PathCon is able to provide interpretable explanations by identifying relations that provide the context and paths that are important for a given predicted relation.

Rosenbaum: This is the video of a machine-learning simulation learning to walk and facing obstacles, and it's there only because I like it. Also, it's a kind of metaphor for me trying to build the CI/CD pipeline. I'm going to be talking about CI/CD for machine learning, which is also being called MLOps. The words are hard, we don't have to really define these things, but we do have to define some other things and we're going to talk about definitions a lot actually. I'm going to start by introducing myself. I'm on the left, this picture is from DevOpsDays Chicago, our mascot is a DevOps Yak. You can come check out the conference. I work for Microsoft on the Azure DevOps team. I come from a developer background, and then, I did a lot of things with DevOps CI/CD and such. I'm not a data scientist, I did some classes on machine learning just so I can get context on this, but I'm coming to this primarily from a developer perspective. I also run another conference, this is a shameless plug, it's DeliveryConf, it's the first year it's happening, it's going to be in Seattle, Washington, on January 21 and 22. You should register for it right now because it's going to be awesome. The first thing I want to do is I want to set an agenda.

Inferring intent from observed behavior has been studied extensively within the frameworks of Bayesian inverse planning and inverse reinforcement learning. These methods infer a goal or reward function that best explains the actions of the observed agent, typically a human demonstrator. Another agent can use this inferred intent to predict, imitate, or assist the human user. However, a central assumption in inverse reinforcement learning is that the demonstrator is close to optimal. While models of suboptimal behavior exist, they typically assume that suboptimal actions are the result of some type of random noise or a known cognitive bias, like temporal inconsistency.

While most agree that artificial intelligence will have a huge impact on society, not everyone is sure whether that impact will be for good or ill. One man who believes that the fears of the doom-and-gloom merchants are groundless is analyst, author and futurologist, Tom Koulopoulos. ZDNet: Tell me about your new book, The Bottomless Cloud? Koulopoulos: Clearly, we are at a point where the noise level around AI has reached an apex – a lot of very big promises along with scepticism and outright fear. What clouds the issue is that we we have extremes at both ends of that spectrum – making promises that are much greater than what we are going to able to live on in the near term and we are painting a picture of a dystopian future that will somehow be the end of humanity as we know it.