Below is a list of the topics I am planning to cover. Note that while these topics are numerated by lectures, note that some lectures are longer or shorter than others. Also, we may skip over certain topics in favor of others if time is a concern. While this section provides an overview of potential topics to be covered, the actual topics will be listed in the course calendar.
AI and Games is a crowdfunded series about research and applications of artificial intelligence in video games. If you like my work please consider supporting the show over on Patreon for early-access and behind-the-scenes updates. 'The AI of Sea of Thieves' is released in association with the UKIE's '30 Years of Play' programme: celebrating the past, present and future of the UK interactive entertainment industry. Welcome to part 3 of the AI of Sea of Thieves here on AI and Games. In parts 1 & 2 I looked at how Rare's online pirate game balances the AI systems at play across each server plus how skeleton and shark AI are built to keep players on their toes both on land and in the water.
Join us for a cyber security community event with your fellow Check Point customers at the Croton Reservoir Tavern in Manhattan on 19th September 2019 from 11am to 2pm Eastern Time! We guarantee that you'll leave this event having learned something new that you can use to improve your security posture. In addition, this event provides an opportunity to meet your fellow Check Point customers in the region.
Artificial intelligence is a bit like a car. If you don't fill up the tank, it's not going to go anywhere. Artificial intelligence is fueled by data; therefore, it needs to be fed a consistent amount of data or else it's not going to learn or be effective. Artificial intelligence is about teaching the computer to do something for you. It takes a monumental amount of information and data to build models and teach the computer to learn on its own.
Conversations with customers have become the need of the hour for businesses. Now we are witnessing a paradigm shift from mass-centered to granular, account-based approach. Banks and other financial institutions, who work closely with customers and rely heavily on customer relationships, have always leveraged technology to assist them. First, it was internet banking in the late 90s, then mobile banking when the smartphone revolution took over the world. Now, with the advent of AI and machine cognizance, conversational banking is on the rise. Conversational banking is nothing but communication between a bank and its customer through text, voice or visual interface. It adds that extra touch of personalization in customer relationships. Conversational banking, though highly effective, comes with the hardship of effective implementation given the sheer volume of customers banks serve (or any B2C business for that matter). That is why AI becomes extremely important in conversational banking.
Explainable machine learning seeks to provide various stakeholders with insights into model behavior via feature importance scores, counterfactual explanations, and influential samples, among other techniques. Recent advances in this line of work, however, have gone without surveys of how organizations are using these techniques in practice. This study explores how organizations view and use explainability for stakeholder consumption. We find that the majority of deployments are not for end users affected by the model but for machine learning engineers, who use explainability to debug the model itself. There is a gap between explainability in practice and the goal of public transparency, since explanations primarily serve internal stakeholders rather than external ones.
Seemingly, one of the most controversial things about Tesla cars is its Autopilot feature, a driver-assist feature that helps drivers navigate and pilot their vehicle. Oddly, while news of exciting Autopilot features comes out regularly, general information about exactly what Autopilot is, what the options are, and what it can and cannot do seem to be few and far between. I have tried to collect and answer the biggest questions about Autopilot below to help prospective buyers know what the system is and is not, as well as to inform journalists about the system in case they find themselves trying to cover a news story regarding the system. When the next questionable news story comes out, please feel free to link this article for anyone wondering about the system. Please note that all of the below information refers to Tesla vehicles containing Autopilot 2.0 hardware or higher in them (vehicles built since October of 2016). Although, the majority of the information will apply to all Tesla vehicles that are Autopilot enabled.
Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed.
This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. The tutorial was organized by Matthew Peters, Swabha Swayamdipta, Thomas Wolf, and me. In this post, I highlight key insights and takeaways and provide updates based on recent work. The slides, a Colaboratory notebook, and code of the tutorial are available online. For an overview of what transfer learning is, have a look at this blog post. Transfer learning is a means to extract knowledge from a source setting and apply it to a different target setting. In the span of little more than a year, transfer learning in the form of pretrained language models has become ubiquitous in NLP and has contributed to the state of the art on a wide range of tasks.
This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. The tutorial was organized by Matthew Peters, Swabha Swayamdipta, Thomas Wolf, and me. In this post, I highlight key insights and takeaways and provide updates based on recent work. The slides, a Colaboratory notebook, and code of the tutorial are available online. For an overview of what transfer learning is, have a look at this blog post. In the span of little more than a year, transfer learning in the form of pretrained language models has become ubiquitous in NLP and has contributed to the state of the art on a wide range of tasks.