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The Machine Ethics podcast DeepDive: AI and games

AIHub

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. This first Deepdive episode we talk to Amandine Flachs, Tommy Thompson and Richard Bartle about AI in games, its history, its uses and where it's going. After supporting startups founders for more than 10 years, she is now looking to help game developers create smarter and more human-like game AIs using machine learning. Amandine is still involved in the startup ecosystem as a mentor, venture scout and through her series of live AMAs with early-stage entrepreneurs. She can be found on Twitter @AmandineFlachs.


Explainable AI for B5G/6G: Technical Aspects, Use Cases, and Research Challenges

arXiv.org Artificial Intelligence

When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG), promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.


Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence

arXiv.org Artificial Intelligence

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.


Alphabet's Next Billion-Dollar Business: 10 Industries To Watch - CB Insights Research

#artificialintelligence

Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).


Introduction to Natural Language Processing (NLP)

#artificialintelligence

In this Machine Learning tutorial, we'll build a video game with Unity, TensorFlow and Python. We'll show you how easy it is to add ML-powered intelligence to video games or simulations, and how inference on smartphones is easier than it's ever been: modern, powerful tools like Unity's ML-Agents, Python, and TensorFlow make the complex easy. In this session, we'll build a little smartphone game, train a bot to play it using reinforcement learning, Python, and TensorFlow, and deploy it to a smartphone. We'll show you how easy it is to add ML-powered intelligence to video games or simulations, and how inference on smartphones is easier than it's ever been: modern, powerful tools like Unity's ML-Agents, Python, and TensorFlow make the complex easy. First, we'll spend 10 minutes of the session: Second, we'll spend 10 minutes of the session: Finally, we'll spend the last 10 minutes of the session: This is an engaging, fast-paced, and surprisingly in-depth exploration of how powerful modern game engines can be used for quick, relatively easy, but incredibly powerful state of the art machine learning and training, and how powerful inference on-device is, for mobile AI.


Let's Build A Video Game With Unity and TensorFlow

#artificialintelligence

In this session, we'll build a little smartphone game, train a bot to play it using reinforcement learning, Python, and TensorFlow, and deploy it to a smartphone. We'll show you how easy it is to add ML-powered intelligence to video games or simulations, and how inference on smartphones is easier than it's ever been: modern, powerful tools like Unity's ML-Agents, Python, and TensorFlow make the complex easy. First, we'll spend 10 minutes of the session: * showcasing the absolute basics game engines * creating an arcade game, live on stage * adding some art, to make the game look pretty! Second, we'll spend 10 minutes of the session: * implementing an agent, using Python and TensorFlow, that is rewarded for playing the game * training the agent to play * giving the agent some character Finally, we'll spend the last 10 minutes of the session: * preparing our trained model for deployment onto a smartphone * building the game and optimizing both the gameplay and ML-components for a smartphone * showing the audience the game, running live on a phone! This is an engaging, fast-paced, and surprisingly in-depth exploration of how powerful modern game engines can be used for quick, relatively easy, but incredibly powerful state of the art machine learning and training, and how powerful inference on-device is, for mobile AI.


Adobe Study: 50% Of Customers Say Ads Impact Their Holiday Shopping Decisions - B&T

#artificialintelligence

Through the power of AI, Adobe has today predicted online shopping during the 2019 holiday season (Nov. 1 through Dec. 31) to reach $143.7bn. And according to the research, powered by Adobe Sensei, Adobe's AI and machine learning technology, advertisements will play a significant role in where these dollars are spent. Fifty per cent of consumers state that ads during the holiday shopping season impact their purchasing decisions while email continues to be the most preferred way to get an offer while holiday shopping. Smartphone visits to retail sites from social media have tripled in the past three years from four per cent to 11 per cent. However, visits coming from social platforms result in lower conversions compared to other channels like search or email.


IBM's AI flies back and forth through time in Flappy Bird

ZDNet

The smartphone video game Flappy Bird was removed from smartphones in 2014 by its creator, Dong Nguyen, because it was too addictive. Specifically, International Business Machines scientists this week unveiled research into how machines can continually learn tasks, including playing Flappy Bird, improving over time rather than learning one level of play and stopping at that. Known as lifelong learning, or continuous learning, the area has been studied for decades but remains a formidable research challenge. Aside from offering an important new tool for AI, the work is something of a meditation on what it means for learning to take place both forward and backward in time. Flappy Bird was one of their chief tests.


Talking with machines with Dr. Layla El Asri - Microsoft Research

#artificialintelligence

Humans are unique in their ability to learn from, understand the world through and communicate with language… Or are they? Perhaps not for long, if Dr. Layla El Asri, a Research Manager at Microsoft Research Montreal, has a say in it. She wants you to be able to talk to your machine just like you'd talk to another person. The hard part is getting your machine to understand and talk back to you like it's that other person. Today, Dr. El Asri talks about the particular challenges she and other scientists face in building sophisticated dialogue systems that lay the foundation for talking machines. She also explains how reinforcement learning, in the form of a text game generator called TextWorld, is helping us get there, and relates a fascinating story from more than fifty years ago that reveals some of the safeguards necessary to ensure that when we design machines specifically to pass the Turing test, we design them in an ethical and responsible way. Layla El Asri: In a video game, most of the time you only have a few actions that you can take. You just need to learn when you should go right, when you should go left, when you should go up, when you should go down. But when it comes to dialogue, you need to learn how to make a sentence that is grammatically correct, and then you need to learn how to make a sentence that makes sense in the global context of the dialogue, or a sentence that brings new information in the dialogue that is going to make the person you are talking to satisfied with the sentence. Your action space is just huge because it's not just up/down, right/left, it's all the sentences you could imagine! Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Host: Humans are unique in their ability to learn from, understand the world through and communicate with language… Or are they? Perhaps not for long, if Dr. Layla El Asri, a Research Manager at Microsoft Research Montreal, has a say in it. She wants you to be able to talk to your machine just like you'd talk to another person.


Best pre-Cyber Monday deals: Xbox One X, Beats, Bose, Apple iPad, KitchenAid, Instant Pot, Echo Plus

Mashable

Don't feel bad if you missed out on Black Friday. There are still a lot of great deals out there from top retailers like Amazon, Walmart, Best Buy, and Macy's in the lead up to Cyber Monday on Nov. 26. There are still a number a fantastic deals on electronics like video games, 4K TVs, and headphones. You can save $80 on Bose SoundSport Free truly wireless earbuds, which are priced at $169 at Walmart, while you can also save $101 on the Xbox One X (1TB) console, which is priced at $399 on Amazon. For the kitchen, you can save $30 on the Instant Pot Duo60 (6-quart), which is going for $69.99 at Macy's, while KitchenAid's Artisan mini tilt-head stand mixer is on sale for just $199.99 -- a $130 savings.