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AI is already part of our lives even though we might not realise it. It is in our phones, filtering spam, identifying Facebook friends, and classifying our images on Instagram. It is in our homes in the form of Siri, Alexa and other AI assistants. It is in our cars and our planes. AI is literally everywhere. Artworks generated by AI have won international prizes, and have been sold at auction. But what does AI mean for the world of design? This issue of AD explores the nature of AI, and considers its potential for architecture. But this is no idle speculation. Architects have already started using AI for architectural design and fabrication. Yet – astonishingly – there has been almost no debate about AI within the discipline of architecture so far. Surely, nothing can be more important for the profession of architecture right now. The issue looks at all aspects of AI: its potential to assist architects in designing buildings so that it becomes a form of ‘augmented intelligence’; its capacity to design buildings on its own; and whether AI might open up an extraordinary new chapter in architectural design. Contributors: Refik Anadol; Daniel Bolojan; Alexa Carlson; Sofia Crespo and Feileacan McCormick; Gabriel Esquivel, Jean Jaminet and Shane Bugni; Behnaz Farahi; Theodoros Galanos and Angelos Chronis; Eduard Haiman; Wanyu He; Damjan Jovanovic and Lidija Kljakovic; Immanuel Koh; Maria Kuptsova; Sandra Manninger; Lev Manovich; Achim Menges and Thomas Wortmann; Wolf dPrix, Karolin Schmidbaur and Efilena Baseta; M Casey Rehm; and Hao Zheng and Masoud Akbarzadeh. Featured architects: Alisa Andrasek, Coop Himmelb(l)au, Lifeforms.io, Nonstandardstudio,SPAN, Kyle Steinfeld, Studio Kinch and Xkool Technology.
"In the 21st century, the use of artificial intelligence has expanded outside of the fields ofscience and technology and into the field of the creative arts. The use of generative AI hasdivided artists since its emergence. However, artists willing to embrace new technology in theirpractice have had positive results incorporating AI technology into their work. Refik Anadol, aself-proclaimed AI and “data artist”, incorporates artificial intelligence into his large-scale AIinstallations. Anadol’s series Machine Hallucinations, uses a generative adversarial network withan adaptive discriminator and an original latent space browser program in order to transform amassive visual dataset into what the artist refers to as a “data painting”: a form of AI artwork.Using Anadol’s series as a case study, this dissertation will explore the idea of AI-generatedartwork becoming its own medium of art. The notions of creativity and originality are critical inthe discourse of AI-generated artwork. The scientific and artistic communities have differentnotions of what it means to be creative. Whether or not the artist or the machine is responsiblefor the aspect of originality in an AI-generative work relies heavily on the intention of theartwork and the agency of the author. This question of originality is further exacerbated by theinability to copyright AI-generated work in most countries due to a lack of judicial precedent onthe topic. In another manner, the perception of AI by the general public stands to also be adecisive factor in AI becoming a recognized medium of art. However, the critical issues in thediscourse of AI becoming its own medium, such as image appropriation, the reliance oncomputer technology, and the notion of creativity, bear some resemblance to the issues faced bythe mixed-media, photography, and video mediums in their emergence. This dissertation aims toaddress these critical issues that stand in the way of AI becoming a recognized medium of art." -- Prodided by the Author.
A brilliant new theory of the mind that upends our understanding of how the brain interacts with the world “This thoroughly readable book will convince you that the brain and the world are partners in constructing our understanding.” —Sean Carroll, New York Times bestselling author of The Biggest Ideas in the Universe: Space, Time, and Motion For as long as we’ve studied human cognition, we’ve believed that our senses give us direct access to the world. What we see is what’s really there—or so the thinking goes. But new discoveries in neuroscience and psychology have turned this assumption on its head. What if rather than perceiving reality passively, your mind actively predicts it? Widely acclaimed philosopher and cognitive scientist Andy Clark unpacks this provocative new theory that the brain is a powerful, dynamic prediction engine, mediating our experience of both body and world. From the most mundane experiences to the most sublime, reality as we know it is the complex synthesis of sensory information and expectation. Exploring its fascinating mechanics and remarkable implications for our lives, mental health, and society, Clark nimbly illustrates how the predictive brain sculpts all human experience. Chronic pain and mental illness are shown to involve subtle malfunctions of our unconscious predictions, pointing the way towards more effective, targeted treatments. Under renewed scrutiny, the very boundary between ourselves and the outside world dissolves, showing that we are as entangled with our environments as we are with our onboard memories, thoughts, and feelings. And perception itself is revealed to be something of a controlled hallucination. Unveiling the extraordinary explanatory power of the predictive brain, The Experience Machine is a mesmerizing window onto one of the most significant developments in our understanding of the mind.
Walmsley offers a succinct introduction to major philosophical issues in artificial intelligence for advanced students of philosophy of mind, cognitive science and psychology. Whilst covering essential topics, it also provides the student with the chance to engage with cutting edge debates.
A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.
A landmark insider’s tour of how social media affects our decision-making and shapes our world in ways both useful and dangerous, with critical insights into the social media trends of the 2020 election and beyond “The book might be described as prophetic. . . . At least two of Aral’s three predictions have come to fruition.”—New York NAMED ONE OF THE BEST BOOKS OF THE YEAR BY WIRED • LONGLISTED FOR THE PORCHLIGHT BUSINESS BOOK AWARD Social media connected the world—and gave rise to fake news and increasing polarization. It is paramount, MIT professor Sinan Aral says, that we recognize the outsize effect social media has on us—on our politics, our economy, and even our personal health—in order to steer today’s social technology toward its great promise while avoiding the ways it can pull us apart. Drawing on decades of his own research and business experience, Aral goes under the hood of the most powerful social networks to tackle the critical question of just how much social media actually shapes our choices, for better or worse. He shows how the tech behind social media offers the same set of behavior influencing levers to everyone who hopes to change the way we think and act—from Russian hackers to brand marketers—which is why its consequences affect everything from elections to business, dating to health. Along the way, he covers a wide array of topics, including how network effects fuel Twitter’s and Facebook’s massive growth, the neuroscience of how social media affects our brains, the real consequences of fake news, the power of social ratings, and the impact of social media on our kids. In mapping out strategies for being more thoughtful consumers of social media, The Hype Machine offers the definitive guide to understanding and harnessing for good the technology that has redefined our world overnight.
The use of artificial intelligence (AI) in various fields is of major importance to improve the use of resourses and time. This book provides an analysis of how AI is used in both the medical field and beyond. Topics that will be covered are bioinformatics, biostatistics, dentistry, diagnosis and prognosis, smart materials, and drug discovery as they intersect with AI. Also, an outlook of the future of an AI-assisted society will be explored.
The special issue of c't KI-Praxis provides tests and practical instructions for working with chatbots. It explains why language models make mistakes and how they can be minimised. This not only helps when you send questions and orders to one of the chatbots offered online. If you do not want to or are not allowed to use the cloud services for data protection reasons, for example, you can also set up your own voice AI. The c't editorial team explains where to find a suitable voice model, how to host it locally and which service providers can host it. The fact that generative AI is becoming increasingly productive harbours both opportunities and risks. Suitable rules for the use of AI in schools, training and at work help to exploit opportunities and minimise risks.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.