{"product_id":"the-new-fire-isbn-9780262548489","title":"The New Fire","description":"\u003cb\u003eArtificial intelligence is revolutionizing the world. Two AI policy experts reveal how democracies can come out on top—sharing cutting-edge research and insights from the intersection of AI and geopolitics.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eArtificial intelligence is revolutionizing the modern world. It is ubiquitous—in our homes and offices, in the present and most certainly in the future. Today, we encounter AI as our distant ancestors once encountered fire. If we manage AI well, it will become a force for good, lighting the way to many transformative inventions. If we deploy it thoughtlessly, it will advance beyond our control. If we wield it for destruction, it will fan the flames of a new kind of war, one that holds democracy in the balance. As AI policy experts Ben Buchanan and Andrew Imbrie show in \u003ci\u003eThe New Fire\u003c\/i\u003e, few choices are more urgent—or more fascinating—than how we harness this technology and for what purpose.\u003cbr\u003e\u003cbr\u003eThe new fire has three sparks: data, algorithms, and computing power. These components fuel viral disinformation campaigns, new hacking tools, and military weapons that once seemed like science fiction. To autocrats, AI offers the prospect of centralized control at home and asymmetric advantages in combat. It is easy to assume that democracies, bound by ethical constraints and disjointed in their approach, will be unable to keep up. But such a dystopia is hardly preordained. Combining an incisive understanding of technology with shrewd geopolitical analysis, Buchanan and Imbrie show how AI can work for democracy. With the right approach, technology need not favor tyranny.Introduction 1\u003cbr\u003eI Ignition \u003cbr\u003e1 Data 13\u003cbr\u003e2 Algorithm 33\u003cbr\u003e3 Compute 59\u003cbr\u003e4 Failure 83\u003cbr\u003eII Fuel\u003cbr\u003e5 Inventing 107\u003cbr\u003e6 Killing 135\u003cbr\u003e7 Hacking 157\u003cbr\u003e8 Lying 183\u003cbr\u003eIII Wildfire \u003cbr\u003e9 Fear 211\u003cbr\u003e10 Hope 231\u003cbr\u003eAcknowledgments 251\u003cbr\u003eNotes 255\u003cbr\u003eIndex 317\u003cb\u003e\u003cb\u003eBen Buchanan\u003c\/b\u003e\u003c\/b\u003e is on leave from his professorship at Georgetown University to serve in the Biden-Harris Administration as the Assistant Director of the White House Office of Science and Technology Policy. Previously, he was also a Senior Faculty Fellow and Director of the CyberAI Project at the Center for Security and Emerging Technology (CSET) at Georgetown. He is the author of \u003ci\u003eThe Hacker and the State \u003c\/i\u003eand \u003ci\u003eThe Cybersecurity Dilemma\u003c\/i\u003e.  \u003cbr\u003e \u003cbr\u003e\u003cb\u003eAndrew Imbrie\u003c\/b\u003e is a Senior Fellow at CSET. He is currently on leave from Georgetown while serving in the State Department. He is the author of \u003ci\u003ePower on the Precipice\u003c\/i\u003e.\u003cbr\u003e \u003cbr\u003eThe views and opinions expressed in this book are the authors’ alone and do not necessarily represent the views of the US government or Department of State. \u003ci\u003eThe New Fire\u003c\/i\u003e was completed prior to their entry into government serviceBefore the Roman Empire and the Greek philosophers, before records of Chinese dynasties and Hindu kings, there were algorithms.1 Babylonian mathematicians discovered that a series of well-organized and repeatable steps—an algorithm—could be devised to accomplish a particular task, beginning with some input and concluding with some output. If the input and the steps were the same each time, the output would also be the same. This idea was essential to order and structure in mathematics and all that was built upon that foundation. Two plus two will always equal four.\u003cbr\u003e Profound as this insight was on its own, the crux of algorithmic thinking was that different sets of steps could be devised for different tasks, yielding different results even with the same inputs; the answer to the calculation two \u003ci\u003eminus\u003c\/i\u003e two will always be zero. The Babylonians crafted one process for multiplying numbers and an inverse one for dividing them.2 Greek thinkers used more complex algorithms in their quest to find prime numbers. Islamic scholars in the ninth century developed innovative algorithms as they discovered algebra. In the 1800s, Charles Babbage and Ada Lovelace began to imagine what algorithms could do as part of general-purpose computing machines, which had not yet been invented. Lovelace in particular recognized that the numbers in algorithms didn’t have to represent quantities but could represent more abstract concepts; images or sounds, for example, could be converted to a numerical form and then given to a machine.3\u003cbr\u003e The AI advances described in the previous chapter continued this history, first with expert systems and then with machine learning. While AlexNet and other data-centric supervised learning systems attracted interest in machine learning in 2012, and while GANs broadened the conception of what the technology could do, it was the subsequent algorithmic innovation described in this chapter that offered a second vital spark for the new fire. With ever more powerful and more efficient algorithms, AI could accomplish greater feats.4\u003cbr\u003e Most significantly, algorithmic breakthroughs cemented the salience of AI to geopolitics. In changing what the technology could do, these algorithms also changed who cared about it. AI had once primarily been the sphere of technical engineers striving to maximize the performance of their systems. Now, it became the domain both of evangelists, who applied it to notable scientific problems, and of warriors within governments, who aimed to gain a strategic edge over rival nations. The gap between these two worldviews began to widen as it became more obvious just how powerful AI’s algorithms would be.\u003cbr\u003e  \u003cbr\u003e \u003cb\u003eCHAPTER 2\u003c\/b\u003e\u003cbr\u003e 1. This comparison does not include the Xia dynasty, a part of traditional Chinese historiography but one for which there are no contemporaneous records.\u003cbr\u003e 2. In essence, these new operations were new one-step algorithms.\u003cbr\u003e 3. L. F. Menabrea and Ada Augusta Lovelace, \u003ci\u003eSketch of the Analytical Engine Invented by Charles Babbage\u003c\/i\u003e,\u003ci\u003e Esq,\u003c\/i\u003e vol. 3 (1842; repr., London: Scientific Memoirs, 2014).\u003cbr\u003e 4. Danny Hernandez and Tom B. Brown, “Measuring the Algorithmic Efficiency of Neural Networks,” arXiv, May 8, 2020, http:\/\/arxiv.org\/abs\/2005.04305.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46305407664357,"sku":"NP9780262548489","price":27.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262548489.jpg?v=1767740666","url":"https:\/\/k12savings.com\/products\/the-new-fire-isbn-9780262548489","provider":"K12savings","version":"1.0","type":"link"}