The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

High-performance medicine: the convergence of human and artificial intelligence

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

Artificial Intelligence: the global landscape of ethics guidelines.

The Net Delusion: The Dark Side of Internet Freedom by Evgeny Morozov New York: Public Affairs, 2011 409 pages $16.99 [ILLUSTRATION OMITTED] In January 2010, Secretary of State Hillary Clinton gave a highly touted speech on Internet freedom in which she stated, "The freedom to connect is like the freedom of assembly, only in cyberspace. It allows individuals to get online, come together, and hopefully cooperate. Once you're on the Internet, you don't need to be a tycoon or a rock star to have a huge impact on society." Evgeny Morozov, in his book The Net Delusion, takes great issue with the implication, however, that the so-called "Arab Spring" and "Twitter Revolution" were caused by unfettered access to the Internet. Instead, Morozov, a research academic, provides a cautionary tale about what he argues is any attempt to establish a monocausal relationship to meaningful political change (especially when that single focus is information technology). The book opens with a discussion of cyber-utopianism and Internet-centrism--mind-sets that focus on the positive "emancipatory" aspects of Internet communication while ignoring the downsides. The argument throughout centers on nation-state policy, or lack thereof, that attacks the "wicked" problem of authoritarianism by, as a colleague of mine has dubbed it, "wiring the world." Morozov, expectantly, but importantly, cites the hedonistic world portrayed by Huxley and the "Big Brother" world of Orwell to consider both the proactive and reactive approaches to Internet freedom by authoritarian regimes. Interestingly, he notes that there is often a mix of both. Such regimes certainly use the anonymity and openness of the Internet to spy on their people and shutdown undesirable sites. But there is also a subtle approach that belies the jackboot on the keyboard methodology. While China may be known more for suppressing the Internet and for employing the masses to counter antiregime rhetoric, Russia imposes no formal Internet censorship. It relies on entertainment (porn is specifically cited) to soothe the masses, assuming that given options for political discourse and anything else, most opt for "anything else." Hitler would understand. And in nations where freedom is not widely understood from a western perspective, any bit of additional mindless diversion may be viewed as liberty by the populace. Perhaps most importantly, Morozov rails against social media determinism as driving the end of authoritarianism, labeling it "an intellectually impoverished, lazy way to study the past, understand the present, and predict the future." He does not dismiss the value of Facebook and Twitter to quickly mobilize like-minded individuals. He notes as well that the development of that very like-mindedness is complex and potentially can be manipulated by authoritarian governments using the same Internet freedom. …

The Net Delusion: The Dark Side of Internet Freedom

Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model, trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data via model-based source attribution. We have released multiple full-sized, pretrained versions of CTRL at this https URL.

CTRL: A Conditional Transformer Language Model for Controllable Generation

In recent months a machine learning based free software tool has made it easy to create believable face swaps in videos that leaves few traces of manipulation, in what are known as "deepfake" videos. Scenarios where these realistic fake videos are used to create political distress, blackmail someone or fake terrorism events are easily envisioned. This paper proposes a temporal-aware pipeline to automatically detect deepfake videos. Our system uses a convolutional neural network (CNN) to extract frame-level features. These features are then used to train a recurrent neural network (RNN) that learns to classify if a video has been subject to manipulation or not. We evaluate our method against a large set of deepfake videos collected from multiple video websites. We show how our system can achieve competitive results in this task while using a simple architecture.

Deepfake Video Detection Using Recurrent Neural Networks

Artificial intelligence and machine learning capabilities are growing at an unprecedented rate. These technologies have many widely beneficial applications, ranging from machine translation to medical image analysis. Countless more such applications are being developed and can be expected over the long term. Less attention has historically been paid to the ways in which artificial intelligence can be used maliciously. This report surveys the landscape of potential security threats from malicious uses of artificial intelligence technologies, and proposes ways to better forecast, prevent, and mitigate these threats. We analyze, but do not conclusively resolve, the question of what the long-term equilibrium between attackers and defenders will be. We focus instead on what sorts of attacks we are likely to see soon if adequate defenses are not developed.

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

The present debate over the creation and potential deployment of lethal autonomous weapons, or ‘killer robots’, is garnering more and more attention. Much of the argument revolves around whether such machines would be able to uphold the principle of noncombatant immunity. However, much of the present debate fails to take into consideration the practical realties of contemporary armed conflict, particularly generating military objectives and the adherence to a targeting process. This paper argues that we must look to the targeting process if we are to gain a fuller picture of the consequences of creating or fielding lethal autonomous robots. This paper argues that once we look to how militaries actually create military objectives, and thus identify potential targets, we face an additional problem: the Strategic Robot Problem. The ability to create targeting lists using military doctrine and targeting processes is inherently strategic, and handing this capability over to a machine undermines existing comman...

The Strategic Robot Problem: Lethal Autonomous Weapons in War

Autonomous weapons systems (AWSs) pose many challenges in complex battlefield environments. Previous discussions of them have largely focused on technological or policy issues. In contrast, we focu...

“Trust but Verify”: The Difficulty of Trusting Autonomous Weapons Systems

Introduction: Kant, Global Justice and R2P 1. Kantian Provisional Duties 2. Provisional Protection: R2P as a Provisional Duty 3. Kant's Permissive Laws 4. Permissible Coercion 5. Provisional to Peremptory: Institutionalizing a Duty to Protect 6. Conclusion: R2P and the Real World - Libya and Syria

Global Justice, Kant and the Responsibility to Protect : A Provisional Duty

There needs to be a change in thinking about AI. Those dealing with AI must insist on greater clarity about its definition. If policy makers and other leaders are not clear about what the term mean...

Heather M. Roff

Papers

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

The Strategic Robot Problem: Lethal Autonomous Weapons in War

“Trust but Verify”: The Difficulty of Trusting Autonomous Weapons Systems

Global Justice, Kant and the Responsibility to Protect : A Provisional Duty

The frame problem: The AI “arms race” isn’t one