Asked at the start of the final 1988 presidential debate whether he would support the death penalty if his wife were raped and murdered, Michael Dukakis, a lifelong opponent of capital punishment, quickly and coolly said no. It was a surprising, deeply personal, and arguably inappropriate question, but in demonstrating an unwavering commitment to his principles, Dukakis had handled it well. "The reporters sensed it instantly," wrote Roger Simon about the scene at the debate immediately after Dukakis gave his response. "Even though the 90-minute debate was only seconds old, they felt it was already over for Dukakis." Dukakis' poll numbers plummeted, his campaign never recovered, and George H. W. Bush became the 41st President of the United States.

Large-scale adoption of technologies such as AI in sectors like healthcare, agriculture and education needs to be backed by a robust data ecosystem, intellectual property rights and collaboration between private players, according to a report by NITI Aayog. The discussion paper on'National Strategy for Artificial Intelligence #AIforALL', released by NITI Aayog on Monday, states that while AI has the potential to provide incremental value to a range of sectors, adoption is still driven from a commercial perspective. It should also be used to create societal impact, it said. Amitabh Kant, CEO, NITI Aayog, said in a tweet that AI should be leveraged to provide quality solutions at scale across education, health, agriculture and efficiency and safety in Smart Cities and Smart Mobility. But there are gaps to be bridged to reap the benefits of AI on a large scale.

Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored. Utilizing this information is a challenge because it is not obvious how to correctly incorporate these censored examples into a model. We study how three categories of loss functions, namely partial likelihood methods, rank methods, and our classification method based on a Wasserstein metric (WM) and the non-parametric Kaplan Meier estimate of the probability density to impute the labels of censored examples, can take advantage of this information. The proposed method allows us to have a model that predict the probability distribution of an event. If a clinician had access to the detailed probability of an event over time this would help in treatment planning. For example, determining if the risk of kidney graft rejection is constant or peaked after some time. Also, we demonstrate that this approach directly optimizes the expected C-index which is the most common evaluation metric for ranking survival models.

Smart factories that use breakthrough technologies to drive efficiencies within production processes and across value chains have captured the attention of manufacturing executives. Digitalization, so the story goes, offers a wide array of advantages. They include predictive maintenance that will reduce downtime through the creation of "digital twins," enhanced quality control, demand-driven production, inventory optimization, reduced energy and material costs, and improved safety and environmental performance. Numerous estimates attempt to quantify the value proposition. Consulting firm McKinsey says the economic impact could be between $1.2 and $3.7 trillion by 2025.

It's the year 2075 and the newest generation doesn't remember life before AI. Even more frightening, they don't know the meaning of personal privacy – at least not in the way their grandparents remember it. Someone is always watching you, whether it be the government, your employer, insurance companies, the bad date you had last week, or some random hacker. Personalized surveillance is just a fact of life now. Nothing lives or dies without being monitored.

Artificial Intelligence is quickly becoming ubiquitous in personal and professional lives in ways we both observe and others we don't see as readily. Artificial Intelligence is used to influence life-changing decisions, such as whether or not you get hired to that dream job, who you will date, and whether or not you'll be approved for a loan for your first home. However, we have little insight into how critical decisions are made with AI. As a result, there is increasing demand (and legislation) to ensure the influence of these technologies is understood. What is it we seek when we ask for explainability in AI, as in the GDPR's Article 22? Explainable by whom and to whom?

Technology is infiltrating dispute resolution at multiple points in the litigation process, from claims portals to online dispute resolution (ODR) platforms to court modernisation. Attendees at this Gazette roundtable discuss the IT and cultural challenges of bringing civil justice online. Tony Guise, director of eARB and eCOURT platforms refers to chapter 43 of Lord Justice Jackson's Review of Civil Litigation Costs: Final Report, which defines effective information technology (IT) as'a central place to which and from which one can find all of the documents relating to a piece of civil litigation'. But in recent years litigation technology has broadened significantly from the document management and case management capability outlined by Lord Justice Jackson, into a confusing collection of online systems and applications that deal with various aspects of dispute resolution. Masood Ahmed, associate professor at the University of Leicester, defines litigation technology as the online processes for dispute resolution.

Digital texts have become an increasingly important source of data for social studies. However, textual data from open platforms are vulnerable to manipulation (e.g., censorship and information inflation), often leading to bias in subsequent empirical analysis. This paper investigates the problem of data distortion in text classification when controlling type I error (a relevant textual message is classified as irrelevant) is the priority. The default classical classification paradigm that minimizes the overall classification error can yield an undesirably large type I error, and data distortion exacerbates this situation. As a solution, we propose the Neyman-Pearson (NP) classification paradigm which minimizes type II error under a user-specified type I error constraint. Theoretically, we show that while the classical oracle (i.e., optimal classifier) cannot be recovered under unknown data distortion even if one has the entire post-distortion population, the NP oracle is unaffected by data distortion and can be recovered under the same condition. Empirically, we illustrate the advantage of NP classification methods in a case study that classifies posts about strikes and corruption published on a leading Chinese blogging platform.

United States and Chinese patent practitioners this week called for considerations to change patent legislation and allow patenting algorithms in the future. They spoke at a 30 May conference of the European Patent Office in Munich on "Patenting Artificial Intelligence." Please login or subscribe to read the full story.

As artificial intelligence (AI) technologies and platforms become integral to advanced operations in nearly every industry, blockchain is inserting itself as a means to enhance AI applications in both form and function. Blockchain has the potential to allow AI technologies to become more collaborative in nature and therefore increase their operating efficiency. Additionally, the potential for bolstered revenue streams is also apparent, as blockchain is projected to grow to $20 billion by 2024 according to Transparency Market Research and the Grand View Research projects the AI market will be worth more than $35 billion by 2025. As previously noted, leaders in the AI landscape are turning to blockchain to finetune various applications. Active tech companies in the markets this week include Gopher Protocol Inc. (OTC:GOPH), Overstock.com