Are our bank accounts secure? Are our phone systems secure? These are all questions we ask ourselves on a daily basis and despite much wariness around the safety of technology, when we are in need of help about a delivery or service, we, without much question, hand over personal details to chatbots. Chatbots have been designed to make our lives a little easier, with simple verification questions they can answer common customer service inquiries without the need to sit on hold waiting for an agent. But with the rise of GDPR, it is important for organisations to communicate to customers how the data that we provide Artificial Intelligence (AI) driven chatbots is being used and stored.

When it comes to artificial intelligence (AI), countless conference sessions and seminars have dedicated inconceivable amount of hours asking what-if questions, with terrifying examples from across science fiction acting as the bleak backgrounds. Terminator's Skynet, Agents in The Matrix, and Ava in Ex Machina are just some of the fictional antagonists which have stemmed from humanity's own creations. But one franchise has spent over 50 years diving deeper than its contemporaries to depict scenarios of AI enhancing life, and in some cases not so – and that is Star Trek. Gene Roddenberry's utopic vision of the future has led to some of the most thought-provoking media to come to life. Topics of race and discrimination, death, and morality are some of the cornerstone topics that kept it relevant across multiple iterations for so long.

As AI models and services are used in a growing number of highstakes areas, a consensus is forming around the need for a clearer record of how these models and services are developed to increase trust. Several proposals for higher quality and more consistent AI documentation have emerged to address ethical and legal concerns and general social impacts of such systems. However, there is little published work on how to create this documentation. This is the first work to describe a methodology for creating the form of AI documentation we call FactSheets. We have used this methodology to create useful FactSheets for nearly two dozen models. This paper describes this methodology and shares the insights we have gathered. Within each step of the methodology, we describe the issues to consider and the questions to explore with the relevant people in an organization who will be creating and consuming the AI facts in a FactSheet. This methodology will accelerate the broader adoption of transparent AI documentation.

The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, and that extend to settings with multiple competing events. However, many machine learning methods for survival analysis only consider the standard setting with right-censored data and proportional hazards assumption. The methods that do provide extensions usually address at most a subset of these challenges and often require specialized software that can not be integrated into standard machine learning workflows directly. In this work, we present a very general machine learning framework for time-to-event analysis that uses a data augmentation strategy to reduce complex survival tasks to standard Poisson regression tasks. This reformulation is based on well developed statistical theory. With the proposed approach, any algorithm that can optimize a Poisson (log-)likelihood, such as gradient boosted trees, deep neural networks, model-based boosting and many more can be used in the context of time-to-event analysis. The proposed technique does not require any assumptions with respect to the distribution of event times or the functional shapes of feature and interaction effects. Based on the proposed framework we develop new methods that are competitive with specialized state of the art approaches in terms of accuracy, and versatility, but with comparatively small investments of programming effort or requirements for specialized methodological know-how.

One of the key challenges in automated synthesis planning is to generate diverse and reliable chemical reactions. Many reactions can be naturally represented using graph transformation rules referred broadly to as reaction templates. Using reaction templates enables accurate and interpretable predictions but can suffer from limited coverage of the reaction space. On the other hand, template-free methods can increase the coverage but can be prone to making trivial mistakes and are challenging to interpret. A promising idea for constructing more interpretable template-free models is to model a reaction as a sequence of graph edits of the substrates. We extend this idea to retrosynthesis and scale it up to large datasets. We propose Molecule Edit Graph Attention Network (MEGAN), a template-free neural model that encodes reaction as a sequence of graph edits. We achieve competitive performance on both retrosynthesis and forward synthesis and in particular state-of-the-art top-k accuracy for larger K values. Crucially, the latter shows excellent coverage of the reaction space of our model. In summary, MEGAN brings together the strong elements of template-free and template-based models and can be applied to both retro and forward synthesis tasks.

The future of jobs has been used to justify the major changes to university education announced last week. Fees for courses that, according to the government, lead to jobs with a great future will fall, while those with a poor future will rise. But can the government predict the jobs of the future? And do proposed fee changes match those jobs that will grow? Read more: The government is making'job-ready' degrees cheaper for students – but cutting funding to the same courses In the research I have done on the future of work, several things are clear.

In the last few months, millions of dollars have been stolen from unemployment systems during this time of immense pressure due to coronavirus-related claims. A skilled ring of international fraudsters has been submitting false unemployment claims for individuals that still have steady work. The attackers use previously acquired Personally Identifiable Information (PII) such as social security numbers, addresses, names, phone numbers, and banking account information to trick public officials into accepting the claims. Payouts to these employed people are then redirected to money laundering accomplices who pass the money around to veil the illicit nature of the cash before depositing it into their own accounts. The acquisition of the PII that enabled these attacks, and the pattern of money laundering that financial institutions failed to detect highlight the importance of renewed security.

For years the tech industry, alongside some academics, has attempted to make a case for using "crime predicting" algorithms that, according to its proponents, would make the world a safer place. But experts who study artificial intelligence (AI) warn that reliance on, or blind faith in, any sort of predictive algorithm will only worsen the existing racism that pervades the criminal justice system. A public letter from more than 1,000 artificial intelligence experts from Harvard, MIT, Google, and Microsoft released this week wants to drive the point home. The experts addressed their letter to the Springer publishing company, urging it to cancel its plan to publish a paper in favor of using predictive algorithms for crime detection. The paper claims that the technology can predict the likelihood of an individual committing a crime with "80 percent accuracy" but experts say this technology will only help the "tech-to-prison-pipeline," Motherboard reports.

Artificial Intelligence (AI) is now enjoying massive acceptance from consumers and organisations worldwide. Hence, more and more companies are stepping up their game by adopting Artificial Intelligence into their functionalities. In this article, we will discuss the absolute wins of the year 2019 in terms of breakthrough AI solutions and their impact. Here are some of the AI success stories and top news for the year 2019. In May 2019, Samsung created a system that could transform facial images into a video sequence.

Amazon followed suit a couple of days later putting a temporary, year-long ban on facial recognition contracts with American police departments. Finally, Microsoft said that they, too, would no longer sell facial recognition to American police departments without federal regulation. Details aside, these statements all share the implicit confession of the danger that facial recognition poses to human rights and democracy. This self-containment coming out of Big Tech does not, however, address these very same dangers that exist in the EU. Although these technologies are used within EU member states as well, the decisions from IBM, Amazon and Microsoft only apply to the American context.