Earth's oceans are having a rough time right now. They're oily, hot, acidic, full of dead fish--and their levels are rising. But even though these things are true, it can be hard to grok (or muster up the will to care about) the oceans' subtle changes over decades. Every time you go to the beach, everything's still as blue and salty and vast as it ever was. But these changes directly impact human life (just ask the Marshall Islands). So to make the ocean's plight more relatable, a Swedish sustainability group is putting out a message that will hit you where it counts: right in the nerd.

Salesforce has officially unveiled Einstein, a set of artificial intelligence (AI) capabilities it says will help users of its platform serve their customers better. Billing the technology as "AI for everyone", Salesforce is putting Einstein's capabilities into all its clouds, bringing machine learning, deep learning, predictive analytics, and natural language processing into each piece of its customer relationship management platform. In Salesforce's Sales Cloud, for instance, machine learning will power predictive lead scoring, a new tool that can analyse all data related to leads -- including standard and custom fields, activity data from sales reps, and behavioural activity from prospects -- to generate a predictive score for each lead. The models will continuously improve over time by learning from signals like lead source, industry, job title, Web clicks and emails. Another tool will analyse CRM data combined with customer interactions such as inbound emails from prospects to identify buying signals earlier in the sales process and recommend next steps to increase the sales rep's ability to close a deal.

The live-action adaptation of "Ghost in the Shell" starring Scarlett Johansson as the cyberpunk-fighting cyborg just dropped its first bit of footage. No doubt manga, anime and genre fans everywhere will have something to say about this brief glimpse of the new dystopia. The first footage from the feature film dropped during the finale of "Mr. Robot," because if a show about hackers taking down evil corporations entertains you, wait until you breathe in the cyberpunk horror that is "Ghost in the Shell." The movie takes place in a fictional, futuristic Japanese city and follows "The Major" (Johansson) and the members of a covert task force within the Japanese National Public Safety Commission made up of former detectives and military operatives.

We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation. Our new point process allows better approximation in application domains where events and intensities accelerate each other with correlated levels of contagion. We generalize a recent algorithm for simulating draws from Hawkes processes whose levels of excitation are stochastic processes, and propose a hybrid Markov chain Monte Carlo approach for model fitting. Our sampling procedure scales linearly with the number of required events and does not require stationarity of the point process. A modular inference procedure consisting of a combination between Gibbs and Metropolis Hastings steps is put forward. We recover expectation maximization as a special case. Our general approach is illustrated for contagion following geometric Brownian motion and exponential Langevin dynamics.

Bibliographic analysis considers author's research areas, the citation network and paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents using a non-parametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. We propose a novel and efficient inference algorithm for the model to explore subsets of research publications from CiteSeerX. Our model demonstrates improved performance in both model fitting and a clustering task compared to several baselines.

Modern humans first left Africa 100,000 years ago in a series of slow-paced migration waves and arrived in southern Europe around 80,000-90,000 years ago, far earlier than previously believed, according to a new study. The research suggests that humans spread out across the globe in four migration events driven by climate change, connected to variations in the Earth's orbit. The results challenge traditional models that suggest there was a single exodus out of Africa around 60,000 years ago. Chris Stringer, Research Leader in Human Origins at the Natural History Museum London told MailOnline the research is'the most comprehensive climate, vegetation and human-dispersal modelling study published so far'. 'While the earliest [migration] wave had only limited further penetration across the rest of Eurasia, they [the researchers] argue that modern humans could have arrived in small numbers in China and southern Europe by about 80,000 years,' he explained.

The new way of thinking about AI is to see it doing time-consuming tasks, freeing up space for accountants to do the serious thinking and to exercise professional judgement on more complex matters. Despite some people's fears, AI's advocates say it can be a job-creator, not a job-killer. For years, there have been fears that Artificial Intelligence (AI) – smart machines that work and react like humans while having self-learning capabilities – will redefine the role of accountants. Now innovative firms are investing in AI so they can be at the forefront of cognitive technologies. What does the evolution from automation and data-analytics software to AI mean for accountants?

If you need an example of one of the many ways artificial intelligence is transforming our lives, look no further than research labs. These days, the problems we expect scientists to tackle, and the discoveries they're called on to make, are more complex than ever. Even the best and brightest need help now and then. So, in 2009, scientists at Aberystwyth University in Wales created Adam, an artificial-intelligence program that functions as a robotic biologist. Adam was first tested on yeast.

Artificial intelligence (AI) is steadily growing more sophisticated, particularly in regards to advanced decision making capabilities, making it increasingly appealing to corporations. A survey from Narrative Science shows that 62 per cent of organisations will be using AI technologies by 2018, while Gartner is predicting that three million employees worldwide will be supervised by AI in 2018. The question that remains is what will this transformation look like? While computers are capable of making intelligent decisions about financial trading, medical diagnoses and even flying planes or driving cars, several factors have kept businesses from adopting AI broadly. One of these is accessibility.

Bibliographic analysis considers the author's research areas, the citation network and the paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a nonparametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. This gives rise to the Citation Network Topic Model (CNTM). We propose a novel and efficient inference algorithm for the CNTM to explore subsets of research publications from CiteSeerX. The publication datasets are organised into three corpora, totalling to about 168k publications with about 62k authors. The queried datasets are made available online. In three publicly available corpora in addition to the queried datasets, our proposed model demonstrates an improved performance in both model fitting and document clustering, compared to several baselines. Moreover, our model allows extraction of additional useful knowledge from the corpora, such as the visualisation of the author-topics network. Additionally, we propose a simple method to incorporate supervision into topic modelling to achieve further improvement on the clustering task.