A fast performing, dictionary-based tagger [1] constitutes EXTRACT's core. The tagger relies on a set of dictionaries that map biological names to corresponding terms in biological ontologies, or to pertinent records in public biological databases.

IBM Zurich researchers have developed a generic artificial-intelligence preprocessing building block for accelerating Big Data machine learning algorithms by at least 10 times over existing methods. The approach, which IBM presented Monday (Dec. "Our motivation was how to use hardware accelerators, such as GPUs [graphic processing units] and FPGAs [field-programmable gate arrays], when they do not have enough memory to hold all the data points" for Big Data machine learning, IBM Zurich collaborator Celestine Dünner, co-inventor of the algorithm, told EE Times in advance of the announcement. "To the best of our knowledge, we are first to have generic solution with a 10x speedup," said co-inventor Thomas Parnell, an IBM Zurich mathematician. "Specifically, for traditional, linear machine learning models -- which are widely used for data sets that are too big for neural networks to train on -- we have implemented the techniques on the best reference schemes and demonstrated a minimum of a 10x speedup."

A virtual assistant, Alisa, is throwing its name in the hat to run against incumbent Vladmir Putin in the 2018 Russian presidential elections. First, let's just dismiss this idea as stupid. Okay, now let's give it a second look – because it actually makes a lot of sense. Maybe it's time to, academically at least, consider alternative political systems based on more logical and rational thought processes – like one that could elect Russia's Alisa. So far, the virtual assistant has over 80,000 "votes" from citizens requesting a place for it on the ballot next year.

Do you see an image yet?" he asks me. Ahh, yes, now I see it," I reply nervously. A minute ago I was on a real road, but now I'm rolling down a fake forested highway in a simulation created by Ubisoft. Meanwhile, Renault's Level 4 autonomous system has taken the piloting chores (with a professional, joystick-equipped driver backing it up in the passenger seat). It's a bizarre experience, but I don't feel sick, because the Symbioz is transmitting real road motion to the headset.

Fortunately, as Hawking pointed out, some European policymakers have already begun legislative work on this front. In February of this year, the European Parliament adopted a resolution calling for the establishment of new rules governing AI and robotics. We are asking the European Commission to propose measures that will maximize the economic benefits of these technologies, while also guaranteeing a standard level of safety and security. Although I disagree with some of the proposals currently on offer, the fact that we are at least having a debate on the matter is a positive development.

IBM researchers have developed a program that can predict the products of organic chemistry reactions.1 Modelled on the latest language translation systems – like Google's artificial neural network – the AI picked the right product 80% of the time despite not having been taught any organic chemistry rules. 'What this tool is trying to do is imitate a top pro chemist in more or less the entire domain of organic chemistry,' says Teodoro Laino, one of the researchers involved in the study at IBM in Zurich, Switzerland. His ambitious goal is shared by other chemists who have been attempting to create a functioning AI chemist since the 1970s, when organic chemist E J Corey kick-started the field by creating a chemical knowledge database. However, making a tool based on chemistry knowledge can be time-consuming; Bartosz Grzybowski's team took 10 years to encode their Chematica retrosynthesis program with 20,000 chemical rules. Moreover, a knowledge-based AI has difficulty tackling reactions that lie outside of its rule set. 'There's a way to learn organic chemistry that's not memorising chemical rules, by just trying to find out the underlying patterns in reactions and trying to rationalise them,' Laino says, explaining the approach that his team took.

There is a chip implanted in my hand. Two years ago, a tattooed man backstage at a Berlin tech show used a syringe to insert the chip. The process was no more painful than having my ear pierced, and just as quick. The chip was the size of a grain of rice and gave me a new way to control my phone, but if I'm honest, I underwent the procedure purely for journalistic reasons. Yes, I'll do just about anything for a story.

Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, M.D., Ph.D., from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images. The researchers found that the AUC of the DLS for referable diabetic retinopathy was 0.936, and sensitivity and specificity were 90.5 and 91.6 percent, respectively.

These and many other insights are from the Salesforce Fourth Annual State of Marketing - Marketing Embraces the AI Revolution published last week. The report is available for download here (50 pp., PDF, no opt-in). The survey is based on interviews with 3,500 marketers worldwide conducted by Salesforce Research through a third-party survey firm in April 2017. The 3,500 respondents are full-time marketing leaders in Australia, New Zealand, Brazil, Canada, France, Germany, Japan, Netherlands, U.K., Ireland and U.S. Respondents were segmented into high-performing, moderate-performing or under-performing groups. High-performing organizations are defined as those who are "extremely satisfied" with the current outcomes realized as a direct result of their company's marketing investment.

Many in Russia, in fact, seem to prefer their AI helpers with sass and a dark sense of humor. The Moscow-based tech giant Yandex launched a Russian-speaking personal assistant called Alice this October (pictured above, sans snark). And unlike Siri or Alexa, the program relies less on scripted responses than on what it's learned by consuming conversational data mined from the Web, news articles, and even a little Russian literature. As a result, Alice can respond to a much wider range of queries. However, some of program's responses can be a bit surprising.