Thinking about how important the funnel of a customer is for a company, we appreciate when new tools make the process easier. This is why we built Marketeer and today we are presenting version 3.0 with Marketeer Intelligent CRM. I have been working with artificial intelligence for a while – since the end of the 90s to be exact meanwhile I was running an advertising company. In 2012 I decided to invest time and money into building a new product that would help me make communication easier and effective for my clients. And today, we have it!

Giving up the controls was as breathtakingly simple as touching two turquoise coloured buttons below the steering wheel with both thumbs. A melodious bell chimed, a line of LEDs stretching across the dashboard switched from red to yellow to aqua blue, and the steering wheel withdrew slowly and serenely from my sweaty grasp. But any nervousness I felt stemmed far more from being required to steer a multimillion-euro research vehicle the few kilometres from German car manufacturer Audi's headquarters in Ingolstadt, Bavaria, on to the autobahn, than the fact that "Jack" had now taken over the driving. I had signed a liability waiver before embarking on the road test, which required me to accept the risks of a piloted journey on the autobahn, including possible injury or death. But once Jack was calling the shots, it took remarkably little time to get used to the idea.

A group of researchers from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) have developed a way to train artificial intelligence to read and interpret pathology images. Scientists tested the artificial intelligence (AI) during a competition at the annual International Symposium of Biomedical Imaging, where it was tasked to look for breast cancer in images of lymph nodes. It turns out it can detect breast cancer accurately 92 percent of the time and won in two separate categories during the contest. Andrew Beck from BIDMC says they used the deep learning method, which is commonly used to train AI to recognize speech, images and objects. They fed the machine with hundreds of slides marked to indicate which parts have cancerous cells and which have normal ones.

Silent Hill composer Akira Yamaoka and one half of the two-man studio behind PlayStation-exclusive Sound Shapes, Shaw-Han Liem are scheduled to perform as well. If none of those names make you want to book a flight to Kyoto, Japan maybe, just maybe word that Seaman creator Yoot Saito will be in attendance too. Once again, Indie Megabooth is helping organize the event and it all goes down July 9th and 10th. When our own Jessica Conditt spoke with Indie Megabooth's President and CEO Kelly Wallick last year, Wallick said of BitSummit that it had "a tremendous impact on how not only local developers see their own community, but how the greater international community does as well."

Researchers have developed a series of AI-based systems that can interpret pathology images and identify the presence and absence of metastatic cancer. The AI systems could lead to new and improved diagnostic methods and treatment. A group of researchers from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School in Boston have teamed up to develop new diagnostic methods based on artificial intelligence (AI). Humayun Irshad, PhD research fellow at Harvard Medical School and one of the lead authors on the research, says that their group is using all kinds of different computational methods to improve diagnostic techniques. "We are developing robust and efficient computational methods to improve diagnostic and prognostic assessment of pathological samples," Irshad says.

Machine learning started out as the idea of giving a machine human intelligence. The discipline was originally intertwined with artificial intelligence (AI), as scientists wove together the fields of computer science, mathematics, statistics, probability, expert systems and neural networks. The original benchmark for machine learning and artificial intelligence was the Turing Test, created by British mathematician and computer scientist Alan Turing. "A computer was said to be able to'thin' if a human interrogator could not tell it apart, through conversation, from a human being". Since then, machine learning has been reorganized as a separate field from AI, with the aim of finding solutions to solvable problems using methods based in statistics and probability theory.

Accenture's technology R&D head urges China to scale up smart machine trials at home and abroad, Chen Yingqun and Zhang Xia report. China should step up its efforts to adopt artificial intelligence in its industries to boost the country's economic transformation, according to French technology expert Marc Carrel-Billiard. The development of artificial intelligence is a hot topic in China, he said, especially since the central government unveiled the Made in China 2025 strategy, which largely aims to upgrade the manufacturing industry with high-technology over the next decade. AI refers to machines or systems that can understand, learn and act independently, allowing them to take on cognitive functions otherwise performed by a human, such as problem-solving. Carrel-Billiard said such technology is important due to the shift toward greater connectivity, either through cloud computing or smart networks.

The fact that Catherine - who's learned that her ex-husband Theodore has taken up with Samantha, a honey-voiced Operating System who screens his emails, entertains his fantasies and sends his writing off to publishers - comes off as judgmental is testament to Jonze's filmmaking skills. But it's also proof of how deeply we've internalised the notion that artificial intelligence is an extension of male desires and that, really, few things may be hotter than the hard-to-nail promise of female servitude. As Laurie Penny writes in an April 2016 article in The New Statesman, the issue of whether or not robots are slaves designed to serve their masters or sentient beings with inner lives and autonomous instincts has long paralleled the questions we ask of women in the world. READ MORE: * New Zealand could become first country to use Domino's pizza delivery robot * Drones, self-drive cars and'car butler' in our near future * Professor hopes robots will take over the rehabilitation world * Robots could threaten up to half New Zealand's jobs in next 20 years * Robots fooling humans they love something that can't love them back: AI expert * Self-learning robot escapes Russian facility, disrupts traffic * New robot from Google shows off human-like qualities Robots may take on domestic tasks and give working mothers more time. From Metropolis, the 1927 Fritz Lang classic in which Maria, a cyborg whose sultry ways plunge the city and its workers into chaos (she's later burned at a stake) to Austin Powers: International Man of Mystery, the hit 1997 spy film whose comely fembots are programmed to ensnare the bumbling Powers with his own libido, female robots are often cast as temptresses or destroyers, coincidentally enough, the same roles reserved for flesh-and-blood women.

Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible framework for fully nonparametric hypothesis tests of independence, which can capture any type of departure from independence, including nonlinear associations and multivariate interactions. However, these approaches come with an at least quadratic computational cost in the number of observations, which can be prohibitive in many applications. Arguably, it is exactly in such large-scale datasets that capturing any type of dependence is of interest, so striking a favourable tradeoff between computational efficiency and test performance for kernel independence tests would have a direct impact on their applicability in practice. In this contribution, we provide an extensive study of the use of large-scale kernel approximations in the context of independence testing, contrasting block-based, Nystrom and random Fourier feature approaches. Through a variety of synthetic data experiments, it is demonstrated that our novel large scale methods give comparable performance with existing methods whilst using significantly less computation time and memory.

Artificial intelligence may be the new face of medical diagnostics. For the first time, a flavor of A.I. called deep learning is being implemented in new ultrasound imaging equipment to aid in breast exams and help patients avoid unnecessary biopsies. A new feature in Samsung Medison's ultrasound system uses a deep-learning algorithm to make recommendations about whether a breast abnormality is benign or cancerous. The "S-Detect for Breast" feature is now included in an upgrade to the company's RS80A ultrasound system and is commercially available in parts of Europe, the Middle East and Korea and is pending FDA approval in the U.S., according to PR manager Doug Kim. Deep learning relies on large amounts of data to inform complex decision-making algorithms, has aided in everything from speech and image recognition software to pharmaceutical research.