Insilico Medicine will unveil a newly-developed Artificial Intelligence (AI) drug discovery engine at the Re-Work Machine Intelligence Summit in Berlin, Germany, to be held June 29-30, 2016. The AI engine is capable of predicting therapeutic use, toxicity, and adverse effects of thousands of molecules. Insilico says that this drug-discovery engine has the potential to transform the pharmaceutical industry and double the number of drugs on the market by "developing multi-modal deep-learned and parametric biomarkers as well as multiple drug-scoring pipelines for drug discovery and drug repurposing, and hypothesis and lead generation." By using AI coupled with a deep understanding of pharmaceutical R&D processes, Insilico hopes to overcome hurdles to drug discovery, such as failure rates due to irreproducible experiments with poor choices of animal models and the inability to translate the results from animal models directly to humans. Up until now, Insilico has dealt mainly with nutraceuticals and cosmetics, signing an exclusive agreement with Life Extension, a vendor of major nutraceutical products, to develop a set of geroprotectors, which are natural products that mimic a young, healthy state in multiple old tissues.

When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook's automated photo tagging to self-driving cars. More recently we've also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results. In this post I'll try to summarize what CNNs are, and how they're used in NLP. The intuitions behind CNNs are somewhat easier to understand for the Computer Vision use case, so I'll start there, and then slowly move towards NLP.

The line between fiction and reality is blurring. Some years ago, driverless cars and drones delivering packages at our doorsteps would have seemed like science fiction. However, these and other new technological advances continue to astound us and make our lives easier. For instance, Dag Kittlaus, who created the virtual assistant Siri, recently showcased another artificial intelligence (AI) platform, Viv, at TechCrunch Disrupt, New York. Based on the demo shown, Viv seems to be a more sophisticated and powerful a virtual assistant.

BOSTON, June 30, 2016 (GLOBE NEWSWIRE) -- indico, an innovator in the machine learning and artificial intelligence space, will make a presentation on deep learning at the Sentiment Analysis Symposium, which takes place in New York, July 12th. Dr. Daniel Kuster, a researcher at indico, will focus on the differences between deep learning and traditional machine learning approaches, and how the advantages of deep learning can be exploited to quickly gain new insights about what people say online, and how they say it. The presentation will take place at Fordham University's Lincoln Center Campus in New York City. Machine learning is becoming the tool of choice for analyzing text and image data. While traditional text processing solutions rely on the ability of experts to encode domain knowledge, machine learning models learn directly from the data.

Kaname Hayashi is known as the "father of Pepper." Hayashi is the "father of Pepper," the charming humanoid robot from Japanese carrier SoftBank Mobile and French company Aldebaran Robotics. Pepper, with its circular doe eyes and welcoming smile, is billed as a robot that can read your emotions. It's available for sale and has even enrolled in school. Like any proud parent whose kids leave home, Hayashi had a void to fill.

More than 30 private companies working to advance artificial intelligence technologies have been acquired in the last 5 years by corporate giants competing in the space, including Google, IBM, Yahoo, Intel, and, more recently, Apple and Salesforce. There have been 5 major acquisitions already in 2016. Google has been the most prominent global player, with 9 acquisitions in the category under its belt (follow all of Google's M&A activity here through our real-time Google acquisitions tracker). In 2013, the corporate giant picked up deep learning and neural network startup DNNresearch from the computer science department at the University of Toronto. This acquisition reportedly helped Google make major upgrades to its image search feature.

We would expect all of these stages to be relevant to varying degrees; the Isle of Man and the industries it is home to are no exception. Whilst some of these technologies may seem far away – and some of them still are – being ready for them will be a key differentiator for workplaces and governments alike. Automation and Robotics varies hugely by industry, sector, and country. There are a number of fields where these technologies are already fully implemented, especially where the removal of human input is for safety reasons; i.e. in dangerous situations such as hazardous environments. However, in a number of fields, this is still in its infancy and improvements can still easily be made – none more so than certain sections of professional services. Machine learning is embedded in several high-tech industries, and is starting to enter early adopter and mainstream usage in both the home and the workplace.

An Amazon warehouse is a flurry of activity. Towering hydraulic arms lift heavy boxes toward the rafters. And an army of stubby orange robots slide along the floor like giant, sentient hockey pucks, piled high with towers of consumer gratification ranging from bestsellers to kitchenware. Those are Kiva robots, once the marvel of warehouses everywhere. Amazon whipped out its wallet and threw down 775 million to purchase these robot legions in 2012.

Go to the movies or turn on a TV, and you'll quickly learn to fear artificial intelligence. The power of rogue computers is one of Hollywood's most popular topics. Call it an existential fear of the unknown, or perhaps a sense of our own mortality. Whatever our future actually holds, we're fascinated today by how we might share it with machines that become smarter, nimbler and -- gulp -- meaner than we are. Even a techno-sophisticate like Elon Musk frets about us "summoning the demon," and research companies like OpenAI have been founded with the goal of ensuring that AI doesn't turn around and bite us. While researchers creating AI in Silicon Valley and collegiate computer science labs have their own theories about what this software will become, they've seen the movies just like us, and they know what's on our minds.

According to the UK-based company What3words, I live at offers.reform.curve in Brooklyn. I work for Condé Nast, which has offices in downtown Manhattan at words.artists.names--but Afterward, I'll be drinking at trick.pills.prompting. These little word-salads are cute, but you might be wondering: Why do I need a new address? If you live in the developed world, you probably don't; Google Maps knows where you live, and so does the mail carrier.