McDonald's is partnering with Dynamic Yield, a leader in personalized tech, to customize drive-thru experiences. McDonald's hopes that an investment in technology is the special sauce that will transform drive-thru ordering. The fast-food giant announced Tuesday that it has an agreement to acquire Apprente, a Silicon Valley-based start-up specializing in conversational voice-based ordering technology. McDonald's said the technology understands different accents and is "expected to allow for faster, simpler and more accurate order taking at the Drive Thru" and could also be incorporated into kiosks and mobile ordering. "Building our technology infrastructure and digital capabilities are fundamental to our Velocity Growth Plan and enable us to meet rising expectations from our customers, while making it simpler and even more enjoyable for crew members to serve guests," Steve Easterbrook, McDonald's president and CEO, said in a news release.

Based on some recent conversations, I realized that text preprocessing is a severely overlooked topic. A few people I spoke to mentioned inconsistent results from their NLP applications only to realize that they were not preprocessing their text or were using the wrong kind of text preprocessing for their project. With that in mind, I thought of shedding some light around what text preprocessing really is, the different techniques of text preprocessing and a way to estimate how much preprocessing you may need. For those interested, I've also made some text preprocessing code snippets in python for you to try. To preprocess your text simply means to bring your text into a form that is predictable and analyzable for your task. A task here is a combination of approach and domain. For example, extracting top keywords with tfidf (approach) from Tweets (domain) is an example of a Task.

The National Health Service reported that over 70 million prescriptions for antidepressants alone were prescribed in England last year. According to a Scientific American study from 2016, more than one in six US adults takes a psychiatric drug. However, most mental disorders are treated based on symptoms instead of the patient's underlying biology. BlackThorn Therapeutics wants to change that. The AI-based neurobehavioural health startup is using machine learning and artificial intelligence to personalise psychiatric medication.

Digitization has become the dominant theme in discussions about the future of supply chains. Wherever there is a problem, there is the promise of a technological solution, using some combination of artificial intelligence or machine learning, big data, automation, and the Internet of Things. On one side of the room, there is a dynamic and fast-growing cohort of technology vendors. When we studied around 300 supply-chain technology companies, we found that they make much of features like AI and machine learning in marketing materials. In the overwhelming majority of cases, however, these are either niche features or of unclear value to the end user.

Smart Services Summit 2019 (hosted by Swiss Alliance for Data-Intensive Services) Location: Swisscom Business Campus, Turbinenstrasse 30, 8005 Zürich... We are happy to announce the 2nd Data Service Alliance Match-Making Event, to be held on October 2, 2019 from 1:00pm to 3:30pm, at FHNW camp... The General Assembly 2019 of the Data Service Alliance will take place on October 02, 2019 at FHNW campus, Riggenbachstrasse 16 (O... In collaboration with Zazuko Knowledge Graphs are getting a lot of attention in the industry and the public sector. CLEF 2019 is the 10th CLEF conference continuing the popular CLEF campaigns which have run since 2000 contributing to the systematic evaluation of inf... Organised by: University of Zurich, Digital Society InitiativeOn Monday September 16, the Digital Society Initiative of University of Zurich is organi... AI, big data and XR are transforming the industry from its core again & in order to utilize the full potential, international collaboration and ma...

Armed forces in Latin America & the Caribbean are faced with the challenge of having to operate with a multi-dimensional mandate. In times of heightened civil unrest they are required to undertake peace-keeping operations, gang warfare driven by the arms-for-drugs trade calls for counter-insurgence style deployments and seasonal natural disasters often require their services to support the essential services under extreme conditions. With limited resources, every opportunity to prevent the unnecessary expenditure while maintaining effectiveness needs to be taken. In this post I will demonstrate how the application of the K-means clustering algorithm, in the context of how Naval Forces in Latin America and the Caribbean, can be used to schedule efficient Naval deployments and reduce the number of unnecessary operations. For this example I simulated 200 data points that represent the location of incidents that would result in the need for Naval resources to be deployed in the Caribbean Sea. The data have a timestamp that indicates the time of day of each incident on a 24-hour clock cycle.

Over the past decade, computer vision has had a tremendous impact. From self-automated cars to space exploration, its various uses in surveillance and image analysis has bettered the world. The in-depth analysis of video footage and images using deep machine learning models has become a powerful tool that most companies and even governments are taking notice of. Recognition and tracking - the computer tries to link any pattern or feature in the image to a previous feature or activity in its database. Prediction - lastly it predicts possible behaviours of the object, and how it will react in the future.

The artificial intelligence industry is expected to reach $59.8 billion by 2025. With use cases in almost every industry vertical, artificial intelligence is predicted to be the future of technology by thought leaders including Bill Gates. From sales forecasting to improving productivity, the application of artificial intelligence (AI) is immense for companies worldwide. As companies race to scale up their AI capabilities, the demand for experts in the field is expected to rise. As companies bet big time on AI, recruiters are paying impressive salaries to hire AI talent.

What could be the link between machine vision and a supposedly smart horse? Clever Hans or der Kluge Hans, was a horse raised in Germany between 1895 and 1916. The animal was claimed to possess superior intelligence and became famous among the public and scientists around the world. By tapping its hoof on the ground, Hans was able to add, subtract, multiply, divide and do many other arithmetic and intellectual tasks. Several scientists at the time investigated the "intelligence" of this controversial horse.

Commonly known among the general public as the co-creator of Siri, but also the creator of CHIC (Computer Human Interaction Center), co-creator of Nuance Communications (now World leader in speech recognition), Luc Julia has been a researcher at the French CNRS and is nowadays at the head of the Paris based AI Lab of Samsung. He has spent nearly 25 years in Silicon Valley and has released a book: "There is no such thing as Artificial Intelligence." As a French researcher in AI, but also as a scientist working for an industrial player like Samsung, he answered the questions IEN Europe asked him with regard to the developments of AI, its relation with IoT, its application in the world of industry, the commitments made at a French level, and his projects at Samsung. Luc Julia: The title of my book is "There is no such thing as Artificial intelligence". Artificial intelligence that does not exist is the one that we talk too often these ten last years in the media or cinema. It does not exist because it is a fantasy.