An open-source disaster response tool that uses visual recognition and learns through artificial intelligence and cloud tools began as an idea that a self-taught developer had at IBM's Call for Code hackathon in Puerto Rico last year. IBM announced DroneAid on Oct. 2 as an open-source project through Code and Response, the company's $25 million program dedicated to the creation and deployment of open-source solutions tackling real-world problems. DroneAid uses visual recognition technology to detect and count SOS icons on the ground gleaned from drone video streams and automatically plots the emergency needs on a map for first responders. Developer Pedro Cruz had planned to use optical character recognition to detect messages, but reading different handwriting and languages complicated that approach. Instead, the tool relies on a subset of the U.N. Office for the Coordination of Humanitarian Affairs' 500 humanitarian icons – symbols that DroneAid can learn and first responders can quickly understand.

Data Science is such a broad field that includes several subdivisions like data preparation and exploration; data representation and transformation; data visualization and presentation; predictive analytics; machine learning, etc. For beginners, learning the fundamentals of data science can be a very daunting task especially if you don't have proper guidance as to the necessary training required, or what courses to take, and in what order. Before discussing the steps necessary to become a data scientist, let's discuss the skills that every data scientist should have in his skills set toolbox. I started learning data science about a year ago. It was quite challenging from the beginning, but let me share with you the approach that worked for me.

The government has unveiled plans to invest £13m in postgraduate conversion courses in data science and artificial intelligence. The initiative will see universities and higher education providers partner with industry to develop new courses that train graduates, who may have studied a non-STEM degree, in the skills required to take up jobs in the field. The funding forms part of a wider £400m investment in maths, digital and technical education through the government's AI sector deal, which was launched last year amid criticism that ministers were failing to protect the UK's tech scene ahead of Brexit. Under the new initiative, the Office for Students and the Department for Digital, Culture, Media and Sport have allocated £3m to course development and £10m to scholarships for candidates from underrepresented backgrounds, including female, disabled and black students. It is hoped that 2,500 students will have enrolled in one of the new courses by 2023.

How do you defeat "deepfakes"? According to Google, you develop more of them. Google just released a large, free database of deepfake videos to help research develop detection tools. Google collaborated with "Jigsaw", a tech "incubator" founded by Google, and the FaceForesenics Benchmark Program at the Technical University of Munich and the University Federico II of Naples. They worked with several paid actors to create hundreds of real videos and then used popular deepfake technologies to generate thousands of fake videos.

Sensitive synthetic skin enables robots to sense their own bodies and surroundings – a crucial capability if they are to be in close contact with people. Inspired by human skin, a team at the Technical University of Munich (TUM) has developed a system combining artificial skin with control algorithms and used it to create the first autonomous humanoid robot with full-body artificial skin. The artificial skin developed by Prof. Gordon Cheng and his team consists of hexagonal cells about the size of a two-euro coin (i.e. about one inch in diameter). Each is equipped with a microprocessor and sensors to detect contact, acceleration, proximity and temperature. Such artificial skin enables robots to perceive their surroundings in much greater detail and with more sensitivity.

Trade wars are hammering manufacturers, from Shanghai to Stuttgart to Seattle. But, awful as today's economic outlook appears, Industry 4.0 is alive and well, its most ardent backers say. Industry 4.0 is the catch-all term for the implementation by businesses of big data, improved robotics and artificial intelligence systems. And it's still expected to be a major driver in global growth over the next decade, and beyond. By 2035, this A.I.-powered push will provide a $14 trillion boost to the global economy, consulting giant Accenture predicts.

Data Science and AI offer us various use cases which can be adapted to different industries and in this meetup we are going to analyze their impact on blockchain and how it can be used to protect digital privacy in a hyper-digitalized world. Speaker Danko Nikolic will focus on how AI will be useful for cryptocurrencies while speaker Mihael Modic will focus on the impact of AI on protecting your digital identity. It aims at bringing together blockchain enthusiastic enterprises and individuals that want to get to know the technology, as well as understand how it can be used and applied. He's an early digital native and internet veteran, entirely focused on blockchain and other distributed ledger technologies, entrepreneur and management consultant with over 20 years of experience. His most important achievement was the theory of hierarchical adaptations, aka practopoiesis.

Has google learned it from the machine or is it the other way round? Google is to larry, What Machine learning is to future! The biggest buzzword of all times in the overall technology market is machine learning. With no surprise, Google has mastered it, and with all its applications it has made our life easy! So, are you questioning yourself of what all achievements do Google possess? Don't take that pain, we will decode that in this article.

Although it is important to be proficient in understanding the inner workings of the algorithm, it is far more essential to be able to communicate the findings to an audience who may not have any theoretical / practical knowledge of machine learning. Just showing that the algorithm predicts well is not enough. You have to attribute the predictions to the elements of the input data that contribute to your accuracy. Thankfully, the random forest implementation of sklearn does give an output called "feature importances" which helps us explain the predictive power of the features in the dataset. But, there are certain drawbacks to this method that we will explore in this post, and an alternative technique to assess the feature importances that overcomes these drawbacks.

The first decade of the 21st century introduced us to sweeping mobile and social revolutions largely driven by names like Jobs, Zuckerberg and Bezos. In the second decade that's now closing, things got a little more… complicated. During those years, a new collection of faces have joined the earlier tech titans to continue moving us into the future. A person wears a Guy Fawkes mask, which today is a trademark and symbol for the online hacktivist group Anonymous. More a decentralized collective than a personality, Anonymous was the name claimed by the loose affiliation of hackers who brought "hacktivism" into the mainstream. During the first half of the decade, Anonymous launched attacks against targets like ISIS, the governments of the US and Tunisia, and corporations such as Sony and PayPal.