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An Overview of National AI Strategies – Politics AI – Medium

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The race to become the global leader in artificial intelligence (AI) has officially begun. In the past fifteen months, Canada, China, Denmark, the EU Commission, Finland, France, India, Italy, Japan, Mexico, the Nordic-Baltic region, Singapore, South Korea, Sweden, Taiwan, the UAE, and the UK have all released strategies to promote the use and development of AI. No two strategies are alike, with each focusing on different aspects of AI policy: scientific research, talent development, skills and education, public and private sector adoption, ethics and inclusion, standards and regulations, and data and digital infrastructure. This article summarizes the key policies and goals of each strategy, as well as related policies and initiatives that have announced since the release of the initial strategies. It also includes countries that have announced their intention to develop a strategy or have related AI policies in place. I plan to continuously update this article as new strategies and initiatives are announced. If a country or policy is missing (or if something in the summary is incorrect), please leave a comment and I will update the article as soon as possible. I also plan to write an article for each country that provides an in-depth look at AI policy. Once these articles are written, I will include a link to the bottom of each country's summary. June 28: Publication of original article, included Australia, Canada, China, Denmark, EU Commission, Finland, France, Germany, India, Japan, Singapore, South Korea, UAE, US, and UK.


A Project Based Introduction to TensorFlow.js

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In this post we introduce how to use TensorFlow.js by demonstrating how it was used in the simple project Neural Titanic. In this project, we show how to visualize the evolution of the predictions of a single layer neural network as it is being trained on the tabular Titanic Dataset for the task of binary classification of passenger survival. Here's a look at what we will produce and a link to the live demo: This article assumes that you have a basic understanding of modern frontend JavaScript development and a general awareness of basic machine learning topics. Please let me know in the comments if you have any questions. Neural networks are finally getting their rightfully deserved day in the sun after many years of development by a rather small research community spearheaded by the likes of Geoffrey Hinton, Yoshua Bengio, Andrew Ng, and Yann LeCun. Traditional statistical models do very well on structured data, i.e. tabular data, but have notoriously struggled with unstructured data like images, audio, and natural language.


Why Should You Integrate Machine Learning Into Your Mobile App?

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Machine Learning Apps are fast invading into our everyday lives as the technology is progressing towards delivering smarter mobile-centric solutions. Embedding mobile apps with Machine Learning, a promising segment of AI, is spelling out a lot of advantages for the adopting companies to stand out amidst the clutter and rake in sizeable profits. Many organizations are investing heavily in Machine Learning to reap its benefits. Based on a prediction, Machine Learning as a service market will touch $5,537 million by 2023 while growing at a CAGR of 39 per cent from 2017-2023. Machine Learning Applications refer to a set of apps with Artificial Intelligence mechanisms that are designed to create a universal approach throughout the web to solve similar problems.


Thought Leader Thursday: Use of Machine Learning in the Automotive Industry - Altair Innovation Intelligence

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Engineering design and operational decisions depend largely on engineers' understanding of applications. This includes assumptions made to simplify problems in order to solve them. However, these assumptions often times introduce errors compared to the actual behavior of an application. Increasing access to sensor or virtual data and computational resources, combined with democratization of advanced Machine Learning (ML) algorithms leads to greater use of ML. This brings field data and engineering knowledge together allowing for an increased level of overall accuracy in decision-making and design performance improvement.


Thought Leader Thursday: Use of Machine Learning in the Automotive Industry - Altair Innovation Intelligence

#artificialintelligence

Engineering design and operational decisions depend largely on engineers' understanding of applications. This includes assumptions made to simplify problems in order to solve them. However, these assumptions often times introduce errors compared to the actual behavior of an application. Increasing access to sensor or virtual data and computational resources, combined with democratization of advanced Machine Learning (ML) algorithms leads to greater use of ML. ML was first defined by computer scientist Arthur Samuel as "a field of study that gives computers the ability to learn without being explicitly programmed".


AI solutions required for fast-paced application development?

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According to Gartner, "to keep pace with the demands of digital transformation initiatives, application development teams will augment their efforts with AI "co-developers" to streamline programming efforts". Gartner predicts that "by 2022, at least 40% of new application development projects will have virtual AI co-developers on their team." Mendix is a digital transformation enabler platform that helps businesses to build web and mobile applications without the need to code – what's called low-code development. The company has recently launched Mendix Assist, which uses machine learning and AI analysis of over 5 million application logic flows to deliver 90% accuracy on next-step suggestions and reduce the cost of development defects by 10x. The platform allows people from across the business with no coding skills to collaborate, build and continuously improve apps at speed and scale.


Real World Robotics: Advances in robotics can empower smaller manufacturers

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Once found mostly in manufacturing environments with extremely high volumes, robots are now being used in smaller organizations, and in a wider variety of applications. The cost of implementing robotic systems has fallen significantly, plus it is now easier to apply robotics in more ways. The reason is simple: over the last few years, controls have become more user-friendly, requiring fewer programming resources. System design has become easier with the availability of online tools to help end-users and OEMs build systems directly. Servicing has also become easier and faster thanks to standardized components and powerful diagnostics.


How Researchers Are Building Models To Safeguard Private Data In Machine Learning

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More machine learning applications are permeating in the tech ecosystem and the data that goes into ML systems is being derived from all sorts of sources -- regardless of its sensitivity. ML algorithms do not realise the aspect of sensitivity as it always looks at data as a way to establish and learn patterns, rather than looking into the who's who of the data. Miscreants might take advantage of this and circumvent the ML systems itself, which can have devastating effects altogether. If that happens, the purpose of ML will completely fail. To counter this, and establish a secure and safe ML environment, researchers are strictly working towards building privacy in ML models.


Machine learning and artificial intelligence in a brave new world

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Machine learning, one of the key building blocks of AI, has been a part of the technological world since the 1950s, when the earliest programmers asked computers to make sense of large sets of data. Programmers have increasingly refined the ability of machines to study data in order to detect patterns that allow computers to then organise information, identify relationships, make predictions and detect anomalies. Today, modern applications of AI have already given us self-driving cars and virtual assistants and have helped us detect fraud and manage resources like electricity more efficiently. Sectors as diverse as retail, sports, banking, manufacturing and healthcare have all found applications for machine learning and AI. Today's machines are now capable of performing narrowly defined tasks with great precision, but--and it's an important caveat--that precision is only as good as the quality and, in some cases, the quantity of the data that drives the model.


Numus – personal finance at new levels – Besim on Data

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Most of existing Personal Finance applications are boring, because they are all dependent on manual data input, then follwing the right segments of costs, income or balance, for each of them to be placed on right category, sub-category, type etc… just boooring! In addition, you have to consider manual input fails together with the impossibility of live update your financial status, to make it even worst experience. These and many other reasons make the existing Personal Finance applications nearly useless. Lately, in the era of #Fintech revolution, there are indications that many startups are providing or will provide Financial data as a service. Even, EU came with new regulation called PSD2 that will make financial data more accesible in a format of Open data concept.