Finnish specialist helps make artificial intelligence human-centric - thisisFINLAND


Data is being collected, analysed and utilised everywhere. Artificial intelligence algorithms process data to produce automated decisions, recommendations and services. New artificial intelligence applications are springing up at an accelerating pace. "Our positive expectations regarding the data economy won't come true if citizens and consumers do not trust that artificial intelligence is used to drive human wellbeing," Haataja says. In the future, the competitiveness of companies and countries will depend to a great extent on their ability to utilise data and artificial intelligence.

Can self-driving cars make cycling safer in urban areas? CyclingTips


Whether we like it or not, driverless cars seem to be on their way. It might still be many years until the technology is ubiquitous on our roads, but when automated vehicles (AVs) do arrive, they're going to shake things up in a huge way. One of the biggest benefits AVs are expected to bring is improvements to road safety. Human error is the biggest cause of road trauma -- remove human drivers from the equation and the number of those injured or killed on our roads should decrease significantly. As vulnerable road users, cyclists can rightly feel a sense of excitement at the prospect of driverless vehicles.

Artificial Intelligence: Evolving Risks and Responsibilities


From speech recognition, big data analysis and machine learning to bold new healthcare advances, financial and business advice, talking computers, robots and self-driving cars, the rapid development and adoption of artificial intelligence ("AI") is becoming widespread in more and more areas of daily life. While unleashing opportunities for businesses and communities across the world, AI technologies have also brought about a host of new risks. The implications of AI for companies' legal and ethical responsibilities are now being discussed among governments and a variety of business and non-governmental groups worldwide. A raft of new voluntary ethical codes and even some legislative proposals relating to AI have also started to appear. The trend is that while companies and other organizations that develop, implement or use AI systems are and will be expected to comply with existing data privacy and other legal norms, they will soon have to adhere to a number of new transparency and other ethical expectations as well--the outlines of which are beginning to converge.

Who's afraid of Intelligent Machines?


Fictional robots have always been supremely intelligent and physically much stronger than feeble humans. The reality is that yes, they can be made more robust than us, but as intelligent? And yet as soon as the word'robot' is mentioned, most people think of the Terminator movies with the evil controlling intelligence of Skynet and humanoid robots looking like chrome-plated human skeletons. Is humanity destined to be enslaved by sentient machines because of our obsession with creating'Artificial Intelligence' (AI)? We humans have always been fascinated by'Automata' – machines that perform complex tasks without any apparent human intervention.

To AI, or not to AI ? That is the question.


Just as the phrase'Big Data' had everyone captivated about 10 years ago, the buzzword of today that has businesses aflutter is undoubtedly Artificial Intelligence (AI). Open any journal or industry publication and it seems that everyone is talking about AI. Whether it's using voice-activated assistants like Alexa, image recognition or the promise of self-driving cars, AI seems to be impacting many areas of our lives and aspects of business. AI is defined as computers producing human-like intelligence. In theory, it could include everything from self-aware robots taking over the world at one extreme, to statistical methods at the other.

Eight Surprising Predictions for AI in 2020.


Earlier this week, Gil Press of Forbes published a piece that explored the AI-related predictions from 120 professionals from various industries. I've taken the liberty of exploring the ten opinions that I feel are most relevant, interesting, and valuable to TDS and Medium readers. I've linked to Gil's piece above, so feel free to read all the other great foretellings from the soothsayers of today's AI world. But first, let's take a look at the future of AI from the eyes of some of the world's CEO's, VP's, Marketers, and Engineers. "With AI actually baked into the chips themselves, a whole new era of computing at the source is being empowered -- and we are only at the beginning. AI chips are already improving vehicles' abilities to process visual data more efficiently, paving the way for autonomous vehicles of the future. For smart cities, AI chips will assist with crucial tasks such as real-time traffic monitoring, locating missing persons, and finding stolen vehicles. For smart homes, chips will ensure more privacy and reliability by processing data at the source. Demand for these new technologies will set the stage for a variety of new applications and use cases, fueling the activity of next-generation products and refinement of product needs. A new age of AI chips means a new age of technology".

120 AI Predictions For 2020


Me: "Alexa, tell me what will happen in 2020." Amazon AI: "Here's what I found on Wikipedia: The 2020 UEFA European Football Championship…[continues to read from Wikipedia]" Me: "Alexa, give me a prediction for 2020." Amazon AI: "The universe has not revealed the answer to me." Well, some slight improvement over last year's responses, when Alexa's answer to the first question was "Do you want to open'this day in history'?" As for the universe, it is an open book for the 120 senior executives featured here, all involved with AI, delivering 2020 predictions for a wide range of topics: Autonomous vehicles, deepfakes, small data, voice and natural language processing, human and augmented intelligence, bias and explainability, edge and IoT processing, and many promising applications of artificial intelligence and machine learning technologies and tools. And there will be even more 2020 AI predictions, in a second installment to be posted here later this month. "Vehicle AI is going to be ...

Osaka Metro unveils ticket gate with facial recognition tech

The Japan Times

OSAKA – Osaka Metro Co. showed a next-generation automated ticket gate with a facial recognition system to the media Monday. Aiming to introduce such gates at all of its train stations in fiscal 2024, ahead of the 2025 World Expo in the city of Osaka, the subway operator will start testing the gates Tuesday with some 1,200 employees. Through the test, the Osaka-based company hopes to identify problems and make improvements. This will be the first such test by a Japanese railway operator, according to Osaka Metro. The test, which is set to run through September 2020, will be conducted at four stations: Dome-mae Chiyozaki, Morinomiya, Dobutsuen-mae and Daikokucho.

The 10 Best Examples Of How Companies Use Artificial Intelligence In Practice


All the world's tech giants from Alibaba to Amazon are in a race to become the world's leaders in artificial intelligence (AI). These companies are AI trailblazers and embrace AI to provide next-level products and services. Here are 10 of the best examples of how these companies are using artificial intelligence in practice. Chinese company Alibaba is the world's largest e-commerce platform that sells more than Amazon and eBay combined. Artificial intelligence (AI) is integral in Alibaba's daily operations and is used to predict what customers might want to buy.

Demystifying Black-box Models with Symbolic Metamodels

Neural Information Processing Systems

Understanding the predictions of a machine learning model can be as crucial as the model's accuracy in many application domains. However, the black-box nature of most highly-accurate (complex) models is a major hindrance to their interpretability. To address this issue, we introduce the symbolic metamodeling framework -- a general methodology for interpreting predictions by converting "black-box" models into "white-box" functions that are understandable to human subjects. A symbolic metamodel is a model of a model, i.e., a surrogate model of a trained (machine learning) model expressed through a succinct symbolic expression that comprises familiar mathematical functions and can be subjected to symbolic manipulation. We parameterize symbolic metamodels using Meijer G-functions -- a class of complex-valued contour integrals that depend on scalar parameters, and whose solutions reduce to familiar elementary, algebraic, analytic and closed-form functions for different parameter settings.