No longer fiction: Flying car startups aim to begin test flights as early as next year

USATODAY - Tech Top Stories

After the recent New York helicopter crash, many are starting to question the safety of choppers. With Uber Air debuting its helicopter ride-sharing service next month, are helicopters a safe mode for transportation? As Uber forges ahead with plans for a flying taxi service in 2023, other startups are unveiling futuristic air mobility vehicles, suggesting that a Jetsons-like transportation system may be closer than you think. Massachusetts-based Alaka'i Technologies showed an electric human-carrying drone last month that it claims can carry five passengers, and the American-Israeli startup NFT --short for Next Future Transportation -- hopes its new folding-wing vehicle will halve travel times by both driving on the street and flying through the air during commutes. This comes as Uber sets its sights on phase one of Uber Air, releasing a fleet of Uber Copters in NYC over the summer.

Open Data for Machine Learning


The P2P Foundation includes the definition of Open Data as the philosophy and practice requiring that certain data be freely available to everyone, without restrictions from copyright. In recent years as an exercise of transparency governments and city councils create open data portals where the authorities push a lot of data to be freely accessible to citizens. Nowadays is easy to find a dataset of almost everything, in a few clicks you can find, as for instance datasets related to territory (parking spaces of a city), population (Level of education), governance (electoral results)... The benefits of Open Data from an ethical movement essentially focus on empowering the resident with data that somehow can be used for his own profit. Clearly stated in the article "5 benefits of open government data" [3] we found: As Stated Before Open Data can unlock the potential of Machine Learning.

Machine Learning Can Predict Heart Attack or Death More Accurately Than Humans


Machine learning, a branch of artificial intelligence, has become more accurate than human medical professionals in predicting incidence of heart attack or death in patients at risk of coronary artery disease. Machine learning, a branch of artificial intelligence, was more accurate than human medical professionals in predicting myocardial infarction (MI) or death among patients suspected of having coronary artery disease (CAD), according to an abstract presented at the 2019 International Conference on Nuclear Cardiology and Cardiac CT, held May 12-14 in Lisbon, Portugal. Physicians routinely make treatment decisions using risk scores, which are based on few variables and are typically only moderately accurate for individual patients. Machine learning can use repetition and adjustment to exploit large quantities of data and identify complex patterns that may go unnoticed by humans. "Humans have a very hard time thinking further than three dimensions (a cube) or four dimensions (a cube through time)," said the study's lead researcher, Luis Eduardo Juarez-Orozco, MD, PhD, in a statement.

University of Oxford Spin-out Mind Foundry Launches Machine Learning Platform That Quickly Transforms Business Problem Owners Into Citizen Data Scientists


Mind Foundry, a technology spin-out from the University of Oxford's Machine Learning Research Group (MLRG), today announced the commercial launch of a revolutionary humanised machine learning platform. For the first time the new cloud-based platform allows anyone, of any technical ability and in any size of organisation, to swiftly unlock the full value of ever increasing volumes of data to make decisions on complex business issues without the need for data scientists. The platform was developed through work with some of the world's largest investment firms, telecommunications providers, manufacturers and heavy industry companies. Organisations can proactively solve business problems by easily leveraging the predictive power of their existing data. The platform automatically builds appropriate machine learning solutions for business problems in minutes or hours, rather than weeks or months, and provides full transparency and auditability of solutions.

Generative Adversarial Network(GAN) using Keras


GAN is an unsupervised deep learning algorithm where we have a Generator pitted against an adversarial network called Discriminator. Discriminators are a team of cops trying to detect the counterfeit currency. Counterfeiters and cops both are trying to beat each other at their game. Generator's objective will be to generate data that is very similar to the training data. Data generated from Generator should be indistinguishable from the real data.

Some Things I Wish I Had Known Before Scaling Machine Learning Solutions: Part I


Recently, I've been touring different conferences presenting a talk about best practices for implementing large scale machine learning solutions. The idea is to present a series of non-obvious ideas that result incredibly practical in the implementation of machine intelligence applications in the real world. All the lessons have been based on our experiences at Invector Labs working with large organizations and ambitious startups in the implementation of machine learning capabilities. During those exercises, we quickly realized that many of our assumptions of machine learning apps were really flawed and that there was a huge gap between the advancements in AI research and the practical viability of those ideas. In this two-part article, I would like summarize some of those ideas that hopefully will result valuable to machine learning practitioners and aspirational data scientists.

How it works: Visa's artificial intelligence (A.I.) for payment authorization and fraud detection


Merchants want to make a sale. Consumers want a fast checkout experience without false declines. Financial institutions want to mitigate fraud. The A.I. technology powering Visa Advanced Authorization can help.

AI Expo Africa launches AI Art Challenge - Screen Africa


In recent years, art-creating AI has pushed the boundaries of how we define art. AI Expo Africa, the largest business focused AI event in Africa, have launched a grand challenge to create an original piece of artwork or music that leverages AI. There will be one winner for each category (visual art & music) and winners must be citizens of an African country and must be living in Africa. Awards will be presented to the winner at the exclusive AI Expo Africa VIP event on the opening night of AI Expo Africa in Cape Town, South Africa, on 3 September 2019 – with the winning music track(s) being played during the event and art work on display. Both works will be auctioned during an exclusive event on 4 September.

Africa: MTN Group Launches Africa's First AI Service for Momo


The MTN Group has launched Africa's first Mobile Money (MoMo) Artificial Intelligence (AI) service or "chatbot". A statement issued by the Group's Corporate Affairs on Tuesday said the chatbot went live in Ivory Coast in May and would be rolled out across MTN's MoMo footprint in the next few months. The AI mobile money "assistant" enables customers to engage with MTN's MoMo services, including payments on various social media platforms such as WhatsApp and Facebook Messenger, and via SMS. The statement said the service would also be included over time, in MTN's own newly released advanced instant messaging service "Ayoba". It said the chatbot was an AI guide that assists users to navigate MTN's MoMo services and provide other useful information.

Microsoft has been rated the most environmentally friendly company. Here's what it's doing right.


Artificial intelligence can already help make medical diagnoses and weed out terrorists. Now Microsoft wants AI to make our planet greener. Ranked the No. 1 environmentally friendly company by the nonprofit Just Capital, the tech giant is finding innovative ways to combat climate change. Chief among them: a grant launched in 2017 that funds the use of AI to address global warming. The tech company also released code that can aid developers building such algorithms.