Electric drones booked through smartphones pick people up from office rooftops, shortening travel time by hours, reducing the need for parking and clearing smog from the air. This vision of the future is driving the government's "flying car" project. Major carrier All Nippon Airways, electronics company NEC Corp. and more than a dozen other companies and academic experts hope to have a road map for the plan ready by the year's end. "This is such a totally new sector Japan has a good chance for not falling behind," said Fumiaki Ebihara, the government official in charge of the project. For now, nobody believes people are going to be zipping around in flying cars any time soon.

NEW DELHI – Japanese companies are looking to tap India's medical market with funding and technological solutions to make health care more accessible in the world's second-most populous country. Japan-based venture capital firms like Spiral Ventures and India Japan Partnership Fund LLP are either funding local health tech startups or exploring new investment opportunities in the health care sector, and electronics giant Panasonic Corp. is offering solutions to improve rural health care. India has a huge health care gap between rich and poor and mismatches between doctors and patients. The situation is made worse by low government spending on health care at 1.3 percent of gross domestic product, the lowest among the BRICS grouping of Brazil, Russia, India, China and South Africa. Spiral Ventures has invested in four health tech startups that offer digital solutions for the local market and is scouting for more such startups in which to invest, according to a top company official.

We propose the genetic algorithm for time window optimization, which is an embedded genetic algorithm (GA), to optimize the time window (TW) of the attributes using feature selection and support vector machine. This GA is evolved using the results of a trading simulation, and it determines the best TW for each technical indicator. An appropriate evaluation was conducted using a walk-forward trading simulation, and the trained model was verified to be generalizable for forecasting other stock data. The results show that using the GA to determine the TW can improve the rate of return, leading to better prediction models than those resulting from using the default TW.

The maverick of personal computing is looking for its next big thing in spaces like healthcare, AR, and autonomous cars, all while keeping its lead in consumer hardware. With an uphill battle in AI, slowing growth in smartphones, and its fingers in so many pies, can Apple reinvent itself for a third time? Get the detailed analysis on Apple's trove of patents, acquisitions, earnings calls, recent product releases, and organizational structure. In many ways, Apple remains a company made in the image of Steve Jobs: iconoclastic and fiercely product focused. But today, Apple is at a crossroads. Under CEO Tim Cook, Apple's ability to seize on emerging technology raises many new questions. Looking for the next wave, Apple is clearly expanding into augmented reality and wearables with the Apple Watch and AirPods wireless headphones. Apple's HomePod speaker system is poised to expand Siri's footprint into the home and serve as a competitor to Amazon's blockbuster Echo device and accompanying virtual assistant Alexa. But the next "big one" -- a success and growth driver on the scale of the iPhone -- has not yet been determined. Will it be augmented reality, auto, wearables? Apple is famously secretive, and a cloud of hearsay and gossip surrounds the company's every move. Apple is believed to be working on augmented reality headsets, connected car software, transformative healthcare devices and apps, as well as smart home tech, and new machine learning applications. We dug through Apple's trove of patents, acquisitions, earnings calls, recent product releases, and organizational structure for concrete hints at how the company will approach its next self-reinvention. Given Apple's size and prominence, we won't be covering every aspect of its business or rehashing old news. There's strong evidence Apple is once again actively "cannibalizing itself," putting massive resources behind consumer tech that will render its own iPhone obsolete. Augmented reality is the company's biggest bet.

IMAGE: The image shows how an AI tool analyzes a slice of cancerous tissue to create a map that tells apart two lung cancer types, with squamous cell carcinoma in red,... view more A new computer program can analyze images of patients' lung tumors, specify cancer types, and even identify altered genes driving abnormal cell growth, a new study shows. Led by researchers at NYU School of Medicine and published online in Nature Medicine, the study found that a type of artificial intelligence (AI), or "machine learning" program, could distinguish with 97 percent accuracy between adenocarcinoma and squamous cell carcinoma--two lung cancer types that experienced pathologists at times struggle to parse without confirmatory tests. The AI tool was also able to determine whether abnormal versions of 6 genes linked to lung cancer--including EGFR, KRAS, and TP53--were present in cells, with an accuracy that ranged from 73 to 86 percent depending on the gene. Such genetic changes or mutations often cause the abnormal growth seen in cancer, but can also change a cell's shape and interactions with its surroundings, providing visual clues for automated analysis. Determining which genes are changed in each tumor has become vital with the increased use of targeted therapies that work only against cancer cells with specific mutations, researchers say.

Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most efficient and widely used supervised learning algorithms are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor (LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this paper is to implement these elegant learning algorithms on eleven different datasets from the UCI machine learning repository to observe the variation of accuracies for each of the algorithms on all datasets. Analyzing the accuracy of the algorithms will give us a brief idea about the relationship of the machine learning algorithms and the data dimensionality. All the algorithms are developed in Matlab. Upon such accuracy observation, the comparison can be built among KNN, SVM, LMNN, and ENN regarding their performances on each dataset.

In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction. We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of synonyms (synsets), constructs embeddings for these sets, and establishes sense-aware relationships between matching synsets. Evaluation on two gold standard datasets for English and Russian shows that the method successfully recognizes hypernymy relationships that cannot be found with standard Hearst patterns and Wiktionary datasets for the respective languages.

WASHINGTON (WASHINGTON POST) - A new poll shows that in several countries around the world, large majorities of people believe it is most likely that robots will be doing much of the work done by humans within 50 years. The effects of this technological leap are not viewed optimistically by most, however. Instead, people largely say they think humans will struggle to find meaningful work and inequality will rise, the research found. The poll was conducted earlier this year by the Pew Research Centre in Greece, Japan, Canada, Argentina, Poland, Brazil, South Africa, Italy and Hungary. Pew also compared the responses in those countries to a poll done in the United States in 2015 that asked about automation. In general, the poll found that majorities in most countries were in agreement that robots would soon do humans' work, with only limited differences in their views of how this would affect society despite some countries being advanced economically and others still developing.

Technology is not something completely new to schools. Think about making presentations: in the old days, students would present their projects on poster boards, but today several audio and video resources are used. New technologies change how business is done and how services are provided, and education is also affected by it. AI is one of the main technologies responsible for changing the way education is provided. Its machine learning feature, which is the learning ability of machines, make it very adjustable.

Facebook is expanding its fake news spotting systems to include photos and videos as part of its ongoing battle to halt the spread of misinformation on its service. Following successful trials in France, India, and Mexico, the company said it will now roll-out the system in 17 countries worldwide in a bid to staunch what it has branded'misinformation in these new visual formats.' The Artificial Intelligence (AI) system feeds potentially fake content to human fact-checkers, who use visual verification techniques such as reverse image searching and analysing image metadata to check the veracity of photos and videos. Previously, the company's efforts to tackle misinformation had been focused on rooting out false articles and webpage links. Russian agents and other malicious groups seeking to influence democratic elections in the US and elsewhere have repeatedly used images and video.