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A new rumor about an #Apple_health_gadget is spreading out of Taiwan, and it could be a new product that is separate from the #Apple_Watch.

Apple is talking to suppliers about a new product that is packed with sensors and can collect data like heart rate, pulse, blood sugar, and other biometric statistics, the Chinese-language Economic Daily News reports.

Apple is reportedly in talks with a slew of manufacturing partners, including TSMC, Foxconn, TPK, Zhen Ding Technology, and others. The device would launch at the same time as the 2017 iPhone, according to the report.

Rumor has it that Apple has been working on a sensor that can measure blood sugar since before the Apple Watch launched. But Alphabet's Verily has reportedly had trouble building its own "noninvasive" glucometer integrated into a contact lens, and biotech professionals have told me the technology isn't ready for prime time.

Apple will need its sensors to be noninvasive, meaning they won't require users to draw blood or break the skin.

Apple clearly has long-term plans in health. The company has hired doctors and nurses and also runs around-the-clock biometric labs.

A device separate from the Apple Watch would be surprising, though Apple CEO Tim Cook has talked about a separate health product that could require approval from the Food and Drug Administration. He has also talked about adding more sensors to the Apple Watch, most notably at a conference earlier this year.

"I love the Watch," Cook said. "One day, this is my prediction, we will look back and we will wonder: How can I ever have gone without the Watch? Because the holy grail of the watch is being able to monitor more and more of what's going on in the body."

"We've gotten into the health arena," he said in another interview. "We started looking at wellness, and that took us to pulling a string to think about research; then we pulled a string a little further, and it took us to some patient care stuff; and we pulled a string that's taking us into some other stuff."

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Serious security flaws that could give attackers complete access to a phone's data have been found in software used on tens of millions of Android devices.

The bugs were uncovered by Checkpoint researchers looking at software running on chipsets made by US firm Qualcomm.Qualcomm processors are found in about 900 million Android phones, the company said.However, there is no evidence of the vulnerabilities currently being used in attacks by cyberthieves."I'm pretty sure you will see these vulnerabilities being used in the next three to four months," said Michael Shaulov, head of mobility product management at Checkpoint."It's always a race as to who finds the bug first, whether it's the good guys or the bad."

Affected devices included:

· BlackBerry Priv and Dtek50

· Blackphone 1 and Blackphone 2

· Google Nexus 5X, Nexus 6 and Nexus 6P

· HTC One, HTC M9 and HTC 10

· LG G4, LG G5, and LG V10

· New Moto X by Motorola

· OnePlus One, OnePlus 2 and OnePlus 3

· US versions of the Samsung Galaxy S7 and Samsung S7 Edge

· Sony Xperia Z Ultra

Mr Shaulov said six months of work to reverse engineer Qualcomm's code revealed the problems. The flaws were found in software that handles graphics and in code that controls communication between different processes running inside a phone.

Exploiting the bugs would allow an attacker to gradually be able to take more control over a device and gain access to its data. Checkpoint handed information about the bugs and proof of concept code to Qualcomm earlier this year.

In response, Qualcomm is believed to have created patches for the bugs and started to use the fixed versions in its factories.

It has also distributed the patches to phone makers and operators. However, it is not clear how many of those companies have issued updates to customers' phones.

Checkpoint has created a free app called QuadRooter Scanner that can be used to check if a phone is vulnerable to any of the bugs, by looking to see if the patches for them have been downloaded and installed. In addition, Mr Shaulov said Android owners should only download apps from the official Google Play store to avoid falling victim to malicious programs.

"People should call whoever sold them their phone, their operator or the manufacturer, and beg them for the patches," said Mr Shaulov.

In a statement, Qualcomm said: "We were notified by the researcher about these vulnerabilities between February and April of this year, and made patches available for all four vulnerabilities to customers, partners, and the open source community between April and July. "We continue to work proactively both internally as well as with security researchers to identify and address potential security vulnerabilities."

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An artificial intelligence can sense the taunt.

Without a helpful hashtag, picking up on sarcasm online can be hard even for humans. For literal-minded computers, it’s often a major headache. But now a machine learning system can automatically recognize when individuals are being sarcastic.

Mining people’s comments on social media is big business. Advertisers track people’s attitudes and moods, companies and governments follow public opinion. But people being sarcastic and saying the opposite of what they actually are super tricky to pick up on. So concerned is the US Secret Service that it listed sarcasm detection as a desired feature in a 2014 tender for a social media analytics service.

Computers can exploit small textual clues, such as use of exclamation marks, to detect sarcasm with some degree of accuracy. But without context, it is hard identify the tone of a comment.

Looking at information like the relationship between a comment’s author and audience or where the comment is posted online makes a big difference, pushing the accuracy up to around 80 per cent. But coding these features by hand is laborious, and selecting which to use depends largely on intuition.

Now Silvio Amir at the University of Lisbon, Portugal, and colleagues has turned to machine learning. They have trained their system to identify sarcasm on Twitter simply by looking at a user’s past tweets. “We can get away without looking at all this external information,” says Amir.

Using just these tweets, the system builds up a picture of a person that is rich enough to guess when they are being sarcastic. “It intuitively makes sense,” says Amir. “Tell me what you talk about and I can tell you who you are.”

Amir’s system predicts sarcasm with an accuracy of 87 per cent, slightly better than existing approaches. However, by learning to detect sarcasm without human input, the system should be very easy to use.

Amir also says the approach should work for any language and any online platform where posting history is available. “The key innovation is realizing you can build a model of the user merely based on what they have said in the past,” says Amir. The team will present their findings next week at CoNLL, a Google-sponsored conference on natural language processing in Berlin.

Mark Carman at Monash University in Melbourne, who studies sarcasm detection, thinks it would be relatively straightforward to integrate the approach with other types of social media analysis, such as tracking people’s emotions or stock market trends.