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Baidu President Ya-Qin Zhang at Apollo meetup
Baidu President Ya-Qin Zhang announces Apollo 1.5 at a meetup in Sunnyvale, California.

Wayne Cunningham/Roadshow

In July, Chinese technology company Baidu made its Apollo 1.0 self-driving car software available as open source on Github, using the Apache/BSD license. By Day 4 of the release, it was the most downloaded C software on the site.

At an Apollo meetup hosted by Baidu at its Sunnyvale, California, offices, company president Ya-Qin Zhang announced Apollo 1.5, a major iteration of the software, just three months after the initial release.

“Tesla, Apple, Waymo, essentially everyone is building their own platform, their own technology. So at the beginning of the year we contemplated our own strategy. We looked at the history of PCs and mobile, and we believe an open system is more powerful, more vibrant, in the longer term. So we decided to open Apollo, both the IP, the technology and the source code.”

The meetup was one way that Baidu is promoting the open-source nature of Apollo. Situated near US tech companies such as Google and Yahoo, the Sunnyvale location made it possible for engineers to walk or bike over after work. The local streets teem with self-driving research cars honing their real-world driving capabilities.

Baidu has been testing its open-source Apollo software with its own self-driving car fleet in China.


Baidu is hardly alone in developing self-driving car technology. A multitude of companies, from automakers to automotive technology suppliers to large tech companies, have all been working on the problem. The technology has the potential to reduce or eliminate the more than 1 million deaths caused by cars around the world each year.

For the Apollo 1.5 release, Zhang said, “1.5 is a lot more capable, it has all the perception features, the sensors like radar and lidar. It has end-to-end learning technology. It has high-definition mapping access.”

Sensor and high-definition mapping access are two of the biggest updates to the software. The current technology trend for self-driving cars is to load them with high-definition maps of specific roadways. These maps not only show the location of the roads, but also include fixed objects, such as infrastructure, around those roads.

The self-driving car compares what its sensors detect with its stored maps to determine its exact location. The maps also show the car where it can safely drive, and include trajectories to take when making turns in intersections. The car uses its onboard sensors to make sure it can follow the trajectory given to it on the map without hitting another car, pedestrian or anything else in the environment not depicted on the map.

These updates show that Apollo is behind the curve somewhat compared with systems being tested by other companies. Google’s Waymo, for example, has been using lidar sensors with its self-driving software for years. However, Baidu’s open-source approach may help it catch up much more quickly.

Open source could also help Baidu in the Chinese automotive marketplace. Zhang said that the country has over 200 automobile brands, making it difficult to sign partnership deals to develop the technology with so many. With open source, any of those companies can download Apollo and begin to develop a self-driving car on its own.

At its meetup, Baidu displayed a couple of its US research vehicles, equipped with a sensor array on the roof.

Wayne Cunningham/Roadshow

Baidu has partnered with a few Chinese automakers on its self-driving car technology. Zhang said, “We announced a partnership with Beijing Automotive, one of the biggest automakers in China, that will make Level 3 cars by 2019 and Level 4 cars by 2021.” In autonomous vehicle engineering lingo, Level 3 means cars can handle some driving tasks, but must share driving with a human. Level 4 cars can handle all driving tasks by themselves, although they still have controls that let a human take over.

Open-sourcing Apollo wouldn’t seem to do much for Baidu’s bottom line. However, Zhang said, “Our business model in China is very clear. We are going to charge for services, such as car services, mapping services, and for some of the technology we provide, like driving simulation.”

Baidu is a major purveyor of digital maps in China, and has been developing its own high-definition maps in the country. Zhang said that it has already created high-definition maps of over 300,000 kilometers (186,411 miles) of Chinese highways.

With high-definition maps playing such an important role for self-driving technology, any company that wants to develop for China will have to work with Baidu. Open-sourcing Apollo will make that easier.

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Google Doodle honors ‘Prince of Mathematicians’ Johann Carl Friedrich Gauss






Maths is the latest to receive the Google Doodle homage.

Johann Carl Friedrich Gauss, otherwise known as “The Prince of Mathematicians”, made instrumental contributions to number theory, algebra, geophysics, mechanics and statistics.

Gauss was born on April 30 in 1777 in Brunswick, a city in the north of Germany, near Wolfsburg. Despite poor working-class parents and an illiterate mother, Gauss was a child prodigy, believed to have been able to add up every number from 1 to 100 at 8-years-old.

One of his first major equations was working out his date of birth, which his mother hadn’t recorded. He used the only information she had: that it was a Wednesday, eight days before an Easter holiday.

At university when he was 19, Gauss discovered a heptadecagon, or a 17-sided polygon. He requested that a regular heptadecagon be inscribed on his tombstone, but it was too difficult for the stonemason, who said it would just look like a circle.

 A heptadecagon.


László Németh/Wikipedia

And remember your prime numbers? That year Gauss was involved with proving the prime number theorem, helping understand how prime numbers are distributed among the integers, or whole numbers.

Again the same year, a productive one for Gauss, he discovered the quadratic reciprocity law, which allows mathematicians to determine the solvability of any quadratic equation in modular arithmetic.

At 24, Gauss’ work on number theory, which he completed when he was 21, was published as a textbook. Not only did it involve his original work, but it reconciled that of other mathematicians. It would be considered his magnum opus and had an extraordinary impact on the field.

Oh, and add to those achievements a discovery in astronomy — in the same year, 1801, Gauss calculated the orbit of an asteroid called Ceres.

After a much-accomplished life, Gauss died aged 77 on Feb. 23, 1855.

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How Cambridge Analytica works and turned ‘likes’ into political tool



How Cambridge analytica works

The algorithm at the heart of the Facebook data breach sounds almost too dystopian to be real. It trawls through the most apparently trivial, throwaway postings –the “likes” users dole out as they browse the site – to gather sensitive personal information about sexual orientation, race, gender, even intelligence and childhood trauma. So exactly how cambridge analytica works and why it turned like in to a real world political tool.

A few dozen “likes” can give a strong prediction of which party a user will vote for, reveal their gender and whether their partner is likely to be a man or woman, provide powerful clues about whether their parents stayed together throughout their childhood and predict their vulnerability to substance abuse. And it can do all this without delving into personal messages, posts, status updates, photos or all the other information Facebook holds.

how cambridge analytica works

Some results may sound more like the result of updated online sleuthing than sophisticated data analysis; “liking” a political campaign page is little different from pinning a poster in a window.

But five years ago psychology researchers showed that far more complex traits could be deduced from patterns invisible to a human observer scanning through profiles. Just a few apparently random “likes” could form the basis for disturbingly complex character assessments.

When users liked “curly fries” and Sephora cosmetics, this was said to give clues to intelligence; Hello Kitty likes indicated political views; “Being confused after waking up from naps” was linked to sexuality. These were just some of the unexpected but consistent correlations noted in a paper in the Proceedings of the National Academy of Sciences journal in 2013. “Few users were associated with ‘likes’ explicitly revealing their attributes. For example, less than 5% of users labelled as gay were connected with explicitly gay groups, such as No H8 Campaign,” the peer-reviewed research found.

The researchers, Michal Kosinski, David Stillwell and Thore Graepel, saw the dystopian potential of the study and raised privacy concerns. At the time Facebook “likes” were public by default.

Cambridge Analytica whistleblower: ‘We spent $1m harvesting millions of Facebook profiles’ How Cambridge Analytica works.

“The predictability of individual attributes from digital records of behaviour may have considerable negative implications, because it can easily be applied to large numbers of people without their individual consent and without them noticing,” they said.

“Commercial companies, governmental institutions, or even your Facebook friends could use software to infer attributes such as intelligence, sexual orientation or political views that an individual may not have intended to share.”

To some, that may have sounded like a business opportunity. By early 2014, Cambridge Analytica CEO Alexander Nix had signed a deal with one of Kosinski’s Cambridge colleagues, lecturer Aleksandr Kogan, for a private commercial venture, separate from Kogan’s duties at the university, but echoing Kosinski’s work.

The academic had developed a Facebook app which featured a personality quiz, and Cambridge Analytica paid for people to take it, advertising on platforms such as Amazon’s Mechanical Turk.

The app recorded the results of each quiz, collected data from the taker’s Facebook account – and, crucially, extracted the data of their Facebook friends as well.

The results were paired with each quiz-taker’s Facebook data to seek out patterns and build an algorithm to predict results for other Facebook users. Their friends’ profiles provided a testing ground for the formula and, more crucially, a resource that would make the algorithm politically valuable.

How Cambridge Analytica works

To be eligible to take the test the user had to have a Facebook account and be a US voter, so tens of millions of the profiles could be matched to electoral rolls. From an initial trial of 1,000 “seeders”, the researchers obtained 160,000 profiles – or about 160 per person. Eventually a few hundred thousand paid test-takers would be the key to data from a vast swath of US voters.

It was extremely attractive. It could also be deemed illicit, primarily because Kogan did not have permission to collect or use data for commercial purposes. His permission from Facebook to harvest profiles in large quantities was specifically restricted to academic use. And although the company at the time allowed apps to collect friend data, it was only for use in the context of Facebook itself, to encourage interaction. Selling data on, or putting it to other purposes, – including Cambridge Analytica’s political marketing – was strictly barred.

It also appears likely the project was breaking British data protection laws, which ban sale or use of personal data without consent. That includes cases where consent is given for one purpose but data is used for another.

The paid test-takers signed up to T&Cs, including collection of their own data, and Facebook’s default terms allowed their friends’ data to be collected by an app, unless their privacy settings allowed this. But none of them agreed to their data possibly being used to create a political marketing tool or to it being placed in a vast campaign database.

How Cambridge Analytica works

Kogan maintains everything he did was legal and says he had a “close working relationship” with Facebook, which had granted him permission for his apps.

Facebook denies this was a data breach. Vice-president Paul Grewal said: “Protecting people’s information is at the heart of everything we do, and we require the same from people who operate apps on Facebook. If these reports are true, it’s a serious abuse of our rules.”

Graphic to show key players in Cambridge Analytica story

The scale of the data collection Cambridge Analytica paid for was so large it triggered an automatic shutdown of the app’s ability to harvest profiles. But Kogan told a colleague he “spoke with an engineer” to get the restriction lifted and, within a day or two, work resumed.

Within months, Kogan and Cambridge Analytica had a database of millions of US voters that had its own algorithm to scan them, identifying likely political persuasions and personality traits. They could then decide who to target and craft their messages that was likely to appeal to them – a political approach known as “micro-targeting”.

Facebook announced on Friday that it was suspending Cambridge Analytica and Kogan from the platform pending information over misuse of data related to this project.

Facebook denies that the harvesting of tens of millions of profiles by GSR and Cambridge Analytica was a data breach. It said in a statement that Kogan “gained access to this information in a legitimate way and through the proper channels” but “did not subsequently abide by our rules” because he passed the information onto third parties.

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