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Facebook has recently come underintense scrutinyfor sharing the data of millions of users without their knowledge. We&ve also learned that Facebook is usingAIto predict users&futurebehavior and selling that data to advertisers. Not surprisingly, Facebookbusiness model and how it handles its users& data has sparked a long-awaited conversation — and controversy — about data privacy. These revelations will undoubtedly force the company to evolve their data sharing and protection strategy and policy.
More importantly, ita call to action: We need acodeofethics.
As theAIrevolution continues to accelerate, new technology is being developed to solve key problems faced by consumers, businesses and the world at large. It is the next stage of evolution for countless industries, from security and enterprise to retail and healthcare. I believe that in the nearfuture, almost all new technology will incorporate some form ofAIor machine learning, enabling humans to interact with data and devices in ways we can&t yet imagine.
Moving forward, our reliance onAIwill deepen, inevitably causing many ethical issues to arise as humans turn over to algorithms their cars, homes and businesses. These issues and their consequences will not discriminate, and the impact will be far-reaching — affecting everyone, including public citizens, small businesses utilizingAIor entrepreneurs developing the latest tech. No one will be left untouched.I am aware of a few existing initiatives focused on more research, best practices and collaboration; however, itclear that theremuch more work to be done.
For the future of AIto become as responsible as possible, we&ll need to answer some tough ethical questions.
Researchers, entrepreneurs and global organizations must lay the groundwork for acodeofAIethicsto guide us through these upcoming breakthroughs and inevitable dilemmas. I should clarify that this won&t be a singlecodeofethics— each company and industry will have to come up with their own unique guidelines.
For thefutureofAIto become as responsible as possible, we&ll need to answer some tough ethical questions. I do not have the answers to these questions right now, but my goal is to bring more awareness to this topic, along with simple common sense, and work toward a solution. Here are some of the issues related toAIand automation that keep me up at night.
The ethicsof driverless cars
With the invention of the car came the invention of the car accident. Similarly, anAI-augmented car will bring with it ethical and business implications that we must be prepared to face. Researchers and programmers will have to ask themselves what safety and mobility trade-offs are inherent in autonomous vehicles.
Ethical challenges will unfold as algorithms are developed that impact how humans and autonomous vehicles interact. Should these algorithms be transparent For example, will a car rear-end an abruptly stopped car or swerve and hit a dog on the side of the street Key decisions will be made by a fusion processor in split seconds, runningAI, connecting a carvast array of sensors. Will entrepreneurs and small businesses be kept in the dark while these algorithms dominate the market
Driverless cars will also transform the way consumers behave. Companies will need to anticipate this behavior and offer solutions to fill those gaps. Now is the time to start predicting how this technology will change consumer needs and what products and services can be created to meet them.
The battle against fake news
As our news media and social platforms become increasinglyAI-driven, businesses from startups to global powerhouses must be aware of their ethical implications and choose wisely when working this technology into their products.
We&re already seeingAIbeing used to create and defend against political propaganda and fake news. Meanwhile, dark money has been used for social media ads that can target incredibly specific populations in an attempt to influence public opinion or even political elections. What happens when we can no longer trust our news sources and social media feeds
AIwill continue to give algorithms significant influence over what we see and read in our daily lives. We have to ask ourselves how much trust we can put in the systems that we&re creating and how much power we can give them. I think itup to companies like Facebook, Google and Twitter — andfutureplatforms — to put safeguards in place to prevent them from being misused. We need the equivalent of Underwriters Laboratories (UL) for news!
The futureof the automated workplace
Companies large and small must begin preparing for thefutureof work in the age of automation. Automation will replace some labor and enhance other jobs. Many workers will be empowered with these new tools, enabling them to work more quickly and efficiently. However, many companies will have to account for the jobs lost to automation.
Businesses should begin thinking about what labor may soon be automated and how their workforce can be utilized in other areas. A large portion of the workforce will have to be trained for new jobs created by automation in what is becoming commonly referred to as collaborative automation. The challenge will come when deciding on how to retrain and redistribute employees whose jobs have been automated or augmented. Will it be the government, employers or automation companies In the end, these sectors will need to work together as automation changes the landscape of work.
No one will be left untouched.
Ittrue thatAIis the next stage of tech evolution, and that iteverywhere. It has become portable, accessible and economical. We have now, finally, reached theAItipping point. But that point is on a precarious edge, see-sawing somewhere between anAIdreamland and anAInightmare.
In order to surpass theAIhype and take advantage of its transformative powers, itessential that we getAIright, starting with theethics. As entrepreneurs rush to develop the latestAItech or use it to solve key business problems, each has a responsibility to consider theethicsof this technology. Researchers, governments and businesses must cooperatively develop ethical guidelines that help to ensure a responsible use ofAIto the benefit of all.
From driverless cars to media platforms to the workplace,AIis going to have a significant impact on how we live our lives. But asAIthought leaders and experts, we shouldn&t just deliver the technology — we need to closely monitor it and ask the right questions as the industry evolves.
It has never been a more exciting time to be an entrepreneur in the rise ofAI,but therea lot of work to be done now and in thefutureto ensure we&re using the technology responsibly.
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Read more: The future of AI relies on a code of ethics
Write comment (95 Comments)Payment provider PayPal continues apace with its acquisitions streak to bring more modern tools into its platform to serve its 237 million customers. Today the company announced that it is buying Simility, a fraud prevention specialist, for $120 million in cash.
PayPal had been an investor in Simility (it owns three percent of the company, it says), along with Accel, Trinity Ventures and others. The startup had raised just under $25 million and was last valued at $52.75 million, according to figures from PitchBook, making this a decent return for its backers. The deal is expected to close in Q3.
Online fraud involving either buyers or sellers — and sometimes people within organizations themselves — has been one of the biggest limiting factors to the growth of e-commerce, and that has only become more of an issue as digital transactions have become more mature and spread to more platforms.
Similityapproach is to use a set of APIs and beacons that essentially monitor digital transactions and buying activity wherever they happen to take place: on mobile, web or in physical environments. Augmenting these with machine learning and feeds from other data sources, it creates something it calls &adaptive& risk management: a changing approach and protection strategy based on what the threat of the moment might be.
Acquiring a company like this makes sense on two levels for PayPal: not just for its own systems, but for that of its customers, who make PayPal-powered transactions on the web, on mobile and at physical points of sales.
&Digital commerce has exploded, and fraudsters have taken note, adapting and developing new methods to carry out their crimes,& said Bill Ready, chief operating officer, PayPal, in a statement. &PayPal has been at the forefront of developing innovative fraud prevention and risk management solutions for nearly 20 years, but until now, merchants haven&t been able to configure those solutions to manage the unique complexities of their businesses. Together with Simility, we will be able to put more control in the hands of our merchants to fight fraud while helping make commerce experiences faster and more secure.&
Ready in a separate blog described the companystrategy currently as an effort to create a one-stop shop for all things commerce, and simplification is also an aspect of this deal:Simility already has a number of customers that also work with PayPal, such as PayPalformer owner eBay/StubHub, OfferUp, DickSporting Goods and Rebtel. The acquisition will mean a more integrated approach for them where their PayPal services have a stronger layer of fraud protection on them, and they also get used to help form a bigger picture about the overall state of fraud that the companies.
PayPal said that after the deal closes, it will also extend Similitytools to the rest of the merchants on its platform.
&Our vision for Simility was to create an adaptive risk management platform that empowers organizations operating in a digital world to manage an evolving fraud and risk landscape where data breaches are the new normal,& said Rahul Pangam, co-founder and CEO, Simility, in a statement. &We are excited to enter the next phase of our growth with PayPal and are thrilled to join them to help drive the next generation of payment and commerce solutions while scaling our business together.&
PayPal has made a number of acquisitions over the last few weeks, all pointing to adding new technologies and tools to reflect our changing times and how that is playing out in the world of payments. They have included European mobile payments and financial services business iZettle, payments aggregator Hyperwallet and AI-based CRM specialist Jetlore.
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Read more: PayPal to buy Simility, a specialist in AI-based fraud and risk management, for $120M
Write comment (93 Comments)Starting in July, you might have to pay a little extra to see the newest blockbusters when using MoviePass . The movie subscription service has plans to instate surge pricing for non-annual subscribers starting at $2 for movies and times the app deems &very popular,&Business Insider reports.
This comes one day after AMC announced its plan for a competing movie subscription service called AMC Stubs A-List, coming in June. In MoviePass CEO Mitch Lowestatement to Business Insider, he seemed unfazed by AMCentrance into the subscription game, saying instead it &validates that subscription is really here to stay.&
But for MoviePass to stay, it might have to start changing it business plan. The subscription serviceparent companyHelios and Matheson Analytics announced today that MoviePass& monthly losses soared to $40 million in May. The company also expects its cash deficit to reach $45 million by June. These numbers are in part a reflection of the545,000 new users who joined the service between May 1st and June 15th.
In addition to a few additional dollars to see the next dinosaur movie or superhero film this summer, MoviePass said itplans to roll out two new programs come August that will bring in a little extra money as well. Starting at the end of August, users will be able to use the app to watch IMAX or Real 3D movies for an additional $2 to $6 and have the option to bring a friend (though, they&ll have to pay almost full price for the additional ticket) through the app. To start, these features will have to be applied to separate films, but MoviePass has plans to combine them.
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Read more: MoviePass introduces surge pricing for the summer
Write comment (93 Comments)If you were still waiting patiently for the virtual reality features that Microsoft promised in 2016, then I have some bad news for you. During E3 last week,Microsoft chief marketing officer for gaming, Mike Nichols,toldGamesIndustry.biz that the company had no plans to fulfill that promise.
&We don&t have any plans specific to Xbox consoles in virtual reality or mixed reality,& Nichols toldGamesIndustry.biz.
This goes against a promise that Microsoft made two years ago when Xbox chief Phil Spencer told The Verge that the Xbox One X (then dramatically known as Xbox Scorpio) would support &[the kind of] high-end VR that you see happening in the PC space.&
The release of the Xbox One X came and went without any news of VR integration, but in the interim, Microsoft did make strides toward VR and mixed reality tech for PC gaming with the release of the Windows Mixed Reality headsets for Windows 10.
According to Nichols, it seems like Microsoft may be sticking to this PC gaming territory for awhile.
&PC is probably the best platform for more immersive VR and MR … but as it relates to Xbox, no,& he said.
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Read more: Microsoft backpedals on VR promise
Write comment (98 Comments)Someday we&ll have an app that you can point at a weird bug or unfamiliar fern and have it spit out the genus and species. But right now computer vision systems just aren&t up to the task. To help things along, researchers have assembled hundreds of thousands of images taken by regular folks of critters in real life situations — and by studying these, our AI helpers may be able to get a handle on biodiversity.
Many computer vision algorithms have been trained on one of several large sets of images, which may have everything from people to household objects to fruits and vegetables in them. Thatgreat for learning a little about a lot of things, but what if you want to go deep on a specific subject or type of image You need a special set of lots of that kind of image.
For some specialties, we have that already: FaceNet, for instance, is the standard set for learning how to recognize or replicate faces. But while computers may have trouble recognizing faces, we rarely do — while on the other hand, I can never remember the name of the birds that land on my feeder in the spring.
Fortunately, I&m not the only one with this problem, and for years the community of the iNaturalist app has been collecting pictures of common and uncommon animals for identification. And it turns out that these images are the perfect way to teach a system how to recognize plants and animals in the wild.

Could you tell the difference
You might think that a computer could learn all it needs to from biology textbooks, field guides and National Geographic. But when you or I take a picture of a sea lion, it looks a lot different from a professional shot: the background is different, the angle isn&t perfect, the focus is probably off and there may even be other animals in the shot. Even a good computer vision algorithm might not see much in common between the two.
The photos taken through the iNaturalist app, however, are all of the amateur type — yet they have also been validated and identified by professionals who, far better than any computer, can recognize a species even when itoccluded, poorly lit or blurry.
The researchers, from Caltech, Google, Cornell and iNaturalist itself, put together a limited subset of the more than 1.6 million images in the appdatabases, presented this week at CVPR in Salt Lake City. They decided that in order for the set to be robust, it should have lots of different angles and situations, so they searched for species that have had at least 20 different people spot them.
The resulting set of images (PDF) still has more than 859,000 pictures of over 5,000 species. These they had people annotate by drawing boxes around the critter in the picture, so the computer would know what to pay attention to. A set of images was set aside for training the system, another set for testing it.

Examples of bounding boxes being put on images.
Ironically, they can tell ita good set because existing image recognition engines perform so poorly on it, not even reaching 70 percent first-guess accuracy. The very qualities that make the images themselves so amateurish and difficult to parse make them extremely valuable as raw data; these pictures haven&t been sanitized or set up to make it any easier for the algorithms to sort through.
Even the systems created by the researchers with the iNat2017 set didn&t fare so well. But thatokay — finding where thereroom to improve is part of defining the problem space.
The set is expanding, as others like it do, and the researchers note that the number of species with 20 independent observations has more than doubled since they started working on the data set. That means iNat2018, already under development, will be much larger and will likely lead to more robust recognition systems.
The team says they&re working on adding more attributes to the set so that a system will be able to report not just species, but sex, life stage, habitat notes and other metadata. And if it fails to nail down the species, it could in the future at least make a guess at the genus or whatever taxonomic rank itconfident about — e.g. it may not be able to tell if itanthopleura elegantissima or anthopleura xanthogrammica, but itdefinitely an anemone.
This is just one of many parallel efforts to improve the state of computer vision in natural environments; you can learn more about the ongoing collection and competition that leads to the iNat data sets here, and other more class-specific challenges are listed here.
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Read more: Species-identifying AI gets a boost from images snapped by citizen naturalists
Write comment (94 Comments)A massive database of current U.S. Immigration and Customs Enforcement (ICE) employees scraped from public LinkedIn profiles has been removed from the tech platforms hosting the data. The project was undertaken by Sam Lavigne, self-described artist, programmer and researcher in response to recent revelations around ICEdetention practices at the southern U.S. border.
Lavigne posted the database to GitHub on Tuesday and by Wednesday the repository had been removed. The database included the name, profile photo, title and city area of every ICE employee who listed the agency as their employer on the professional networking site. A more in-depth version of the data pulled all public LinkedIn data from the pool of users, including previous employment, education history and any other information those users opted to make public. The total database lists this information for 1,595 ICE employees, from the agencyCTO on down to low-level workers and interns.
The project accompanied a Medium post about the projectaims that has since been removed by the platform:
While I don&t have a precise idea of what should be done with this data set, I leave it here with the hope that researchers, journalists and activists will find it useful…
I find it helpful to remember that as much as internet companies use data to spy on and exploit their users, we can at times reverse the story, and leverage those very same online platforms as a means to investigate or even undermine entrenched power structures. Ita strange side effect of our reliance on private companies and semi-public platforms to mediate nearly all aspects of our lives.
The data set appears to have violated GitHub and Medium guidelines against doxing. Mediumanti-harassment policy specifically forbids doxing and defines it broadly, preventing &the aggregation of publicly available information to target, shame, blackmail, harass, intimidate, threaten, or endanger.&
Because it doesn&t include personal identifying information like home addresses, phone numbers or other non-public details, Lavigneproject isn&t really doxing in the normal sense of the word, though that hasn&t made it less controversial.
GitHubown policy leading to the dataremoval is less clear, though the company told The Verge the repository was removed due to &doxxing and harassment.& The platformterms of service forbid uses of GitHub that &violate the privacy of any third party, such as by posting another personpersonal information without consent.& This leaves some room for interpretation, and it is not clear that data from a public-facing social media profile is &personal& under this definition. GitHub allows researchers to scrape data from external sites in order to aggregate it &only if any publications resulting from that research are open access.&
While Lavigneaggregation efforts were deemed off-limits by some tech platforms, they do raise compelling questions. What kinds of public data, in aggregate, run afoul of anti-harassment rules Why can this kind of data be scraped for the purposes of targeted advertising or surveillance by law enforcement but not be collected in a user-facing way The ICE database raised these questions and plenty more, but for some tech companies the question of hosting the data proved too provocative from the start.
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Read more: A huge spreadsheet naming ICE employees gets yanked from GitHub and Medium
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