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Archivo del Autor: Belen De Leon

Twitter unveils new political ad guidelines set to go into effect this summer

Following the unrelenting wave of controversy around Russian interference in the 2016 presidential election, Twitter announced new guidelines today for political advertisements on the social networking site.

The policy, which will go into effect this summer ahead of midterm elections, will look towards preventing foreign election interference by requiring organizations to self-identify and certify that they are based in the U.S., this will entail organization registered by the Federal Elections Committee to present their FEC ID, while other orgs will have to present a notarized form, the company says.

Orgs buying political ads will also have to comply with a stricter set of rules for how they present their profiles. Twitter will mandate that the account header, profile photo and organization name are consistent with how the organization presents itself online elsewhere, a policy likely designed to ensure that orgs don’t try to obfuscate their identity or present their accounts in a way that would confuse users that the account belonged to a political organization.

In a blog post, the company noted that there would also be a special type of identifying badge for promoted content from these certified advertisers in the future.

Back in April — in the midst of Facebook’s Cambridge Analytica scandal — Twitter publicly shared its support for the Honest Ads Act. This Political Campaigning Policy will be followed up by the company’s work on a unified Ads Transparency Center which the company has promised “will dramatically increase transparency for political and issue ads, providing people with significant detail on the origin of each ad.”


Source: TechCrunch

The AI in your non-autonomous car

Sorry. Your next car probably won’t be autonomous. But, it will still have artificial intelligence (AI).

While most of the attention has been on advanced driver assistance systems (ADAS) and autonomous driving, AI will penetrate far deeper into the car. These overlooked areas offer fertile ground for incumbents and startups alike. Where is the fertile ground for these features? And where is the opportunity for startups?

Inside the cabin

Inward-facing AI cameras can be used to prevent accidents before they occur. These are currently widely deployed in commercial vehicles and trucks to monitor drivers to detect inebriation, distraction, drowsiness and fatigue to alert the driver. ADAS, inward-facing cameras and coaching have shown to drastically decrease insurance costs for commercial vehicle fleets.

The same technology is beginning to penetrate personal vehicles to monitor driver-related behavior for safety purposes. AI-powered cameras also can identify when children and pets are left in the vehicle to prevent heat-related deaths (on average, 37 children die from heat-related vehicle deaths in the U.S. each year).

Autonomous ridesharing will need to detect passenger occupancy and seat belt engagement, so that an autonomous vehicle can ensure passengers are safely on board a vehicle before driving off. They’ll also need to identify that items such as purses or cellphones are not left in the vehicle upon departure.

AI also can help reduce crash severity in the event of an accident. Computer vision and sensor fusion will detect whether seat belts are fastened and estimate body size to calibrate airbag deployment. Real-time passenger tracking and calibration of airbags and other safety features will become a critical design consideration for the cabin of the future.

Beyond safety, AI also will improve the user experience. Vehicles as a consumer product have lagged far behind laptops, tablets, TVs and mobile phones. Gesture recognition and natural language processing make perfect sense in the vehicle, and will make it easier for drivers and passengers to adjust driving settings, control the stereo and navigate.

Under the hood

AI also can be used to help diagnose and even predict maintenance events. Currently, vehicle sensors produce a huge amount of data, but only spit out simple codes that a mechanic can use for diagnosis. Machine learning may be able to make sense of widely disparate signals from all the various sensors for predictive maintenance and to prevent mechanical issues. This type of technology will be increasingly valuable for autonomous vehicles, which will not have access to hands-on interaction and interpretation.

AI also can be used to detect software anomalies and cybersecurity attacks. Whether the anomaly is malicious or just buggy code, it may have the same effect. Vehicles will need to identify problems quickly before they can propagate on the network.

Cars as mobile probes

In addition to providing ADAS and self-driving features, AI can be deployed on vision systems (e.g. cameras, radar, lidar) to turn the vehicle into a mobile probe. AI can be used to create high-definition maps that can be used for vehicle localization, identifying road locations and facades of addresses to supplement in-dash navigation systems, monitoring traffic and pedestrian movements and monitoring crime, as well as a variety of new emerging use cases.

Efficient AI will win

Automakers and suppliers are experimenting to see which features are technologically possible and commercially feasible. Many startups are tackling niche problems, and some of these solutions will prove their value. In the longer-term, there will be so many features that are possible (some cataloged here and some yet unknown) that they will compete for space on cost-constrained hardware.

Making a car is not cheap, and consumers are price-sensitive. Hardware tends to be the cost driver, so these piecewise AI solutions will need to be deployed simultaneously on the same hardware. The power requirements will add up quickly, and even contribute significantly to the total energy consumption of the vehicle.

It has been shown that for some computations, algorithmic advances have outpaced Moore’s Law for hardware. Several companies have started building processors designed for AI, but these won’t be cheap. Algorithmic development in AI will go a long way to enabling the intelligent car of the future. Fast, accurate, low-memory, low-power algorithms, like XNOR.ai* will be required to “stack” these features on low-cost, automotive-grade hardware.

Your next car will likely have several embedded AI features, even if it doesn’t drive itself.

* Full disclosure: XNOR.ai is an Autotech Ventures portfolio company.


Source: TechCrunch

The latest Asus gaming monitor will be insanely expensive

Hot on the heels of Acer’s $2,000 Predator X27 desktop monitor, a similar model manufactured by Asus for the same price will ship in June. Both are based on Nvidia’s G-Sync HDR reference design focusing on four key areas.

The post The latest Asus gaming monitor will be insanely expensive appeared first on Digital Trends.


Source: Digital trends

Hitlist’s new premium service puts a travel agent in your pocket

Hitlist, a several-years old app for finding cheap flights has begun rolling out a subscription tier that will turn it into something more akin to your own mobile travel agent. While the core app experience which monitor airlines for flight deals will continue to be free, the new premium upgrade will unlock a handful of other useful features, including advanced filtering, exclusive members-only fares, and even custom travel advice from the Hitlist team.

The idea, says founder and CEO Gillian Morris, goes back to the original idea that inspired her to create Hitlist in the first place.

“Going back to the very beginning, Hitlist was essentially me giving travel advice to friends,” she says. “People had the time, inclination, and money to travel, but didn’t book because they got lost in the search process. When I sent custom advice, like ‘you said you wanted to go to Istanbul, there are $500 direct round trips in May available right now, that’s a good price and the weather will be good and the tulip festival, this unique cultural experience, will be happening’ – 4 out of 5 people would book,” Morris explains.

“I wouldn’t be able to scale that level of advice at the beginning, so we focused on just the flight deals. But now we have four years’ worth of data that we can learn from – browsing and searching within Hitlist – and we can start to build more sophisticated models that will inside and enable people to travel at scale,” she says.

The new subscription feature will offer users the ability to better filter airline deals by things like the carrier, number of stops, and the time of day of both the departure and return.

It’s also working with airlines to market “closed group” fares that aren’t accessible through flight search engines, but are available to select travel agents and other resellers that market to a closed user group. These will be flagged in the app as “members-only” fares.

Hitlist says it’s currently working with one airline and, through a third party, with several more. But because this is still in a pilot phase and is only live with select users, it can’t say which.

Meanwhile, the app will continue to focus on helping users find the best, low-cost fares – not only by tracking deals – but also by bundling low-cost carriers and traditional airlines together. However, it won’t promote dates that are likely to be cancelled by airlines, nor will it venture into legally gray areas like skipping legs of a flight (like Skiplagged) to find cheaper fares.

Beyond just finding cheap flights – which remains a competitive space – Hitlist aims to offer users a more personalized experience, more like what you would have gotten with a travel agent in the past.

For starters, it developed a proprietary machine learning algorithm that sorts through over 50 million fares’ worth of data per day to find deals that appeal to each individual user. It also learns from how you use it – browsing flights, or how you react to alerts, for example.

“The app gets to know you better over time, just like a human travel agent would,” says Morris. “With the premium upgrade, we’re gaining more insight to the traveler’s preferences that helps us to develop even more sophisticated A.I. to provide advice and make sure you’re getting the best deal.”

When you find a flight you like, Hitlist will direct you over to a partner’s site – like the airline or online travel agency such as CheapOair.

Where the app differs from others who are also trying to replace the travel agent – like Lola, Pana or Hyper – is that Hitlist doesn’t offer a chat interface. Morris feels that ultimately, travelers don’t want to talk to a chatbot – they just want to browse and discover, then have an experience that’s tailored for them as the app gets smarter about what they like.

That’s where Hitlist’s editorially curated suggestions come in, which can be as broad as “escape to Mexico” or as weird and quirky as “best cities to find wild kittens.” (Yes really.)

Hitlist will also help travelers by offering a variety of travel advice to help them make a decision – similar to how Morris used to advise her friends. For example, it might suggest the best days to fly (similar to Google Flights or Hopper), or tell you about the baggage fees, or even what sort of events might be happening at a destination.

Since its launch, Hitlist has grown to over a million mostly millennial travelers, who have collectively saved over $25 million on their flights by booking at the right time.

The new subscription plan is live now on iOS as an in-app purchase for $4.99 per month, but offers a better rate for quarterly or annual subscriptions, at $4.00/mo and $3/mo, respectively. It will roll out on Android later in the year.


Source: TechCrunch

Fortnite tallied almost $300 million in April, with no signs of slowing down – CNET

The money keeps rolling in for Epic Games’ battle royale shooter, which doubled its revenue in just two months.
Source: CNET

Microsoft patent could make playing ‘NBA 2K18’ in VR feel more realistic

Microsoft’s accessories for virtual reality will make it feel more real when you’re playing a game of virtual hoops. You will be able to feel drag as you’re trying to make a shot, and virtual checks will feel like real ones.

The post Microsoft patent could make playing ‘NBA 2K18’ in VR feel more realistic appeared first on Digital Trends.


Source: Digital trends

Uber's Self-Driving Car Saw the Woman It Killed, Report Says

The National Transportation Safety Board says the car had trouble identifying Elaine Herzberg as a human, and then wasn’t made to hit the brakes to avoid hitting her.
Source: Wired

Navigating the risks of artificial intelligence and machine learning in low-income countries

On a recent work trip, I found myself in a swanky-but-still-hip office of a private tech firm. I was drinking a freshly frothed cappuccino, eyeing a mini-fridge stocked with local beer, and standing amidst a group of hoodie-clad software developers typing away diligently at their laptops against a backdrop of Star Wars and xkcd comic wallpaper.

I wasn’t in Silicon Valley: I was in Johannesburg, South Africa, meeting with a firm that is designing machine learning (ML) tools for a local project backed by the U.S. Agency for International Development.

Around the world, tech startups are partnering with NGOs to bring machine learning and artificial intelligence (AI) to bear on problems that the international aid sector has wrestled with for decades. ML is uncovering new ways to increase crop yields for rural farmers. Computer vision lets us leverage aerial imagery to improve crisis relief efforts. Natural language processing helps usgauge community sentiment in poorly connected areas. I’m excited about what might come from all of this. I’m also worried.

AI and ML have huge promise, but they also have limitations. By nature, they learn from and mimic the status quo–whether or not that status quo is fair or just. We’ve seen AI or ML’s potential to hard-wire or amplify discrimination, exclude minorities, or just be rolled out without appropriate safeguards–so we know we should approach these tools with caution. Otherwise, we risk these technologies harming local communities, instead of being engines of progress.

Seemingly benign technical design choices can have far-reaching consequences. In model development, tradeoffs are everywhere. Some are obvious and easily quantifiable — like choosing to optimize a model for speed vs. precision. Sometimes it’s less clear. How you segment data or choose an output variable, for example, may affect predictive fairness across different sub-populations. You could end up tuning a model to excel for the majority while failing for a minority group.

Image courtesy of Getty Images

These issues matter whether you’re working in Silicon Valley or South Africa, but they’re exacerbated in low-income countries. There is often limited local AI expertise to tap into, and the tools’ more troubling aspects can be compounded by histories of ethnic conflict or systemic exclusion. Based on ongoing research and interviews with aid workers and technology firms, we’ve learned five basic things to keep in mind when applying AI and ML in low-income countries:

  1. Ask who’s not at the table. Often, the people who build the technology are culturally or geographically removed from their customers. This can lead to user-experience failures like Alexa misunderstanding a person’s accent. Or worse. Distant designers may be ill-equipped to spot problems with fairness or representation. A good rule of thumb: if everyone involved in your project has a lot in common with you, then you should probably work hard to bring in new, local voices.
  2. Let other people check your work. Not everyone defines fairness the same way, and even really smart people have blind spots. If you share your training data, design to enable external auditing, or plan for online testing, you’ll help advance the field by providing an example of how to do things right. You’ll also share risk more broadly and better manage your own ignorance. In the end, you’ll probably end up building something that works better.
  3. Doubt your data. A lot of AI conversations assume that we’re swimming in data. In places like the U.S., this might be true. In other countries, it isn’t even close. As of 2017, less than a third of Africa’s 1.25 billion people were online. If you want to use online behavior to learn about Africans’ political views or tastes in cinema, your sample will be disproportionately urban, male, and wealthy. Generalize from there and you’re likely to run into trouble.
  4. Respect context. A model developed for a particular application may fail catastrophically when taken out of its original context. So pay attention to how things change in different use cases or regions. That may just mean retraining a classifier to recognize new types of buildings, or it could mean challenging ingrained assumptions about human behavior.
  5. Automate with care. Keeping humans ‘in the loop’ can slow things down, but their mental models are more nuanced and flexible than your algorithm. Especially when deploying in an unfamiliar environment, it’s safer to take baby steps and make sure things are working the way you thought they would. A poorly-vetted tool can do real harm to real people.

AI and ML are still finding their footing in emerging markets. We have the chance to thoughtfully construct how we build these tools into our work so that fairness, transparency, and a recognition of our own ignorance are part of our process from day one. Otherwise, we may ultimately alienate or harm people who are already at the margins.

The developers I met in South Africa have embraced these concepts. Their work with the non-profit Harambee Youth Employment Accelerator has been structured to balance the perspectives of both the coders and those with deep local expertise in youth unemployment; the software developers are even foregoing time at their hip offices to code alongside Harambee’s team. They’ve prioritized inclusivity and context, and they’re approaching the tools with healthy, methodical skepticism. Harambee clearly recognizes the potential of machine learning to help address youth unemployment in South Africa–and they also recognize how critical it is to ‘get it right’. Here’s hoping that trend catches on with other global startups too.


Source: TechCrunch

Apple’s macOS reaches a historic milestone in its life span

It’s a milestone for Apple’s MacOS — the desktop operating system is now in service for 17 years and 29 days, which is the exact same amount of time that the original classic Mac OS was used by Apple before MacOS replaced it.

The post Apple’s macOS reaches a historic milestone in its life span appeared first on Digital Trends.


Source: Digital trends

This Digital Pill Prototype Uses Bacteria to Sense Stomach Bleeding

Researchers are cooking up pill-sized sensors to detect medical molecules and possibly diagnose other gastrointestinal ailments.
Source: Wired

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