Collections is a better way to organize those photos you snap as mental notes

Wi-Fi password sticker on your router? Snap. Cute sweater in a store’s window display? Snap. Party invitation? Snap. Cool gift idea for mom? Snap. If any of this sounds familiar to you, then you probably also use your iPhone’s camera to take photos of the things you want to remember – maybe even more often than you use Notes to write things down. If your mental notes are more visual in nature, then you may want give the new app Collections a go instead of relying only on your Camera Roll.

I know, I know…isn’t visual bookmarking already handled by Pinterest?

Well, okay, sure. You can go that route.

But using Pinterest feels heavy. There’s a vast collection of images to explore and search. A Home feed of new stuff to look at. (Why, Pinterest, are you showing me spider tattoos? Why?). People to follow. A feed of notifications to check in on. (Where I get to write back to people things like, “hi, you’re messaging the wrong Sarah Perez. I don’t know you.” Ugh, too often. Stupid common name.)

Collections is just a little app for you to use.

It’s not overwrought. Its simple interface just helps you to better organize those photos you’ve snapped for inspiration, ideas, mental notes, or whatever else you may need to refer back to – like clothes you like, restaurants you passed by and want to try later, art or design ideas, the best photos of your dog, events you want to go to, screenshots, gift ideas, travel inspiration, or really anything else you could think of.

But unlike saving these things to the Camera Roll, where they quickly get lost into a feed of photos, Collections lets you write down little details – like the vendor or price, or your notes. For example, “Great gift for mom. Shop owner says it also comes in blue. Having a summer sale in 2 weeks.” 

While your collections are largely meant just for you, if you ever want to share them, you can use iCloud to do so – friends and family won’t have to sign up for a new service to view your shares, just download the app. You can also share them to social media, iMessage, email, messaging apps, and elsewhere, if you choose.

If you prefer to keep your collections private, you can turn off iCloud syncing during setup to keep them saved to local storage only.

On iPad, the app is even better because it supports drag-and-drop – meaning you drag images from other apps to your collections.

The app was designed by a team of two indie developers, Emile Bennett and Dave Roberts, based in Chamonix, France and Liverpool, U.K., respectively. Bennett had previously launched a budgeting app called Pennies, but built Collections because it’s something he wanted for himself.

“I often find myself in clothes shops just ‘window shopping’. I’ll find a shirt, or a pair of shoes, or yet another over-priced GoreTex outdoor jacket  – I’ve got a bit of a thing about them…I have too many! – and I think “yeah I like this, but I’m not going to buy it now, I’ll pick it up another time,’” he tells TechCrunch.

“So I’d take a few photos, the item, the tag, maybe me wearing it and also maybe the shop front so I remember where it is. I’d always think ‘it’s in my photo stream, I’ll remember it later.’ But, of course, that doesn’t happen as the photos just get lost down in your stream, and even if I did find and remember the photos, there’s no context around them,” he says.

He tried Evernote and Notes to keep tracking of these things, but found Evernote was too bloated and Notes was too text-centric. He also feels Pinterest is too focused on discovery and public sharing to be used for collecting your own private inspirations.

One of the best things about Collections, in my opinion, is that there’s no sign-up. Radical idea, right? Bennett is sick of it, too.

“I’m really passionate about not forcing people to sign up to my apps – I want your data to be yours, I don’t want you to have to sign up to a new service just to use this app,” he says. “I think we’re all getting a bit of ’sign-up fatigue’ these days. Most apps do it because it’s the way they make their money – they give you the app for free, make you sign up to use it, collect your data, and then use that data to make their money. That’s really against my ethos,” says Bennett.

Instead, Collections is a $2.99 download.

Hey people, this is the kind of app development we should be encouraging.

Bennett gave me a few promo codes to try out the app with friends, but I forgot about that, and purchased it.

So here you go, first come, first served:

M77J6T7WLHWJ
N3X9APPT9THE
KNJMTXMY6FFJ
TRT4E77MTR4H
Promo codes are just free downloads. It’s not a scheme to make money, cynics. Nobody’s getting paid here. I just like this app and figured I’d share. Have a good weekend. 


Source: Tech Crunch

Tesla brings on new VP of engineering from Snap

Tesla announced a number of new hires today, including Stuart Bowers, who is joining as VP of engineering. Bowers is joining Tesla from Snap, where he worked as VP of monetization engineering. Other new hires include Neeraj Manrao, who left Apple to become Tesla’s director of energy manufacturing, and Kevin Mukai, who is now director of product engineering at Tesla’s Gigafactory.

“We’re excited to welcome a group of such talented people as we continue to ramp Model 3 and accelerate towards a more sustainable future,” Tesla wrote on its blog. “We’ll be announcing more hires in the coming days, so stay tuned.”

These new hires come following a couple of departures. In April, Tesla VP of Autopilot Jim Keller left for Intel, with Pete Bannon serving as Keller’s replacement. Bannon is a former Apple chip engineer who helped design Apple’s A5-AP chips. Earlier this month, Sameer Qureshi left a senior manager Autopilot role at Tesla to lead Lyft’s autonomous driving efforts.

Here’s the full list of new hires, via Tesla’s blog:

  • Stuart Bowers is joining as VP of Engineering, responsible for a broad range of Tesla’s software and hardware engineering. Stuart has 12 years of software experience and a background in applied mathematics, and is joining Tesla from Snap. There, he was most recently VP of Monetization Engineering, leading the team with a focus on machine learning and ad infrastructure. Prior to Snap, Stuart was the eighth engineer hired at Facebook’s Seattle office where he worked on data infrastructure and machine learning for search.
  • Neeraj Manrao has joined Tesla as Director of Energy Manufacturing. Neeraj comes from Apple, where he led the technical operations team.
  • Kevin Mukai has started as Director of Production Engineering at Gigafactory. Kevin was most recently at ThinFilm Electronics, where he served as Senior Director of Process Engineering, and before that at SunPower as Director of Process & Equipment Engineering. Kevin has extensive experience in advanced factory design and development.
    James Zhou started last month as CFO, China. James previously served as CFO for Asia Pacific and India for Ingersoll Rand, and prior to that held a number of financial leadership positions at General Electric and General Motors.
  • Alexandra Veitch joined last month as Senior Director for North American Government Relations and Policy. Alexandra comes to Tesla from CSRA. Before that, she served as Special Assistant to the President and Legislative Affairs Liaison in the White House under the Obama Administration. Her government service also includes time at the Department of Homeland Security and as a staff member in both the U.S. Senate and House of Representatives.
  • Kate Pearson is our new Director of Field Delivery Operations. She previously worked as VP of Digital Acceleration at Walmart eCommerce, where she led online grocery and last-mile delivery.
  • Mark Mastandrea started earlier this month as Director, Vehicle Delivery Operations. He comes from Amazon, where he was their Director of Logistics Operations, leading last-mile delivery in North America and working on the design and development of AmazonFresh pickup.
  • Myriam Attou recently started as Regional Sales Director in EMEA. Coming from La Perla, and before that Burberry, she has a long track record of delivering strong results in sales, customer experience and service excellence.


Source: Tech Crunch

Facebook has a very specific Pepe the Frog policy, report says

Facebook doesn’t ban fictional characters with hateful content as a rule, but interestingly Pepe the frog is well enough established as a hate speech symbol that Facebook has a very particular policy devoted only to the cartoon frog.

Motherboard got their hands on some content moderation policy documents from Facebook that show Pepe, a cartoon frog harmlessly created by cartoonist Matt Furie, has earned himself the bizarre honor of being the only cartoon that employees reviewing content are encouraged to delete when depicted in “the context of hate.”

documents obtained by Motherboard

While Pepe’s popularity as a meme seems to be waning, the policy was likely born out of the classification of the frog as a hate symbol by the ADL and other orgs. Pepe was a generic meme long before he was adopted by the alt-right, but an army of internet photoshoppers managed to produce a lot of messed up stuff in a short amount of time. It’s interesting that Facebook has put such an emphasis on this cartoon alone while not having an issue with characters like Homer Simpson having nazi imagery illustrated alongside them as also depicted in the internal docs.

We’ve reached out to Facebook for more details.


Source: Tech Crunch

Fortnite had a $296 million April

Just how big Fortnite? Very big. Very, very big. $296 million in April, big. That’s according to SuperData Research (via The Verge), a service that tracks digital game sales. The number, which includes sales from the console, mobile and PC versions of the game, is up $73 million from just last month.

The sandbox’s survival game’s April numbers are also more than double the $126 million it earned back in February. The title is currently atop Superdata’s console chart, and is number five on the PC, several slots ahead of PlayerUnknown’s Battlegrounds. According to the survey it “once again it broke the record for most additional content revenue in a single month by a console game.”

As for mobile, the title didn’t crack the top 10, but the smartphone has clearly been a driving force in recent growth. Fortnite arrive on iOS in the middle of March, and its upcoming jump to Android is likely to push the title’s success even further. And then, of course, there’s the prize pools.

Earlier this month, Epic added a competition that let players compete for large sums of its in-game currency, V-Bucks. And just this week, the company announced plans to spend $100 million real world dollars on Fornite eSports competitions for the next two years.


Source: Tech Crunch

Yahoo shuts down social savings app Tanda only months after launch

Well, that didn’t take long. Yahoo Finance’s new social savings app Tanda, which launched just this January, is already shutting down. The company announced the news of the app’s closure via a blog post, which vaguely hinted at a lack of traction. That appears to be true – the app isn’t even in the top 1,500 in the Finance category on the App Store, according to Sensor Tower’s data.

It had been installed around 37,000 times to date across both iOS and Android.

Still, tens of thousands in the first few months isn’t an entirely horrible showing for app that received almost no attention, marketing effort or media outreach. (We happened upon it practically by accident – not because Yahoo reached out to press. Yes, even though Yahoo is owned by Oath which also owns us, there wasn’t any internal heads-up. Or even any external pitching. In case you’re wondering!)

The app had allowed people to save money together for short-term goals using the concept of a “money pool” where a group of friends pay a fixed amount to the saving pot monthly, and every month someone takes the pot home. You didn’t “win” this pot, you took turns claiming it. In the end, it was just another way to save money, but the social element helped you stay on track.

Money pools are popular outside the U.S., in places like Mexico and the Philippines, Yahoo notes. It may have been hard to convince the U.S. audience to give them a shot, though.

In any event, Yahoo says Tanda is no more.

“While we garnered valuable insights around how consumers can benefit from financial planning tools and the opportunity for Yahoo Finance to offer a diversified suite of financial products, we’ve made the decision to begin sunsetting Tanda this week,” the blog post reads.

“Every trial run helps brands better optimize, and create a better experience for users. We’ve learned a lot from launching and running Tanda, and then scaling it back. Key learnings around audience segments, engagement rates, consumer preferences, and UX will inform the projects we are creating, and how we improve the ones that are already in the market to fuel future innovation,” it says.

Still, that was a fast learning experience, guys.

In an email sent to Tanda users, the company says the app will be shut down starting on May 29.

Any funds owed to you will be refunded in full, and then your Tanda account will be deactivated, the email states.

Yahoo declined to comment further on the reasons behind the shutdown, but said the Tanda team will continue to support Yahoo Finance.


Source: Tech Crunch

Netflix magic market number larger than big cable company’s magic market number

Netflix’s market cap is now larger than Comcast, which is pretty much just a symbolic thing given that the companies are valued very differently but is like one of those moments where Apple was larger than Exxon and may be some kind of watershed moment for technology. Or not.

A couple notes on this largely symbolic and not really important thing:

  • Netflix users are going up. That’s a number that people look at. It’s why Netflix’s magic market number is going up.
  • People are cutting cable TV cords. Netflix has no cable TV cords. It does, however, require a cord connected to the internet. So it still needs a cord of some sort, unless everything goes wireless.
  • Netflix is spending a lot of money on content. People consume content. Cable is also content, but it is expensive content. Also, Comcast will start bundling in Netflix into its cable subscriptions.
  • They have a very different price-to-earnings ratio. Comcast is valued as a real company. Netflix is valued as a… well, something that is growing that will maybe be a business more massive than Comcast. Maybe.
  • Comcast makes much more money than Netflix. Netflix had $3.7 billion in revenue in Q1. Comcast had $22.8 billion and free cash flow of $3.1 billion. Netflix says it will have -$3 billion to -$4 billion in free cash flow in 2018.

Anyway, Netflix will report its next earnings in a couple months, and this number is definitely going to change, because it’s pretty arbitrary given that Netflix is not valued like other companies. The stock price doesn’t swing as much as Bitcoin, but things can be pretty random.

In the mean time, Riverdale Season 2 is on Netflix, so maybe that’s why it’s more valuable than Comcast . See you guys in a few hours.


Source: Tech Crunch

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: Tech Crunch

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: Tech Crunch

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: Tech Crunch

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: Tech Crunch