Author Archives: Elizabeth Agatha

Launch with TechCrunch in the Startup Battlefield competition at Disrupt SF 2018



Hey, this message goes out to all you early-stage startup founders with the drive and determination to take your company all the way to the land of the unicorns. Now’s the time to apply to TechCrunch’s Startup Battlefield competition — the world’s best start-up pitch competition going down at Disrupt San Francisco 2018 on September 5-7. If you require incentive, consider this: we bumped the top prize this year to a very cool $100,000. That sure would help your bottom line, now wouldn’t it?

If you’re not tuned in to how Startup Battlefield works, we’ll break it down for you. Seasoned TechCrunch editors review all applications in a highly-competitive vetting process. How competitive? The acceptance rate ranges from 3 to 6 percent. Factors that influence the decision include the team, the product and the market potential. Anywhere from 15-30 pre-Series A startups will make the final cut. This is a good time to point out that competing in Startup Battlefield doesn’t cost a thing, and that TechCrunch does not charge startups any fees or take any equity.

All competing teams receive free expert pitch training from the Startup Battlefield team who, trust us, have seen it all when it comes to startup pitch competitions. You’ll be primed and ready for round one where you’ll deliver a six-minute pitch and demo to a panel of expert judges — and then answer any questions they may have. The cream of the crop — roughly five teams — will advance to round two for a repeat pitch performance in front of a fresh set of judges.

Every exciting, heart-pounding minute takes place in front of a live audience numbering in the thousands, and we live-stream it to the world on TechCrunch.com, YouTube, Facebook and Twitter. And it’ll be available later on-demand.

But the benefits of competing go far beyond winning the $100,000 cash prize. More than 400 media outlets will attend Disrupt SF’18, and an intense media spotlight will shine brightly on all Startup Battlefield contestants. Just making it into the initial Startup Battlefield cohort can draw significant investor interest. Aircall, a cloud-based call center solution, stands as a prime example.

The French startup competed in round one of Startup Battlefield SF 2015. It didn’t make the finals, but the company just received another round of funding to the tune of $29 million. Since its debut at Startup Battlefield three years ago, the company has gone on to raise a total of $40.5 million. Not too shabby for an “also-ran.”

All startups who compete will join the ranks of the Startup Battlefield alumni community. This impressive group of over 800 companies has collectively raised more than $8 billion in funding and produced more than 100 exits. Companies like Mint, Dropbox, Yammer, Fitbit, Getaround and Cloudflare — just to drop a few names. Networking as part of this community has its advantages.

So many reasons to apply, and we haven’t even talked about all the other fabulous happenings at Disrupt SF. More than 10,000 attendees will stream through Startup Alley, home to more than 1,200 of the best early-stage startups around. Twelve different tech tracks, four stages of unique programming, speakers, Q & A Sessions, after parties and world-class networking — made even easier with CrunchMatch, our curated investor-to-startup matching platform.

Disrupt San Francisco 2018 takes place on September 5-7 at Moscone Center West. Apply to Startup Battlefield. You have nothing to lose and everything to gain.

Source

Tradeshift fires-up blockchain to address late payment problem



While the cryptocurrency world continues to swirl around in a daze of troughs and highs, startups are continuing to make use of the fundamental underlying strengths of blockchain technology.

A new entrant in this race is Tradeshift, a leading players in supply-chain payments and marketplaces, which is today launching its new service which enables supports blockchain-based finance, or writing all transactions to a public ledger in order to create transparency and securing a record.

While this doesn’t involve the use of currencies like actual Bitcoin or Ethereum, “having the transactions on a public ledger ensures full transparency and the ability for companies to prove that they have legit transactions,” says CEO and cofounder Christian Lanng.

SO what this all means is that Tradeshift’s cloud platform will bring supply chain payments, supply chain finance, and blockchain-based early payments together into one unified end-to-end solution, called “Tradeshift Pay”.

They are aiming at a $9 trillion problem, which is the capital trapped in “accounts receivable” as a result of old-fashioned payment practices and the disconnection between large business buyers and their suppliers.

In other words, this could be a boon for small suppliers who find it hard to get paid when their invoices aren’t mapped to a ledger as strong as a blockchain.

With this single unified wallet, buyers can use several payment options, including virtual card payments of invoices and purchase orders, dynamic discounting, supply chain finance through bank partners, or blockchain-based payments.

Source

Whisk, the smart food platform that makes recipes shoppable, acquires competitor Avocando



Whisk, the U.K. startup that has built a B2B data platform to power various food apps, including making online recipes ‘shoppable’, has acquired Avocando, a competitor based in Germany.

The exact financial terms of the deal remain undisclosed, although TechCrunch understands it was all-cash and that Whisk is acquiring the tech, customer base, integrations, and team. Related to this, Avocando’s founders are joining Whisk.

“The team is joining Whisk to help scale a joint global vision to help leading businesses create integrated and meaningful digital food experiences using cutting-edge technology,” says Whisk in a statement.

To that end, Whisk’s “smart food platform” enables app developers, publishers and online supermarkets/grocery stories to do a number of interesting things.

The first relates to making recipes shoppable i.e. making it incredibly easy to order the ingredients needed to cook a recipe listed online or in an app. Specifically, Whisk’s platform parses ingredients in a recipe, and matches it to products at local grocery stores based on user preferences (e.g. “50g of butter, cubed” matched to “250g Tesco Salted Butter”). It then interfaces with the store to fill the users basket with the needed items.

The second is recipe personalisation. Based on user preferences (e.g. disliked ingredients, diet, previous behaviour, deals at a favourite store, and trending recipes based on location), Whisk is able to create personalised recipe feeds, search results, and meal plans.

The third aspect is an Internet-of-Things play. This is seeing Whisk’s data power experiences that connect IoT devices with different parts of a user’s journey. Think: smart fridges connected to recipes.

“As the e-commerce grocery market quickly accelerates across Europe, players are increasingly looking for ways to connect recipe content to grocery retailers and provide consumers with personalized nutrition, planning and purchase options right from the comfort of their kitchen,” says the startup.

Whisk says its platform powers experiences for over 100,000,000 monthly users through the applications of its clients. They include retailers like Walmart, Amazon, Instacart, and Tesco who use Whisk to enable online grocery shopping via recipes. On the IoT front, Samsung is using Whisk to build smart food applications that take user preferences, what’s in their fridge, what offers are in the supermarket, and recommends recipes. Other customers include publishers, such as the BBC, and food brands like McCormick, Nestle, Unilever, and General Mills.

Meanwhile, Whisk says it is currently focused on the U.S., U.K. and Australia, and with today’s acquisition will expand services across Europe. “Together, Germany, France and Spain represent a larger e-commerce grocery market than both the U.S. and U.K. individually, with the largest online recipe usage per capita figures in the world,” adds the company.

Source

Microsoft acquires conversational AI startup Semantic Machines to help bots sound more lifelike



Microsoft announced today that it has acquired Semantic Machines, a Berkeley-based startup that wants to solve one of the biggest challenges in conversational AI: making chatbots sound more human and less like, well, bots.

In a blog post, Microsoft AI & Research chief technology officer David Ku wrote that “with the acquisition of Semantic Machines, we will establish a conversational AI center of excellence in Berkeley to push forward the boundaries of what is possible in language interfaces.”

According to Crunchbase, Semantic Machines was founded in 2014 and raised about $20.9 million in funding from investors including General Catalyst and Bain Capital Ventures.

In a 2016 profile, co-founder and chief scientist Dan Klein told TechCrunch that “today’s dialog technology is mostly orthogonal. You want a conversational system to be contextual so when you interpret a sentence things don’t stand in isolation.” By focusing on memory, Semantic Machines’ AI can produce conversations that not only answer or predict questions more accurately, but also flow naturally.

Instead of building its own consumer products, Semantic Machines focused on enterprise customers. This means it will fit in well with Microsoft’s conversational AI-based products, including Microsoft Cognitive Services and Azure Bot Service, which are used by one million and 300,000 developers, respectively, and virtual assistants Cortana and Xiaolce.

Source

OnePlus 6 review: Flagship Killer



There are no shortcuts to greatness and no smartphone maker knows this more than Chinese startup OnePlus.

To fully understand and appreciate the company’s new OnePlus 6 Android smartphone, we need to take a brief trip back a couple of years.

In 2014, the company nobody had heard of had, against all odds, successfully launched its first phone, the OnePlus One.

The sales pitch was remarkably simple: The best Android hardware paired with virtually unmodified (aka “stock”) Android software with prices that’d cost hundreds less than premium phones from Samsung, LG, HTC, Sony, or whoever.

Android diehards flocked to OnePlus. Through word of mouth and gimmicky “invite-to-buy” tactics, the company delivered what Samsung and Google’s Nexus devices couldn’t. Read more…

More about Mobile, Gadgets, Android, Smartphones, and Reviews

Progressive advocacy groups call on the FTC to “make Facebook safe for democracy”



A team of progressive advocacy groups, including MoveOn and Demand Progress, are asking the Federal Trade Commission to “make Facebook safe for democracy.” According to Axios, the campaign, called Freedom From Facebook, will launch a six-figure ad campaign on Monday that will run on Facebook, Instagram and Twitter, among other platforms.

The other advocacy groups behind the campaign are Citizens Against Monopoly, Content Creators Coalition, Jewish Voice for Peace, Mpower Change, Open Markets Institute and SumOfUs. Together they are calling on the FTC to “break up Facebook’s monopoly” by forcing it to spin off Instagram, WhatsApp and Messenger into separate, competing companies. They also want the FTC to require interoperability so users can communicate against competing social networks and strengthen privacy regulations.

Freedom From Facebook’s site also includes an online petition and privacy guide that links to FB Purity and the Electronic Frontier Foundation’s Privacy Badger, browser extensions that help users streamline their Facebook ad preferences and block online trackers, respectively.

The FTC recently gained a new chairman after President Donald Trump’s pick for the position Joseph Simons was sworn in early this month, along with four new commissioners also nominated by Trump. Simons is an antitrust lawyer who has represented large tech firms like Microsoft and Sony. The FTC is currently investigating whether or not Facebook’s involvement with Cambridge Analytica violated a previous legal agreement it had with the commission, but many people are wondering if it and other federal agencies are capable of regulating tech companies, especially after many lawmakers seemed confused about how social media works during Facebook CEO Mark Zuckerberg’s Congressional hearing last month.

Despite its data privacy and regulatory issues, Facebook is still doing well from a financial perspective. Its first-quarter earnings report showed strong user growth and revenue above Wall Street’s expectations.

TechCrunch has contacted Freedom From Facebook and Facebook for comment.

Source

Are algorithms hacking our thoughts?



As Facebook shapes our access to information, Twitter dictates public opinion, and Tinder influences our dating decisions, the algorithms we’ve developed to help us navigate choice are now actively driving every aspect of our lives.

But as we increasingly rely on them for everything from how we seek out news to how we relate to the people around us, have we automated the way we behave? Is human thinking beginning to mimic algorithmic processes? And is the Cambridge Analytica debacle a warning sign of what’s to come–and of happens when algorithms hack into our collective thoughts?

It wasn’t supposed to go this way. Overwhelmed by choice–in products, people, and the sheer abundance of information coming at us at all times–we’ve programmed a better, faster, easier way to navigate the world around us. Using clear parameters and a set of simple rules, algorithms help us make sense of complex issues. They’re our digital companions, solving real-world problems we encounter at every step, and optimizing the way we make decisions. What’s the best restaurant in my neighborhood? Google knows it. How do I get to my destination? Apple Maps to the rescue. What’s the latest Trump scandal making the headlines? Facebook may or may not tell you.

Wouldn’t it be nice if code and algorithms knew us so well — our likes, our dislikes, our preferences — that they could anticipate our every need and desire? That way, we wouldn’t have to waste any time thinking about it: We could just read the one article that’s best suited to reinforce our opinions, date whoever meets our personalized criteria, and revel in the thrill of familiar surprise. Imagine all the time we’d free up, so we could focus on what truly matters: carefully curating our digital personas and projecting our identities on Instagram.

It was Karl Marx who first said our thoughts are determined by our machinery, an idea that Ellen Ullman references in her 1997 book, Close to the Machine, which predicts many of the challenges we’re grappling with today. Beginning with the invention of the Internet, the algorithms we’ve built to make our lives easier have ended up programming the way we behave.

Photo courtesy of Shutterstock/Lightspring

Here are three algorithmic processes and the ways in which they’ve hacked their way into human thinking, hijacking our behavior.

1. Product Comparison: From Online Shopping to Dating

Amazon’s algorithm allows us to browse and compare products, save them for later, and eventually make our purchase. But what started as a tool designed to improve our e-commerce experience now extends much beyond that. We’ve internalized this algorithm and are applying it to other areas of our lives–like relationships.

Dating today is much like online shopping. Enabled by social platforms and apps, we browse endless options, compare their features, and select the one that taps into our desires and perfectly fits our exact personal preferences. Or just endlessly save it for later, as we navigate the illusion of choice that permeates both the world of e-commerce and the digital dating universe.

Online, the world becomes an infinite supply of products, and now, people. “The web opens access to an unprecedented range of goods and services from which you can select the one thing that will please you the most,” Ullman explains in Life in Code. “[There is the idea] that from that choice comes happiness. A sea of empty, illusory, misery-inducing choice.”

We all like to think that our needs are completely unique–and there’s a certain sense of seduction and pleasure that we derive from the promise of finding the one thing that will perfectly match our desires.

Whether it’s shopping or dating, we’ve been programmed to constantly search, evaluate and compare. Driven by algorithms, and in a larger sense, by web design and code, we’re always browsing for more options. In Ullman’s words, the web reinforces the idea that “you are special, your needs are unique, and [the algorithm] will help you find the one thing that perfectly meets your unique need and desire.”

In short, the way we go about our lives mimics the way we engage with the Internet. Algorithms are an easy way out, because they allow us to take the messiness of human life, the tangled web of relationships and potential matches, and do one of two things: Apply a clear, algorithmic framework to deal with it, or just let the actual algorithm make the choice for us. We’re forced to adapt to and work around algorithms, rather than use technology on our terms.

Which leads us to another real-life phenomenon that started with a simple digital act: rating products and experiences.

2. Quantifying People: Ratings & Reviews

As with all other well-meaning algorithms, this one is designed with you and only you in mind. Using your feedback, companies can better serve your needs, provide targeted recommendations just for you, and serve you more of what you’ve historically shown to like, so you can carry on mindlessly consuming it.

From your Uber ride to your Postmate delivery to your Handy cleaning appointment, nearly every real-life interaction is rated on a scale of 1-5 and reduced to a digital score.

As a society we’ve never been more concerned with how we’re perceived, how we perform, and how we compare to others’ expectations. We’re suddenly able to quantify something as subjective as our Airbnb host’s design taste or cleanliness. And the sense of urgency with which we do it is incredible — you’re barely out of your Uber car when you neurotically tap all five stars, tipping with wild abandon in a quest to improve your passenger rating. And the rush of being reviewed in return! It just fills you with utmost joy.

Yes, you might be thinking of that dystopian Black Mirror scenario, or that oddly relatable Portlandia sketch, but we’re not too far off from a world where our digital score simultaneously replaces and drives all meaning in our lives.

We’ve automated the way we interact with people, where we’re constantly measuring and optimizing those interactions in an endless cycle of self-improvement. It started with an algorithm, but it’s now second nature.

As Jaron Lainier wrote in his introduction to Close to the Machine, “We create programs using ideas we can feed into them, but then [as] we live through the program. . .we accept the ideas embedded in it as facts of nature.”

That’s because technology makes abstract and often elusive, desirable qualities quantifiable. Through algorithms, trust translates into ratings and reviews, popularity equals likes, and social status means followers. Algorithms create a sort of Baudrillardian simulation, where each rating has completely replaced the reality it refers to, and where the digital review feels more real, and certainly more meaningful, than the actual, real-life experience.

In facing the complexity and chaos of real life, algorithms help us find ways to simplify it; to take the awkwardness out of social interaction and the insecurity that comes with opinions and real-life feedback, and make it all fit neatly into a ratings box.

But as we adopt programming language, code, and algorithms as part of our own thinking, are human nature and artificial intelligence merging into one? We’re used to think of AI as an external force, something we have little control over. What if the most immediate threat of AI is less about robots taking over the world, and more about technology becoming more embedded into our consciousness and subjectivity?

In the same way that smartphones became extensions of our senses and our bodies, as Marshall McLuhan might say, algorithms are essentially becoming extensions of our thoughts. But what do we do when when they replace the very qualities that make us human?

And, as Lainier asks, “As computers mediate human language more and more over time, will language itself start to change?”

Image: antoniokhr/iStock

3. Automating Language: Keywords and Buzzwords

Google indexes search results based on keywords. SEO makes websites rise to the top of search results, based on specific tactics. To achieve this, we work around the algorithm, figure out what makes it tick, and sprinkle websites with keywords that make it more likely to stand out in Google’s eyes.

But much like Google’s algorithm, our mind prioritizes information based on keywords, repetition, and quick cues.

It started as a strategy we built around technology, but it now seeps into everything we do–from the the way we write headlines to how we generate “engagement” with our tweets to how we express ourselves in business and everyday life.

Take the buzzword mania that dominates both the media landscape and the startup scene. A quick look at some of the top startups out there will show that the best way to capture people’s attention–and investors’ money–is to add “AI,” “crypto” or “blockchain” into your company manifesto.

Companies are being valuated based on what they’re signifying to the world through keywords. The buzzier the keywords in the pitch deck, the higher the chances a distracted investor will throw some money at it. Similarly, a headline that contains buzzwords is far more likely to be clicked on, so the buzzwords start outweighing the actual content. Clickbait being one symptom of that.

Where do we go from here?

Technology gives us clear patterns; online shopping offers simple ways to navigate an abundance of choice. Therefore there’s no need to think — we just operate under the assumption that algorithms know best. We don’t exactly understand how they work, and that’s because code is hidden: we can’t see it, the algorithm just magically presents results and solutions. As Ullman warns in Life in Code, “When we allow complexity to be hidden and handled for us, we should at least notice what we are giving up. We risk becoming users of components. . .[as we] work with mechanisms that we do not understand in crucial ways. This not-knowing is fine while everything works as expected. But when something breaks or goes wrong or needs fundamental change, what will we do except stand helpless in the face of our own creations?”

Cue fake news, misinformation, and social media targeting in the age of Trump.

Image courtesy of Intellectual Take Out.

So how do we encourage critical thinking, how do we spark more interest in programming, how do we bring back good old-fashioned debate and disagreement? What can we do to foster difference of opinion, let it thrive, and allow it to challenge our views?

When we operate within the bubble of distraction that technology creates around us, and when our social media feeds consist of people who think just like us, how can we expect social change? What ends up happening is we operate exactly as the algorithm intended us to. The alternative is questioning the status quo, analyzing the facts and arriving at our own conclusions. But no one has time for that. So we become cogs in the Facebook machine, more susceptible to propaganda, blissfully unaware of the algorithm at work–and of all the ways in which it has inserted itself into our thought processes.

As users of algorithms rather than programmers or architects of our own decisions, our own intelligence become artificial. It’s “program or be programmed” as Douglas Rushkoff would say. If we’ve learned anything from Cambridge Analytica and the 2016 U.S. elections, it’s that it is surprisingly easy to reverse-engineer public opinion, to influence outcomes, and to create a world where data, targeting, and bots lead to a false sense of consensus.

What’s even more disturbing is that the algorithms we trust so much–the ones that are deeply embedded in the fabric of our lives, driving our most personal choices–continue to hack into our thought processes, in increasingly bigger and more significant ways. And they will ultimately prevail in shaping the future of our society, unless we reclaim our role as programmers, rather than users of algorithms.

Source

Nvidia’s researchers teach a robot to perform simple tasks by observing a human



Industrial robots are typically all about repeating a well-defined task over and over again. Usually, that means performing those tasks a safe distance away from the fragile humans that programmed them. More and more, however, researchers are now thinking about how robots and humans can work in close proximity to humans and even learn from them. In part, that’s what Nvidia’s new robotics lab in Seattle focuses on and the company’s research team today presented some of its most recent work around teaching robots by observing humans at the International Conference on Robotics and Automation (ICRA), in Brisbane, Australia.

Nvidia’s director of robotics research Dieter Fox.

As Dieter Fox, the senior director of robotics research at Nvidia (and a professor at the University of Washington), told me, the team wants to enable this next generation of robots that can safely work in close proximity to humans. But to do that, those robots need to be able to detect people, tracker their activities and learn how they can help people. That may be in small-scale industrial setting or in somebody’s home.

While it’s possible to train an algorithm to successfully play a video game by rote repetition and teaching it to learn from its mistakes, Fox argues that the decision space for training robots that way is far too large to do this efficiently. Instead, a team of Nvidia researchers led by Stan Birchfield and Jonathan Tremblay, developed a system that allows them to teach a robot to perform new tasks by simply observing a human.

The tasks in this example are pretty straightforward and involve nothing more than stacking a few colored cubes. But it’s also an important step in this overall journey to enable us to quickly teach a robot new tasks.

The researchers first trained a sequence of neural networks to detect objects, infer the relationship between them and then generate a program to repeat the steps it witnessed the human perform. The researchers say this new system allowed them to train their robot to perform this stacking task with a single demonstration in the real world.

One nifty aspect of this system is that it generates a human-readable description of the steps it’s performing. That way, it’s easier for the researchers to figure out what happened when things go wrong.

Nvidia’s Stan Birchfield tells me that the team aimed to make training the robot easy for a non-expert — and few things are easier to do than to demonstrate a basic task like stacking blocks. In the example the team presented in Brisbane, a camera watches the scene and the human simply walks up, picks up the blocks and stacks them. Then the robot repeats the task. Sounds easy enough, but it’s a massively difficult task for a robot.

To train the core models, the team mostly used synthetic data from a simulated environment. As both Birchfield and Fox stressed, it’s these simulations that allow for quickly training robots. Training in the real world would take far longer, after all, and can also be more far more dangerous. And for most of these tasks, there is no labeled training data available to begin with.

“We think using simulation is a powerful paradigm going forward to train robots do things that weren’t possible before,” Birchfield noted. Fox echoed this and noted that this need for simulations is one of the reasons why Nvidia thinks that its hardware and software is ideally suited for this kind of research. There is a very strong visual aspect to this training process, after all, and Nvidia’s background in graphics hardware surely helps.

Fox admitted that there’s still a lot of research left to do be done here (most of the simulations aren’t photorealistic yet, after all), but that the core foundations for this are now in place.

Going forward, the team plans to expand the range of tasks that the robots can learn and the vocabulary necessary to describe those tasks.

Source

With at least $1.3 billion invested globally in 2018, VC funding for blockchain blows past 2017 totals



Although bitcoin and blockchain technology may not take up quite as much mental bandwidth for the general public as it did just a few months ago, companies in the space continue to rake in capital from investors.

One of the latest to do so is Circle, which recently announced a $110 million Series E round led by bitcoin mining hardware manufacturer Bitmain. Other participating investors include Tusk VenturesPantera CapitalIDG Capital PartnersGeneral CatalystAccel PartnersDigital Currency GroupBlockchain Capital and Breyer Capital.

This round vaults Circle into an exclusive club of crypto companies that are valued, in U.S. dollars, at $1 billion or more in their most recent venture capital round. According to Crunchbase data, Circle was valued at $2.9 billion pre-money, up from a $420 million pre-money valuation in its Series D round, which closed in May 2016. According to Crunchbase data, only Coinbase and Robinhood — a mobile-first stock-trading platform which recently made a big push into cryptocurrency trading — were in the crypto-unicorn club, which Circle has now joined.

But that’s not the only milestone for the world of venture-backed cryptocurrency and blockchain startups.

Back in February, Crunchbase News predicted that the amount of money raised in old-school venture capital rounds by blockchain and blockchain-adjacent startups in 2018 would surpass the amount raised in 2017. Well, it’s only May, and it looks like the prediction panned out.

In the chart below, you’ll find worldwide venture deal and dollar volume for blockchain and blockchain-adjacent companies. We purposely excluded ICOs, including those that had traditional VCs participate, and instead focused on venture deals: angel, seed, convertible notes, Series A, Series B and so on. The data displayed below is based on reported data in Crunchbase, which may be subject to reporting delays, and is, in some cases, incomplete.

A little more than five months into 2018, reported dollar volume invested in VC rounds raised by blockchain companies surpassed 2017’s totals. Not just that, the nearly $1.3 billion in global dollar volume is greater than the reported funding totals for the 18 months between July 1, 2016 and New Year’s Eve in 2017.

And although Circle’s Series E round certainly helped to bump up funding totals year-to-date, there were many other large funding rounds throughout 2018:

There were, of course, many other large rounds over the past five months. After all, we had to get to $1.3 billion somehow.

All of this is to say that investor interest in the blockchain space shows no immediate signs of slowing down, even as the price of bitcoin, ethereum and other cryptocurrencies hover at less than half of their all-time highs. Considering that regulators are still figuring out how to treat most crypto assets, massive price volatility and dubious real-world utility of the technology, it may surprise some that investors at the riskiest end of the risk capital pool invest as much as they do in blockchain.

Notes on methodology

Like in our February analysis, we first created a list of companies in Crunchbase’s bitcoin, ethereum, blockchaincryptocurrency and virtual currency categories. We added to this list any companies that use those keywords, as well as “digital currency,” “utility token” and “security token” that weren’t previously included in the above categories. After de-duplicating this list, we merged this set of companies with funding rounds data in Crunchbase.

Please note that for some entries in Crunchbase’s round data, the amount of capital raised isn’t known. And, as previously noted, Crunchbase’s data is subject to reporting delays, especially for seed-stage companies. Accordingly, actual funding totals are likely higher than reported here.

Source

Nancy Drew is the most important game series no one talks about



This post is part of Mashable’s You’re Old Week. Break through the haze of nostalgia with us and see what holds up, what disappoints, and what got better with time.

I will never forget the first time I became Nancy Drew.

As a little girl, I wasn’t allowed to play video games — which meant I played video games, but only through well-executed schemes. At the crux of my early, forbidden play experiences was the Nancy Drew computer game series. I’d sneak them into the Scholastic book order forms we’d get at school, right under my parents’ noses.

While the rest of the house slept, I sleuthed until night turned into dawn. By the light of the blue screen, I squinted at hastily written notes scrawled down in a notebook about potential leads. I questioned suspects with a balance of skepticism and open mindedness, because that’s how you got to the bottom of things. Read more…

More about Entertainment, Nostalgia, Feminism, Nancy Drew, and You Re Old Week