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The Pursuit of Creativity Can Make Algorithms Much Smarter

https://www.wired.com/story/the-pursuit-of-creativity-can-make-algorithms-much-smarter

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The steppingstone’s potential can be seen by analogy with biological evolution. In nature, the tree of life has no overarching goal, and features used for one function might find themselves enlisted for something completely different. Feathers, for example, likely evolved for insulation and only later became handy for flight.

Biological evolution is also the only system to produce human intelligence, which is the ultimate dream of many AI researchers. Because of biology’s track record, Stanley and others have come to believe that if we want algorithms that can navigate the physical and social world as easily as we can—or better!—we need to imitate nature’s tactics. Instead of hard-coding the rules of reasoning, or having computers learn to score highly on specific performance metrics, they argue, we must let a population of solutions blossom. Make them prioritize novelty or interestingness instead of the ability to walk or talk. They may discover an indirect path, a set of steppingstones, and wind up walking and talking better than if they’d sought those skills directly.

New, Interesting, Diverse

After Picbreeder, Stanley set out to demonstrate that neuroevolution could overcome the most obvious argument against it: “If I run an algorithm that’s creative to such an extent that I’m not sure what it will produce,” he said, “it’s very interesting from a research perspective, but it’s a harder sell commercially.”

He hoped to show that by simply following ideas in interesting directions, algorithms could not only produce a diversity of results, but solve problems. More audaciously, he aimed to show that completely ignoring an objective can get you there faster than pursuing it. He did this through an approach called novelty search.

The system started with a neural network, which is an arrangement of small computing elements called neurons connected in layers. The output of one layer of neurons gets passed to the next layer via connections that have various “weights.” In a simple example, input data such as an image might be fed into the neural network. As the information from the image gets passed from layer to layer, the network extracts increasingly abstract information about its contents. Eventually, a final layer calculates the highest-level information: a label for the image.

a man smiling
For Kenneth Stanley, a computer scientist at Uber AI Labs and the University of Central Florida, the steppingstone principle explains innovation.Photograph: Asa Mathat

In neuroevolution, you start by assigning random values to the weights between layers. This randomness means the network won’t be very good at its job. But from this sorry state, you then create a set of random mutations — offspring neural networks with slightly different weights — and evaluate their abilities. You keep the best ones, produce more offspring, and repeat. (More advanced neuroevolution strategies will also introduce mutations in the number and arrangement of neurons and connections.) Neuroevolution is a meta-algorithm, an algorithm for designing algorithms. And eventually, the algorithms get pretty good at their job.

To test the steppingstone principle, Stanley and his student Joel Lehman tweaked the selection process. Instead of selecting the networks that performed best on a task, novelty search selected them for how different they were from the ones with behaviors most similar to theirs. (In Picbreeder, people rewarded interestingness. Here, as a proxy for interestingness, novelty search rewarded novelty.)

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In one test, they placed virtual wheeled robots in a maze and evolved the algorithms controlling them, hoping one would find a path to the exit. They ran the evolution from scratch 40 times. A comparison program, in which robots were selected for how close (as the crow flies) they came to the exit, evolved a winning robot only 3 out of 40 times. Novelty search, which completely ignored how close each bot was to the exit, succeeded 39 times. It worked because the bots managed to avoid dead ends. Rather than facing the exit and beating their heads against the wall, they explored unfamiliar territory, found workarounds, and won by accident. “Novelty search is important because it turned everything on its head,” said Julian Togelius, a computer scientist at New York University, “and basically asked what happens when we don’t have an objective.”

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These ten enterprise M&A deals totaled over $40B in 2019

It would be hard to top the 2018 enterprise M&A total of a whopping $87 billion, and predictably this year didn’t come close. In fact, the top 10 enterprise M&A deals in 2019 were less than half last year’s, totaling $40.6 billion. This year’s biggest purchase was Salesforce buying Tableau for $15.7 billion, which would…

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These ten enterprise M&A deals totaled over $40B in 2019

It would be hard to top the 2018 enterprise M&A total of a whopping $87 billion, and predictably this year didn’t come close. In fact, the top 10 enterprise M&A deals in 2019 were less than half last year’s, totaling $40.6 billion.

This year’s biggest purchase was Salesforce buying Tableau for $15.7 billion, which would have been good for third place last year behind IBM’s mega deal plucking Red Hat for $34 billion and Broadcom grabbing CA Technologies for $18.8 billion.

Contributing to this year’s quieter activity was the fact that several typically acquisitive companies — Adobe, Oracle and IBM — stayed mostly on the sidelines after big investments last year. It’s not unusual for companies to take a go-slow approach after a big expenditure year. Adobe and Oracle bought just two companies each with neither revealing the prices. IBM didn’t buy any.

Microsoft didn’t show up on this year’s list either, but still managed to pick up eight new companies. It was just that none was large enough to make the list (or even for them to publicly reveal the prices). When a publicly traded company doesn’t reveal the price, it usually means that it didn’t reach the threshold of being material to the company’s results.

As always, just because you buy it doesn’t mean it’s always going to integrate smoothly or well, and we won’t know about the success or failure of these transactions for some years to come. For now, we can only look at the deals themselves.

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Jumia, DHL, and Alibaba will face off in African ecommerce 2.0

The business of selling consumer goods and services online is a relatively young endeavor across Africa, but ecommerce is set to boom. Over the last eight years, the sector has seen its first phase of big VC fundings, startup duels and attrition. To date, scaling e-commerce in Africa has straddled the line of challenge and…

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Jumia, DHL, and Alibaba will face off in African ecommerce 2.0

The business of selling consumer goods and services online is a relatively young endeavor across Africa, but ecommerce is set to boom.

Over the last eight years, the sector has seen its first phase of big VC fundings, startup duels and attrition.

To date, scaling e-commerce in Africa has straddled the line of challenge and opportunity, perhaps more than any other market in the world. Across major African economies, many of the requisites for online retail — internet access, digital payment adoption, and 3PL delivery options — have been severely lacking.

Still, startups jumped into this market for the chance to digitize a share of Africa’s fast growing consumer spending, expected to top $2 billion by 2025.

African e-commerce 2.0 will include some old and new players, play out across more countries, place more priority on internet services, and see the entry of China.

But before highlighting several things to look out for in the future of digital-retail on the continent, a look back is beneficial.

Jumia vs. Konga

The early years for development of African online shopping largely played out in Nigeria (and to some extent South Africa). Anyone who visited Nigeria from 2012 to 2016 likely saw evidence of one of the continent’s early e-commerce showdowns. Nigeria had its own Coke vs. Pepsi-like duel — a race between ventures Konga and Jumia to out-advertise and out-discount each other in a quest to scale online shopping in Africa’s largest economy and most populous nation.

Traveling in Lagos traffic, large billboards for each startup faced off across the skyline, as their delivery motorcycles buzzed between stopped cars.

Covering each company early on, it appeared a battle of VC attrition. The challenge: who could continue to raise enough capital to absorb the losses of simultaneously capturing and creating an e-commerce market in notoriously difficult conditions.

In addition to the aforementioned challenges, Nigeria also had (and continues to have) shoddy electricity.

Both Konga — founded by Nigerian Sim Shagaya — and Jumia — originally founded by two Nigerians and two Frenchman — were forced to burn capital building fulfillment operations most e-commerce startups source to third parties.

That included their own delivery and payment services (KongaPay and JumiaPay). In addition to sales of goods from mobile-phones to diapers, both startups also began experimenting with verticals for internet based services, such as food-delivery and classifieds.

While Jumia and Konga were competing in Nigeria, there was another VC driven race for e-commerce playing out in South Africa — the continent’s second largest and most advanced economy.

E-tailers Takealot and Kalahari had been jockeying for market share since 2011 after raising capital in the hundreds of millions of dollars from investors Naspers and U.S. fund Tiger Global Management.

So how did things turn out in West and Southern Africa? In 2014, the lead investor of a flailing Kalahari — Naspers — facilitated a merger with Takealot (that was more of an acquisition). They nixed the Kalahari brand in 2016 and bought out Takelot’s largest investor, Tiger Global, in 2018. Takealot is now South Africa’s leading e-commerce site by market share, but only operates in one country.

In Nigeria, by 2016 Jumia had outpaced its rival Konga in Alexa ratings (6 vs 14), while out-raising Konga (with backing of Goldman Sachs) to become Africa’s first VC backed, startup unicorn. By early 2018, Konga was purchased in a distressed acquisition and faded away as a competitor to Jumia.

Jumia went on to expand online goods and services verticals into 14 Africa countries (though it recently exited a few) and in April 2019 raised over $200 million in an NYSE IPO — the first on a major exchange for a VC-backed startup operating in Africa.

Jumia’s had bumpy road since going public — losing significant share-value after a short-sell attack earlier in 2019 — but the continent’s leading e-commerce company still has heap of capital and generates $100 million in revenues (even with losses).

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Airbnb’s New Year’s Eve guest volume shows its falling growth rate

Hello and welcome back to our regular morning look at private companies, public markets and the gray space in between. It’s finally 2020, the year that should bring us a direct listing from home-sharing giant Airbnb, a technology company valued at tens of billions of dollars. The company’s flotation will be a key event in…

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Airbnb’s New Year’s Eve guest volume shows its falling growth rate

Hello and welcome back to our regular morning look at private companies, public markets and the gray space in between.

It’s finally 2020, the year that should bring us a direct listing from home-sharing giant Airbnb, a technology company valued at tens of billions of dollars. The company’s flotation will be a key event in this coming year’s technology exit market. Expect the NYSE and Nasdaq to compete for the listing, bankers to queue to take part, and endless media coverage.

Given that that’s ahead, we’re going to take periodic looks at Airbnb as we tick closer to its eventual public market debut. And that means that this morning we’re looking back through time to see how fast the company has grown by using a quirky data point.

Airbnb releases a regular tally of its expected “guest stays” for New Year’s Eve each year, including 2019. We can therefore look back in time, tracking how quickly (or not) Airbnb’s New Year Eve guest tally has risen. This exercise will provide a loose, but fun proxy for the company’s growth as a whole.

The numbers

Before we look into the figures themselves, keep in mind that we are looking at a guest figure which is at best a proxy for revenue. We don’t know the revenue mix of the guest stays, for example, meaning that Airbnb could have seen a 10% drop in per-guest revenue this New Year’s Eve — even with more guest stays — and we’d have no idea.

So, the cliche about grains of salt and taking, please.

But as more guests tends to mean more rentals which points towards more revenue, the New Year’s Eve figures are useful as we work to understand how quickly Airbnb is growing now compared to how fast it grew in the past. The faster the company is expanding today, the more it’s worth. And given recent news that the company has ditched profitability in favor of boosting its sales and marketing spend (leading to sharp, regular deficits in its quarterly results), how fast Airbnb can grow through higher spend is a key question for the highly-backed, San Francisco-based private company.

Here’s the tally of guest stays in Airbnb’s during New Years Eve (data via CNBC, Jon Erlichman, Airbnb), and their resulting year-over-year growth rates:

  • 2009: 1,400
  • 2010: 6,000 (+329%)
  • 2011: 3,1000 (+417%)
  • 2012: 108,000 (248%)
  • 2013: 250,000 (+131%)
  • 2014: 540,000 (+116%)
  • 2015: 1,100,000 (+104%)
  • 2016: 2,000,000 (+82%)
  • 2017: 3,000,000 (+50%)
  • 2018: 3,700,000 (+23%)
  • 2019: 4,500,000 (+22%)

In chart form, that looks like this:

Let’s talk about a few things that stand out. First is that the company’s growth rate managed to stay over 100% for as long as it did. In case you’re a SaaS fan, what Airbnb pulled off in its early years (again, using this fun proxy for revenue growth) was far better than a triple-triple-double-double-double.

Next, the company’s growth rate in percentage terms has slowed dramatically, including in 2019. At the same time the firm managed to re-accelerate its gross guest growth in 2019. In numerical terms, Airbnb added 1,000,000 New Year’s Eve guest stays in 2017, 700,000 in 2018, and 800,000 in 2019. So 2019’s gross adds was not a record, but it was a better result than its year-ago tally.

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