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Archivo del Autor: Belen De Leon

Early Go-Jek investor NSI Ventures goes independent and rebrands to Openspace

NSI Ventures, the Singapore-based VC firm affiliated with PE firm Northstar Group that invested early in ride-sharing unicorn Go-Jek, is going independent after it announced it has rebranded to Openspace Ventures.

NSI Ventures was started by Hian Goh, an entrepreneur who sold his startup Asia Food Channel in 2013, and finance exec Shane Chesson in 2014. The firm was initially conceived as the venture capital arm of Northstar, which manages some $2 billion in assets with a focus on Indonesia.

In a statement, Goh paid tribute to Northstar’s support but said that “the moment has come for us to bring Openspace Ventures to the next stage, as an independent, Southeast Asia-focused venture fund manager.”

Following its spin-out, Northstar Group co-founder and managing partner Patrick Walujo will become a senior advisor to the firm, providing “strategic advice” on investments in Indonesia.

Openspace is best known in Southeast Asia for its early investment in Go-Jek, the Indonesian ride-sharing company that is valued at over $4 billion and in the process of expanding across Southeast Asia. To date it has invested in 19 companies, including online fashion brand Love Bonito, roti bread maker Zimplistic, restaurant discovery service Chope, and digital insurance startup Axinan. It announced its second $125 million fund last December, a final close for which is expected this year.

The firm said that 12 of its 15 investment deals have since secured follow-on funding, with a total of $2.2 billion poured in. Much of that comes from Go-Jek, but even removing two recent rounds that total around $2 billion, leaves good numbers for the rest of its portfolio.

In fact, a recent report from investment tracker Prequin listed the firm as the third highest performing investment fund worldwide for those created between 2003-2015. That shows progress but the proof is in the pudding, and for VCs that means LP returns. Openspace, like others in exit-starved Southeast Asia, is yet to have a liquidation event despite making promising progress.


Source: TechCrunch

Business solutions firm Africa’s Talking closes $8.6M round led by IFC

Business APIs in Africa are getting a boost from global venture capital thanks to a new $8.6 million round for Africa’s Talking—a Kenyan based enterprise software company.

The new financing was led by IFC Venture Capital, with participation from Orange Digital Ventures and the investor that’s upending most Silicon Valley mores, Social Capital, led by former Facebook exec Chamath Palihapitiya.

Capital from the round will be used to hire and build capacity at the company’s Nairobi headquarters; expand its presence in other geographies around Africa; and invest in research and development in IoT, analytics, payments, and cloud offerings, according to chief executive Samuel Gikandi.

The company will open a tech studio for “engineers and developers to collocate with Africa’s Talking for 12-18 months…to build new products and companies,” Gikandi said.

Africa’s Talking operates in Kenya, Uganda, Rwanda, Malawi, Nigeria, Ethiopia, and Tanzania and maintains a private cloud space in London through Rackspace. The company works with developers to create solution focused APIs across SMS, voice, payment, and airtime services.

“We’re helping software developers in Africa connect to local infrastructure,” said Gikandi. “We find companies that have local infrastructure, whether its mobile operators, banks, or data-centers, then we partner with them and build a platform that simplifies access to that local infrastructure and open it up to software developers.”

Africa’s Talking has a network of 20,000 software developers and 1000 clients  including fintech lending startup Talasolar power financier and retailer M-Kopa; and financial services giant Ecobank, according to Gikandi.

The company gets paid through mobile wallets, earning fees on a portion of the transactional business its solutions generate.

Founded in 2010, the Series A round represents the company’s first significant venture capital investment. “We’ve primarily bootstrapped the business and became profitable in 2012,” said Gikandi.

The private company does not release financials or confirm net revenues, but Gikandi said that the company’s profitability was a stipulation for investment from its new backers (unlike the requirements for startups coming from Silicon Valley).

As the company looks to expand geographically in the African continent, observers can likely expect Africa’s Talking services to begin cropping up in francophone countries, otherwise, why else would Orange Digital Ventures’ invest in the round, since Orange has a presence in over 20 French speaking African nations.

IFC Ventures will be an active investor, with Africa Regional Head Wale Ayeni joining the board.

“The caliber of the tech team was a reason [to invest],” says Ayeni. “They’ve been able to build a cloud based digital platform and produce a product that people are willing to pay for. It’s rare to see that kind of high caliber tech talent create something that can scale that rapidly…and we’re looking forward to what they’ll build in the future.”


Source: TechCrunch

In the age of Cambridge Analytica what are reasonable data norms?

Things really escalated quickly in this month’s Facebook-Cambridge Analytica scandal.

While it’s usually best to just sit back with a bucket of popcorn and watch reality business drama unfold, I was surprised by the severe reactions insinuating Facebook’s eagerness to profit at the expense of its users’ data, creating paranoia around data analytics and equating data driven targeting to an underhanded practice of mind control.

Perhaps this is because it’s being bundled up with the clearly unethical issues of fake news and foreign interference, both of which are distinct from the issue of data harvesting through Facebook’s API.

The scandal surrounding Facebook’s graph API 1.0 and 2.0 might not have been rooted in malicious intent. In fact, a key component of the solution lies in forming a shared understanding amongst platforms, regulators and users, of what data can reasonably be considered private.

“Facebook gave out its users data!”

Indeed it did. But it is important to gauge the motivation and intent behind doing this. For starters, this wasn’t a “leak” as many have called it. Graph API 1.0 was a conscious feature Facebook rolled out under its Platform vision to allow other developers to utilize Facebook data to give rise to presumably useful new apps and use-cases.

Core features of popular apps like Tinder, Timehop and various Zynga social games are powered by their ability to access users’ preexisting Facebook content, social connections and information, instead of having users build up that information from scratch for each app they use.

It was also not a “loophole”. Limitations and procedures for accessing data were clearly stated in the API’s documentation, available publicly for everyone to read. It did not hide the fact that a user’s friends’ data could also be accessed.

This was a product and architectural decision; and a bad decision in hindsight, because it lacked basic precautions against bad actors. But after all, this was API 1.0 and as with all first versions in the new agile world, there is always going to be significant learnings and course correction.

importantly, Facebook did not make any money from developers accessing data through the API. Therefore, the growing narrative insinuating some deceptive, profiteering motives with regards to user data does not resonate with me.

“Data is dangerous since it can be used for psychographic profiling in political campaigns!”

The media outcry has been sounding alarms and highlighting how data can be used to create segments and psychographic profiles to influence people with pinpoint precision. It’s important to realize that this is data-driven marketing and is not something new.

It has been a widely practiced and constantly improving marketing practice utilized across industries and even in political campaigns, across parties. To assume that it would not have happened if Facebook’s data was never accessible is incorrect.

In 2008 the Obama campaign used Facebook data to win the election.

The ability to capture data online and in the physical world is only getting better, and there is a growing industry whose core function is specifically capturing and selling data. The core issue here is neither the data source nor data analytics, but rather when a useful scientific tool has been used to add sophistication to an unethical or illegal activity.

Let’s elaborate using a simple example of a fictitious company ‘Homer Donuts’ which decides to run a series of ad campaigns for its different segments. For the price conscious segment their ad says “Homer Donuts is now 20% cheaper!”, for the health conscious segment it says “Try Homer Donuts’ new low-calorie airfried donuts!” and for the convenience conscious segment it says “Donuts delivered to your doorstep in 15 minutes“.

Understanding your target audience in granular segments and customizing your ad’s positioning, messaging and placement for each segment is a core part of data-driven marketing. None of this is wrong or manipulative. However, if they create a fake article titled “Homer donuts cures baldness! ” and show it as ads to us vulnerable bald folks, unless somehow miraculously true, that is fake news: intentionally and knowingly spreading false information in a way to mislead for benefit or towards an agenda.

Differentiation between the tools and the improper utilization is important lest businesses start having to feel apologetic and tentative in striving for advancements in data science and analytics.

The industry needs for a framework for shared responsibility on privacy

Facebook made a critical error of judgement in indiscriminately allowing anyone to access to conscenting users and that user’s entire list of friends. It was overly naive not to anticipate manipulation by bad actors and take precautionary measures. That said, it’s important to realize that it’s not in Facebook’s power alone to protect user data. There is need for a framework and shared understanding of privacy expectation levels amongst platforms, users and regulators to guide corporate practices, social behaviour and potential regulations.

Orkut (c. 2005) and Facebook (c. 2007) were my first exposure to social networks. I remember asking myself back then: Why would I write a message to a friend publicly on their “wall” instead of emailing them privately?

There needs to be a guiding principle on types of user data and the level to which a platform can utilize or analyze it.

The concept was completely alien and unintuitive to me at the time. And yet a short few years later wishing birthdays, sharing posts or writing a message to a friend in sight of a broader audience, whether to flaunt one’s friendship or to invite others to participate, became the new norm around the world. We tend to forget today that whereas services like chat messengers and email are varying degrees of private, things we write, post or share on social networks are varying degrees of public.

There needs to be a guiding principle on types of user data and the level to which a platform can utilize or analyze it. This level needs to be commensurate to the implicit privacy expectation based on where it is shared, the intended audience and the data’s purpose.

When Kim Kardashian shares a selfie on Instagram with her 109 million followers, it would unreasonable for her to be outraged if that photo finds its way into the hands of people outside of the platform. At a much smaller scale, when you share something with your social network of 500 friends and acquaintances, while it’s not technically public, you can’t reasonably expect it to be very private data.

It is very possible for anyone in that audience to further speak about, record or share your content with people outside your selected audience. Conversely, if you are having a private one-on-one chat with a person, your expectation and intent of privacy is a lot higher, despite the fact that it can still be shared on by that person.


To illustrate this framework, let’s take the example of Facebook’s most criticized error in the data harvesting scandal: allowing users to be able to grant access to their friends’ information in addition to their own. Essentially, it would have been the equivalent of any user manually collecting all the data that he had rights to view on Facebook, be it his own, his friends’ or public content, and passing it on to the third-party app or whomever else he wishes.

So while Facebook added fuel to the fire by making it systematically easier and more effortless for a user to collect all this data and pass it on with a simple click of one button, the fire still exists; data can still be shared outside intended audiences even without the API’s provision.

The objective is not to absolve platforms of the responsibility to keep its users data safe, but to reinforce the understanding to all users that social networks by design cannot be foolproof data-safes and to adjust social media user’s norms in addition to counter those risks.

It is important to isolate and view all the different issues under consideration here. We are at a pivotal juncture in history where tech companies, regulators and lawmakers are actively reviewing the acceptability of evolved social norms. Along with addressing serious threats of fake news and foreign intervention, more granular and grey-area questions like what data should be considered private, the obligations of companies in protection of that data even if the data owner consents, and the acceptable boundaries of data-driven influencing in business and political settings must be debated taking all aspects, good and bad, into consideration.

Anyway, time to share this on Facebook.


Source: TechCrunch

5 things to know about Google's new Tasks app – CNET

Finally, Google has a dedicated to-do app that integrates with Gmail.
Source: CNET

Samsung makes a lot of money from chips, but phones may struggle – CNET

Despite reporting record profits, Samsung says it’s seeing the effects of a “sluggish” smartphone market. Which could mean more promotions for the Galaxy S9.
Source: CNET

Facebook: We take 'false news' in your timeline seriously – CNET

Google, LinkedIn, AirBNB and Microsoft join Facebook at a conference on abuse of online services in explaining how they use algorithms to fight fraud.
Source: CNET

Allegro.AI nabs $11M for a platform that helps businesses build computer vision-based services

Artificial intelligence and the application of it across nearly every aspect of our lives is shaping up to be one of the major step changes of our modern society. Today, a startup that wants to help other companies capitalise on AI’s advances is announcing funding and emerging from stealth mode.

Allegro.AI, which has built a deep learning platform that companies can use to build and train computer-vision-based technologies — from self-driving car systems through to security, medical and any other services that require a system to read and parse visual data — is today announcing that it has raised $11 million in funding, as it prepares for a full-scale launch of its commercial services later this year after running pilots and working with early users in a closed beta.

The round may not be huge by today’s startup standards, but the presence of strategic investors speaks to the interest that the startup has sparked and the gap in the market for what it is offering. It includes MizMaa Ventures — a Chinese fund that is focused on investing in Israeli startups, along with participation from Robert Bosch Venture Capital GmbH (RBVC), Samsung Catalyst Fund and Israeli fund Dynamic Loop Capital. Other investors (the $11 million actually covers more than one round) are not being disclosed.

Nir Bar-Lev, the CEO and cofounder (Moses Guttmann, another cofounder, is the company’s CTO), started Allegro.AI first as Seematics in 2016 after he left Google, where he had worked in various senior roles for over 10 years. It was partly that experience that led him to the idea that with the rise of AI, there would be an opportunity for companies that could build a platform to help other less AI-savvy companies build AI-based products.

“We’re addressing a gap in the industry,” he said in an interview. Although there are a number of services, for example Rekognition from Amazon’s AWS, which allow a developer to ping a database by way of an API to provide analytics and some identification of a video or image, these are relatively basic and couldn’t be used to build and “teach” full-scale navigation systems, for example.

“An ecosystem doesn’t exist for anything deep-learning based.” Every company that wants to build something would have to invest 80-90 percent of their total R&D resources on infrastructure, before getting to the many other apsects of building a product, he said, which might also include the hardware and applications themselves. “We’re providing this so that the companies don’t need to build it.”

Instead, the research scientists that will buy in the Allegro.AI platform — it’s not intended for non-technical users (not now at least) — can concentrate on overseeing projects and considering strategic applications and other aspects of the projects. He says that currently, its direct target customers are tech companies and others that rely heavily on tech, “but are not the Googles and Amazons of the world.”

Indeed, companies like Google, AWS, Microsoft, Apple and Facebook have all made major inroads into AI, and in one way or another each has a strong interest in enterprise services and may already be hosting a lot of data in their clouds. But Bar-Lev believes that companies ultimately will be wary to work with them on large-scale AI projects:

“A lot of the data that’s already on their cloud is data from before the AI revolution, before companies realized that the asset today is data,” he said. “If it’s there, it’s there and a lot of it is transactional and relational data.

“But what’s not there is all the signal-based data, all of the data coming from computer vision. That is not on these clouds. We haven’t spoken to a single automotive who is sharing that with these cloud providers. They are not even sharing it with their OEMs. I’ve worked at Google, and I know how companies are afraid of them. These companies are terrified of tech companies like Amazon and so on eating them up, so if they can now stop and control their assets they will do that.”

Customers have the option of working with Allegro either as a cloud or on-premise product, or a combination of the two, and this brings up the third reason that Allegro believes it has a strong opportunity. The quantity of data that is collected for image-based neural networks is massive, and in some regards it’s not practical to rely on cloud systems to process that. Allegro’s emphasis is on building computing at the edge to work with the data more efficiently, which is one of the reasons investors were also interested.

“AI and machine learning will transform the way we interact with all the devices in our lives, by enabling them to process what they’re seeing in real time,” said David Goldschmidt, VP and MD at Samsung Catalyst Fund, in a statement. “By advancing deep learning at the edge, Allegro.AI will help companies in a diverse range of fields—from robotics to mobility—develop devices that are more intelligent, robust, and responsive to their environment. We’re particularly excited about this investment because, like Samsung, Allegro.AI is committed not just to developing this foundational technology, but also to building the open, collaborative ecosystem that is necessary to bring it to consumers in a meaningful way.”

Allegro.AI is not the first company with hopes of providing AI and deep learning as a service to the enterprise world: Element.AI out of Canada is another startup that is being built on the premise that most companies know they will need to consider how to use AI in their businesses, but lack the in-house expertise or budget (or both) to do that. Until the wider field matures and AI know-how becomes something anyone can buy off-the-shelf, it’s going to present an interesting opportunity for the likes of Allegro and others to step in.

 

 

 


Source: TechCrunch

China turns to tech to monitor, shame and rate citizens – CNET

The government is using surveillance cameras, facial recognition and smart glasses to score people on social behavior, which can lead to punishment.
Source: CNET

Sprint pitchman laments life in corporate America – CNET

Commentary: Paul Marcarelli, best known as Verizon’s “Can You Hear Me Now?” pitchman, says American companies make employees feel dispensable.
Source: CNET

Why Facebook's Troubles Haven't Dented Its Profits

Facebook’s growth accelerated, despite concerns about the Cambridge Analytica scandal, the #deletefacebook movement, and potential advertiser defections.
Source: Wired

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