Explorium reveals $19.1M in total funding for machine learning data discovery platform – gpgmail


Explorium, a data discovery platform for machine learning models, received a couple of unannounced funding rounds over the last year — a $3.6 million seed round last September and a $15.5 million Series A round in March. Today, it made both of these rounds public.

The seed round was led by Emerge with participation of F2 Capital. The Series A was led by Zeev Ventures with participation from the seed investors. The total raised is $19.1 million.

The company founders, who have a data science background, found that it was problematic to find the right data to build a machine learning model. Like most good startup founders confronted with a problem, they decided to solve it themselves by building a data discovery platform for data scientists.

CEO and co-founder, Maor Shlomo says that the company wanted to focus on the quality of the data because not much work has been done there. “A lot of work has been invested on the algorithmic part of machine learning, but the algorithms themselves have very much become commodities. The challenge now is really finding the right data to feed into those algorithms,” Sholmo told gpgmail.

It’s a hard problem to solve, so they built a kind of search engine that can go out and find the best data wherever it happens to live, whether it’s internally or in an open data set, public data or premium databases. The company has partnered with thousands of data sources, according to Schlomo, to help data scientist customers find the best data for their particular model.

“We developed a new type of search engine that’s capable of looking at the customers data, connecting and enriching it with literally thousands of data sources, while automatically selecting what are the best pieces of data, and what are the best variables or features, which could actually generate the best performing machine learning model,” he explained.

Shlomo sees a big role for partnerships, whether that involves data sources or consulting firms, who can help push Explorium into more companies.

Explorium has 63 employees spread across offices in Tel Aviv, Kiev and San Francisco. It’s still early days, but Sholmo reports “tens of customers.” As more customers try to bring data science to their companies, especially with a shortage of data scientists, having a tool like Explorium could help fill that gap.


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Why am I seeing this ad? AI, ML & human error in advertising – gpgmail


Ad platforms create equal opportunities for businesses but not equal outcomes.

They’re mostly marketed as self-service and easy to use, however, there are new features added regularly and open-ended ways to set, structure and target. Meaning, countless ways to spend—creating winners and losers in advertising.

This is where machines and digital advertisers are needed, to provide a profitable outcome.

Enter AI, ML and experts as freelancers, via agencies or housed in some of the world’s biggest companies, equipped with ample data, tech and educational resources to match people with companies via ads on search, social, and elsewhere on the web.

But, are the machines still in infancy or too heavily relied upon and do the experts always get it right?

Well, how often are you seeing ads that are irrelevant to what you wanted or where you were or who you are?

An irrelevant ad is an ad paid for by the company advertising but can return zero value as it’s of no use to the person receiving the ad.

As a digital advertiser via my company Adboy.com, I’m always curious as to why I was served an ad and if the company paying makes or loses money from it.

Something I’ve noticed is that in easily avoidable errors, ads can be served to existing customers, people with irrelevant needs and people that can’t be or are far less likely to become customers.

With this article, I’m going to give you the lenses of a fastidious digital advertiser. You’ll spot errors like these for yourself and know how they could occur, what the negative impact could be and how they can be avoided.

Advertising to existing customers


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Huawei’s Kirin 990 SoC Is the First Chip With an Integrated 5G Modem


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Huawei’s year has been anything but good, but the company has pushed ahead with new technology introductions and smartphone designs. The Chinese firm has now announced its latest SoC, the Kirin 990. The new chip will ship in two flavors — the Kirin 990, and the Kirin 990 5G. These two chips are based on the same SoC design, but there are some significant differences between them.

First, the Kirin 990 5G is built on TSMC’s 7nm+ process node, which utilizes EUV. The Kirin 990, in contrast, is a standard 7nm design. It seems as though Huawei will be the first customer to ship a part that uses EUV for manufacturing. Huawei’s stated reason for using EUV for the 5G variant is that it allowed for a smaller die. Die size on the 5G part is larger than 100mm2, while the LTE chip is less than 90mm2. Transistor counts are also significantly different, with the LTE chip at 8B and the 5G chip at 10.3B.

Kirin-990-Comparison

One interesting fact that Anandtech mentions is that the Kirin 990 was originally expected to use ARM’s Cortex-A77 CPU, not the Cortex-A76. Apparently the Huawei team didn’t like how the Cortex-A77 clocked on TSMC’s 7nm process node. The A77 had higher peak performance, but overall power efficiency between the A76 and A77 was practically identical on 7nm and the A76 design was capable of hitting much higher clocks. Supposedly the A77 tops out around 2.2GHz on 7nm at the moment and the design may not be used widely until 5nm CPUs are available.

The new ARM Mali-G76 implementation is substantially wider than the 10-core implementation used on the previous generation Kirin 980. GPU power efficiency can often be improved by using a wider GPU clocked at lower frequencies, and Huawei believes the new GPU design will still be more power-efficient than the old Kirin 980, despite being substantially wider.

The NPU design is a homegrown Huawei effort. Where the company previously licensed an NPU from Cambricon, the new Kirin 990 uses Huawei’s Da Vinci architecture. Huawei intends to scale this AI processing block from servers to smartphones. It supports both INT8 and FP16 on both cores, whereas the older Cambricon design could only perform INT8 on one core. There’s also a new ‘Tiny Core’ NPU. It’s a smaller version of the Da Vinci architecture focused on power efficiency above all else, and it can be used for polling or other applications where performance isn’t particularly time critical. The 990 5G will have two “big” NPU cores and a single Tiny Core, while the Kirin 990 (LTE) has one big core and one tiny core.

Huawei’s Balong modem will support sub-6GHz 5G signals with a maximum of 2.3Gbps download and 1.25Gbps upload. Overall CPU performance improvements from the Kirin 980 to the Kirin 990 are modest — Huawei claims single-threaded gains of 9 percent and multi-threaded boosts of 10 percent. Power efficiency, however, has improved significantly. The top-end cores are supposedly 12 percent more efficient, the “middle” cores of Huawei’s Big.Little.littlest are supposedly 35 percent more efficient, and the low-end Cortex-A55 chips are 15 percent more efficient. Most workloads are supposed to run on the middle cores for maximum performance/watt.

It seems unlikely that these devices will come to the US market in any numbers, though you may be able to buy them from third-party resellers if the Trump Administration doesn’t take further action against the company. While devices are going to start carrying 5G modems from this point forward, I’ve yet to see a 5G phone I’d actually recommend. While it’s true that the first generation of LTE devices didn’t exactly cover themselves in glory, the first generation of LTE devices didn’t overheat and shutdown when summer temperatures rose above 85F / 29.4C. They didn’t require you to be literally standing underneath an LTE access point in order to see faster service, either. Verizon has already stated that outside city centers, its 5G network will closely resemble “good 4G,” which raises the question of what, exactly, consumers are paying all this money for.

The first LTE devices were the HTC Evo 4G, the Samsung Craft, and the HTC Thunderbolt. They sold for $200, $350, and $250, respectively, though this was in the era of two-year contracts. Apple’s first LTE device was the iPhone 5, which cost $649 if purchased without a contract. Assuming Apple and the other AndroidSEEAMAZON_ET_135 See Amazon ET commerce manufacturers continue to offer 5G as a luxury feature, we’ll likely only see it on devices at or above the $1000 price point for the next 12 months. I wouldn’t pay $1000 for a phone under any circumstances, but I definitely wouldn’t step up to a $1000+ device to buy a feature that I’ve got no chance of using at any point in the next few years.

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Facebook is making its own deepfakes and offering prizes for detecting them – gpgmail


Image and video manipulation powered by deep learning, or so-called “deepfakes,” represent a strange and horrifying facet of a promising new field. If we’re going to crack down on these creepy creations, we’ll need to fight fire with fire; Facebook, Microsoft, and many others are banding together to help make machine learning capable of detecting deepfakes — and they want you to help.

Though the phenomenon is still new, we are nevertheless in an arms race where the methods of detection vie with the methods of creation. Ever more convincing fakes appear regularly, and though while they are frequently benign, the possibility of having your face flawlessly grafted into a compromising position is very much there — and many a celebrity has already had it done to them.

Facebook, as part of a coalition with Microsoft, the Partnership for AI, and several universities including Oxford, Berkeley, and MIT, is working to empower the side of good with better detection techniques.

“The most interesting advances in AI have happened when there’s a clear benchmark on a dataset to write papers against,” said Facebook CTO Mike Schroepfer in a media call yesterday. The dataset for object recognition might be millions of images of ordinary objects, while the dataset for voice transcription would be hours of different kinds of speech. But there’s no such set for deepfakes.

We talked about this challenge at our Robotics and AI event earlier this year in what I thought was a very interesting discussion:

Fortunately Facebook is planning on dedicating around $10 million in resources to make this Deepfake Detection Challenge happen.

“Creation of these datasets can be challenging, because you want to make sure that everyone participating in it is clear and gives consent so they aren’t surprised by the usage of it,” Schroepfer continued. And since most deepfakes are made without any consent whatsoever, they’re not really permissible for usage in an academic context.

So Facebook and its partners are making the deepfake content out of whole cloth, he said. “You want a dataset of source video, and then a dataset of personalities you can map onto that. Then we’re spending engineering time implementing the latest most advanced deepfake techniques to generate altered videos as part of the dataset.”

And while you’re entirely justified in wondering, no, they aren’t using Facebook data to do this. They’ve got paid actors.

This dataset will be provided to interested parties, who will be able to build solutions and test them, putting the results on a leaderboard. At some point there will be cash prizes given out, though the details are a ways off. With luck this will spur serious competition among academics and researchers.

“We need the full involvement of the research community in an open environment to develop methods and systems that can detect and mitigate the ill-effects of manipulated multimedia,” said the University of Maryland’s Rama Chellappa in a news release. “By making available a large corpus of genuine and manipulated media, the proposed challenge will excite and enable the research community to collectively address this looming crisis.​”

Initial tests of the dataset are planned for the International Conference on Computer Vision in October, with the full launch happening at NeurIPS in December.


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Teaching ethics in computer science the right way with Georgia Tech’s Charles Isbell – gpgmail


The new fall semester is upon us, and at elite private colleges and universities, it’s hard to find a trendier major than Computer Science. It’s also becoming more common for such institutions to prioritize integrating ethics into their CS studies, so students don’t just learn about how to build software, but whether or not they should build it in the first place. Of course, this begs questions about how much the ethics lessons such prestigious schools are teaching are actually making a positive impression on students.

But at a time when demand for qualified computer scientists is skyrocketing around the world and far exceeds supply, another kind of question might be even more important: Can computer science be transformed from a field largely led by elites into a profession that empowers vastly more working people, and one that trains them in a way that promotes ethics and an awareness of their impact on the world around them?

Enter Charles Isbell of Georgia Tech, a humble and unassuming star of inclusive and ethical computer science. Isbell, a longtime CS professor at Georgia Tech, enters this fall as the new Dean and John P. Imlay Chair of Georgia Tech’s rapidly expanding College of Computing.

Isbell’s role is especially given Georgia Tech’s approximately 9,000 online graduate students in Computer Science. This astronomical number of students in the CS field is the result of a philosophical decision made at the university to create an online CS master’s degree treated as completely equal to on-campus training.

Another counterintuitive philosophical decision made at Georgia Tech — for which Isbell proudly evangelized while speaking at conferences like the MIT Technology Review’s EmTech Next, where I met him in June — is to admit every student who has the potential to earn a degree, rather than making any attempt at “exclusivity” by rejecting worthy candidates. In the coming years all of this may lead, Isbell projected at EmTech Next, to a situation in which up to one in eight of all people in the US who hold a graduate degree in CS will have earned it at Georgia Tech.

isbell 1

Isbell speaks to Gideon Lichfield, Editor-in-chief of the MIT Technology Review, at its EmTech Next conference in June. Image via MIT Technology Review.

“What they’ve done is pretty remarkable,” said Casey Fiesler, a 3x recent graduate of Georgia Tech and a founding faculty member and CS professor at the University of Colorado’s College of Media, Communication, and Information.

And it’s promising that Fiesler, who has become known in the tech ethics field for her comparative study of curricula and teaching approaches, told me, “ethics can be integrated into online [CS] courses just as easily as it can be into face to face courses.”

Still, it is as daunting as it is impressive to think about how one public school like Georgia Tech might be able to successfully and ethically educate such an enormous percentage of the students in arguably the most influential academic field in the world today. So I was glad to be able to speak to Isbell, an expert on statistical machine learning and artificial intelligence, for this gpgmail series on the ethics of technology.

Our conversation below covers the difference between equality and equity; cultural issues around women in American CS, and what it would look like for ethics to be so integrated into the discussion of computing that students and practitioners wouldn’t even think of it as ethics.

Greg Epstein: Around 1/8 of Computer Science graduate degrees will be delivered by your school in the coming years; you’re thinking inclusively about providing a relatively huge number of opportunities for people who would not otherwise get the opportunity to become computer scientists. How have you achieved that?

Charles Isbell: There’s an old joke about organizations: don’t tell me what your values are, show me your budget and then I’ll tell you what your values are. Because you spend money on the things that you care about.


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Elliptic banks $23M to shrink crypto risk, eyeing growth in Asia – gpgmail


Crypto means risk. To UK company Elliptic it also means business. The startup has just closed a $23M Series B to step up growth for a crypto risk-management play that involves selling tech and services to help others navigate the choppy darks of cryptocurrencies.

The round was led by financial services and asset management firm SBI Group, a Tokyo-based erstwhile subsidiary of SoftBank . Also joining as a new investor this round is London-based AlbionVC. Existing investors including SignalFire, Octopus Ventures and Santander Innoventures also participated. SBI Group’s Tomoyuki Nii and Ed Lascelles of AlbionVC are also joining Elliptic’s board.

Flush with a sizeable injection of Series B capital, Elliptic is especially targeting business growth at Asia — with a plan to open new offices in Japan and Singapore. It says client revenues in the region have risen 11x over the past two years.

We last spoke to Elliptic back in 2016 when it had just raised a $5M Series A.

The 2013-founded startup began by testing the crypto waters with a storage product before zeroing in on financial compliance as a pain-point worth its time. It went on to develop machine learning tech that screens transactions to identify suspicious patterns and, via them, dubious transactors.

Now it offers an integrated suite of products and services for financial institutions and crypto businesses to screen volumes of crypto-flows that sum to billions of dollars in transactions per day — analyzing them for links to illicit activity such as money laundering, terrorist financing, sanctions evasion, and other financial crimes.

It’s focused on selling anti-money laundering compliance, crypto forensics and cryptocurrency investigation services to the private sector — though has also sold tools direct to law enforcement agencies in the past.

Billions of dollars in financial services terms is of course just a tiny drop in a massive ocean of money movements. And growth in the crypto risk-management space has clearly required more than a little patience, from a startup perspective.

Three years ago Elliptic’s first blockchain analytics product had 10-20 Bitcoin companies as customers. That’s now up to 100+ crypto businesses and financial institutions using its products to shrink their risk of financial crime when dealing with crypto-assets. But the more three than year gap between Elliptic’s Series A and B is notable.

“To date, we’ve focused on product development and assembling the right team as the market has matured. This new funding will help us expand in the right way, namely by making the push into Asia without diluting our focus on the US and EMEA,” says co-founder and CEO James Smith when asked about the gap between financing rounds.

He declines to comment on how far off Elliptic is from achieving breakeven or profitability yet.

“We provide best-in-class transaction monitoring products for crypto-assets, which are trusted by crypto exchanges and financial institutions worldwide,” he adds of its product suite. “Our products are used as key components of larger compliance processes that are designed to minimise money laundering risks.”

With the addition of SBI Group to its investor roster Elliptic gains a strategic partner in Asia to help push what it dubs “bank-grade risk data” at a new wave of established financial institutions it believes are eyeing crypto with growing appetite for risk as larger players wade in.

Larger players like Facebook . Elliptic’s PR name-drops the likes of Facebook’s Libra cryptocurrency, Line Corporation’s LINK and central bank digital currencies, as markers of a rise in mainstream attention on crypto assets. And it says Series B funds will be used to accelerate product development to support “an emerging class of asset-backed crypto-assets”.

Regulatory attention on crypto — which has been rising globally for years but looks set to zip up several gears now that Facebook has ripped the curtain off of an ambitious global digital currency plan which also has buy-in from a number of other household tech and fintech names — is another claimed feed in for Elliptic’s business. More crypto implies growing risk.

It also points to the intergovernmental Financial Action Task Force’s global regulatory framework for crypto-assets as an example of some of the wider risk-based requirements and now wrapped around those dealing in crypto.

The focus on Asia for business expansion is a measure of relative maturity of interest in opportunities around crypto-assets and localized attention to regulation, according to Smith.

“Revenue growth is certainly very strong in this region. We have been working with customers in Asia for a number of years and have seen first-hand how vibrant their crypto-asset ecosystems are. Countries such as Singapore and Japan have developed clear crypto-asset regulatory frameworks, and businesses based in these countries are serious about meeting their compliance obligations,” he says.

“We have also found that traditional financial institutions in Asia are particularly keen to engage with crypto-assets, and we will be working with them as they take their first steps into this new asset class.”

“We believe that crypto-assets will play an increasingly important role in our everyday lives and are shaping the future of banking. Our investment in Elliptic is a further commitment to this belief and to SBI Holding’s appetite to help build the digital asset-related ecosystem,” adds Yoshitaka Kitao, CEO of the SBI Group, in a supporting statement.

“Elliptic’s pioneering approach is enabling the transparency, integrity, and trust necessary for this vision to become reality. We are seeing a growing demand for their services across our portfolio of crypto-assets related companies and view Elliptic as best-placed to meet this considerable opportunity.”

While Elliptic’s business is focused on reducing the risk for other businesses of inadvertently transacting with criminals using crypto to launder money or otherwise shift assets under the legal radar, the proportion of transactions that such illicit activity represents in the Bitcoin space represents a tiny fraction of overall transactions.

“According to our analysis, approximately $1BN in Bitcoin has been spent on the dark web, so far in 2019, on items ranging from narcotics to stolen credit cards. This represents a very small share of all Bitcoin activity — less than 0.5% of Bitcoin payments over this period,” says Smith.

Not that that diminishes the regulatory risk. Nor, therefore, the business opportunity for Elliptic to sell support services to help others avoid touching the hot stuff.

“Crypto money launderers are continually developing new techniques to cover their tracks — from the use of mixers to transacting in privacy coins such as monero,” Smith adds. “We are also constantly innovating to keep pace with this and help our clients to detect money laundering. For example our work with researchers from MIT and IBM demonstrated the application of deep learning techniques to the identification of illicit crypto-asset transactions.”


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Facebook Is Building a Minecraft AI Because Games May Be Great Training Tools


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It turns out that video games may be an excellent method of teaching skills to artificial intelligence assistants. That’s the theory of a group of researchers working for Facebook, who have focused on Minecraft as a potential teaching tool for building generalist AI — a so-called ‘virtual assistant.’ The research team isn’t trying to build an artificial intelligence that’s super-good at classifying images or other content — it wants to build a generalist AI that can perform a much larger number of tasks reasonably well.

This is, to-date, an under-studied area of research. The authors’ write:

There has been measured progress in this setting as well, with the mainstreaming of virtual personal assistants. These are able to accomplish thousands of tasks communicated via natural language, using multi-turn dialogue for clarifications or further specification. The assistants are able to interact with other applications to get data or perform actions.

Nevertheless, many difficult problems remain open. Automatic natural language understanding (NLU) is still rigid and limited to constrained scenarios. Methods for using dialogue or other natural language for rich supervision remain primitive. In addition, because they need to be able to reliably and predictably solve many simple tasks, their multi-modal inputs, and the constraints of their maintenance and deployment, assistants are modular systems, as opposed to monolithic ML models. Modular ML systems that can improve themselves from data while keeping well-defined interfaces are still not well studied.

According to the team, they picked Minecraft because it offered a regular distribution of tasks with “hand-holds for NLU research,” as well as enjoyable opportunities for human-AI interaction, with plenty of opportunities for human-in-the-loop research. Minecraft, for those of you who haven’t played or heard of it, is a block-based crafting and exploration game in which players explore a 3D voxel grid universe populated with various types of materials, neutral characters, and enemies. The team’s goal is to build an AI virtual assistant that can be given instructions in natural language by a Minecraft player, and that can reliably complete some of the primary tasks that player might engage in, including gathering materials, building structures, fighting mobs, and crafting items.

The authors of the paper target three specific achievements: Create synergy between machine-learning and non-machine-learning components, allowing them to work together; create a “grounded” natural language simulation that allows the AI to understand what players want it to do, and can communicate its success or failure back to the end-user; and create an AI that shouldn’t just be capable of doing what the player wants it to do, but that also its performance should improve based on observation of the human player.

They write:

We intend that the player will be able to specify tasks through dialogue (rather than by just issuing commands), so that the agent can ask for missing information, or the player can interrupt the agent’s actions to clarify. In addition, we hope dialogue to be useful for providing rich supervision. The player might label attributes about the environment, for example “that house is too big”, relations between objects in the environment (or other concepts the bot understands), for example “the window is in the middle of the wall”, or rules about such relations or attributes. We expect the player to be able to question the agent’s “mental state” to give appropriate feedback, and we expect the bot to ask for confirmation and use active learning strategies.

The machine learning code being used for the Facebook-Minecraft bot is available on GitHub. Better AI tools could be useful in many games, though they could also raise serious questions about what constitutes cheating in multiplayer.

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Apple is turning Siri audio clip review off by default and bringing it in house – gpgmail


The top line news is that Apple is making changes to the way that Siri audio review, or ‘grading’ works across all of its devices. First, it is making audio review an explicitly opt-in process in an upcoming software update. This will be applicable for every current and future user of Siri.

Second, only Apple employees, not contractors, will review any of this opt-in audio in an effort to bring any process that uses private data closer to the company’s core processes.

Apple has released a blog post outlining some Siri privacy details that may not have been common knowledge as they were previously described in security white papers.

Apple apologizes for the issue.

“As a result of our review, we realize we haven’t been fully living up to our high ideals, and for that we apologize. As we previously announced, we halted the Siri grading program. We plan to resume later this fall when software updates are released to our users — but only after making the following changes…”

It then outlines three changes being made to the way Siri grading works.

  • First, by default, we will no longer retain audio recordings of Siri interactions. We will continue to use computer-generated transcripts to help Siri improve.
  • Second, users will be able to opt in to help Siri improve by learning from the audio samples of their requests. We hope that many people will choose to help Siri get better, knowing that Apple respects their data and has strong privacy controls in place. Those who choose to participate will be able to opt out at any time.
  • Third, when customers opt in, only Apple employees will be allowed to listen to audio samples of the Siri interactions. Our team will work to delete any recording which is determined to be an inadvertent trigger of Siri.

Apple is not implementing any of these changes, nor is it lifting the suspension on the Siri grading process that it halted until the software update becomes available for its operating systems that will allow users to opt in. Once people update to the new versions of its OS, they will have the chance to say yes to the grading process that uses audio recordings to help verify requests that users make of Siri. This effectively means that every user of Siri will be opted out of this process once the update goes live and is installed.

Apple says that it will continue using anonymized computer generated written transcripts of your request to feed its machine learning engines with data, in a fashion similar to other voice assistants. These transcripts may be subject to Apple employee review.

Amazon and Google had previous revelations that their assistants were being helped along by human review of audio, and they have begun putting opt-ins in place as well.

Apple is making changes to the grading process itself as well, noting that, for example, “the names of the devices and rooms you setup in the Home app will only be accessible by the reviewer if the request being graded involves controlling devices in the home.”

A story in The Guardian in early August outlined how Siri audio samples were sent to contractors Apple had hired to evaluate the quality of responses and transcription that Siri produced for its machine learning engines to work on. The practice is not unprecedented, but it certainly was not made as clear as it should have been in Apple’s privacy policies that humans were involved in the process. There was also the matter that contractors, rather than employees, were being used to evaluate these samples. One contractor described as containing sensitive and private information that, in some cases, may have been able to be tied to a user, even with Apple’s anonymizing processes in place.

In response, Apple halted the grading process worldwide while it reviewed the process. This post and updates to its process are the result of that review.

Apple says that around 0.2% of all Siri requests got this audio treatment in the first place, but given that there are 15B requests per month, the quick maths tell us that though it is statistically insignificant, the raw numbers could be quite high.

The move away from contractors was signaled by Apple releasing employees in Europe, as noted by Alex Hearn earlier on Wednesday.

Apple is also publishing an FAQ on how Siri’s privacy controls fit in with its grading process, you can read that in full here.

The blog post from Apple and the FAQ provide some details to consumers about how Apple handles the grading process, how it is minimizing the data given to data reviewers in the grading process and how Siri privacy is preserved.


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Y Combinator graduate PredictLeads helps VCs hunt for unicorns – gpgmail


The Slovenian founders behind PredictLeads, another recent Y Combinator graduate, applied to the prestigious accelerator five times before they were admitted.

Their business, which helps venture capital firms and sales teams identify high growth companies, i.e. potential investments and potential customers, had come a long way since it was founded in 2016. And earlier this year — finally — YC gave them the green light to complete its three-month accelerator program.

“We almost ran out of money in 2017 and then I took a loan from my mother because that bank wouldn’t give me the loan at that point,” PredictLeads chief executive officer Roq Xever tells gpgmail. “But by then, the data was getting much better and we were able to make higher-value sells and that got us to profitability.”

You read that right. Unlike most of today’s tech startups, PredictLeads is profitable, though, only out of pure necessity: “We didn’t know we would ever get into YC to raise the money we needed, so we structured the company to make more money than we spent.”

Xever leads the small PredictLeads team alongside marketing chief Miha Stanovnik and chief technology officer Matic Perovsek. Xever tells gpgmail it wasn’t until they realized the opportunity to sell their product to VCs that YC became interested. Today, PredictLeads has eight venture firms as customers, the names of which they were not able to disclose.

The tool helps investors track companies they’ve considered in the past. PredictLeads notifies users if certain companies start getting traction so they can reevaluate the deal and helps investors become aware of startups they may not have otherwise heard of.

More and more venture capital firms are turning to third-party tools to help them make sense of and leverage data in the investment and company-tracking process, leading to the birth of new data-focused companies. Social Capital co-founder Chamath Palihapitiya is spinning out a company from his venture capital fund-turned-family-office, gpgmail learned earlier this year. The new entity, temporarily dubbed CaaS (short for capital-as-a-service) Technologies, will focus on providing data-driven insights to VC firms, for example.

Startups have also realized the importance of data. Narrator, another recent YC graduate, is betting big on this trend. The startup wants to become the operating system for data science by providing companies software that claims to fulfill the same service as a data team for the price of an analyst.

PredictLeads, for its part, collects data from websites, press releases, news articles, blogs and career sites, then uses supervised machine learning to extract and structure the data. The startup tracks 20 million public and private companies.

Now that it’s a graduate of YC, the team is in the process of moving its headquarters to the U.S. Either New York or San Francisco, says Xever, who’s currently navigating the difficult visa application process.

The startup is today raising a $1.5 million seed financing at a $10 million valuation. They plan to use the capital to expand their service to cater to quant funds, build a Salesforce app to better support sales teams, and, of course, expand their small team.


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Why now is the time to get ready for quantum computing – gpgmail


For the longest time, even while scientists were working to make it a reality, quantum computing seemed like science fiction. It’s hard enough to make any sense out of quantum physics to begin with, let alone the practical applications of this less than intuitive theory. But we’ve now arrived at a point where companies like D-Wave, Rigetti, IBM and others actually produce real quantum computers.

They are still in their infancy and nowhere near as powerful as necessary to compute anything but very basic programs, simply because they can’t run long enough before the quantum states decohere, but virtually all experts say that these are solvable problems and that now is the time to prepare for the advent of quantum computing. Indeed, Gartner just launched a Quantum Volume metric, based on IBM’s research, that looks to help CIOs prepare for the impact of quantum computing.

To discuss the state of the industry and why now is the time to get ready, I sat down with IBM’s Jay Gambetta, who will also join us for a panel on Quantum Computing at our TC Sessions: Enterprise event in San Francisco on September 5, together with Microsoft’s Krysta Svore and Intel’s Jim Clark.


10 minutes mail – Also known by names like : 10minemail, 10minutemail, 10mins email, mail 10 minutes, 10 minute e-mail, 10min mail, 10minute email or 10 minute temporary email. 10 minute email address is a disposable temporary email that self-destructed after a 10 minutes. https://tempemail.co/– is most advanced throwaway email service that helps you avoid spam and stay safe. Try tempemail and you can view content, post comments or download something