Ideaforge

The Gillmor Gang — Doc Searls, Frank Radice, Esteban Kolsky, Michael Markman, and Steve Gillmor . Recorded live Friday, May 11, 2018. Audio networks, a new Beatles, and other digital cliffhangers

@stevegillmor, @dsearls, @fradice, @mickeleh, @ekolsky

Produced and directed by Tina Chase Gillmor @tinagillmor

Liner Notes

Live chat stream

The Gillmor Gang on Facebook

Source: Tech Crunch

A little more than three years ago, Apple announced a new MacBook with a “butterfly” keyboard that was 40 percent thinner and ostensibly four times more stable than the previous “scissor” mechanism that MacBooks employed.

The promise was to more evenly distribute pressure on each key. Not everyone loved this “reinvention,” however, and now, Apple is facing a class action lawsuit over it.

According to a complaint lodged in the Northern District Court of California yesterday and first spied by the folks over at AppleInsider, “thousands” of MacBook and MacBook Pro laptops produced in 2015 and 2016 experienced failure owing to dust or debris the Butterly design that rendered the machines useless. The complaint further alleges that Apple “continues to fail to disclose to consumers that the MacBook is defective, including when consumers bring their failed laptops into the ‘Genius Bar’ (the in-store support desk) at Apple stores to request technical support.”

It just not a lack of disclosures that’s problematic, the suit continues. Customers who think the issue will be covered by their warranties are sometimes in for an unpleasant surprise. As stated in the filing: “Although every MacBook comes with a one-year written warranty, Apple routinely refuses to honor its warranty obligations. Instead of fixing the keyboard problems, Apple advises MacBook owners to try self-help remedies that it knows will not result in a permanent repair. When Apple does agree to attempt a warranty repair, the repair is only temporary—a purportedly repaired MacBook fails again from the same keyboard problems. For consumers outside of the warranty period, Apple denies warranty service, and directs consumers to engage in paid repairs, which cost between $400 and $700. The keyboard defect in the MacBook is substantially certain to manifest.”

The lawsuit was filed on behalf of two users, ZIxuan Rao and Kyle Barbaro, and more broadly “on behalf of all others similarly situated.” It was brought by Girard Gibbs, a San Francisco-based law firm that has battled with Apple numerous times in the past, including filing a class-action suit centered on the iPod’s “diminishing battery capacity.” (Apple appears to have settled that one.)

We’ve reached out to Apple for comment.

Interestingly, AppleInsider appears to have provided the fodder for this new lawsuit, or some of it at least. Last month, the outlet reported findings of its own separate investigation into the problem after hearing enough anecdotes to support a deep dive. It says that after collecting service data for the first year of release for the 2014, 2015, and 2016 MacBook Pros, it concluded that —  excluding Touch Bar failures — the 2016 MacBook Pro keyboard has been failing its users twice as often in the first year of use as the 2014 or 2015 MacBook Pro models.

AppleInsider says it collected its data from “assorted Apple Genius Bars in the U.S.” that it has worked with for several years, as well as  Apple-authorized third-party repair shops.

The investigation clearly resonated with MacBook owners, because soon after, more than 17,000 people signed a Change.org petition demanding that Apple recall all MacBooks with butterfly switch keyboards.

That petition — which cites among others the highly regarded writer and UI designer John Gruber, who has called the keyboard “one of the biggest design screwups in Apple history” —  is gaining steam again today, presumably fueled by this new lawsuit. As of this writing, roughly 18,000 people have provided their signature.

Source: Tech Crunch

Digital services have frequently been in collision — if not out-and-out conflict — with the rule of law. But what happens when technologies such as deep learning software and self-executing code are in the driving seat of legal decisions?

How can we be sure next-gen ‘legal tech’ systems are not unfairly biased against certain groups or individuals? And what skills will lawyers need to develop to be able to properly assess the quality of the justice flowing from data-driven decisions?

While entrepreneurs have been eyeing traditional legal processes for some years now, with a cost-cutting gleam in their eye and the word ‘streamline‘ on their lips, this early phase of legal innovation pales in significance beside the transformative potential of AI technologies that are already pushing their algorithmic fingers into legal processes — and perhaps shifting the line of the law itself in the process.

But how can legal protections be safeguarded if decisions are automated by algorithmic models trained on discrete data-sets — or flowing from policies administered by being embedded on a blockchain?

These are the sorts of questions that lawyer and philosopher Mireille Hildebrandt, a professor at the research group for Law, Science, Technology and Society at Vrije Universiteit Brussels in Belgium, will be engaging with during a five-year project to investigate the implications of what she terms ‘computational law’.

Last month the European Research Council awarded Hildebrandt a grant of €2.5 million to conduct foundational research with a dual technology focus: Artificial legal intelligence and legal applications of blockchain.

Discussing her research plan with TechCrunch, she describes the project as both very abstract and very practical, with a staff that will include both lawyers and computer scientists. She says her intention is to come up with a new legal hermeneutics — so, basically, a framework for lawyers to approach computational law architectures intelligently; to understand limitations and implications, and be able to ask the right questions to assess technologies that are increasingly being put to work assessing us.

“The idea is that the lawyers get together with the computer scientists to understand what they’re up against,” she explains. “I want to have that conversation… I want lawyers who are preferably analytically very sharp and philosophically interested to get together with the computer scientists and to really understand each other’s language.

“We’re not going to develop a common language. That’s not going to work, I’m convinced. But they must be able to understand what the meaning of a term is in the other discipline, and to learn to play around, and to say okay, to see the complexity in both fields, to shy away from trying to make it all very simple.

“And after seeing the complexity to then be able to explain it in a way that the people that really matter — that is us citizens — can make decisions both at a political level and in everyday life.”

Hildebrandt says she included both AI and blockchain technologies in the project’s remit as the two offer “two very different types of computational law”.

There is also of course the chance that the two will be applied in combination — creating “an entirely new set of risks and opportunities” in a legal tech setting.

Blockchain “freezes the future”, argues Hildebrandt, admitting of the two it’s the technology she’s more skeptical of in this context. “Once you’ve put it on a blockchain it’s very difficult to change your mind, and if these rules become self-reinforcing it would be a very costly affair both in terms of money but also in terms of effort, time, confusion and uncertainty if you would like to change that.

“You can do a fork but not, I think, when governments are involved. They can’t just fork.”

That said, she posits that blockchain could at some point in the future be deemed an attractive alternative mechanism for states and companies to settle on a less complex system to determine obligations under global tax law, for example. (Assuming any such accord could indeed be reached.)

Given how complex legal compliance can already be for Internet platforms operating across borders and intersecting with different jurisdictions and political expectations there may come a point when a new system for applying rules is deemed necessary — and putting policies on a blockchain could be one way to respond to all the chaotic overlap.

Though Hildebrandt is cautious about the idea of blockchain-based systems for legal compliance.

It’s the other area of focus for the project — AI legal intelligence — where she clearly sees major potential, though also of course risks too. “AI legal intelligence means you use machine learning to do argumentation mining — so you do natural language processing on a lot of legal texts and you try to detect lines of argumentation,” she explains, citing the example of needing to judge whether a specific person is a contractor or an employee.

“That has huge consequences in the US and in Canada, both for the employer… and for the employee and if they get it wrong the tax office may just walk in and give them an enormous fine plus claw back a lot of money which they may not have.”

As a consequence of confused case law in the area, academics at the University of Toronto developed an AI to try to help — by mining lots of related legal texts to generate a set of features within a specific situation that could be used to check whether a person is an employee or not.

“They’re basically looking for a mathematical function that connected input data — so lots of legal texts — with output data, in this case whether you are either an employee or a contractor. And if that mathematical function gets it right in your data set all the time or nearly all the time you call it high accuracy and then we test on new data or data that has been kept apart and you see whether it continues to be very accurate.”

Given AI’s reliance on data-sets to derive algorithmic models that are used to make automated judgement calls, lawyers are going to need to understand how to approach and interrogate these technology structures to determine whether an AI is legally sound or not.

High accuracy that’s not generated off of a biased data-set cannot just be a ‘nice to have’ if your AI is involved in making legal judgment calls on people.

“The technologies that are going to be used, or the legal tech that is now being invested in, will require lawyers to interpret the end results — so instead of saying ‘oh wow this has 98% accuracy and it outperforms the best lawyers!’ they should say ‘ah, ok, can you please show me the set of performance metrics that you tested on. Ah thank you, so why did you put these four into the drawer because they have low accuracy?… Can you show me your data-set? What happened in the hypothesis space? Why did you filter those arguments out?’

“This is a conversation that really requires lawyers to become interested, and to have a bit of fun. It’s a very serious business because legal decisions have a lot of impact on people’s lives but the idea is that lawyers should start having fun in interpreting the outcomes of artificial intelligence in law. And they should be able to have a serious conversation about the limitations of self-executing code — so the other part of the project [i.e. legal applications of blockchain tech].

“If somebody says ‘immutability’ they should be able to say that means that if after you have put everything in the blockchain you suddenly discover a mistake that mistake is automated and it will cost you an incredible amount of money and effort to get it repaired… Or ‘trustless’ — so you’re saying we should not trust the institutions but we should trust software that we don’t understand, we should trust all sorts of middlemen, i.e. the miners in permissionless, or the other types of middlemen who are in other types of distributed ledgers… ”

“I want lawyers to have ammunition there, to have solid arguments… to actually understand what bias means in machine learning,” she continues, pointing by way of an example to research that’s being done by the AI Now Institute in New York to investigate disparate impacts and treatments related to AI systems.

“That’s one specific problem but I think there are many more problems,” she adds of algorithmic discrimination. “So the purpose of this project is to really get together, to get to understand this.

“I think it’s extremely important for lawyers, not to become computer scientists or statisticians but to really get their finger behind what’s happening and then to be able to share that, to really contribute to legal method — which is text oriented. I’m all for text but we have to, sort of, make up our minds when we can afford to use non-text regulation. I would actually say that that’s not law.

“So how should be the balance between something that we can really understand, that is text, and these other methods that lawyers are not trained to understand… And also citizens do not understand.”

Hildebrandt does see opportunities for AI legal intelligence argument mining to be “used for the good” — saying, for example, AI could be applied to assess the calibre of the decisions made by a particular court.

Though she also cautions that huge thought would need to go into the design of any such systems.

“The stupid thing would be to just give the algorithm a lot of data and then train it and then say ‘hey yes that’s not fair, wow that’s not allowed’. But you could also really think deeply what sort of vectors you have to look at, how you have to label them. And then you may find out that — for instance — the court sentences much more strictly because the police is not bringing the simple cases to court but it’s a very good police and they talk with people, so if people have not done something really terrible they try to solve that problem in another way, not by using the law. And then this particular court gets only very heavy cases and therefore gives far more heavy sentences than other courts that get from their police or public prosecutor all life cases.

“To see that you should not only look at legal texts of course. You have to look also at data from the police. And if you don’t do that then you can have very high accuracy and a total nonsensical outcome that doesn’t tell you anything you didn’t already know. And if you do it another way you can sort of confront people with their own prejudices and make it interesting — challenge certain things. But in a way that doesn’t take too much for granted. And my idea would be that the only way this is going to work is to get a lot of different people together at the design stage of the system — so when you are deciding which data you’re going to train on, when you are developing what machine learners call your ‘hypothesis space’, so the type of modeling you’re going to try and do. And then of course you should test five, six, seven performance metrics.

“And this is also something that people should talk about — not just the data scientists but, for instance, lawyers but also the citizens who are going to be affected by what we do in law. And I’m absolutely convinced that if you do that in a smart way that you get much more robust applications. But then the incentive structure to do it that way is maybe not obvious. Because I think legal tech is going to be used to reduce costs.”

She says one of the key concepts of the research project is legal protection by design — opening up other interesting (and not a little alarming) questions such as what happens to the presumption of innocence in a world of AI-fueled ‘pre-crime’ detectors?

“How can you design these systems in such a way that they offer legal protection from the first minute they come to the market — and not as an add-on or a plug in. And that’s not just about data protection but also about non-discrimination of course and certain consumer rights,” she says.

“I always think that the presumption of innocence has to be connected with legal protection by design. So this is more on the side of the police and the intelligence services — how can you help the intelligence services and the police to buy or develop ICT that has certain constrains which makes it compliant with the presumption of innocence which is not easy at all because we probably have to reconfigure what is the presumption of innocence.”

And while the research is part abstract and solidly foundational, Hildebrandt points out that the technologies being examined — AI and blockchain — are already being applied in legal contexts, albeit in “a state of experimentation”.

And, well, this is one tech-fueled future that really must not be unevenly distributed. The risks are stark.   

“Both the EU and national governments have taken a liking to experimentation… and where experimentation stops and systems are really already implemented and impacting decisions about your and my life is not always so easy to see,” she adds.

Her other hope is that the interpretation methodology developed through the project will help lawyers and law firms to navigate the legal tech that’s coming at them as a sales pitch.

“There’s going to be, obviously, a lot of crap on the market,” she says. “That’s inevitable, this is going to be a competitive market for legal tech and there’s going to be good stuff, bad stuff, and it will not be easy to decide what’s good stuff and bad stuff — so I do believe that by taking this foundational perspective it will be more easy to know where you have to look if you want to make that judgement… It’s about a mindset and about an informed mindset on how these things matter.

“I’m all in favor of agile and lean computing. Don’t do things that make no sense… So I hope this will contribute to a competitive advantage for those who can skip methodologies that are basically nonsensical.”

Source: Tech Crunch

After 26 years, Boston Dynamics is finally getting ready to start selling some robots. Founder Marc Raibert says that the company’s dog-like SpotMini robot is in pre-production and preparing for commercial availability in 2019. The announcement came onstage at TechCrunch’s TC Sessions: Robotics event today at UC Berkeley.

“The SpotMini robot is one that was motivated by thinking about what could go in an office — in a space more accessible for business applications — and then, the home eventually,” Raibert said onstage.

Boston Dynamics’ SpotMini was introduced late last year and took the design of the company’s “bigger brother” quadruped Spot. While the company has often showcased advanced demos of its emerging projects, SpotMini has seemed uniquely productized from the start.

On its website, Boston Dynamics highlights that SpotMini is the “quietest robot [they] have built.” The device weighs around 66 pounds and can operate for about 90 minutes on a charge.

The company says it has plans with contract manufacturers to build the first 100 SpotMinis later this year for commercial purposes, with them starting to scale production with the goal of selling SpotMini in 2019. They’re not ready to talk about a price tag yet, but they detailed that the latest SpotMini prototype cost 10 times less to build than the iteration before it.

Just yesterday, Boston Dynamics posted a video of SpotMini in autonomous mode navigating with the curiosity of a flesh-and-blood animal.

The company, perhaps best known for gravely frightening conspiracy theorists and AI doomsdayers with advanced robotics demos, has had quite the interesting history.

It was founded in 1992 after being spun out of MIT. After a stint inside Alphabet Corp., the company was purchased by SoftBank last year. SoftBank has staked significant investments in the robotics space through its Vision Fund, and, in 2015, the company began selling Pepper, a humanoid robot far less sophisticated than what Boston Dynamics has been working on.

You can watch the entire presentation below, which includes a demonstration of the latest iteration of the SpotMini.

Source: Tech Crunch

The visual data sets of images and videos amassed by the most powerful tech companies have been a competitive advantage, a moat that keeps the advances of machine learning out of reach from many. This advantage will be overturned by the advent of synthetic data.

The world’s most valuable technology companies, such as Google, Facebook, Amazon and Baidu, among others, are applying computer vision and artificial intelligence to train their computers. They harvest immense visual data sets of images, videos and other visual data from their consumers.

These data sets have been a competitive advantage for major tech companies, keeping out of reach from many the advances of machine learning and the processes that allow computers and algorithms to learn faster.

Now, this advantage is being disrupted by the ability for anyone to create and leverage synthetic data to train computers across many use cases, including retail, robotics, autonomous vehicles, commerce and much more.

Synthetic data is computer-generated data that mimics real data; in other words, data that is created by a computer, not a human. Software algorithms can be designed to create realistic simulated, or “synthetic,” data.

This synthetic data then assists in teaching a computer how to react to certain situations or criteria, replacing real-world-captured training data. One of the most important aspects of real or synthetic data is to have accurate labels so computers can translate visual data to have meaning.

Since 2012, we at LDV Capital have been investing in deep technical teams that leverage computer vision, machine learning and artificial intelligence to analyze visual data across any business sector, such as healthcare, robotics, logistics, mapping, transportation, manufacturing and much more. Many startups we encounter have the “cold start” problem of not having enough quality labelled data to train their computer algorithms. A system cannot draw any inferences for users or items about which it hasn’t yet gathered sufficient information.

Startups can gather their own contextually relevant data or partner with others to gather relevant data, such as retailers for data of human shopping behaviors or hospitals for medical data. Many early-stage startups are solving their cold start problem by creating data simulators to generate contextually relevant data with quality labels in order to train their algorithms.

Big tech companies do not have the same challenge gathering data, and they exponentially expand their initiatives to gather more unique and contextually relevant data.

Cornell Tech professor Serge Belongie, who has been doing research in computer vision for more than 25 years, says,

In the past, our field of computer vision cast a wary eye on the use of synthetic data, since it was too fake in appearance. Despite the obvious benefits of getting perfect ground truth annotations for free, our worry was that we’d train a system that worked great in simulation but would fail miserably in the wild.  Now the game has changed: the simulation-to-reality gap is rapidly disappearing. At the very minimum, we can pre-train very deep convolutional neural networks on near-photorealistic imagery and fine tune it on carefully selected real imagery.

AiFi is an early-stage startup building a computer vision and artificial intelligence platform to deliver a more efficient checkout-free solution to both mom-and-pop convenience stores and major retailers. They are building a checkout-free store solution similar to Amazon Go.

Amazon.com Inc. employees shop at the Amazon Go store in Seattle. ©Amazon Go; Photographer: Mike Kane/Bloomberg via Getty Images

As a startup, AiFi had the typical cold start challenge with a lack of visual data from real-world situations to start training their computers, versus Amazon, which likely gathered real-life data to train its algorithms while Amazon Go was in stealth mode.

Avatars help train AiFi shopping algorithms. ©AiFI

AiFi’s solution of creating synthetic data has also become one of their defensible and differentiated technology advantages. Through AiFi’s system, shoppers will be able to come into a retail store and pick up items without having to use cash, a card or scan barcodes.

These smart systems will need to continuously track hundreds or thousands of shoppers in a store and recognize or “re-identify” them throughout a complete shopping session.

AiFi store simulation with synthetic data. ©AiFi

Ying Zheng, co-founder and chief science officer at AiFi, previously worked at Apple and Google. She says,

The world is vast, and can hardly be described by a small sample of real images and labels. Not to mention that acquiring high-quality labels is both time-consuming and expensive, and sometimes infeasible. With synthetic data, we can fully capture a small but relevant aspect of the world in perfect detail. In our case, we create large-scale store simulations and render high-quality images with pixel-perfect labels, and use them to successfully train our deep learning models. This enables AiFi to create superior checkout-free solutions at massive scale.

Robotics is another sector leveraging synthetic data to train robots for various activities in factories, warehouses and across society.

Josh Tobin is a research scientist at OpenAI, a nonprofit artificial intelligence research company that aims to promote and develop friendly AI in such a way as to benefit humanity as a whole. Tobin is part of a team working on building robots that learn. They have trained entirely with simulated data and deployed on a physical robot, which, amazingly, can now learn a new task after seeing an action done once.

They developed and deployed a new algorithm called one-shot imitation learning, allowing a human to communicate how to do a new task by performing it in virtual reality. Given a single demonstration, the robot is able to solve the same task from an arbitrary starting point and then continue the task.

©Open AI

Their goal was to learn behaviors in simulation and then transfer these learnings to the real world. The hypothesis was to see if a robot can do precise things just as well from simulated data. They started with 100 percent simulated data and thought that it would not work as well as using real data to train computers. However, the simulated data for training robotic tasks worked much better than they expected.

Tobin says,

Creating an accurate synthetic data simulator is really hard. There is a factor of 3-10x in accuracy between a well-trained model on synthetic data versus real-world data. There is still a gap. For a lot of tasks the performance works well, but for extreme precision it will not fly — yet.

Many large technology companies, auto manufacturers and startups are racing toward delivering the autonomous vehicle revolution. Developers have realized there aren’t enough hours in a day to gather enough real data of driven miles needed to teach cars how to drive themselves.

One solution that some are using is synthetic data from video games such as Grand Theft Auto; unfortunately, some say that the game’s parent company Rockstar is not happy about driverless cars learning from their game. 

A street in GTA V (left) and its reconstruction through capture data (right). ©Intel Labs,Technische Universität Darmstadt

May Mobility is a startup building a self-driving microtransit service. Their CEO and founder, Edwin Olson, says,

One of our uses of synthetic data is in evaluating the performance and safety of our systems. However, we don’t believe that any reasonable amount of testing (real or simulated) is sufficient to demonstrate the safety of an autonomous vehicle. Functional safety plays an important role.

The flexibility and versatility of simulation make it especially valuable and much safer to train and test autonomous vehicles in these highly variable conditions. Simulated data can also be more easily labeled as it is created by computers, therefore saving a lot of time.

Jan Erik Solem is the CEO and co-founder of Mapillary*, helping create better maps for smarter cities, geospatial services and automotive. According to Solem,

Having a database and an understanding of what places look like all over the world will be an increasingly important component for simulation engines. As the accuracy of the trained algorithms improves, the level of detail and diversity of the data used to power the simulation matters more and more.

Neuromation is building a distributed synthetic data platform for deep learning applications. Their CEO, Yashar Behzadi says,

To date, the major platform companies have leveraged data moats to maintain their competitive advantage. Synthetic data is a major disruptor, as it significantly reduces the cost and speed of development, allowing small, agile teams to compete and win.

The challenge and opportunity for startups competing against incumbents with inherent data advantage is to leverage the best visual data with correct labels to train computers accurately for diverse use cases. Simulating data will level the playing field between large technology companies and startups. Over time, large companies will probably also create synthetic data to augment their real data, and one day this may tilt the playing field again. Many speakers at the annual LDV Vision Summit in May in NYC will enlighten us as to how they are using simulated data to train algorithms to solve business problems and help computers get closer to general artificial intelligence.

*Mapillary is an LDV Capital portfolio company.

Source: Tech Crunch

Google Clips’ AI-powered “smart camera” just got even smarter, Google announced today, revealing improved functionality around Clips’ ability to automatically capture specific moments — like hugs and kisses. Or jumps and dance moves. You know, in case you want to document all your special, private moments in a totally non-creepy way.

I kid, I kid!

Well, not entirely. Let me explain.

Look, Google Clips comes across to me as more of a proof-of-concept device that showcases the power of artificial intelligence as applied to the world of photography rather than a breakthrough consumer device.

I’m the target market for this camera — a parent and a pet owner (and look how cute she is) — but I don’t at all have a desire for a smart camera designed to capture those tough-to-photograph moments, even though neither my kid nor my pet will sit still for pictures.

I’ve tried to articulate this feeling, and I find it’s hard to say why I don’t want this thing, exactly. It’s not because the photos are automatically uploaded to the cloud or made public — they are not. They are saved to the camera’s 16 GB of onboard storage and can be reviewed later with your phone, where you can then choose to keep them, share them or delete them. And it’s not even entirely because of the price point — though, arguably, even with the recent $50 discount it’s quite the expensive toy at $199.

Maybe it’s just the camera’s premise.

That in order for us to fully enjoy a moment, we have to capture it. And because some moments are so difficult to capture, we spend too much time with phone-in-hand, instead of actually living our lives — like playing with our kids or throwing the ball for the dog, for example. And that the only solution to this problem is more technology. Not just putting the damn phone down.

What also irks me is the broader idea behind Clips that all our precious moments have to be photographed or saved as videos. They do not. Some are meant to be ephemeral. Some are meant to be memories. In aggregate, our hearts and minds tally up all these little life moments — a hug, a kiss, a smile — and then turn them into feelings. Bonds. Love.  It’s okay to miss capturing every single one.

I’m telling you, it’s okay.

At the end of the day, there are only a few times I would have even considered using this product — when baby was taking her first steps, and I was worried it would happen while my phone was away. Or maybe some big event, like a birthday party, where I wanted candids but had too much going on to take photos. But even in these moments, I’d rather prop my phone up and turn on a “Google Clips” camera mode, rather than shell out hundreds for a dedicated device.

Just saying.

You may feel differently. That’s cool. To each their own.

Anyway, what I think is most interesting about Clips is the actual technology. That it can view things captured through a camera lens and determine the interesting bits — and that it’s already getting better at this, only months after its release. That we’re teaching AI to understand what’s actually interesting to us humans, with our subjective opinions. That sort of technology has all kinds of practical applications beyond a physical camera that takes spy shots of Fido.

The improved functionality is rolling out to Clips with the May update, and will soon be followed by support for family pairing, which will let multiple family members connect the camera to their device to view content.

Here’s an intro to Clips, if you missed it the first time. (See below)

Note that it’s currently on sale for $199. Yeah, already. Hmmm. 

Source: Tech Crunch

With so much controversy swirling around the advertising-driven business models typified by Facebook and Google, and the increasing rigors of regulations like GDPR, it’s no wonder the blockchain world is starting to whet its appetite at the prospect of paying users for attention with crypto assets.

Now a company involved in the production of Hollywood blockbusters featuring the likes of James Franco, Selena Gomez, Alec Baldwin, Heidi Klum and Al Pacino is backing a new startup to reward viewers in this manner.

Hollywood producer Andrea Iervolino (best known for backing the James Franco film “In Dubious Battle” based on the novel by the Nobel Prize-winning author John Steinbeck) has decided to enter the fray by launching a new blockchain platform called TaTaTu. The startup’s aim is to bring a social, crypto economy to the entertainment industry.

Iervolino says the platform allows users to get rewarded for the content they watch and share with others through the use of crypto tokens. Of course, whether it can actually pull that off remains to be seen. Many other startups are trying to play in this space. But where Iervolino might just have an edge is in his Hollywood connections.

The idea is that the TaTaTu token can also be used by advertisers to run their ads on the platform. Organizations will also be able to earn tokens by uploading content to the platform. The more content an organization brings to the platform, the more revenue they earn. TaTaTu aims to show ads to viewers and will even share advertising revenues with them in return for their attention.

But it doesn’t stop there. Users are supposed to invite their friends via their social media to join TaTaTu, and then watch and create videos that can be shared with friends, chat with other members and share the content they like. TaTaTu will give its users the possibility to be rewarded for their social entertainment activity. TaTaTu plans not only movies and videos, but also music, sports and games. So this is quite a grand vision which, frankly, will be tricky to pull off outside of perhaps just sticking to one vertical like movies. This is like trying to do YouTube and Netflix at the same time, on a blockchain. Good luck with that.

But Iervolino is putting his money where his mouth is. The AMBI Media Group, a consortium of vertically integrated film development, production, finance and distribution companies (which counts End of Watch, Apocalypto and The Merchant of Venice among its title) and which he co-runs with Monaco-based businesswoman Lady Monika Bacardi, is said to have put in $100 million via a token pre-sale.

Building the platform will be CTO Jonathan Pullinger who started working in the Bitcoin space in late 2012, developing crypto mining software and building mining rigs. Since then he has worked on several blockchain projects, including Ethereum smart contracts (ERC-20 tokens and other solidity based solutions), Hyperledger, Fabric, the Waves Platform and lightning nodes.

Source: Tech Crunch

Google’s YouTube is the first streaming app that will actually tell users to stop watching. At its Google I/O conference this week, the company introduced a series of new controls for YouTube that will allow users to set limits on their viewing, and then receive reminders telling them to “take a break.” The feature is rolling out now in the latest version of YouTube’s app, along with others that limit YouTube’s ability to send notifications, and soon, one that gives users an overview of their binge behavior so they can make better-informed decisions about their viewing habits.

With “Take a Break,” available from YouTube’s mobile app Settings screen, users can set a reminder to appear every 15, 30, 60, 90 or 180 minutes, at which point the video will pause. You can then choose to dismiss the reminder and keep watching, or close the app.

The setting is optional, and is turned off by default, so it’s not likely to have a large impact on YouTube viewing time at this point.

Also new is a feature that lets you disable notification sounds during a specified time period each day — say, for example, from bedtime until the next morning. When users turn on the setting to disable notifications, it will, by default, disable them from 10 PM to 8 AM local time, but this can be changed.

Combined with this is an option to get a scheduled digest of notifications as an alternative. This setting combines all the daily push notifications into a single combined notification that is sent out only once per day. This is also off by default, but can be turned on in the app’s settings.

And YouTube is preparing to roll out a “time watched profile” that will appear in the Account menu and display your daily average watch time, and how long you’ve watched YouTube videos today, yesterday and over the past week, along with a set of tools to help you manage your viewing habits.

While these changes to YouTube are opt-in, it’s an interesting — and arguably responsible — position to take in terms of helping people manage their sometimes addictive behaviors around technology.

And it’s not the only major change Google is rolling out on the digital well-being front — the company also announced a series of Android features that will help you get a better handle on how often you’re using your phone and apps, and give you tools to limit distractions — like a Do Not Disturb setting, alerts that are silenced when the phone is flipped over and a “Wind Down” mode for nighttime usage that switches on the Do Not Disturb mode and turns the screen to gray-scale.

The digital well-being movement at Google got its start with a 144-page Google Slides presentation from product manager Tristan Harris, who was working on Google’s Inbox app at the time. After a trip to Burning Man, he came back convinced that technology products weren’t always designed with users’ best interests in mind. The memo went viral and found its way to then-CEO Larry Page, who promoted Harris to “design ethicist” and made digital well-being a company focus.

There’s now a Digital Wellbeing website, too, that talks about Google’s broader efforts on this front. On the site, the company touts features in other products that save people time, like Gmail’s high-priority notifications that only alert you to important emails; Google Photos’ automated editing tools; Android Auto’s distracted driving reduction tools; Google Assistant’s ability to turn on your phone’s DND mode or start a “bedtime routine” to dim your lights and quiet your music; Family Link’s tools for reducing kids’ screen time; Google WiFi’s support for “internet breaks;” and more.

Google is not the only company rethinking its role with regard to how much its technology should infiltrate our lives. Facebook, too, recently re-prioritized well-being over time spent on the site reading news, and saw its daily active users decline as a result.

But in Google’s case, some are cynical about the impact of the new tools — unlike Facebook’s changes, which the social network implemented itself, Google’s tools are opt-in. That means it’s up to users to take control over their own technology addictions, whether that’s their phone in general, or YouTube specifically. Google knows that the large majority won’t take the time to configure these settings, so it can pat itself on the back for its prioritization of digital well-being without taking a real hit to its bottom line.

Still, it’s notable that any major tech platform is doing this at all — and it’s at least a step in the right direction in terms of allowing people to reset their relationship with technology.

And in YouTube’s case, the option to “Take a Break” is at the very top of its Settings screen. If anyone ever heads into their settings for any reason, they’ll be sure to see it.

The new features are available in version 13.17 and higher of the YouTube mobile app on both iOS and Android, which is live now.

The changes were announced on May 8 during the I/O keynote, and will take a few days to roll out to all YouTube users. The “time watched profile,” however, will ship in the “coming months,” Google says.

Source: Tech Crunch

Remember Klout?

The influencer market service that purportedly let social media influencers get free stuff is finally closing its doors this month.

Perhaps, like me, you’re surprised that Klout is still running in 2018, but time is nearly up. The closure will happen May 25 — you have until then to see what topics you’re apparently an expert on. The shutdown comes more than four years after it was acquired by social media software company Lithium Technologies for a reported $200 million. The plan was for Lithium to IPO, but that never happened.

Lithium operates a range of social media services, including products that handle social media marketing campaigns and engagement with customers, and now it has decided that Klout is no longer part of its vision.

“The Klout acquisition provided Lithium with valuable artificial intelligence (AI) and machine learning capabilities but Klout as a standalone service is not aligned with our long-term strategy,” CEO Pete Hess wrote in a short note.

Hess said those apparent AI and ML smarts will be put to work in the company’s other product lines.

Interestingly, it appears that the EU’s General Data Protection Regulation (GDPR) on data is partially involved here. A Lithium spokesperson told TechCrunch that “the upcoming deadline for GDPR implementation simply expedited our plans to sunset Klout,” though the primary reason is said to be a new focus on messaging-based services.

The game isn’t entirely over. Hess teased a potential Klout replacement in the form of “a new social impact scoring methodology based on Twitter” that Lithium is apparently planning to release soon. I’m pretty sure someone out there is already pledging to bring Klout back on the blockchain and is frantically writing up an ICO whitepaper as we speak because that’s how it is these days.

RIP Klout

Source: Tech Crunch