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Archivo del Autor: Belen De Leon

2020 Mercedes-Benz GLE-Class starts under $55,000 – Roadshow

If you want the mild hybrid, you’ll have to reach north of $60,000 for it.
Source: CNET

Nintendo Switch forecasted to outsell the PS4 in 2019

The Switch has been a monster hit for Nintendo by nearly every measure. The convertible console is precisely the success the company needed after a few years in the wildness following the Wii U flop and smartphone foot dragging.

Strategy Analytics predicts more good things for the platform, predicting that Nintendo will surpass Sony in console sales next year. The margins are admittedly pretty thin, with Nintendo selling 17.3 million Switches to Sony’s 17.1 million PS4/PS4 Pro (Microsoft’s in a distant third here at an even 10 million), but if it holds, it will be an impressive feat nonetheless. 

That number would put Nintendo ahead of the pack for the first time in 10 years, back in the Wii/PS3/Xbox 360 days. The company’s gearing up to release one of the console’s biggest titles yet, with the new Super Smash Bros. due out next week, and rumors have been swirling around update hardware for 2019, which would be pretty standard fare for Nintendo.

While those sales would propel the company to the front of the pack, Sony’s still got a much larger overall user base, accounting for around half of consoles currently in use — an impressive 84 percent of which are PS4s.


Source: TechCrunch

South Korea indicts group for allegedly leaking Samsung flexible display tech to Chinese company

Prosecutors in South Korea have indicted the chief executive and eight other employees of Toptec Co for allegedly selling information about Samsung’s flexible OLED displays to a Chinese company. The charges detail that the company received more than $13.8 million for the information, Bloomberg reports.

Toptec, a Samsung supplier that manufactures display-related equipment, has denied the charges in a statement. “Our company has never provided Samsung Display’s industrial technology or business secrets to a Chinese client. Our company will fully cooperate with legal proceedings to find the truth in court.” The company’s share price is down 20 percent at the time of writing.

Samsung’s flexible display tech probably makes you think of their weird and yet-to-be-released foldable phone that they just showed off earlier this month. Samsung’s been deep in the flexible display business for a while though even if their bends have been less acute like the designs of much older handsets like the Galaxy S6 Edge.

The Chinese company was not named in the suit though there are a number of companies working to produce flexible displays for smartphones.

South Korea’s national interests are deeply intertwined with the business dealings of Samsung, and the threat of intellectual property theft to China is one which they seem to be taking very seriously. We have reached out to Samsung for comment.


Source: TechCrunch

Hulu with Live TV adds Discovery Networks programming to its core and add-on packages

In September, Hulu and Discovery announced a partnership for live and on-demand programming that would see Discovery’s content coming to Hulu’s streaming service. Today, as promised, those channels are going live with the launch of Discovery Networks programming across all of Hulu with Live TV packages.

That means Hulu with Live TV subscribers will now have access to five new channels in the core package, including Discovery, TLC, Motor Trend, Animal Planet and ID. This is the first time the content from these networks has been available live on Hulu’s service.

Hulu’s on-demand service subscription plan, however, offers a number of library episodes from the networks, including shows from Food Network, TLC and HGTV.

In addition to the expansion of the core package, Hulu’s new add-on bundles focused on Entertainment and Spanish programming are being expanded with Discovery Networks content, as well.

Earlier this month, Hulu announced the launch of these two new add-ons, which are small bundles of channels subscribers can opt to add on top of their core package. The $4.99 per month “Español” add-on, for example, offers live news and sports networks, including ESPN Deportes, NBC Universo, CNN En Español and History Channel En Español.

Now, it will include Discovery en Español and Discovery Familia as a result of the Hulu-Discovery partnership.

Meanwhile, the $7.99 per month Entertainment add-on has been offering live network feeds of LMN (Lifetime Movie Network), FYI, DIY Network, Cooking Channel and CNBC World.

It now gains Destination America, Discovery Family, Science, Discovery Life and AHC thanks to the deal.

Combined, the new channels bring a variety of popular lifestyle content to Hulu’s Live TV service, including shows like Fixer Upper, Chopped, 90 Day Fiancé, Naked and Afraid, Cupcake Wars, Deadliest Catch, Property Brothers, Alaskan Bush People and House Hunters, for example.

These sorts of home improvement shows and other light reality fare is something that on-demand services, like Netflix, haven’t quite caught up to. Netflix more recently has been rolling out originals like Nailed It! and Sugar Rush in the competition cooking space to help engage the reality TV audience, but hasn’t really had a breakout hit in the home improvement space.

Not doing more in reality TV could be to Netflix’s disadvantage. Hulu’s data has shown this content is often heavily binged, with one-third of Alaskan Bush People’s audience binge-watching the entire series, for instance.

And on Sundays, viewers are streaming multiple episodes of the other titles, it says, including 90 Day Fiancé, Property Brothers, House Hunters, Fixer Upper, Deadliest Catch, Naked and Afraid, Chopped and Cupcake Wars. The latter, Cupcake Wars, also saw its audience streaming 50 percent more hours this year than the last, the company noted.


Source: TechCrunch

Microsoft’s customizable PC could arrive in 2020 with new Surface Monitor

Microsoft’s Surface Studio may finally go modular by 2020, according to author Brad Sams. Microsoft could be working on a Surface Studio monitor that allows easy upgrades, though it’s unclear what components are upgradable.

The post Microsoft’s customizable PC could arrive in 2020 with new Surface Monitor appeared first on Digital Trends.


Source: Digital trends

AWS launches a managed Kafka service

Kafka is an open-source tool for handling incoming streams of data. Like virtually all powerful tools, it’s somewhat hard to set up and manage. Today, Amazon’s AWS is making this all a bit easier for its users with the launch of Amazon Managed Streaming for Kafka. That’s a mouthful, but it’s essentially Kafka as a fully managed, highly available service on AWS. It’s now available on AWS as a public preview.

As AWS CTO Werner Vogels noted in his AWS re:Invent keynote, Kafka users traditionally had to do a lot of heavy lifting to set up a cluster on AWS and to ensure that it could scale and handle failures. “It’s a nightmare having to restart all the cluster and the main nodes,” he said. “This is what I would call the traditional heavy lifting that AWS is really good at solving for you.”

It’s interesting to see AWS launch this service, given that it already offers a very similar tool in Kinesis, a tool that also focuses on ingesting streaming data. There are plenty of applications on the market today that already use Kafka, and AWS is clearly interested in giving those users a pathway to either move to a managed Kafka service or to AWS in general.

As with all things AWS, the pricing is a bit complicated, but a basic Kafka instance will start at $0.21 per hour. You’re not likely to just use one instance, so for a somewhat useful setup with three brokers and a good amount of storage and some other fees, you’ll quickly pay well over $500 per month.

more AWS re:Invent 2018 coverage


Source: TechCrunch

Mueller: Cohen Lied About Trump Organization's Moscow Project

Trump’s former lawyer has testified that the Trump Organization pursued a real estate deal with Moscow deep into the 2016 presidential campaign.
Source: Wired

The Hoberman Sphere Maker Toys With Origami Structures

Chuck Hoberman is looking to make “transformative designs” that borrow from the art of paper folding.
Source: Wired

The Infinite Lifespan of Memes

Brandon Moore, creator of the “What are those?” meme, died this week. But his cultural legacy endures.
Source: Wired

Agtech startup Imago AI is using computer vision to boost crop yields

Presenting onstage today in the 2018 TC Disrupt Berlin Battlefield is Indian agtech startup Imago AI, which is applying AI to help feed the world’s growing population by increasing crop yields and reducing food waste. As startup missions go, it’s an impressively ambitious one.

The team, which is based out of Gurgaon near New Delhi, is using computer vision and machine learning technology to fully automate the laborious task of measuring crop output and quality — speeding up what can be a very manual and time-consuming process to quantify plant traits, often involving tools like calipers and weighing scales, toward the goal of developing higher-yielding, more disease-resistant crop varieties.

Currently they say it can take seed companies between six and eight years to develop a new seed variety. So anything that increases efficiency stands to be a major boon.

And they claim their technology can reduce the time it takes to measure crop traits by up to 75 percent.

In the case of one pilot, they say a client had previously been taking two days to manually measure the grades of their crops using traditional methods like scales. “Now using this image-based AI system they’re able to do it in just 30 to 40 minutes,” says co-founder Abhishek Goyal.

Using AI-based image processing technology, they can also crucially capture more data points than the human eye can (or easily can), because their algorithms can measure and asses finer-grained phenotypic differences than a person might pick up on or be easily able to quantify just judging by eye alone.

“Some of the phenotypic traits they are not possible to identify manually,” says co-founder Shweta Gupta. “Maybe very tedious or for whatever all these laborious reasons. So now with this AI-enabled [process] we are now able to capture more phenotypic traits.

“So more coverage of phenotypic traits… and with this more coverage we are having more scope to select the next cycle of this seed. So this further improves the seed quality in the longer run.”

The wordy phrase they use to describe what their technology delivers is: “High throughput precision phenotyping.”

Or, put another way, they’re using AI to data-mine the quality parameters of crops.

“These quality parameters are very critical to these seed companies,” says Gupta. “Plant breeding is a very costly and very complex process… in terms of human resource and time these seed companies need to deploy.

“The research [on the kind of rice you are eating now] has been done in the previous seven to eight years. It’s a complete cycle… chain of continuous development to finally come up with a variety which is appropriate to launch in the market.”

But there’s more. The overarching vision is not only that AI will help seed companies make key decisions to select for higher-quality seed that can deliver higher-yielding crops, while also speeding up that (slow) process. Ultimately their hope is that the data generated by applying AI to automate phenotypic measurements of crops will also be able to yield highly valuable predictive insights.

Here, if they can establish a correlation between geotagged phenotypic measurements and the plants’ genotypic data (data which the seed giants they’re targeting would already hold), the AI-enabled data-capture method could also steer farmers toward the best crop variety to use in a particular location and climate condition — purely based on insights triangulated and unlocked from the data they’re capturing.

One current approach in agriculture to selecting the best crop for a particular location/environment can involve using genetic engineering. Though the technology has attracted major controversy when applied to foodstuffs.

Imago AI hopes to arrive at a similar outcome via an entirely different technology route, based on data and seed selection. And, well, AI’s uniform eye informing key agriculture decisions.

“Once we are able to establish this sort of relation this is very helpful for these companies and this can further reduce their total seed production time from six to eight years to very less number of years,” says Goyal. “So this sort of correlation we are trying to establish. But for that initially we need to complete very accurate phenotypic data.”

“Once we have enough data we will establish the correlation between phenotypic data and genotypic data and what will happen after establishing this correlation we’ll be able to predict for these companies that, with your genomics data, and with the environmental conditions, and we’ll predict phenotypic data for you,” adds Gupta.

“That will be highly, highly valuable to them because this will help them in reducing their time resources in terms of this breeding and phenotyping process.”

“Maybe then they won’t really have to actually do a field trial,” suggests Goyal. “For some of the traits they don’t really need to do a field trial and then check what is going to be that particular trait if we are able to predict with a very high accuracy if this is the genomics and this is the environment, then this is going to be the phenotype.”

So — in plainer language — the technology could suggest the best seed variety for a particular place and climate, based on a finer-grained understanding of the underlying traits.

In the case of disease-resistant plant strains it could potentially even help reduce the amount of pesticides farmers use, say, if the the selected crops are naturally more resilient to disease.

While, on the seed generation front, Gupta suggests their approach could shrink the production time frame — from up to eight years to “maybe three or four.”

“That’s the amount of time-saving we are talking about,” she adds, emphasizing the really big promise of AI-enabled phenotyping is a higher amount of food production in significantly less time.

As well as measuring crop traits, they’re also using computer vision and machine learning algorithms to identify crop diseases and measure with greater precision how extensively a particular plant has been affected.

This is another key data point if your goal is to help select for phenotypic traits associated with better natural resistance to disease, with the founders noting that around 40 percent of the world’s crop load is lost (and so wasted) as a result of disease.

And, again, measuring how diseased a plant is can be a judgement call for the human eye — resulting in data of varying accuracy. So by automating disease capture using AI-based image analysis the recorded data becomes more uniformly consistent, thereby allowing for better quality benchmarking to feed into seed selection decisions, boosting the entire hybrid production cycle.

Sample image processed by Imago AI showing the proportion of a crop affected by disease

In terms of where they are now, the bootstrapping, nearly year-old startup is working off data from a number of trials with seed companies — including a recurring paying client they can name (DuPont Pioneer); and several paid trials with other seed firms they can’t (because they remain under NDA).

Trials have taken place in India and the U.S. so far, they tell TechCrunch.

“We don’t really need to pilot our tech everywhere. And these are global [seed] companies, present in 30, 40 countries,” adds Goyal, arguing their approach naturally scales. “They test our technology at a single country and then it’s very easy to implement it at other locations.”

Their imaging software does not depend on any proprietary camera hardware. Data can be captured with tablets or smartphones, or even from a camera on a drone or using satellite imagery, depending on the sought for application.

Although for measuring crop traits like length they do need some reference point to be associated with the image.

“That can be achieved by either fixing the distance of object from the camera or by placing a reference object in the image. We use both the methods, as per convenience of the user,” they note on that.

While some current phenotyping methods are very manual, there are also other image-processing applications in the market targeting the agriculture sector.

But Imago AI’s founders argue these rival software products are only partially automated — “so a lot of manual input is required,” whereas they couch their approach as fully automated, with just one initial manual step of selecting the crop to be quantified by their AI’s eye.

Another advantage they flag up versus other players is that their approach is entirely non-destructive. This means crop samples do not need to be plucked and taken away to be photographed in a lab, for example. Rather, pictures of crops can be snapped in situ in the field, with measurements and assessments still — they claim — accurately extracted by algorithms which intelligently filter out background noise.

“In the pilots that we have done with companies, they compared our results with the manual measuring results and we have achieved more than 99 percent accuracy,” is Goyal’s claim.

While, for quantifying disease spread, he points out it’s just not manually possible to make exact measurements. “In manual measurement, an expert is only able to provide a certain percentage range of disease severity for an image example; (25-40 percent) but using our software they can accurately pin point the exact percentage (e.g. 32.23 percent),” he adds.

They are also providing additional support for seed researchers — by offering a range of mathematical tools with their software to support analysis of the phenotypic data, with results that can be easily exported as an Excel file.

“Initially we also didn’t have this much knowledge about phenotyping, so we interviewed around 50 researchers from technical universities, from these seed input companies and interacted with farmers — then we understood what exactly is the pain-point and from there these use cases came up,” they add, noting that they used WhatsApp groups to gather intel from local farmers.

While seed companies are the initial target customers, they see applications for their visual approach for optimizing quality assessment in the food industry too — saying they are looking into using computer vision and hyper-spectral imaging data to do things like identify foreign material or adulteration in production line foodstuffs.

“Because in food companies a lot of food is wasted on their production lines,” explains Gupta. “So that is where we see our technology really helps — reducing that sort of wastage.”

“Basically any visual parameter which needs to be measured that can be done through our technology,” adds Goyal.

They plan to explore potential applications in the food industry over the next 12 months, while focusing on building out their trials and implementations with seed giants. Their target is to have between 40 to 50 companies using their AI system globally within a year’s time, they add.

While the business is revenue-generating now — and “fully self-enabled” as they put it — they are also looking to take in some strategic investment.

“Right now we are in touch with a few investors,” confirms Goyal. “We are looking for strategic investors who have access to agriculture industry or maybe food industry… but at present haven’t raised any amount.”


Source: TechCrunch

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