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DataRobot expands platform and announces Zepl acquisition

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DataRobot, the Boston-based automated machine learning startup, had a bushel of announcements this morning as it expanded its platform to give technical and non-technical users alike something new. It also announced it has acquired Zepl, giving it an advanced development environment where data scientists can bring their own code to DataRobot. The two companies did not share the acquisition price.

Nenshad Bardoliwalla, SVP of Product at DataRobot says that his company aspires to be the leader in this market and it believes the path to doing that is appealing to a broad spectrum of user requirements from those who have little data science understanding to those who can do their own machine learning coding in Python and R.

“While people love automation, they also want it to be [flexible]. They don’t want just automation, but then you can’t do anything with it. They also want the ability to turn the knobs and pull the levers,” Bardoliwalla explained.

To resolve that problem, rather than building a coding environment from scratch, it chose to buy Zepl and incorporate its coding notebook into the platform in a new tool called Composable ML. “With Composable ML and with the Zepl acquisition, we are now providing a really first class environment for people who want to code,” he said.

Zepl was founded in 2016 and raised $13 million along the way, according to Crunchbase data. The company didn’t want to reveal the number of employees or the purchase price, but the acquisition gives it advanced capabilities, especially a notebook environment to call its own to attract those more advanced users to the platform.The company plans to incorporate the Zepl functionality into the platform, while also leaving the stand-alone product in place.

Bardoliwalla said that they see the Zepl acquisition as an extension of the automated side of the house, where these tools can work in conjunction with one another with machines and humans working together to generate the best models. “This [generates an] organic mixture of the best of what a system can generate using DataRobot AutoML and the best of what human beings can do and kind of trying to compose those together into something really interesting […],” Bardoliwalla said.

The company is also introducing a no-code AI app builder that enables non-technical users to create apps from the data set with drag and drop components. In addition, it’s adding a tool to monitor the accuracy of the model over time. Sometimes, after a model is in production for a time, the accuracy can begin to break down as the data the model is based is no longer valid. This tool monitors the model data for accuracy and warns the team when it’s starting to fall out of compliance.

Finally the company is announcing a model bias monitoring tool to help root out model bias that could introduce racist, sexist or other assumptions into the model. To avoid this, the company has built a tool to identify when it sees this happening both in the model building phase and in production. It warns the team of potential bias, while providing them with suggestions to tweak the model to remove it.

DataRobot is based in Boston and was founded in 2012. It has raised over $750 million and has a valuation of over $2.8 billion, according to Pitchbook.

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Source: https://techcrunch.com/2021/05/11/datarobot-expands-platform-and-announces-zepl-acquisition/

AI

Why You Need a Truck Accident Lawyer to Represent You

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The trucking industry is an essential part of the economy and it’s an ongoing measure for our goods to travel far without issues. Truck drivers put an incredible amount of effort into what they do every day, but most people still overlook this entire process and refer to trucking as an easy job where you just sit and drive. That couldn’t be further from the truth. 

The Reality of Driving a Truck for a Living

Truck driving is hard and stressful, even the initial procedure of becoming a driver takes a lot of time and concentration. You’ll find yourself spending a lot of time alone and on the road, driving for hours on end. Your only interactions will be with customers and strangers. Racking up around 3,000 miles each week on the job is more common than you might think, and late-night shifts happen regularly when a driver needs to meet a specific deadline. 

Trucking is not merely a job, but a lifestyle—and it’s not an easy one and not for everyone. But if you like having a lot of time for yourself, meeting new people, and experiencing the feeling of a long road trip, this hard and stressful job can be a pleasant and unique adventure. Of course, adventure is exempt from facing the unexpected, especially when it involves driving a large vehicle for many hours. 

Nowadays, truck accidents are becoming more common and they’re far more dangerous than a typical car crash. When you find yourself in an accident involving a truck, there’s a good chance someone got seriously hurt, and it’s important that you focus and follow the correct steps to avoid causing even more damage. 

But following the right steps by yourself after a traumatic event is not easy, and that’s why a lawyer can be crucial.

How a Lawyer Handles a Truck Accident and How Liability Works

A truck accident will usually cause a lot of damage and involve multiple parties. Identifying the at-fault party won’t be easy. The responsibility for your injuries could fall on the truck driver, the trucking company, the truck manufacturer, and so on. These variables make gathering evidence for your case even more difficult, and it’s not something you want to do yourself while you’re dealing with injuries. 

If you want to receive proper compensation, a lawyer will examine your case and ensure you receive everything you deserve after identifying the liable party. He will also take care of calculating economic and non-economic damages, as a truck accident will likely incur both. Medical bills associated with the accident will probably be your first thought, and if you deal with the entire case by yourself, you’ll probably receive a settlement offer that will only cover those. Truck accident lawyers in Vegas can help you understand how that would be a big mistake. 

An accident can change your life in many ways that do not involve money at all, and you need to be adequately compensated for all non-economic damages along with all your medical bills. Emotional distress, pain, and all kinds of suffering that sometimes don’t appear immediately need to be taken into consideration. A lawyer can ensure nothing is missed. 

If multiple parties are involved, you’ll need to handle negotiations with each and every one of them, especially if there’s more than one party responsible for the event. There’s no way around it, a truck accident lawyer is fundamental if you want to be adequately compensated. Always hire one while you take care of your injuries—you won’t regret it.

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Source: https://1reddrop.com/2021/09/27/why-you-need-a-truck-accident-lawyer-to-represent-you/?utm_source=rss&utm_medium=rss&utm_campaign=why-you-need-a-truck-accident-lawyer-to-represent-you

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AI

Why You Need a Truck Accident Lawyer to Represent You

Published

on

The trucking industry is an essential part of the economy and it’s an ongoing measure for our goods to travel far without issues. Truck drivers put an incredible amount of effort into what they do every day, but most people still overlook this entire process and refer to trucking as an easy job where you just sit and drive. That couldn’t be further from the truth. 

The Reality of Driving a Truck for a Living

Truck driving is hard and stressful, even the initial procedure of becoming a driver takes a lot of time and concentration. You’ll find yourself spending a lot of time alone and on the road, driving for hours on end. Your only interactions will be with customers and strangers. Racking up around 3,000 miles each week on the job is more common than you might think, and late-night shifts happen regularly when a driver needs to meet a specific deadline. 

Trucking is not merely a job, but a lifestyle—and it’s not an easy one and not for everyone. But if you like having a lot of time for yourself, meeting new people, and experiencing the feeling of a long road trip, this hard and stressful job can be a pleasant and unique adventure. Of course, adventure is exempt from facing the unexpected, especially when it involves driving a large vehicle for many hours. 

Nowadays, truck accidents are becoming more common and they’re far more dangerous than a typical car crash. When you find yourself in an accident involving a truck, there’s a good chance someone got seriously hurt, and it’s important that you focus and follow the correct steps to avoid causing even more damage. 

But following the right steps by yourself after a traumatic event is not easy, and that’s why a lawyer can be crucial.

How a Lawyer Handles a Truck Accident and How Liability Works

A truck accident will usually cause a lot of damage and involve multiple parties. Identifying the at-fault party won’t be easy. The responsibility for your injuries could fall on the truck driver, the trucking company, the truck manufacturer, and so on. These variables make gathering evidence for your case even more difficult, and it’s not something you want to do yourself while you’re dealing with injuries. 

If you want to receive proper compensation, a lawyer will examine your case and ensure you receive everything you deserve after identifying the liable party. He will also take care of calculating economic and non-economic damages, as a truck accident will likely incur both. Medical bills associated with the accident will probably be your first thought, and if you deal with the entire case by yourself, you’ll probably receive a settlement offer that will only cover those. Truck accident lawyers in Vegas can help you understand how that would be a big mistake. 

An accident can change your life in many ways that do not involve money at all, and you need to be adequately compensated for all non-economic damages along with all your medical bills. Emotional distress, pain, and all kinds of suffering that sometimes don’t appear immediately need to be taken into consideration. A lawyer can ensure nothing is missed. 

If multiple parties are involved, you’ll need to handle negotiations with each and every one of them, especially if there’s more than one party responsible for the event. There’s no way around it, a truck accident lawyer is fundamental if you want to be adequately compensated. Always hire one while you take care of your injuries—you won’t regret it.

Recommended Products

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://1reddrop.com/2021/09/27/why-you-need-a-truck-accident-lawyer-to-represent-you/?utm_source=rss&utm_medium=rss&utm_campaign=why-you-need-a-truck-accident-lawyer-to-represent-you

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This AI Performs Seamless Video Manipulation Without Deep Learning or Datasets

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Have you ever wanted to edit a video to remove or add someone, change the background, make it last a bit longer, or change the resolution to fit a specific aspect ratio without compressing or stretching it? For those of you who already ran advertisement campaigns, you certainly wanted to have variations of your videos for AB testing and see what works best. Well, this new research by Niv Haim et al. can help you do all of the about in a single video and in HD! Indeed, using a simple video, you can perform any tasks I just mentioned in seconds or a few minutes for high-quality videos. You can basically use it for any video manipulation or video generation application you have in mind. It even outperforms GANs in all ways and doesn’t use any deep learning fancy research nor requires a huge and impractical dataset! And the best thing is that this technique is scalable to high-resolution videos

image

Louis Bouchard Hacker Noon profile picture

Louis Bouchard

I explain Artificial Intelligence terms and news to non-experts.

Have you ever wanted to edit a video to remove or add someone, change the background, make it last a bit longer, or change the resolution to fit a specific aspect ratio without compressing or stretching it? For those of you who already ran advertisement campaigns, you certainly wanted to have variations of your videos for AB testing and see what works best.

Well, this new research by Niv Haim et al. can help you do all of the about in a single video and in HD!

Indeed, using a simple video, you can perform any tasks I just mentioned in seconds or a few minutes for high-quality videos. You can basically use it for any video manipulation or video generation application you have in mind. It even outperforms GANs in all ways and doesn’t use any deep learning fancy research nor requires a huge and impractical dataset!

And the best thing is that this technique is scalable to high-resolution videos…

Watch the video

References

►Read the full article: https://www.louisbouchard.ai/vgpnn-ge…
►Paper covered: Haim, N., Feinstein, B., Granot, N., Shocher, A., Bagon, S., Dekel, T., & Irani, M. (2021). Diverse Generation from a Single Video Made Possible. ArXiv, abs/2109.08591.
►The technique that was adapted from images to videos: Niv Granot, Ben Feinstein, Assaf Shocher, Shai Bagon, and Michal Irani. Drop the gan: In defense of patches nearest neighbors as single image generative models. arXiv preprint arXiv:2103.15545, 2021.
►Code (available soon): https://nivha.github.io/vgpnn/
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

Video Transcript

00:00

have you ever wanted to edit a video

00:02

remove or add someone change the

00:04

background make it last a bit longer or

00:06

change the resolution to fit a specific

00:08

aspect ratio without compressing or

00:10

stretching it for those of you who

00:12

already ran advertisement campaigns you

00:14

certainly wanted to have variations of

00:16

your videos for a b testing and see what

00:19

works best well this new research by niv

00:22

haim ital can help you do all of these

00:24

out of a single video and in high

00:27

definition indeed using a simple video

00:29

you can perform any tasks i just

00:32

mentioned in seconds or in a few minutes

00:34

for high quality videos you can

00:36

basically use it for any video

00:38

manipulation or video generation

00:40

application you have in mind it even

00:42

outperforms guns in any ways and doesn’t

00:45

use any deep learning fancy research nor

00:48

requires a huge and impractical data set

00:51

and the best thing is that this

00:52

technique is scalable to high resolution

00:55

videos it is not only for research

00:57

purposes with 256 by 256 pixel videos oh

01:01

and of course you can use it with images

01:04

let’s see how it works the model is

01:06

called video based generative patch

01:08

nearest neighbors vgpnn instead of using

01:11

complex algorithms and models like gans

01:14

or transformers the researchers that

01:16

developed vgpn opt for a much simpler

01:19

approach but revisited the nearest

01:22

neighbor algorithm first they downscale

01:24

the image in a pyramid way where each

01:26

level is a flower resolution than the

01:28

one above then they add random noise to

01:31

the coarsest level to generate a

01:33

different image similar to what guns do

01:36

in the compressed space after encoding

01:38

the image note that here i will say

01:40

image for simplicity but in this case

01:42

since it’s applied to videos the process

01:45

is made on three frames simultaneously

01:48

adding a time dimension but the

01:49

explanation stays the same with an extra

01:52

step at the end the image at the

01:54

coarsest scale with noise added is

01:56

divided into multiple small square

01:59

patches all patches in the image with

02:01

noise added are replaced with the most

02:04

similar patch from the initial scaled

02:06

down image without noise this most

02:09

similar patch is measured with the

02:11

nearest neighbor algorithm as we will

02:13

see most of these patches will stay the

02:15

same but depending on the added noise

02:17

some patches will change just enough to

02:19

make them look more similar to another

02:21

patch in the initial image this is the

02:24

vpn output you see here these changes

02:27

are just enough to generate a new

02:29

version of the image then this first

02:31

output is upscaled and used to compare

02:34

with the input image of the next scale

02:36

to act as a noisy version of it and the

02:38

same steps are repeated in this next

02:41

iteration we split these images into

02:43

small patches and replace the previously

02:45

generated ones with the most similar

02:48

ones at the current step let’s get into

02:50

this vpn module we just covered as you

02:53

can see here the only difference from

02:55

the initial step with noise added is

02:58

that we compare the upscale generated

03:00

image here denoted as q with an upscaled

03:03

version of the previous image just so it

03:06

has the same level of details denoted as

03:09

k basically using the level below as

03:12

comparisons we compare q and k and then

03:15

select corresponding patches in the

03:17

image from this current level v to

03:20

generate the new image for this step

03:22

which will be used for the next

03:24

iteration as you see here with the small

03:26

arrows k is just an upscale version of

03:28

the image we created downscaling v in

03:31

the initial step of this algorithm where

03:33

we created the pyramidal scaling

03:35

versions of our image this is done to

03:38

compare the same level of sharpness in

03:40

both images as the upscale generated

03:42

image from the previous layer q will be

03:45

much more blurry than the image at the

03:48

current step v and it will be very hard

03:50

to find similar patches this is repeated

03:53

until we get back to the top of the

03:54

pyramid with high resolution results

03:57

then all these generated patches are

03:59

folded into a video and voila you can

04:02

repeat this with different noises or

04:04

modifications to generate any variations

04:06

you want on your videos let’s do a quick

04:09

recap the image is downscaled at

04:11

multiple scales noise is added to the

04:13

corsa scale image which is divided into

04:16

small square patches each noisy patch is

04:18

then replaced with the most similar

04:20

patches from the same compressed image

04:23

without noise causing few random changes

04:26

in the image while keeping realism both

04:28

the newly generated image and image

04:31

without noise of this step are upscaled

04:33

and compared to find the most similar

04:36

patches with the nearest neighbor again

04:38

these most similar patches are then

04:40

chosen from the image at the current

04:42

resolution to generate a new image for

04:45

the step again and we repeat this

04:47

upscaling and comparing steps until we

04:49

get back to the top of the pyramid with

04:52

high resolution results of course the

04:54

results are not perfect you can still

04:56

see some artifacts like people appearing

04:58

and disappearing at weird places or

05:00

simply copy-pasting someone in some

05:02

cases making it very obvious if you

05:05

focus on it still it’s only the first

05:07

paper attacking video manipulations with

05:09

the nearest neighbor algorithm and

05:11

making it scalable to high resolution

05:13

videos it’s always awesome to see

05:15

different approaches i’m super excited

05:18

to see the next paper improving upon

05:20

this one also the results are still

05:22

quite impressive and they could be used

05:24

as a data augmentation tool for models

05:26

working on videos due to their very low

05:29

run time allowing other models to train

05:31

on larger and more diverse data sets

05:33

without much cost if you are interested

05:35

in learning more about this technique i

05:37

will strongly recommend reading their

05:38

paper it is the first link in the

05:40

description thank you for watching and

05:42

to everyone supporting my work on

05:44

patreon or by commenting and liking the

05:46

videos here on youtube

05:54

you

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Artificial Intelligence

DeFi and Web 3.0: Unleashing creative juices with decentralized finance

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Decentralized technologies are starting to revolutionize the world of finance, with cryptocurrencies applied in different ways to recreate traditional financial instruments. However, since cryptocurrencies aren’t backed by anything but people’s faith in them, they are extremely volatile. That means, when it comes to loaning value with crypto, neither party can be sure that they will get a fair deal.

There needs to be a way to secure the value of the assets loaned, which can be done by backing them up with a value in the real world. Here is where the tokenization of real assets comes in. This process is pretty straightforward when we consider tangible assets like a building or gold bars, but what about intangible assets like intellectual property?

Related: Understanding the systemic shift from digitization to tokenization of financial services

The rise of the creator economy has led to intangible assets accounting for over 90% of the S&P 500’s market value, a figure that is only set to grow. There needs to be a way to unlock more creativity to realize the potential of human capital.

Kickstarting creator financing

Finding a start with financing in the creator economy is a great challenge, especially for newcomers. As many entrepreneurs in this segment discover, sometimes it is much easier to give away a good idea than to create a business out of that idea.

Creativity, by definition, disrupts what came before; it’s about new ideas, new technologies, new products, new services and new ways of doing things. Driven in large part by the digital revolution, many creative industries are not just innovative in what they do but in how they do it.

Related: Bull or bear market, creators are diving headfirst into crypto

Raising funds may be difficult for several reasons. For one, banks and investors tend to be conservative. They like certainty and are unlikely to be impressed by an enthusiastic entrepreneur convinced that an entirely new and untried idea — whether it is a design, a software tool, a fashion concept or a video game — will be a commercial success. Furthermore, banks want collateral for their loans, but many creative businesses have no capital assets to offer.

Stumbling blocks in the state of play

Investors specializing in creative industries may indeed recognize an entrepreneur’s genius. But in return for their investment, they often want some ownership of the idea and, therefore, some control over its development and marketing. This may not seem acceptable to the creative entrepreneur who prefers debt-finance in the form of a loan rather than equity finance in the form of sharing ownership and control over the work with the investor.

Alex Shkor, the founder of DEIP — a company that is building a protocol for the creator economy — explained to me, “For creators to be able to tokenize their works and collateralize them for funding, there needs to be a set of smart contracts, which can register assets on-chain, issue NFTs, evaluate assets and manage both collateralization and liquidation in case of default.”

Loan framework for the creative economy

Just as loans can be issued in the real economy based on collateral, so can they be in the creator economy.

Imagine a game developer (let’s call them Jane) who begins working on a side project. After a while and some positive encouragement from friends and family, Jane decides to take the leap into converting their side project into a full-time job. But a few months down the line, and with slower progress than first anticipated, Jane’s funds start to dwindle; they begin to consider full-time roles again. This situation is a common one for budding creators out there.

However, with a decentralized platform for intellectual assets, Jane’s progress on their work could be assessed by a decentralized assessment system that pools the expertise of people in the domain to give the unfinished creation an appraisal guided by the intrinsic value of the idea. This inherent value is used as the input for the collateralization calculation, the loan value that it can be issued for. Jane can use the loan offered to them for whatever they like; in this case, to support themself while they finish the game’s development.

Moreover, with or without collateral, a small loan can be issued to newcomers. If Jane doesn’t have any project, ready-made or part-made creation, they still have the chance for initial financing as a newcomer to the platform. The loan amount will be smaller as it is unsecured, and the loan itself is backed by the segment decentralized autonomous organization (DAO) and budgets originating from its ecosystem fund. Sources of this fund come from transaction fees and bandwidth allocation payments of the underlying blockchain.

If loans are paid back on time, Jane’s personal credit rating will be upgraded. In this case, if Jane would like to apply for another loan, the collateralization factor will be less, enabling them to borrow more.

Should Jane default on their loan, any collateralized assets are assumed by the platform and can be sold off to recoup the funds via smart liquidation contracts. If Jane hasn’t collateralized anything, the default risk is realized by the platform and covered by the DAO.

As long as the creator’s credit history is solid and positively confirmed with each new loan, the next tranche can be issued with iteratively improved terms and conditions. Credit history becomes an integral and immutable part of the reputational profile of the creator. As Shkor noted:

“he whole purpose of Web 3.0 is to enable a decentralized creator economy nd all the tech for this already exists.”

He continued, “We just need to foster adoption of these technologies in real industries, in creative industries, for the assets produced by creators. It will not only increase liquidity of the creator economy assets, it will also open a flow of capital to creators.”

The views, thoughts and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

Alexandra Luzan is a Ph.D. student researching the connection between new technologies and art at Ca’ Foscari University in Venice. For about a decade, Alexandra has been organizing tech conferences and other events in Europe dedicated to blockchain technology and artificial intelligence. She is equally interested in the relationship between blockchain tech and art.


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Source: https://cointelegraph.com/news/defi-and-web-3-0-unleashing-creative-juices-with-decentralized-finance

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