Connect with us

AI

Skyqraft raises $2.2M seed for its powerline issue detection system

Avatar

Published

on

Skyqraft, the Swedish startup using AI and drones for electricity powerline inspection, has raised $2.2 million in seed funding, capital it will use to further develop its technology and expand its operations in Europe and in the U.S.

Leading the seed round is Subvenio Invest, with participation from pre-seed backer Antler, Next Human Ventures and unnamed angel investors.

Founded in March 2019 and launched that September, Skyqraft provides what it calls “smart” infrastructure inspections for powerlines. It uses drones, combined with AI, to gather images and detect risk automatically.

This is in contrast to the status quo, where powerlines are typically inspected by teams of people and helicopters, which is time-consuming and potentially dangerous. The idea behind Skyqraft is to enable safer powerline inspections in a more cost-efficient and environmentally sustainable way.

“Powerline inspections most importantly are not environmentally friendly, very costly and unsafe with the use of helicopters and people,” Skyqraft co-founder and CMO Sakina Turabali told TechCrunch when Skyqraft announced its pre-seed funding. “We provide smart infrastructure inspections using unmanned airplanes by gathering images and 360 videos and feeding that data into a machine learning system that automatically detects any risk to the powerlines.”

Skyqraft says the system can process high volumes of image data and is able to detect equipment issues “rapidly and with high accuracy”. By using Skyqraft, the Swedish company claims utility companies can shorten a 25km powerline inspection from two days to “three minutes”.

Image Credits: Skyqraft

That proposition appears to already be resonating with customers, which include the three largest utility companies in Sweden jointly representing 85% of the Swedish market. Additionally, Skyqraft says it is also negotiating a series of larger-scale pilots in the U.S. in 2021 with the global utility company Iberdrola.

Source: https://techcrunch.com/2021/01/19/skyqraft-seed/

AI

Australian FinTech company profile #122 – Unhedged

Avatar

Published

on

1. Company Name: Unhedged

2. Website: www.unhedged.com.au

3. Key Staff & Titles: Peter Bakker – Founder & CEO, Mike Cohen – Co-Founder & COO, Glen VanBavinckhove – CTO, Jeremy Beasley – Growth, Jeremy Machet – Growth, and 6 others who are building like crazy

4. Location(s): Melbourne and Sydney

5. In one sentence, what does your fintech do?: Unhedged uses AI to deliver algorithmic returns to the everyday investor

6. How / why did you start your fintech company?: Being an Algotrader and working with rich people I got annoyed that these advanced tools were not available to my friends. When I looked up the returns of robo-investors I got really annoyed and thought: there must be a better way

7. What is the best thing your company has achieved or learnt along the way (this can include awards, capital raising etc)?: Raised 500K in 3 days which was faster then I ever raised before.

8. What’s some advice you’d give to an aspiring start-up?: Watch your cashflow: companies die of lack of cash, not lack of ideas

9. What’s next for your company? And are you looking to expand overseas or stay focussed on Australia?: Lauchinh the fund in April/May, a crowd fund raise in June and launching the retail product in July….

10. What other fintechs or companies do you admire?: Finserv (most stable earnings and growth), Blackrock: amazing money machine. Ellevest: a narrow target markets that works. CacheInvest: fundmanager as a service

11. What’s the most interesting or funniest moment that’s happened in your company’s lifetime?:
An investor transferring 100K without any documentation nor live fund (we returned the cash). We are still wondering how he knew where to transfer to.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://australianfintech.com.au/australian-fintech-company-profile-122-unhedged/

Continue Reading

Artificial Intelligence

Flawed data is putting people with disabilities at risk

Avatar

Published

on

Data isn’t abstract — it has a direct impact on people’s lives.

In 2019, an AI-powered delivery robot momentarily blocked a wheelchair user from safely accessing the curb when crossing a busy road. Speaking about the incident, the person noted, “It’s important that the development of technologies [doesn’t put] disabled people on the line as collateral.”

Alongside other minority groups, people with disabilities have long been harmed by flawed data and data tools. Disabilities are diverse, nuanced and dynamic; they don’t fit within the formulaic structure of AI, which is programmed to find patterns and form groups. Because AI treats any outlier data as “noise” and disregards it, too often people with disabilities are excluded from its conclusions.

Disabilities are diverse, nuanced and dynamic; they don’t fit within the formulaic structure of AI, which is programmed to find patterns and form groups.

Take for example the case of Elaine Herzberg, who was struck and killed by a self-driving Uber SUV in 2018. At the time of the collision, Herzberg was pushing a bicycle, which meant Uber’s system struggled to categorize her and flitted between labeling her as a “vehicle,” “bicycle,” and “other.” The tragedy raised many questions for people with disabilities; would a person in a wheelchair or a scooter be at risk of the same fatal misclassification?

We need a new way of collecting and processing data. “Data” ranges from personal information, user feedback, resumes, multimedia, user metrics and much more, and it’s constantly being used to optimize our software. However, it’s not done so with the understanding of the spectrum of nefarious ways that it can and is used in the wrong hands, or when principles are not applied to each touchpoint of building.

Our products are long overdue for a new, fairer data framework to ensure that data is managed with people with disabilities in mind. If it isn’t, people with disabilities will face more friction, and dangers, in a day-to-day life that is increasingly dependent on digital tools.

Misinformed data hampers the building of good tools

Products that lack accessibility might not stop people with disabilities from leaving their homes, but they can stop them from accessing pivot points of life like quality healthcare, education and on-demand deliveries.

Our tools are a product of their environment. They reflect their creators’ worldview and subjective lens. For too long, the same groups of people have been overseeing faulty data systems. It’s a closed loop, where underlying biases are perpetuated and groups that were already invisible remain unseen. But as data progresses, that loop becomes a snowball. We’re dealing with machine-learning models — if they’re taught long enough that “not being X” (read: white, able-bodied, cisgendered) means not being “normal,” they will evolve by building on that foundation.

Data is interlinked in ways that are invisible to us. It’s not enough to say that your algorithm won’t exclude people with registered disabilities. Biases are present in other sets of data. For example, in the United States it’s illegal to refuse someone a mortgage loan because they’re Black. But by basing the process heavily on credit scores — which have inherent biases detrimental to people of color — banks indirectly exclude that segment of society.

For people with disabilities, indirectly biased data could potentially be frequency of physical activity or number of hours commuted per week. Here’s a concrete example of how indirect bias translates to software: If a hiring algorithm studies candidates’ facial movements during a video interview, a person with a cognitive disability or mobility impairment will experience different barriers than a fully able-bodied applicant.

The problem also stems from people with disabilities not being viewed as part of businesses’ target market. When companies are in the early stage of brainstorming their ideal users, people’s disabilities often don’t figure, especially when they’re less noticeable — like mental health illness. That means the initial user data used to iterate products or services doesn’t come from these individuals. In fact, 56% of organizations still don’t routinely test their digital products among people with disabilities.

If tech companies proactively included individuals with disabilities on their teams, it’s far more likely that their target market would be more representative. In addition, all tech workers need to be aware of and factor in the visible and invisible exclusions in their data. It’s no simple task, and we need to collaborate on this. Ideally, we’ll have more frequent conversations, forums and knowledge-sharing on how to eliminate indirect bias from the data we use daily.

We need an ethical stress test for data

We test our products all the time — on usability, engagement and even logo preferences. We know which colors perform better to convert paying customers, and the words that resonate most with people, so why aren’t we setting a bar for data ethics?

Ultimately, the responsibility of creating ethical tech does not just lie at the top. Those laying the brickwork for a product day after day are also liable. It was the Volkswagen engineer (not the company CEO) who was sent to jail for developing a device that enabled cars to evade U.S. pollution rules.

Engineers, designers, product managers; we all have to acknowledge the data in front of us and think about why we collect it and how we collect it. That means dissecting the data we’re requesting and analyzing what our motivations are. Does it always make sense to ask about someone’s disabilities, sex or race? How does having this information benefit the end user?

At Stark, we’ve developed a five-point framework to run when designing and building any kind of software, service or tech. We have to address:

  1. What data we’re collecting.
  2. Why we’re collecting it.
  3. How it will be used (and how it can be misused).
  4. Simulate IFTTT: “If this, then that.” Explain possible scenarios in which the data can be used nefariously, and alternate solutions. For instance, how users can be impacted by an at-scale data breach? What happens if this private information becomes public to their family and friends?
  5. Ship or trash the idea.

If we can only explain our data using vague terminology and unclear expectations, or by stretching the truth, we shouldn’t be allowed to have that data. The framework forces us to break down data in the most simple manner. If we can’t, it’s because we’re not yet equipped to handle it responsibly.

Innovation has to include people with disabilities

Complex data technology is entering new sectors all the time, from vaccine development to robotaxis. Any bias against individuals with disabilities in these sectors stops them from accessing the most cutting-edge products and services. As we become more dependent on tech in every niche of our lives, there’s greater room for exclusion in how we carry out everyday activities.

This is all about forward thinking and baking inclusion into your product at the start. Money and/or experience aren’t limiting factors here — changing your thought process and development journey is free; it’s just a conscious pivot in a better direction. While the upfront cost may be a heavy lift, the profits you’d lose from not tapping into these markets, or because you end up retrofitting your product down the line, far outweigh that initial expense. This is especially true for enterprise-level companies that won’t be able to access academia or governmental contracts without being compliant.

So early-stage companies, integrate accessibility principles into your product development and gather user data to constantly reinforce those principles. Sharing data across your onboarding, sales and design teams will give you a more complete picture of where your users are experiencing difficulties. Later-stage companies should carry out a self-assessment to determine where those principles are lacking in their product, and harness historical data and new user feedback to generate a fix.

An overhaul of AI and data isn’t just about adapting businesses’ framework. We still need the people at the helm to be more diverse. The fields remain overwhelmingly male and white, and in tech, there are numerous firsthand accounts of exclusion and bias toward people with disabilities. Until the teams curating data tools are themselves more diverse, nations’ growth will continue to be stifled, and people with disabilities will be some of the hardest-hit casualties.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://techcrunch.com/2021/04/19/flawed-data-is-putting-people-with-disabilities-at-risk/

Continue Reading

Artificial Intelligence

Why data and analytics are critical on the farm

Avatar

Published

on

From the introduction of the steel plow in 1837 to the adoption of advanced technologies like GPS, IoT, and AI, farmers have always looked at how technology and innovation can enhance the work they’re doing. This is incredibly important as they are tasked with an enormous job: to create the food, fuel, and fiber for 7.7 billion people worldwide.

With researchers projecting that our population will grow to nearly 10 billion by 2050, the ability to increase crop yields while lowering traditional inputs’ levels is more important than ever. In a typical year, farmers experience many challenges out of their control, such as unpredictable weather, varying soil types, and fluctuating markets. To better manage such variables, farmers rely on data to make timely, informed, and precise decisions.

The Value of Data in Agriculture Today

Nearly every farmer in the world relies on a mix of historical and real-time data to make informed decisions in their fields. A 2020 study by Purdue University of 800 farmers highlights that only a small minority of 7% do not collect any data related to their yield, soil sampling, or satellite imagery. Data collected through AI, IoT, and advanced robotics is vital for farmers to know what’s happening with each seed, each plant, and each machine.

Through various data points captured on the field and in their equipment, farmers continuously explore data for all variable conditions on the farm to ensure operations run seamlessly at a speed and scale unattainable through manual labor. Every year machines are deployed throughout each step of the farming cycle, and as they perform their jobs, they’re gathering a vast amount of information–from planting conditions all the way to crop success. These insights are gathered year-over-year, and that historical data is working in tandem with real-time data to help a farmer make the most informed decision possible. This is incredibly important as agriculture is one of the most unpredictable industries.

Adopting new technologies is also imperative for enabling interoperability between different software and hardware agriculture solutions to process large volumes of data while making businesses more efficient, sustainable, and profitable.

Capturing Data at Every Stage

Data collection on a farm is more complicated than data collection in a strictly office-based business because key data on a farm can come from places humans aren’t physically able to go to or conditions where a human can’t see. That’s why data and analytics capabilities on a farm have had to become so advanced. For example, a farmer must do multiple jobs simultaneously, so through connected machines and screens that provide real-time data, they can get insight into the most critical functions at all times. Additionally, AI helps farmers “see” beyond human capacity, monitor what’s happening in real-time, and gather data that is used to create insights at any point throughout the growing season.

Farmers leverage data to make smart decisions throughout the life of a single crop. Farmers know exactly where each seed goes into the ground, and overall conditions during planting as data-driven planters can vary the rate at which they plant seeds to eliminate overuse – from the target rate and depth of the seed to how hard it to push the seed into the soil. These machines are also able to self-steer themselves, precisely place seeds and develop accurate geospatial data insights. In the next stage, advanced spraying technology treats each plant individually, applying the exact amount of nutrients needed to protect the plant and surrounding plants and soil.

Farmers are then able to monitor the growth of their planted seeds remotely continuously. Once crops are ready to be harvested, the powerful combination of data and technology enables the equipment to precisely separate grains from the rest of the plant without damage to the kernel. With more and more data being collected for each seed in each stage, farmers leverage AI to combine more data points and better understand the impact of each independent decision taken on or off the field.

Additionally, predictive and preventative maintenance is made possible by collecting and monitoring machine data from any farmer opting into that. This, in turn, enables dealers to detect any issues proactively and from faraway locations, providing support in many cases before a farmer even knows there’s a problem. Many updates and fixes can be done over the air, and with these proactive alerts, downtime can be kept to a minimum.

Access to real-time and historical data and advanced automation transform the smart farming industry as more farmers embrace technology like AI and machine learning to aggregate trends, boost innovation, manage risks, save costs, and enhance supply chain management. The availability of smart sensor data enables farmers to make more informed decisions and comprehend in-the-moment conditions that can impact their operations. Sensors also continuously collect more data with time so that farmers can identify recurring patterns, predict future trends, and prioritize necessary changes.

Data’s Role in Farming’s Bright Future

Big data is key to producing quality food sustainably that can feed today’s growing population. The U.S. Department of Agriculture estimated that farms could add $47 to $65 billion annually to the domestic gross economy by implementing “broadband e-connectivity and next-generation precision agriculture technology.” Technology and analytics, combined with a farmer’s experience and determination, have the power to transform a network of fields into an efficient and highly profitable business. Thanks to data and analytics, the future of farming have never looked so bright.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.fintechnews.org/why-data-and-analytics-are-critical-on-the-farm/

Continue Reading

Artificial Intelligence

Why data and analytics are critical on the farm

Avatar

Published

on

From the introduction of the steel plow in 1837 to the adoption of advanced technologies like GPS, IoT, and AI, farmers have always looked at how technology and innovation can enhance the work they’re doing. This is incredibly important as they are tasked with an enormous job: to create the food, fuel, and fiber for 7.7 billion people worldwide.

With researchers projecting that our population will grow to nearly 10 billion by 2050, the ability to increase crop yields while lowering traditional inputs’ levels is more important than ever. In a typical year, farmers experience many challenges out of their control, such as unpredictable weather, varying soil types, and fluctuating markets. To better manage such variables, farmers rely on data to make timely, informed, and precise decisions.

The Value of Data in Agriculture Today

Nearly every farmer in the world relies on a mix of historical and real-time data to make informed decisions in their fields. A 2020 study by Purdue University of 800 farmers highlights that only a small minority of 7% do not collect any data related to their yield, soil sampling, or satellite imagery. Data collected through AI, IoT, and advanced robotics is vital for farmers to know what’s happening with each seed, each plant, and each machine.

Through various data points captured on the field and in their equipment, farmers continuously explore data for all variable conditions on the farm to ensure operations run seamlessly at a speed and scale unattainable through manual labor. Every year machines are deployed throughout each step of the farming cycle, and as they perform their jobs, they’re gathering a vast amount of information–from planting conditions all the way to crop success. These insights are gathered year-over-year, and that historical data is working in tandem with real-time data to help a farmer make the most informed decision possible. This is incredibly important as agriculture is one of the most unpredictable industries.

Adopting new technologies is also imperative for enabling interoperability between different software and hardware agriculture solutions to process large volumes of data while making businesses more efficient, sustainable, and profitable.

Capturing Data at Every Stage

Data collection on a farm is more complicated than data collection in a strictly office-based business because key data on a farm can come from places humans aren’t physically able to go to or conditions where a human can’t see. That’s why data and analytics capabilities on a farm have had to become so advanced. For example, a farmer must do multiple jobs simultaneously, so through connected machines and screens that provide real-time data, they can get insight into the most critical functions at all times. Additionally, AI helps farmers “see” beyond human capacity, monitor what’s happening in real-time, and gather data that is used to create insights at any point throughout the growing season.

Farmers leverage data to make smart decisions throughout the life of a single crop. Farmers know exactly where each seed goes into the ground, and overall conditions during planting as data-driven planters can vary the rate at which they plant seeds to eliminate overuse – from the target rate and depth of the seed to how hard it to push the seed into the soil. These machines are also able to self-steer themselves, precisely place seeds and develop accurate geospatial data insights. In the next stage, advanced spraying technology treats each plant individually, applying the exact amount of nutrients needed to protect the plant and surrounding plants and soil.

Farmers are then able to monitor the growth of their planted seeds remotely continuously. Once crops are ready to be harvested, the powerful combination of data and technology enables the equipment to precisely separate grains from the rest of the plant without damage to the kernel. With more and more data being collected for each seed in each stage, farmers leverage AI to combine more data points and better understand the impact of each independent decision taken on or off the field.

Additionally, predictive and preventative maintenance is made possible by collecting and monitoring machine data from any farmer opting into that. This, in turn, enables dealers to detect any issues proactively and from faraway locations, providing support in many cases before a farmer even knows there’s a problem. Many updates and fixes can be done over the air, and with these proactive alerts, downtime can be kept to a minimum.

Access to real-time and historical data and advanced automation transform the smart farming industry as more farmers embrace technology like AI and machine learning to aggregate trends, boost innovation, manage risks, save costs, and enhance supply chain management. The availability of smart sensor data enables farmers to make more informed decisions and comprehend in-the-moment conditions that can impact their operations. Sensors also continuously collect more data with time so that farmers can identify recurring patterns, predict future trends, and prioritize necessary changes.

Data’s Role in Farming’s Bright Future

Big data is key to producing quality food sustainably that can feed today’s growing population. The U.S. Department of Agriculture estimated that farms could add $47 to $65 billion annually to the domestic gross economy by implementing “broadband e-connectivity and next-generation precision agriculture technology.” Technology and analytics, combined with a farmer’s experience and determination, have the power to transform a network of fields into an efficient and highly profitable business. Thanks to data and analytics, the future of farming have never looked so bright.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.fintechnews.org/why-data-and-analytics-are-critical-on-the-farm/

Continue Reading
Esports5 days ago

Free Fire World Series APK Download for Android

Esports3 days ago

C9 White Keiti Blackmail Scandal Explains Sudden Dismissal

Esports3 days ago

Overwatch League 2021 Day 1 Recap

Esports5 days ago

Dota 2: Top Mid Heroes of Patch 7.29

Esports3 days ago

Fortnite: Epic Vaults Rocket Launchers, Cuddlefish & Explosive Bows From Competitive

Esports4 days ago

Don’t Miss Out on the Rogue Energy x Esports Talk Giveaway!

Esports3 days ago

Gamers Club and Riot Games Organize Women’s Valorant Circuit in Latin America

Esports4 days ago

Fortnite: DreamHack Cash Cup Extra Europe & NA East Results

Blockchain4 days ago

CoinSmart Appoints Joe Tosti as Chief Compliance Officer

Blockchain5 days ago

Bitfinex-Hacker versenden BTC im Wert von 750 Millionen USD

Blockchain4 days ago

April Continuum Blockchain Legislation Summit ContinuumBlockLegs

Blockchain3 days ago

15. BNB Burn: Binance zerstört Coins im Wert von 600 Mio. USD

Fintech5 days ago

Zip Co raises $400 million for international expansion

Esports4 days ago

2021 Call of Duty Mobile World Championship Announced

Esports4 days ago

Position 5 Faceless Void is making waves in North American Dota 2 pubs after patch 7.29

Fintech4 days ago

Mambu research reveals global consumers are hesitant to use Open Banking

Esports5 days ago

COD Mobile Season 3 Tokyo Escape

Esports5 days ago

Fortnite: Mero Joins FNCS-Winning Teammates On ENDLESS

Esports3 days ago

LoL gameplay design director pulled, transferred to Riot’s MMO

Blockchain5 days ago

Ethereum in Blockchain Software Development

Trending