Mediaboss Marketing

With hundreds, if not thousands, of road accidents occurring within the time that it takes you to finish reading this article, the importance of dash cameras is ever essential. Its a wonder that more modern-day cars dont come with DVRs fitted as standard. Regardless, many have resorted to the perfectly capable aftermarket camera as a simple precaution in case of an unfortunate mishap.

While these dash cams have provided a lot of helpful evidence since their introduction (not to mention plenty of content for the likes of us to work with), their limitations mean that sometimes crucial info is missed. Enter the dash cam from Israeli start-up, Nexar. The company has taken the simple concept of a regular camera mounted to the windshield and reinvented it to collect data on the spot and piece together the moments leading up to the incident.

The Drive reports that, in the event of a collision, Nexars connected dash cam will locate any vehicles that have been involved in the accident and retraces their respective paths leading up to the impact using data gathered from its GPS and motion sensors. It then displays this, along with camera footage, providing a comprehensive report on a virtual dashboard. It effectively allows you or the authorities to reconstruct the event. The data is shown side-by-side with actual footage, with information such as vehicle speed and G-force.

Nexar claims their dash cams will automatically detect 90% of accidents to create a report. The data and metrics collected by the camera and sensors can be assembled into an easy-to-view report that can then be shared with insurers and authorities as evidence.

Japanese auto insurer Mitsui Sumitomo Insurance has shown interest in the idea. It has already helped deploy the cameras, stating that its helping hundreds of thousands of Drivers who have added Nexars dash cam into their vehicles.

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Recreate Your Very Own Seconds From Disaster Episode With This Machine Learning Dash Cam - CarScoops

Dive Brief:

This partnership suggests the chain is relying more heavily on AI and machine learning systems to improve its system.

"Machine learning models can provide significant value to an organization in several business applications, but without a solid [machine learning]operations pipeline, that value cannot be truly realized," Zack Fragoso, manager, data science and AI at Domino's, said in the release.

The chain began testing AI in 2018 with an Alexa-like voice recognition application called DOM. In 2019, Domino's began piloting Dragontail Systems' AI technology at its Australian and New Zealand locations to scan each pizza and ensure they measured up to quality standards. The pilot was expanded after improving quality scores by 15% in its first month.

The chain also deployed AI technology across its Malaysia and Singapore division to automate delivery operations and consolidate operations. Further,during its most recent Q4 earnings call, CEO Ritch Allison said the chain has added AI-enabled forecasting alongside GPS driver tracking, enhanced makeline and cutting technologies to "better match demand with capacity in our stores."

"These innovations are all designed to increase speed, accuracy and efficiency, allowing us to continue to better serve our customers,"he said.

As all these technologies are deployed disparately across a system as big as Domino's, it's important to streamline them to optimize management weigh viability to scale. Such a streamline is particularly critical now. While Domino's has been ahead of the curve on this technology, its rivals are catching up. Pizza Hut parent Yum Brands recently announced the acquisition of AI company Kvantumto improve consumer insights, for example. Pizza Hut U.K.also started using an AI-powered data analytics solution in 2020.

Meanwhile, Papa John's in November added kea's Indecisive AI to its system, which helps the chain better manage phone calls, easing some labor pressure.

On Domino's Q4 call, Allison said the company will continue to invest in technology to both drive efficiencies in store and improve its corporate team's ability to support the business. The company will likely be able to maintain a technology advantage through both those investments and through its fledgling Innovation Garage, which is focused specifically on advantageous technology innovations.

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Domino's teams with Datatron to streamline AI and machine learning tech - Restaurant Dive

There are a lot of options out there for scanning film these days, but theres just something about building a device yourself. This one from Benjamin Bezine does so using Lego and a Raspberry Pi. What makes this solution a little special, though, is that it uses machine learning and vision AI in order to determine the edges of frames, so you dont need to sit and operate it manually or worry about the number of turns changing as roll thickens and thins out or counting sprockets.

Its called RoboScan, and Benjamins been working on it for a while now (its his lockdown project) but hes not finished yet! Its an open-source project and hes been uploading the code to GitHub. Only 80 images were used to train the Raspberry Pi so that it knows what to look for, but it seems that its very effective with just that limited set.

RoboScan uses a backlight behind the film in order to provide even illumination across the frame, and when it detects that a complete frame is in the correct position, it tells the DSLR or mirrorless camera to which its attached to shoot a photo. The camera you choose to use should be compatible with libgphoto2, but that is a large list of cameras.

The first version was a proof of concept, Benjamin writes, but was far too sensitive, imprecise and had too many manual operations that could introduce user error. So now it uses machine learning to detect when a photo is correctly framed in front of the backlight using a Google Coral TPU. The whole thing is controlled by the web, and youre even able to add metadata.

Its a very cool project and a great use of Lego and AI. Fantastic if youve got a whole load of film you need to scan in and dont want to have to do it manually!

If you want to have a go at building your own or just find out more about RoboScan, you can find the full Lego model with a complete parts list over on Mecabricks and the source code can be downloaded from GitHub.

[via Raspberry Pi]

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Automating scanning film with the help of Lego, a Raspberry Pi and a little machine learning - DIYphotography

It is one of the fundamentals of all the best esports betting sites, yet it is something that still remains something of a mystery to many of those that enjoy betting on esports. Have you ever wondered how companies come up with the latest esports betting odds?

The sheer number of tournaments there are nowadays, means that it is almost impossible for humans to assess and update all the different markets and odds available on a typical esports event, so something more advanced is required. This is where software like machine learning applications come into their own.

As the name suggests, machine learning is the process by the way a computer program can develop an algorithm that uses the data it collects to effectively predict what will happen in the future. This type of software is not just used for esports betting technology but serves a huge number of uses in other fields too.

For example, similar applications are used to help predict future weather patterns using the same process, triggering billions of data points to process and predict what the weather will do. In terms of AI betting predictions, a similar process is followed, only this time to come up with the odds for a typical esports market or event.

Data is fundamental for any betting industry, and one of the big advantages of esports is that its data is readily available and accessible. Companies like PandaScore, Oddin, and Sportsflare are just three that have taken this approach to formulate esports odds using AI betting predictions.

This is particularly important when companies like to offer Live Odds on sports events. The ever-changing nature of In-Play betting means that things can change in an instant. This is almost impossible for a human to assess, but machine learning applications and assess the data in real time and make adjustments to the odds extremely quickly to reflect the ever-changing nature of esports contests.

As PandaScore founder and CEO Flavien Guillocheau explained:

Bookmakers are some of the most data-dependent companies out there. With esports tournaments and fantasy leagues becoming a more significant component of sportsbook offerings around the world, access to PandaScores abundance of real time esports odds and data is vital to all the bookmakers we work with. Our vision is to help esports grow by providing the data resources for any esports business.

Of course, it is one thing to create software that claims it can use data to predict betting outcomes and help set odds, but the proof of the pudding so to speak is when the software is put to work in a real world setting.

The good news here for esports betting services is that in the real world, the predictive models have worked extremely well.

Data provided by Sportsflare based on predictions for a CS:GO map winner market revealed exceptionally accurate predictions that were very well calibrated with the actual outcomes of those games.

The net result of this is that sites like Unikrn betting can be sure that the odds that their Ai betting predictions software are churning out, are not only incredibly accurate but that over time, that accuracy will only improve as more data becomes available.

AI has a huge role to play in the future of esports. It can not only be used to predict outcomes of events and help formulate odds, but it can also be used to help track fraudulent play, catching people attempting to cheat before they have had the chance to claim any sort of reward from their activities.

In fact, AI has so many potential applications in the esports betting industry that we are only seeing the tip of the iceberg in terms of how it can be used to benefit the industry and ensure that both punters and esports betting companies can thrive together.

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The role of AI Betting Predictions and Machine Learning in Esports Odds - EsportsBets

Entries for the AI & Machine Learning Awards 2021 in one week

Artificial intelligence adoption is increasing at an unprecedented rate, with new products, projects and solutions appearing every day, in every industry sector. Computing's AI & Machine Learning Awards 2021 recognise and honour the very best of these developments, each of which has the potential to drive massive change. But if you think you deserve a spot, you'd better hurry: entries close on the 1st April.

From data entry to chatbots to healthcare and the environment, AI has applications in every industry, sector, and role. Even the most basic implementations can free a workforce from time-consuming manual tasks, with more complex developments providing real insight into customer data.

Anexpert panel of judgesfrom some of the UK's most recognised organisations will assess each entry, including Rachel Anne Jones (CIO - Valuation Office Agency at HMRC); Aidan Hancock (Group CIO at Network Rail) and Sudip Trivedi (Head of Data and Analytics at London Borough of Camden).

TheAI & Machine Learning Awardsrecognise the best companies, individuals, and projects in the AI space today. The awards cover every corner of the industry: security, ethics, data analysis, innovation and more, as well as showcasing the technology heroes and projects that deserve industry-wide praise.

We refresh the categories for our awards each year to ensure they present the most up-to-date view of the industry. As well as returning favourites like Best Emerging Technology in AI andAI/ML Team of the Year, the 2021 awards have three new categories:Business Transformation of the Year, Best Marketing Automation Project, and a Special Award for Pandemic Performance. See below for a full list of categories for 2021.

Entries forAI & Machine Learning Awards 2021 close in just a few days, so make sure to enter soon.

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Entries for the AI & Machine Learning Awards 2021 close in one week - http://www.computing.co.uk