Category Archives: Machine Learning

Footy is back - thankfully - but it's hard to ignore the fact that stadium seats are more or less completely empty for each game.

But, along with the cardboard cut-outs making headlines around the world, one company went above and beyond toreplicate the crowd sounds to fans at home

Tim ONeill is a soundengineer with aFX Global, a company put together almost exclusively totry andmake it sound like a stadium was just as full of noise as it normally would be.

Speaking to Triple M Riverina, O'Neill explained that, when watching Round 1 of the AFL, he felt that the silence behind the play was not going to be good enough as a fan.

So he took the logical next step to... invent new technology to provide the next best thing.

Listen below:

You get the flavour of the stadium but every time we reinterpret it for that match, its unique so you never get that kind of fatigue of something youve heard before, so every game will be different," O'Neill explained.

Hear the full chat below:

Don't miss a minute ofthe action; download theTriple M NRL appnow to listen to the call live or to Catch-Up at anytime.

Link:
Machine Learning, AI, And One Guy Operating The Entire System: How An Aussie Invented The Technology Responsible For The NRL's Crowd Noise - Triple M

Since the evolution of social media platforms like Instagram, Snapchat, and Facebook, we have seen a lot of beauty apps pop-up in the market. Even most of the budget phones of recent times come with beauty filters baked in the default camera app. All these apps apply a ton of filters to your pictures and that can be very annoying. Well, now theres an app that can not only detect, but also remove edits from an image.

Created by Redditor, Akshat Jagga (u/chancemehmu), Mirage is an app that can detect and remove edits by image-editing software on an existing image. According to the Haryana-based developer, the app uses machine learning to perform the tasks.

As shown in a video (below) posted by the developer on Reddit, the app works even on screenshots of pictures.

I recently made an app that uses Machine Learning to detect & undo photoshopped/edited images! Looking for feedback on Mirage. from iphone

You can feed the app an image or a screenshot. It will then analyze it for a few seconds, and show two versions of the image. These will show the original input image, and the image with highlights around areas that have been edited. This can be seen in the video as well.

The next screen shows a similar view. Only in this one, the picture on the right shows the original image, before all the effects and filters. This is the one the app makes after removing the edits from the highlighted areas.

Now, we do not have any idea what kind of machine learning algorithm the app is using to detect and remove the edits of the images. However, it definitely looks interesting.

The app is available on both the App Store and the Play Store. While the App Store version costs $1.99, on the Play Store you can get it for free. However, bear in mind that you will need to subscribe to the app before you are able to use it, which is really annoying.

Download Mirage (Android, iOS)

Original post:
This App Uses Machine Learning to Detect and Remove Edits from Images - Beebom

Recently, a team of researchers from MIT CSAIL recommended that researchers should focus on three key areas that prioritise to deliver computing speed-ups, which are new algorithms, higher-performance software and more specialised hardware, and the need for moving away from focusing on creating only smaller hardware.

The researchers stated that semiconductor miniaturisation is running out of steam as a viable way to grow computer performance, and industries will soon face challenges in their productivity. However, the opportunities for growth in computing performance will still be available if the researchers focus more on software, algorithms, including hardware architecture.

Transistors have brought a plethora of advances and growth in computer performance over the past few decades. These improvements in computer performance come from decades of miniaturisation of computer components, for instance, from a room-sized computer to a cellphone. For decades, programmers have been able to prioritise writing code quickly rather than writing it so that it runs quickly since smaller, faster computer chips have always been able to pick up the slack.

In 1975, Intel founder Gordon Moore predicted the regularity of this miniaturisation trend, which is now called Moores law the number of transistors on computer chips would double every 24 months.

The researchers broke down their recommendations into the categories, they are software, algorithms, and hardware architecture as mentioned below.

According to the researchers, software can be made more efficient by performance engineering such as restructuring the software to make it run faster. Performance engineering can remove inefficiencies in programs, also known as software bloat. Software bloating is an issue that arises from traditional software-development strategies that aim to minimise applications development time rather than the time it takes to run. Performance engineering can also tailor the software to the hardware on which it runs, for example, to take advantage of parallel processors and vector units.

Algorithms offer more-efficient ways to solve problems. The researchers stated that the biggest benefits coming from algorithms are for new problem domains. For instance, machine learning and new theoretical machine models that better reflect emerging hardware.

According to the researchers, hardware architectures can be streamlined through processor simplification, where a complex processing core is replaced with a simpler core that requires fewer transistors. Then, the freed-up transistor budget can be redeployed in other ways. For example, by increasing the number of processor cores running in parallel, which can lead to large efficiency gains for problems that can exploit parallelism.

Also, another form of streamlining is domain specialisation, where hardware is customised for a particular application domain. This type of specialisation discards processor functionality that is not needed for the domain and can allow more customisation to the specific characteristics of the domain by decreasing floating-point precision for artificial intelligence and machine-learning applications.

Researchers have been following Moores law for a few decades now, i.e the overall processing power for computers will double every two years. Software development in the Moore era has generally focused on minimising the time it takes to develop an application, rather than the time it takes to run that application once it is deployed.

The researchers stated that as miniaturisation wanes, the silicon-fabrication improvements at the Bottom will no longer provide the predictable, broad-based gains in computer performance that society has enjoyed for more than 50 years.

In the post-Moore era, performance engineering, development of algorithms, and hardware streamlining will be most effective within big system components. From engineering-management and economic points of view, these changes will be easier to implement if they occur within big system components that include reusable software with typically more than a million lines of code or hardware of comparable complexity or a similarly large software-hardware hybrid.

comments

The rest is here:
Semiconductor Miniaturisation Is Running Out Of Steam. Time To Focus On Smarter Algorithms - Analytics India Magazine

The 19 Impact on Global Machine Learning Artificial intelligence market research report added by Market Study Report, LLC, is a thorough analysis of the latest trends prevalent in this business. The report also dispenses valuable statistics about market size, participant share, and consumption data in terms of key regions, along with an insightful gist of the behemoths in the 19 Impact on Global Machine Learning Artificial intelligence market.

The 19 Impact on Global Machine Learning Artificial intelligence market report provides a granular assessment of the business space, while elaborating on all the segments of this business space. The document offers key insights pertaining to the market players as well as their gross earnings. Moreover, details regarding the regional scope and the competitive scenario are entailed in the study.

Request a sample Report of 19 Impact on Global Machine Learning Artificial intelligence Market at:https://www.marketstudyreport.com/request-a-sample/2684709?utm_source=coleofduty.com&utm_medium=AG

This report studies the 19 Impact on Global Machine Learning Artificial intelligence market status and outlook of global and major regions, from angles of players, countries, product types and end industries, this report analyzes the top players in global 19 Impact on Global Machine Learning Artificial intelligence industry, and splits by product type and applications/end industries. This report also includes the impact of COVID-19 on the 19 Impact on Global Machine Learning Artificial intelligence industry.

Emphasizing the key factors of the 19 Impact on Global Machine Learning Artificial intelligence market report:

Thorough analysis of the geographical landscape of 19 Impact on Global Machine Learning Artificial intelligence market:

Highlighting on the competitive landscape of 19 Impact on Global Machine Learning Artificial intelligence market:

.

Ask for Discount on 19 Impact on Global Machine Learning Artificial intelligence Market Report at:https://www.marketstudyreport.com/check-for-discount/2684709?utm_source=coleofduty.com&utm_medium=AG

Additional factors of 19 Impact on Global Machine Learning Artificial intelligence market research report:

.

.

Research objectives:

For More Details On this Report: https://www.marketstudyreport.com/reports/covid-19-impact-on-global-machine-learning-artificial-intelligence-market-size-status-and-forecast-2020-2026

Related Reports:

1. COVID-19 Impact on Global Gamification of Learning Market Size, Status and Forecast 2020-2026Read More: https://www.marketstudyreport.com/reports/covid-19-impact-on-global-gamification-of-learning-market-size-status-and-forecast-2020-2026

2. COVID-19 Impact on Global Chain Hotel Market Size, Status and Forecast 2020-2026Read More: https://www.marketstudyreport.com/reports/covid-19-impact-on-global-chain-hotel-market-size-status-and-forecast-2020-2026

Contact Us:Corporate Sales,Market Study Report LLCPhone: 1-302-273-0910Toll Free: 1-866-764-2150 Email: [emailprotected]

Read this article:
19 Impact on Global Machine Learning Artificial intelligence Market to Grow at a Stayed CAGR from 2020 to 2026 - Cole of Duty

Telecommuters: tired of the constant embarrassment of showing up to video conferences wearing nothing but your underwear? Save the humiliation and all those pesky trips down to HR with Safe Meeting, the new system that uses the power of artificial intelligence to turn off your camera if you forget that casual Friday isnt supposed to be that casual.

The following infomercial is brought to you by [Nick Bild], who says the whole thing is tongue-in-cheek but we sense a certain degree of necessity is the mother of invention here. Its true that the sudden throng of remote-work newbies certainly increases the chance of videoconference mishaps and the resulting mortification, so whatever the impetus, Safe Meeting seems like a great idea. It uses a Pi cam connected to a Jetson Nano to capture images of you during videoconferences, which are conducted over another camera. The stream is classified by a convolutional neural net (CNN) that determines whether it can see your underwear. If it can, it makes a REST API call to the conferencing app to turn off the camera. The video below shows it in action, and that it douses the camera quickly enough to spare your modesty.

We shudder to think about how [Nick] developed an underwear-specific training set, but we applaud him for doing so and coming up with a neat application for machine learning. Hes been doing some fun work in this space lately, from monitoring where surfaces have been touched to a 6502-based gesture recognition system.

Go here to see the original:
Machine Learning Takes The Embarrassment Out Of Videoconference Wardrobe Malfunctions - Hackaday

The Global Machine Learning Chip Market Size Is Expected To Reach $8,272 Million In 2022 From $4,495 Million In 2015, Growing At A Cagr Of 9.4% From 2016 To 2022. The Global Machine Learning Chip Market report draws precise insights by examining the latest and prospective industry trends and helping readers recognize the products and services that are boosting revenue growth and profitability. The study performs a detailed analysis of all the significant factors, including drivers, constraints, threats, challenges, prospects, and industry-specific trends, impacting the market on a global and regional scale. Additionally, the report cites worldwide market scenario along with competitive landscape of leading participants.

Click To Get Sample Copy of Report @ https://www.premiummarketinsights.com/sample/AMR00013157

Leading Players in the Machine Learning Chip Market:

The Machine Learning Chip market analysis is intended to provide all participants and vendors with pertinent specifics about growth aspects, roadblocks, threats, and lucrative business opportunities that the market is anticipated to reveal in the coming years. This intelligence study also encompasses the revenue share, market size, market potential, and rate of consumption to draw insights pertaining to the rivalry to gain control of a large portion of the market share.

By Type

By Application

Competitive landscape

The Machine Learning Chip Industry is extremely competitive and consolidated because of the existence of several established companies that are adopting different marketing strategies to increase their market share. The vendors engaged in the sector are outlined based on their geographic reach, financial performance, strategic moves, and product portfolio. The vendors are gradually widening their strategic moves, along with customer interaction.

Machine Learning Chip Market Segmented by Region/Country: US, Europe, China, Japan, Middle East & Africa, India, Central & South America

Go For Interesting Discount Here: @ https://www.premiummarketinsights.com/discount/AMR00013157

Points Covered in the Report:

Fundamentals of Table of Content:

1 Report Overview1.1 Study Scope1.2 Key Market Segments1.3 Players Covered1.4 Market Analysis by Type1.5 Market by Application1.6 Study Objectives1.7 Years Considered

2 Global Growth Trends2.1 Machine Learning Chip Market Size2.2 Machine Learning Chip Growth Trends by Regions2.3 Industry Trends

3 Market Share by Key Players3.1 Machine Learning Chip Market Size by Manufacturers3.2 Machine Learning Chip Key Players Head office and Area Served3.3 Key Players Machine Learning Chip Product/Solution/Service3.4 Date of Enter into Machine Learning Chip Market3.5 Mergers & Acquisitions, Expansion Plans

4 Breakdown Data by Product4.1 Global Machine Learning Chip Sales by Product4.2 Global Machine Learning Chip Revenue by Product4.3 Machine Learning Chip Price by Product

5 Breakdown Data by End User5.1 Overview5.2 Global Machine Learning Chip Breakdown Data by End User

Access full Report Description, For More Inquiry@ https://www.premiummarketinsights.com/inquiry/AMR00013157

Thanks for reading this article; you can also customize this report to get select chapters or region-wise coverage with regions such as Asia, North America, and Europe.

About Premium market insights:

Premiummarketinsights.comis a one stop shop of market research reports and solutions to various companies across the globe. We help our clients in their decision support system by helping them choose most relevant and cost effective research reports and solutions from various publishers. We provide best in class customer service and our customer support team is always available to help you on your research queries.

Contact US:

Sameer Joshi Call: US: +1-646-491-9876, Apac: +912067274191Email: [emailprotected]

Go here to read the rest:
Machine Learning Chip Market Is Thriving Worldwide to reach $8,272 Million by 2022 | Advanced Micro Devices, Inc., Google Inc., Graphcore, Intel...