The ML Olympiad – organised by groups including ML GDE, TFUG, and other ML communities – aims to provide developers with hands-on opportunities to learn and practice machine learning skills by tackling real-world challenges.

Over the previous two rounds, an impressive 605 teams participated across 32 competitions, generating 105 discussions and 170 notebooks.

This year’s lineup includes challenges spanning areas like healthcare, sustainability, natural language processing (NLP), computer vision, and more. Competitions are hosted by expert groups and developers from around the world.
Here are this year’s challenges:

• Smoking Detection in Patients

Hosted by Rishiraj Acharya (AI/ML GDE) in collaboration with TFUG Kolkata, this competition tasks participants with predicting smoking status using bio-signal ML models.

• TurtleVision Challenge

Organised by Anas Lahdhiri under MLAct, this challenge calls for the development of a classification model to differentiate between jellyfish and plastic pollution in ocean imagery.

• Detect Hallucinations in LLMs

Luca Massaron (AI/ML GDE) presents a unique challenge of identifying hallucinations in answers provided by a Mistral 7B instruct model.

• ZeroWasteEats

Anushka Raj, alongside TFUG Hajipur, seeks ML solutions to mitigate food wastage, a critical concern in today’s world.

• Predicting Wellness

Hosted by Ankit Kumar Verma and TFUG Prayagraj, this competition involves predicting the percentage of body fat in men using multiple regression methods.

• Offbeats Edition

Ayush Morbar from Offbeats Byte Labs invites participants to build a regression model to predict the age of crabs.

• Nashik Weather

TFUG Nashik challenges participants to forecast the weather condition in Nashik, India, leveraging machine learning techniques.

• Predicting Earthquake Damage

Usha Rengaraju presents a task of predicting the level of damage to buildings caused by earthquakes, based on various factors.

• Forecasting Bangladesh’s Weather

TFUG Bangladesh (Dhaka) aims to predict rainfall, average temperature, and rainy days for a particular day in Bangladesh.

• CO2 Emissions Prediction Challenge

Md Shahriar Azad Evan and Shuvro Pal from TFUG North Bengal seek to predict CO2 emissions per capita for 2030 using global development indicators.

• AI & ML Malaysia

Kuan Hoong (AI/ML GDE) challenges participants to predict loan approval status, addressing a crucial aspect of financial inclusion.

• Sustainable Urban Living

Ashwin Raj and BeyondML task participants with predicting the habitability score of properties, promoting sustainable urban development.

• Toxic Language (PTBR) Detection

Hosted in Brazilian Portuguese, this challenge by Mikaeri Ohana, Pedro Gengo, and Vinicius F. Caridá (AI/ML GDE) involves classifying toxic tweets.

• Improving Disaster Response

Yara Armel Desire of TFUG Abidjan invites participants to predict humanitarian aid contributions in response to disasters worldwide.

• Urban Traffic Density

Kartikey Rawat from TFUG Durg calls for the development of predictive models to estimate traffic density in urban areas.

• Know Your Customer Opinion

TFUG Surabaya presents a challenge of classifying customer opinions into Likert scale categories.

• Forecasting India’s Weather

Mohammed Moinuddin and TFUG Hyderabad task participants with predicting temperatures for specific months in India.

• Classification Champ

Hosted by TFUG Bhopal, this competition involves developing classification models to predict tumour malignancy.

• AI-Powered Job Description Generator

Akaash Tripathi from TFUG Ghaziabad challenges participants to build a system that automatically generates job descriptions using Generative AI and chatbot interface.

• Machine Translation French-Wolof

GalsenAI presents a challenge of accurately translating French sentences into Wolof, offering a platform to enhance language translation capabilities.

• Water Mapping using Satellite Imagery

Taha Bouhsine of ML Nomads tasks participants with water mapping using satellite imagery for dam drought detection.

Google is supporting each community host this round through its Google for Developers program.

Participants are encouraged to search for “ML Olympiad” on Kaggle, follow #MLOlympiad on social media, and get involved in the competitions that most interest them.

With such a diverse array of real-world machine learning challenges, the ML Olympiad represents an excellent opportunity for developers to put their skills to the test and gain valuable experience.

(Image Credit: Google)

See also: Microsoft: China plans to disrupt elections with AI-generated disinformation

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Critical minerals are raw, non-fuel materials that are vital for manufacturing products that are essential to national security.

DARPA is teaming up with the US Geological Survey (USGS) to explore how machine learning and AI can accelerate critical mineral assessments.

“The USGS’s critical mineral resource assessments are at the heart of our domestic supply and production of critical minerals,” said Dr Anne Fischer, Deputy Director of DARPA’s Defense Sciences Office.

“We want to have a measurable, immediate impact on the USGS’s ability to reach some of its objectives, especially in ways that are critical to national security.”

DARPA says that assessing the current list of 50 critical minerals is labour-intensive and takes too long to meet today’s supply chain needs.

The AI competition will include two challenges:

• Map Georeferencing Challenge: Participants will receive a dataset of 1,000 or more maps of various types for training and validating models with the goal of fitting coordinate points that can be referenced to known locations in one or more base maps.
• Map Feature Extraction Challenge: Participants will be provided with a training set consisting of maps with each legend item labelled and characterised (as point, line, or polygon) and a binary pixel map reflecting the feature’s coverage in the map. The goal of the challenge is to identify all features in a map that appear in the map’s legend.

“DARPA is known for fostering innovation by creating problem-focused research communities, and we’re excited to put the results of this competition to immediate use,” said Sarah Ryker, associate director for energy and mineral resources at the USGS.

“The United States is under-mapped, and the Bipartisan Infrastructure Law provides a historic opportunity to catch up – if we can precisely target our investments in new mapping. We hope that new capabilities emerging from these challenges will enable us to do exactly that.”

For each of the challenges, $10,000 will be awarded for first place. Second place will take home $3,000. $1,000 will be awarded for third place.

While the rewards on offer aren’t huge for the AI industry, they’re a nice bonus and participants can take pride in contributing to projects that could make a real difference.

Registration for the map georeferencing challenge is now open. Registration for the map feature extraction challenge opens on 29th August.

You can find out more information and register for the challenges here.

(Photo by Elena Mozhvilo on Unsplash)

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Nuance gained renown for originally creating the backend of that little old virtual assistant called Siri (you might have heard of it?)

The company has since continued to focus on building its speech recognition capabilities and has a number of solutions which span particular industries such as healthcare to general omni-channel customer experience services.

Earlier this year, Microsoft decided Nuance is worth coughing up $19.7 billion for.

As such large deals often do, the proposed acquisition caught the eyes of several global regulators. In the case of the EU, it was referred to the Commission’s regulators on 16 November.

The regulator said on Tuesday that the proposed acquisition “would raise no competition concerns” within the bloc and that “Microsoft and Nuance offer very different products” after looking at potential horizontal overlaps between the companies’ transcription solutions.

Vertical links in the healthcare space were also analysed but it was determined that “competing transcription service providers in healthcare do not depend on Microsoft for cloud computing services” and that “transcription service providers in the healthcare sector are not particularly important users of cloud computing services”.

Furthermore, the regulator concluded:

• Microsoft-Nuance will continue to face stiff competition from rivals in the future.
• There’d be no ability/incentive to foreclose existing market solutions.
• Nuance can only use the data it collects for its own services.
• The data will not provide Microsoft with an advantage to shut out competing software providers.

The EU’s decision mirrors that of regulators in the US and Australia. However, the UK’s Competition and Markets Authority (CMA) announced its own investigation earlier this month.

When it announced the deal, Microsoft said that it aims to complete its acquisition by the end of 2021. The CMA is accepting comments until 10 January 2022 so it seems that Microsoft may have to hold out a bit longer.

(Photo by Annie Spratt on Unsplash)

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The CMA (Competition & Markets Authority) has been investigating whether the deal is anti-competitive. In August, it declared the deal does indeed raise “serious competition concerns”.

Arm founder Hermann Hauser went further and suggested the merger would amount to “surrendering the UK’s most powerful trade weapon to the US”.

Among the concerns are that Nvidia could limit competitors’ access to key technologies. The CMA claims to have received “a substantial number” of concerns from rivals and some have even offered to invest in Arm if it helps the company to remain independent.

Dorries has ‘quasi-judicial’ powers under the Enterprise Act 2002 to intervene in mergers on public interest grounds.

As part of its ‘Phase One’ report, the CMA determined the merger has the possibility of a “substantial lessening of competition across four key markets”. Those markets are data centres, Internet of Things, the automotive sector, and gaming.

Beyond the impact on competition, evidence provided from departments across government have also led the Secretary of State to deem that national security could be harmed from the merger and warrants further investigation.

“I have carefully considered the Competition and Market Authority’s ‘Phase One’ report into NVIDIA’s proposed takeover of Arm and have decided to ask them to undertake a further in-depth ‘Phase Two’ investigation,” commented Dorries.

“Arm has a unique place in the global technology supply chain and we must make sure the implications of this transaction are fully considered. The CMA will now report to me on competition and national security grounds and provide advice on the next steps.”

The CMA now has 24 weeks to conduct Phase Two of its investigation, although this could be extended by eight weeks if necessary. Upon receiving the report, the Digital Secretary could take action to remedy any adverse effects to the public interest or refer it back to the CMA.

David Bicknell, Principal Analyst, and Dr Lil Read, Analyst, on the Thematic Research Team at GlobalData, commented:

“This latest government probe is another nail in the coffin for the proposed merger between Nvidia and Arm. With proceedings likely to extend into late 2022 at the earliest, Nvidia should just abandon the deal and focus on its future away from Arm.

Ordering a security review signals that the UK government doesn’t want this bid to succeed. Couple this with an already underway EU investigation and the further prospect of a China referral, and Nvidia faces some tough questions. It is unlikely it will want the continued uncertainty of a bid that is going nowhere.

We think it is time for Nvidia to move on, and for Softbank to return Arm to where it found it—the stock market.

Nvidia’s future beyond Arm extends into the metaverse, a virtual world where users share experiences and interact in real-time within simulated scenarios. Nvidia is already targeting the metaverse as a key pillar of its future – a wise decision as GlobalData believes it will be the next big technology megatheme.”

Nvidia, for its part, has promised to work with UK regulators to alleviate concerns. The company has already pledged to keep Arm in the UK and hire more staff.

The company’s announcement of a new AI centre in Cambridge last year – which features an Arm/Nvidia-based supercomputer, set to be one of the most powerful in the world – was expected to be part of a bid to show UK regulators of the firm’s commitment to the country.

“Arm is an incredible company and it employs some of the greatest engineering minds in the world,” said Jensen Huang, CEO of Nvidia. “But we believe we can make Arm even more incredible and take it to even higher levels.”

“We want to propel it – and the UK – to global AI leadership.”

(Image Credit: UK Parliament under CC BY 3.0 license)

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The company has sponsored a competition on Kaggle which challenges data scientists to create models which predict if a device is likely to become infected with malware given a current machine state.

In a blog post, Microsoft wrote:

“The competition provides academics and researchers with varied backgrounds a fresh opportunity to work on a real-world problem using a fresh set of data from Microsoft.

Results from the contest will help us identify opportunities to further improve Microsoft’s layered defenses, focusing on preventative protection.

Not all machines are equally likely to get malware; competitors will help build models for identifying devices that have a higher risk of getting malware so that preemptive action can be taken.”

Participants are supplied with 9.4GB of anonymised data gathered from 16.8M devices to build their models.

A pot of $25,000 prize money will be used to incentivise participation spread out as:

• 1st Place – $12,000
• 2nd Place – $7,000
• 3rd Place – $3,000
• 4th Place – $2,000
• 5th Place – $1,000

The best performing entry, thus far, has achieved 68.9 percent accuracy – though it’s likely this will be improved before the end.

Entries must be submitted before the competition closes on March 13, 2019.

You can find out more and enter on Kaggle here.

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