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AI Weekly: What can AI tell us about social unrest, virus structures, and carbon emissions?

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Did you miss a session from the Future of Work Summit? Applying data science to predict unrest. AI that can anticipate the next variant of COVID-19's structure. That's a few of the headlines in AI this week, which ran the gamut from the dour (how AI might prevent the next attack on the U.S. Capitol) to the uplifting (making air travel greener). It's caveated optimism, but nonetheless a breath of fresh air in a community that's becoming increasingly cynical about the technology's potential to do good.


Process Mining Trends to Watch for in 2022

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Process mining has evolved into a mainstream approach to discover and improve business processes, and its market is projected to grow by 40-50% by passing $1 billion in 2022. It is being applied to numerous sectors and departments, ranging from healthcare to logistics. Case studies have shown that retailers, telco, and finance companies were some of the top beneficiaries of process mining. In this article, we explore experts' opinions, and we leverage our own research to predict process mining trends in 2022, and how businesses can benefit from these trends. Wil van der Aalst, the founder of process mining, states that he observes a shift towards more integrated tools and capabilities in the process mining market.


Dubai can't shake off the stain of smuggled African gold

The Japan Times

In the moon-like landscape of northern Sudan, informal gold miners toil with spades and pickaxes to extract their prize from shallow pits that pockmark the terrain. Mining ore in the sweltering heat of the Nubian desert is the first stage of an illicit network that has exploded in the past 18 months following a pandemic-induced spike in the gold price. African governments desperate to recoup lost revenue are looking to Dubai to help stop the trade. Interviews with government officials across Africa reveal smuggling operations that span at least nine countries and involve tons of gold spirited over borders. That's a cause for international concern because the funds from contraband minerals dealing in Africa fuel conflict, finance criminal and terrorist networks, undermine democracy and facilitate money laundering, according to the Organisation for Economic Cooperation and Development. While it's impossible to say precisely how much is lost to smugglers each year, United Nations trade data for 2020 show a discrepancy of at least $4 billion between the United Arab Emirates' declared gold imports from Africa and what African countries say they exported to the UAE.


Top 100 Most Read Interviews of Influential Tech Leaders by Analytics Insight

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'Business is an art and business leaders are artists', a well said a statement that is proving to be true every time a top leader takes amazing decisions for his organization. Although businesses rise and fall as times change, leaders never fail to be at the forefront to give their best. However, the key to long-term sustained success is great leadership and the ability of an executive to embrace the evolving trends. While talking about trends, the first thing that comes to our mind is artificial intelligence and disruptive technologies that are driving the next generation towards major digitization. The idea of technology came to practical usage when men thought that they needed machines to replace human activities. The core of such machines is to mimic or outperform human cognition. Although the concept of artificial intelligence came into existence in the 1950s, it didn't get fruition till the 1990s when technology hit the mainstream applications. Since then, the rise of technology has been enabled by exponentially faster and more powerful computers and large, complex datasets. Today, we have many futuristic technologies like machine learning, autonomous systems, data analytics, data science, and AR/VR in play. On the other hand, the enormous inflow of data has also contributed to this growth. In the digital world, development is highly reliant on technological advancement. Organizations across diverse industries are processing data to find insights and data-driven answers. Apart from laymen and consumers, it is the business leaders and corporate executives who have joined the bandwagon of the population to use artificial intelligence to the fullest. These trailblazing leaders are now increasingly using technology to optimize performance and experiment with new explorations. Their success story is what the world needs to hear. Analytics Insight has listed the top 100 such interviews that describe the journey of tech leaders and companies. Engineering and mining companies have faced a growing range of pressures in recent years, including price volatility, the need to drill down deeper to find new resources, and an industry-wide skills shortage. To address these challenges, many mining companies have embraced digital technology to enhance engineering design and develop smart mines'. Ausenco is a tech-savvy engineering company that delivers innovative, value-add consulting services, project delivery, asset operations, and maintenance solutions to the mining and metals, oil and gas, and industrial sectors….


Researcher Position - AI and Machine Learning, Halmstad University, Sweden 2022

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The applicant must hold a doctoral degree in Artificial Intelligence/Data Mining/Machine Learning/Information Technology or related fields. The applicant needs to demonstrate a strong research profile in the fields related to topics of interest for CAISR research environment, including recent activities with high impact.


A New AI Lexicon: Exporting AI

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AI/ML models are often exported. For example, large tech companies tend to congregate in particular parts of the world and sell software-as-a-service, platform-as-a-service, even surveillance-as-a-service in neatly-bound packages on a subscription basis to individuals, companies, and authorities around the world [1]. As a service/product/labour, AI/ML systems are also frequently exceptionalized where sleek models are intentionally portrayed to magically appear from thin air, skipping the commodity chain altogether. Software and virtual products are often decoupled from their material entanglements -- divorced from the vast lithium farms of the Atacama Desert, the cold data centers underneath the Alps, the data annotation centers scattered across the world, and the digital graveyards in the Korle Lagoon [2], [3]. As a feature of our capitalist society, all global supply chains have hidden components -- whether it is the obfuscation of sweatshops that operate on child labor or the efforts made towards washing the blood off of the diamond industry.


DIOS Drills First Priority Artificial Intelligence Target on K2 & Related Gold-Copper Soil Anomalies

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MONTREAL, Dec. 09, 2021 (GLOBE NEWSWIRE) -- DIOS Exploration Inc. ("DIOS") (TSX-V: DOS) reports successful drilling of first priority Artificial Intelligence (AI) target on wholly-owned K2 property, with good related gold-copper-As in B-soil anomalies. Windfall Geotek (TSX-V:WIN, OTC: WINKF), a leader in the use of Artificial Intelligence (AI) with advanced knowledge-extraction techniques since 2005 in the mining sector, provided a significant size AI Gold target covering a 0.87 km2 area on K2. A 3.5 km-long minimum electromagnetic conductor (ground VLF) associated with a good eastwest magnetic structure crosses this AI target. DIOS drilled two holes in section for 260 m in the middle of the 0.87 km2 AI target, cross-cutting the 3.5 km EM and magnetic structure. Both drill holes started in mineralized rocks, hitting a 53.65 meters thick sequence of one to forty percent disseminated (& in stringers) sulfides, that is pyrrhotite-pyrite, within chert/ mafic-intermediate volcanic tuffs/ graphitic argillites overlying a several m thick (3 -7.32 meters) massive sulfide horizon (70-90% pyrrhotite-pyrite).


Brainnwave, Hatch Form Co-Venture for AI-Augmented Metals, Mining, & More

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Brainnwave, founded in 2014, provides an augmented business intelligence platform that leverages advanced machine learning and statistical models to transform data. Now, the Edinburgh-based company has teamed up with a company nearly 60 years its senior: Hatch, which delivers engineering, construction, and consulting solutions for industries spanning mining, metallurgy, energy, and infrastructure. The new co-venture will combine Brainnwave's AI-powered analytics with Hatch's sector knowledge to deliver improved solutions for those industries. At the launch of the co-venture, Hatch announced a Series A investment in Brainnwave, not disclosing the amount of the investment. "This partnership made sense because both organizations are like-minded in their entrepreneurial approach, willingness to do things differently and challenge the status quo, and propensity to develop game-changing solutions," said Steve Coates, CEO and co-founder of Brainnwave.


25 Github Repositories Every Python Developer Should Know - KDnuggets

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Well, the answer to all your questions is Github. Learning how to code is easy but learning how to write better code is tough. Github can show you exactly what you need to know. It is like a Goldmine for developers where gold is the code written by other developers. With the help of GitHub, you can learn how to write better code, how good code looks, and the steps you need to follow to become a better developer. According to Stackoverflow, Python is the most preferred language.


Few-Shot Machine Learning Explained: Examples, Applications, Research

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Data is what powers machine learning solutions. Quality datasets enable training models with the needed detection and classification accuracy, though sometimes the accumulation of sufficient and applicable training data that should be fed into the model is a complex challenge. For instance, to create data-intensive apps human annotators are required to label a huge number of samples, which results in complexity of management and high costs for businesses. In addition to that, there is the difficulty associated with data acquisition related to safety regulations, privacy, or ethical concerns. When we have a limited dataset including only a finite number of samples per class, few-shot learning may be useful.