Improvement of statistical learning models in order to increase efficiency in solving classification or regression problems is still a goal pursued by the scientific community. In this way, the support vector machine model is one of the most successful and powerful algorithms for those tasks. However, its performance depends directly from the choice of the kernel function and their hyperparameters. The traditional choice of them, actually, can be computationally expensive to do the kernel choice and the tuning processes. In this article, it is proposed a novel framework to deal with the kernel function selection called Random Machines. The results improved accuracy and reduced computational time. The data study was performed in simulated data and over 27 real benchmarking datasets.

While most of humanity is suddenly on the net, the value of this singularity is hampered by the lack of credible digital identities: Social networking, person-to-person transactions, democratic conduct, cooperation and philanthropy are all hampered by the profound presence of fake identities, as illustrated by Facebook's removal of 5.4Bn fake accounts since the beginning of 2019. Here, we introduce the fundamental notion of a \emph{genuine personal identifier}---a globally unique and singular identifier of a person---and present a foundation for a decentralized, grassroots, bottom-up process in which every human being may create, own, and protect the privacy of a genuine personal identifier. The solution employs mutual sureties among owners of personal identifiers, resulting in a mutual-surety graph reminiscent of a web-of-trust. Importantly, this approach is designed for a distributed realization, possibly using distributed ledger technology, and does not depend on the use or storage of biometric properties. For the solution to be complete, additional components are needed, notably a mechanism that encourages honest behavior and a sybil-resilient governance system.

Could the same computer algorithms that teach autonomous cars to drive safely help identify nearby asteroids or discover life in the universe? NASA scientists are trying to figure that out by partnering with pioneers in artificial intelligence (AI)--companies such as Intel, IBM and Google--to apply advanced computer algorithms to problems in space science. Machine learning is a type of AI. It describes the most widely used algorithms and other tools that allow computers to learn from data in order to make predictions and categorize objects much faster and more accurately than a human being can. Consequently, machine learning is widely used to help technology companies recognize faces in photos or predict what movies people would enjoy.

CAIML #9 took place on November 14 at factor-a – part of Dept, demonstrating how AI can be used for social good and to address societal challenges. "Aid organizations and governments are applying great effort in resolving the negative impacts of food insecurity induced crisis like famines or mass migration. One of the most limiting resources these actors face is the lack of preparation time for consistent and sustainable planning for emergency relief like setting refugee camps or securing supply with food and energy. Hence, increasing the lead time for preparation is an essential step and will result in saving many lives. The aim of this research is to increase the lead time by developing a ML based mathematical prediction model that is able to compute the probability for food insecure areas by learning from historical data. For performing such computations, our prediction model is developed and trained on historic open access data for the Horn of Africa (2009-2018). We used precipitation and vegetation data derived by remote sensing, as well as socio-economic, medical, armed conflict and disaster data. To overcome spatial inconsistencies in the input data and to meet the requirements of spatially homogenous input for neural networks, all data has been converted to geo-referenced raster maps. Disaster and armed conflict data has been fitted to districts while local food market prices have been interpolated. The IPC has been used as the food security label. In order to find a prediction model, deep learning methods have been used. Several analyses were applied on the collected data such as multicollinearity checks and principal component analyses. Preliminary cross-validated results have encouraged us to further investigate the detection of food insecure areas using open access data."

Researchers at Carnegie Mellon University have developed a system that can accurately locate a shooter based on video recordings from as few as three smartphones. When demonstrated using three video recordings from the 2017 mass shooting in Las Vegas that left 58 people dead and hundreds wounded, the system correctly estimated the shooter's actual location--the north wing of the Mandalay Bay hotel. The estimate was based on three gunshots fired within the first minute of what would be a prolonged massacre. Alexander Hauptmann, research professor in CMU's Language Technologies Institute, said the system, called Video Event Reconstruction and Analysis (VERA), won't necessarily replace the commercial microphone arrays for locating shooters that public safety officials already use, although it may be a useful supplement for public safety when commercial arrays aren't available. One key motivation for assembling VERA was to create a tool that could be used by human rights workers and journalists who investigate war crimes, terrorist acts and human rights violations, Hauptmann said.

Everything you need to know about Duchess of Sussex Meghan Markle and her new life as part of the British royal family. There's something about that "Markle sparkle" that has the world transfixed, seeing as Meghan Markle has now been named the world's "most powerful dresser" in a 2019 report from Lyst, a fashion search engine. It was a big year for the Duchess of Sussex, who stylishly seized the spotlight at dozens of public appearances and royal tours, and even when introducing the world to baby Archie -- and according to Lyst, shoppers took notice. There's something about that "Markle sparkle" that has the world transfixed, as Meghan Markle has been named the world's most powerful dresser of 2019. According to Lyst's annual Year in Fashion roundup, each of the Duchess' numerous fashion statements sparked a 216-percent average increase in search for similar items.

Cirta is a new machine-learning challenge for high-energy physics on Zindi, the Africa-based data-science challenge platform. Launched this autumn at the International Conference on High Energy and Astroparticle Physics (TIC-HEAP), Constantine, Algeria, Cirta challenges participants to provide machine-learning solutions for identifying particles in LHC experiment data. Cirta* is the first particle-physics challenge to specifically target computer scientists in Africa, and puts the public TrackML challenge dataset to new use. Created by ATLAS computer scientists Sabrina Amrouche and Dalila Salamani, the Cirta challenge aims to bring new blood into the growing field of machine learning for particle physics. "Zindi has a strong community of computer scientists based on the continent, and we're looking forward to reviewing their creative solutions to the challenge," says Salamani.

Artificial intelligence technology is advancing and bringing opportunities for society but also profound challenges for individual freedom. AI is a powerful enabler of surveillance technology, such as facial recognition, and many countries are grappling with appropriate rules for use, weighing the security benefits against privacy risks. Authoritarian regimes, however, lack strong institutional mechanisms to protect individual privacy--a free and independent press, civil society, an independent judiciary--and the result is the widespread use of AI for surveillance and repression. This dynamic is most acute in China, where the Chinese government is pioneering new uses of AI to monitor and control its population. China has already begun to export this technology along with laws and norms for illiberal uses to other nations. As AI-enabled surveillance technology spreads around the globe, how it is used poses profound challenges for the future of democracy, liberty, and individual freedom.

AI and machine learning algorithms require data. But the bulk of that data is of no use if it isn't first labeled by human annotators. This predicament has given rise to a cottage industry of startups, including Scale AI, which recently raised $100 million for its extensive suite of data labeling services. That's not to mention Mighty AI, Hive, Appen, and Alegion, which together occupy a data annotation tools segment that's anticipated to be worth $1.6 billion by 2025. CloudFactory is yet another vying for attention.

AI training data provider Samasource has raised a $14.8 million Series A funding round led by Ridge Ventures. The San Francisco headquartered company delivers Fortune 100 companies with the inputs they need for machine learning development in fields including autonomous transportation, e-commerce and robotics. And it does so with a global work-force of data-specialists, a large number of whom are located in East Africa. In addition to San Francisco, New York and the Hague, Samasource has offices and teams in Kenya and Uganda. The company has a global staff of 2900 and is the largest AI and data annotation employer in East Africa, according to CEO and founder Leila Janah.