Agremo is one of the fastest growing startup in Europe that develops intuitive agricultural sensing and drone analysis platform for drone operators, growers, and agronomists. For several years now, AgroTech startups from all around the world, have been making major and significant changes in the agricultural sector. Their goal is to help make this industry much more digitized and more modern. With drones which are sensors equipped for scanning land and subsequently processing large amounts of data, users get powerful tools that take their business to a whole new level. Among them is Agremo, a Serbian startup backed by South Central Ventures and StartLabs, which has developed an advanced AI platform that allows farmers to analyze fields and seedlings using drone photos.

The high technological and strategic stakes mean major technology companies as well as ambitious start-ups and government-funded research centers are all in the race to build the world's first universal quantum computer. In contrast to today's classical computers, where information is encoded in bits (0 or 1), quantum computers process information stored in quantum bits (qubits). These are hosted by quantum mechanical objects like electrons, the negatively charged particles of an atom. Quantum states can also be binary and can be put in one of two possibilities, or effectively both at the same time--known as quantum superposition--offering an exponentially larger computational space with an increasing number of qubits. This unique data crunching power is further boosted by entanglement, another magical property of quantum mechanics where the state of one qubit is able to dictate the state of another qubit without any physical connection, making them all 1's for example.

"High demand and tight labor markets have made candidates with AI skills highly competitive, but hiring techniques and strategies have not kept up," said Peter Krensky, research director at Gartner. "In the recent Gartner AI and Machine Learning Development Strategies Study, respondents ranked "skills of staff" as the No. 1 challenge or barrier to the adoption of AI and machine learning (ML)." Departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, and research and development. These business units are using AI talent for customer churn modeling, customer profitability analysis, customer segmentation, cross-sell and upsell recommendations, demand planning, and risk management. A significant portion of AI use cases are reported from asset-centric industries supporting projects such as predictive maintenance, workflow and production optimization, quality control and supply chain optimization.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. The late Stephen Hawking called artificial intelligence the biggest threat to humanity. But Hawking, albeit a revered physicist, was not a computer scientist. Elon Musk compared AI adoption to "summoning the devil." But Elon is, well, Elon. And there are dozens of movies that depict a future in which robots and artificial intelligence go berserk.

Most of the daily news and recently published scientific papers on research, innovations, and applications in artificial intelligence (AI) refer to what is known as machine learning--algorithms using massive amounts of data and various methodologies to find patterns, support decisions, make predictions, or, for the deep learning part, self-identify important features in data. However, AI is a complex concept to grasp, and most people have little understanding of what it really is. AI was founded as an academic discipline in 1956 and, despite its youth, already has a rich history [1,2]. In more than 60 years of exploration and progress, AI has become a large field of research and development involving multidisciplinary approaches to address many challenges, from theoretical frameworks, methods, and tools to real implementations, risk analysis, and impact measures. The definition of AI is a moving target and changes over time with the evolution of the field. Since its early days, the field of AI has allowed the development of many techniques supporting decision support and prediction, as it is usually made by humans. As early as 1958, a perceptron was expected to be able "to walk, talk, see, write, reproduce itself and be conscious of its existence," which led a large scientific controversy between neural network and symbolic reasoning approaches [3].

Artificial intelligence technology has continued to grow in recent years, stunning the world with its latest innovations. But, some are admittedly growing weary about AI and its continuous growth. With talk of robots one day replacing humans for labor, concerns of an increasingly tech dependent world grow stronger. A report from Oxford researchers stated that 47% of American jobs will be at risk by 2030 because of automation. However, AI is truly changing the world - providing innovation that can change how we approach healthcare, the environment, and the day to day act of living.

In a blog post from November 2019, Arielle Fleisher, transportation policy director for SPUR (the San Francisco Bay Area Planning and Urban Research Association), called for new ways of thinking about public transit. "Transit doesn't have to stay exactly as it is today," Fleisher wrote. "The world has changed in ways that should impact transit design: Virtually every person has a device that shares their location in real time. This alone begs for innovation and experimentation in the transit sector. We need to embrace a larger view of what transportation is for and who it serves."

Autonomous vehicles were supposed to make human drivers obsolete. But the coronavirus pandemic is exposing how a technology designed to be human-free still relies on a large workforce of contract laborers at almost every level. The Verge reached out to 10 autonomous vehicle developers to find out what they were doing in response to the coronavirus outbreak. Almost all of them said they would be grounding their fleets for at least several weeks as they monitor the spread of the virus. But the fate of human backup drivers who ride around in the vehicles is less certain.

A new AI-powered system monitors coughing sounds to understand where the coronavirus is spreading. It then analyzes the data to predict the progress of COVID-19 and other respiratory diseases. These insights could guide public health responses to the pandemic, such as the allocation of medical supplies, travel restriction, and vaccine campaigns. "I've been interested in non-speech body sounds for a long time," said researcher Tauhidur Rahman, an assistant professor of computer and information sciences at the University of Massachusetts Amherst. I thought if we could capture coughing or sneezing sounds from public spaces where a lot of people naturally congregate, we could utilize this information as a new source of data for predicting epidemiologic trends.

Bayesian Optimization is the state of the art technique for the optimization of black boxes, i.e., functions where we do not have access to their analytical expression nor its gradients, they are expensive to evaluate and its evaluation is noisy. The most popular application of bayesian optimization is the automatic hyperparameter tuning of machine learning algorithms, where we obtain the best configuration of machine learning algorithms by optimizing the estimation of the generalization error of these algorithms. Despite being applied with success, bayesian optimization methodologies also have hyperparameters that need to be configured such as the probabilistic surrogate model or the acquisition function used. A bad decision over the configuration of these hyperparameters implies obtaining bad quality results. Typically, these hyperparameters are tuned by making assumptions of the objective function that we want to evaluate but there are scenarios where we do not have any prior information about the objective function. In this paper, we propose a first attempt over automatic bayesian optimization by exploring several heuristics that automatically tune the acquisition function of bayesian optimization. We illustrate the effectiveness of these heurisitcs in a set of benchmark problems and a hyperparameter tuning problem of a machine learning algorithm.