This dialog paper offers a preview and provides a foretaste of an upcoming work on the axiomatization of basic interactive algorithms. The modern notion of algorithm was elucidated in the 1930s--1950s. It was axiomatized a quarter of a century ago as the notion of ``sequential algorithm'' or ``classical algorithm''; we prefer to call it ``basic algorithm" now. The axiomatization was used to show that for every basic algorithm there is a behaviorally equivalent abstract state machine. It was also used to prove the Church-Turing thesis as it has been understood by the logicians. Starting from the 1960s, the notion of algorithm has expanded -- probabilistic algorithms, quantum algorithms, etc. -- prompting introduction of a much more ambitious version of the Church-Turing thesis commonly known as the ``physical thesis.'' We emphasize the difference between the two versions of the Church-Turing thesis and illustrate how nondeterministic and probabilistic algorithms can be viewed as basic algorithms with appropriate oracles. The same view applies to quantum circuit algorithms and many other classes of algorithms.

Manipulation of the decision-making process can have serious consequences that can negatively affect individuals [26], society, or organizations [19]. Prejudice, external pressures, bribery, or multiple factors can influence decision-makers, leading to sub-optimal outcomes or harm. In political elections, propaganda, disinformation, or bribery can manipulate voters and influence elections [15], leading to long-term societal consequences. We can take various measures to prevent manipulation and ensure transparency [2] and objectivity in decision-making. For example, the number of decision-makers can be increased to make manipulation more difficult [35], or the decision-making processes may be subject to external oversight (or review) to ensure compliance with ethical and legal standards [16].

In this article, we show that the completion problem, i.e. the decision problem whether a partial structure can be completed to a full structure, is NP-complete for many combinatorial structures. While the gadgets for most reductions in literature are found by hand, we present an algorithm to construct gadgets in a fully automated way. Using our framework which is based on SAT, we present the first thorough study of the completion problem on sign mappings with forbidden substructures by classifying thousands of structures for which the completion problem is NP-complete. Our list in particular includes interior triple systems, which were introduced by Knuth towards an axiomatization of planar point configurations. Last but not least, we give an infinite family of structures generalizing interior triple system to higher dimensions for which the completion problem is NP-complete.

Boolean MaxSAT, as well as generalized formulations such as Min-MaxSAT and Max-hybrid-SAT, are fundamental optimization problems in Boolean reasoning. Existing methods for MaxSAT have been successful in solving benchmarks in CNF format. They lack, however, the ability to handle 1) (non-CNF) hybrid constraints, such as XORs and 2) generalized MaxSAT problems natively. To address this issue, we propose a novel dynamic-programming approach for solving generalized MaxSAT problems with hybrid constraints -- called \emph{Dynamic-Programming-MaxSAT} or DPMS for short -- based on Algebraic Decision Diagrams (ADDs). With the power of ADDs and the (graded) project-join-tree builder, our versatile framework admits many generalizations of CNF-MaxSAT, such as MaxSAT, Min-MaxSAT, and MinSAT with hybrid constraints. Moreover, DPMS scales provably well on instances with low width. Empirical results indicate that DPMS is able to solve certain problems quickly, where other algorithms based on various techniques all fail. Hence, DPMS is a promising framework and opens a new line of research that invites more investigation in the future.

Old days of hard and not always profitable human labor is over, Smart Farming powered by Machine Learning with its high-precision algorithms is a new concept emerging today. Aiming to increase the quantity and quality of products, this cutting-edge movement makes sustainable productivity growth for everyone working in the agriculture realm. Farming goes digital and now we are observing 4th Agricultural Revolution. Everyday machines learn to solve complicated tasks, and they are doing it better with time. So, what is Machine learning applications in farming today and why should farmers care?

Playing repeated matrix games (RMG) while maximizing the cumulative returns is a basic method to evaluate multi-agent learning (MAL) algorithms. Previous work has shown that UCB, M3, S or Exp3 algorithms have good behaviours on average in RMG. Besides, hedging algorithms have been shown to be effective on prediction problems. An hedging algorithm is made up with a top-level algorithm and a set of basic algorithms. To make its decision, an hedging algorithm uses its top-level algorithm to choose a basic algorithm, and the chosen algorithm makes the decision. This paper experimentally shows that well-selected hedging algorithms are better on average than all previous MAL algorithms on the task of playing RMG against various players. S is a very good top-level algorithm, and UCB and M3 are very good basic algorithms. Furthermore, two-level hedging algorithms are more effective than one-level hedging algorithms, and three levels are not better than two levels.

This idea is really interesting. Sadly I don't have nearly enough linear algebra experience to understand the details though. Would IRL still be feasible if the state was not explicit? It seems like this technique depends on prior knowledge of the state machine, but from what I understand about deep reinforcement learning, the state may be very complex, and the value function could actually be a deep neural network.

This involves grouping different data points (customers, products, movies, etc.) Hierarchical clustering is based around organizing data points into a set of similar clusters, then recursively grouping clusters together until you are left with a single cluster. Because the algorithm has to run through every data point and compare groups of data points to other groups of data points, the run time increases dramatically. Usually the algorithm progresses by randomly assigning data points as centroids, followed by assigning data points to the appropriate clusters.

The human brain is the most sophisticated organ in the human body. The things that the brain can do, and how it does them, have even inspired a model of artificial intelligence (AI). Now, a recent study published in the journal Frontiers in Systems Neuroscience shows how human intelligence may be a product of a basic algorithm. This algorithm is found in the Theory of Connectivity, a "relatively simple mathematical logic underlies our complex brain computations," according to researcher and author Joe Tsien, neuroscientist at the Medical College of Georgia at Augusta University, co-director of the Augusta University Brain and Behavior Discovery Institute and Georgia Research Alliance Eminent Scholar in Cognitive and Systems Neurobiology. He first proposed the theory in October 2015.

They gave the animals various combinations of four different foods (rodent biscuits, pellets, rice, and milk). Using electrodes placed at specific areas of the brain, they were able to "listen" to the neurons' response. The scientists were able to identify all 15 different combinations of neurons or cliques that responded to the assortment of food combinations, as the Theory of Connectivity would predict. Furthermore, these neural cliques seem prewired in the brain, as they appeared immediately as soon as the food choices did.