Many mathematical algorithms that we use in data science and machine learning require numeric data. And many algorithms tend to be very complex to implement (such as Support Vector Machines or Local Linear Embedding, which we previously discussed). But, association rule mining is perfect for categorical (non-numeric) data and it involves nothing more than simple counting! What we have here is a simple algorithm with not so simplistic results! The ratio of actionable insights discovery potential (high) to algorithm complexity (low) is quite large and atypical, IMHO.

Echo-sounder data registered by buoys attached to drifting FADs provide a very valuable source of information on populations of tuna and their behaviour. This value increases when these data are supplemented with oceanographic data coming from CMEMS. We use these sources to develop Tuna-AI, a Machine Learning model aimed at predicting tuna biomass under a given buoy, which uses a 3-day window of echo-sounder data to capture the daily spatio-temporal patterns characteristic of tuna schools. As the supervised signal for training, we employ more than 5000 set events with their corresponding tuna catch reported by the AGAC tuna purse seine fleet.

Deep learning is on the frontline in a new age of piracy, outwitting attacks with pre-emptive tech, explains Yarden Gross, CEO and co-founder of Orca AI. Almost a decade has passed since piracy raged off Somalia, and yet the danger posed by maritime hijackings is as present as ever. The global pandemic last year sparked a resurgence of attacks, with piracy incidents doubling across Asia, in a worrying uptick also seen in the Gulf of Mexico and West Africa. The fallout from coronavirus, including the loss of key security personnel, turned quarantined vessels into easy targets. This wave has since receded a little, with the International Maritime Bureau reporting a 44 per cent YoY dip in piracy and armed robbery incidents in 2021.

The prediction of behavior in dynamical systems, is frequently subject to the design of models. When a time series obtained from observing the system is available, the task can be performed by designing the model from these observations without additional assumptions or by assuming a preconceived structure in the model, with the help of additional information about the system. In the second case, it is a question of adequately combining theory with observations and subsequently optimizing the mixture. In this work, we proposes the design of time-continuous models of dynamical systems as solutions of differential equations, from non-uniform sampled or noisy observations, using machine learning techniques. The performance of strategy is shown with both, several simulated data sets and experimental data from Hare-Lynx population and Coronavirus 2019 outbreack. Our results suggest that this approach to the modeling systems, can be an useful technique in the case of synthetic or experimental data.

Swarms of drones have gotten a lot of time in the spotlight lately, mostly for their use in potential military operations. The U.S. military is testing out swarm operations in simulations, while the British Army is using live drones operating in swarms during actual training operations. Other militaries are also interested in deploying swarms. One of the biggest advantages a swarm of drones has when performing military operations is its resiliency. If a swarm enters combat and several individual drones get shot down or otherwise incapacitated, it really doesn't reduce the combat effectiveness of the swarm, nor the tactics that it uses.

Recently, Github and OpenAI released one of the most anticipated AI-based tools for developers -- Github Copilot. The Artificial Intelligence (AI) tool is advertised as a pair programming assistant that does much more than usual code autocomplete tools out there. By no means is Copilot a tool intended to substitute developers in any way. Instead, the tool is meant to be used as an assistant that can facilitate many of the "boring" and "repetitive" parts of programming and lets coders worry about parts of the process that require human thinking and reasoning. It is important to note that GitHub Copilot is based on a recent deep learning model published by OpenAI in a paper called "Evaluating Large Language Models Trained on Code". This research paper introduces Codex, a GPT-like language model fine-tuned on publicly available code from GitHub.

We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts. We tested GPT-3, GPT-Neo/J, GPT-2 and a T5-based model. The best model was truthful on 58% of questions, while human performance was 94%. Models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. The largest models were generally the least truthful. For example, the 6B-parameter GPT-J model was 17% less truthful than its 125M-parameter counterpart. This contrasts with other NLP tasks, where performance improves with model size. However, this result is expected if false answers are learned from the training distribution. We suggest that scaling up models alone is less promising for improving truthfulness than fine-tuning using training objectives other than imitation of text from the web.

The crew of SpaceX's Inspiration4, the first all-civilian spaceflight to orbit, will be used to collect a huge amount of health data that will be used to help future humans travel off-planet. The four humans riding the Dragon capsule are US billionaire Jared Isaacman, who commissioned the flight, St. Jude physician's assistant Hayley Arcenaux, data engineer Chris Sembroski and geoscientist and artist Sian Proctor. The mission, scheduled for 15 September, will orbit the planet at 575 kilometres for three days before returning to Earth, descending into the Atlantic Ocean. This is the furthest distance from Earth for any human spaceflight since the Hubble Space Telescope repair missions, SpaceX says. The crew will collect a range of medical data including ECG (electrocardiograph) activity, movement, sleep, heart rate and rhythm, blood oxygen saturation, cabin noise and light intensity – which will be used to help assess changes in behavioural and cognitive performance over time.

Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting (CEF). Our framework combines two formalisms: a) symbolic automata which are used to encode complex event patterns; and b) prediction suffix trees which can provide a succinct probabilistic description of an automaton's behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. Our experimental results demonstrate the benefits, in terms of accuracy, of being able to capture such long-term dependencies. This is achieved by increasing the order of our model beyond what is possible with full-order Markov models that need to perform an exhaustive enumeration of all possible past sequences of a given order. We also discuss extensively how CEF solutions should be best evaluated on the quality of their forecasts.

If it looks like a penguin and swims like a penguin – but it's actually a robot – then it must be the latest advance in marine sensory equipment. The Quadroin is an autonomous underwater vehicle (AUV): a 3D-printed self-propelled machine designed to mimic a penguin in order to measure the properties of oceanic eddies. It was developed by Burkard Baschek while head of Germany's Institute of Coastal Ocean Dynamics at the Helmholtz Centre Hereon in Geesthacht after he watched more than $20,000 of his equipment sink to the bottom of the Pacific Ocean. Eddies are small ocean currents that other research methods have struggled to capture. They influence all the animals and plants in the seas as well as the Earth's climate, driving roughly 50% of all phytoplankton production.