This paper shows that the fuzzy temporal logic can model figures of thought to describe decision-making behaviors. In order to exemplify, some economic behaviors observed experimentally were modeled from problems of choice containing time, uncertainty and fuzziness. Related to time preference, it is noted that the subadditive discounting is mandatory in positive rewards situations and, consequently, results in the magnitude effect and time effect, where the last has a stronger discounting for earlier delay periods (as in, one hour, one day), but a weaker discounting for longer delay periods (for instance, six months, one year, ten years). In addition, it is possible to explain the preference reversal (change of preference when two rewards proposed on different dates are shifted in the time). Related to the Prospect Theory, it is shown that the risk seeking and the risk aversion are magnitude dependents, where the risk seeking may disappear when the values to be lost are very high.

Killer robots are a'grave threat to humanity' and should be banned, the world's largest gathering of scientists was told. As yet, robot soldiers and security guards armed with lethal weapons are largely science fiction concepts. But advances in artificial intelligence mean it is only a matter of time before robots with the power to select and attack targets without human input become widespread. Killer robots, similar to the Terminator played by Arnold Schwarzenegger (pictured), are a'grave threat to humanity' and should be banned, the world's largest gathering of scientists was told (stock image) Scientists and human rights campaigners told the American Association for the Advancement of Science annual meeting in Washington DC lethal droids able to select targets without human help represent the'third revolution' in warfare after gun powder and nuclear weapons. Just as international agreements greatly restricted the use of landmines, similar international agreements should be used to prevent robotic killers becoming established.

Microsoft has announced a new initiative that will see more than 3 million Brazilian students getting trained in themes around artificial intelligence (AI). During the Microsoft AI Tour event in São Paulo, the firm's chief executive Satya Nadella announced the pro-bono partnership with school networks SESI and SENAI to offer AI training in high school courses. During the announcement, Nadella pointed out that governments should accelerate the adoption of automation but create new capabilities at the same time. "We have a tremendous opportunity to generate advances in digital technologies – and specifically in AI – to empower each person and each organization in Brazil to achieve more," Nadella told delegates at the event on Tuesday (12). Four courses are available through the school network's online platform, covering the introduction to AI and data science, data science fundamentals development of solutions using Azure, Bot and IoT.

In this paper, we address the stability of a broad class of discrete-time hypercomplex-valued Hopfield-type neural networks. To ensure the neural networks belonging to this class always settle down at a stationary state, we introduce novel hypercomplex number systems referred to as Hopfield-type hypercomplex number systems. Hopfield-type hypercomplex number systems generalize the well-known Cayley-Dickson algebras and real Clifford algebras and include the systems of real numbers, complex numbers, dual numbers, hyperbolic numbers, quaternions, tessarines, and octonions as particular instances. Apart from the novel hypercomplex number systems, we introduce a family of hypercomplex-valued activation functions called Hopfield-type activation functions. Broadly speaking, a Hopfield-type activation function projects the activation potential onto the set of all possible states of a hypercomplex-valued neuron. Using the theory presented in this paper, we confirm the stability analysis of several discrete-time hypercomplex-valued Hopfield-type neural networks from the literature. Moreover, we introduce and provide the stability analysis of a general class of Hopfield-type neural networks on Cayley-Dickson algebras.

Neuroscientists at Columbia University have discovered a ground-breaking way of turning thoughts into speech, that could potentially give individuals who have lost their ability to speak a voice. Professor Mesgarani and his team from Colombia University are using Artificial Intelligence (AI) to recognise patters that appear in someone's brain when they listen to speech. The AI is similar to the algorithms used by Apple for Siri and Amazon for Alexa. Using computer processing software, scientists monitor brainwaves of patients who are unable to vocalise thoughts. They also use neural networks, along with the technology able to channel brain activity into the device to be translated into speech.

Ecosia, a German startup with an internet search engine, today, has brought in enough revenues to enable it to plant 50 million trees. This equates to the removal of 2.5 million tonnes of Co2 from the atmosphere, according to the company. Ecosia has used the profits from advertisements on its search engine to plant trees in Kenya, Brazil, Indonesia, Spain, Tanzania, Madagascar, Colombia, Peru, Senegal, Burkina Faso, Haiti, Morocco, Ethiopia, Uganda, Ghana and Nicaragua. Ecosia has partnered with Bing, Microsoft's search engine, to get results for users, but receives a majority portion of any revenues. After covering its internal costs, everything left goes towards planting trees; Ecosia is a non-profit organization.

There is growing interest in being able to run neural networks on sensors, wearables and internet-of-things (IoT) devices. However, the computational demands of neural networks make them difficult to deploy on resource-constrained edge devices. To meet this need, our work introduces a new recurrent unit architecture that is specifically adapted for on-device low power acoustic event detection (AED). The proposed architecture is based on the gated recurrent unit (`GRU') but features optimizations that make it implementable on ultra-low power micro-controllers such as the Arm Cortex M0+. Our new architecture, the Embedded Gated Recurrent Unit (eGRU) is demonstrated to be highly efficient and suitable for short-duration AED and keyword spotting tasks. A single eGRU cell is 60x faster and 10x smaller than a GRU cell. Despite its optimizations, eGRU compares well with GRU across tasks of varying complexities. The practicality of eGRU is investigated in a wearable acoustic event detection application. An eGRU model is implemented and tested on the Arm Cortex M0-based Atmel ATSAMD21E18 processor. The Arm M0+ implementation of the eGRU model compares favorably with a full precision GRU that is running on a workstation. The embedded eGRU model achieves a classification accuracy 95.3%, which is only 2% less than the full precision GRU.

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Artificial Intelligence (AI) is necessary to address the large and growing deficit in retina and healthcare access globally. And mobile AI diagnostic platforms running in the Cloud may effectively and efficiently distribute such AI capability. Here we sought to evaluate the feasibility of Cloud-based mobile artificial intelligence for detection of retinal disease. And to evaluate the accuracy of a particular such system for detection of subretinal fluid (SRF) and macula edema (ME) on OCT scans. A multicenter retrospective image analysis was conducted in which board-certified ophthalmologists with fellowship training in retina evaluated OCT images of the macula. They noted the presence or absence of ME or SRF, then compared their assessment to that obtained from Fluid Intelligence, a mobile AI app that detects SRF and ME on OCT scans. Investigators consecutively selected retinal OCTs, while making effort to balance the number of scans with retinal fluid and scans without. Exclusion criteria included poor scan quality, ambiguous features, macula holes, retinoschisis, and dense epiretinal membranes. Accuracy in the form of sensitivity and specificity of the AI mobile App was determined by comparing its assessments to those of the retina specialists. At the time of this submission, five centers have completed their initial studies. This consists of a total of 283 OCT scans of which 155 had either ME or SRF ("wet") and 128 did not ("dry"). The sensitivity ranged from 82.5% to 97% with a weighted average of 89.3%. The specificity ranged from 52% to 100% with a weighted average of 81.23%. CONCLUSION: Cloud-based Mobile AI technology is feasible for the detection retinal disease. In particular, Fluid Intelligence (alpha version), is sufficiently accurate as a screening tool for SRF and ME, especially in underserved areas. Further studies and technology development is needed.

Abstract--Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increasing interest in quantification of CAC, i.e. coronary calcium scoring,especially as an unrequested finding for screening and research, automatic methods have been proposed. Current automatic calcium scoring methods are relatively computationally expensive and only provide scores for one type of CT. To address this, we propose a computationally efficient method that employs two ConvNets: the first performs registration to align the fields of view of input CTs and the second performs direct regression of the calcium score, thereby circumventing timeconsuming intermediateCAC segmentation. Optional decision feedback provides insight in the regions that contributed to the calcium score. Experiments were performed using 903 cardiac CT and 1,687 chest CT scans. The method predicted calcium scores in less than 0.3 s. Intra-class correlation coefficient between predicted and manual calcium scores was 0.98 for both cardiac and chest CT. The method showed almost perfect agreement between automatic and manual CVD risk categorization in both datasets, with a linearly weighted Cohen's kappa of 0.95 in cardiac CT and 0.93 in chest CT. Performance is similar to that of state-of-the-art methods, but the proposed method is hundreds of times faster. By providing visual feedback, insight is given in the decision process, making it readily implementable in clinical and research settings. I. INTRODUCTION Cardiovascular disease (CVD) is the global leading cause of death [1]. To reduce the burden of cardiovascular disease the World Health Organization underlines the need for early detection and treatment of individuals with CVD or those who are at high cardiovascular risk due to the presence of one or more risk factors [2]. Quantification of CAC, i.e. calcium scoring, is typically performed in dedicated Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. Bob D. de Vos, Jelmer M. Wolterink, Nikolas Lessmann, and Ivana Išgum are with the Image Sciences Institute of the University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.