Simulation and Experimental System Terner Aseton-Butanol-Ethanol with Batch Distillation

Result of Aseton-Butanol-Ethanol (ABE) terner system simulation (ABE) in the form of temperature profile, liquida composition profile and vapour in bottom utilized as reference in ABE terner system experiment, with komparasi result of ABE terner system experiment and simulation will know how far deviation obtained. For the dissociation of ABE terner system done by research simulationly before done by research experimentally, so that in determination of research variable can more directional and expense of cheaper research. Simulation of ABE terner system by batch distillation use rigorous method, model DAEs and Matlab Ianguage. Result of from ABE terner system simulation later then comparation use Metanol-Ethanol-Propanol (MEP) terner system which forming homolog deret. Usage of MEP terner system in ABE terner system comparation, because MEP terner system mixture prediction form zeotropik mixture. Result of simulation in the form of temperature profile, liquida composition profile and vapor composition profile function of time dimensionless () either in bottom and also[in distilate. Is afterwards done by ABE terner system experiment with operating pressure 1 atmospher, ABE mixture volume 350 ml, ABE feed composition : 0.8, 0.1, 0.1 ( mole fraction) and : 0, 1, 2,3. Result of ABE terner system simulation comparation with MEP terner system come near result which same and show zeotropik mixture. Result of experiment and simulation in the form of temperature profile, liquida composition profile and vapor composition profile time dimensionless function either in bottom and also in distilate show result come near is samely. So that ABE terner system simulation after comparation with MEP terner system can wear by reference in ABE terner system experimen

AI Education: Open-Access Educational Resources on AI

Open-access AI educational resources are vital to the quality of the AI education we offer. Avoiding the reinvention of wheels is especially important to us because of the special challenges of AI Education. AI could be said to be “the really interesting miscellaneous pile of Computer Science”. While “artificial” is well-understood to encompass engineered artifacts, “intelligence” could be said to encompass any sufficiently difficult problem as would require an intelligent approach and yet does not fall neatly into established Computer Science subdisciplines. Thus AI consists of so many diverse topics that we would be hard-pressed to individually create quality learning experiences for each topic from scratch. In this column, we focus on a few online resources that we would recommend to AI Educators looking to find good starting points for course development. [excerpt

Apports des Smart Contracts aux blockchains et comment créer une nouvelle crypto-monnaie

Dans ce travail de recherche, est expliqué ce qu'est la blockchain et son fonctionnement, mais aussi ce qu'est une crypto-monnaie et un Smart Contract. En particulier, quelles sont les possibilités et les problématiques émergentes avec l'arrivée des Smart Contracts. De plus, ce travail aboutit à la réalisation pratique d'un projet, utilisant des Smart Contracts pour créer une crypto-monnaie locale Business to Business, permettant aussi de faire des prêts à 0% d'intérêt. Ce projet est également conçu comme une aide montrant comment programmer des Smart Contracts. Il apportera les outils et les connaissances nécessaires dans le but de pouvoir créer, soi-même, sa propre crypto-monnaie ou toute autre application décentralisée

Global Solutions vs. Local Solutions for the AI Safety Problem

There are two types of artificial general intelligence (AGI) safety solutions: global and local. Most previously suggested solutions are local: they explain how to align or “box” a specific AI (Artificial Intelligence), but do not explain how to prevent the creation of dangerous AI in other places. Global solutions are those that ensure any AI on Earth is not dangerous. The number of suggested global solutions is much smaller than the number of proposed local solutions. Global solutions can be divided into four groups: 1. No AI: AGI technology is banned or its use is otherwise prevented; 2. One AI: the first superintelligent AI is used to prevent the creation of any others; 3. Net of AIs as AI police: a balance is created between many AIs, so they evolve as a net and can prevent any rogue AI from taking over the world; 4. Humans inside AI: humans are augmented or part of AI. We explore many ideas, both old and new, regarding global solutions for AI safety. They include changing the number of AI teams, different forms of “AI Nanny” (non-self-improving global control AI system able to prevent creation of dangerous AIs), selling AI safety solutions, and sending messages to future AI. Not every local solution scales to a global solution or does it ethically and safely. The choice of the best local solution should include understanding of the ways in which it will be scaled up. Human-AI teams or a superintelligent AI Service as suggested by Drexler may be examples of such ethically scalable local solutions, but the final choice depends on some unknown variables such as the speed of AI progres

Evaluating Visual Conversational Agents via Cooperative Human-AI Games

As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. In this work, we design a cooperative game - GuessWhich - to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call ALICE, is provided an image which is unseen by the human. Following a brief description of the image, the human questions ALICE about this secret image to identify it from a fixed pool of images. We measure performance of the human-ALICE team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with ALICE. We compare performance of the human-ALICE teams for two versions of ALICE. Our human studies suggest a counterintuitive trend - that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.Comment: HCOMP 201

Sinorhizobium Meliloti, A Bacterium Lacking The Autoinducer-2 (AI-2) Synthase, Responds To AI-2 Supplied By Other Bacteria

Many bacterial species respond to the quorum-sensing signal autoinducer-2 (AI-2) by regulating different niche-specific genes. Here, we show that Sinorhizobium meliloti, a plant symbiont lacking the gene for the AI-2 synthase, while not capable of producing AI-2 can nonetheless respond to AI-2 produced by other species. We demonstrate that S. meliloti has a periplasmic binding protein that binds AI-2. The crystal structure of this protein (here named SmlsrB) with its ligand reveals that it binds (2R,4S)-2-methyl-2,3,3,4-tetrahydroxytetrahydrofuran (R-THMF), the identical AI-2 isomer recognized by LsrB of Salmonella typhimurium. The gene encoding SmlsrB is in an operon with orthologues of the lsr genes required for AI-2 internalization in enteric bacteria. Accordingly, S. meliloti internalizes exogenous AI-2, and mutants in this operon are defective in AI-2 internalization. S. meliloti does not gain a metabolic benefit from internalizing AI-2, suggesting that AI-2 functions as a signal in S. meliloti. Furthermore, S. meliloti can completely eliminate the AI-2 secreted by Erwinia carotovora, a plant pathogen shown to use AI-2 to regulate virulence. Our findings suggest that S. meliloti is capable of \u27eavesdropping\u27 on the AI-2 signalling of other species and interfering with AI-2-regulated behaviours such as virulence

Federated AI for building AI Solutions across Multiple Agencies

The different sets of regulations existing for differ-ent agencies within the government make the task of creating AI enabled solutions in government dif-ficult. Regulatory restrictions inhibit sharing of da-ta across different agencies, which could be a significant impediment to training AI models. We discuss the challenges that exist in environments where data cannot be freely shared and assess tech-nologies which can be used to work around these challenges. We present results on building AI models using the concept of federated AI, which al-lows creation of models without moving the training data around.Comment: Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, US

Futur campus santé de Sion: vers une collaboration entre services documentaires? : état des lieux et pistes de réflexion

La HES-SO Valais Wallis a pour projet de construire à l’horizon 2020 un nouveau bâtiment regroupant ses formations dans le domaine de la santé (soins infirmiers et physiothérapie) et du social (filières de niveau école supérieure dans l’éducation de l’enfance et l’action socio-professionnelle). Le choix de la localisation de cette nouvelle infrastructure s’est porté sur le site de Champsec à Sion qui regroupe actuellement plusieurs institutions sanitaires valaisannes (Hôpital du Valais, SUVA, Observatoire valaisan de la santé, etc.). Il existe ainsi un potentiel de synergie possible entre partenaires et futurs voisins. Ce travail a pour objectif de déterminer dans un premier temps l’intérêt des différents partenaires à une collaboration sur le plan de la documentation, d’établir un état de la situation actuelle et de proposer des pistes pour une future collaboration institutionnelle