12 research outputs found
Intelligenza artificiale e sicurezza: opportunitĆ , rischi e raccomandazioni
L'IA (o intelligenza artificiale) ĆØ una disciplina in forte espansione negli ultimi anni e lo sarĆ sempre piĆ¹ nel prossimo futuro: tuttavia ĆØ dal 1956 che lāIA studia lāemulazione dellāintelligenza da parte delle macchine, intese come software e in certi casi hardware. LāIA ĆØ nata dallāidea di costruire macchine che - ispirandosi ai processi legati allāintelligenza umana - siano in grado di risolvere problemi complessi, per i quali solitamente si ritiene che sia necessario un qualche tipo di ragionamento intelligente.
La principale area di ricerca e applicazione attuale dellāIA ĆØ il machine learning (algoritmi che imparano e si adattano in base ai dati che ricevono), che negli ultimi anni ha trovato ampie applicazioni grazie alle reti neurali (modelli matematici composti da neuroni artificiali) che a loro volta hanno consentito la nascita del deep learning (reti neurali di maggiore complessitĆ ). Appartengono al mondo dellāIA anche i sistemi esperti, la visione artificiale, il riconoscimento vocale, lāelaborazione del linguaggio naturale, la robotica avanzata e alcune soluzioni di cybersecurity.
Quando si parla di IA c'ĆØ chi ne ĆØ entusiasta pensando alle opportunitĆ , altri sono preoccupati poichĆ© temono tecnologie futuristiche di un mondo in cui i robot sostituiranno l'uomo, gli toglieranno il lavoro e decideranno al suo posto. In realtĆ l'IA ĆØ ampiamente utilizzata giĆ oggi in molti campi, ad esempio nei cellulari, negli oggetti smart (IoT), nelle industry 4.0, per le smart city, nei sistemi di sicurezza informatica, nei sistemi di guida autonoma (drive o parking assistant), nei chat bot di vari siti web; questi sono solo alcuni esempi basati tutti su algoritmi tipici dellāintelligenza artificiale. Grazie all'IA le aziende possono avere svariati vantaggi nel fornire servizi avanzati, personalizzati, prevedere trend, anticipare le scelte degli utenti, ecc.
Ma non ĆØ tutto oro quel che luccica: ci sono talvolta problemi tecnici, interrogativi etici, rischi di sicurezza, norme e legislazioni non del tutto chiare.
Le organizzazioni che giĆ adottano soluzioni basate sullāIA, o quelle che intendono farlo, potrebbero beneficiare di questa pubblicazione per approfondirne le opportunitĆ , i rischi e le relative contromisure. La Community for Security del Clusit si augura che questa pubblicazione possa fornire ai lettori un utile quadro dāinsieme di una realtĆ , come lāintelligenza artificiale, che ci accompagnerĆ sempre piĆ¹ nella vita personale, sociale e lavorativa.AI (or artificial intelligence) is a booming discipline in recent years and will be increasingly so in the near future.However, it is since 1956 that AI has been studying the emulation of intelligence by machines, understood as software and in some cases hardware. AI arose from the idea of building machines that-inspired by processes related to human intelligence-are able to solve complex problems, for which it is usually believed that some kind of intelligent reasoning is required.
The main current area of AI research and application is machine learning (algorithms that learn and adapt based on the data they receive), which has found wide applications in recent years thanks to neural networks (mathematical models composed of artificial neurons), which in turn have enabled the emergence of deep learning (neural networks of greater complexity). Also belonging to the AI world are expert systems, computer vision, speech recognition, natural language processing, advanced robotics and some cybersecurity solutions.
When it comes to AI there are those who are enthusiastic about it thinking of the opportunities, others are concerned as they fear futuristic technologies of a world where robots will replace humans, take away their jobs and make decisions for them. In reality, AI is already widely used in many fields, for example, in cell phones, smart objects (IoT), industries 4.0, for smart cities, cybersecurity systems, autonomous driving systems (drive or parking assistant), chat bots on various websites; these are just a few examples all based on typical artificial intelligence algorithms. Thanks to AI, companies can have a variety of advantages in providing advanced, personalized services, predicting trends, anticipating user choices, etc.
But not all that glitters is gold: there are sometimes technical problems, ethical questions, security risks, and standards and legislation that are not entirely clear.
Organizations already adopting AI-based solutions, or those planning to do so, could benefit from this publication to learn more about the opportunities, risks, and related countermeasures. Clusit's Community for Security hopes that this publication will provide readers with a useful overview of a reality, such as artificial intelligence, that will increasingly accompany us in our personal, social and working lives
Genetics and breeding of [i]durum wheat[/i]
UMR AGAP - Ć©quipe GEĀ²pop - GĆ©nomique Ć©volutive et gestion des populationsabsen
A panel of elite accessions of durum wheat (Triticum durum Desf.) suitable for association mapping studies
Abstract The effectiveness of association mapping (AM) based on linkage disequilibrium (LD) is currently being tested in a number of crops. An important prerequisite for the application of AM is the availability of collections of accessions with a suitable level of genetic variation for target traits and with limited spurious LD due to the presence of population structure. Herein, the results of a genomewide molecular characterization of a collection of elite durum wheat accessions well-adapted to Mediterranean environments are presented. Ninety-seven highly polymorphic simple sequence repeats and 166 amplified fragment length polymorphism markers were used to characterize 189 durum accessions, mainly cultivars and advanced breeding lines. Genome-wide significant and sizeable LD indices at a centimorgan scale were observed, while LD mainly decayed within 10 cM. On the other hand, effects due to spurious LD were notably lower than those previously observed in a durum wheat collection sampling durum gene pools of more diverse origin. These results, coupled with the high level of genetic variability detected for a number of important morpho-physiological traits and their high heritability, indicate the suitability of this collection for AM studies targeting agronomically important traits
Italian Re Evolution : Design in Italian Society in the Eighties
A thorough presentation of the evolution of Italian design since World War II and an analysis of consumer habits in Italian society. Includes an essay by Umberto Eco on the history and specificity of Italian design
Interrogating open issues in cancer precision medicine with patient-derived xenografts
Patient-derived xenografts (PDXs) have emerged as an important platform to elucidate new treatments and biomarkers in oncology. PDX models are used to address clinically relevant questions, including the contribution of tumour heterogeneity to therapeutic responsiveness, the patterns of cancer evolutionary dynamics during tumour progression and under drug pressure, and the mechanisms of resistance to treatment. The ability of PDX models to predict clinical outcomes is being improved through mouse humanization strategies and the implementation of co-clinical trials, within which patients and PDXs reciprocally inform therapeutic decisions. This Opinion article discusses aspects of PDX modelling that are relevant to these questions and highlights the merits of shared PDX resources to advance cancer medicine from the perspective of EurOPDX, an international initiative devoted to PDX-based research
Tocilizumab for patients with COVID-19 pneumonia. The single-arm TOCIVID-19 prospective trial
BackgroundTocilizumab blocks pro-inflammatory activity of interleukin-6 (IL-6), involved in pathogenesis of pneumonia the most frequent cause of death in COVID-19 patients.MethodsA multicenter, single-arm, hypothesis-driven trial was planned, according to a phase 2 design, to study the effect of tocilizumab on lethality rates at 14 and 30 days (co-primary endpoints, a priori expected rates being 20 and 35%, respectively). A further prospective cohort of patients, consecutively enrolled after the first cohort was accomplished, was used as a secondary validation dataset. The two cohorts were evaluated jointly in an exploratory multivariable logistic regression model to assess prognostic variables on survival.ResultsIn the primary intention-to-treat (ITT) phase 2 population, 180/301 (59.8%) subjects received tocilizumab, and 67 deaths were observed overall. Lethality rates were equal to 18.4% (97.5% CI: 13.6-24.0, P=0.52) and 22.4% (97.5% CI: 17.2-28.3, P<0.001) at 14 and 30 days, respectively. Lethality rates were lower in the validation dataset, that included 920 patients. No signal of specific drug toxicity was reported. In the exploratory multivariable logistic regression analysis, older age and lower PaO2/FiO2 ratio negatively affected survival, while the concurrent use of steroids was associated with greater survival. A statistically significant interaction was found between tocilizumab and respiratory support, suggesting that tocilizumab might be more effective in patients not requiring mechanical respiratory support at baseline.ConclusionsTocilizumab reduced lethality rate at 30 days compared with null hypothesis, without significant toxicity. Possibly, this effect could be limited to patients not requiring mechanical respiratory support at baseline.Registration EudraCT (2020-001110-38); clinicaltrials.gov (NCT04317092)