How is the Role of AI in Talent Acquisition Evolving?

[Excerpt] Artificial Intelligence (AI) is a rapidly emerging technology that is at the forefront of industry innovation and efficiency. Despite the rapid technological advances and energy surrounding AI’s potential, there are pitfalls in terms of AI’s application in the Talent Acquisition (TA) process and many companies have yet to identify strategic points of AI implementation. Through our research, we have identified a variety of AI products available at specific points in the TA lifecycle. We weigh the most apparent drawbacks of using AI in TA as it currently exists, and share research and benchmarks of companies implementing AI in TA. We have identified key areas of AI use in the TA lifecycle, and evidence as to the utility and validity of the technology use along with predictions for future engagement with AI and preparing your workforce

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

ALLAMANDA CATHARTICA LINN.: EXTRACTION AND PHARMACEUTICAL EVALUATION OF VARIOUS EXTRACTS OF LEAVES AND FLOWERS

Objective: Extraction and evaluation of the antifungal activity of extracts from leaves and flowers of Allamanda cathartica (Apocynaceae) Linn.Methods: Leaves and flowers of A. cathartica were collected, dried and extracted by using well-established methods for alkaloids, flavonoids, steroids and crude extracts in polar and non-polar solvents. Extracts were screened for antifungal activity using ‘Disc Diffusion Assay' against Candida albicans (Yeast), Aspergillus flavus and Tricophyton mentagrophyte (fungi). Inhibition zone (IZ), Activity index (AI), Minimum inhibitory concentration (MIC), Minimum fungicidal concentration (MFC) and Total activity (TA) were studied. Mean and standard deviation has also been calculated.Results: C. albicans found to be the most susceptible organism while T. mentagrophyte found to be resistant. Alkaloid of leaf (IZ= 22 mm, AI= 0.65±0.02, MIC= 0.156 mg/ml, MFC= 0.078 mg/ml, TA= 278.85 ml/g) showed the best activity against C. albicans. Pet ether extract (IZ= 12 mm, AI= 0.40±0.02, MIC= 0.312 mg/ml, MFC= 0.156 mg/ml, TA= 57.24 ml/g) and water extract (IZ= 13 mm, AI= 0.43±0.02, MIC= 0.312 mg/ml, MFC= 0.156 mg/ml, TA= 49.68 ml/g) of flower also showed very good activities against C. albicans. The range of MIC and MFC found to be 1.25-0.156 mg/ml and 0.625-0.078 mg/ml, respectively.Conclusion: Results indicate the good antifungal potency of extracts against tested microorganisms. Hence, may be explored for the formation of new antifungal drugs

Habitual differences of Phyllonorycter salictella (Zeller, 1846 ) and P. heringiella ( Grönlien, 1932) (Lepidoptera: Gracillariidae) in two Finnish materials; a problem pair

From Phyllonorycter salictella (Zeller,1846) a sister species P. heringiella (Grönlien, 1932) is here extracted on the basis of the number and colour of costal strigulae at the tip of the wing and the brightness of the golden coloration of the forewing. P. salictella is reported from the Finnish biogeographical provinces AI, Ab, N, Ka, Ta, Kl, Oa, Tb, Om, Oba, and Obb and P. heringiella from Al, Ab, N, Ka, Ta, Sa, and Oba. It is assumed that the golden hue, which sometimes covers the normally white strigulae of the forewing, occurs in freshly emerged moths and disappears after first flying efforts

Investigating German Higher Education Institutions\u27 Transfer Activities: New Measurements Based on Web Mining

In recent years, higher education institutions (HEI) have expanded their involvement in diverse transfer activities (TA), extending beyond traditional teaching and research roles. These TA are often heterogeneous and informal, which makes measuring their full scope and effects challenging. In this article, we propose a new and straightforward to implement approach for mastering this task. In a first step, we theoretically derive three different dimensions of transfer, namely the transfer of knowledge, technology and personnel. For each of these categories, we develop an artificial intelligence (AI) optimized keyword list. Finally, we use these lists and apply web mining techniques and natural language processing (NLP) to measure TA from German HEI. To this end, we analyze a total of 299,229 texts from 376 German HEI websites. Our study shows that our proposed approach represents an effective and valuable tool for measuring TA from HEI and provides a foundation for further research

KI Systeme: Aktuelle Trends und Entwicklungen aus Perspektive der Technikfolgenabschätzung

Künstliche Intelligenz (KI) ist - ebenso wie damit verknüpfte Techniken wie maschinelles Lernen und Big Data - in aller Munde. Die große Dynamik und Tragweite dieser Entwicklungen zeigen sich bereits in zahlreichen Anwendungsgebieten von Wirtschaft, Gesellschaft und Wissenschaft. Technikfolgenabschätzung (TA) von KI hat in diesem Zusammenhang zunächst die Aufgabe, etwaige überzogene öffentliche Erwartungen und Befürchtungen in sachliche, realistische Perspektiven zu transformieren. In einem zweiten Schritt kann TA entlang begründbarer Entwicklungsziele von KI und legitimer gesellschaftlicher Wertvorstellungen Impulse für die weitere, wünschbare Gestaltung von KI geben. Wenn TA diese Orientierungsaufgabe nah am technologischen Kern wahrnimmt, findet sie dabei große gestalterische Freiräume in frühen Phasen der Technikentwicklung vor. Die damit zusammenhängenden Gedanken werden im vorliegenden Einleitungskapitel konkretisiert und auf die Beiträge zu diesem Themenschwerpunkt angewendet.Artificial intelligence (AI) is on everyone's lips - as well as the associated technologies of machine learning and big data. The enormous dynamics and consequences of these developments become already evident in numerous areas of application in business, society and science. In this context, technology assessment (TA) of AI initially has the task of transforming any excessive public expectations and fears to the factual level. In a second step, TA can provide impulses for the further, desirable design of AI based on reasonable development goals of AI and legitimate societal values. If TA conducts this orientation task close to the technological core, it can consider wide scopes of options for action in the early phases of technology development. Related thoughts are put into concrete terms in this article and will be related to the authors’ contributions to this topical focus

Beam Foil Interaction Studies for the Future Stripper of GANIL

International audienceThe GANIL 3 - cyclotron accelerator complex is tobe upgraded in 1989 by increasing the beam energy atthe stripper. In view of this modification, the effectof carbon stripper foils of various thicknesses (from50 to 250 $\mu$g/cm2) on the energy spread of AI, Kr, Xeand Ta beams was measured using the aralysing sectionof the 270° spectrometer. Energies ranging from 3.2 to6.5 MeV/A (i.e. corresponding to the future energyrange) were used, and intensities of several hundredsof electrical nanoamperes were concentrated over a fewmm2 spot

Żabińskiite, ideally Ca(Al_(0.5)Ta_(0.5))(SiO_4)O, a new mineral of the titanite group from the Piława Górna pegmatite, the Góry Sowie Block, southwestern Poland

Żabińskiite, ideally Ca(Al_(0.5)Ta_(0.5))(SiO_4)O, was found in a Variscan granitic pegmatite at Piława Górna, Lower Silesia, SW Poland. The mineral occurs along with (Al,Ta,Nb)- and (Al,F)-bearing titanites, a pyrochlore-supergroup mineral and a K-mica in compositionally inhomogeneous aggregates, ∼120 μm × 70 μm in size, in a fractured crystal of zircon intergrown with polycrase-(Y) and euxenite-(Y). Żabińskiite is transparent, brittle, brownish, with a white streak, vitreous lustre and a Mohs hardness of ∼5. The calculated density for the refined crystal is equal to 3.897 g cm^(–3), but depends strongly on composition. The mineral is non-pleochroic, biaxial (–), with mean refractive indices ≥1.89. The (Al,Ta,Nb)-richest żabińskiite crystal, (Ca_(0.980)Na_(0.015))Σ=0.995(Al_(0.340) Fe^(3+)_(0.029) Ti_(0.298)V_(0.001)Zr_(0.001)Sn_(0.005)Ta_(0.251)Nb_(0.081))Σ=1.005[(Si_(0.988)Al_0.012)O_(4.946)F_(0.047)(OH)_(0.007))Σ=5.000]; 60.7 mol.% Ca[Al_(0.5)(Ta,Nb)_(0.5)](SiO_4)O; is close in composition to previously described synthetic material. Żabińskiite is triclinic (space group symmetry Ai and has unit-cell parameters a = 7.031(2) Å, b = 8.692(2) Å, c = 6.561(2) Å, α = 89.712(11)°, β = 113.830(13)°, γ = 90.352(12)° and V = 366.77 (11) Å3. It is isostructural with triclinic titanite and bond-topologically identical with titanite and other minerals of the titanite group. Żabińskiite crystallized along with (Al,Ta,Nb)-bearing titanites at increasing Ti and Nb, and decreasing Ta activities, almost coevally with polycrase-(Y) and euxenite-(Y) from Ca-contaminated fluxed melts or early hydrothermal fluids

Dijagnosticiranje mogućnosti i potencijala umjetne inteligencije u području nacionalne sigurnosti i obrane

Artificial Intelligence (AI) is recognised as a disruptive technology that rapidly changes a wide range of businesses and industries. It also can transform functions (processes and systems) related to national security and defence. Although it is not primarily being developed in the defence sector, AI is poised to change the character of future conflict by improving the effectiveness, efficiency and speed of defence-related activities. However, while AI has enormous potential to replace or assist human factors in decision-making, it still cannot replace humans in strategic thinking. The paper first reviews the meaning, current developments, and challenges related to AI technology. It then scrutinises the impact of AI on national security and defence in three areas: defence capabilities, decision making and strategic thinking. The article concludes with a prospect on AI in general and in the security and defence domain, expecting its most significant value in support of decision-making.Umjetna inteligencija (UI) prepoznata je kao remetilačka tehnologija koja brzo mijenja širok raspon poslovanja i industrija. Isto tako, ta tehnologija ima potencijal transformirati funkcije (procese i sustave) vezane uz nacionalnu sigurnost i obranu. Iako se primarno ne razvija u sektoru obrane, umjetna inteligencija je spremna promijeniti karakter budućih sukoba povećavajući učinkovitost, djelotvornost i brzinu aktivnosti koje se provode u području nacionalne obrane. Međutim, iako umjetna inteligencija ima veliki potencijal zamjene ili pomoći ljudskim čimbenicima u donošenju odluka, još uvijek ne može zamijeniti ljude u strateškom razmišljanju. Rad prvo daje pregled značenja, trenutačnog razvoja i izazova povezanih s UI tehnologijom, nakon čega se analizira utjecaj umjetne inteligencije na nacionalnu sigurnost i obranu u tri područja: obrambene sposobnosti, donošenje odluka i strateško promišljanje. U zaključku se daje perspektiva daljnjeg razvoja i uporabe UI, općenito i u domeni sigurnosti i obrane, očekujući njezinu najznačajniju vrijednost u podršci donošenju odluka