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Los habitantes del planeta están cada vez más conectados. Se estima que actualmente el número de usuarios de teléfonos inteligentes a escala world supera la marca de los 3,000 millones, y que esta cantidad siga creciendo paulatinamente en los próximos años.  

Tal cantidad de usuarios demanda cada vez más de conexiones continuas y confiables proporcionadas por las redes de las empresas de telecomunicaciones, las cuales tienen que ser estables, confiables e integrar la tecnología de punta para funcionar las 24 horas del día.  

Actualmente, el mercado de las telecomunicaciones es uno de los más avanzados tecnológicamente y altamente competitivo, pero es también el objetivo de un gran número de intentos de fraude que pudieran afectar su rentabilidad y reputación.  

La encuesta de Pérdida por Fraude 2019 de la Asociación de Management de Fraude de Comunicaciones revela que el mercado de telecom registró en ese año ingresos por $1,625 millones de dólares, con una pérdida world por fraude del 1.74%, lo que equivale a $28,300 millones de dólares anuales.   

Es, sin duda, una cifra importante, por lo que las telcos necesitan combatir el fraude de una manera ágil, efectiva y de forma anticipada a las jugadas de los defraudadores, para reducir las pérdidas por fraude y brindar una mejor experiencia de servicio a sus clientes, con la ayuda de tecnología de punta.  

Específicamente, necesitan enfocar sus iniciativas de prevención en la reducción de pérdidas por fraude en las suscripciones, mediante la detección y la prevención, el uso de reglas de negocio, modelos analíticos y el análisis de redes de vínculos.  

Si bien es basic reducir el porcentaje de fraude, en specific en las tasas de aprobación de nuevas suscripciones, es crítico abrir canales de autenticación al cliente, sin dejar huecos por los que puedan colarse los estafadores.  

Y es que estos actores están poniendo en la mira aquellos servicios que integran equipos de gama alta para obtener mayor beneficio de sus acciones.  

Principales desafíos de los operadores de telecomunicaciones

En este contexto, ¿cuáles son los principales desafíos de los operadores de telecomunicaciones en todo el mundo? Si bien son numerosos y variados, estos son los seis principales:

  1. Reducir las pérdidas de fraude en las suscripciones
  2. Agilizar y visualizar en tiempo actual el proceso de identificación y validación de los nuevos suscriptores
  3. Minimizar las adquisiciones en cuentas que puedan generar grandes pérdidas
  4. Reducir el robo de dispositivos de gama alta
  5. Optimizar y agilizar la experiencia del cliente
  6. Mejorar su reputación y elevar la lealtad del cliente   

Mediante la analítica avanzada, y adoptando un enfoque híbrido, las telcos pueden anticiparse a los fraudes durante los procesos de suscripción de usuarios potencialmente maliciosos. 

Mediante la #analíticaavanzada, y adoptando un enfoque híbrido, los telcos pueden anticiparse a los #fraudes durante los procesos de suscripción de usuarios potencialmente maliciosos. Click To Tweet

En este sentido, un enfoque híbrido contempla reglas de negocio, técnicas analíticas, así como inteligencia synthetic (IA) y machine studying (ML) que ayudan a elevar la detección de fraudes hasta un 20%, y al mismo tiempo reducir los falsos positivos entre 40% y 70%.  

Por otro lado, al poder generar alertas oportunas en el momento de realizar una suscripción, se eleva la productividad del proceso de suscripciones entre un 15% y 20%, aproximadamente. Asimismo, al bloquear las solicitudes potencialmente fraudulentas antes de que se full una renovación o adquisición de un servicio ayuda a prevenir y reducir las pérdidas por fraude.  

 Así, lo anterior redunda en una mejor satisfacción del cliente cuando se protegen sus intereses, como datos, información financiera, tipo de planes, identificando y bloqueando a quienes intentan suplantar o robar la identidad de los usuarios. En paralelo, se cut back el riesgo por daño a la reputación de la empresa.  

Ataque frontal al fraude 

El enfoque analítico híbrido cuenta con las capacidades para combatir el fraude en áreas como las de suscripciones, distribuidores, tarjetas de crédito y chips SIM, entre otros. De igual forma, ayuda a las empresas de telecomunicaciones a aprovechar todos sus datos para identificar los perfiles que puedan representar algún riesgo, así como a detectar el fraude de manera más rápida y efectiva usando la IA y el ML para reconocer los vínculos, anomalías en el comportamiento y actividades de alto riesgo.  

Por otra parte, agiliza y refuerza el trabajo de investigación mediante alertas, búsquedas avanzadas, visualización de vínculos entre redes y mapas geoespaciales, el análisis de textos y la gestión de casos y flujos de trabajo. Las telcos pueden rastrear y monitorear las actividades fraudulentas dentro de su organización y generar reportes detallados sobre los hallazgos. Aquí, la retroalimentación analítica aprovecha las investigaciones para establecer reglas y modelos para contrarrestar los cambios en las tendencias observadas.  

Un enfoque híbrido seguirá teniendo gran relevancia, en especial cuando organizaciones de telecomunicaciones y usuarios coinciden en que el fraude en suscripciones es el principal método utilizado a escala world, y se observa un crecimiento progresivo en el número de teléfonos inteligentes y dispositivos conectados en todo el mundo. 

 



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Physical training is the next hurdle for artificial intelligence, researcher says

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Let one million monkeys clack on one million typewriters for one million years and, the adage goes, they’re going to reproduce the works of Shakespeare. Give infinite monkeys infinite time, they usually nonetheless won’t respect the bard’s poetic turn-of-phrase, even when they will kind out the phrases. The identical holds true for synthetic intelligence (AI), in response to Michael Woolridge, professor of pc science on the College of Oxford. The problem, he stated, just isn’t the processing energy, however quite a scarcity of expertise.

His perspective was revealed on July 25 in Clever Computing.

“Over the previous 15 years, the velocity of progress in AI usually, and (ML) specifically, has repeatedly taken seasoned AI commentators like myself unexpectedly: we’ve needed to frequently recalibrate our expectations as to what’s going to be potential and when,” Wooldridge stated.

“For all that their achievements are to be lauded, I believe there may be one essential respect during which most massive ML fashions are vastly restricted: the world and the truth that the fashions merely haven’t any expertise of it.”

Most ML fashions are inbuilt digital worlds, equivalent to video video games. They will practice on large datasets, however for bodily purposes, they’re lacking important info. Wooldridge pointed to the AI underpinning autonomous automobiles for instance.

“Letting unfastened on the roads to be taught for themselves is a nonstarter, so for this and different causes, researchers select to construct their fashions in digital worlds,” Wooldridge stated. “And on this manner, we’re getting excited a couple of technology of AI techniques that merely haven’t any capacity to function within the single most necessary surroundings of all: our world.”

Language AI fashions, however, are developed and not using a pretense of a world in any respect—however nonetheless undergo from the identical limitations. They’ve developed, so to talk, from laughably horrible predictive texts to Google’s LaMDA, which made headlines earlier this 12 months when a now-former Google engineer claimed the AI was sentient.

“Regardless of the validity of [the engineer’s] conclusions, it was clear that he was deeply impressed by LaMDA’s capacity to converse—and with good cause,” Wooldridge stated, noting that he doesn’t personally imagine LaMDA is sentient, neither is AI close to such a milestone.

“These foundational fashions reveal unprecedented capabilities in pure language technology, producing prolonged items of natural-sounding textual content. Additionally they appear to have acquired some competence in common sense reasoning, one of many holy grails of AI analysis over the previous 60 years.”

Such fashions are , feeding on huge datasets and coaching to know them. For instance, GPT-3, a predecessor to LaMDA, skilled on all the English textual content obtainable on the web. The quantity of coaching knowledge mixed with important computing energy makes the fashions akin to , the place they transfer previous slender duties to start recognizing patterns and make connections seemingly unrelated to the first activity.

“The guess with basis fashions is that their in depth and broad coaching results in helpful competencies throughout a variety of areas, which may then be specialised for particular purposes,” Wooldridge stated. “Whereas symbolic AI was predicated on the belief that intelligence is primarily an issue of data, basis fashions are predicated on the belief that intelligence is primarily an issue of knowledge. To simplify, however not by a lot, throw sufficient coaching knowledge at massive fashions, and hopefully, competence will come up.”

This “may is true” strategy scales the fashions bigger to supply smarter AI, Wooldridge argued, however this ignores the bodily know-how wanted to really advance AI.

“To be honest, there are some indicators that that is altering,” Wooldridge stated, pointing to the Gato system. Introduced in Could by DeepMind, the inspiration mannequin, skilled on massive language units and on robotic knowledge, might function in a easy however bodily surroundings.

“It’s great to see the primary child steps taken into the bodily world by basis fashions. However they’re simply child steps: the challenges to beat in making AI work in our world are no less than as massive—and doubtless bigger—than these confronted by making AI work in simulated environments.”


A Google software engineer believes an AI has become sentient. If he’s right, how would we know?


Extra info:
Michael Wooldridge, What Is Lacking from Modern AI? The World, Clever Computing (2022). DOI: 10.34133/2022/9847630

Offered by
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Bodily coaching is the subsequent hurdle for synthetic intelligence, researcher says (2022, September 27)
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How to stop cities from being turned into AI jungles

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How to stop cities from being turned into AI jungles

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Within the Metropolis of London, safety cameras may even be present in cemeteries. In 2021, the mayor’s workplace launched an effort to determine pointers for analysis round rising expertise. Credit score: Acabashi/Wikimedia, CC BY

As synthetic intelligence grows extra ubiquitous, its potential and the challenges it presents are coming more and more into focus. How we steadiness the dangers and alternatives is shaping up as one of many defining questions of our period. In a lot the identical method that cities have emerged as hubs of innovation in tradition, politics, and commerce, so they’re defining the frontiers of AI governance.

Some examples of how cities have been taking the lead embody the Cities Coalition for Digital Rights, the Montreal Declaration for Responsible AI, and the Open Dialogue on AI Ethics. Others may be present in San Francisco’s ban of facial-recognition technology, and New York Metropolis’s push for regulating the sale of automated hiring systems and creation of an algorithms management and policy officer. City institutes, universities and different academic facilities have additionally been forging forward with a variety of AI ethics initiatives.

These efforts level to an rising paradigm that has been known as AI Localism. It is part of a bigger phenomenon typically referred to as New Localism, which includes cities taking the lead in regulation and policymaking to develop context-specific approaches to a wide range of issues and challenges. Now we have additionally seen an elevated uptake of city-centric approaches within international law frameworks.

In so doing, municipal authorities are filling gaps left by inadequate state, nationwide or international governance frameworks associated to AI and different complicated points. Current years, for instance, have seen the emergence of “broadband localism,” by which native governments deal with the digital divide; and “privacy localism,” each in response to challenges posed by the elevated use of knowledge for regulation enforcement or recruitment.

AI localism encompasses all kinds of points, stakeholders, and contexts. Along with bans on AI-powered facial recognition, native governments and establishments are taking a look at procurement guidelines pertaining to AI use by public entities, public registries of ‘ AI techniques, and public teaching programs on AI. However whilst initiatives and case research multiply, we nonetheless lack a scientific technique to evaluate their effectiveness—and even the very want for them. This limits policymakers’ skill to develop acceptable regulation and extra typically stunts the expansion of the sector.

Constructing an AI Localism framework

Under are ten ideas to assist systematize our strategy to AI Localism. Thought of collectively, they add as much as an incipient framework for implementing and assessing initiatives around the globe:

  • Ideas present a North Star for governance: Establishing and articulating a transparent set of guiding ideas is an important place to begin. For instance, the Emerging Technology Charter for London, launched by the mayoral workplace in 2021 to stipulate “sensible and moral pointers” for analysis round rising expertise and smart-city expertise pilots, is one instance. Related initiatives exist in Nantes, France, which rolled out a data charter to underscore the native authorities’s dedication to information sovereignty, safety, transparency, and innovation. Such efforts assist events chart a course that successfully balances the potential and challenges posed by AI whereas affirming a dedication to openness and transparency on for the general public.
  • Public engagement supplies a social license: Establishing belief is crucial to fostering accountable use of expertise in addition to broader acceptance and uptake by the general public. Types of —crowdsourcing, consciousness campaigns, mini-assemblies, and extra—will help to construct belief, and needs to be a part of a deliberative course of undertaken by policymakers. For instance, the California Division of Truthful Employment and Housing held their first virtual public hearing with residents and employee advocacy teams on the rising use of AI in hiring and human assets, and the potential for technological bias in procurement.
  • AI literacy permits significant engagement: The aim of AI literacy is to encourage familiarity with the expertise itself in addition to with related moral, political, financial and cultural points. For instance, the Montreal AI Ethics Institute, a non-profit centered on advancing AI literacy, supplies free, well timed, and digestible details about AI and AI-related happenings from the world over.
  • Faucet into native experience: Policymakers ought to faucet into cities’ AI experience by establishing or supporting analysis facilities. Two examples are the Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE), a pan-European challenge that takes a European focus to AI makes use of in cities and “How Busy Is Toon,” a web site developed by Newcastle Metropolis Council and Newcastle College to supply real-time transit details about the town heart.
  • Innovate in how transparency is supplied: To construct belief and foster engagement, AI Localism ought to embody time-tested transparency ideas and practices. For instance, Amsterdam and Helsinki disclose AI use and clarify how algorithms are employed for particular functions. As well as, AI Localism can innovate in how transparency is supplied, instilling consciousness and techniques to determine and overcome “AI blind spots” and different types of unconscious bias.
  • Set up means for accountability and oversight: One of many sign options of AI Localism is a recognition of the necessity for accountability and oversight to make sure that ideas of responsive governance are being adhered to. Examples embody New York Metropolis’s Algorithms Management and Policy Officer, Singapore’s Advisory Council on the Ethical Use of AI and Data, and Seattle’s Surveillance Advisory Working Group.
  • Sign boundaries by binding legal guidelines and insurance policies: Ideas are solely pretty much as good as they’re carried out or enforced. Ratifying laws, akin to New York Metropolis’s Biometrics Privacy Law, which requires clear notices that biometric information is being collected by companies, limits how companies can use such information. It additionally prohibits promoting and making the most of the information sends a transparent message to customers that their information rights and protections are upheld and holds firms accountable to respecting privateness privileges.
  • Use procurement to form accountable AI markets: As municipal and different governments have finished in different areas of public life, cities ought to use procurement insurance policies to encourage accountable AI initiatives. As an example, the Berkeley, California Council handed an ordinance requiring that public departments justify using new surveillance applied sciences and that the advantages of those instruments outweigh the harms previous to procurement.
  • Set up information collaboratives to sort out asymmetries: Information collaboratives are an rising type of intersectoral partnership, by which personal information is reused and deployed towards the general public good. Along with yielding new insights and improvements, such partnerships will also be highly effective instruments for breaking down the information asymmetries that each underlie and drive so many wider socio-economic inequalities. Encouraging information collaboratives, by figuring out potential partnerships and matching provide and demand, is thus an vital part of AI Localism. Preliminary efforts embody the Amsterdam Data Exchange, which permits for information to be securely shared to deal with native points.
  • Make good governance strategic: Too many AI methods do not embody governance and too many governance approaches will not be strategic. It’s thus crucial that cities have a transparent imaginative and prescient on how they see information and AI getting used to enhance native well-being. Charting an AI strategy, as was undertaken by the Barcelona Metropolis Council in 2021, can create avenues to embed good AI use throughout companies and open up AI consciousness to residents to make accountable information use and issues a typical thread moderately than a siloed train inside native authorities.

AI Localism is an emergent space, and each its apply and analysis stay in flux. The expertise itself continues to alter quickly, providing one thing of a transferring goal for governance and regulation. Its state of flux highlights the necessity for the kind of framework outlined above. Moderately than enjoying catch-up, responding reactively to successive waves of technological innovation, policymakers can reply extra constantly, and responsibly, from a principled bedrock that takes under consideration, the customarily competing wants of assorted stakeholders.


To build sustainable cities, involve those who live in them


Supplied by
The Conversation


This text is republished from The Conversation below a Inventive Commons license. Learn the original article.The Conversation

Quotation:
The way to cease cities from being became AI jungles (2022, September 27)
retrieved 27 September 2022
from https://techxplore.com/information/2022-09-cities-ai-jungles.html

This doc is topic to copyright. Aside from any honest dealing for the aim of personal examine or analysis, no
half could also be reproduced with out the written permission. The content material is supplied for data functions solely.



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Python Program to Find the Factorial of a Number

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Factorial Python

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  1. What is Factorial
  2. Factorial Formula
  3. 10 factorial
  4. factorial of 5
  5. factorial of 0
  6. Factorial program in Python
    1. Factorial program in Python using function
    2. Factorial program in Python using for loop
    3. Factorial program in Python using recursion
  7. Count Trailing Zeroes in Factorial
  8. Frequently asked questions

Downside Assertion: We intend to make use of Python to cowl the fundamentals of factorial and computing factorial of a quantity.

What’s Factorial?

In easy phrases, if you wish to discover the factorial of a optimistic integer, preserve multiplying it with all of the optimistic integers lower than that quantity. The ultimate consequence that you just get is the factorial of that quantity. So if you wish to discover the factorial of seven, multiply 7 with all optimistic integers lower than 7, and people numbers could be 6,5,4,3,2,1. Multiply all these numbers by 7, and the ultimate result’s the factorial of seven.

In case you are seeking to construct your experience in Python factorial program, contemplate getting licensed. This free course on Factorial Program in Python provides you full steerage on the topic and in addition a certificates on completion which is certain to make your CV stand out.

Components of Factorial

Factorial of a quantity is denoted by n! is the product of all optimistic integers lower than or equal to n:
n! = n*(n-1)*(n-2)*…..3*2*1

10 Factorial

So what’s 10!? Multiply 10 with all of the optimistic integers that are lower than 10.
10! =10*9*8*7*6*5*4*3*2*1=3628800

Factorial of 5

To seek out ‘5!’ once more, do the identical course of. Multiply 5 with all of the optimistic integers lower than 5. These numbers could be 4,3,2,1
5!=5*4*3*2*1=120

Factorial of 0

Since 0 isn’t a optimistic integer, as per conference, the factorial of 0 is outlined to be itself.
0!=1

Factorial program in python
Factorial of a quantity

Computing that is an attention-grabbing drawback. Allow us to take into consideration why easy multiplication could be problematic for a pc. The reply to this lies in how the answer is applied.

1! = 1
2! = 2
5! = 120
10! = 3628800
20! = 2432902008176640000
30! = 9.332621544394418e+157

The exponential rise within the values exhibits us that factorial is an exponential perform, and the time taken to compute it could take exponential time.

Factorial Program in Python

We’re going to undergo 3 methods by which we are able to calculate factorial:

  • Utilizing a perform from the maths module
  • Iterative strategy(Utilizing for loop)
  • Recursive strategy

Factorial program in Python utilizing the perform

That is probably the most simple technique which can be utilized to calculate the factorial of a quantity. Right here we’ve a module named math which incorporates a number of mathematical operations that may be simply carried out utilizing the module.

import math
num=int(enter("Enter the quantity: "))
print("factorial of ",num," (perform): ",finish="")
print(math.factorial(num))

TEST THE CODE

Enter – Enter the quantity: 4
Output – Factorial of 4 (perform):24

Factorial program in python utilizing for loop

def iter_factorial(n):
    factorial=1
    n = enter("Enter a quantity: ")
    factorial = 1
    if int(n) >= 1:
        for i in vary (1,int(n)+1):
            factorial = factorial * i
        return factorial
  
num=int(enter("Enter the quantity: "))

print("factorial of ",num," (iterative): ",finish="")
print(iter_factorial(num))

TEST THE CODE

Enter – Enter the quantity: 5
Output – Factorial of 5 (iterative) : 120

Take into account the iterative program. It takes a whole lot of time for the whereas loop to execute. The above program takes a whole lot of time, let’s say infinite. The very function of calculating factorial is to get the end in time; therefore, this strategy doesn’t work for big numbers.

Factorial program in Python utilizing recursion

def recur_factorial(n):
    """Perform to return the factorial
    of a quantity utilizing recursion"""
    if n == 1:
        return n
    else:
        return n*recur_factorial(n-1)

num=int(enter("Enter the quantity: "))

print("factorial of ",num," (recursive): ",finish="")
print(recur_factorial(num))

TEST THE CODE

Enter – Enter – Enter the quantity : 4
Output – Factorial of 5 (recursive) : 24

On a 16GB RAM pc, the above program may compute factorial values as much as 2956. Past that, it exceeds the reminiscence and thus fails. The time taken is much less when in comparison with the iterative strategy. However this comes at the price of the house occupied.

What’s the answer to the above drawback?
The issue of computing factorial has a extremely repetitive construction.

To compute factorial (4), we compute f(3) as soon as, f(2) twice, and f(1) thrice; because the quantity will increase, the repetitions improve. Therefore, the answer could be to compute the worth as soon as and retailer it in an array from the place it may be accessed the subsequent time it’s required. Due to this fact, we use dynamic programming in such circumstances. The circumstances for implementing dynamic programming are

  1. Overlapping sub-problems
  2. optimum substructure 

Take into account the modification to the above code as follows:

def DPfact(N):
    arr={}
    if N in arr:
        return arr[N]
    elif N == 0 or N == 1:
        return 1
        arr[N] = 1
    else:
        factorial = N*DPfact(N - 1)
        arr[N] = factorial
    return factorial
    
num=int(enter("Enter the quantity: "))

print("factorial of ",num," (dynamic): ",finish="")
print(DPfact(num))

TEST THE CODE

Enter – Enter the quantity: 6
Output – factorial of 6 (dynamic) : 720

A dynamic programming answer is extremely environment friendly by way of time and house complexities.

Rely Trailing Zeroes in Factorial utilizing Python

Downside Assertion: Rely the variety of zeroes within the factorial of a quantity utilizing Python

num=int(enter("Enter the quantity: "))
  
# Initialize consequence 
rely = 0
# Hold dividing n by 
# powers of 5 and 
# replace Rely 
temp = 5
whereas (num / temp>= 1):
    rely += int(num / temp) 
    temp *= 5

# Driver program  
print("Variety of trailing zeros", rely)

Output
Enter the Quantity: 5
Variety of trailing zeros 1

Discover ways to discover if a string is a Palindrome.

Discover ways to print the Fibonacci Series in Python. Additionally, learn artificial intelligence online with the assistance of this AI Course.

Incessantly requested questions

What’s factorial in math?

Factorial of a quantity, in arithmetic, is the product of all optimistic integers lower than or equal to a given optimistic quantity and denoted by that quantity and an exclamation level. Thus, factorial seven is written 4! that means 1 × 2 × 3 × 4, equal to 24. Factorial zero is outlined as equal to 1. The factorial of Actual and Unfavorable numbers don’t exist.

What’s the method of factorial?

To calculate the factorial of a quantity N, use this method:

Factorial=1 x 2 x 3 x…x N-1 x N

Is there a factorial perform in Python?

Sure, we are able to import a module in Python often called math which incorporates virtually all mathematical features. To calculate factorial with a perform, right here is the code:

import math
num=int(enter("Enter the quantity: "))
print("factorial of ",num," (perform): ",finish="")
print(math.factorial(num))

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