martes, 7 de noviembre de 2023

¿What is Artificial Intelligence?

 

What is artificial intelligence?

While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004 paper (link resides outside ibm.com), " It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable." (McCarthy, 2004)

However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, "Computing Machinery and Intelligence"(link resides outside ibm.com), which was published in 1950. In this paper, Turing, often referred to as the "father of computer science", asks the following question, "Can machines think?"  From there, he offers a test, now famously known as the "Turing Test", where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its publish, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics. (Turing, 1950)



How does artificial intelligence (AI) work?

“Using math and logic, a computer system simulates the reasoning that humans use to learn from new information and make decisions. An artificially intelligent computer system makes predictions or takes actions based on patterns in existing data and can then learn from its errors to increase its accuracy.” (Microsoft, 2006)

 

Characteristics

1.     Ability to learn and improve: AI systems can learn from data and improve their performance over time.

2.     Automation: AI can automate repetitive tasks and increase efficiency in various industries.

3.     Decision-making: AI can analyze large amounts of data and make decisions based on that data.

4.     Personalization: AI can be used to personalize experiences for users, such as in the case of recommendation systems.

5.     Prediction: AI can predict outcomes based on historical data and patterns.

Purpose

The applications of artificial intelligence (AI) in different sectors have become agendas for discussions in the highest circle of experts. The applications of AI can help society and can harm society even by jeopardizing human rights. The purpose of this study is to examine the evolution of AI and its impacts on human rights from social and legal perspectives. (Chatterjee, 2021)

The Future

So, what is in store for the future? In the immediate future, AI language is looking like the next big thing. In fact, it’s already underway. I can’t remember the last time I called a company and directly spoke with a human. These days, machines are even calling me! One could imagine interacting with an expert system in a fluid conversation or having a conversation in two different languages being translated in real time. We can also expect to see driverless cars on the road in the next twenty years (and that is conservative). In the long term, the goal is general intelligence, that is a machine that surpasses human cognitive abilities in all tasks. This is along the lines of the sentient robot we are used to seeing in movies. To me, it seems inconceivable that this would be accomplished in the next 50 years. Even if the capability is there, the ethical questions would serve as a strong barrier against fruition. When that time comes (but better even before the time comes), we will need to have a serious conversation about machine policy and ethics (ironically both fundamentally human subjects), but for now, we’ll allow AI to steadily improve and run amok in society. (Anyoha, 2017)

 


Everything about the evolution of artificial intelligence

Artificial Intelligence has grown into a formidable tool in recent years allowing robots to think and act like humans. Furthermore, it has attracted the attention of IT firms all around the world and is seen as the next major technological revolution following the growth of mobile and cloud platforms. It’s even been dubbed the “4th industrial revolution” by some. Researchers have developed software that uses Darwinian evolution ideas, such as “survival of the fittest,” to construct AI algorithms that improve generation to generation with no need for human intervention. The computer was able to recreate decades of AI research in only a few days, and its creators believe that one day it will be able to find new AI techniques. In this article, we will learn about how AI is evolving day by day.  (Chowdhury, 2021)




CHAT BOT

“A chatbot is a computer program that simulates human conversation to solve customer queries. When a customer or a lead reaches out via any channel, the chatbot is there to welcome them and solve their problems.” (Watts, 2022)

How Chatbots Work


Chatbots are most used on business websites. When you have spent a couple of minutes on a website, you can see a chat or voice messaging prompt pop up on the screen. Those are chatbots. Chatbots had a humble start as computer programs that used keywords and pattern matching to respond to users’ questions based on a pre-written script. (Watts, 2022)


 

Benefits of Chatbots

We’ve already discussed that chatbots improve customer experience. But enhanced customer experience is not the only benefit of using chatbots. An organization has many advantages of using chatbots for business growth, process efficiency and cost reduction:

-        Cost-effectiveness: A chatbot is a one-time investment. Once it has been developed and implemented, you can scale down on hiring people for customer support. Instead, people can be engaged in solving complex problems and creating strategies for business growth. Chatbots also do not make mistakes for established processes, unlike human agents.

-        Save time: Chatbots can handle routine repetitive tasks and do them much faster than humans.

-        Available 24/7: Chatbots can be available to customers 24/7 without much effort. This can, in turn, lead to more satisfied customers who wouldn’t hesitate to recommend your product or services to their network.

-        Reduce customer wait time: Each chatbot can interact with multiple customers simultaneously, reducing the size of the wait queue. People can get real-time answers to their queries when interacting with chatbots.

-        Identify business leads: You already know that AI chatbots are experts at identifying patterns and keywords. As chatbots handle the initial support interaction with customers or prospects, they can be programmed to identify leads by listening to the words and phrases used by the customers. (Watts, 2022)



What Is Data Crunching?

“Data crunching refers to key initial steps required to prepare large volumes of raw data for analysis. It includes stripping out unwanted information and formatting, translating data into the required format and structuring it for analysis or processing by other applications.” (Morris, 2021)

Data Crunching Explained


Data crunching is needed to convert raw data into a form suitable for analysis. It commonly involves clearing out proprietary formatting and unwanted data, converting number and date formats and reformatting and structuring the information. It can also involve eliminating duplicated and erroneous data. Data crunching may be needed for a variety of different reasons. A company may need to convert information from external data feeds so it can apply its existing business intelligence tools to the data. Also, if the company’s departments use different applications, it may need to massage data into a common format so it can report on information from across the entire business. (Morris, 2021)


 


Data Crunching Benefits

Converting raw data into a usable form can be extremely time-consuming for data scientists, so it makes sense to automate data crunching as much as possible using programming languages or other tools. An efficient data crunching process:

-Saves time: This means companies can save time by focusing their analysis efforts on the most relevant data. Automating data crunching also accelerates the process of cleaning up raw data, so companies also have more up-to-date information available for analysis.

-Saves money: The time savings translate into lower analysis costs. Highly paid data scientists and business analysts can use their time more efficiently analyzing the most valuable information instead of hunting through vast amounts of raw data. (Morris, 2021)


What Is Machine Learning (ML)?

“The basic concept of machine learning in data science involves using statistical learning and optimization methods that let computers analyze datasets and identify patterns.” (Berkely, 2020)

Since a machine learning algorithm update autonomously, the analytical accuracy improves with each run as it teaches itself from the data it analyzes. This iterative nature of learning is both unique and valuable because it occurs without human intervention — empowering the algorithm to uncover hidden insights without being specifically programmed to do so. (Berkely, 2020)


The purpose of machine learning is to use machine learning algorithms to analyze data. By leveraging machine learning, a developer can improve the efficiency of a task involving large quantities of data without the need for manual human input. Around the world, strong machine learning algorithms can be used to improve the productivity of professionals working in data science, computer science, and many other fields. (Berkely, 2020)

 

Evolution of Machine Learning

According to Forbes, the origins of machine learning date back to 1950. Speculating on how one could tell if they had developed a truly integrated artificial intelligence (AI), Alan Turing created what is now referred to as the Turing test, which suggests that one way of testing whether or not the AI is capable of understanding language is to see if it’s able to fool a human into thinking they are speaking to another person. (Marr, A Short History of Machine Learning , 2016)




What Are Artificial Neural Networks

“An artificial neural network is an attempt to simulate the network of neurons that make up a human brain so that the computer will be able to learn things and make decisions in a humanlike manner.” (Marr, Forbes , 2018)

How do artificial neural networks work?

Artificial neural networks use different layers of mathematical processing to make sense of the information it’s fed. Typically, an artificial neural network has anywhere from dozens to millions of artificial neurons—called units—arranged in a series of layers. The input layer receives various forms of information from the outside world. This is the data that the network aims to process or learn about. From the input unit, the data goes through one or more hidden units. (Marr, Forbes , 2018)

What are artificial neural networks used for?

“As the networks process and learn from data they can classify a given data set into a predefined class, it can be trained to predict outputs that are expected from a given input and can identify a special feature of data” (Marr, Forbes , 2018)

 

What is the Turing Test?

“The Turing Test involves three players: a computer, a human respondent, and a human interrogator. All three are placed in separate rooms or in the same room but physically separated by terminals.” (Kekatos, 2023)

The interrogator asks both players a series of questions and, after a period, tries to determine which player is the human and which is the computer. If the interrogator fails to determine which player is which, the computer is declared the winner, and the machine is described as able to think. (Kekatos, 2023)


“The game involves a human guessing if a player is a computer or another human.” (Kekatos, 2023)


 


The challenges of artificial intelligence by using real life examples

As such, there exists much as-yet-untapped potential, with growing career prospects. Many top employers seek professionals with the skills, expertise, and knowledge to propel their organizational aims forward. Career pathways may include:

 

1.     Robotics and self-driving/autonomous cars (such as Waymo, Nissan, Renault)

2.     Healthcare (for instance, multiple applications in genetic sequencing research, treating tumors, and developing tools to speed up diagnoses including Alzheimer’s disease)

3.     Academia (leading universities in AI research include MIT, Stanford, Harvard, and Cambridge)

4.     Retail (Amazon shops and other innovative shopping options)

5.     Banking (Brooks, 2022)

                                                                  

 What are the risks of artificial intelligence?

 

“The use of artificial intelligence systems in decision-making processes carries certain risks due to the potential direct or indirect impacts of the implementation of these technologies. Some of these risks include:’’ (Pombo, 2022)

 

The leakage of personal data that can compromise people's well-being.

 

Extreme surveillance and subsequent manipulation by private or government organizations with access to the information that feeds artificial intelligence technologies.”

 

The "echo chambers" or "filter bubbles" that occur when you are exposed to the same ideas, news and/or facts, which is a common phenomenon among social media users and ends up strengthening preconceived biases. This is especially dangerous among decision-makers in any area, but even more so among those working on public policy. (Pombo, 2022)



“Disadvantages of Artificial Intelligence”

 

Limits of adaptation: Unlike what happens with human beings, machines that work with Artificial Intelligence lack the adaptive capacity of humans. For example, in crisis situations or changing environments, machines have very limited ability to adapt and solve problems. However, this is a problem that, probably in the future, can be solved thanks to Machine Learning. Although, for now, it is still a major disadvantage when it comes to talking about Artificial Intelligence. (https://www.beetrack.com/es/blog/ventajas-y-desventajas-de-la-inteligencia-artificial, 2023)

 

It encourages unemployment: Although the use of Artificial Intelligence can encourage humans to focus on more complex and responsible tasks, in many cases, its use can lead to unemployment. This is especially a problem in the case of lower-skilled jobs, where intelligent machines are far more efficient at carrying them out than any human worker. (https://www.beetrack.com/es/blog/ventajas-y-desventajas-de-la-inteligencia-artificial, 2023)

 Ethical Challenges

 

1. The lack of transparency of AI tools: AI decisions are not always intelligible to humans.

2. AI is not neutral: AI-based decisions are susceptible to inaccuracies, discriminatory outcomes, embedded or embedded biases.

3. Surveillance practices for data collection and the privacy of court users. (unesco, 2023)


Weak Artificial Intelligence: What It Is and How It Has Impacted Society

 

Universities of the Pacific Rim (APRU) with the support of Google, the professor of the Faculty of Physical and Mathematical Sciences (FCFM) and researcher at the Center for Mathematical Modeling, Felipe Tobar, is developing a work on weak Artificial Intelligence and what its social consequences are. The academic explores the daily use of devices classified as "intelligent machines", and the concerns generated by the lack of knowledge that exists regarding their limits. (Ramires, 2020)

 

Artificial Intelligence and Authorship

 

A tweet has recently circulated giving instructions on how to remove a peanut butter sandwich from a video recorder, written in the style of a Bible verse. It's a lot of fun, at least until you realize it was written by an AI bot. At that stage he becomes very intelligent but loses all humor. It seems that ingenuity lies in the clever use of language; the self-conscious parody of a shared understanding of the form being imitated. Once it has been revealed that the author is a computer program, all of this is lost; It's simply a tool that applies rules you've learned. (levene, 2023)


Evaluating the Effectiveness of Artificial Intelligence Systems in Intelligence Analysis

 

He U.S. military and intelligence community have shown interest in developing and deploying artificial intelligence (AI) systems to support intelligence analysis, both as an opportunity to leverage new technology and as a solution for an ever-proliferating data glut. However, deploying AI systems in a national security context requires the ability to measure how well those systems will perform in the context of their mission. (Daniel Ish, 2021)

 

To address this issue, the authors begin by introducing a taxonomy of the roles that AI systems can play in supporting intelligence—namely, automated analysis, collection support, evaluation support, and information prioritization—and provide qualitative analyses of the drivers of the impact of system performance for each of these categories. (Daniel Ish, 2021)

 

The authors then single out information prioritization systems, which direct intelligence analysts' attention to useful information and allow them to pass over information that is not useful to them, for quantitative analysis. Developing a simple mathematical model that captures the consequences of errors on the part of such systems, the authors show that their efficacy depends not just on the properties of the system but also on how the system is used. Through this exercise, the authors show how both the calculated impact of an AI system and the metrics used to predict it can be used to characterize the system's performance in a way that can help decisionmakers understand its actual value to the intelligence mission. (Daniel Ish, 2021)


 Key Findings

- Using metrics not matched to actual priorities obscures system performance and impedes informed choice of the optimal system.

- Metric choice should take place before the system is built and be guided by attempts to estimate the real impact of system deployment.

- Effectiveness, and therefore the metrics that measure it, can depend not just on system properties but also on how the system is used.

- A key consideration for decisionmakers is the number of resources devoted to the mission outside those devoted to building the system. (Daniel Ish, 2021)

 

Recommendations

1. Begin with the right metrics. This requires having a detailed understanding of the way an AI system will be used and choosing metrics that reflect success with respect to this utilization.

2. Reevaluate (and retune) regularly. Because the world around the system continues to evolve after deployment, system evaluation must continue as a portion of regular maintenance.

3. Speak the language. System designers have a well-established set of metrics for capturing the performance of AI systems, and being conversant in these traditional metrics will ease communication with experts during the process of designing a new system or maintaining an existing one.

4. Conduct further research into methods of evaluating AI system effectiveness. (Shacklett, 2019)

 

The true costs and ROI of implementing AI in the enterprise in 2019, web content evaluator Market Muse revealed that 80% of IT and corporate business leaders wanted to learn more about the cost of implementing existing artificial intelligence (AI) technology; 74% were interested in how much more it would cost over present expenditure levels to implement AI in their enterprises; and 69% wanted more information about how to measure the return on investment (ROI) for a new AI solution. (Shacklett, 2019)

Bibliografía

Anyoha, R. (2017). Special Edition on Artificial Intelligence. https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/.

Berkely, U. (26 de Junio de 2020). University of California, Berkeley. Obtenido de University of California, Berkeley: https://ischoolonline.berkeley.edu/blog/what-is-machine-learning/

Brooks, R. (21 de March de 2022). University of York . Obtenido de University of York : https://online.york.ac.uk/artificial-intelligence-and-its-impact-on-everyday-life/

Chatterjee, S. S. (2021). Evolution of artificial intelligence and its impact on human rights: from sociolegal perspective. . International Journal of Law and Management, 64(2), 184-205.

Chowdhury, M. (2021). The Evolution of Artificial Intelligence: Past, Present & Future. https://www.analyticsinsight.net/the-evolution-of-artificial-intelligence-past-present-future/.

Kekatos, M. (21 de July de 2023). ABCNews. Obtenido de ABCNews: https://abcnews.go.com/US/turing-test-determines-computers/story?id=101486628

Marr, B. (19 de February de 2016). A Short History of Machine Learning . Obtenido de A Short History of Machine Learning : https://www.forbes.com/sites/bernardmarr/2016/02/19/a-short-history-of-machine-learning-every-manager-should-read/?sh

Marr, B. (24 de Septiembre de 2018). Forbes . Obtenido de Forbes : https://www.forbes.com/sites/bernardmarr/2018/09/24/what-are-artificial-neural-networks-a-simple-explanation-for-absolutely-anyone/

McCarthy, J. (2004). Artificial Intelligence . https://www.ibm.com/topics/artificial-intelligence.

Microsoft. (2006). Work of AI. https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-artificial-intelligence#self-driving-cars.

Morris, A. (2021). What Is Data Crunching & Why Is It Important? https://www.netsuite.com/portal/resource/articles/erp/data-crunching.shtml.

Turing, A. (1950). Artificial Intelligence . https://www.ibm.com/topics/artificial-intelligence.

Watts, R. (2022). What Is A Chatbot? Everything You Need To Know. https://www.forbes.com/advisor/business/software/what-is-a-chatbot/.

 A.Pilar, S. (21 de mayo de 2023). https://www.rtve.es/noticias/20230527/como-inteligencia-artificial-chatgpt-estan-cambiando-ambito-educativo/2447088.shtml. Obtenido de https://www.rtve.es/noticias/20230527/como-inteligencia-artificial-chatgpt-estan-cambiando-ambito-educativo/2447088.shtml: https://www.rtve.es/noticias/20230527/como-inteligencia-artificial-chatgpt-estan-cambiando-ambito-educativo/2447088.shtml

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Barreiro, N. (27 de Abril de 2023). https://anelis.com/10-casos-de-exito-de-empresas-que-han-implementado-inteligencia-artificial/. Obtenido de https://anelis.com/10-casos-de-exito-de-empresas-que-han-implementado-inteligencia-artificial/: https://anelis.com/10-casos-de-exito-de-empresas-que-han-implementado-inteligencia-artificial/

Barrera, T. (3 de Diciembre de 2021). https://thetechfashionista.com/es/inteligencia-articifial-en-la-industria-de-la-moda/. Obtenido de https://thetechfashionista.com/es/inteligencia-articifial-en-la-industria-de-la-moda/: https://thetechfashionista.com/es/inteligencia-articifial-en-la-industria-de-la-moda/

Daniel Ish, J. E. (2021). https://www.rand.org/pubs/research_reports/RRA464-1.html. Obtenido de https://www.rand.org/pubs/research_reports/RRA464-1.html: https://www.rand.org/pubs/research_reports/RRA464-1.html

Dilmegani, C. (9 de Octubre de 2023). https://research.aimultiple.com/ai-usecases/. Obtenido de https://research.aimultiple.com/ai-usecases/: https://research.aimultiple.com/ai-usecases/

https://www.beetrack.com/es/blog/ventajas-y-desventajas-de-la-inteligencia-artificial. (8 de 5 de 2023). Obtenido de https://www.beetrack.com/es/blog/ventajas-y-desventajas-de-la-inteligencia-artificial: https://www.beetrack.com/es/blog/ventajas-y-desventajas-de-la-inteligencia-artificial

levene, A. (23 de febrero de 2023). https://publicationethics.org/news/artificial-intelligence-and-authorship. Obtenido de https://publicationethics.org/news/artificial-intelligence-and-authorship: https://publicationethics.org/news/artificial-intelligence-and-authorship

Pombo, c. (7 de 12 de 2022). https://blogs.iadb.org/conocimiento-abierto/es/riesgos-inteligencia-artificial/. Obtenido de https://blogs.iadb.org/conocimiento-abierto/es/riesgos-inteligencia-artificial/: https://blogs.iadb.org/conocimiento-abierto/es/riesgos-inteligencia-artificial/

RAGHUNANDAN, M. (25 de Septiembre de 2018). https://www.kolabtree.com/blog/es/5-ejemplos-del-mundo-real-de-la-ai-en-la-sanidad/. Obtenido de https://www.kolabtree.com/blog/es/5-ejemplos-del-mundo-real-de-la-ai-en-la-sanidad/: https://www.kolabtree.com/blog/es/5-ejemplos-del-mundo-real-de-la-ai-en-la-sanidad/

Ramires, F. (16 de junio de 2020). https://uchile.cl/noticias/164364/inteligencia-artificial-debil-que-es-y-como-ha-impactado. Obtenido de https://uchile.cl/noticias/164364/inteligencia-artificial-debil-que-es-y-como-ha-impactado: https://uchile.cl/noticias/164364/inteligencia-artificial-debil-que-es-y-como-ha-impactado

Ruiz, A. C. (22 de junio de 2021). https://www.universidadviu.com/es/actualidad/nuestros-expertos/inteligencia-artificial-ventajas-y-desventajas. Obtenido de https://www.universidadviu.com/es/actualidad/nuestros-expertos/inteligencia-artificial-ventajas-y-desventajas: https://www.universidadviu.com/es/actualidad/nuestros-expertos/inteligencia-artificial-ventajas-y-desventajas

Samuel.s.Pilar. (27 de mayo de 2023). https://www.rtve.es/noticias/20230527/como-inteligencia-artificial-chatgpt-estan-cambiando-ambito-educativo/2447088.shtml. Obtenido de https://www.rtve.es/noticias/20230527/https://www.rtve.es/noticias/20230527/como-inteligencia-artificial-chatgpt-estan-cambiando-ambito-educativo/2447088.shtmlcomo-inteligencia-artificial-chatgpt-estan-cambiando-ambito-educativo/2447088.shtml: https://www.rtve.es/noticias/20230527/como-inteligencia-artificial-chatgpt-estan-cambiando-ambito-educativo/2447088.shtml

Shacklett, M. (1 de Abril de 2019). https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-ai-in-the-enterprise/. Obtenido de https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-ai-in-the-enterprise/: https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-ai-in-the-enterprise/

unesco. (24 de ABRIL de 2023). https://www.unesco.org/es/artificial-intelligence/recommendation-ethics/cases. Obtenido de https://www.unesco.org/es/artificial-intelligence/recommendation-ethics/cases: https://www.unesco.org/es/artificial-intelligence/recommendation-ethics/cases

world, t. l. (27 de febrero de 2023). https://thelogisticsworld.com/manufactura/como-la-inteligencia-artificial-nos-lleva-a-un-nuevo-capitulo-en-la-manufactura-industrial/#:~:text=Los%20avances%20en%20tecnolog%C3%ADa%20han%20permitido%20a%20los,vanguardia%20en%20el%20campo%20de%20la%20manufac. Obtenido de https://thelogisticsworld.com/manufactura/como-la-inteligencia-artificial-nos-lleva-a-un-nuevo-capitulo-en-la-manufactura-industrial/#:~:text=Los%20avances%20en%20tecnolog%C3%ADa%20han%20permitido%20a%20los,vanguardia%20en%20el%20campo%20de%20la%20manufac: https://thelogisticsworld.com/manufactura/como-la-inteligencia-artificial-nos-lleva-a-un-nuevo-capitulo-en-la-manufactura-industrial/#:~:text=Los%20avances%20en%20tecnolog%C3%ADa%20han%20permitido%20a%20los,vanguardia%20en%20el%20campo%20de%20la%20manufac

 

¿What is Artificial Intelligence?

  What is artificial intelligence? While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, Jo...