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La inteligencia artificial (IA) es un área de la informática que se centra en el desarrollo de sistemas y algoritmos capaces de simular el pensamiento y el comportamiento humano. Busca crear máquinas con la capacidad de aprender, razonar, percibir, comprender y actuar de manera autónoma en entornos cambiantes y complejos.

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La inteligencia artificial se aplica en una amplia variedad de campos y sectores, incluyendo:

-Medicina

-Industria

-Servicios Financieros

-Comercio electrónico

-Transporte

-Educación

-Entretenimiento

-Atención al cliente

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asistentes virtuales, software de análisis de imágenes, motores de búsqueda, sistemas de reconocimiento de voz y rostro e inteligencia artificial integrada: robots, drones, vehículos autónomos, entre algunos otros ejemplos. A continuación presento algunos de los Softwares IA mas populares:

Chat GPT

Google Bard

MidJourney

Synthesia

AIVA

Deep L

Jasper

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-Eficiencia y productividad

-Innovación

-Asistencia en la toma de decisiones

-Mejora de la precisión

-Personalización

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-Desplazamiento laboral

-Privacidad y seguridad

-Desafíos éticos

-Dependencia tecnológica

-Sesgo algorítmico

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La inteligencia artificial representa un avance tecnológico significativo con el potencial de transformar profundamente diversas áreas de la sociedad y la economía. Sus aplicaciones van desde la optimización de procesos industriales hasta la personalización de servicios en línea y la asistencia en la toma de decisiones médicas. Sin embargo, este avance no está exento de desafíos y consideraciones éticas.

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What is artificial intelligence (AI)?

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind

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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."

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.

Stuart Russell and Peter Norvig then proceeded to publish, Artificial Intelligence: A Modern Approach (link resides outside ibm.com), becoming one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting:

Human approach:

Systems that think like humans

Systems that act like humans

Ideal approach:

Systems that think rationally

Systems that act rationally

Alan Turing's definition would have fallen under the category of "systems that act like humans."

At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

Over the years, artificial intelligence has gone through many cycles of hype, but even to
skeptics, the release of OpenAI's ChatGPT seems to mark a turning point. The last time generative AI loomed this large, the breakthroughs were in computer vision, but now the leap forward is in natural language processing. And it's not just language: Generative models can also learn the grammar of software code, molecules, natural images, and a variety of other data types.

The applications for this technology are growing every day, and we're just starting to
explore the possibilities. But as the hype around the use of AI in business takes off,
conversations around ethics become critically important. To read more on where IBM stands within the conversation around AI ethics, read more here.

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Weak AI-also called Narrow AI or Artificial Narrow Intelligence (ANI)-is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. 'Narrow' might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM watson, and autonomous vehicles.

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)-also known as superintelligence-would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman, rogue computer assistant in 2001: A Space Odyssey.

Since deep learning and machine learning tend to be used interchangeably, it's worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning.

Deep learning is actually comprised of neural networks. "Deep" in deep learning refers to a neural network comprised of more than three layers-which would be inclusive of the inputs and the output-can be considered a deep learning algorithm. This is generally represented using the diagram below.

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman noted in same MIT lecture from above. Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn.

"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn't necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the hierarchy of features which distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, allowing us to scale machine learning in more interesting ways.

Generative AI refers to deep-learning models that can take raw data - say, all of Wikipedia or the collected works of Rembrandt - and "learn" to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified
representation of their training data and draw from it to create a new work that's similar,
but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. The rise of deep learning, however, made it possible to extend them to images, speech, and other complex data types. Among the first class of models to achieve this cross-over feat were variational autoencoders, or VAEs, introduced in 2013. VAEs were the first deep-learning models to be widely used for generating realistic images and speech.

"VAEs opened the floodgates to deep generative modeling by making models easier to
scale," said Akash Srivastava, an expert on generative AI at the MIT-IBM Watson AI Lab.
"Much of what we think of today as generative AI started here."

Early examples of models, like GPT-3, BERT, or DALL-E 2, have shown what's possible. The future is models that are trained on a broad set of unlabeled data that can be used for different tasks, with minimal fine-tuning. Systems that execute specific tasks in a single domain are giving way to broad AI that learns more generally and works across domains and problems. Foundation models, trained on large, unlabeled datasets and fine-tuned for an array of applications, are driving this shift.

When it comes to generative AI, it is predicted that foundation models will dramatically
accelerate AI adoption in enterprise. Reducing labeling requirements will make it much
easier for businesses to dive in, and the highly accurate, efficient AI-driven automation they enable will mean that far more companies will be able to deploy AI in a wider range of mission-critical situations. For IBM, the hope is that the power of foundation models can eventually be brought to every enterprise in a frictionless hybrid-cloud environment.

There are numerous, real-world applications of AI systems today. Below are some of the most common use cases:

Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search-e.g. Siri-or provide more accessibility around texting.

Customer service: Online virtual agents are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.

Computer vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.

Recommendation engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.

Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.

The idea of 'a machine that thinks' dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

1950: Alan Turing publishes Computing Machinery and Intelligence. In the paper, Turing-famous for breaking the Nazi's ENIGMA code during WWII-proposes to answer the question 'can machines think?' and introduces the Turing Test to determine if a computer can demonstrate the same intelligence (or the results of the same intelligence) as a human. The value of the Turing test has been debated ever since.

1956: John McCarthy coins the term 'artificial intelligence' at the first-ever AI conference at Dartmouth College. (McCarthy would go on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw, and Herbert Simon create the Logic Theorist, the first-ever running AI software program.

1967: Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that 'learned' though trial and error. Just a year later, Marvin Minsky and Seymour Papert publish a book titled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research projects.

1980s: Neural networks which use a backpropagation algorithm to train itself become widely used in AI applications.

1997: IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch).

2011: IBM watson beats champions Ken Jennings and Brad Rutter at Jeopardy!

2015: Baidu's Minwa supercomputer uses a special kind of deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human.

2016: DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves!). Later, Google purchased DeepMind for a reported USD 400 million.

2023: A rise in large language models, or LLMs, such as ChatGPT, create an
enormous change in performance of AI and its potential to drive enterprise value.
With these new generative AI practices, deep-learning models can be pre-trained on
vast amounts of raw, unlabeled data.

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What is Artificial Intelligence (AI) ? | IBM

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Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

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Por David J / 28 de diciembre 2023

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La Inteligencia Artificial se ha convertido en trending topic durante los últimos años y nos hemos encontrado con que estas tecnologías han avanzado a un ritmo tan rápido que incluso sus propios desarrolladores se han visto abrumados por el potencial que tienen. Dejando a un lado los debates sobre qué peligros pueden tener, nos hemos querido centrar en las mejores IAs que existen a día de hoy para que puedas ver su potencial y las incluyas en tu trabajo, para desarrollar nuevas ideas o simplemente para pasar un buen rato. ¡Allá vamos con el ranking! 🚀

Chat GPT es una plataforma de inteligencia artificial desarrollada por la empresa que se ha hecho súper famosa y ha despertado el interés de mucha gente. Tras su lanzamiento en el año 2022, necesitó tan solo 5 días para llegar a la extraordinaria cifra de 1 millón de usuarios y no ha parado de crecer desde entonces. Es considerada una de las mejores IA gracias a todas las características que tiene y a la increíble capacidad de respuestas que puede dar, ¿para qué puede ser útil?

Generación de textos

Puede crear letras de canciones, poesías, historias, guiones...

Marketing

Puede dar ideas de campañas de marketing, crear textos para las campañas, copys para social media, audiencias...

Viajes

Puede hacerte rutas y guías para tu próximo viaje. Por ejemplo: hacer una ruta de 3 días por París.

Traducción

Puede traducirte un texto a muchos idiomas.

Conversaciones

Puede conversar contigo de cualquier tema ya que dispone de toneladas de contenido. Notarás que hay ciertas preguntas sensibles que no te va a contestar.

Operaciones matemáticas

Puede realizar todo tipo de operaciones matemáticas.

Desarrollo de código

Puede dar ejemplos de código para desarrolladores web.

El gigante Google no se iba a quedar atrás y lanzó Bard, su plataforma de inteligencia artificial al mercado en 2023. Es una interfaz a modo de chat, parecida a la que hemos visto en ChatGPT, pero con alguna funcionalidad extra como que va a estar conectada a internet desde un inicio, se podrá integrar con otras aplicaciones y permitirá subir imágenes dentro del chat. Google ha desarrollado su propio modelo de lenguaje experimental llamado para competir con otros sistemas de IA del mercado.

Pasamos a una inteligencia artificial de generación de imágenes, y para nosotros uno de los mejores motores de creación gráfica del momento. Midjourney es una plataforma IA que funciona a través de y a la que le puedes mandar (indicaciones u órdenes) para que te genere nuevas imágenes que nunca antes habían existido, todo ello desarrollado por una máquina. Esta herramienta es perfecta para crear contenido visual para tu web, carteles publicitarios, fondos de pantalla o simplemente cuadros para tu casa.

Estas son un par de imágenes generadas por Midjourney que generaron mucha polémica al hacerse virales ya que parecen fotos de verdad.

Ahora vamos con una de las mejores IA para creación de vídeos sin personas reales, únicamente con avatares generados por inteligencia artificial. La plataforma Synthesia es una de las referentes en este tipo de tecnología gracias a su modelo de generación de vídeo con avatares y guiones personalizados. Una servicio que ya ha sido usado por grandes empresas como Amazon o Accenture y que tiene un precio relativamente económico.

Es perfecta para crear contenidos como la formación en el uso de plataformas o explicaciones sobre el funcionamiento de productos, y todo de forma muy rápida. Ya que estamos hablando de formación, también puedes ver inteligencias artificiales ppt que puede ser usada en entornos académicos.

¿Te gusta la música? Pues saltamos al mundo musical de la mano de AIVA, una de las mejores IAs en generación de canciones por sistemas de inteligencia artificial. Su forma de uso es muy sencilla, solo debes seleccionar los instrumentos que quieres que aparezcan en la canción, la duración de la pieza y el estilo para que AIVA cree una canción desde cero.

Aquí tienes unos cuantos ejemplos de canciones creadas con IA gracias a AIVA.

Y ahora nos centramos en el mundo de la traducción de texto y las IAs ¿te da pereza traducir mucho texto? DeepL puede parecer un traductor más, pero usando la inteligencia artificial ha conseguido dar mejores resultados que grandes colosos como Google. Lanzado en 2017, ya se ha convertido en una de las plataformas más usadas a nivel mundial gracias a su facilidad para traducir documentos, textos, páginas webs y a sus conexiones vía API para mover grandes volúmenes de texto. ¿La has probado ya?

Y acabamos este ranking de mejores IAs con una enfocada totalmente al marketing, si eres un especialista en marketing digital esta herramienta es para ti. Esta plataforma está creada con la ayuda de especialistas en SEO y copywriters y te va a ayudar a crear textos de todo tipo para tu blog, emails, página web o posts en redes sociales. Más de 4.000 reseñas en Trust Pilot y decenas de grandes empresas avalan esta gran plataforma.

Entre sus funcionalidades se destacan el chat de IA, Art para generar imágenes, un traductor o el Brand Voice.

Y hasta aquí el análisis de las mejores IA del momento, realmente tienes una infinidad al alcance de tu mano pero hemos destacado las que están siendo más relevantes durante este año. Si te has quedado con ganas de más inteligencias artificiales, puedes echar un ojo a nuestro post de robots con inteligencia artificial .

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El boom de las Inteligencias Artificiales nos ha hecho pensar en cuáles son las mejores. Aquí tienes la guía definitiva sobre las top IAs del momento!

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García Moreno Julio Jorge

Medina Espinoza Evelyn Aimé

Moreno Félix Adalberto

Salazar Cota Juan Alexis

Sánchez Soto Ashley Victoria

Valle Lagarde Eliseo