8. Inteligencia Artificial
Conjunto de tecnologías
que permiten Inteligencia
de máquina que simulan
los elementos del
pensamiento humano
Aprendizaje profundo
Aprendizaje automático
Redes neuronales
Búsquedas
Razonamiento probabilístico
etc.
Visión
Habla
Lenguaje
Conocimiento
Resolución de
problemas
etc.
9. Language
Speech
Search
Machine
Learning
Knowledge Vision
Spell
check
Speech API
Entity linking
Recommendation
API
Bing
autosuggest
Computer
vision
Emotion
Forecasting
Text to
speech
Thumbnail
generation
Anomaly
detection
Custom
recognition
(CRIS)
Bing
image search
Web language
model
Customer
feedback
analysis
Academic
knowledge
OCR, tagging,
captioning
Sentiment
scoring
Bing
news search
Bing
web search
Text analytics
Cognitive Services APIs
10. IA, Machine Learning, Deep Learning
“Deep Learning”, Ian Goodfellow, 2016
“What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?”, Michael Copeland, 2016
15. Deep LearningBasado en los avances en las redes neuronales artificiales
Labrador
Redes más grandes y más profundas
Muchas capas; algunos hasta 150 capas
Miles de millones de parámetros aprendibles
Feed Forward, Recurrente, Convolucional,
Disperso, etc.
Entrenamiento en grandes
conjuntos de datos
10,000+ horas de
Entrenamiento
Millones de imágenes
Años de datos de clics
Cálculo altamente paralelizado
Trabajos de formación de larga duración (días,
semanas, meses)
Aceleración con GPU
Avances recientes en más potencia de la
computadora y datos grandes
http://playground.tensorflow.org
What is ‘artificial intelligence’? There are many definitions, and certainly a lot of confusion, as well as hype around this term today. Just the same as all major computing waves; if you think back to the level of confusion and hype around the term ‘cloud computing’ back around 10 years ago.
Here we try to boil it down to the essentials. Artificial intelligence is a set of technologies that enable ‘machine intelligence’ to simulate or augment elements of human thinking. For example, those technologies on the left, such as machine learning and neural networks. And those elements of human thinking on the right, such as vision, speech, and language.
There is a very subtle, but important, aspect in this definition. We are not advocating that machine intelligence should ‘replace’ human intelligence. But, machine intelligence can be used effectively to ‘augment’ or improve the human experience, accomplished via a set of more specialized use cases that map to individual elements of human thinking.
That, is where we think, that at least today, is the greatest opportunity for you as well.
Here we offer a bit more clarity around the more detailed terms, as there is a si
And as you may already know, everybody is doing some form of AI today. The biggest technology companies today, all have major investments in AI. Amazon with Alexa, Apple with Siri, Facebook has a lot of investments in computer vision, Google’s AlphaGo recently defeated the human champion in Go which is a much more complex game than chess, and Microsoft’s Skype Translator and investments in natural language processing. Of course, there are a lot more AI stuff that these companies are all investing in; just listing a few more visible examples here. And there are thousands of companies of all sizes all building AI capabilities across the map.