AI, Machine Learning and Deep Learning are getting an hype nowadays even if most part of the algorithms and models at their core are around since long time:
1805 Least Squares
1812 Bayes' Theorem
1913 Markov Chains
1950 Turing's Learning Machine
1957 Perceptron
1967 Nearest Neighbor
1970 Automatic Differentiation
1972 TF-IDF
1980 Neocognitron
1981 Explanation Based Learning
1982 Recurrent Neural Network
1970 Back Propagation
1989 Reinforcement Learning
1995 Random Forest Algorithm
1995 Support Vector Machines
1997 LSTM
So what are the reasons that speed up and accelerated the implementation and made possible today for the theory to become reality?
There are several factors:
- Cheaper computation: in the past hardware was a constraining factor for AI/ML/DL. Late advance in hardware (coupled with improved tools and software
frameworks) and new computational models (in particular around GPUs) have accelerated AI/ML/DL adoption.
- Cheaper storage: the increased number of available data means more space needed for storage. Advance in hardware, cost reduction and improved performance made possible the implementation of new storage systems without the typical limitations of relational databases.
- More advanced algorithms: less expensive compute and storage enable development and training of more advanced algorithms. As a result, DL is nowsolving specific problems like image classification or fraud detection with astonishing accuracy (and more sophisticated algorithms will continue to improve the state of the art).
- More and bigger investments: investment in AI is no longer confined to universities or research institutes, but is done from many other entities such as tech giants, governments, startups and large enterprises across almost every industry sector.
- Bigger data availability: AI/ML/DL need a huge amount of data to learn. The digital transformation of society is providing tons of raw material to fuel their advances. Big data coming from diverse sources such as IoT sensors, social and mobile computing, healthcare and many more new applications can be used to train models.
But often just getting data from any possible data source, in particular from the edge, requires moving mountains. Please attend my talk at the DataWorks Summit in Berlin on April 18th if you want to learn how to make edge data ingestion and analytics easier using a single tool, StreamSets Data Collector Edge, which is an ultralight, platform independent and small-footprint Open Source solution written in Go for streaming data from resource-constrained sensors and personal devices (like medical equipment or smartphones) to Apache Kafka, HDFS, Elastic Search and many other destinations.
1805 Least Squares
1812 Bayes' Theorem
1913 Markov Chains
1950 Turing's Learning Machine
1957 Perceptron
1967 Nearest Neighbor
1970 Automatic Differentiation
1972 TF-IDF
1980 Neocognitron
1981 Explanation Based Learning
1982 Recurrent Neural Network
1970 Back Propagation
1989 Reinforcement Learning
1995 Random Forest Algorithm
1995 Support Vector Machines
1997 LSTM
So what are the reasons that speed up and accelerated the implementation and made possible today for the theory to become reality?
There are several factors:
- Cheaper computation: in the past hardware was a constraining factor for AI/ML/DL. Late advance in hardware (coupled with improved tools and software
frameworks) and new computational models (in particular around GPUs) have accelerated AI/ML/DL adoption.
- Cheaper storage: the increased number of available data means more space needed for storage. Advance in hardware, cost reduction and improved performance made possible the implementation of new storage systems without the typical limitations of relational databases.
- More advanced algorithms: less expensive compute and storage enable development and training of more advanced algorithms. As a result, DL is nowsolving specific problems like image classification or fraud detection with astonishing accuracy (and more sophisticated algorithms will continue to improve the state of the art).
- More and bigger investments: investment in AI is no longer confined to universities or research institutes, but is done from many other entities such as tech giants, governments, startups and large enterprises across almost every industry sector.
- Bigger data availability: AI/ML/DL need a huge amount of data to learn. The digital transformation of society is providing tons of raw material to fuel their advances. Big data coming from diverse sources such as IoT sensors, social and mobile computing, healthcare and many more new applications can be used to train models.
But often just getting data from any possible data source, in particular from the edge, requires moving mountains. Please attend my talk at the DataWorks Summit in Berlin on April 18th if you want to learn how to make edge data ingestion and analytics easier using a single tool, StreamSets Data Collector Edge, which is an ultralight, platform independent and small-footprint Open Source solution written in Go for streaming data from resource-constrained sensors and personal devices (like medical equipment or smartphones) to Apache Kafka, HDFS, Elastic Search and many other destinations.
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