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What is the next big thing in AI and ML? – The Launchpad – Medium

#artificialintelligence

The past year has been rich in events, discoveries and developments in AI. It is hard to sort through the noise to see if the signal is there and, if it is, what is the signal saying. This post attempts to get you exactly that: I'll try to extract some of the patterns in the AI landscape over the past year. And, if we are lucky, we'll see how some of the trends extend into the near future. Make no mistake: this is an opinion piece. I am not trying to establish some comprehensive record of accomplishments for the year. I am merely trying to outline some of these trends. Another caveat: this review is US-centric. A lot of interesting things are happening, say, in China, but I, unfortunately, am not familiar with that exciting ecosystem.


Your AI skills are worth less than you think

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We are in the middle of an AI boom. Machine Learning experts command extraordinary salaries, investors are happy to open their hearts and checkbooks when meeting AI startups. And rightly so: this is one of those transformational technologies that occur once per generation. The tech is here to stay, and it will change our lives. That doesn't mean that making your AI startup succeed is easy. I think there are some important pitfalls ahead of anyone trying to build their business around AI. In 2015 I was still at Google and started playing with DistBelief (which they would later rename to TensorFlow).


Your AI skills are worth less than you think – Inside Inovo – Medium

#artificialintelligence

We are in the middle of an AI boom. Machine Learning experts command extraordinary salaries, investors are happy to open their hearts and checkbooks when meeting AI startups. And rightly so: this is one of those transformational technologies that occur once per generation. The tech is here to stay, and it will change our lives. That doesn't mean that making your AI startup succeed is easy. I think there are some important pitfalls ahead of anyone trying to build their business around AI.


Making AI, Machine Learning Work for You!

#artificialintelligence

Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.


Making AI, Machine Learning Work for You!

#artificialintelligence

Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.