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I'm somewhat qualified to talk about AI and to call it hype is itself hype. As in, it's looking at outliers and trying to understand the market based on them al

I'm somewhat qualified to talk about AI and to call it hype is itself hype. As in, it's looking at outliers and trying to understand the market based on them alone. It's deciding that because there's no glamorous reality shifting changes there must be no impact at all. For example, IBM failed but every pharma company has a decent bunch of PhDs building machine learning models for a variety of reasons. Those models aren't as headline catching but rather they improve certain unglamorous business processes by X%. Taken as a whole that adds up to a lot of value for those companies. Facebook probably makes tens of billions per year due to AI. In a way AI has become mundane and doesn't require specialized centralized companies to be implemented which makes fewer headlines. edit: I also suspect it's a bias due to people being used to traditional industries. To make a new drug you need a lab, infrastructure, equipment, well trained experts and so on. Lot's of investment so it's centralized and visible. To make an AI model requires some data, a laptop and an internet connection.

six2seven 6y ago
> every pharma company has a decent bunch of PhDs building machine learning models for a variety of reasons. Those models aren't as headline catching but rather they improve certain unglamorous business processes by X%. Taken as a whole that adds up to a lot of value for those companies. This! Although I am not an expert in AI field or AI-based methods, I have been working as an s/w engineer / plumber / fireman in projects deploying some AI-based methods and models in production. These methods are nothing catchy, that would unlikely get into headlines, nothing like self-driving cars, etc. But they are just pretty effective applied in a quite well defined scope improving parts of existing processes. These would be considered by many as 'boring' and are related e.g. with improving the data acquisition processes, such as, improving quality of text extraction from images or improving recognition of named entities from free text by using a tailored neural network model in the system. Nothing big nor fancy, but for the business it makes a significant difference. So, on one hand, yes, there's a lot of hype for applying AI-based methods for just anything and wanting to show to investors using the keywords for getting more $$$. But on the other hand, having a well scoped use-case with clear understanding on what method and why, it can bring pretty good results. However, as a community, we are still trying to catch-up with all the recent advancements in AI-based field, and trying to understand when to use XYZ and why over well established 'classical' approaches. These are / will be another tools in our toolbox and this fact cannot be ignored.

six2seven 6y ago

> every pharma company has a decent bunch of PhDs building machine learning models for a variety of reasons. Those models aren't as headline catching but rather they improve certain unglamorous business processes by X%. Taken as a whole that adds up to a lot of value for those companies. This! Although I am not an expert in AI field or AI-based methods, I have been working as an s/w engineer / plumber / fireman in projects deploying some AI-based methods and models in production. These methods are nothing catchy, that would unlikely get into headlines, nothing like self-driving cars, etc. But they are just pretty effective applied in a quite well defined scope improving parts of existing processes. These would be considered by many as 'boring' and are related e.g. with improving the data acquisition processes, such as, improving quality of text extraction from images or improving recognition of named entities from free text by using a tailored neural network model in the system. Nothing big nor fancy, but for the business it makes a significant difference. So, on one hand, yes, there's a lot of hype for applying AI-based methods for just anything and wanting to show to investors using the keywords for getting more $$$. But on the other hand, having a well scoped use-case with clear understanding on what method and why, it can bring pretty good results. However, as a community, we are still trying to catch-up with all the recent advancements in AI-based field, and trying to understand when to use XYZ and why over well established 'classical' approaches. These are / will be another tools in our toolbox and this fact cannot be ignored.