Joel Camacho's Profile
Great post – I can only imagine HOW MUCH data C.H. Robinson has collected in order to be able to run algorithms that try to provide insights across the entire world. I was curious to learn that supply chain companies are tackling problems with ML. The supply chain industry is an immediate use case where blockchain can unlock huge benefits. The reason for that is because blockchain essentially forces different parts of the supply chain to interact with each other digitally than with exchanges of paper. More data on the supply chain can be collected if more of the data is being processed digitally. I’m curious to learn more what can be unlocked by combining blockchain technology with ML.
Really great post and very informative. The “Contract Intelligence” initiative is a real time saver. There is so much time spent by backoffice/operations employees going over contracts that do not change in content by a whole lot. Appreciate the stats on the man hours saved by using ML technology to extract pertinent information from contracts. I can see law firms harnessing type of ML technology to reduce the amount of time associates / paralegals spent reading through contracts.
I would have to say that M&A and IPOs are very different. The capital markets part of investment banking – where IPOs will fall under – have routine steps that bankers take in order to execute the deal. I definitely agree any process that has measurable, predictable steps can be automated and even better so with ML. On the other hand from my experience in investment banking, M&A tends to have some steps that are the same regardless of the situation, but most are not. I would be curious to learn more as to how the author thought about the potential of ML in M&A situations.
On the consumer banking part of the article, I didn’t think about the data play with this strategy. I just thought that GS wanted to get more deposits in the door through Marcus so it can grow its balance sheet and lend against it, but the data angle makes complete sense. I’m going to check out Marquee when it comes out and see what type of data GS may be collecting from its retail investors.
I was drawn to this post because I remember seeing the video of Duplex’s reveal. I was completely left in awe at how human-like Duplex was when scheduling an appointment on behalf of a person. I am now aware that Google made Duplex really well at scheduling appointments but not at other things. It makes sense that they started with a relatively simple use case. This use case is relatively simple because scheduling appointments has pretty much a define set of variables and potential outcomes. It will be interesting to see how they tackle on harder problems like having Duplex understand double meaning, intent, puns, and other parts of speech that are natural to humans but not straightforward for machines.
The article also brings up the potential of legal issues which is a really good point. I personally wouldn’t want to be recorded by Duplex, especially when I’m acting on behalf of my company where I work with sensitive information.
This is a great post. I didn’t realize how much of the gold is lost during extraction. I find it fascinating that there are places in the gold mining supply chain where a company can implement ML-powered strategies to reduce waste but are hesitant to do so. The article sights that mining companies are worried of being the first mover because competitors will copy or buy the technology. I would imagine that ultimately the algorithm is only as good as its data. If a competitor doesn’t have the same pools of data, then the competitor will not be able to fully copy the tech. I wonder if there are other factors making mining companies hesitant to roll this technology fully.