Tech executives strongly believe in the power of AI as a whole, but that doesn’t mean that they’re convinced by every id
This whole post was written using my autocorrection script and I saved at least an hour on this post alone by typing much quicker, having it correct all my typos and expand custom abbreviations into full words, thereby saving me time and strain on my fingers. When I press Win-T my currently selected sentence will be converted to title case, Win-G selects the last-typed word and searches for that term on Google — so many small quick hacks that save a great deal of time over the course of a workday.
I hope this post could showcase some optimization and automation potential in a pretty regular life — and that is still only scratching the surface. This becomes even more true when you look at semi-automation like for example finding an easy way to quickly sort through emails and push them into folders. I could see a system like the Tumblr app where you long-press a button on your phone and a little circle menu opens with all your blogs under that account, then you swipe to one and the email gets added there.
For one, the hardware or cloud storage space to handle bigger datasets might not be available. In addition, modularity of machine learning models doesn’t always work the same at large scales as it does on small ones.
Finally, data sourcing may not be easy or even possible. This can be due to silo-structures in companies, as discussed earlier, or due to other challenges in obtaining more data.
“It’s still classic, but we’re ready to do darker, richer, and warmer,” Lauren Schneider, the co-founder of the design firm Transition State, told Insider of her thoughts on the material.
Why is Machine Learning Deployment Hard? After several AI projects, I realized that deploying Machine Learning (ML) models at scale is one of the most important…towardsdatascience.com
It’s such a great invention and it works flawlessly. You get automated texts when something is delivered, if you have a job without homeoffice work you save a full vacation day on every single delivery — it’s just great.
This whole post was written using my autocorrection script and I saved at least an hour on this post alone by typing much quicker, having it correct all my typos and expand custom abbreviations into full words, thereby saving me time and strain on my fingers. When I press Win-T my currently selected sentence will be converted to title case, Win-G selects the last-typed word and searches for that term on Google — so many small quick hacks that save a great deal of time over the course of a workday.
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he registers I talked about. Modern RISC CPUs cannot do operations on numbers that are not in a register like this. For example, it cannot add two numbers residing in RAM in two different locations. Instead, it has to pull these two numbers into a separate register. That is what we do in this simple example. We pull in the number at memory location 150 in the RAM and put it into register r1 in the CPU. Next, we put the contents of address 200 into register r2. Only then can the numbers be added with the add r1, r2 instruction.I am so glad that these delivery boxes exist here in Germany now, that has made everything so much simpler, streamlined, comfortable for everyone. I’m sure the delivery folks live just as fine without talking to me and I can take my bicycle at midnight to get my package if I feel like it.
In addition, engineering isn’t always deemed essential for data scientists. This is a problem because engineers might not always understand all the details of what a data scientist envisions, or might implement things differently due to miscommunication. Therefore, data scientists who can deploy their models have a competitive edge over those who can’t, as StackOverflow points out.
Not only is this a waste of time and resources. It can also lead to additional confusion when stakeholders don’t know which version of the code to use, and who to turn to if they encounter any bugs.
Insider spoke to interior designers about the 2020 decor trends they wouldn’t be sad to leave behind, whether because they’re impractical, look old-fashioned, or are overused.
Light-colored wood has been a popular choice for homes in recent years because it brightens a space, but designers aren’t loving it anymore. It doesn’t add any depth to a home.
Although data scientists have an advantage if they’re able to implement their models, they should clearly communicate with the engineers about what needs to be done by whom. This way, they’ll save the company’s time and resources.