machine learning

“People make this sort of distinction of either it's machine learning or it's people. Either you've got a team in India or, you know, Romania working through the data, trying to make sense of it or you've got some algorithm working on it, but I think there is a…”

“You can't do machine learning with lat-longs.”

“I think that machine learning is, has hit an inflection point in the past few years artificial intelligence, machine learning, whatever we want to call it To the point where, ah, we're starting to see, ah, these, ah, dreams that we've had for 30 plus years in …”

“So, one interesting thing is it's actually really, really easy to fit data. It's actually, it's quite boring. You know, you have, you know, a really large, complex model. Maybe it's a neural network. Maybe it's a random forest. Or maybe it's just a really larg…”

“So much of the existing work on fairness and transparency in machine learning, not that there's very much of it, focuses on predictive models rather than models for exploratory or explanatory analyses”

“The thing that makes data science different from machine learning is not just getting epsilon better predictive accuracy on learning, you know, cat's faces from pictures. It's this thing where you interact with somebody from a different discipline, and then so…”

“Our take on this is we should be building tools for trained professionals along with a feedback loop to tell them how they're doing. Machine learning and other technologies I think you can apply on top to make those people slightly more efficient.”

“And Spark was, ah, you know, we targeted first some workloads which are not covered by Hadoop, and from all this experience I mentioned earlier, we look at iterative, iterative computations to support machine learning, as well as interactive computation, right…”

“If you look at the machine learning, it's, fundamentally, it's an iterative algorithm, and every iteration is turned into a Hadoop job. So between the iteration, you write the data and read the data from HDFS, so that's why it's very slow.”

“But in most cases, customers aren't willing to pay for a product that only gets them 80% of the way. You have to kind of like specialize and focus on the problem to make sure that you get to being 100%.”

“Deep learning is a set of algorithms that is really not that different to machine learning in general. It can do anything that general machine learning can do, and in many cases better, but it really shines when you have unstructured data.”

“So the old traditions are much better at dealing with abstract knowledge The new traditions are much better at learning things quickly.”

“And so, as a result, there is no great machine learning technique in natural language.”

“And machine learning is the game of kings.”

“In terms of developing real application, I think you should really try to balance both, because it's true that in the example I showed, If you have a very good process to basically already isolate all the entities and if you want basically to use something lik…”

“When somebody, when a vendor or a startup is going to build a product that solves your problem, They must solve a generic formulation of the problem. They have to solve everybody's version of your same problem. When you want to solve your problem, you just nee…”

“We're seeing this in machine learning primarily in the open source world in an ongoing basis, almost something new every day.”

“Machine learning has not gotten to the point where you can just point at a data and say, give me the insights.”

“So a lot of the luxury that other companies have, like doing large scale machine learning based on massive amount of usage data, or doing large scale A-B testing to decide which model works better, doesn't work for us.”

“Machine learning and even statistical tools are becoming kind of commoditized.”

“I think actually most companies will not be able to use, ah, machine learning. Even though deep learning may be commodity within the machine learning community is still way too small to be commodity overall.”

“In that world, even the most advanced models, deep learning, and machine learning frameworks can't beat them because they have access to this kind of core underlying data.”

“And so we could see, it's very possible we could see more machine learning innovations, or at least in certain areas, Like maybe in healthcare, for instance. We might see more machine learning innovations that happen in China than happen in, in other places.”

“So we use it for a number of things throughout our business. In this context, we were speaking specifically about auto tagging because humans, it's hard to scale humans to capture every element about a product that you may want to capture. And so we're attempt…”

“Notice I did not quite use buzzwords like NLP, or deep learning, or data science, or machine learning, which I could very well have used, but I don't think it's that useful.”

“They have started using machine learning to suggest answers in real time to the responders of these text messages”

“I think a lot of the great products in the future are going to be built this way, and consequently, I think this style of software engineering is becoming mainstream and becoming this sort of differentiated tool set for software engineers and machine learning …”

“Neurons and synapses is not the right abstraction. Models should be programs, specifically programs that simulate the system that generated the data.”

“All data science And all AI and all ML is fundamentally vertically specific. Which is the dirty secret about AI and ML powered businesses. They are fundamentally vertical problems.”

“You can sort of, like, you know, you can download TensorFlow, download some data sets, and over the weekend, probably come up with, you know, if you're a good programmer, come up with something that can do, like, 80% accuracy of whatever, let's say OCR or some…”

“Credo number three, two, actually, is that I think that, well, machine learning is to be automated in the next few years.”

“When they log into their Shopify store in the admin, they see a bunch of, like, home cards that tells them do this or, like market for this product or make this change in your theme. And those are all driven by machine learning,”

“The first time you go from any rule base or non-machine learning to machine learning actually is the time that you get the highest lift.”

“So actually all of our machine learning offerings that I was talking about are actually built for the individual use case of the merchant.”

“There's a lot of hype and focus and good work in machine learning. But I do think if we take a step back and look at causal inference, we're gonna actually solve a lot of problems with just that arm. So I hope over the next few years, like, people invest in th…”

“As of last two years, Shopify Capital is entirely machine learning driven.”

“I'd argue Uber is an ML company because they use it for demand management and pricing and everything else.”

“Security is actually like a, Sort of forefront industry for machine learning in some ways because you have a massive amount of different types of data where you have too much data to go inspect manually. And you want to be able to infer a bunch of different be…”

“I think five of the six companies that I am involved with actively today would say, like, machine learning is a core tenet of, like, what makes your product work.”

“I would say we are so much in the beginning that we can't even really picture where we're going to be 20 years from now.”

“There's been enormous progress. The five major paradigms are exactly the same as they were then.”

“With the existing machine learning technology, of what we can do with that, how much have we done? Very little so far. So there's enormous amount for, enormous scope to do things there. Not just in finance, but in a lot of other fields, right?”

“So if you want to be really practical in modern use cases, focus on tabular data with schemas that do really simple machine learning, like do fraud detection, do inventory forecasting do simple, straightforward predictions, and just about every business in the…”

“A lot of people will say they did machine learning, but if they did it more than 10 years ago, they did it in a pretty obsolete way.”

“I believe that NLP today is the most important field of machine learning.”

“And this is where we believe that machine learning is not really the hard part, but building and maintaining the data pipeline is”

“We've moved away from, okay, I'm going to run a few, set up a warehouse and get a few reports and get an insight into the operation of my organizations to actually I want to run, you know, include ML, a data-driven way of solving problems into every feature of…”

“Again, if you look in five years, I think that this distinction will have been sanded over and will not be salient anymore.”

“If you describe behavior, human behavior in that way, you get to very, very granular descriptions of what's happened in the past, and those are highly, highly predictive for machine learning.”

“Increasingly people are finding that this data exhaust is not well suited to AI, to advanced ML use cases.”

“No matter how good you are at statistics or machine learning, it's hard to make sense of data without having some quality inputs.”

“I bet 99% of the global 2000 is using machine learning for something that they actually really care about.”

“I think that health care is historically like eight years behind everyone else, but there's also a very massive upside there right now for people who are going to be using machine learning and data science to solve problems.”

“There's a tenant of machine learning where like you just throw out the outliers because they're going to mess up your distribution and you kind of don't want to deal with them. For security, what you do is you find the outliers and you hyper focus on them beca…”

“And it's something that I think traditional machine learning hasn't really been agile enough to deal with. Right. Like the act of doing machine learning is fundamentally changing the problem you're trying to solve.”

“But that doesn't mean that image recognition was overhyped.”

“And this is clearly what happened with the last wave of machine learning. There was a brief moment where people said it needs all this data. Only Google's got all the data. There's going to be, like, three people who've got enough data to do AI, and that turne…”

“And it took a while to work out that the right level of abstraction was to think that this is pattern recognition.”

“One of the ways I used to talk about machine learning is that it gives you infinite interns. Like you would like someone to listen to every call coming into the call center and tell me if the customer is angry. Like you've got a million calls a day. You don't …”

“I definitely see a lot of people building very good applications without traditional ML background.”

“It's a vast majority of GF-AI systems have seen, like, you have, like, traditional or, like, analytical ML components with GF-AI.”

“I transitioned to product design, which is a rare transition, I think. But it actually, like, really helped me in my path to starting this company. Because think about it, product design plus machine learning, like, hard to beat as a combination.”

“AIs seem Significantly better at engineering and grungy stuff and sort of just keeping trying than they seem to be at conceptual breakthroughs, but their ability to do sort of these. Breakthroughs, especially in easy to verify domains are improving. And like, …”