AI News, How to break into machinelearning
How to break into machinelearning
Photo by Erich Ferdinand An engineer recently asked me how she could turn an interest in machine learning into a full-time job.
This can be a daunting prospect, because the whole field has until recently been very separate from traditional engineering, with only a few specialists at large companies using it in production, often far from traditional product teams.
This may sound like madness, but machine learning is rapidly invading almost every product area, so whether you’re in games or enterprise software, your group needs to at least stay up to date with what’s happening with the technology.
You may have to fight your own imposter syndrome, but becoming the go-to person for everyone’s questions about machine learning is a fantastic way to teach yourself the essentials.
Even if you don’t end up building anything in production, at least you’ll be able to point at relevant research and experiments if you decide to change to a new position.
If your job’s not offering you the opportunities in machine learning you want, then joining that community is a great way to teach yourself a lot of practical skills.
Most of machine learning is the software equivalent of banging on the side of the TV set until it works, so don’t be discouraged if you have trouble seeing an underlying theory behind all your tweaking!
There is a long tradition of mentorship in machine learning, especially around deep learning, but I think we should be doing a lot better job of capturing all that oral tradition.
As long as you’re happy to keep eating humble pie, that means writing up your own tentative understanding and getting it reviewed is a lot more effective way of getting others to share their knowledge than asking flat out!
Don’t learn Machine Learning in 24 hours
This is a witty and a tad bit satirical headline, taking a dig at all those coffee table programming books that aim to teach you programming in 24 hours, 7 days, 10, days, *insert a ridiculously short time line*.
Yes, you may come to grips with the syntax, nature, and style of a programming language in 24 hours, but that doesn’t mean you’ve become adept at the art of programming.
Of course you could write a Hello World program in C++ in 24 hours, or a program to find the area of a circle in 24 hours, but that’s not the point.
Found this on a very funny Facebook page Even if we were to forget the 10,000 hours rule for a second, you can’t do machine learning in 7 lines of code.
Because those 7 lines of code do not explain how you did in the bias-variance tradeoff, what your accuracy value means, or whether accuracy is an appropriate metric of performance in the first place, whether your model overfits, how your data is distributed, and if you’ve chosen the right model to fit the data you have, etc.
But after reading this, you will realise the entire gamut of concepts you need to understand in order to be fluent with this field, to think in ML, so to speak.
An unfortunate result of hype is that we “drown in information and starve for knowledge”. So many people do it, that we frequently lose sight of the bigger picture.
Learning machine learning? Six articles you don’t want to miss
Digital disruption has revolutionized the way we live and do business — and machine learning is the latest wave of that revolution.
Whether it’s by remaking medicine with innovations from Watson Health or by transforming online shopping with intelligent recommendations, machine learning is remaking industries across the board. And yet we’re just at the beginning, which is why IBM works to provide the best possible tools in the industry for data scientists.
Machine Learning: how to go from Zero to Hero
Even if you’re not a Java buff, the presentation Jim gives on all things Machine Learning is a pretty cool 1.5+ hour introduction into ML concepts, which includes more info on many of the examples above.
This course has no time limits and helps you learn ML while killing time in line on your phone.
For some of you that’s a show stopper, but for others, that’s why you’re going to put yourself through it and collect a certificate saying you did.
But more so, if you do make it through, you’ll have a deep understanding of the implementation of Machine Learning that will catapult you into successfully applying it in new and world-changing ways.
If you’re not interested in writing the algorithms, but you want to use them to create the next breathtaking website/app, you should jump into TensorFlow and the crash course.
You can efficiently utilize ML as a service in many ways with tech giants who have trained models ready.
They were my inspiration to get started, and though I’m still a newb in the ML world, I’m happy to light the path for others as we embrace this awe-inspiring age we find ourselves in.
What most people don’t understand about AI and the the state of machine learning
Though the term was officially coined in the 1950s, Artificial Intelligence (AI) is a concept that dates back to ancient Egyptian automatons and early myths of Greek robots.
Notable attempts to define AI include the 1956 Dartmouth conference and the Turing test, and passionate AI advocates persist to explain the concept to the world in a way that is distinguishable and digestible.
We create an AI capable of learning, show it how to learn the solution to a task, and subsequently, it will simply figure out a solution to any related problem, right?
It would appear that big companies like Google, Microsoft, and Apple think so: they’re capitalizing on this intuitive expectation to persuade people that their AI systems will solve many of their customers’ problems.
Over the last decade, learning systems have glamorously solved object recognition, speech recognition, speech synthesis, language translation, image creation, and gameplay.
People without deep technical background in machine learning often perceive machines’ improvements in performing specialized tasks like these as an AI’s rapidly increasing set of combined abilities.
Upon learning this, a client with a background in engineering stated, However, this reasoning is based on the assumption that once a machine learning algorithm has been developed to solve one problem, that same algorithm can be easily applied to solve a different problem.
The resulting machine learning system needs to be tailored to fit to the data from the specific application, and the training algorithms need to be tuned to find a high-performing solution.
In many cases, each team is dedicated to one specific application domain with the goal to research incremental methods to improve the current best machine learning approach in that domain.
Biologists research ‘the mechanisms that cause an animal to change the way it responds to a particular circumstance after an experience alters the meaning of that circumstance’.
experts modify the program code of the learning algorithm (similar to the gene code of the sea hare), changing its abilities and predispositions to adapt to various experiences.
The core message here: you cannot simply pour raw data into a general AI and expect something meaningful to come out — this kind of AI simply doesn’t exist yet.
For a success story, you need good planning, a mathematically sound problem statement, sufficient training data, a lot of machine learning expertise, and software development capacities.
- On Thursday, May 23, 2019
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