AI News, How to break into machinelearning
- On Monday, June 4, 2018
- By Read More
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
Recently, I came across a wonderful article by Peter Norvig — “Teach yourself programming in 10 years”.
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.
And no expert (or even one comfortable with its ins and outs) did.” 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.
An understanding and intuition of machine learning can support our understanding of the challenges and solutions Google's engineers are facing, while also opening our minds to ML's broader implications.
spent a year taking online courses, reading books, and learning about learning (...as a machine).
This post is the fruit borne of that labor -- it covers 17 machine learning resources (including online courses, books, guides, conference presentations, etc.) comprising the most affordable and popular machine learning resources on the web (through the lens of a complete beginner).
I genuinely hope you find this research useful, and I encourage comments on which materials prove most helpful (especially ones not included)!
*Free, but there is the cost of running an AWS EC2 instance (~$70 when I finished, but I did tinker a ton and made a Rick and Morty script generator, which I ran many epochs [rounds] of...)
Read content focused on teaching the breadth of machine learning -- building an intuition for what the algorithms are trying to accomplish (whether visual or mathematically).
Below contains a high-level summary of my reviews on all of the classes I took, along with a plan for how I would approach learning machine learning if I could start over.
Eventually it transformed into an idea: a review guide on the most affordable and popular machine learning resources on the web (through the lens of a complete beginner).
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.
AI and machine learning: What you do and don’t need to know for SEO
Artificial intelligence (AI) is a field of technology that is surrounded by both hype and misconceptions.
It is predicted that $60 billion will be spent by brands on AI technology by 2025, so this hype is having a direct impact on where companies allocate their budgets.
Although we tend to imagine eerily human robots that mimic our mannerisms, AI is actually a very broad field that encompasses a range of disciplines –
The headlines are typically reserved for AI applications like driverless cars and delivery drones, but this overlooks the fact that AI has the potential to improve every aspect of our lives.
Machine learning, which is a subset of artificial intelligence, is built on algorithms that take in data from their surroundings and take actions without being specifically programmed to do so.
AI helps us to find these insights in our customer data just as it allows search engines to match queries to the most relevant responses.
Through a process called deep learning, computer-based algorithms can interpret these assets AI can now unearth important statistics hidden within our analytics platforms.
The initial fear that AI would take over our jobs is increasingly replaced by a general sense of optimism of how AI can augment our abilities and extend what we can achieve.
Some examples of AI applications that do not directly impact the search and content industry would include: Needless to say, many in the search industry will be interested in these fields, but knowledge of these topics is not a prerequisite for SEO success today.
For marketers aiming to get started with AI today, I would recommend the following steps: From there, you will have a fantastic case study to demonstrate the positive impact AI can have to the rest of your organization.
- On Friday, February 28, 2020
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