I remember the first time I ever tried to learn to code.
It prints Hello World, he replied.
Whats
Ignore that for now.
This is exactly how humans learn best.
We learn Python,thenC,thenassembly, not the other way around.
In its short lifespan, TensorFlow has already become way,waymore user-friendly than it was five years ago.
Even writing a simple print statement was a challenge.
Just earlier this fall, TensorFlow 2.0 officially launched, making the framework significantly more developer-friendly.
But if you havent or youre just learning, youve probably got some questions.
Like, what is Dropout?
What are these dense layers, how many do you need and where do you put them?
Where were going
So what will the future of easy-to-use ML tools look like?
Also disclaimer it is whatIspend all my time thinking about as an engineer at Google.
For one, well start to see many more developers using pre-trained models for common tasks, i.e.
rather than collecting our own data and training our own neural networks, well just use Googles/Amazons/Microsofts models.
Many cloud providers already do something like this.
Googles freeTeachable Machinesite lets users collect data and train models in the internet tool using a drag-and-drop interface.
Meanwhile,Google Cloud AutoMLis an automated model-training framework for enterprise-scale workloads.
Its not ok if a medical version of Alexa thinks your doctor prescribed you Enulose instead of Adderall.
Understanding when and how models should be used in production is and will always be a nuanced problem.
Its especially tricky in cases where:
Take medical imaging.
Were globally short on doctors and ML models are oftenmore accuratethan trained physicians at diagnosing disease.
But would you want an algorithm to have the last say on whether or not you have cancer?
Same thing with models that help judges decide jail sentences.Models can be biased, but so are people.
Explainability
Machine Learning models are notoriously opaque.
Thats why theyre sometimes called black boxes.
denying loans to people from a specific age group or zip code).
We still havent entirely cracked this problem as an industry, but were making progress.
Googles Facial Recognition Model Card shows the limitations of this particular model.
This is true of almost any sufficiently complex field.
So if youre a software developer lucky enough to possess additional expertise, youre already ahead of the curve.
She also likes solving her own life problems with AI, and talks about it on YouTube.
