Machine Learning Strata

THE BEGINNING IS NOW.... !!!

Intelligent systems, like those powered by the latest genre of machine learning software, aren’t just getting smarter: they’re getting smarter faster.Understanding the rate at which these systems develop can be a particularly challenging part of navigating technological change.

What we might call “machine teaching”—when devices communicate gained knowledge to one another—is a radical step up in the speed at which these systems improve.Connected devices are now sharing knowledge between themselves, escalating the speed at which they learn.Sometimes it is cooperative, for example when one machine learns from another like a hive mind. But sometimes it is adversarial, like in an arms race between two systems playing chess against each other.

Data is the fuel of machine learning, but even for machines, some data is hard to get—it may be risky, slow, rare, or expensive. In those cases, machines can share experiences or create synthetic experiences for each other to augment or replace data. It turns out that this is not a minor effect, it actually is self-amplifying, and therefore exponential.

As machines begin to learn from their environments in new and powerful ways, their development is accelerated by communicating what they learn with each other. The collective intelligence, spread across the planet, can accelerate each individual machine’s predictive ability. Where it may take one driverless car significant time to learn to navigate a particular city—one hundred driverless cars navigating that same city together, all sharing what they learn—can improve their algorithms in far less time.

As other AI-powered devices begin to leverage this shared knowledge transfer, we could see an even faster pace of development. So if you think things are developing quickly today, remember we’re only just getting started.

Agree?? Leave your comments below.

It is no doubt that the sub-field of machine learning has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought before.

So if you want to learn more about machine learning, how do you start?

Enroll in Udacity’s Intro to Machine Learning online course or study research areas that are logical and artificial, focusing primarily on the use of logic to model human-like planning, reasoning and problem solving.
You may spark up a discussion on theory/core concepts and hands-on problem solving for ML.
Last but not the least, grab experience from multiple tech talks on deep learning, neural networks, data architecture — and Machine Learning conferences with a lot of well-known professionals in the field.