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.

What should everyone know about machine learning?

1) Machine learning means learning from data; AI is a buzzword. Machine learning lives up to the hype.

2) Machine learning is about data and algorithms, but mostly data. There’s a lot of excitement about advances in machine learning algorithms. But data is the key ingredient that makes machine learning possible.

3) Unless you have a lot of data, you should stick to simple models. Machine learning trains a model from patterns in your data, exploring a space of possible models defined by parameters.

4) Machine learning can only be as good as the data you use to train it. The phrase “garbage in, garbage out” predates machine learning, but it aptly characterizes a key limitation of machine learning.

5) Machine learning only works if your training data is representative. Machine learning should warn that it’s only guaranteed to work for data generated by the same distribution that generated its training data.

6) Most of the hard work for machine learning is data transformation. Most of your time and effort goes into data cleansing and feature engineering — that is, transforming raw features into features that better represent the signal in your data.

7) Deep learning is a revolutionary advance, but it isn’t a magic bullet. Deep learning has earned its hype by delivering advances across a broad range of machine learning application areas.

8) Machine learning systems are highly vulnerable to operator error. When machine learning systems fail, it’s rarely because of problems with the machine learning algorithm. More likely, you’ve introduced human error into the training data, creating bias or some other systematic error.

9) Machine learning can inadvertently create a self-fulfilling prophecy. In many applications of machine learning, the decisions you make today affect the training data you collect tomorrow.

10) AI is not going to become self-aware, rise up, and destroy humanity. A surprising number of people seem to be getting their ideas about artificial intelligence from science fiction movies. We should be inspired by science fiction, but not so credulous that we mistake it for reality.

- courtesy Quora.

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.

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably.

They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference.

Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world.

In short, the best answer is that:

Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.

And,

Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.