The Future is Now
The Future is Now

Three Ways We Are Already Using Machine Learning

by Tiffany Harper about a year ago in tech

Get to know how you are already connected with machine learning in our everyday life.

Three Ways We Are Already Using Machine Learning

You’ve probably heard all of the hype about how AI is here with us already, giving us things like instant machine translation and self-driving cars. However, as a regular person, it can be difficult for you to visualize how all of this is affecting your regular life from one moment to the next. It might help to be given some examples of how machine learning and AI, in general, are being implemented in our everyday lives.

Janet Stark, a researcher at a cheap paper writing service and college essay writing service writes a lot about machine learning and technology in general, seeing how it is slowly pervading every area of our lives. We asked her if she could give us at least 3 examples of how machine learning is already here and she was generous enough to do so.

And this is how we all already use Machine Learning:

1. Baidu

Baidu is a Chinese search engine that is providing some serious competition for Google. It is also investing heavily in the benefits of machine learning and AI, just like Google. One of their greatest developments, however, is their Deep Voice program, which is a deep neural network that can generate synthetic human voices. These voices can hardly be distinguished from actual human voices. The neural network can learn all the nuances of speech, such as pitch, pronunciation, accent, cadence, and so on, and create very accurate sounding human voices.

What started as an idle experiment of the researchers at Baidu has now become a large project that promises to revolutionize the world of natural language processing, including voice pattern recognition and voice search.

2. Google

Google is working on so many areas of research and development that it has become easier to list the areas that they aren’t involved in. trying to summarize the areas where Google is involved would be an exercise in futility.

Google has been working hard over the past few years, looking into areas like medical devices, a technology that reverses aging, and, as is directly relevant to this article, neural networks.

The most famous neural network in the world would have to be Google’s DeepMind, which has often been dubbed “The machine that dreams”. However, as far as Google or their parent company Alphabet is concerned, they are exploring all areas of machine learning. This will inevitably lead to many developments in such areas as speech translation, natural language processing, prediction systems, and search rankings.

3. Twitter

Twitter has been part of many different controversies concerning the way the service is run. However, perhaps the most controversial recent development is their move toward an algorithm powered feed.

Twitter’s machine learning algorithms evaluate each tweet and score it according to a range of metric, then determine how they appear on your timeline. The whole point is to display the tweets that will lead to the highest engagement rate, based on the individual and their preferences. As a result, your feed is algorithmic. Understandably, not everyone supports this.

As the human race, we have been making some massive leaps in our technological achievements. That’s a good thing on the one hand because it is bringing the future to us at a rapid rate. On the other hand, it puts us at the danger of taking the massive leaps we’re taking for granted. Some of the applications we’ll mention in this article would have been the stuff of science fiction only a decade ago and would have been dismissed as impossible. However, they are here with us now because the rate of progress at which our researchers and innovators are moving is both amazing and powerful. Here is a closer look at the future of machine learning whether it turns out to be bad or good:

More Effective Learning for Machines

At the moment, as much as our machines learn pretty fast and pretty effectively, we still face a bottleneck in the sense that the current techniques being used to teach computers are a little slow, so to speak. In the near future, AI programs will be able to learn in a more effective manner. We will soon see AI programs that are so good at what they do that they will be able to recognize their own internal architecture, alter it, and improve it to give them the capability to learn even more. All of this will likely happen without the supervision or intervention of human beings.

Automated Countermeasures Against Cyber Attack

Cybercrime and variants of it have risen to high rates around the world and companies have been forced to seriously consider the tactics and strategies they use to respond to attacks that are leveled against them online. AI and machine learning will take a central role in this fight, being able to monitor the threats as they materialize in real time, prevent them, and respond to them, powerfully extinguishing breaches, DDoS attacks, and so on. AI programs will be able to do all of this in an automated fashion without the help of human beings. They will also be able to study the changing nature of their attacks and alter their own strategies to match them in real time.

Generative Models

Generative models are quite convincing already, like the ones being used by Baidu in its voice search algorithms. We will soon not be able to tell the difference between something generated by a human being and the same thing generated by a computer. AI programs will produce more and more sophisticated voices, images, and entire identities that will be indistinguishable from human ones.

Better Training for Machine Learning

Even the most sophisticated and powerful AI program can only learn as well as you teach it. The training model you use is as important to the effectiveness with which a machine learns as the learning algorithms implemented in the machine. Many machine learning systems usually need large volumes of data for the training to take place. As we improve the learning models, they will require fewer data to learn and so will be able to learn much faster without as much data.

Tiffany  Harper
Tiffany Harper
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Tiffany Harper

Tiffany Harper began her career as a journalist in the educational publishing house. Now she works as an experienced expert writer, mostly in education, business, and technology area. Please, contact her on Twitter.

See all posts by Tiffany Harper