What Are Neural Networks, Why They Are So Popular And What Problems Can Solve
Over the past year there were a lot of news about various developments based upon neural networks.
Microsoft created how-old.net service, that quite accurately estimates your ages, and also developed their Fetch! project that allows to determine a breed of your dog on the basis of its photo. It is also worth remembering MSQRD startup, acquired by Facebook and incredible launch of Prisma App that had 20 millions installs within 1 month from launch.
I wrote about how Prisma works in my last post, take a look if you are interested in the topic.
Photo processed by Prisma
So, let's figure out what are neural networks and which tasks they can solve?
Neural networks are one of the trends in the development of artificial intelligence systems. The idea behind this concept is similar to human nervous system — namely, its ability to learn and to correct errors. The main feature of any neural network — it’s capability to act on the basis of previous experience, making fewer mistakes over time.
Neural network simulates not only the activity but also structure of the human nervous system. Network consists of a large number of individual computing elements ("neurons"). In most cases, each "neuron" refers to a specific layer of the network. Input data are sequentially processed at all the layers. Network knows parameters of each "neuron" and the order of the whole system that can be changed depending on the results obtained in the previous datasets. Difference between neural networks and other machine learning algorithms lies in the approach to training but basically they can solve the same problems.
Forecasting, decision making, pattern recognition, optimization and data analysis are among the main applications of neural networks. Most of major IT companies use them to make their services more useful and to create natural reaction to users behavior. Neural networks are the basis of many image recognition and speech synthesis systems. They are used in some navigation systems, algorithms of industrial robots or unmanned vehicles. Algorithms based on neural networks also protect information systems from malicious attacks and help to identify illegal internet content.”
Prisma in action
Here are trends of machine learning algorithms development that IMO will change our life within next 3 years:
- Classification and recognition of objects in images
- The emergence of bots-consultants, technical support or personal assistants
- Development of voice interaction interfaces for the Internet of things
- Quality monitoring service in call-centers Intelligent
- Monitoring and security systems video Analytics services
- Services that allow to find any person on internet upon its photo
- Intelligent self-learning systems for production processes and devices management
- Simultaneous interpreter bots for conferences and personal use (no language barriers anymore)
- Image processing for photo effects and art filters
Classification of objects using machine learning. Image source
Why neural networks have become so popular just now?
Scientists are developing artificial neural networks for more than 70 years. The first attempt to formalize a neural network refers to 1943, when two American scientists Warren McCulloch and Walter Pitts) submitted an article about the logical basis of human ideas and nerve activity.
Until recently the speed of the neural networks was too low to be widely disseminated, and therefore, these systems were mainly used in the development of computer vision algorithms and other learning algorithms were used in different areas.
Most importantly, what happened now — appeared variety of tricks to make neural networks much less prone to retraining.
Modern graphics cards allow hundreds times faster train and usage of neural networks. Now there is a large and public array of marked images (ImageNet), that can be used as training data.
Nowadays pre-trained off-the-shelf network can be easily found, on the basis of which you can make your own application without doing a long training phase of the neural network. All these factors ensure the application of the neural networks in various fields.
Nice writeup! I'm expecting that neural-net-based curation bots will really work well here on Steem. I don't think I have the expertise at this point to code one up myself, but it seems very likely that you could train a NN bot to predict post success, and just rake in the curation rewards. Thoughts?
If you like what I write, check out my series on the game theory of Steem!
Very high-quality content on a trends of machine learning algorithms development that IMO will change our life within next 3 years. I am very excited to share this post with my students who are visiting Dissertation Help - https://www.dissertationhelp.uk/ for getting dissertation assistance in the UK. Thanks.
I upvoted You
I'd argue that deep networks, seen as recursive generative linear models, owe much of their success to increased computational power but also to the transformations (RKHS) and optimization developments. On the flip-side, GPU makers are rewiring their hardware to make deep nets go. Exciting times ahead!
I would agree. Using Moores law, as the densities double and the price per chip decreases, the computational power on a given density of silicon increases at a stable price. Pair that with the computational ingenuity using/creating neural networks and the future has infinite potential.
Quantum computers will revolutionize this area and make neural network based algorithms ubiquitous.
We have to wait
I don't work with them, personally, but it's my understanding that if you want to use your own training set with Deep Learning, you basically need thousands of samples, and your own computing cluster.
It depends on the task, but basically you are right. Training stage requires a lot of computational power. If someone implements Deep Learning at Amazon servers, he probably spends few million dollars for just server infrastructure
It does require "a lot" of computing power. But considering what leaps and bounds we've come in the last few years, you would be amazed at what you can do with a normal desktop PC or even a smartphone these days.
yeah. Computers and processors are developed rapidly. Moreover there are some amazing parallelization technologies that can essentially alleviate computational requirements.
I think in a few years training of neural network will barely be a big problem.
Keep up the great work @krishtopa
Upvoted
1
It's great that you're spreading the word! I'm actually a big fan of neural networks and I've been trying to make significant contributions to further its advancement. I know I'm just a tiny droplet, but isn't ocean but a collection of many tiny droplets?
My favorite potential applications are with healthcare and robotics. I've been trying to catch up with the current technologies, so I better get back to studying. I have a lot of ground to catch up.
Hi, @jedau. Glad to hear you're also interested in the topic. I'm pretty sure that your droplet helps to keep this ocean sustainable.
Could you please tell us about healthcare applications in more details? I've never thought about use-cases in medicine.
At the top of my head, some of the applications for healthcare could include: clinical diagnosis, signal analysis for ECG and heart rate, image analysis for x-rays and MRIs, and also drug development.
yeah, everything that is somehow improves healthcare technologies is fascinating.
Thanks for the tip, need to read more on this topic
I must say this is great stuff! Hope you can write more on how machine learning can reduce language barriers. i think i have seen some of this in the web on this
Fascinating stuff.
Thanks, @Condra.
I'm glad you like it!
Better late than never. I have just read your post tonight!
Neural networks are indeed very powerful.
In my field (particle physics), there are used a lot, in particular for trying to identify objects that could leave similar tracks in huge detectors.