Building AI: How Bots Are Training Themselves to Become More Efficient

Building AI: How Bots Are Training Themselves to Become More Efficient

By | 2019-02-13T15:30:57+00:00 January 10th, 2019|AI, Technology|0 Comments

Building AI – Google and other leading names in the industry such as Apple, Microsoft, and Amazon are fighting for a small pool of researchers. Who are looking for automated ways to deal with the shortage of artificial intelligence experts. It is a dream of researchers that artificial intelligent machines can build other artificial intelligent machines by themselves. In May 2017, researchers at Google Brain announced the creation of AutoML, an artificial intelligent machine that has the ability to develop its own kind of Artificial Intelligent machines.

The researchers automated the design of artificial intelligence models using an approach called reinforcement learning. The AutoML acts as a controller that develops a Child AI network for a specific task. The particular AI child generated is called NASNet. It recognized objects like cars, people, traffic lights, backpacks, handbags, and a lot of things in a video in real time. Recently, AutoML created a Child of AI that even performed so exceptionally, that it outperformed all of its human made counterparts.

AutoML also evaluates NASNet’s performance and even uses the collected data to improve the further output. According to the research, NASNet was 82.7% accurate at the time of predicting images on Image Net’s validation set. In addition to this, a less demanding version of NASNet outperformed on the mobile platforms by 3.1%.

Why Is AI Building AI?

This is because of the basic economics demand and supply mismatch. It is difficult to find a resourceful and experienced data scientist. And if you find one, fitting it in your bill would come across to be little out of your budget estimation for this role. It is a real problem for every kind of organization. Though Google and Facebook have a strong financial shield. But even they struggle when it comes to finding a quality resource to add to their team.

Decondia - AI algorithms learn how to develop new AI systems

Being the master of deep learning takes even years of research and countless hours of productive work. This is the reason that we are experiencing increasing rollout of tools and systems that enable you to build AI programs, a chatbot or an analytic platform.

Building an AI from an AI sounds so magical!

Deep learning is more than just writing algorithms. The problem is that before developing an efficient algorithm there is a lot of complex work involved. The algorithm that is developed evolves and is refined over time after processing tons of data. In short, there is a lot of work involved before figuring out what works the best for the specific program.

Building AI – An AI that builds another AI means you are handling that critical part of the process to a machine. Developing an algorithm with the only purpose of analysing other algorithms. And figuring out which algorithm works the best, is not an easy game.  The initial purpose is to eliminate a good deal of hard work involved in the development of basic machine learning algorithms.

And while considering AutoML’s NASNet, it provides efficient, accurate computer vision algorithm. Then that provides a number of potential applications. Companies can use them to develop highly sophisticated, AI enabled bots to help visually impaired people regain their eyesight. NASNet can also help designers improve self-driven cars technology. The quicker an autonomous car can recognize the object in its path, the faster it can react to tackle them.

Building AI

The Google researchers also claimed, that NASNet can also provide useful applications and have open-source for interference on image classification and object detection. There is plenty of useful applications provided by AutoML and NASNet. But the generation of AI from an AI also raises some concerns.

Decondia - Integration of more AI and ML will lead to a fully automated society

For example – What if AutoML  develops fast paced systems that our society won’t be able to keep up with? The time is not far when we can involve NASNet in automated surveillance systems. It would be perhaps sooner than the regulations can put into the place to control such system.

AI can turn dangerous, if not monitored and controlled on time. And for this Amazon, Facebook, Google, Apple, and various other leading organizations have become a part of the AI Partnership to Benefit People and Society – an institution that focuses on the responsible development of AI.  Additionally, the Institute of Electrical and Electronic Engineers has proposed a goal of developing ethical standards of AI. Recently, DeepMind – a research company owned by Google’s parent company announced the development of the group that focuses on the moral and ethical implications of AI.

Various governments are also working on regulations to prevent the use of Artificial Intelligence for dangerous purposes, such autonomous weapons. And till the time as humans maintain control of the overall functionality of AI development.

Our previous blog post – AI 2019 – What to expect from Artificial Intelligence in the upcoming year