So, what will the future look like with intelligent machines? AI has been growing exponentially in the past decade and touching our lives in ways you might not notice. For example, every time you use Google, AI is being used to show you the best results.
Every time you ask SIRI a question, neuro-language-processing and speech recognition are being used to provide you with an answer. Luxury cars can learn your driving style and adapt to it, to optimise efficiency, ride, and handling. So, AI is already very much present in our lives.
The backbone of AI is machine learning. If we want to make machines learn based on their knowledge and make decisions, firstly we can use algorithms to find meaning in random data. Secondly, we can use learning algorithms to find relationships so they can learn from that knowledge.
If we take Google as an example, we can gain a great understanding of how AI works. Google holds 10-15 Exabytes of data. To put that in perspective, if one PC contains 500GB then that figure is equivalent to 30 million PCs.
That’s a lot of data, and data is one of the fuels of AI. Let’s take Google Translate; its learning process is getting better over time. It turns out that Google Translate can get better by reading more articles. The learning algorithm is what powers computers to learn.
Let’s think about how the brain identifies images. Visual signals from our retina are relayed to the primal visual cortex. There are three different processing systems in our brain; one processes information about colour, the second about shape, and the third about movement and orientation.
If we want to create an application that identifies a logo, the computer needs to understand the image. It needs to look at each of the features of the image, process it, and compare it with what it already has in its memory to identify the logo.
To summarise, we input data, we use a learning algorithm to find meaning, and then we can use some learning networks to improve this process and learn even more.
Our brains, after thousands of years of evolution, are able to process information. We take in information and process it, and then it gives you some output. We can learn over time.
If we want to use image recognition or speech recognition, it would take too much manpower to identify each image or sound. So, we can help a computer learn by giving it its own knowledge about something. We just need to train the computer.
We can also combine image processing with neural networks. For example, in the Google self-driving car project, they use image processing and laser and ultrasonic sensors to enable the car to navigate safely.
One of the biggest breakthroughs in AI was when the IBM computer Deep Blue defeated a world chess champion under tournament rules. Calculation however does not equal true intelligence.
Google’s AlphaGo programme was used to defeat the South Korean ‘Go champion’ and used reinforcement learning as well as neural networks, which resembles our own learning processes.
AI will give us unprecedented opportunities to change our world in the same way as the industrial revolution did 200 years ago when humans harnessed coal and steam for power.
We also experienced great change in the 1990s when millions of computers reached the homes of consumers across the globe.
In 10 years from now, scientists could be using AI to find mutations in human DNA databases, to discover cures for diseases. Perhaps your car might be able to drive you to work! The possibilities are endless.
Although we are capable of creating machines that can learn and think, it will not replace human biological intelligence, it will enhance our world and our future. In the end, it will be up to us, and how we decide to use AI will influence how things might change in the future.
Let’s look back at history and see how good we are at handling major change.
Up until 1440, books were a very rare and expensive commodity. In 1440 the printing press was invented, and we could suddenly print all sorts of things, but that only changed very gradually.
Weaving and producing textiles was originally a cottage industry, but with mechanisation, textiles became much easier to produce, and as textiles became cheaper, they became more widely available to the masses.
New technology brings great advances but great disruption. Once new technology comes along, we should not ignore it, we must make decisions on how to use it, and use it to improve our future.