I’m sure you’ve ever used at least one of these
- Google Translate
- Android’s speech recognition system
- Google Photos
But behind all of these tools we use in our everyday lives, how did these become what they are today?
Machines are (or were) kind of dumb. People needed to relentlessly guide and tell a machine precisely what to do and how to do it. To do a set of instructions. Expecting to give a machine an unmistakable arrangement of guidelines used as far as possible what machines could do. They can’t learn by themselves.
But today, machines are substantially more fit thanks to some extent to artificial intelligence (AI). These frameworks or neural networks are made to mimic the human brain to do certain tasks like problem-solving. Computer-based intelligence frameworks “realize” what to do by figuring out loads of past models. These artificial neural networks we call are influenced by how the human brain processes information. Just like how humans have neurons, neural networks have nodes.
It all started in 2011 when the Google Brain team started. They saw the potential of AI and machine learning.
They saw advancements inside the lab, but they wanted to bring this technology out from academia and to everyday commercial products.
Propelled by promising scholastic papers, this team thought of an idea to make this idea more feasible to draft and train. Something that could be common in society’s lives. Something that could deliver adapting so precisely thus broad that it would open up new skylines for this present reality utilization of AI.
By linking 16,000 computer processors, the team built one of the world’s largest neural networks for machine learning
Random thumbnails of photographs of cats extracted from 10 million YouTube videos were fed into the neural network to test its capabilities, but they didn’t tell the machines what a cat looks like in advance. What could a neural network with this large of a size and score achieve ? The simulation taught itself to recognize cats, which was far better than any previous machine learning effort!
Not only did they taught the machine how to learn, but they also taught it to create. So the machine was able to create a whole new cat by combining all of the features that they learned based on those cat thumbnails.
I guess machines are not as dumb now 🤔💭
The project proved that software-based neural networks accurately replicated a central theory of how the human brain works: individual neurons are trained to detect significant artifacts. But, more significantly, it demonstrated that, when fed massive quantities of data, machine learning algorithms can enhance the usefulness and capability of products we use every day.
Now, this technology is behind all of your favorite Google products.
From breaking down language barriers with google translate to looking at the analytics of corona cases in your city.
Even better, the team (now Google AI) has been leveraging this technology for social good — opening up new avenues for solving challenges and making significant differences in society. With AI, we now have yet another method to investigate and answer difficult unanswered questions. Is it possible to monitor disease as it spreads in order to eradicate it sooner? What if we could forecast natural disasters ahead of time? Or do you want to make a significant difference in the lives of people with disabilities?
Monitoring the COVID-19 pandemic
In trying to battle COVID-19, researchers from all over the world have used modeling techniques to identify trends in data and map the disease’s spread. Modeling a complex global event is difficult, particularly but there are many unknowns about the virus itself, as well as many variables such as human nature, emerging science and policy, and socio-economic issues. Google teams are distributing tools and services to the wider scientific group of epidemiologists, analysts, and researchers seeking to counter the virus’s health and economic effects. Accurate information and tools are vital in such unprecedented times like these. Mapping trends and data, or even seeing these maps with certain numbers and flagged places so we can avoid going out to these places to stop the spread of the virus. Saving lives.
Forecasting floods accurately and efficiently
The Google Flood Forecasting Initiative has been working with governments for many years to build systems with alert technology that can predict when and where flooding will occur, as well as keep people safe and aware. The focus of much of this work right now is India, where floods pose a significant threat to hundreds of millions of people. Where people can get injures, get sick, and even die. This technology can help citizens prepare and mitigate risks such as health risks and losing valuable items when floods come unprepared.
Understanding people with atypical speech impairment
Project Euphonia is an initiative aimed at making it easier to understand people who speak in atypical ways. They do this by using several methods like analyzing speech recordings in order to improve speech recognition models. But using voice-activated technology can also be aggravating. But with tools such as Google home, doing everyday tasks that may be a burden for these people can be made easier for them. With assistive tech, disabled people can feel freer when doing certain tasks, bridging the gap for their disablities.
It all started with a crazy idea.
Moonshot thinking is when you choose a huge problem and propose to create a radical solution to the problem using disruptive technology.
Moonshot thinking was inspired by President John F. Kennedy’s speech at Rice University in 1962, Where he said, “We choose to go to the moon in this decade”. He lit the fire that motivated an entire nation, and even the whole world to align itself to go to the moon. We might argue that JFK did not set that target because he knew how to achieve it or because he promised it would be simple. But, he clearly stated that we were going to accomplish something extraordinary, setting a deadline and motivating people to take action.
This inspired Astro Teller to build X (where the Google Brain team started), where people can build and develop once ‘crazy ideas’ to new technological advances that save and improve lives.