Research indicates, With Artificial Intelligence On Our Side, It is now easy to predict If It would rain in two hours.
The Nowcasting system has been developed by a team of science experts at Google-owned London AI Lab DeepMind who were joined by subject stalwarts at the University of Exeter and they collaborated with Met Office.
Earlier with obsolete methods, complex equations had to be visited with which forecasts were made for six hours to two weeks time.
But the short-term predictions are now a reality, with a refined AI system designed to make precise forecasts even for critical storms and floods.
Truly, climate change has made it anticipation tough for us for adverse weather conditions, given that the rains have become more severe and more frequent now than before and to our horror, such is likely to cause damage to life and property.
Niall Robinson, who heads Met Office partnerships and product innovation discloses, “Extreme weather has catastrophic consequences, including loss of life and, as the effects of climate change suggest, these types of events are set to become more common,”
“As such, better short-term weather forecasts can help people stay safe and thrive.”
The system is designed to uncover the models indicating rainfall when it scanned through the radar maps of the UK covering a time span from 2016 through 2018 as a base, while its run over the 2019 maps was considered as a test and the result sought the approval from 50 senior meteorologists from Met Office who found a towering accuracy rate of 89%.
The research got a place in the journal Nature and was described as “”Meteorologists significantly preferred the [AI] approach to competing methods.”
Mr. Shakir Mohamed, senior scientist at DeepMind shares, “It’s very early days but this trial shows that AI could be a powerful tool, enabling forecasters to spend less time trawling through ever growing piles of prediction data and instead focus on better understanding the implications of their forecasts.
“This will be integral for mitigating the adverse effects of climate change today, supporting adaptation to changing weather patterns and potentially saving lives.”
Now to predict the structures of almost every protein made by the human body, AI technology has been harnessed.
Now, with this development in place, discovering new drugs has been possible to treat ailments and for other applications.
In living organisms, proteins are considered vital building blocks and every cell within us teems with it.
Now, to fathom the shapes of proteins, such is important for medical advancement, but till now, not much has been researched about.
To unfold the structure of around 350,000 proteins relating to humans and other organisms, researchers made use of a program that is known as AlphaFold.
Within the nuclei of human cells, there is DNA, where human proteins making instructions are kept.
The human genome expresses around 20,000 of such proteins and such are collectively referred to as “proteome”.
Such results from AlphaFold also fetched comments from Dr. Demis Hassbis, who co-founded AI company DeepMind and is currently its chief executive too, “We believe it’s the most complete and accurate picture of the human proteome to date.
“We believe this work represents the most significant contribution AI has made to advancing the state of scientific knowledge to date.
“And I think it’s a great illustration and example of the kind of benefits AI can bring to society.” He added: “We’re just so excited to see what the community is going to do with this.”
Proteins are comprised of chains smaller building blocks which are called amino acids and such chains bend in multiple and diverse ways and take the 3D shape. A protein’s design unrolls its function inside the human body.
By AlphaFold predicts 350,000 protein structures where 20,000 are stored in human proteome and some model organisms from scientific research point of view, such as E coli, yeast, the fruit fly and the mouse.
Such a breakthrough is outlined by researchers of DeepMind alongside the team from the European Molecular Biology Laboratory (EMBL) in Nature, a prime journal.
Now, emphasizing the structural position of about 58% amino acids present inside the human body, AlphaFord made such a confident prediction.
Prediction, backed by confidence was made for the positions of 35.7% which is twice to the number confirmed through experiments.
Among the conventional techniques implemented to work out protein structures, there were X-ray crystallography, cryogenic electron microscopy and certain others like this. But such have been pretty tough to implement and Prof John McGeehan, structural biologist revealed before the media, “It takes a huge amount of money and resources to do structures,”
As such, 3D shapes are commonly considered as kind of targeted scientific investigation but there is not a single project until now, through which we have been able to determine structures for all the proteins developed by our body.
However, structure confirms just 17% of the proteome covered in experiments. Now on such predictions put forth by AlphaFord, Prof McGeehan remarked, “It’s just the speed – the fact that it was taking us six months per structure and now it takes a couple of minutes. We couldn’t really have predicted that would happen so fast.”
“When we first sent our seven sequences to the DeepMind team, two of those we already had the experimental structures for. So we were able to test those when they came back. It was one of those moments – to be honest – where the hairs stood up on the back of my neck because the structures [AlphaFold] produced were identical.”
From EMBL side, Prof Edith Heard remarked,” “This will be transformative for our understanding of how life works. That’s because proteins represent the fundamental building blocks from which living organisms are made.”
Now, we are in a better position to look for new areas which include new drugs and treatments for disease, making crops of future which would resist climate change impact and enzymes that would split the plastic which would pervade our environment.
The team led by Prof McGeehan already scavenges through AlphaFold’s data in a bid to develop faster enzymes for plastic degradation.
He maintained that through such project, predictions were made for proteins belonging to interest whose structures could be resolved through experiment so as to speed up their observation by “multiple years”.
Dr. Ewan Birney, who sits in the director’s seat at EMBL’s European Bioinformatics Institute commented on structures predicted by AlphaFord as “one of the most important datasets since the mapping of the human genome”.
As of now, in a bid to make AlphaFold code and protein structure predictions accessible to scientific community around the globe, collaboration is formed between DeepMind and EMBL.
Dr. Hassabis opined DeepMind reckoned to vastly bolster the coverage in the database to almost every sequenced protein that is understood by science – i.e. structures spanning 100 million.