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How artificial intelligence can revolutionise science	How artificial intelligence can revolutionise science
Talk about of artificial intelligent (AI) alway will focus on its discrimination or bias, or ai may take over human's jobs, may even more the extinction of human race.	DEBATE about artificial intelligence (AI) tends to focus on its potential dangers: algorithmic bias and discrimination, the mass destruction of jobs and even, some say, the extinction of humanity.
There are some stories are horrible, but there are some good sides the this technology, others people are focus on its potential in solving big and hard problem.	As some observers fret about these dystopian scenarios, however, others are focusing on the potential rewards. ai could, they claim, help humanity solve some of its biggest and thorniest problems.
And they say AI will make this happen in a special way like by accelerating the speed of science discovery such as medical, climate or green technology.	And, they say, ai will do this in a very specific way: by radically accelerating the pace of scientific discovery, especially in areas such as medicine, climate science and green technology.
Some influences in the AI filed like Demis Hassabis or Yann LeCun believe that AI will change the science and move to a new era of discovery.	Luminaries in the field such as Demis Hassabis and Yann LeCun believe that AI can turbocharge scientific progress and lead to a golden age of discovery.
Will they right?	Could they be right?

The saying like that are worth to think casrfully when hear too much of bad saying like the AI may cause big unemployment or robot may kill us.	Such claims are worth examining, and may provide a useful counterbalance to fears about large-scale unemployment and killer robots.
Many technologies before are over valued.	Many previous technologies have, of course, been falsely hailed as panaceas.
When the telegraph way invented, in 1850s it way prised would bring peace to the world, also the plane in 1900s， experts in 1990s said the internet would make inequality and destroy nationalism.	The electric telegraph was lauded in the 1850s as a herald of world peace, as were aircraft in the 1900s; pundits in the 1990s said the internet would reduce inequality and eradicate nationalism.
But we have old experience that AI can solve the world's problems, in the history, in some fileds, there was some periods when the new technology near and the new technology really make make the world change, by bring new scientific discovery and invented new things.	But the mechanism by which AI will supposedly solve the world’s problems has a stronger historical basis, because there have been several periods in history when new approaches and new tools did indeed help bring about bursts of world-changing scientific discovery and innovation.
In the 17th century the invection of microscopes and telescopes open a new era, in this era, by using the tools will encouraged scientifist to using observations but not the wisdom from old books, also the introduce of scientific journals give them a new way to share their findings.	In the 17th century microscopes and telescopes opened up new vistas of discovery and encouraged researchers to favour their own observations over the received wisdom of antiquity, while the introduction of scientific journals gave them new ways to share and publicise their findings.
The result in astronomy,physiscs the other fields was very good, and the most import is the invection of pendulum clock lead to stream engine that is the thing lead us to the Industrial Revoltion.	The result was rapid progress in astronomy, physics and other fields, and new inventions from the pendulum clock to the steam engine—the prime mover of the Industrial Revolution.

Then in 19th century, from laboratories research we got chemical fertilizer pharmaceuticals, the the most inport thins transistor which is the base of computer.	Then, starting in the late 19th century, the establishment of research laboratories, which brought together ideas, people and materials on an industrial scale, gave rise to further innovations such as artificial fertiliser, pharmaceuticals and the transistor, the building block of the computer.
From the mid-20th century, computer go back to make a new sicence based on simulation and modelling, which lead to new design form of weapons aircrafe and weather forecasting.	From the mid-20th century, computers in turn enabled new forms of science based on simulation and modelling, from the design of weapons and aircraft to more accurate weather forecasting.

And the cimputer revoluion still in going	And the computer revolution may not be finished yet.
As we report before in the special Science section, AI tools has been widely use in almost every fields of science, the adoption rate varies from 7.2% in physics and astronomy in the 2022 to 1.4% in veterinary science.	As we report in a special Science section, AI tools and techniques are now being applied in almost every field of science, though the degree of adoption varies widely: 7.2% of physics and astronomy papers published in 2022 involved AI, for example, compared with 1.4% in veterinary science.
AI is being used in many ways.	AI is being employed in many ways.
It can find the possible candidates in analysis, for example find the properties of molecules in drug discovery or find the things needed in batteries or solar cells	It can identify promising candidates for analysis, such as molecules with particular properties in drug discovery, or materials with the characteristics needed in batteries or solar cells.
It can go throught thousands of data which produced by CERN or telescopes data, and find the patterns in them.	It can sift through piles of data such as those produced by particle colliders or robotic telescopes, looking for patterns.
And AI can even use in complex system such as protieins folding or the information of galaxies to help analysis data for scientist.	And AI can model and analyse even more complex systems, such as the folding of proteins and the formation of galaxies.
AI tools also have beed used in find antibiotics, Higgs boson and the accents in wolves in wild.	AI tools have been used to identify new antibiotics, reveal the Higgs boson and spot regional accents in wolves, among other things.

All these are welcome.	All this is to be welcomed.
But the journel and lab improve itself and unlock more power to make new discovery, by include people and ideas to mingle in a new way and colaborate in more lage scale.	But the journal and the laboratory went further still: they altered scientific practice itself and unlocked more powerful means of making discoveries, by allowing people and ideas to mingle in new ways and on a larger scale.
AI also have the potential to do this job.	AI, too, has the potential to set off such a transformation.

	Two areas in particular look promising.
	The first is “literature-based discovery” (LBD), which involves analysing existing scientific literature, using ChatGPT-style language analysis, to look for new hypotheses, connections or ideas that humans may have missed.
	LBD is showing promise in identifying new experiments to try—and even suggesting potential research collaborators.
	This could stimulate interdisciplinary work and foster innovation at the boundaries between fields.
	LBD systems can also identify “blind spots” in a given field, and even predict future discoveries and who will make them.

	The second area is “robot scientists”, also known as “self-driving labs”.
	These are robotic systems that use AI to form new hypotheses, based on analysis of existing data and literature, and then test those hypotheses by performing hundreds or thousands of experiments, in fields including systems biology and materials science.
	Unlike human scientists, robots are less attached to previous results, less driven by bias—and, crucially, easy to replicate. They could scale up experimental research, develop unexpected theories and explore avenues that human investigators might not have considered.

	The idea that AI might transform scientific practice is therefore feasible.
	But the main barrier is sociological: it can happen only if human scientists are willing and able to use such tools.
	Many lack skills and training; some worry about being put out of a job.
	Fortunately, there are hopeful signs.
	AI tools are now moving from being pushed by AI researchers to being embraced by specialists in other fields.

	Governments and funding bodies could help by pressing for greater use of common standards to allow AI systems to exchange and interpret laboratory results and other data.
	They could also fund more research into the integration of AI smarts with laboratory robotics, and into forms of AI beyond those being pursued in the private sector, which has bet nearly all its chips on language-based systems like ChatGPT.
	Less fashionable forms of AI, such as model-based machine learning, may be better suited to scientific tasks such as forming hypotheses.

	The adding of the artificial
	In 1665, during a period of rapid scientific progress, Robert Hooke, an English polymath, described the advent of new scientific instruments such as the microscope and telescope as “the adding of artificial organs to the natural”.
	They let researchers explore previously inaccessible realms and discover things in new ways, “with prodigious benefit to all sorts of useful knowledge”.
	For Hooke’s modern-day successors, the adding of artificial intelligence to the scientific toolkit is poised to do the same in the coming years—with similarly world-changing results.