The Upsurge of Artificial Intelligence Scientist

Artificial intelligence is growing at a rapid pace. However, the downside is that there aren’t enough AI engineers to understand technology. Thus, tech professionals will need to evolve with newer AI tools and technologies to take control of the new AI role.

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rtificial Intelligence (AI) is already underway.

Perhaps, not the way you may have been led to think.

Though AI has been a recurring topic since the 1950s, it is only now that the field started gaining traction due to the advancement in technology and algorithms.

Most companies are excited to join the new fray of the AI trend. And, why wouldn’t they? With modern AI, deep learning techniques, and natural language processing (NLP), organizations are ready to embark on the AI journey. Opportunities in the Artificial Intelligence career will play a vital role in the foreseeable future.

Unraveling the journey of AI scientist

Integration of simulation time

Studying the Milky Way in person is a challenge for scientists, thus often end up developing simulations. Most importantly, traveling backward or forward in time, or even across the galaxy is not an option yet. However, you can still combine relevant variables to give it a closer look, just to ensure whether it is a molecule or just a galaxy.

Now the area where the simulation ponders is also known to be the home of AI and machine learning (ML) systems. Wherein data handling and pattern detection can be easily managed by the ML system.

Below are some of the renowned works where the unleashing of AI is seen.

Using the deep neural architecture search, some of the researchers could speed up scientific simulation up to 2 billion times in almost 10 scientific cases. These cases include climate science, high energy density physics, seismology, astrophysics, with the help of the same super architecture hyperparameters, algorithms, and architecture.

Picture having an AI system that uses scientific simulation to learn things that moves rather quickly beyond the given speed limit. Well, we already have researchers working on speeding simulations. Researchers created a model by introducing subnetworks. This subnetwork helps in exchanging information but through a limited network. The network data fed is of the movement of the sun and Mars. Once the data is fed, the network then finds relevant physical parameters and laws to make positive insights. For example, Copernicus concluded the solar system to be heliocentric.

In contrast, though the demand for AI engineers will increase, we are yet to grapple with problems associated with the advancement of technology.

AI and machine learning as powerful tools

Artificial intelligence-based systems along with machine learning have started developing tools used in research. And the capacity of handling swaths of data is now helping scientists have a better understanding of different research topics like multi-factorial mental health conditions, genomic regulations, and mass extinctions.

Though AI systems help scientists detect newer patterns and generate different hypotheses, machine learning isn’t asking the right questions either designing its experiments. Therefore, it may be justified to say these tools are fairly dumb to use in these contexts.

Taking the big intuitive leap

The theory of gravity can be easily picked by an AI-robot when it watches an Apple fall down the ground. But will a robot have the capability of dreaming about benzene rings? These are certain questions whose response depends upon the intuitive leaps taken by AI. Though we know robots are yet to can do such things. Whereas intuition and creativity depend upon more than just pattern detection and data. Something that would take you into amazement.

Besides, figuring out heliocentrism all by yourself through a simple simulation is not that bad. All in all, it took everyone a while to come up with a new antibiotic molecule that had no structure amongst the known ones that did have a little of the benzene ring story to it.

Is it time for robots?

AI can discover new materials while the robotic system is automated to perform tests in material science.

Though AI-driven robots sound dystopian, it is already here.

Adam is a lab robot, the very first of its kind that is infused with machine learning which can easily predict and test the gene function in baker’s yeast. In a few years, Adam was joined by Eve. Eve helps screen and select the compound which may be proven effective against any one of the tropical disease.

However, this is unlike any other test wherein the preliminary tests are first conducted on candidates. Now is an ideal time for the aspirant looking to build an AI career.

With technological advancement happening at a rapid pace, we all know what’s coming in the foreseeable future.

This could be great news for the technology industry. Although the history of AI is far stretched, technology companies are already excited about the development. In simple terms, AI technologies have started creating a dire need for scientists capable to take up AI tasks. As AI establishes its place in the world, scientists will evolve from their current roles to become AI scientists. Or perhaps a newer category of expert professionals in the AI realm.

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Artificial Intelligence, Machine Learning, Deep Learning and Data Mining is what that gives me kick.

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