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The Role of Artificial Intelligence in Instrument Automation

The Role of Artificial Intelligence in Instrument Automation

From self-driving cars to Facebook’s artificial intelligenceArtificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. () botsA bot is a piece of code, which does a predefined set of actions on behalf of someone. Bots are used to manage Twitter Followers, they answer email requests or order more supplies as soon a certain item runs low. to speak their own language, and yes, even that series of “self-aware machine” movies starring Arnold Schwarzenegger, the appeal of technology—and just how far it can be stretched—will always capture imaginations and headlines globally.

SwissCognitiveWith already integrated into our daily lives—Amazon has sold over 11 million echo devices to date, and Microsoft’s personal assistant now has more than 141 million monthly users—you can see why the subject generates so much interest and sells so many box office tickets. Although the Skynet fictions of the silver screen frequently cast in a negative light (why are the robots always trying to kill us?), in the real world, scientific organizations and researchers are increasingly considering new technologies to revolutionize how they work. In the future, will form an integral part of all laboratory roadmaps.

The current laboratory landscape

There are a variety of potential uses for artificial intelligence within the laboratory, and how they interplay with humans and automated instruments. Natural language processingNatural Language Processing (NLP) is the part of the technology world concerned with language. Natural language means that the language is produced by or for humans. This website for example is all written in natural language. NLP ranges from speech recognition, to language synthesis, it also involves tasks such as machine translation or information retrieval. () could be used to transcribe a researcher’s , or enable instruments to start or stop tasks. Likewise, instruments could self-calibrate if inconsistencies are spotted, or notify users if steps in a process are missed. But, before organizations can take advantage of technologies, or even consider how they would work in practice, they need to ensure they’re using the right equipment.

In modern labs, scientific organizations and researchers are using a wide set of instruments and tools to record their data. Older technologies, including outmoded paper forms and notebooks, are still frequently used, meaning devices remain unconnected and data remains in silos. It’s not an ideal starting point. Thankfully we’re starting to see an uptick in organizations purchasing and implementing Internet of Things (IoT)-enabled technology and other smart devices to manage their data.

For organizations to truly take advantage of , the ability to connect data to data is critical, so, naturally, they will question how systems could fit in with their pre-existing automated instruments. With in its infancy, it’s likely that technology and automated instrument technology will evolve in tandem—after all, the impact of relies significantly on the quality of data available. Instead, when choosing technology for their laboratories, organizations just need to make sure their providers have future-proofed their products. How easy are they to upgrade? Can they connect to other cloud-based technologies?

In the laboratory, artificial intelligence is actually more about augmented intelligence. That is, if data is captured correctly, then new systems and programs can learn based on algorithmsAn algorithm is a fixed set of instructions for a computer. It can be very simple like "as long as the incoming number is smaller than 10, print "Hello World!". It can also be very complicated such as the algorithms behind self-driving cars. and training sets from any past information available in the system. It’s also why organizations should capture information from every experiment, even if the results aren’t what was expected or intended: all data can tell a story. When complete sets of data are recorded, alongside content and any other relevant information, and automation in the lab can really start to break new ground. […]

  1. H. Douglas Cook

    When I first dreamed of AI it was in the 1960’s when we lived just a few blocks from Disneyland. While I always knew it would come about, I thought it would be the perfect application for the 80 core CPU that came out a few years ago. Little did I think that when developing tooling long for a larger, faster IC chip, did I think in my lifetime we would get to an 80 core CPU.
    When it comes to automotive technologies, I believe it should be an assistant to driving and not a replacement. Kind of like an advanced version of the cruise control where those small plastic reflectors that let you know your changing lanes could actually have a data code in them to help direct and reflect the position on the roadway, and it’s location. What a marvelous world it would be to only have to set your destination and allow the car to be driven, by an onboard computer system. The biggest hurdle is that the chip can not get to hot, or it would miss it’s calculations.
    Just a random thought, think about it.
    I know if I was in a position to develop more advanced interactive mechanisms, I would do so. But life was not as generous to this old soul.

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