Generative AI is in the news a lot this year, from developers of AI technologies to industry experts discussing the value and impact on their industry. The word “Generative” is new to many people, but most of us have known the term 'Artificial Intelligence' (AI), for our entire life. That may be because AI has been around since Dwight D. Eisenhower was the U.S. President (1953-1960).
It all started in 1955 when, an Assistant Professor of Mathematics at Dartmouth University named John McCarthy, organized a group to discuss and develop ideas about thinking machines. The young professor picked the name 'Artificial Intelligence' for the new field of study. His subsequent 1956 summer workshop is widely considered to be the founding event of Artificial Intelligence as a field.
This certainly begs the question: Why are so many companies actively embracing Generative AI now?
Artificial Intelligence is the science of using machines to simulate human decision making.
The concept of AI was born long before humanity had the ability to employ it. For decades after Professor McCarthy first coined the term “artificial intelligence” (AI), people throughout the U.S. and around the world were capturing letters typed on typewriters and captured on carbon paper. Hand written meeting notes and reports on a wide range of topics were organized in paper folders. These documents were stored in tens of thousands of file cabinets in tens of thousands of offices. Cooled rooms containing microfilm and newsreels contained a great deal more information. In millions of homes, family and personal information could be found in photo albums, audio tapes and personal/family videos. And none of it was digital.
It was a nearly insurmountable effort to collect and deliver massive amounts of information to a ‘machine’ (computer) as the 1956 Dartmouth team completed their 1st meeting. It would be quite a while until humanity made significant progress in capturing digital data.
The 1980s brought rapid change. Computers replaced microfilm for storing information. Businesses adopted the personal computer as their device of choice. Microfilm and the typewriter began to fade into history. By the end of the decade, a treasure trove of information that might have been captured on microfilm or carbon paper in the previous decade was now captured digitally, by tens of millions of people.
In the 1990s, many of us bought our first cellular phone. Cell phone usage grew rapidly and the device’s functions and benefits evolved quickly. Within the first 10 years of the new millennium, the humble cell phone had transformed into a hand-held computer that could send and receive all types of digital data, equipped with a camera for taking excellent (digital) pictures.
Welcome to the 21st century. WiFi signal strength was getting better as was cell phone signal strength. Coverage to more and more places allowed WiFi to support the growth of new social media programs. More recently, the Internet of Things (IoT) allowed each of us to capture and share any type of data wirelessly from virtually anywhere.
In 2023, AI programs can access nearly any of humanity’s digital data. The next question is, can AI effectively work with the immense amount of data to simulate human thinking?
Machine Learning & the Generative Model
Wikipedia credits Tony Jabara for defining two specific types of machine learning.
Generative Model - A statistical model of the joint probability distribution on a given observable variable X and target variable Y.
Discriminative Model - A model of the conditional probability of the target Y given an observation X.
According to TechTarget, Generative AI was introduced in the 1960s in chatbots. But it was not until 2014, with the introduction of generative adversarial networks, that it could create convincingly authentic images, videos and audio of real people.
Until 2014, most significant machine learning models were released by academia. Since then, industry has taken over, per Stanford University's Human-Centered Artificial Intelligence. After more than 6 decades of R&D and technological advances, many of the roadblocks to AI success have been eliminated.
Society’s collective data is now created and stored in digital form. That data can be accessed by either a cable connected to computers and servers, or via wireless access to systems and devices which are miles away. Generative models that are at the heart of AI programs can now create new content by learning from the voluminous available data, then use all of that data to simulate human thinking.
Why are so many companies embracing Generative AI now?
Because the landscape is now fertile for AI implementation.
NTI’s role as technology consultants is to provide the required design for the cabling infrastructure and wireless communications systems that assure AI will work as planned. This includes a structured cabling system design and end-points such as CCTV cameras, access control and audio visual systems. Other companies are the experts for AI models and solutions. NTI specializes in providing the design for the systems that support and allow Generative AI to work.
Since 1998, NTI has provided design, documentation and consulting services for Division 27 and 28 systems, often referred to as low voltage systems. The pace of progress of technology requires us to remain students of our industry. We are proud to be considered as thought leaders in low voltage having worked on 1,800 projects over the past 25 years. We strive to provide a ‘best in class’ solution based on the owner’s vision and project budget.
NTI's commitment is to provide our clients with objective, superior technology consulting and design services. NTI’s leadership team averages 20+ years of experience in the industry. NTI's staff have earned credentials such as RCDD, PMP and CTS-D. NTI design and documentation teams work in either Revit or AutoCAD, as required. Once design is complete, our construction administration services confirm our design intent so that all systems work as planned on opening day.
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