Technology that Speaks the Same Language
By Paul Golata, Mouser Electronics
Introduction
Goo Goo Gaga is a simple interjection. It is onomatopoeia from the Greek “onoma” for
“name” and “poiein” for “to make,” meaning “to make a
name”. It is a phrase that resembles the sounds that a baby makes before they are old enough and smart enough
to carry on a conversation (Figure 1). Somehow, as if by osmosis, babies naturally learn to speak
and ultimately converse in the native language they find themselves living in. If only it were like this for
computers and machines. Instead, these items need to be programmed by humans. Natural language processing (NLP), a
subset within the domain of artificial intelligence (AI), combines high technology with the linguistics of human
language, enabling machines and humans to communicate. It provides machines with the ability to understand both
written and spoken human communication. It allows humans and machines to speak the same language and to talk to each
other to exchange information and ideas. This article will look at how AI is helping us to have conversations with
our machines.
Figure 1: A young boy babbling on a mobile phone. He is too young to have a
meaningful discussion.
Human Language
The field of linguistics studies the methods and manner whereby processed information is communicated both
internally and externally to things with intelligence. Humans use what is called natural language. Natural language
can be coupled with technology. Conversational AI is helping to merge humans' sophisticated communication and
intellectual abilities with their technological capabilities.
Human communication happens by way of complex symbols that are perceivable to the senses. Notable human examples
include speech (hearing), written or sign language (visual), and physical contacts such as handshakes and hugs
(touch). Raymond Kurzweil, who was hired by Google in 2012 with the mission of bringing natural language insight and
understanding to the company, asserts that human language was the first invention of humanity. Language is a way
that humans can work together and build a society, have culture, and create technology.
All intelligence-manipulating communication requires a method to structure language. Grammar, syntax, and discourse
provide structure to a language so that its constituent components may be appropriately understood and interpreted.
Author, critic, and educator Neil Postman (1931–2003) believes that language is “pure ideology”
and should be viewed as an “invisible technology.” By this, Postman means that language is not neutral.
It is a reflection of the starting assumptions and its use frames the entire informational content that intelligence
utilizes.
Interpretation is the art of adequately receiving communication and processing it in the manner that was intended
by the communicator. The circumstances surrounding the specific grammar, syntax, and discourse employed are called
the context. It provides the external and internal environment into which the information is being processed. The
context that language finds itself in is a critical key in ascertaining what a communication means from the
perspective of the communicator’s intention. Because the intention of the communicator matters to the context,
the issue of agency is brought to bear. If intention is not part of the communicated message, then whatever is
transmitted cannot produce meaningful action since it will have only been derived from an original set of
happenstance, from which it is not possible to assert meaning.
Humans can create and utilize symbols to express themselves in new and unique ways without limitation. A human can
cry in pain, read Shakespeare, or sing an opera (Figure 2). These symbols come to have meaning as a
result of social interaction and agreement. Because human language is based upon social purpose, it allows both
change over time and unlimited variety as society develops new symbols to communicate what people experience.
Figure 2: The title page from an antique book of the plays of Shakespeare.
Other life forms can communicate in a manner that is natural yet distinct and different from human language. This
communication is generally a form of signaling understood within the species, but it does not involve the
manipulation of symbols and creative thought. For example, a dog’s bark may provide information to other dogs
who receive it and understand in a manner beyond general human understanding. Animals may also use other ways to
communicate that are not inherently understandable to humanity at initial glances, such as the abilities of bees to
do a dance that indicates the direction to fly to obtain pollen for the hive. Scientists recognize that, even though
animals may communicate with other animals of their species, there is no animal, including apes and chimpanzees,
that can manipulate signs and symbols to the degree that humanity can. Animals only work and communicate regarding
particular contexts and do not communicate regarding universal or abstract relationships.
From Humans to Machines
In contrast, machines and computers do not use human language. Their intelligence happens in the form of AI. All AI
utilizes programming, which enables it to receive information, compute, and act in an attempt to make sense of what
it is experiencing. Humans have created these machines and programming languages to be able to participate in what
the machine is capable of doing.
These languages follow a specific set of rules that have been agreed upon by social and primarily scientific
convention. Because of the general desire to be utilized universally, they are most frequently constructed with
formality; that is, there is a universally agreed-upon method to the logic contained within the artificial language.
Artificial languages (machine code or code) can be set up to perform specific predefined tasks.
Programming is the art and science of writing machine code. It is performed by manipulating the functionally
equivalent elements found in human language, including grammar, syntax, semantics, and discourse. Programming is
initially set up by humans but can be assigned to be done by machines (robots/computers) after the initial setup. An
algorithm is a set of instructions that have been formatted and arranged to achieve a specific function. Programming
code is generally broken down into a long series of discrete binary digital signals. These signals, representing
particular ON and OFF sequences, are then stored, analyzed, and processed in conjunction with the available
intelligence of the machine. All AI programming is based upon human conceptions of structure. AI semantics and
syntax thus function in a manner that emulates humans rather than, for instance, another species like apes,
dolphins, or rats.
Large Model Sizes
Human language is vast and complicated. It is a collection of shared knowledge and wisdom. Understanding and
meaning are derived from experience and context. The tremendous amount of variables means that the model size for
one language, such as English, is vast. When expanded to understand other languages that operate in different ways
such as Chinese, French, German, Hindi, Japanese, Spanish, etc. the model sizes required are genuinely staggering.
Language models must train on the most extensive and broadest data sets available to capture the most exceptional
level of nuance implied in the message. The upshot is that AI and NLP models must be able to handle a vast amount of
data and access it quickly and efficiently to have everything needed for understanding.
High Computation Demands
Machines must be able to train themselves quickly from a vast field of language in order to understand humans. This
requires high computational capabilities. GPUs, FPGAs, CPUs, ASICs, crossover processors, and microcontroller units
(MCUs) are necessary elements for any successful implementation. Let’s look further at how a crossover
processor might be part of a conversational AI solution.
Applications processors and MCUs are employed in embedded applications. Applications processors provide excellent
integration and performance, while MCUs are easy-to-use and low cost. NXP Semiconductors has placed these two
products together to provide one part that can simultaneously provide high performance, low latency, power
efficiency, and security in a low cost part. This product is ideally suited to handle a variety of human language
and voice-assistance applications.
NXP
Semiconductors i.MX RT106A Crossover Processor is a solution specific variant of the i.MX RT1060 family
of MCUs, targeting cloud-based embedded voice applications (Figure 3). It features NXP’s
advanced implementation of the Arm® Cortex®-M7 core, which operates at speeds up
to 600MHz to provide high CPU performance and the best real-time response. i.MX RT106A based solutions enable system
designers to easily add voice control capabilities to a wide variety of smart appliances, smart home, smart retail,
and smart industry devices. The i.MX RT106A is licensed to run NXP turnkey voice-assistant software solutions, which
may include a far-field audio front-end softDSP, wake-word inference engine, media player/streamer, and a host of
associated items.
Figure 3: NXP Semiconductors i.MX RT106A Crossover Processor. (Source: Mouser
Electronics)
Instant Inferencing
Machines must also be able to train themselves quickly from the massive field of human language and be able to draw
exceptionally fast inferencing with extremely low latency times if not in real-time. Products like Intel®
Xeon® Second Generation Scalable Gold Processors are enhanced to produce excellent inferencing
results (Figure 4).
Figure 4: Intel® Xeon® Second Generation Scalable Gold
Processors optimized for inferencing. (Source: Mouser Electronics)
These Intel processors are 64-bit, multicore server microprocessors built on 14nm lithography process technology.
The processors are based on the Cascade Lake microarchitecture that allows for higher clock speeds. The processors
are also optimized for demanding mainstream data centers, multi-cloud computing, and network and storage workloads.
These processors offer up to 22 cores/44 threads and feature Intel Turbo Boost Technology 2.0 that ramps up to
4.4GHz. The processors also feature up to four-socket scalability and support up to 46-bits of physical address
space and 48-bits of virtual address space. The devices take embedded AI performance to the next level with new AI
acceleration, including new Intel® Deep Learning Boost.
Conclusion
Technology is talking sense. The implementation of AI and NLP is an example of an emerging technology that will
allow humans and machines to communicate seamlessly and in real-time. Humans and machines are now starting to speak
the same language.
Paul Golata joined
Mouser Electronics in 2011. As a Senior Technical Content Specialist, Mr. Golata is accountable for contributing
to the success in driving the strategic leadership, tactical execution, and overall product line and marketing
direction for advanced technology related products. Mr. Golata provides design engineers with the newest and
latest information delivered through the creation of unique and valuable technical content that facilitates and
enhances Mouser Electronics as the preferred distributor of choice.
Before Mouser Electronics, he served in various Manufacturing, Marketing, and Sales related
roles for Hughes Aircraft Company, Melles Griot, Piper Jaffray, Balzers Optics, JDSU, and Arrow Electronics. Mr.
Golata holds a BSEET from DeVry Institute of Technology – Chicago, IL; an MBA from Pepperdine University –
Malibu, CA; an MDiv w/BL from Southwestern Baptist Theological Seminary – Fort Worth, TX; and a PhD from
Southwestern Baptist Theological Seminary – Fort Worth, TX.
