AI
is a new technology that helps businesses automate processes and make business
decisions. It is being applied to a wide range of tasks in the workplace, from
security intelligence and chatbots to data analysis, customer service, lead
generation, and fraud detection. It is used to improve existing technologies
like autonomous cars, smart cams, and speech recognition and is making products
more intelligent by adding features.
AI’s
rapid rise is due to increases in data volume and velocity and computing technology
advances. This combination triggered an AI renaissance in the 1990s, leading to
NLP, computer vision, robotics, and machine learning breakthroughs. This is
reflected by landmark achievements in the last two decades.
There
are concerns about the ethical use of AI, including the potential for machines
to replace human jobs, bias, and discrimination. These issues can be addressed
through various means: regulation, education, workforce development, ethics
training, data access, and international cooperation. It’s better to focus on
advancing overall goals than trying to crack open the black box of individual
algorithms and regulate specific decision-making processes. This would limit
innovation and create a significant barrier to technology adoption.
Augmented
and Alternative Communication (AAC)
AAC
refers to devices that augment or replace natural speech for those who cannot
speak. AAC systems/devices can include communication boards, touchscreens that
allow typing with the eye or fingers, and speech synthesis software. Originally
called Augmentative Communication, the term alternative was added in 1983 to
highlight that communication devices can augment or replace speech. People use
AAC temporarily following a stroke or other injury/condition or may use it
long-term because their natural speech is impaired.
Many individuals with complex communication needs are using various AAC tools.
The
recent availability of AI in this field is a powerful new tool to improve the
lives of these individuals. However, as with other technologies, there are
essential considerations to remember when using these tools for this purpose.
For
example, there are concerns about language prediction models consistently
generating culturally or developmentally inappropriate words. Also, since many
of these tools are now based on machine learning, there is a risk that others
could use them to bully or harass someone.
The
AAC field is working hard to ensure that these types of tools are used
respectfully and ethically so that they can be productive for individuals with
complex communication needs. In the future
of search, it may be possible that these types of systems can be
individualized so that they will provide a voice that is meaningful and
accessible to each person who uses them.
Machine
Learning (ML)
ML is
the current form of AI that is widely used. This type of AI is designed to
accomplish a specific task, such as making recommendations for an e-commerce
user or predicting the weather. ML is still quite far from human functioning
but can come close to us in these very narrow contexts.
It
wasn’t until recently that ML systems reached the point where they performed at
or even better than human-level performance on some tasks, such as image
classification and speech recognition. This was a key milestone for the AI
industry, opening up vast new possibilities for business applications.
While
ML technology is still evolving, its progress has been remarkable in the last
decade. It is now used in many businesses and industries, from delivering
better recommendations to e-commerce sites to automating administrative tasks
like data entry. It also helps with transcriptions of medical notes and makes
it possible for self-driving cars to navigate complex road conditions safely.
However,
the potential for bias in machine learning is real. When biased information or
data that reflects existing inequities is fed into an algorithm, it can create
or exacerbate discrimination. There are ways to combat this problem, such as
carefully vetting training data and ensuring that an organization supports
ethical artificial intelligence efforts.
Natural
Language Processing (NLP)
Natural
Language Processing (NLP) is a field within AI that allows computers to read
and understand human languages like speech and text. NLP is separate from—but
often used in conjunction with—speech recognition, which converts sound into
machine-readable text. NLP is booming because of considerable improvements in
access to data and computational power, which have enabled it to be applied
across industries.
The
NLP field has come a long way since its beginnings in the 1950s, when computer
scientists first wondered whether computers could be programmed to “understand”
human language. The technology continues to evolve, with new
techniques and algorithms pushing the boundaries of what’s
possible.
NLP
will continue to play an essential role in business, with companies using it to
analyze customer feedback, automate routine tasks, and improve the overall
efficiency of operations.
As
NLP technologies advance, businesses must educate themselves about how these
tools work. Educated employees can help mitigate any concerns, such as privacy
issues or potential biases. Additionally, they can promote broad adoption of
language-based AI tools across the organization, which will be more beneficial
than focusing on one or two use cases.
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