A prominent group of researchers
alarmed by the harmful social effects of artificial intelligence
called Thursday for a ban on automated analysis of facial expressions in hiring
and other major decisions. The AI Now Institute at New
York University said action against such software-driven "effect
recognition" was its top priority because science doesn't justify the technology's use and
there is still time to stop widespread adoption.
The group of professors and other researchers cited as a
problematic example the company HireVue, which sells systems for remote video
interviews for employers such as Hilton and Unilever. It offers AI to analyze facial
movements, tone of voice
and speech patterns, and doesn't disclose scores to the job candidates.
Machine learning is a
part of AI which
provides intelligence to machines with the ability to automatically learn with
experiences without being explicitly programmed.
- It
is primarily concerned with the design and development of algorithms that
allow the system to learn from historical data.
- Machine Learning is
based on the idea that machines can learn from past data, identify
patterns, and make decisions using algorithms.
- Machine learning
algorithms are designed in such a way that they can learn and improve
their performance automatically.
- Machine learning
helps in discovering patterns in data.
Natural
Language processing
NLP plays an
important role in AI
as without NLP, AI
agent cannot work on human instructions, but with the help of NLP, we can instruct an
AI system on our language. Today we are all around AI, and as well as NLP, we can easily ask
Siri, Google or Cortana to help us in our language.
The Input and
output of NLP
applications can be in two forms:
Deep Learning
Deep learning is a
subset of machine
learning which provides the ability to machine to perform human-like tasks
without human involvement. It provides the ability to an AI agent to mimic the
human brain. Deep
learning can use both supervised and unsupervised learning to train an AI
agent.
- Deep
learning is implemented through neural networks architecture hence also
called a deep neural network.
- Deep
learning is the primary technology behind self-driving cars, speech
recognition, image recognition, automatic machine translation, etc.
- The
main challenge for deep
learning is that it requires lots of data with lots of computational
power.
Executives don’t see many job cuts ahead of a
result of tasks being replaced by AI. Is this a realistic
perception?
A recent survey of executives out of IFS tackled
issues of AI perception,
finding a few business leaders predict worker displacement by AI. Close to half, 46%,
predict AI will
actually increase headcounts over the coming decade, while 25% predict no
changes at all to workforce sizes. Only 18% see AI as a tool for
replacing workers.
There are many high hopes for AI — 61% see it boosting
the productivity of their workforces. Another 48% also see AI as adding value to
their products and services. While a majority anticipate productivity increases
from AI, only 29% say such increased productivity will reduce headcounts over
the next 10 years. “Respondents did not make the connection between increased
productivity and reduced headcount,” the report’s authors suggest.
The
vast and growing amounts of data being created, collected and used by the
enterprise makes the deployment of data security solutions
a business imperative. It is essential to implement cybersecurity solutions
and practices to prevent data leaks and breaches, but how do businesses stay
ahead of the growing sophistication of cyber-attacks?
Predictive technologies, such as artificial intelligence
(AI) and machine learning
(ML) can enhance traditional data loss prevention (DLP) solutions to greatly
reduce the risk of breaches or leaks.
AI can
provide critical analysis, and ML uses algorithms to
learn from data—both provide a dynamic framework to predict and solve data
security problems before they occur. The more data patterns ML analyses, the more
processes and self-adjustments can operate based on those learned patterns.
This continuous delivery of insights increases in value with the “intelligence” of the
technology.
Let’s use healthcare as an example of how interoperable machine learning
technology can enhance our lives. Consider high-tech medical procedures like CT
scans that automatically generate large volumes of sensor data for a single
patient as opposed to health information your doctor manually enters into a
proprietary database
during a routine check-up. Without a way to quickly and automatically integrate
these disparate data types for analysis, there is lost the potential for fast
diagnosis of critical illnesses. This has created a demand for optimization
across different information models. Current methods and legacy systems simply
aren’t friendly in terms of interoperability — but recent developments in machine learning are
opening the door for the possibility of stronger, faster translation between
information platforms. The result could be vastly improved medical
care and optimized research practices.
Modeled after the human brain, neural networks are
comprised of a set of algorithms that are designed to recognize patterns. They
interpret sensory data through a sort of machine perception, labeling or
clustering raw input. The patterns they recognize are numerical, contained in
vectors, into which all real-world data, be it images, sound, text or time
series, must be translated. According to a 2017 article in MIT News, neural networks were
first proposed in 1944 by Warren McCullough and Walter Pitts, two University of
Chicago researchers who moved to MIT in 1952 as founding members of what’s
sometimes called the first cognitive science department. Since that time, the
approach has fallen in and out of favor, but today it’s making a serious
comeback.
Ever
since computers were invented, there has been an exponential growth in their
ability and potential to perform various tasks. In order to use computers across diverse
working domains, humans have developed computer systems while increasing their
speed, and reducing size with respect to time.
Artificial Intelligence
(AI) is one of the hottest technology trends on the planet, but for the average
small business owner, it can be terribly intimidating. It’s time to get over
that.
While many small and midsized business
(SMB) leaders say AI
is critical for their business, only one in five are actually doing anything
about it, according to a recent Capterra survey.
This should come as no surprise since
we all know SMBs don’t tend to deploy new technology until the
kinks have been worked out and it becomes more mature. Plus, they have more
pressing priorities to deal with, like finding new customers and paying the
bills. Right?
But here’s the thing: AI isn’t all that new,
and it’s not some temperamental new technology that could come-and-go as
quickly as Palm Pilots, Betamax video players and QR Codes. It’s here to stay,
finding its way into everything from those personalized shopping suggestions we
all get on social media sites to virtual assistants like Amazon’s Alexa and
Apple’s Siri. Increasingly, it’s also seeping into SMB operations.
