"Every
aspect of learning or any other feature of intelligence can be so precisely
described that a machine can be made to simulate it" – This is the maxim
on which AI is build. Machines are made to behave intelligently. It can read
and grasp reams of data; it can determine patterns and spot outliers. Unlike
automation, it learns from mistakes, and like human beings, with more practice,
it becomes better. AI is meant to free up time for people but can never
dispense with the need for human experience and insight. AI is helping
industries like financial services, healthcare, automotive and many others, accelerate
innovation, improve customer experience, and reduce costs.
Bots are
set to replace tax preparers, Online shopping is making the sales rep extinct,
Self checkout reduces the need for cashiers, Robots are replacing medical
technicians, lawyers are replaced with bots, BPO can become machine driven.
The AI
revolution is in full swing, with many monumental achievements like the
revolution in healthcare, where we had a doctor in China doing a remote surgery
from his home town, timely diagonises and treatment are helping patient care,
Chat GPT a generative artificial
intelligence chatbot developed by OpenAI. Launched in 2022 based on the GPT-3.5
large language model, it provides answers to any question you ask, unlike
google which provide multiple options and you have to choose.
The first self-driving car - In 1995,
Mercedes-Benz managed to drive a modified S-Class mostly autonomously from
Munich to Copenhagen.
According
to auto evolution, the 1043 mile ride was made via stuffing effectively a
supercomputer into the boot - the automobile contained 60 transputer chips,
which at the time were the kingdom of the artwork when it came to parallel
computing, that means that it may want to system a lot of riding statistics
shortly - a crucial section of making self-driving motors sufficiently
responsive. The vehicle reached speeds of up to 115mph and was virtually pretty
similar to autonomous automobiles of today, as it could overtake and read road
signs.
But when
and how did it start? Any guess?
The concept
of AI didn't suddenly appear - it is the subject of a deep, philosophical
debate that still rages today: Can a machine honestly think like a human? Can a
machine be human? One of the first people to think about this was René
Descartes, way back in 1637, in a book called Discourse on the Method.
The second
primary philosophical benchmark came courtesy of computer science pioneer Alan
Turing. In 1950 he first described what became known as The Turing Test, and
what he referred to as "The Imitation Game" - a test for measuring
when we can finally declare that machines can be intelligent.
His test
was simple: if a judge cannot differentiate between a human and a machine (say,
through a text-only interaction with both), can the machine trick the judge
into thinking that they are the human one?
“Neural
Network” is the fancy name scientists give to trial and error, the critical
thinking unpinning present-day AI. Essentially, when it comes to coaching an
AI, the first-class way to do it is to have the device guess, acquire feedback,
and bet again - continuously moving the possibilities that it will get to the
correct answer. What's quite splendid then is that the first neural community
was once definitely created way again in 1951. Called "SNARC" - the
Stochastic Neural Analogy Reinforcement Computer - was created by Marvin Minsky
and Dean Edmonds. It was not made of microchips and transistors, however of
vacuum tubes, motors, and clutches.
In 1997,
IBM was responsible for perhaps the most famous chess match of all time, as its
Deep Blue computer bested world chess champion, Garry Kasparov - demonstrating
how powerful machines can be.
To a
positive extent, Deep Blue’s Genius was illusory - IBM itself reckons that its
computing device is not using Artificial Intelligence. Instead, Deep Blue uses
a combination of brute pressure processing - processing thousands of possible
moves every second. IBM fed the system with facts on lots of beforehand games,
and each time the board modified with each movie, Deep Blue wouldn’t be gaining
knowledge of anything new. Still, it would as a substitute be looking up how
preceding grandmasters reacted in identical situations. “He’s playing the
ghosts of grandmaster's past,” as IBM puts it.Whether this counts as AI or no
longer, though, what’s clear is that it was once indeed a substantial milestone
and one that drew much interest not simply to the computational skills of
computers but additionally to the discipline as a whole. Since the face-off
with Kasparov, besting human players at games had come to be a significant, populist
way of benchmarking computer Genius - as we saw once more in 2011 when IBM’s
Watson machine handily trounced two of the game show Jeopardy’s fantastic
players.
Machine
Starts Talking - Siri
Natural
language processing has long been a holy grail of synthetic intelligence - and
integral if we’re ever going to have a world where humanoid robots exist or
where we can bark orders at our units like in Star Trek.
2010S:
WATSON AND OUR DAYS
In the
early 2000s, the story of the voice revolution reached a decisive turning
point: the question answering system, Watson competed with the best champions
of the popular television quiz Jeopardy! and defeated them in total points.
Thus, becoming the first system capable of processing natural language with the
same speed and confidence as a human.
This
victory set the stage for a forthcoming set of digital smart products that you
can control with your voice. Two months after Watson's success, Apple
introduced Siri to the world, then conversational assistants began to pop up
like mushrooms after the rain (2012: Google Assistant, 2013: Cortana, 2014:
Amazon Alexa, 2016:Google Home, 2017: Bixby etc.).
And this is
why Siri, which used to be constructed using the aforementioned statistical
methods, was once so impressive. Created by using SRI International and even
launched as a separate app on the iOS app store, it was rapidly acquired using
Apple itself and deeply integrated into iOS: Today, it is one of the most
excessive-profile fruits of computer learning, as it, along with equivalent
merchandise from Google (the Assistant), Microsoft (Cortana), and of course,
Amazon’s Alexa, has modified the way we have interaction with our units in a
way that would have appeared impossible simply a few years earlier.
Today we
take it for granted - however, you only have to ask all people who ever tried
to use a voice to textual content software before 2010 to respect just how far
we’ve come.
Like voice
recognition, picture awareness is every other most crucial assignment that AI
is helping to beat. In 2015, researchers concluded for the first time that
machines - in this case, two competing structures from Google and Microsoft -
have been better at identifying objects in pictures than humans were, in over
one thousand categories. These “deep learning” systems were successful in
beating the ImageNet Challenge - assume something like the Turing Test,
however, for image attention - and they are going to be essential if photograph
cognizance is ever going to scale beyond human abilities.
GPUs make
AI economical.
One of the
big reasons AI is now such a big deal is because it is only over the last few
years that the cost of crunching so much data has become affordable. According
to Fortune, it was only in the late 2000s that researchers realized that
graphical processing units (GPUs), which had been developed for 3D graphics and
games, were 20-50 times better at deep learning computation than traditional
CPUs.
AlphaGo and
AlphaGoZero conquer all.
In March
2016, another AI milestone was reached as Google’s AlphaGo software beat Lee
Sedol, a top-ranked player of the board game Go, echoing Garry Kasparov’s
historic match. What made it substantial was not simply that Go is an even
different mathematically complex sport than Chess; however, that it was skilled
using a combination of human and AI opponents. Google received 4 out of five of
the matches via reportedly using 1920 CPUs and 280 GPUs.
Perhaps
even extra giant is information from a remaining year - when a later version of
the software, AlphaGo Zero. Instead of the usage of any previous data, as
AlphaGo and Deep Blue had, to research the sport, it undoubtedly played
hundreds of matches towards itself - and after three days of coaching, was
capable of beating the version of AlphaGo which beat Lee Sedol one hundred
video games to nil.
Apple,
Microsoft, Alphabet, are all forrunner in AI innovations. These technologies
not only save time, but also potentially save lives by minimizing human error
and ensuring a safer working environment. In addition, automating repetitive
tasks in design, planning, and management with AI frees up human workers to
focus on more complex and creative aspects.
Artificial
Intelligence, technical automation, and bots are transforming the workplace
culture. Technologies, however, are yet to master Emotional Quotient, and this
is where soft skills like decision-making and empathy of a deserving candidate
are crucial.
AI bias can
creep in when decisions made by AI reflect the conscious or unconscious values
of the people who designed it or data it's based on, for example, when finance
teams make decisions on customers' credit or payment terms. Applying AI to
F&A creates new demands for teams with both business and technical skills.
People need industry and functional knowledge to provide essential context and
review algorithms. Advanced teams are even hiring behavioral scientists and anthropologists.
But they also need technical skills, such as forecasting, data scientists, and
engineers, analytics, design thinking, and agile programming. Once you have the
right people, they need the right infrastructure to work with. With easy access
to intuitive technology at home, a workplace with outdated, clunky systems
won't encourage them to stay.
It will
kill some jobs, leave some untouched and create new ones as well, Jobs, that
are likely to go away due to automation include
call center employees, data entry operators, insurance underwriters, tax
preparers, sales representatives, translators, and fast food employees.
Yet, no
advancement can upstage psychiatrists, storytellers, world-class teachers,
scientists, actors, and thought leaders because these roles need innovative and
personal skills.
The World
Bank estimates up to 69% of today’s job positions will become redundant. But
there is no need to panic. For every job lost, new ones will come up. Look at
history, for proof. The 20th century hadn’t heard anything like Chief
Technology Officer, Chief Delivery Officer, Chief Belief Officer, and Chief
Gardener. It is not that job opportunities are not there. It is just that
skills set requirements have changed. So what is most important is to ensure
that workforce is smart and adaptive and can take up newer roles.
Like every
other the entertainment industry has been debating both the pros (such as the
rise of new art forms) and cons (deepfakes that can replicate a performer’s
face and/or voice, with or without their permission) of the proliferation of
AI.
While AI is
a powerful resource that’s not going away, industries, governments and the
public at large need to stay updated on its developments and think carefully
about the ethical implications of its use.
The question persist: "If ethical principles deny our right from doing evil, that good may come, are we justified in doing good, that the foreseeable consequence is evil?"
Some of the effects of AI to be checked are:
1. Phishing
Messages And Malware
2. Identity
theft: AI-generated deepfakes aren’t just targeting high-profile people.
Fraudsters are leveraging them to steal individuals’ identities so they have
access to bank accounts and confidential information. Luckily, verification
platforms that have multiple identification factors can help deter fraud and
the potential leakage of personal information and documents.
3.
Increasingly Sophisticated Cyberattacks - Hackers are increasingly utilizing AI
for sophisticated cyberattacks.
4.
Disinformation Campaigns
AI-generated
text can be used to create sophisticated disinformation campaigns. By emulating
the writing style of influential figures, AI can generate fake news articles,
social media posts or blog entries that appear authentic. This raises concerns
about the spread of misinformation and the erosion of trust in online content.
5.
Revelation Of Personal Data
AI models
trained on large data sets can capture patterns and knowledge from text,
potentially including sensitive or personal information. This raises concerns
about the privacy and security of individuals’ data, as AI-generated text can
inadvertently reveal private details or be exploited for malicious purposes,
such as social engineering attacks or identity theft.
6.
Reputational Damage
It’s
unsettling that deepfake technology could enable highly damaging revenge
scenarios. A vengeful person could easily make it appear as though someone has
cheated by swapping faces in an intimate video; create a fake video of the
victim saying offensive things, damaging their career (even if the video is
proven to be fake); or blackmail someone with a deepfake video, threatening to
release it publicly unless demands are met.
7.
Impersonating Trusted Individuals
Deepfakes
are on the rise and create security threats for both consumers and businesses.
Bad actors can utilize AI to impersonate bank employees or even family members
over the phone. These phishing attacks are very dangerous—their urgent and
deceptive nature specifically targets human emotions with the ultimate goal of
stealing personally identifiable information and/or money.
8.
Manipulating Election Results
AI
deepfakes can distort democratic discourse and manipulate elections. Deepfakes
can be used to spread misinformation, propaganda and fake news about political
candidates, parties or issues. Political leaders can be impersonated or
discredited, as can political activists or journalists. This can influence
voter behavior, undermine public trust and destabilize democracy. AI use needs
to be controlled.
9.
Autonomous Weapons Systems
I am sure
there will be a time when AI-powered autonomous weapons systems will evolve.
These systems could have the potential to make critical decisions about
targeting and engagement without direct human control. This raises serious
ethical concerns.
10. Image
Manipulation
Most people
do not realize that AI can be used to manipulate images. AI-powered image
manipulation can take an existing image and change elements of it, such as the
background, color and other features. This technology is used for everything
from facial recognition to creating realistic deepfakes. It is a powerful tool
that can be used both ethically and unethically, depending on the application.
11.
Surveillance
One
unsettling way AI can be leveraged is as a surveillance tool. Facial
recognition technology is becoming more common, and there’s a concern among
some that it may be used to keep an eye on people without their knowledge. I
think we need to be cautious and hold companies that use this tech accountable
so people’s rights are not violated.
12.
Adversarial Attacks
AI
adversarial attacks represent a surprising and concerning application of the
technology. These attacks subtly manipulate AI inputs to induce erroneous
outputs, misleading systems including those used in autonomous cars or for
facial recognition. This unfamiliar threat can lead to significant security
risks, making it vital to improve public awareness and system resilience.
13. More
Pervasive And Invasive Advertising
AI can be
used for more pervasive advertising. With AI, one can analyze the emotional
state of a consumer and feed them highly personalized ads, exploiting their
emotional vulnerabilities. AI algorithms can distinguish between happy and sad
faces, understand text sentiments and tone of voice, and read other behavioral
patterns to manipulate a user’s decision-making processes and nudge them into
buying.
14.
Creation Of Echo Chambers
The most
unsettling development to me is the way AI serves up only what people want to
see and know about. The more you click on sites and pages expressing a certain
viewpoint, the more that viewpoint is shown to you. It is causing people to
take sides and think those who don’t believe the same things they do are
misinformed, unintelligent or misguided. In reality, every one of us is only
being shown things that align with our existing viewpoints.
15.
Realistic Digital Influencers
Companies
are creating AI-generated social media influencers that are entirely
computer-generated and designed to appear and act like real people. They can
amass large numbers of followers, endorse products and even collaborate with
other influencers—all without being human. These blurred lines between real and
virtual individuals raise ethical concerns regarding transparency and
authenticity in influencer marketing.
16.
Creation Of Synthetic Data
One way AI
is being leveraged that the general public may not know about is to create
synthetic data, which imitates real data such as images, text, audio or video.
Synthetic data serves several worthwhile purposes, including training machine
learning models, testing software and enhancing privacy. However, there are
also challenges regarding quality, validity, fairness and safeguarding the
rights of the original data owners and users.
17. Medical
Image Interpretation
AI’s
ability to interpret medical images, such as X-rays or MRIs, is astonishing yet
disconcerting. While it can aid in early disease detection, if the algorithms
are flawed or biased, it may lead to misdiagnoses and inappropriate treatments.
It’s essential that we approach AI in healthcare with a balanced understanding
of both its vast potential and the need for rigorous validation.
No, it is not a mosquito. It's an insect spy drone which can be remotely controlled and is equipped with a camera and a microphone. It can land on you, and it may have the potential to take a DNA sample or leave RFID tracking nanotechnology on your skin. It can fly through an open window, or it can attach to your clothing until you take it in your home. One of the current areas of research reportedly being undertaken in the scientific/military field is the development of micro air vehicles (MAVs), tiny flying objects intended to go places that cannot be (safely) reached by humans or other types of equipment. Pl zoom & see actual equipment. This is fifth generation war.
.jpg)
.jpg)
