Artificial
intelligence, or AI, is technology that enables computers and machines to simulât
human intelligence and problem-solving capabilities.
On its own
or combined with other technologies (e.g., sensors, geolocation, robotics) AI
can perform tasks that would otherwise require human intelligence or
intervention. Digital assistants, GPS guidance, autonomous vehicles, and
generative AI tools (like Open AI's Chat GPT) are just a few examples of AI in
the daily news and our daily lives.
As a field
of computer science, artificial intelligence encompasses (and is often
mentioned together with) machine learning and deep learning. These disciplines involve the
development of AI algorithms, modeled after the decision-making processes of
the human brain, that can ‘learn’ from available data and make increasingly
more accurate classifications or predictions over time.
Artificial
intelligence has gone through many cycles of hype, but even to skeptics, the
release of ChatGPT seems to mark a turning point. The last time generative
AI loomed this large, the breakthroughs were in computer vision, but now the
leap forward is in natural language processing (NLP). Today, generative AI
can learn and synthesize not just human language but other data types including
images, video, software code, and even molecular structures.
Applications
for AI are growing every day. But as the hype around the use of AI tools in
business takes off, conversations around ai ethics and responsible ai become critically
important. For more on where IBM stands on these issues, please read Building
trust in AI.
Types of artificiel intelligence: weak AI vs. strong AI
Weak AI—also known as narrow AI or artificial narrow
intelligence (ANI)—is AI trained and focused to perform specific tasks.
Weak AI drives most of the AI that surrounds us today. "Narrow" might
be a more apt descriptor for this type of AI as it is anything but weak: it
enables some very robust applications, such as Apple's Siri, Amazon's Alexa,
IBM watsonx™, and self-driving vehicles.
Strong AI is made up of artificial general intelligence (AGI) and artificial
super intelligence (ASI). AGI, or general AI, is a theoretical form of AI
where a machine would have an intelligence equal to humans; it would be
self-aware with a consciousness that would have the ability to solve problems,
learn, and plan for the future. ASI—also known as superintelligence—would
surpass the intelligence and ability of the human brain. While strong AI is
still entirely theoretical with no practical examples in use today, that
doesn't mean AI researchers aren't also exploring its development. In the
meantime, the best examples of ASI might be from science fiction, such as HAL,
the superhuman and rogue computer assistant in 2001: A Space Odyssey.
Deep learning vs.
machine learning
Machine
learning and deep learning are sub-disciplines of AI, and deep learning is a
sub-discipline of machine learning.
Both machine
learning and deep learning algorithms use neural networks to ‘learn’ from huge amounts of
data. These neural networks are programmatic structures modeled after the
decision-making processes of the human brain. They consist of layers of
interconnected nodes that extract features from the data and make predictions
about what the data represents.
Machine
learning and deep learning differ in the types of neural networks they use, and
the amount of human intervention involved. Classic machine learning algorithms
use neural networks with an input layer, one or two ‘hidden’ layers, and an
output layer. Typically, these algorithms are limited to supervised learning: the data needs to be
structured or labeled by human experts to enable the algorithm to extract
features from the data.
Deep
learning algorithms use deep neural networks—networks composed of an input
layer, three or more (but usually hundreds) of hidden layers, and an output
layout. These multiple layers enable unsupervised learning: they automate extraction
of features from large, unlabeled and unstructured data sets. Because it
doesn’t require human intervention, deep learning essentially enables machine
learning at scale.
The
rise of generative models
Generative AI refers to deep-learning models that can take raw data—say, all
of Wikipedia or the collected works of Rembrandt—and “learn” to generate
statistically probable outputs when prompted. At a high level, generative
models encode a simplified representation of their training data and draw from
it to create a new work that’s similar, but not identical, to the original
data.
Generative models have been used for years in statistics to analyze
numerical data. The rise of deep learning, however, made it possible to extend
them to images, speech, and other complex data types. Among the first class of
AI models to achieve this cross-over feat were variational autoencoders, or
VAEs, introduced in 2013. VAEs were the first deep-learning models to be widely
used for generating realistic images and speech.
VAEs opened
the floodgates to deep generative modeling by making models easier
to scale,” said Akash Srivastava, an
expert on generative AI at the MIT-IBM Watson AI Lab. “Much of what we think of
today as generative AI started here.”
Early
examples of models, including GPT-3, BERT, or DALL-E 2, have shown what’s
possible. In the future, models will be trained on a broad set of unlabeled
data that can be used for different tasks, with minimal fine-tuning. Systems
that execute specific tasks in a single domain are giving way to broad AI
systems that learn more generally and work across domains and problems.
Foundation models, trained on large, unlabeled datasets and fine-tuned for an
array of applications, are driving this shift.
As to the
future of AI, when it comes to generative AI, it is predicted that foundation
models will dramatically accelerate AI adoption in enterprise. Reducing
labeling requirements will make it much easier for businesses to dive in, and
the highly accurate, efficient AI-driven automation they enable will mean that
far more companies will be able to deploy AI in a wider range of
mission-critical situations. For IBM, the hope is that the computing power of
foundation models can eventually be brought to every enterprise in a
frictionless hybrid-cloud environment.
Artificial
intelligence applications
There are
numerous, real-world applications for AI systems today. Below are some of the
most common use cases:
Speech
recognition
Also known
as automatic speech recognition (ASR), computer speech recognition, or
speech-to-text, speech recognition uses NLP to process human speech into a
written format. Many mobile devices incorporate speech recognition into
their systems to conduct voice search—Siri, for example—or provide more
accessibility around texting in English or many widely-used languages. See how Don Johnston used IBM Watson Text to Speech to
improve accessibility in the classroom with our case study.
Customer
service
Online
virtual agents and chatbots are replacing
human agents along the customer journey. They answer frequently asked questions
(FAQ) around topics, like shipping, or provide personalized advice,
cross-selling products or suggesting sizes for users, changing the way we think
about customer engagement across websites and social media platforms. Examples
include messaging bots on e-commerce sites with virtual agents , messaging
apps, such as Slack and Facebook Messenger, and tasks usually done by virtual
assistants and voice assistants. See how Autodesk Inc. used IBM watsonx Assistant to speed up
customer response times by 99% with our case study.
Computer
vision
This AI
technology enables computers and systems to derive meaningful information from
digital images, videos and other visual inputs, and based on those inputs, it
can take action. This ability to provide recommendations distinguishes it from
image recognition tasks. Powered by convolutional neural networks, computer
vision has applications within photo tagging in social media, radiology imaging
in healthcare, and self-driving cars within the automotive industry. See how ProMare used IBM Maximo to set a new course for
ocean research with our case study.
Supply chain
Adaptive
robotics act on Internet of Things (IoT) device information, and structured and
unstructured data to make autonomous decisions. NLP tools can understand human
speech and react to what they are being told. Predictive analytics are applied
to demand responsiveness, inventory and network optimization, preventative
maintenance and digital manufacturing. Search and pattern recognition
algorithms—which are no longer just predictive, but hierarchical—analyze
real-time data, helping supply chains to react to machine-generated, augmented
intelligence, while providing instant visibility and transparency. See how Hendrickson used IBM Sterling to fuel real-time
transactions with our case study.
