Artificial intelligence (AI)
makes it possible for machines to learn from experience, adjust to new
inputs and perform human-like tasks. Most AI examples that you hear
about today – from chess-playing computers to self-driving cars – rely
heavily on deep learning and natural language processing.
Using these technologies, computers can be trained to accomplish
specific tasks by processing large amounts of data and recognizing
patterns in the data.
Artificial Intelligence History
The
term artificial intelligence was coined in 1956, but AI has become more
popular today thanks to increased data volumes, advanced algorithms,
and improvements in computing power and storage.
Early
AI research in the 1950s explored topics like problem solving and
symbolic methods. In the 1960s, the US Department of Defense took
interest in this type of work and began training computers to mimic
basic human reasoning. For example, the Defense Advanced Research
Projects Agency (DARPA) completed street mapping projects in the 1970s.
And DARPA produced intelligent personal assistants in 2003, long before
Siri, Alexa or Cortana were household names.
This
early work paved the way for the automation and formal reasoning that
we see in computers today, including decision support systems and smart
search systems that can be designed to complement and augment human
abilities.
While
Hollywood movies and science fiction novels depict AI as human-like
robots that take over the world, the current evolution of AI
technologies isn’t that scary – or quite that smart. Instead, AI has
evolved to provide many specific benefits in every industry. Keep
reading for modern examples of artificial intelligence in health care,
retail and more.
Why is artificial intelligence important?
AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.
AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.
AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
AI achieves incredible accuracy through deep neural networks – which was previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.
AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.
AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.
How Artificial Intelligence Is Being Used
Every
industry has a high demand for AI capabilities – especially question
answering systems that can be used for legal assistance, patent
searches, risk notification and medical research. Other uses of AI
include:
Health Care: AI applications can provide personalized medicine and X-ray readings. Personal health care assistants can act as life coaches, reminding you to take your pills, exercise or eat healthier.
Retail:AI
provides virtual shopping capabilities that offer personalized
recommendations and discuss purchase options with the consumer. Stock
management and site layout technologies will also be improved with AI.
Manufacturing:AI can analyze factory IoT data as it streams from connected equipment to forecast expected load and demand using recurrent networks, a specific type of deep learning network used with sequence data.
Banking:Artificial Intelligence enhances the speed, precision and effectiveness of human efforts. In financial institutions, AI techniques can be used to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, as well as automate manually intense data management tasks.
Working together with AI
Artificial
intelligence is not here to replace us. It augments our abilities and
makes us better at what we do. Because AI algorithms learn differently
than humans, they look at things differently. They can see relationships
and patterns that escape us. This human, AI partnership offers many
opportunities. It can:
- Bring analytics to industries and domains where it’s currently underutilized.
- Improve the performance of existing analytic technologies, like computer vision and time series analysis.
- Break down economic barriers, including language and translation barriers.
- Augment existing abilities and make us better at what we do.
- Give us better vision, better understanding, better memory and much more.
How Artificial Intelligence Works
AI
works by combining large amounts of data with fast, iterative
processing and intelligent algorithms, allowing the software to learn
automatically from patterns or features in the data. AI is a broad field
of study that includes many theories, methods and technologies, as well
as the following major subfields:
- Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.
- A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
- Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
- Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.
- Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
- Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.
Additionally, several technologies enable and support AI:
- Graphical processing units are key to AI because they provide the heavy compute power that’s required for iterative processing. Training neural networks requires big data plus compute power.
- The Internet of Things generates massive amounts of data from connected devices, most of it unanalyzed. Automating models with AI will allow us to use more of it.
- Advanced algorithms are being developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.
- APIs, or application programming interfaces, are portable packages of code that make it possible to add AI functionality to existing products and software packages. They can add image recognition capabilities to home security systems and Q&A capabilities that describe data, create captions and headlines, or call out interesting patterns and insights in data.
In
summary, the goal of AI is to provide software that can reason on input
and explain on output. AI will provide human-like interactions with
software and offer decision support for specific tasks, but it’s not a
replacement for humans – and won’t be anytime soon.
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