What it is and why it matters
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.
Early work with neural networks stirs excitement for “thinking machines.”
AI has been an integral part of SAS software for years. Today we help customers in every industry capitalize on advancements in AI, and we’ll continue embedding AI technologies like machine learning and deep learning in solutions across the SAS portfolio. Jim Goodnight CEO SAS
Artificial Intelligence trends to watch
Quick, watch this video to hear AI experts and data science pros weigh in on AI trends for the next decade.
What is the role of ethics in the future of AI? How important is big data? Why is domain knowledge crucial for the success of AI?
Most importantly: “It really is who has the data. That’s who will be the king,” says Harper Reid, Technology Pioneer.
Why is artificial intelligence important?
- AI automates repetitive learning and discovery through data. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks. And it does so reliably and without fatigue. Of course, humans are still essential to set up the system and ask the right questions.
- AI adds intelligence to existing products. Many 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. Upgrades at home and in the workplace, range from security intelligence and smart cams to investment analysis.
- AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that algorithms can acquire skills. Just as an algorithm can teach itself to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data.
- AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers used to be impossible. 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.
- AI achieves incredible accuracy through deep neural networks. For example, your interactions with Alexa and Google are all based on deep learning. And these products keep getting more accurate the more you use them. In the medical field, AI techniques from deep learning and object recognition can now be used to pinpoint cancer on medical images with improved accuracy.
- AI gets the most out of data. When algorithms are self-learning, the data itself is an asset. The answers are in the data. You just have to apply AI to find them. Since the role of the data is now more important than ever, 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.
Artificial Intelligence in Today's World
Pondering AI podcast
Is artificial intelligence always biased? Does AI need humans? What will AI do next? Join Kimberly Nevala to ponder AI’s progress with a diverse group of guests, including innovators, activists and data experts.
Your journey to AI success
Determine if you really need artificial intelligence. And learn to evaluate if your organization is prepared for AI. This series of strategy guides and accompanying webinars, produced by SAS and MIT SMR Connections, offers guidance from industry pros.
Five AI technologies that you need to know
Read our quick overview of the key technologies fueling the AI craze. This useful introduction offers short descriptions and examples for machine learning, natural language processing and more.
How Artificial Intelligence Is Being Used
Every industry has a high demand for AI capabilities – including systems that can be used for automation, learning, legal assistance, risk notification and research. Specific uses of AI in industry include:
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.
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.
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.
From ensuring drug safety to getting new therapies to market faster, AI technologies can unleash the full potential of data to solve some of our greatest health challenges.
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.
Artificial Intelligence can make smart cities smarter. It can support national defense with mission readiness and predictive maintenance. Across the board, AI can improve program efficiency and effectiveness.
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.
The principle limitation of AI is that it learns from the data. There is no other way in which knowledge can be incorporated. That means any inaccuracies in the data will be reflected in the results. And any additional layers of prediction or analysis have to be added separately.
Today’s AI systems are trained to do a clearly defined task. The system that plays poker cannot play solitaire or chess. The system that detects fraud cannot drive a car or give you legal advice.
In other words, these systems are very, very specialized. They are focused on a single task and are far from behaving like humans.
What is Composite AI?
Most AI projects today rely on multiple data science technologies. According to Gartner, using a combination of different AI techniques to achieve the best result is called composite AI. For complex problems, the answer isn’t always one technique or another. Instead, the best answer to any problem is often a combination of multiple techniques and technologies, like machine learning, optimization and object detection.
“SAS is pushing the boundaries of the larger composite AI movement that’s happening in the industry,” says Bryan Harris, SAS Chief Technology Officer. “Accelerated digital transformation is demanding more sophisticated decisioning. This requires input from multiple analytic techniques, such as descriptive statistics, natural language processing, deep learning, audio processing, computer vision and more. Companies that can quickly harness these analytic techniques ultimately have a competitive advantage in their digital transformation.”
SAS® Visual Data Mining and Machine Learning
AI is simplified when you can prepare data for analysis, develop models with modern machine-learning algorithms and integrate text analytics all in one product. Plus, you can code projects that combine SAS with other languages, including Python, R, Java or Lua.
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.
- 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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