What is AI, or anything you wanted to know about artificial intelligence (but ChatGPT was afraid to ask)

These days it's all about AI, short for "artificial intelligence," or artificial intelligence. You probably hear about it on social media, in the news and maybe even in your latest binge television series. Although the concept has been around since the 1950s, the term has reached "top trend" status in virtually every industry.

Throughout all this hype, you've probably at some point asked yourself the very practical question - what is AI? It's already having a big impact, from autonomous vehicles to robots doing dangerous work, and even in your neighborhood where it can be used to make the traffic light turn green at just the right time. The applications for AI technology are virtually limitless. Below we look at what AI is, how it works and how industrialized edge devices are driving AI innovations.

What is AI?

AI stands for "artificial intelligence," or artificial intelligence, and focuses on technologies that attempt to replicate the results or output of human intelligence. To do so, AI must work its way through complex information processing, including: learning, reasoning, problem solving, language use and perception.

How does AI work?

AI is not one computer program or application; it is an entire branch of computer science. Broadly speaking, there are two phases of AI: training and inference.

AI training

Training for AI is the process of creating an AI algorithm to perform a desired task by providing the algorithm with a controlled data set. During the training process, the data is analyzed so that the algorithm can discover structure and patterns in the data. The goal is for the software to be able to make informed predictions when new data is provided. Effective AI training requires huge amounts of data and a lot of computing power, often using multicore processors and GPUs.

AI conferencing

AI inferencing is the process of using the trained model to make predictions and turn the data into actionable insights. From a hardware standpoint, GPUs and multicore processors are not always required for inferencing. It is a model that is applied and referenced. So it is not built.

Applications

The field of artificial intelligence is vast and growing by the day. If you do want to break it down, you could divide the applications of AI into several types, including Machine Learning, Deep Learning, NLP (natural language processing), Expert Systems, Robotics and Machine Vision.

• Machine Learning (ML)
  • Machine learning models use data and algorithms to perform specific tasks without being explicitly programmed. The accuracy of these machine learning algorithms gradually improves over time.
    • This is how Plus One Robotics for example, ML solutions for warehouse and distribution machines. They use the Karbon 804 as a platform for inferencing.
• Deep Learning
  • Deep Learning is a type of machine learning that structures algorithms in layers to create an "artificial neural network. Deep learning is a more advanced approach to machine learning and can be used to solve more complex problems. Autonomous driving is a form of deep learning.
    • Bear Flag Robotics develops self-driving autonomous tractors, for example.
• Natural language processing (NLP)
  • NLP algorithms are used to process written or spoken human language. It is used for translation, summarization or to perform an action.
• Expert System
  • An expert system is software that uses AI to solve problems and simulate the judgment of a human expert.
• Robotics
  • Robotics is a part of engineering that uses AI to help machines navigate and manipulate their environment.
    • Thus, it makes Rigorous Technologies robots that help produce logistics tasks. In another example, Advanced.Farm fruit harvesting robots that assist in harvesting.
• Machine Vision
  • Machine Vision uses the latest AI technologies to give industrial equipment the ability to visualize its environment and make quick decisions based on what it "sees.
    • Artemis Vision For example, creates machine vision solutions for quality inspection in a manufacturing environment to capture the smallest details that can be missed by the human eye.

Examples in everyday life

You probably use AI quite often in your daily life without even knowing it. Some examples you may have used even today are:

• The chat feature on a website
  • Q&A bots on a Web site are often powered by generative pre-trained transformers, commonly known as GPT. These allow you to create content and conversational text for Q&A bots, as well as summarize text, generate content and perform searches.
• Search engines on the web
  • AI is used to understand your search query and determine the most relevant results.
• Virtual assistants, such as Alexa or Siri
  • These assistants use natural language processing and machine learning to improve performance over time.
• Traffic Management
  • AI is used to analyze real-time traffic data from various cameras and IoT devices and identify patterns in the data provided to increase safety and control traffic.
• Recommended engine for streaming service
  • Personalized entertainment options are presented to consumers based on data. Based on that data, the engine predicts that consumers will like that content. Check out our Human-in-the-loop machine learning blog to see how the streaming recommendation engine works.

In addition to these everyday examples, many industries depend on AI. Some examples include:

• Health care planning, diagnosis and treatment planning
• Funding to execute transactions at precise times
• Transportation for self-driving vehicles
• Retail for inventory management, customer sentiment and forecasting
• Production for predictive maintenance

All of these examples are just the proverbial tip of the iceberg. There are applications for AI in almost every industry, leading to the incredible growth of Edge AI.

AI at the edge

To enable near real-time decision-making, many companies are moving AI solutions away from the cloud and into the edge, closer to the source of the systems that create the data. The edge of the network may be in a warehouse, on a production line, on a forklift or even in the desert.

Rugged industrial computers with powerful processors are designed to survive in such environments; they are resistant to dirt, dust, vibration and temperature fluctuations. Industrial hardware from OnLogic is available with an integrated or separate GPU and can be installed almost anywhere. They offer the latest technology with all the cores, threads, memory, connectivity and accelerators to keep your AI-on-the-edge solution drive.

Technology for AI solutions

AI's explosive growth and resulting value are closely tied to the explosion of available data, models and advances in technology to process and respond to the information gathered. Some of the key improvements include:

• Larger data sets
  • The Internet of Things (IoT) provides the ability to collect data from a wide range of connected devices and open data sets.
• Pre-trained models
  • The availability of pre-trained open source models allows developers to create solutions faster. Indeed, they can start with an AI model that has already been trained on a large data set to solve a similar problem.
• Processor improvements
  • Multicore CPUs, integrated GPUs and discrete GPUs (Graphical Processing Units) are the basis for the increasing value of AI. They provide the computational power to process and interpret data sets to build an AI algorithm.

Hardware for AI solutions

When it comes to hardware options for AI, we see that different applications have different AI Solutions require.

IoT gateways

If your AI solution is in the cloud, you have a IoT gateway needed. This small but reliable computer is the link between data collected by integrated sensors and the cloud. They play an increasingly important role in AI solutions for collecting, storing and sometimes partially processing incoming data before it is transmitted.

Thus, we have the Karbon 410 designed for reliability under even the most challenging installation conditions, including extreme temperatures and vibration-prone locations. We combined innovative fanless cooling and flexible configuration options with advanced Intel® Atom® processors (formerly Elkhart Lake).

AI at the edge with an integrated GPU

Some of the latest processors with their integrated GPU can easily support AI inference solutions at the edge. The fanless Helix 511 from OnLogic for example, is powered by Intel 12th generation processors with hybrid core architecture and DDR5-memory. This compact powerhouse offers a plethora of I/O, including connectivity for legacy devices and powerful processing for an AI-on-the-edge solution. Separate GPUs are not always necessary and can add significantly to the cost of a computer. By running inference on a CPU or integrated GPU (iGPU), you can lower the cost of an effective AI implementation.

AI at the edge with separate GPU

Want to implement a more robust solution? Powered by a 12th or 13th generation Intel Core™ processor with separate GPU? Then the Karbon 804 incredible computing power and flexibility. We designed this system for the most demanding environments and with PCIe Gen 4-expansion for advanced GPU support. This ruggedized system is an ideal platform for automation, machine learning or AI.

GPU server

For complex workloads, deep learning at the edge and on-site training and inference, a GPU server such as the AC101 a great solution. This platform offers Intel 13th generation processors and advanced GPUs and DDR5 memory. Many companies are using edge servers in their strategies for cloud repatriation, moving computing resources to the edge to avoid latency and reduce operational costs.

Next steps

Ready to get started with your AI solution? Our solution specialists are ready to make sure you have the processing power and I/O to get your AI models moving, even in the most challenging environments. Please contact us.