If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
It took Odysseus ten years to find his way home from the Trojan Wars but a modern day odyssey about to launch involves an autonomous tug boat that will find its own way on a 1,000 mile journey expected to take just a couple of weeks. The project, named Machine Odyssey in a tribute to Homer's epic poem, will take a sea going tug built by Dutch shipbuilders Damen Shipyards from Hamburg, Germany around Denmark. In keeping with the theme, the tug is christened the Nellie Bly, in homage to the American journalist, industrialist, inventor, and charity worker who was widely known for her bold and record-breaking solo trip around the world in 72 days. At the helm won't be an ancient mariner, but rather the ultra modern SM300 autonomy system created by Boston maritime tech firm Sea Machines. "We recognize in today's day and age the effort on a vessel where a lot of it is still very manual today, it's still staring out those windows, it's still manual driving," said Michael Johnson, Sea Machines CEO. "The auto pilots we have are very single sensor with not a lot of feedback. A small crew will be on board to maintain the ship when the voyage is scheduled to begin Oct. 1 and the Nellie Bly's progress will be monitored and commanded back in Boston at Sea Machine's headquarters. But Johnson stresses, that's not the same as operating the ship by remote control. "The goal is 99% of the effort is taken by the autonomy system.
The need for math engines specifically designed to support machine learning algorithms, particularly for inference workloads but also for certain kinds of training, has been covered extensively here at The Next Platform. Just to rattle off a few of them, consider the impending "Cirrus" Power10 processor from IBM, which is due in a matter of days from Big Blue in its high-end NUMA machines and which has a new matrix math engine aimed at accelerating machine learning. Or IBM's "Telum" z16 mainframe processor coming next year, which was unveiled at the recent Hot Chips conference and which has a dedicated mixed precision matrix math core for the CPU cores to share. Intel is adding its Advanced Matrix Extensions (AMX) to its future "Sapphire Rapids" Xeon SP processors, which should have been here by now but which have been pushed out to early next year. Arm Holdings has created future Arm core designs, the "Zeus" V1 core and the "Perseus" N2 core, that will have substantially wider vector engines that support the mixed precision math commonly used for machine learning inference, too. All of these chips are designed to keep inference on the CPUs, where in a lot of cases it belongs because of data security, data compliance, and application latency reasons.
Many of the smart/IoT devices you'll purchase are powered by some form of Artificial Intelligence (AI)--be it voice assistants, facial recognition cameras, or even your PC. These don't work via magic, however, and need something to power all of the data-processing they do. For some devices that could be done in the cloud, by vast datacentres. Other devices will do all their processing on the devices themselves, through an AI chip. But what is an AI chip?
But in the context of Artificial Intelligence and Edge computing, "AI on the edge" is starting to become the norm rather than the exception. Edge AI is garnering attention partly due to the recent advances in edge compute capability and increasingly lightweight AI algorithms. But the real need for edge AI has risen from the changing dynamics of human-machine interactions and user engagement models. "AI on the edge" is an amalgamation of two key technology areas – Artificial Intelligence and Edge computing. Artificial Intelligence or machine intelligence is now a commonplace technology in applications such as computer vision, language processing and complex data analysis.
While AI-powered devices and technologies have become essential parts of our lives, machine intelligence may still contain areas wherein drastic improvements could be made. To fill these metaphorical gaps, non-AI technologies can come in handy. Artificial intelligence (AI) is an'emerging computer technology with synthetic intelligence.' It is widely accepted that the applications of AI we see in our daily lives are just the tip of the iceberg with regards to its powers and abilities. The field of artificial intelligence needs to constantly evolve and keep developing to eliminate the common AI limitations.
AI reduces errors in common semi-skilled tasks such as sorting and categorizing products. Autonomous mobile robots (AMRs), for instance, improve package delivery, including the last mile of delivery which is typically the most expensive. AI helps AMRs with route planning and feature recognition, such as people, obstacles, delivery portals, and doorways. Integrating logistics automation into any environment comes with challenges. It can be as simple as replacing a repetitive process with a powered conveyor or as complex as introducing a collaborative, autonomous robot into the workplace.
Over the course of the last few years, the world has embarked on a transformation that is the result of artificial intelligence (AI). These changes have come about primarily because of advances in the AI technique of deep learning, which processes mass quantities of information and is able to establish relationships and draw conclusions based on the data. While each of the following three companies represents a distinctly different approach to the AI revolution, they have all benefited enormously by being among the early adopters of this groundbreaking technology. Read on to find out why Adobe Systems Incorporated (NASDAQ: ADBE), NVIDIA Corporation (NASDAQ: NVDA), and Micron Technology (NASDAQ: MU) were among the best performing AI stocks of 2017. Adobe is best known for its Portable Document Format (PDF), which is used to create and exchange documents, and its suite of creative software tools like Photoshop, Illustrator, and InDesign.
What do you get when you combine 5G and AI with advanced drone development? One answer is from Qualcomm's recent launch of its Flight RB5 5G Platform, a reference drone containing computing at lower power with AI, 5G, and long-range Wi-Fi 6 connectivity. According to the company in a press release, the drone and reference design contains "enhanced autonomy and intelligence features" powered by the Qualcomm QRB5165 processor. Announced in June 2020, the QRB5165 processor is customized for robotics applications and is coupled with the Qualcomm AI Engine, which delivers 15 trillion operations per second (Tops) of AI performance. This allows it to run complex AI and deep learning workloads, and on-device machine learning and accurate edge inferencing while using lower power, according to a Qualcomm product description.
Artificial intelligence (AI) contains many subfields, including machine learning (ML), which automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without being explicitly programmed where to look or what to conclude. Real artificial intelligence applies machine learning, and other techniques to solve actual problems. Despite the covid-19 pandemic and the current economic climate, artificial intelligence has quickened its progress. AI has the ability to analyze big data sets – pulling together innovative insights and leading to predictive analysis. Here are five significant artificial intelligence trends that are transforming the future of our economies and society.
Visual computing has used OpenCV algorithms to detect objects for decades. Deep learning inference takes computer vision to entirely new levels of sophistication with support for poor lighting, off-angled shots, and subtle flaws. What exactly is deep learning object detection? Deep learning object detection combines two computer vision tasks: localization and classification. In localization, the model identifies objects in an image and draws a bounding box around them.