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) …
"Through 3D printing, fast automation, artificial intelligence, advanced IT systems," Weber said. His lab recently trained a Baxter assembly robot to understand and respond to natural language commands. Researchers from MIT's Computer Science and AI Lab (CSAIL) recently revealed their similar efforts, which they've dubbed ComText -- as in "commands in context". The current problem is that robots generally see the world at a relatively low level -- in pixels and sensor readings -- but humans see it as related concepts, connected to form reasoning and higher order thinking, Paul explained.
On the assembly line in Toyota's low-strung, sprawling Georgetown, Kentucky factory, worker ingenuity pops up in the least expected places. Even as the automaker unveils an updated version of its vaunted production system, called the Toyota New Global Architecture (TNGA), the company has resisted the very modern allure of automation–a particularly contrarian stance to take in the car industry, which is estimated to be responsible for over half of commercial robot purchases in North America. Despite its dry subject, this book had a radical impact inside and outside of the business community–for the first time, unveiling the mysteries of Japanese industrial expertise and popularizing terms like lean production, continuous improvement, andon assembly lines, seven wastes or mudas and product flow. Codified as the Toyota New Global Architecture, this strategy doesn't primarily target labor to reduce production expenses but instead is weighted toward smarter use of materials; reengineering automobiles so their component parts are lighter and more compact and their weight distribution is maxed out for performance and fuel efficiency; more economical global sharing of engine and vehicle models (trimming back more than 100 different platforms to fewer than ten); and a renewed emphasis on elusive lean concepts, such as processes that allow assembly lines to produce a different car one after another with no downtime.
Today, these advanced algorithms are transforming the way the manufacturing industry collects information, performs skilled labor, and predicts consumer behavior. Smart factories with integrated IT systems provide relevant data to both sides of the supply chain more easily, increasing production capacity by 20%. Robots and other automated technology are also integral in improving speed and efficiency, allowing manufacturing companies to "optimize production workflows, inventory, Work in Progress, and value chain decisions." With this new level of predictive accuracy comes an improvement in condition monitoring processes, providing manufacturers "with the scale to manage Overall Equipment Effectiveness (OEE) at the plant level increasing OEE performance from 65% to 85%."
For example, for personalized recommendations, we have been working with learning to rank methods that learn individual rankings over item sets. Figure 1: Typical data science workflow, starting with raw data that is turned into features and fed into learning algorithms, resulting in a model that is applied on future data. This means that this pipeline is iterated and improved many times, trying out different features, different forms of preprocessing, different learning methods, or maybe even going back to the source and trying to add more data sources. Probably the main difference between production systems and data science systems is that production systems are real-time systems that are continuously running.
Even as an IT generalist, it pays to at least get comfortable with the matrix of machine learning outcomes, expressed with quadrants for the counts of true positives, true negatives, false positives (items falsely identified as positive) and false negatives (positives that were missed). For example, overall accuracy is usually defined as the number of instances that were truly labeled (true positives plus true negatives) divided by the total instances. If you want to know how many of the actual positive instances you are identifying, sensitivity (or recall) is the number of true positives found divided by the total number of actual positives (true positives plus false negatives). And often precision is important too, which is the number of true positives divided by all items labeled positive (true positives plus false positives).
Data scientists who want to build machine learning models and put them into production have no shortage of available tools, but choosing the right one comes with some thorny decisions. Note that many open source tools are available for machine learning, as well as other vendor offerings, but we focused exclusively on vendor cloud platforms that span the entire machine learning lifecycle from data ingestion to model development to production. The market for machine learning platforms is heating up, and all of the leading vendors are looking to nab their share. Several vendors have beefed up their offerings in recent months and now offer simple, cloud-based platforms for getting started with machine learning and developing models that can quickly be put into production.
Is machine learning (ML) or artificial intelligence (AI) the key? Companies have worked on many ways to offer plug-and-play sensor packages to collect information, with multiple options to send it where ever it needs to go. To reap the benefits of a higher-performing data network such as IIoT, ML, or Big Data, an interdisciplinary communication network is essential. "OT professionals are focused on keeping manufacturing, plant, and physical equipment in operation for extended periods of time, while IT professionals focus on keeping data flowing and accessible to all facets of an organization," says Dariol.
TL;DR A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It serves as the foundation layer on top of which three internal stakeholders collaborate: product data scientists, central data scientists, and IT infrastructure. Figure 1: A data science platform serves three stakeholders: product, central, and infrastructure. Serverless scaling, as implemented by Algorithmia Enterprise, is horizontal scaling on-demand by encapsulating your model in a dedicated container, deploying that container just-in-time across your compute cluster, and destroying it right after execution to release resources.
After spending two to three months training the AI to piece together coherent sentences and phrases, Saatchi LA began rolling out a campaign last week on Facebook called "Thousands of Ways to Say Yes" that pitches the car through short video clips. First, Saatchi LA wrote 50 scripts based on location, behavioral insights and occupation data that explained the car's features to set up a structure for the campaign. After a few more attempts, "We realized that it was struggling with the words that it had learned to create cohesive sentences," Saatchi LA's Pierantozzi said. About halfway through the process, Watson began putting together sentences, but they weren't connected to each other.
The annual Consumer Electronics Show, in Las Vegas has become the best place to learn about what's coming to cars. This year, however, the wild new capabilities rolling through CES, and into the auto industry, went from an open secret to headline grabbing news. As a result, it was an incredible week for us. Since we unveiled our AI computing platform for autonomous vehicles at CES last year, DRIVE PX 2 has become the core of the AI revolution sweeping the auto industry. That became clear with the show's opening keynote Wednesday from NVIDIA Co-Founder and CEO Jen-Hsun Huang, who announced our new AI Co-Pilot for the car built on DRIVE PX 2, as well as our ever expanding AI Car ecosystem of partners.