A rule-based system may be viewed as consisting of three basic components: a set of rules [rule base], a data base [fact base], and an interpreter for the rules. In the simplest design, a rule … can be viewed as a simple conditional statement, and the invocation of rules as a sequence of actions chained by modus ponens.
– from The Origin of Rule-Based Systems in AI. Randall Davis and Jonathan J. King, reprinted as Ch. 2 of Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley Series in Artificial Intelligence). Bruce G. Buchanan and Edward H. Shortliffe (Eds.). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1984.
Data mining is a popular term used by machine learning developers. The technique refers to extracting meaningful information from the massive dataset. For the aspiring data scientists, it is important to be familiar with data mining techniques. Here are the top data mining techniques that are used by Data Science and Machine Learning experts. Association rule learning is a standard rule-based ML technique used to discover the relationship between variables in datasets.
For a hitherto relative unknown, scoring a $113 million Series C at this time is bound to get some attention. The amount of attention is bound to grow upon learning that the company is backed by, and works with, the likes of Bp, its AI technology is based on IP from NASA and Caltech, and it looks like the closest thing to the vision for AI in the real world today. Beyond Limits, an industrial and enterprise-grade AI technology company active in energy, utilities, and healthcare, today announced a milestone Series C funding round with $113 million closed and another approximately $20 million committed. This round is led by Group 42, a prominent AI and cloud computing company, and Bp ventures, an existing two-time investor and customer of the company. ZDNet caught up with Beyond Limits CEO and Founder AJ Abdallat to discuss business, technology, and applications.
Google's Area 120 incubator today launched Tables, a work-tracking tool with IFTTT-like automation features and support for Google products, including Google Groups, Google Sheets, and more. Currently in beta in the U.S., Tables automates actions like collating data, checking multiple sources of data, and pasting data into other docs for handoff. "Tracking work with existing tech solutions meant building a custom in-house solution or purchasing an off-the-shelf product, but these options are time-consuming, inflexible, and expensive," Tables general manager Tim Gleason explained in a blog post. "Tables helps teams track work and automate tasks to save time and supercharge collaboration -- without any coding required." Using Tables, teams can program bots to schedule recurring email reminders when tasks are overdue, message a Slack or Google Chat room when new form submissions are received, or move a task to someone else's work queue when the status changes.
For a hitherto relative unknown, scoring a $113M Series C at this time is bound to get some attention. The amount of attention is bound to grow upon learning that the company is backed by, and works with, the likes of bp, its AI technology is based on IP from NASA and Caltech, and it looks like the closest thing to the vision for AI in the real world today. Beyond Limits, an industrial and enterprise-grade AI technology company active in energy, utilities and healthcare, today announced a milestone Series C funding round with $113 million closed and another approximately $20 million committed. This round is led by Group 42, a prominent AI and cloud computing company, and bp ventures, an existing two-time investor and customer of the company. ZDNet caught up with Beyond Limits CEO and Founder AJ Abdallat to discuss business, technology, and applications.
Emergent Insight: We all are recipients of the untiring work of artificial intelligence but we don't take time to acknowledge it. Industrial businesses certainly are leveraging the powerful technology as noted in this post by Michael Sharp at Metrology News. In your company tasks and objectives, take a moment to consider how rules-based AI or machine learning can improve productivity, safety or more. Machines, devices and computers usually take over tasks that are mundane and laborious and don't really require a human to do. Why not let AI do the work and switch the human employees to more satisfying roles?
This blog post has been written with the collaboration of Juan Olloniego and Germán Hoffman. Even if machines have done a big part of the heavy lifting for us since the industrial revolution, they still depend on us for their maintenance. As they have that annoying tendency to break from time to time, their conservation becomes essential to keep up with our daily activities. Now, with the industry 4.0, the internet of things, and the artificial intelligence advent, we are letting a new kind of machines take care of their older counterparts. We make these new transistor-based machines look after their ancestors.
It is non-trivial to design engaging and balanced sets of game rules. Modern chess has evolved over centuries, but without a similar recourse to history, the consequences of rule changes to game dynamics are difficult to predict. AlphaZero provides an alternative in silico means of game balance assessment. It is a system that can learn near-optimal strategies for any rule set from scratch, without any human supervision, by continually learning from its own experience. In this study we use AlphaZero to creatively explore and design new chess variants.
The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.
Data Analytics has been the backbone of some of the revolutionary companies that are disrupting the ecosystem. Financial Services is no different. These challenges are picked by our editorial team based on the latest trends and development. Do read it till the end to enhance your understanding of the subject. If you think we are missing anything, let us know in the comments and our team will review and add it into the blog.
Computer assistants and AIs perform an ever-growing range of tasks that are broadly intended to improve our quality of life. This extends to industry as well. But first, what do we mean by artificial intelligence? In simple terms, it's any machine (usually a computer) that does things normally associated with human intelligence, such as reasoning, learning and self-improvement. AI systems in industry are the same technologies you use in daily life but applied to industrial problems.