AI News, The Handbook of Artificial Intelligence
The Handbook of Artificial Intelligence
The Handbook of Artificial Intelligence, Volume II focuses on the improvements in artificial intelligence (AI) and its increasing applications, including programming languages, intelligent CAI systems, and the employment of AI in medicine, science, and education.
The manuscript then examines applications-oriented AI research in medicine and education, including ICAI systems design, intelligent CAI systems, medical systems, and other applications of AI to education.
Explainable Artificial Intelligence (XAI)
The Explainable AI (XAI) program aims to create a suite of machine learning techniques that: New machine-learning systems will have the ability to explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future.
XAI Concept XAI is one of a handful of current DARPA programs expected to enable “third-wave AI systems”, where machines understand the context and environment in which they operate, and over time build underlying explanatory models that allow them to characterize real world phenomena.
The XAI program is focused on the development of multiple systems by addressing challenge problems in two areas: (1) machine learning problems to classify events of interest in heterogeneous, multimedia data;
These two challenge problem areas were chosen to represent the intersection of two important machine learning approaches (classification and reinforcement learning) and two important operational problem areas for the DoD (intelligence analysis and autonomous systems).
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Content Technologies, Inc., an artificial intelligence development company specializing in automation of business processes and intelligent instruction design, has created a suite of smart content services for secondary education and beyond. Cram101, for example, uses AI to help disseminate and breakdown textbook content into digestible “smart”
Learning platforms for the modern workplace are designed to allow employees to master additional skills and receive continuous and automated feedback, and when used strategically have the potential to help improve performance and increase production.
software, for example, uses cognitive science and AI technologies to provide personalized tutoring and real-time feedback for post-secondary education students, particularly incoming college freshman who would otherwise need remedial courses.
Carnegie Learning’s Mika platform Pearson, in collaboration with University College London Knowledge Lab, notes that today’s model-based adaptive systems are also increasingly transparent, allowing educators to understand how a system arrived at a next-step decision and rendering them more effective tools for classroom teaching.
For example, the iTalk2Learn system16, a system engineered and tested by Carnegie Mellon University to assess its effects on young students learning fractions, applied a learner model that explicitly included information about an individual’s mathematics knowledge, cognitive needs, emotional state, as well as feedback received and the students’
While it seems obvious that no one in education is eager for virtual humans to come and replace educators, the idea of creating virtual human guides and facilitators for use in a variety of educational and therapeutic environments is a promising area of development.
SimCoach Prototype Captivating Virtual Instruction for Training (CVIT), for example, is a distributed learning strategy that aims to integrate live classroom methods with best-fit virtual technologies—including virtual facilitators, augmented reality, intelligent tutor, and others—in remote learning and training programs. It’s worth visiting USC’s Creative Technologies prototype page and exploring the many other initiatives currently under development, from immersive training counseling for Army leaders to a personal assistant (PAL3) for life-long learning.
AI’s ability to analyze large amounts of data in real-time (a student’s performance in a particular skill across subjects over the course of a year, for example) and automatically provide new content or specified learning parameters, helps meet students’
Woolf, et al., (2013) has proposed five key areas for ongoing researching in educating using AI: The above seems a useful framework for framing objectives and generating aligned ideas, as researchers and companies continue to move forward in developing AI applications in education.
Everyday Examples of Artificial Intelligence and Machine Learning
With all the excitement and hype about AI that’s “just around the corner”—self-driving cars, instant machine translation, etc.—it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you’re already using—right now? In
You’ve also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases. We distinguish between AI and machine learning (ML) throughout this article when appropriate.
According to a 2015 report by the Texas Transportation Institute at Texas A&M University, commute times in the US have been steadily climbing year-over-year, resulting in 42 hours of rush-hour traffic delay per commuter in 2014—more than a full work week per year, with an estimated $160 billion in lost productivity.
driving to a train station, riding the train to the optimal stop, and then walking or using a ride-share service from that stop to the final destination), not to mention the expected and the unexpected: construction;
Engineering Lead for Uber ATC Jeff Schneider discussed in an NPR interview how the company uses ML to predict rider demand to ensure that “surge pricing”(short periods of sharp price increases to decrease rider demand and increase driver supply) will soon no longer be necessary.
Glimpse into the future In the future, AI will shorten your commute even further via self-driving cars that result in up to 90% fewer accidents, more efficient ride sharing to reduce the number of cars on the road by up to 75%, and smart traffic lights that reduce wait times by 40% and overall travel time by 26% in a pilot study.
“filter out messages with the words ‘online pharmacy’ and ‘Nigerian prince’ that come from unknown addresses”) aren’t effective against spam, because spammers can quickly update their messages to work around them.
In a research paper titled, “The Learning Behind Gmail Priority Inbox”, Google outlines its machine learning approach and notes “a huge variation between user preferences for volume of important mail…Thus, we need some manual intervention from users to tune their threshold.
The researchers tested the effectiveness of Priority Inbox on Google employees and found that those with Priority Inbox “spent 6% less time reading email overall, and 13% less time reading unimportant email.” Glimpse into the future Can your inbox reply to emails for you?
Smart reply uses machine learning to automatically suggest three different brief (but customized) responses to answer the email. As of early 2016, 10% of mobile Inbox users’ emails were sent via smart reply.
A brute force search comparing every string of text to every other string of text in a document database will have a high accuracy, but be far too computationally expensive to use in practice. One MIT paper highlights the possibility of using machine learning to optimize this algorithm.
– Credit Decisions Whenever you apply for a loan or credit card, the financial institution must quickly determine whether to accept your application and if so, what specific terms (interest rate, credit line amount, etc.) to offer. FICO uses ML both in developing your FICO score, which most banks use to make credit decisions, and in determining the specific risk assessment for individual customers.
In early 2016, Wealthfront announced it was taking an AI-first approach, promising “an advice engine rooted in artificial intelligence and modern APIs, an engine that we believe will deliver more relevant and personalized advice than ever before.” While there is no data on the long-term performance of robo-advisors (Betterment was founded in 2008, Wealthfront in 2011), they will become the norm for regular people looking to invest their savings.
In a short video highlighting their AI research (below), Facebook discusses the use of artificial neural networks—ML algorithms that mimic the structure of the human brain—to power facial recognition software.
The company has invested heavily in this area not only within Facebook, but also through the acquisitions of facial-recognition startups like Face.com, which Facebook acquired in 2012 for a rumored $60M, Masquerade (2016, undisclosed sum), and Faciometrics (2016, undisclosed sum).
In June 2016, Facebook announced a new AI initiative: DeepText, a text understanding engine that, the company claims “can understand with near-human accuracy the textual content of several thousand posts per second, spanning more than 20 languages.” DeepText is used in Facebook Messenger to detect intent—for instance, by allowing you to hail an Uber from within the app when you message “I need a ride” but not when you say, “I like to ride donkeys.” DeepText is also used for automating the removal of spam, helping popular public figures sort through the millions of comments on their posts to see those most relevant, identify for sale posts automatically and extract relevant information, and identify and surface content in which you might be interested.
– Pinterest Pinterest uses computer vision, an application of AI where computers are taught to “see”, in order to automatically identify objects in images (or “pins”) and then recommend visually similar pins. Other applications of machine learning at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing.
– Instagram Instagram, which Facebook acquired in 2012, uses machine learning to identify the contextual meaning of emoji, which have been steadily replacing slang (for instance, a laughing emoji could replace “lol”).
This may seem like a trivial application of AI, but Instagram has seen a massive increase in emoji use among all demographics, and being able to interpret and analyze it at large scale via this emoji-to-text translation sets the basis for further analysis on how people use Instagram.
A few months later, it opened its messenger platform to developers, allowing anyone to build a chatbot and integrate Wit.ai’s bot training capability to more easily create conversational bots.
–Recommendations You see recommendations for products you’re interested in as “customers who viewed this item also viewed” and “customers who bought this item also bought”, as well as via personalized recommendations on the home page, bottom of item pages, and through email.
While Amazon doesn’t reveal what proportion of its sales come from recommendations, research has shown that recommenders increase sales (in this linked study, by 5.9%, but in other studies recommenders have shown up to a 30% increase in sales) and that a product recommendation carries the same sales weight as a two-star increase in average rating (on a five-star scale).
By utilizing AI that can learn your purchasing habits, credit card processors minimize the probability of falsely declining your card while maximizing the probability of preventing somebody else from fraudulently charging it.
however, a month later Amazon’s press release boasted a 9x increase in Echo family sales over the previous year’s holiday sales, suggesting that 5 million sold is a significant underestimate.
For example, casual chess players regularly use AI powered chess engines to analyze their games and practice tactics, and bloggers often use mailing-list services that use ML to optimize reader engagement and open-rates.