System detects patterns in Email About likely markers of spam Detected Pattern Emails with "Earn Cash" More likely to be spam email Can use such detected patterns to make automated decisions about future emails Example: Email Spam Filter "Earn Cash" "Earn Cash" detected in 10% of Spam emails 0% of wanted emails Identification Improves Algorithm improves in performance In auto-identifying spam As it is able to examine more data And find additional indicia of spam Algorithm is "learning" over time from additional examples Example: Email Spam Filter "Free" Probability of Spam Contains "Free" 70% Spam Contains "Earn Cash" 90% Spam From Belarus 85% Spam For some (not all) complex tasks Requiring intelligence Intelligent Results Without Intelligence Can get "intelligent" automated results without intelligence By finding suitable Proxies or Patterns People use advanced cognitive skills to translate Proxies for Intelligent Results Without Intelligence Google finds statistical correlations by analyzing previously translated documents Statistical Machine Translation Produces automated translations using statistical likelihood as a "proxy" for underlying meaning Detecting Patterns Proxy Principle for Automation That can serve as Proxies For some underlying Cognitive Task Learning Machine Learning Main Points Pattern Detection Data Self-Programming Summary Major AI Approaches Two Major AI Techniques • Logic and Rules-Based Approach • Machine Learning (Pattern-Based Approach) Hybrid Systems • Many successful AI systems are hybrids of • Machine learning & Rules-Based Hybrids • e.g. Self-driving cars employ both approaches • Human intelligence AI Hybrids • Also, many successful AI systems work best when • They work with human intelligence • AI systems supply information for humans Humans Computers Technology Enhancing (Not Replacing) Humans Humans Alone Computers Alone Examples of AI in Law Today • Machine Learning • AI in Litigation - E-Discovery and "Predictive Coding" • Natural Language Processing (NLP) of Legal Documents • Automated contract analysis • Predictive Analytics for Litigation • Machine Learning Assisted Legal Research • Logic and Rules-Based Approaches • Compliance Engines • Expert Systems • Attorney Workflow Rule Systems • Automated Document Assembly Limits on Artificial Intelligence • Artificial Intelligence Accomplishments • Automate many things that couldn't do before • Limits • Many things still beyond the realm of AI • No thinking computers • No Abstract Reasoning • Often AI systems Have Accuracy Limits • Many things difficult to capture in data • Sometimes Hard to interpret Systems Questions Harry Surden Associate Professor of Law University of Colorado Law School Affiliated Faculty, Stanford CodeX Center Twitter: @HarrySurden Email: hsurden@colorado.edu

For roughly a decade, the SEO industry has been consistently moving further away from discussions about the internal workings of the search engines, starting around the time of the first Panda update. This decision to focus on more strategic and long-term SEO approaches has been a positive one, overall. Marketing strategies built entirely on the whims of a search engine aren't built to stand the test of time. However, there's no denying the value that came from understanding and predicting how search engines would react to certain kinds of tweaks to the HTML or to the backlink profile in the early days of SEO. Most of us have assumed that those days ended when Google embraced machine learning.

However, this reasoning is based on the assumption that once a machine learning algorithm has been developed to solve one problem, that same algorithm can be easily applied to solve a different problem.

It is standard practice in managing payments to block potentially fraudulent transactions via a set of rules. These rules can be very effective in mitigating fraud risk, and practitioners in the industry are comfortable with this approach. Quite often these rules are able to mitigate the losses from fraudulent transactions without producing a correspondingly high alarm rate. For example, a fraud team might create rules based on a location and block transactions from risky zip codes. They might also create rules to block transactions from cards used too frequently by blocking any transactions for cards with more than 4 previous transactions in the past 30 minutes.

Commerce has evolved from its many different forms – from in-store purchases and mail order businesses to online market places and omni-channel commerce. Digital downloads and instant fulfillment services like Uber are transforming the way people consume goods and services. Online shopping has slowly eroded the traditional brick and mortar space of shopping malls. Consumers' retail trips in November and December, the two biggest shopping months out of the year, dropped from 35 billion visits in 2010 to 17.3 billion visits in 2013, according to a report from Cushman & Wakefield. In spite of rapid rise and adoption of modern commerce, fraud prevention is still based on archaic methods that are rigid, resource constrained and time consuming.

President Donald Trump cancelled his visit to the United Kingdom -- his first since taking office -- which was scheduled to happen in February this year, multiple reports revealed Thursday. The reason he gave for cancelling his visit was heavily criticized by British politicians. During his visit, he was supposed to inaugurate the new U.S. embassy in London as well as hold talks with U.K. Prime Minister Theresa May on Feb. 26 -27. Trump put the blame of having to cancel his U.K. visit on former President Barack Obama. After Trump's tweet, a number of British politicians celebrated the fact he will not be visiting their country, adding that the reason he gave for cancelling was untrue.

The core of most modern Machine Learning (ML) systems is automated neural networks (ANNs). The training of ANN's requires large data sets. One misconception of those data sets is the idea that "if we get enough data, we can make the system 100% accurate." Yes, that can happen, but it's not what we really want. Many methods can be used to group data into relevant categories.

The core of most modern Machine Learning (ML) systems is automated neural networks (ANNs). The training of ANN's require large data sets. One misconception of those data sets is the idea that "if we get enough data, we can make the system 100% accurate." Yes, that can happen, but it's not what we really want. Many methods can be used to group data into relevant categories.

Technology leaders like Google, Amazon, Microsoft, and Apple have recently built personal assistants that use Artificial Intelligence to deliver customized search results. This trend is now gaining momentum in healthcare where the doctors expect the computer to assist them in treating their patients when in clinic. In the current times, besides prescribing medicines, doctors have to keep themselves updated on the evolving guidelines and drug discoveries. Doctors today have insufficient time to devote for the diagnosis and treatment of their patients. What they need is a system that can improve patient outcomes in a limited time.

An artificial intelligence company has launched this week what it says is the first autonomous AI tool for determining the "trustworthiness" of a story. Called Unpartial and developed by San Jose, California-based Recognant, the tool is an extension for the Chrome browser. CEO Brandon Wirtz told me that other browsers may be supported at some point. Recognant has described the tool as being a detector of "fake news," but it's actually a "trustworthiness" detector. It doesn't check facts or validate the source, but instead uses generated rules to evaluate the internal validity of a story.