AI News, Artificial Intelligence Can Make Our Lives Easier, But It Can Also Go ... artificial intelligence
Artificial Intelligence Bundle: 3 Books in 1
This book is a good guide to understand how AI will impact every aspect of our daily lives, and the steps you can take to take advantage of it.
Book 2: Machine Learning in Finance While machine learning and finance have generally been seen as separate entities, this book looks at several applications of machine learning in the financial world.
Whether it is predicting the best time to buy a stock in a day trading scenario, or trying to determine the long term value of a stock, financial ratios and common sense have always been used as reliable indicators.
whether it is in the web, supermarkets, social media, etc., analysis of data has become integral to our daily life.
A.I. Took a Test to Detect Lung Cancer. It Got an A.
They created a neural network, with multiple layers of processing, and trained it by giving it many CT scans from patients whose diagnoses were known: Some had lung cancer, some did not and some had nodules that later turned cancerous.
We gave it a final exam on data it’s never seen after we spent a lot of time training, and the result we saw on final exam — it got an A.” Tested against 6,716 cases with known diagnoses, the system was 94 percent accurate.
Pitted against six expert radiologists, when no prior scan was available, the deep learning model beat the doctors: It had fewer false positives and false negatives.
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?
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.
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).
Square, a credit card processor popular among small businesses, charges 2.75% for card-present transactions, compared to 3.5% + 15 cents for card-absent transactions.
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.
We may soon see retailers take it one step further and design your entire experience individually for you. Google already does this with search, even with users who are logged out, so this is well within the realm of possibility for retailers.
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.
- On Friday, June 5, 2020
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