Photo credit: Pixabay It hasn't been two years since AlphaGo beat world champion Lee Sedol in Go, but Google's DeepMind already launched a new AI program to take its place.
On December of 2017, Alpha Zero single handedly defeated a world class chess engine, Stockfish, in only 4 hours. In fact, it had no previous experience with chess besides learning the basic rules, but the results were incredible: the AI went undefeated, winning 28 games and drawing the rest in an 100 game matchup. After this match, it went on to beat its former self AlphaGo in Go as well as Elmo in shogi. With this breakthrough, experts were able to discover more about the thought process of a machine. According to Demis Hassabis, the AI "doesn't play like a human, and it doesn't play like a program . . . It plays in a third, almost alien, way." As he analyzed the games of Alpha Zero, he noticed it played some outlandish yet positionally profound moves. Hassabis offers an explanation for this strange behavior. Rather than reinforcement learning (letting the AI learn from example games), Alpha Zero was taught solely by playing games against itself without any human input. DeepMind also says it takes on an "arguably more human-like approach", one that involves more evaluation and planning instead of calculating lengthy variations. Ever since 1997 when DeepBlue beat the world chess champion Gary Kasparov, computers have revolutionized the game of chess. Now powerful forms of machine learning like AlphaGo are making a drastic impact in the field of board games. Surely enough, it keeps us wondering: who will defeat Alpha Zero?
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Are you interested in working on projects with open-source organizations along with peers from around the world? Getting first-hand experience in the world of project development? Interested in coding, quality control, documentation, or outreach? Just getting into the world of programming? The Google Code-In, which opened for registration on November 28th and will run until January 17th, is an annual event allowing participants to do just that. Pre-university students of all skill levels, ages 13-17, are invited to participate. According to their webpage, over 4500 students from 99 countries have completed work in the contest since 2010. Google partners with a number of open-source organizations (this year’s bunch includes Ubuntu and JBoss), giving participants the opportunity to claim tasks and work with mentors to complete assignments. Assignments range from developing new code for an application or webpage, to installing software and documenting the process, to designing company laptop stickers and t-shirts. The Google Code-In has tasks for everyone and is a great way to be introduced to the world of programming. Additionally, participants get to win prizes ranging from t-shirts to a trip to Google HQ! If you’re interested, we encourage you to sign up at https://codein.withgoogle.com/. Photo Credit: Gigaom
AI has been making great advances in social media. On November 27th, Guy Rosen, VP of Facebook Product Management, announced in a blog post that Facebook is using AI to help identify suicidal users and connect them to help. This tool has been in use in the US for months and will now be implemented in other countries as well. Rosen wrote that in the last month alone, Facebook ‘worked with first responders on over 100 wellness checks based on reports’ thanks to this technology. This tech uses AI for pattern recognition to ‘help accelerate the most concerning reports’ and inform local authorities, writes Rosen. Pattern recognition helps Facebook flag posts and live streams through which users may be expressing suicidal thoughts. It also searches for comments like, ‘Are you ok?’ and ‘Can I help?’ which can be strong indicators of someone needing support. It then prioritizes the posts and sends more pressing ones to be reviewed first. Snapchat, too, has recently unveiled AI image-recognition technology in its latest update. It recognises objects in pictures and then offers image-recognition filters which are tailor made to match the objects in the picture. For example, if you take a picture with food, Snapchat will offer filters with words like, ‘get in my belly’ and ‘eatin’ good’. This is not the first time that the company has incorporated object recognition in its app. Snapchat already allows you to search for certain objects, places and events in ‘stories’. For example, if you search for ‘beach’, you will get snaps of people at beaches, and if you search for ‘football’ you will find snaps of people at football games. This is just the beginning of AI being incorporated into social media and eventually all aspects of our daily lives. Sources: CNN tech Facebook newsroom Flipboard/AI Business Insider Photo Credit: Daily Mail
Researchers at the University of California, San Diego, and Adobe have recently created a way for AI to both learn a person’s style and create images of items that match the style. The system could potentially allow retailers to create personalized clothing, or help predict fashion trends. The two algorithms used are a convolutional neural network (CNN) and a generative adversarial network (GAN). The two networks improve the results and can create multiple item images for each user. There’s still a few obstacles to these AI-generated textiles hitting the market, however. For example, researchers need to turn two-dimensional computer images into 3-D images used to produce an actual piece of clothing. And of course, fashion sense requires knowing which items pair well together. Amazon has been working on using AI to spot fashion trends, and Alibaba, a Chinese retail giant, has introduced FashionAI, which recommends items based on what shoppers brought into the dressing room. Vue.ai is a fashion AI startup that recently revealed a method for creating fake fashion models. Last fall, Burberry launched a Facebook Messenger bot during London Fashion Week, which offered glimpses of the new collection and shared trivia, as well as a live buying option. HighSnobiety is a website covering streetwear trends, which also launched a Sneaker Bot on Facebook Messenger, which quickly conveyed information and news from different brands. This is just the tip of the iceberg when it comes to AI applications in fashion. It’s an exciting field, with many high-profile clients and players. Sources: MIT Tech Review Fast Company Tech Emergence Several companies from Silicon Valley are taking advantage of AI's ability to accurately recognize images in order to benefit consumer's health decisions. For instance, Habit, founded by Nail Grimmer, uses a combination of genetics and machine learning to help personalize the user's diet, the startup Passio uses AI to give nutritional advice, and the New York based company Edamam implements Recipe Analysis API to provide nutritional information to the user.
Not only will artificial intelligence assist consumers, but they will also bring advantages to producers. In the future, AI could be able to help recognize agricultural diseases (researchers at Cornell already trained their own AI to identify brown leaf spot disease on cassava leaves with a 98% accuracy). Other applications of AI in the food industry include reducing the use of herbicides and other harmful chemicals through precision weeding or simply aiding in the harvest of crops. But why is AI so good at decision making? A study done by Stanford reported on by FoodTanks concluded that the artificial neural networks (analogous to the brain's neural networks) are trained with "huge data sets and large-scale computing (deep learning), boosting data-driven solutions for improving decision making." To learn more about the difference in deep learning and machine learning, feel free to check out this article by Forbes. Photo credit: The Medical Futurist
AI excels in many areas, however, one place where AI currently falls short is emotion. AI is unable to detect and replicate human emotions, something that many people are concerned about. However, this may change in the future. There are autonomous, relational, and conversational devices, but so far, there has not been a device that can detect emotion. Currently, an area of AI (emotion AI) is creating algorithms that can detect basic human emotions. Some challenges they face include how to train multi-modal systems and how to get data on less frequent emotions. Nonetheless, emotion AI is progressing quickly, and the MIT Technology Review predicts that technology may become emotion-aware within the next five years. Forbes ties the benefits of emotionally-aware devices into chatbots, explaining how devices would be able to better interact with humans if they were aware of emotion. Emotionally intelligent chatbots would also be much more consumer-friendly. Additionally, Microsoft states that in order for AI to be a positive force, it will need empathy, since empathy is what will truly allow AI to solve for people-problems. In order for AI to be able to truly interact at the human level, they first need to be aware of empathy with compassionate intelligence -- the ability to act with compassion. Sources: MIT Technology Review Forbes Microsoft Photo credit: USACO
Are you interested in spending hours hunched over a computer, debugging until 2 AM? It's not as bad as it sounds, we promise... The United States of America Computing Olympiad (USACO, supposedly pronounced "you-sah-co") is a multi-round competition. During each round, competitors solve various programming problems, ranging in difficulty based on the competitor's level. There are 4 levels: bronze, silver, gold, and platinum. Practice for the USACO using their online training pages, or multiple other competitive programming websites like CodeForces. You'd have the chance to be selected as one of a small group of students to attend the summer training camp. Those who perform well at camp are chosen to represent the United States at the International Olympiad in Informatics (IOI). The IOI 2018 will be held in Japan. The first round of USACO 2017-2018 will be held mid-December. Pictured, left to right, are: Manisha Bahl, director of the Massachusetts General Hospital Breast Imaging Fellowship Program; MIT Professor Regina Barzilay (center); and Constance Lehman, professor at Harvard Medical School and chief of the Breast Imaging Division at MGH’s Department of Radiology.
Image: Jason Dorfman/CSAIL There are over 200,000 cases of breast cancer every year in the United States and 40,000 women die every year due to this. One of the best and most common ways to diagnose breast cancer is through mammograms. However, a drawback of using a mammogram is that they are still imperfect and result in a great many false positives which lead to unnecessary surgeries and biopsies. A cause of these false positives are high risk lesions that appear suspicious on mammograms and are often removed through surgeries. Nonetheless, 90% of these lesions are benign, meaning that thousands of women must go through painful, scarring and unnecessary surgeries. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory, Massachusetts General Hospital and Harvard Medical School turned to artificial intelligence for the answer. The model is trained on more than 600 existing high risk lesions and looks for patterns within family history, demographics, genetics, etc. By using the “random-forest classifier” the model diagnosed 97% of cancers. As the name suggests, a random-forest is made up of multiple decision trees. A decision tree is a predictive model which goes from observations about an item, or the branches, to conclusions about this item, represented by the leaves. Each of the decision trees come to a conclusion and vote on what the data set could be. Majority rules. Researchers hope that this model can be incorporated into clinical practice in the next year. The team, including Regina Barzilay (MIT’s Delta Electronics Professor of Electrical Engineering and Computer Science), Constance Lehman (professor at Harvard Medical School and chief of the Breast Imaging Division at MGH’s Department of Radiology) and Manisha Bahl of MGH. Along with CSAIL graduate students Nicholas Locascio, Adam Yedidia, and Lili Yu, they published an article in the medical journal Radiology. Sources: BBC News MIT News Data Aspirant Photo Credit: Janelle Shane’s Tumblr, Lewis and Quark
The Portland Guinea Pig Rescue is home to many fluffy critters, but coming up with creative names can be a challenge. So researcher Janelle Shane developed an algorithm to generate guinea pig names. Given a list of examples, (”Snickers”, “Pumpkin”, “Ginger”, “Rascal”, etc.), a neural network learned to make more. Neural networks are a type of computer program with thousands of processing nodes that mimic the way human brains learn. They work in a network to “train” on sample datasets with enough examples, adapting over time and identifying patterns. In this case, 628 names was enough to generate “remixes” and new words. For the most part, the neural network excelled at its task, generating names that are both eclectic and cute. The resident puffballs include: Popchop, Fuzzable, Buzzberry, Fleury White, Stargoon, and Princess Pow. However, some of the misfires were names like Madly Mean, Fleshy, and Bho8otteeddeeceul. Go check out the adorable guinea pigs, along with pictures, here, or on the researcher’s own blog. And of course, you can head over to the Portland Guinea Pig Rescue’s website if your life needs a little Popchop or Princess Pow. Sources (besides links in the article): Smithsonian Magazine The Mary Sue The month of November will bring a number of submission and application deadlines that readers may want to take note of.
The NCWIT Aspirations in Computing Award application is due on November 6, 2017. The application includes several questions and essays. The award is based off of aspirations, so don’t worry if you are relatively new to coding! The Google Code-in starts on November 28, 2017. Participants perform a variety of tasks for various open-source organizations in order to win prizes and possibly a trip to Google HQ. Don’t miss these great opportunities! Start preparing/sending in submissions today! |