machine learning

A Guide to Solving Social Problems with Machine Learning


You sit down to watch a movie and ask Netflix for help. Zoolander 2?") The Netflix recommendation algorithm predicts what movie you'd like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don't believe it's possible to forecast which families will wind up on the streets. You'd love to move your city's use of predictive analytics into the 21st century, or at least into the 20th century. You just hired a pair of 24-year-old computer programmers to run your data science team. But should they be the ones to decide which problems are amenable to these tools? Or to decide what success looks like?

Email overload: Using machine learning to manage messages, commitments - Microsoft Research


As email continues to be not only an important means of communication but also an official record of information and a tool for managing tasks, schedules, and collaborations, making sense of everything moving in and out of our inboxes will only get more difficult. The good news is there's a method to the madness of staying on top of your email, and Microsoft researchers are drawing on this behavior to create tools to support users. Two teams working in the space will be presenting papers at this year's ACM International Conference on Web Search and Data Mining February 11–15 in Melbourne, Australia. "Identifying the emails you need to pay attention to is a challenging task," says Partner Researcher and Research Manager Ryen White of Microsoft Research, who manages a team of about a dozen scientists and engineers and typically receives 100 to 200 emails a day. "Right now, we end up doing a lot of that on our own."

Homenum Revelio! AI will destroy sperm donor anonymity


Maybe you're a full-time student trying to come up with next month's ramen budget. Perhaps you've just lost your job and you're trying to earn a few bucks in between interviews. Or it could be you're just happy to find a career doing something you're really good at. For whatever reason, thousands of men donate sperm every year. Most of them expect a certain level of anonymity that no longer exists.

Can Machine Learning Double Your Social Impact? (SSIR)


The next big thing in the social sector has officially arrived. Machine learning is now at the center of international conferences, $25 million dollar funding competitions, fellowships at prestigious universities, and Davos-launched initiatives. Yet amidst all of the hype, it can be difficult to understand which social sector problems machine learning is best positioned to solve, how organizations can practically use it to enhance their impact, and what kind of sector-wide investments can enable the ambitious use of it for social good in the future. Our work at IDinsight, a nonprofit that uses data and evidence to help leaders in the social sector combat poverty, and the work of other organizations offer some insights into these questions. Machine learning uses data (usually a lot) and statistical algorithms to predict something unknown.

Image Classification


Recent advances in deep learning made tasks such as Image and speech recognition possible. Most people talk about these days whilst discussing machine learning / deep learning is Tensorflow and Neural Networks. Deep Learning is nothing but a subset of Machine Learning Algorithms which is specifically good at recognizing patterns but typically requires a large number of data. This post describes a Keras based Convolution Neural Net for image classification from scratch. There are several scripts which use pre-trained models available for image classification such as Google's Inception model.

Artificial Intelligence: It's Time


People may continue to call artificial intelligence and machine learning emerging technologies for decades, but the technology is ready to implement today. In order to avoid falling behind, businesses need to start moving on plans for AI and machine learning now. Oracle Magazine sat down with Ian Swanson, vice president of product management AI and machine learning for Oracle Cloud, to talk about enterprise AI and machine learning today: adoption challenges, ways to succeed, and how Oracle supports innovation. Oracle Magazine: AI and machine learning, in particular, have been emerging technologies for some time. What is the state of these technologies in the enterprise today?

Machine Learning for Anyone who Took Math in 8th Grade


I usually see artificial intelligence explained in one of two ways: through the increasingly sensationalist perspective of the media or through dense scientific literature riddled with superfluous language and field-specific terms. There's a less publicized area between these extremes where I think literature needs to step up a bit. News about "breakthroughs" like that stupid robot Sophia hype up A.I. to be something akin to human consciousness while in reality, Sophia is about as sophisticated as AOL Instant Messenger's SmarterChild. Scientific literature can be even worse, causing even the most driven researcher's eyes to glaze over after a few paragraphs of gratuitous pseudo-intellectual trash. In order to accurately assess A.I., the general population needs to know what it really is.

How companies use collaborative filtering to learn exactly what you want


How do companies like Amazon and Netflix know precisely what you want? Whether it's that new set of speakers that you've been eyeballing, or the next Black Mirror episode -- their use of predictive algorithms has made the job of selling you stuff ridiculously efficient. But as much as we'd all like a juicy conspiracy theory, no, they don't employ psychics. They use something far more magical -- mathematics. Today, we'll look at an approach called collaborative filtering.

Facebook's Yann LeCunn reflects on the enduring appeal of convolutions


Thirty years ago, Yann LeCun pioneered the use of a particular form of machine learning, called the convolutional neural network, or CNN, while at the University of Toronto. That approach, moving a filter over a set of pixels to detect patterns in images, showed promise in cracking problems such as getting the computer to recognize hand-written digits with minimal human guidance. Years later, LeCun, then at NYU, launched a "conspiracy," as he has termed it, to bring machine learning back into the limelight after a long winter for the discipline. The key was LeCun's CNN, which had continued to develop in sophistication to the point where it could produce results in computer vision that stunned the field. The new breakthroughs with CNNs, along with innovations by peers such as Yoshua Bengio, of Montreal's MILA group for machine learning, and Geoffrey Hinton of Google Brain, succeeded in creating a new springtime for AI research, in the form of deep learning.