AI/ML News & Innovations Hub

Statistical significance test for comparing two canonical correlation analyses

I have a colleague who is comparing several different treatments of data via canonical correlation analysis. In other words, given some time-varying signal, $a(t)$, he extracting some vector of features $v_1(t)$. He then supposes that this is a predictor for some other vector $p(t)$. To check this he computes the [first] canonical correlation coefficient, $R_1 = \text{CCA}(v_1(t),p(t))$. And then he tries some new improved feature extractor, $v_2(t)$, and compares again $R_2 = \text{CCA}(v_2(t),p(t))$. I can find tests for the situation that $R_1$, and $R_2$ are different from zero. But what about the test that $R_1$ and $R_2$ are significantly different from each other? Asked on behalf of a colleague, but I'd also be interested in the answer.

How does the shape of a decision boundary in relate between the original and kernel feature space?

I'm trying to get my head around the mathematics and implementation of SVM and hopefully gain some intuition into how kernels work and perhaps being able to, with a bit more confidence, define my own kernel. So far, what I understand (and I might be wrong) is that kernels map the samples in the training set into higher dimensions where the classes in the training set are linearly separable. We can then, in this new dimension use a hyperplane to separate the classes. A hyperplane is, for example, a straight line, in 2 dimensions, or a flat surface (a plane), in 3 dimensions. Questions If the original feature space is in 2 dimensions and the mapping is to 3 dimensions, then, in short, I'm wondering: When mapping a training set, generated around a quadratic function, which originally has features in the form $(X,Y)$ to $(X,Y,X^2)$, see below, doesn't seem linearly separable when plotted in a 3D space. Yet the decision boundary in the original space makes a perfect parabola and classifies samples correctly. How come? Can we and, if so, how can we visualize the decision boundary as it looks in the "extended" feature space? Can we then see how the looks in the extended feature space relate to the looks original space? Experiment In order to get some image of what this looks like I attempted the following, see github for more details: Generated 130 data points around a simple quadratic equation (2 features), Points above the quadratic curve were assigned one class points below anot…

Convolution and deconvolution of random variables of different dimensions

Preliminary: Let's say we have $Y=X+Z$ ($Y$ is data, $X$ is latent variable and $Z$ is noise), where the random variables are all in $\mathbb{R}$. Then an inverse Fourier transform leads to \begin{align} f_X(x)=\frac{1}{2\pi}\int e^{-itx}\varphi_X(t)dt=\frac{1}{2\pi}\int e^{-itx}\frac{\varphi_Y(t)}{\varphi_Z(t)}dt, \end{align} where $\varphi$ are the characteristic functions. Now, suppose we know the noise distribution for $Z$ so we know the characteristic function $\varphi_Z$. If we observe data $\boldsymbol{y} = (y_1, ..., y_n)$ then we can estimate $f_Y$ and thereby estimate $\varphi_Y$ by subsitution. This gives us the estimator $$\hat{f_X}(x) = \frac{1}{2\pi}\int e^{-itx} \frac{\hat{\varphi_Y}(t)}{\varphi_Z(t)}dt = \frac{1}{2\pi}\int e^{-itx} \frac{\int e^{ity}\hat{f}_Y(y)dy}{\varphi_Z(t)}dt.$$ My question: What if $X$ is one-dimensional but the noise and observation are two-dimensional? For example, $X$ is a random variable that takes values on some deterministic line segment (in two dimensional space, but since it is on the line so essentially its one-dimensional), the error $Z$ is a two dimensional random variable so as $Y$. I understand that if we treat $X$ as a two dimensional object we still have $\varphi_X(t)=\varphi_Y(t)/\varphi_Z(t)$, but it says nothing about the fact that $f_X$ is a density on the one dimensional line segment. How should I do deconvolution in this kind of situations?

A Massively Multi-user Datastore, Synced with Mobile Clients

At Square, we manage large amounts of information for our merchants. This includes the data surrounding what a merchant sells — their…

Comment on What’s the Difference Between Ray Tracing and Rasterization? by Polaristar

In reply to Nutti . Yes, but this article discusses the use of ray tracing *in games.* As in, *real-time ray tracing.* We're getting to the point where software and hardware are capable of outputting ray-traced frames at 30 or 60 times a second. This is even explained near the top of the article: "Historically, though, computer hardware hasn’t been fast enough to use these techniques in real time, such as in video games. Moviemakers can take as long as they like to render a single frame, so they do it offline in render farms. Video games have only a fraction of a second. As a result, most real-time graphics rely on another technique, rasterization." This is why it's a pretty historical event.

Need advice on change point (step) detection

I have a time series with lots of steps/jumps (data file here ). A plot is given below. I would like to subtract an appropriate value for each of these square wave features to bring them back down to the baseline of the signal (i.e. remove the jumps so I get a smoothly varying signal). A median filter works really well for removing a small number of outliers in a row, but in this case I probably need a different approach since the square wave jumps can have different durations as seen. A common method I've seen for doing this is to compute first differences of adjacent samples, and look for large differences to detect jumps. I implemented this method but the problem is it often fails, since the one tunable parameter for the method is a threshold value $t$ which the first differences must cross in order to detect a jump: $$ | x_{i+1} - x_i | > t $$ As can be seen in the plot below, the jumps I have are often different sizes, so a constant threshold value isn't the best approach. In particular, in some cases there is an interesting signal where adjacent samples can change by large values without being a jump! I have highlighted such a region in red. Below is a zoomed in view of the red box area. You can see there is a square wave jump followed by an interesting signal. The red arrows depict a place where adjacent samples from an interesting signal have a larger distance between them than some of the jumps in the signal. Therefore a constant threshold method with finite differe…

Constructing a unified path analysis model from several datasets each with different combinations of variables

I have four observational experiments (data sets) that I wish to combine and summarize in a single path analysis model. Each experiment is 3-dimensional but the observables and therefore dimensions/variables are different in each data set. One variable X.1 is common to all datasets. All other variables are present in exactly two experiments. Here are the experiments and associated variables: Experiment 1: {X.1, X.2, Y} Experiment 2: {X.1, X.2, Z} Experiment 3: {X.1, X.3, Y} Experiment 4: {X.1, X.3, Z} For the sake of my study, Y and Z are considered to be the dependent (response) variables in a set of multiple linear regression models (MLRs). For each data set I minimized the Bayesian Information Criterion (BIC) to select the best MLR model. These BIC-minimizing models (one for each experiment, same order as above) are as follows: Y ~ X.1 + X.2 Z ~ X.1 Y ~ X.1 + X.3 Z ~ X.3 I am looking for a way to create a single path analysis model with two outputs, Y and Z , that unifies all four experiments. Is there a smart way to get a single path analysis model out of this? To make such a path model I believe I must first either 1) combine these separate MLR models OR 2) combine the datasets then make a single MLR model. I'm not sure where to start for either of these methods. Thank you for reading and offering advice. BTW, there is a little bit of multicollinearity present in these MLR models, but the Variance Inflation Factor is less than 5 for all predictor variables.

Random Forest for regression--binary response

Kind of a broad question here. But is it okay/possible in R to use a random forest for regression when the response variable is a binary outcome? Essentially what I'm looking for is a probability of something happening. Below is the code I've been using it run it and the warning message. Or am I better off using this in classification mode. My end goal is to predict the probability of something happening, not necessarily predict what "classification" it will be, 1 or 0. m1RF I apologize if this question is in the wrong place.

(started posting on Medium instead)

The current state of this blog (with the last post 2 years ago) makes it look like I’ve disappeared. I’ve certainly become less active on blogs since I’ve joined Tesla, but whenever I do get a chance to post something I have recently been defaulting to doing it on Medium because it is much faster and easier. I still plan to come back here for longer posts if I get any time, but I’ll default to Medium for everything short-medium in length. TLDR Have a look at my Medium blog .

Using artificial intelligence to improve early breast cancer detection

Using artificial intelligence to improve early breast cancer detection Anonymous (not verified) Tue, 10/17/2017 - 13:58 Article October 17 '17 Adam Conner-Simons Every year 40,000 women die from breast cancer in the U.S. alone. When cancers are found early, they can often be cured. Mammograms are the best test available, but they’re still imperfect and often result in false positive results that can lead to unnecessary biopsies and surgeries. Every year 40,000 women die from breast cancer in the U.S. alone. When cancers are found early, they can often be cured. Mammograms are the best test available, but they’re still imperfect and often result in false positive results that can lead to unnecessary biopsies and surgeries. One common cause of false positives are so-called “high-risk” lesions that appear suspicious on mammograms and have abnormal cells when tested by needle biopsy. In this case, the patient typically undergoes surgery to have the lesion removed; however, the lesions turn out to be benign at surgery 90 percent of the time. This means that every year thousands of women go through painful, expensive, scar-inducing surgeries that weren’t even necessary . How, then, can unnecessary surgeries be eliminated while still maintaining the important role of mammography in cancer detection? Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts General Hospital, and Harvard Medical School believe that the answer is to turn to ar…

Detecting walking speed with wireless signals

Detecting walking speed with wireless signals Anonymous (not verified) Mon, 05/01/2017 - 10:25 Video May 01 '17 Adam Conner-Simons | Rachel Gordon We’ve long known that blood pressure, breathing, body temperature and pulse provide an important window into the complexities of human health. But a growing body of research suggests that another vital sign – how fast you walk – could be a better predictor of health issues like cognitive decline, falls, and even certain cardiac or pulmonary diseases. We’ve long known that blood pressure, breathing, body temperature and pulse provide an important window into the complexities of human health. But a growing body of research suggests that another vital sign – how fast you walk – could be a better predictor of health issues like cognitive decline, falls, and even certain cardiac or pulmonary diseases. Unfortunately, it’s hard to accurately monitor walking speed in a way that’s both continuous and unobtrusive. Professor Dina Katabi’s group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has been working on the problem, and believes that the answer is to go wireless. In a new paper, the team presents “WiGait,” a device that can measure the walking speed of multiple people with 95 to 99 percent accuracy using wireless signals. The size of a small painting, the device can be placed on the wall of a person’s house and its signals emit roughly one-hundredth the amount of radiation of a standard cellphone. It builds o…

Comment on Dubai fintech accelerator to assist African startups by Dejene Mulugeta

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Teaching from Simple Abstractions

(You need to know programming to understand this post. If you know what linked lists are, that’s enough to get the general point, but more knowledge would be more helpful.) Within the Programming Languages community, there’s a subcommunity that thinks a lot about education, especially for introductory courses. Two main approaches are SICP approach and […]

Thoughts on the “Meta Trap”

Cross-posted to the EA Forum. Thanks to Ajeya Cotra and Jeff Kaufman for feedback on a draft of this post. Any remaining errors are my own. Last year, Peter Hurford wrote a post titled ‘EA risks falling into a “meta trap”. But we can avoid it.’ Ben Todd wrote a followup that clarified a few […]

Comment on 20 startups to pitch to investors at Angel Fair Africa by Dejene Mulugeta

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Comment on Kenyan recruitment startup Fuzu raises $1.88m by Dejene Mulugeta

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Almost Vegan

About a year and a half ago, I decided to stop consuming animal products, because of the intense suffering on factory farms. (Why focus on this problem among myriads of others? Jacy from Animal Charity Evaluators explains.) For the most part, I actually found being vegan easier than I thought it would be, though it still wasn’t […]

Comment on SA govt partners Global Innovation Fund to back social innovations by Dejene Mulugeta

Which is Exllent.. For.. Global transformation!

Comment on Impact Hub launches entrepreneur fellowship in Burundi, Ethiopia, Sierra Leone by Dejene Mulugeta

this is the aportunity for Africans entrepreneur!!

A Survival Guide to a PhD

This guide is patterned after my “Doing well in your courses” , a post I wrote a long time ago on some of the tips/tricks I’ve developed during my undergrad. I’ve received nice comments about that guide, so in the same spirit, now that my PhD has come to an end I wanted to compile a similar retrospective document in hopes that it might be helpful to some. Unlike the undergraduate guide, this one was much more difficult to write because there is significantly more variation in how one can traverse the PhD experience. Therefore, many things are likely contentious and a good fraction will be specific to what I’m familiar with (Computer Science / Machine Learning / Computer Vision research). But disclaimers are boring, lets get to it! Preliminaries First, should you want to get a PhD? I was in a fortunate position of knowing since young age that I really wanted a PhD. Unfortunately it wasn’t for any very well-thought-through considerations: First, I really liked school and learning things and I wanted to learn as much as possible, and second, I really wanted to be like Gordon Freeman from the game Half-Life (who has a PhD from MIT in theoretical physics). I loved that game. But what if you’re more sensible in making your life’s decisions? Should you want to do a PhD? There’s a very nice Quora thread and in the summary of considerations that follows I’ll borrow/restate several from Justin/Ben/others there. I’ll assume that the second option you are considering is joining a medium…

Deep Reinforcement Learning: Pong from Pixels

--> This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go , simulated quadrupeds are learning to run and leap , and robots are learning how to perform complex manipulation tasks that defy explicit programming. It turns out that all of these advances fall under the umbrella of RL research. I also became interested in RL myself over the last ~year: I worked through Richard Sutton’s book , read through David Silver’s course , watched John Schulmann’s lectures , wrote an RL library in Javascript , over the summer interned at DeepMind working in the DeepRL group, and most recently pitched in a little with the design/development of OpenAI Gym , a new RL benchmarking toolkit. So I’ve certainly been on this funwagon for at least a year but until now I haven’t gotten around to writing up a short post on why RL is a big deal, what it’s about, how it all developed and where it might be going. Examples of RL in the wild. From left to right : Deep Q Learning network playing ATARI, AlphaGo, Berkeley robot stacking Legos, physically-simulated quadruped leaping over terrain. It’s interesting to reflect on the nature of recent progress in RL. I broadly like to think about four separate factors that hold back AI: Compute (the obvious one: Moore’s Law, GPUs, ASICs), Data (in a nice form, not just out there somewhere on the int…

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aisp_new

Autonomous Intelligent Systems This project intertwines Bayesian inference, model-predictive control, distributed information networks, human-in-the-loop and multi-agent systems. The project focuses on the principled handling of uncertainty for distributed modelling in complex environments which are highly dynamic, communication poor, observation costly and time-sensitive. We aim to develop robust, stable, computationally practical and principled approaches which naturally accommodate these rea…

Comment on Applications open for Tony Elumelu Entrepreneurship Programme by Dejene Mulugeta

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Comment on Banks face extinction if they don’t find ways to work with fintech startups by Dejene Mulugeta

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Comment on Impact Hub’s Resilience Africa to launch new hubs, incubators by Dejene Mulugeta

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Short Story on AI: A Cognitive Discontinuity.

The idea of writing a collection of short stories has been on my mind for a while. This post is my first ever half-serious attempt at a story, and what better way to kick things off than with a story on AI and what that might look like if you extrapolate our current technology and make the (sensible) assumption that we might achieve much more progress with scaling up supervised learning than any other more exotic approach. A slow morning Merus sank into his chair with relief. He listened for the satisfying crackling sound of sinking into the chair’s soft material. If there was one piece of hardware that his employer was not afraid to invest a lot of money into, it was the chairs. With his eyes closed, his mind still dazed, and nothing but the background hum of the office, he became aware of his heart pounding against his chest- an effect caused by running up the stairs and his morning dose of caffeine and taurine slowly engulfing his brain. Several strong beats passed by as he found his mind wandering again to Licia - did she already come in? A sudden beep from his station distracted him - the system finished booting up. A last deep sigh. A stretch. A last sip of his coffee. He opened his eyes, rubbed them into focus and reached for his hardware. “Thank god it’s Friday”, he muttered. It was time to clock in. Fully suited up, he began scrolling past a seemingly endless list of options. Filtering, searching, trying to determine what he was in the mood for. He had worked hard a…

application_domains

Applications Current Projects Human Agent Collectives - ORCHID As computation increasingly pervades the world around us, we will increasingly work in partnership with highly inter-connected computational agents that are able to act autonomously and intelligently. Humans and software agents will continually and flexibly establish a range of collaborative relationships with one another, forming human-agent collectives (HACs) to meet their individual and collective goals.

Comment on Death Penalty, Child Pornography & Hacking…10 Things To Know About Nigeria’s Cybercrime Act by Monday News Round Up: Send it Like A Snail Mail | techcabal.com

[…] Death Penalty, Child Pornography & Hacking…10 Things To Know About Nigeria’s Cyberc… […]

Comment on The SA Reserve Bank has Approved EcoCash Zimbabwe Transfer Service by Monday News Round Up: Send it Like A Snail Mail | techcabal.com

[…] EcoCash sends money to Zimbabwe now […]

Comment on African-American Pop Star, Akon Launches Solar Academy in Mali by Wednesday News Roundup: The Business of MasterCard in Somalia | techcabal.com

[…] Akon Launches Solar Power Academy in Mali Akon and his partners through the Akon Lighting Africa initiative has announced they will open a Solar Academy in Bamako, Mali this year to teach solar engineering. […]

Comment on First Mobile-Money Treasury Bond, M-Akiba, Unveiled in Kenya by Monday News Round Up: Send it Like A Snail Mail | techcabal.com

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Comment on Jumia Kenya Partners Postal Corporation of Kenya For Logistics by Monday News Round Up: Send it Like A Snail Mail | techcabal.com

[…] Jumia Kenya is leveraging on Posta Kenya logistics network […]

Comment on Flippy Campus Qualifies for DEMO Africa 2015 by Kenyan Shield Finance Is The Second Startup To Qualify For DEMO Africa Main Event | techcabal.com

[…] after Flippy Campus, a Ghanaian social networking and notification app for University students, booked a space after winning the DEMO Africa mini pitch event held in […]

Comment on Seven Top Nigerian Universities Where Nigerian Developers Come From by Seven Top Nigerian Universities Where Nigerian Developers Come From | WAKAPOST.

[…] Still, we were curious about what universities Nigerian coders come from. So we decided to make a list, based on an informal survey of tech companies and developers who we are familiar with. Some are no surprise, while others that made the list areSeven Top Nigerian Universities Where Nigerian Developers Come From […]

Comment on European Business Angel Network is Ready to Partner with the African Angel Community by Thursday News Roundup: European Business Angel Network ready to support African Angel Community | techcabal.com

[…] European Angel Network Ready to Partner with African Angel Community The European Business Angel Network (EBAN) is ready to the support of the development of the African angel community. […]

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Comment on 88mph halts investments in African startups by Africa’s best-known tech funder is taking a break from investing in startups - Quartz

[…] better known locally based pan-African funders, and any sign that it is halting its operations is bad for sentiment in Africa. Barnwell, who stresses that 88mph is merely taking a break, says there is no reason for […]

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8.7.6. Mathematical Details