May 28, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Human resource  Source

[ AnalyticsWeek BYTES]

>> Top 10 B.O.B. Blog Posts of 2012: Big Data, Patient Experience and Customer Metrics by bobehayes

>> US Army enterprise apps must move to core data centers by analyticsweekpick

>> Data Sources for Cool Data Science Projects: Part 2 by michael-li

Wanna write? Click Here

[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

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The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence… more

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

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A black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. In this groundbreaking and prophetic book, Taleb shows in a playful way that Black Swan events explain almost eve… more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:How do you know if one algorithm is better than other?
A: * In terms of performance on a given data set?
* In terms of performance on several data sets?
* In terms of efficiency?
In terms of performance on several data sets:

– ‘Does learning algorithm A have a higher chance of producing a better predictor than learning algorithm B in the given context?”
– ‘Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets”, A. Lacoste and F. Laviolette
– ‘Statistical Comparisons of Classifiers over Multiple Data Sets”, Janez Demsar

In terms of performance on a given data set:
– One wants to choose between two learning algorithms
– Need to compare their performances and assess the statistical significance

One approach (Not preferred in the literature):
– Multiple k-fold cross validation: run CV multiple times and take the mean and sd
– You have: algorithm A (mean and sd) and algorithm B (mean and sd)
– Is the difference meaningful? (Paired t-test)

Sign-test (classification context):
Simply counts the number of times A has a better metrics than B and assumes this comes from a binomial distribution. Then we can obtain a p-value of the HoHo test: A and B are equal in terms of performance.

Wilcoxon signed rank test (classification context):
Like the sign-test, but the wins (A is better than B) are weighted and assumed coming from a symmetric distribution around a common median. Then, we obtain a p-value of the HoHo test.

Other (without hypothesis testing):
– AUC
– F-Score

Source

[ VIDEO OF THE WEEK]

#FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

 #FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

We chose it because we deal with huge amounts of data. Besides, it sounds really cool. – Larry Page

[ PODCAST OF THE WEEK]

#FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

 #FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

The Hadoop (open source software for distributed computing) market is forecast to grow at a compound annual growth rate 58% surpassing $1 billion by 2020.

Sourced from: Analytics.CLUB #WEB Newsletter

Business Intelligence in the Cloud: The Way to Go

Organizations running Business Intelligence in the cloud realize a number of distinct advantages that vastly outstrip those of on-premise deployments.

The former facilitates unparalleled scalability which, when contrasted with the physical infrastructure required to keep pace on premise, becomes far more cost-efficient than the latter could ever be. As such, it provides a much greater efficiency of resources and, when properly architected, yields performance boons immediately realized by the business.

Furthermore, organizations properly running BI in the cloud get an additional benefit which may very well trump the others, particularly in the event of disaster. In this case, “The technical team hasn’t compromised the performance of their on-premise environment, so they can continue to focus on OLTP,” DH2i CEO Don Boxley said. “The business users that need analytics can get near real-time reporting on that same data. And then the third thing the team gets is automatic failover into the cloud: those OLTP workloads would failover into the cloud.”

The combination of high availability disaster preparation, cost-effective scale, resource efficiency and low latency analytics simply cannot be matched on premises.

Cloud Architecture
Budget conscious organizations attempting to maximize their analytics resources tend to naturally gravitate towards cloud deployments. According to Boxley, the choice is relatively simple for such common scenarios in which, “I don’t have the budget right now to bring in a new machine to run reporting. But if I [use the cloud] I can spin up my node to AWS or Azure or whatever, when I need it.” Optimizing the efficiency of the cloud’s capabilities involves smart availability solutions which dynamically provision resources between clouds, containers, on-premise environments and operating systems (Windows and Linux). Examples of successful architectures for cloud BI include maintaining OLTP systems on premise while continually replicating data to the cloud for reporting purposes. The added benefit is the disaster preparation in which the on-premise systems failover into the cloud as well. Boxley described a healthcare use case in which, if failover did occur, business analytics could still continue: “Those business users, nurses, doctors, and clinicians that are hitting the electronic records system, they would be able to continue doing that. Instead of using the on-premise servers, now they’re using the servers in Amazon.”

Dynamic Pricing
In addition to the efficiency, scale, and resilience benefits of deploying a cloud-based model for BI, this paradigm also facilitates an intrinsic flexibility which is the antithesis of most on-premise deployments. Organizations continuously replicating data to the cloud via cost-effective asynchronous techniques can replicate that data to multiple cloud providers. They also have the option of switching where their workloads and data are going between cloud providers “to support multiple use cases” Boxley mentioned. Transitioning workflows between cloud providers enables organizations to avail themselves of the dynamic pricing found in the cloud, which is another means of boosting ROI. “There are companies out there where all they do is monitor pricing between cloud vendors and give customers insight into which resource to use,” Boxley said. “You can imagine layering something on top of that so now you’re running on AWS because this is the best time for it; now you’re running on Azure. Because I’m replicating the data back and forth, I’m not going to lose anything.”

Multi-Cloud Flexible Analytics
Still, the most compelling use case for the flexibility imbued with the ability to replicate data to and within the cloud is the agility it gives organizations within today’s decentralized data landscape. “It helps to keep these guys [cloud providers] honest—to not have everything in one place,” Boxley said. Viewed from this perspective, the ability to rapidly switch where BI workloads are running in the cloud gives organizations what Boxley termed “protection”, as well as the means to keep up with the evolving demands of swiftly changing business requirements. One of the persistent concerns with the cloud is vendor lock-in, in which it becomes difficult to switch cloud providers. Worse, there are some situations in which cloud vendors take a proprietary stance towards data (or metadata), staking claims to what organization believed was their own data. According to Boxley, the agile approach of asynchronously replicating data to the cloud for BI “makes it easy for customers to be multi-cloud protected. They can support a multi-cloud infrastructure now.”

Cloud Security
Running analytics for BI in the cloud with multi-tenant providers also greatly impacts the security for the said data. Whereas once security was considered a caveat for the cloud, there are many instances in which today it’s considered one of the cloud’s areas of specialization. Boxley recalled a common industry perception in which “Probably nobody’s spending more money on security in terms of IT infrastructure than AWS, Azure, and Google [Cloud]. They’re spending ridiculous amounts of money to secure those environments.” Security is another area which cloud computing positively affects. Organizations replicating data to major multi-tenant cloud providers have multiple layers of security. Unsanctioned data access in these situations requires perpetrators to breach the security of the aforementioned cloud hosts, find the data of the particular organization in their labyrinth of clouds, then deal with other factors such as host or even organizational encryption methods. Regarding the amount of capital major cloud providers are allocating on security, Boxley noted, “Very few companies can match that. The argument is your data’s probably way more secure in those environments than it is on-premise. You’d be hard-pressed to say you’re spending as much money on it as those guys are. Intuitively, that makes sense to me.”

In Practical Terms
Overall, the host of boons of performing BI in the cloud is largely unparalleled by on-premise methods. When buttressed by the proper architecture, cloud BI grants robust security, performance, low latency, scale, beneficial pricing, and efficiency. Even better, it allows for disaster prevention failover measures that enable continuous analytics access in the worst of situations. Regarding the flexible means of moving data that smart availability cloud-ready solutions offer, Boxley observed:

“It’s an easy way to test your assumptions around the cloud without making a whole 100 percent commitment to it when you have a specific use case and want to test those assumptions. Is it cheaper for me to use the cloud versus on premise? Can I get better scalability in the cloud versus on premise? Is the security good enough to meet my requirements? I think this use case allows customers to do that without too much risk.”

Source

Metrics Are The Characters of Data Stories

I like to think that the hero in your data story is your audience. The hero is the person who starts with conflict and, through the narrative journey, is transformed to resolve this conflict. That’s what you want the data story to accomplish for your audience — start with a question and move your audience (hero) to actions that will resolve the question.

At least I used to think it was that simple. Then someone raised their hand at a recent presentation and asked me: “If your audience is the hero, who is the villain in your story?”

I was puzzled. Does there need to be a villain? Who is opposing your audience/hero in a data story? Was there something else in the data story that best represents the hero?

The question made me reconsider whether we could point to another element as characters in our stories. Was there something else that is the focus of attention, that evolves and improves or declines into madness — what was our Batman and what is our Joker?

Perhaps it is worth considering whether metrics are our characters. Metrics are the critical pivot point — the thing that we want to see change for the better. Or be removed entirely — like the seemingly crazy girl on The Bachelor.

So consider:

  1. Like heroes or villains, we only want a few to focus on. Unless you’ve got some powerful engagement, the attention of your audience isn’t able to grasp a massive cast of characters like Game of Thrones. Your metric selections should be carefully curated and intentional in the way that an author creates characters for a book. 

  2. Our metric heroes need to have substance and depth of meaning. Vanity metrics have no place in your data stories.

  3. Metrics can be viewed as good or bad. Understand what your audience wants to happen to this metric. Are they a hero metric that needs to succeed (e.g. revenue) or a villain metric that we want to see shrivel like the Wicked Witch of the West? Some metrics can be flipped around from good to bad, e.g. win rate vs. loss rate.

  4. Bring some identity into how you present each metric. After all, it should be an individual with a character arc. You may not want to create a musical motif for each metric (a la Star Wars), but you could give each metric its own style, color, and/or icon. 

Once you have mastered your character selection, your data stories will not only be easier to understand, but they will appeal even more to your desired audience.

Source: Metrics Are The Characters of Data Stories by analyticsweek

May 21, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data analyst  Source

[ AnalyticsWeek BYTES]

>> Visualizing taxi trips between NYC neighborhoods with Spark and Microsoft R Server by analyticsweek

>> Govt to host Artificial Intelligence-focused Raise 2020 summit in April by administrator

>> This year’s Talend at AWS re:invent recap: Bigger, busier, and better than ever before by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Baseball Data Wrangling with Vagrant, R, and Retrosheet

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Analytics with the Chadwick tools, dplyr, and ggplot…. more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

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A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:Do we always need the intercept term in a regression model?
A: * It guarantees that the residuals have a zero mean
* It guarantees the least squares slopes estimates are unbiased
* the regression line floats up and down, by adjusting the constant, to a point where the mean of the residuals is zero

Source

[ VIDEO OF THE WEEK]

Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast

 Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Torture the data, and it will confess to anything. – Ronald Coase

[ PODCAST OF THE WEEK]

#FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

 #FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

This year, over 1.4 billion smart phones will be shipped – all packed with sensors capable of collecting all kinds of data, not to mention the data the users create themselves.

Sourced from: Analytics.CLUB #WEB Newsletter

Struggling To Work Remotely? Try Out These WFH Tips From Juicers

With much of the world switching gears to remote working due to the coronavirus pandemic, we know how difficult this transition can be. As Juice employees are all working remotely indefinitely, we had some great discussions about how best to do your jobs in a new environment.

Here are a dozen tips from our team that came out of those discussions: 

  1. Open up your windows, let all the natural light come in. That vitamin D will give you a little more energy! 

  2. Create a checklist of things you need to do, and mark them off the list as you complete them. It makes you feel good seeing all your completed tasks and wins for the day! 

  3. Manage your intake of the news. COVID-19/coronavirus is plaguing our news cycle, so it’s important to take in a healthy amount of news surrounding it, and not let it affect your panic level.

  4. Create a work space that feels and looks different from the spaces where you don’t work. E.g. Don’t work from your couch if you like to relax on your couch after a long work day.

  5. Build in break time into your daily workflow or schedule. Add reminders in Google calendar to take a break for 20 minutes after an hour and a half of working. 

  6. Get outside and go for a walk if you can. Obviously continue to socially distance yourself from people. It’s a good thing to get some fresh air in your lungs and walk around a little bit.

  7. Start your morning routine just like you would if you were going to leave home to commute to work.

  8. Find a house project you can easily start and stop for brain breaks. Not chores though, something creative or new.

  9. Have something like “work” pants or “work” shoes that you can put on and take off.

  10. Appreciate that you aren’t wasting time on a morning commute.

  11. Be on the look out for joy and gratitude.

  12. Try Zach’s famous Healthy Frosty Smoothy (I’m finally ready to share the recipe): 1 big scoop chocolate protein powder, 1 frozen banana, ½ cup almond milk, tablespoon flax seeds, tablespoon chia seeds, ½ cup greek yogurt, big scoop Nutella, 2 cups of ice. Blend like a madman.

We hope that these ideas can help you make the transition from a collaborative work environment to working remotely as we continue in these uncertain times. We are committed to not only helping our employees, but in helping you and your employees, as well.

Stay safe, stay healthy, and wash those hands!

Originally Posted at: Struggling To Work Remotely? Try Out These WFH Tips From Juicers by analyticsweek

May 14, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Complex data  Source

[ AnalyticsWeek BYTES]

>> Web scraping using Python by administrator

>> Mitigating Risk from Common Driving Infractions by administrator

>> Bagging and Boosting by administrator

Wanna write? Click Here

[ FEATURED COURSE]

Pattern Discovery in Data Mining

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Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern disc… more

[ FEATURED READ]

Big Data: A Revolution That Will Transform How We Live, Work, and Think

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“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… more

[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE Q&A]

Q:Which kernels do you know? How to choose a kernel?
A: * Gaussian kernel
* Linear kernel
* Polynomial kernel
* Laplace kernel
* Esoteric kernels: string kernels, chi-square kernels
* If number of features is large (relative to number of observations): SVM with linear kernel ; e.g. text classification with lots of words, small training example
* If number of features is small, number of observations is intermediate: Gaussian kernel
* If number of features is small, number of observations is small: linear kernel

Source

[ VIDEO OF THE WEEK]

@SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

 @SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It’s easy to lie with statistics. It’s hard to tell the truth without statistics. – Andrejs Dunkels

[ PODCAST OF THE WEEK]

Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

 Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

Subscribe 

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[ FACT OF THE WEEK]

100 terabytes of data uploaded daily to Facebook.

Sourced from: Analytics.CLUB #WEB Newsletter

Voices in AI – Episode 90: A Conversation with Norman Sadeh

[voices_in_ai_byline]

About this Episode

Episode 90 of Voices in AI features Byron speaking with Norman Sadeh from Carnegie Mellon University about the nature of intelligence and how AI effects our privacy.

Listen to this episode or read the full transcript at www.VoicesinAI.com

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm I’m Byron Reese, today my guest is Norman Sadeh. He is a professor at Carnegie Mellon School of Computer Science. He’s affiliated with Cylab which is well known for their seminal work in AI planning and scheduling, and he is an authority on computer privacy. Welcome to the show.

Carnegie Mellon has this amazing reputation in the AI world. It’s arguably second to none. There are a few university campuses that seem to really… there’s Toronto and MIT, and in Carnegie Mellon’s case, how did AI become such a central focus?

Norman Sadeh: Well, this is one of the birthplaces of AI, and so the people who founded our computer science department included Herbert Simon and Allen Newell who are viewed as two of the four founders of AI. And so they contributed to the early research in that space. They helped frame many of the problems that people are still working on today, and they helped recruit also many more faculty over the years that have contributed to making Carnegie Mellon as the place that many people refer to as being the number one place in AI here in the US.

Not to say that there are not other many good places out there, but CMU is clearly a place where a lot of the leading research has been conducted over the years, whether you are looking at autonomous vehicles – for instance, I remember when I came here to do my PhD back in 1997, there was research going on autonomous vehicles. Obviously the vehicles were a lot clumsier than they are today, not moving quite as fast, but there’s a very, very long history of AI research, here at Carnegie Mellon. The same is true for language technology, the same is true for robotics, you name it. There are lots and lots of people here who are doing truly amazing things.

When I stop and think about [how] 99.9% of the money spent in AI is for so-called Narrow AI—trying to solve a specific problem often using machine learning. But the thing that gets written about and is shown in science fiction is ‘general intelligence’ which is a much more problematic topic. And when I stop to think about who’s actually working on general intelligence, I don’t actually get too many names. There’s OpenAI, Google, but I often hear you guys mentioned: Carnegie Mellon. Would you say there are people in a serious way thinking about how do you solve for general intelligence?

Absolutely. And so going back to our founders again, Allen Newell was one of the first people to develop what you referred to as a general theory of cognition, and obviously that theory has evolved quite a bit, and it didn’t include anything like neural networks. But there’s been a long history of efforts on working on general AI here at CMU.

And you’re completely true, that as an applied [science] university also, we’ve learned that just working on these long-term goals is not necessarily the easiest way to secure funding, and that it really pays to also have shorter term objectives along the way, things that can solve the accomplishments that can help motivate more funding coming your way. And so, it is absolutely correct that many of the AI efforts that you’re going to find, and that’s also true at Carnegie Mellon, will be focused on more narrow types of problems, problems where we’re likely to be able to make a difference in the short to mid-term, rather than just focusing on these longer and loftier goals of building general AI. But we do have a lot of researchers also working on this broader vision of general AI.

And if you were a betting man and somebody said ”Do you believe that general intelligence is kind of an evolutionary [thing]… that basically the techniques we have for Narrow AI, they’re going to get better and better and better, and bigger datasets, and we’re going to get smarter, and that it’s gradually going to become a general intelligence?”

Or are you of the opinion that general intelligence is something completely different than what we’re doing now—and what we’re doing now is just like simulated intelligence—we just kind of fake it (because it’s so narrow) into tasks? Do you think general AI is a completely different thing or it will gradually get to it with the techniques we have?

So AI has become such a broad field that it’s very hard to answer this question in one sentence. You have techniques that have come out under the umbrella of AI that are highly specialized and that are not terribly likely, I believe, to contribute to a general theory of AI. And then you have I think, broader techniques that are more likely to contribute to developing this higher level of functionality that you might refer to as ‘general AI.’

And so, I would certainly think that a lot of the work that has been done in deep learning, neural networks, those types of things are likely over time with obviously a number of additional developments that people have, a number of additional inventions that people have to come up with, but I would imagine that has a much better chance of getting us there than perhaps more narrow, yet equally useful technologies that might have been developed in fields like scheduling and perhaps planning and perhaps other areas of that type where there’s been amazing contributions, but it’s not clear how those contributions will necessarily lead to a general AI over the years. So mixed answer, but hopefully…

You just made passing reference to ‘AI means so many things and it’s such a broad term that may not even be terribly useful,’ and that comes from the fact that intelligence is something that doesn’t have a consensus definition. So nobody agrees on what intelligence is. Is that meaningful? Why is it that something so intrinsic to humans: intelligence, we don’t even agree on what it is? What does that mean to you?

Well, it’s fascinating, isn’t it, that there used to be this joke and maybe it’s still around today, that AI was whatever it is that you could not solve, and as soon as you would solve it, it was no longer viewed as being AI. So in the ‘60s, for instance, there was this program that people still often talk about called Eliza…

Weiznbaum’s chatbots.

Right, exactly, simple Rogerian therapist, basically a collection of rules that was very good at sounding like a human being. Effectively what it was doing is, it was paraphrasing what we would tell you and say, “well, why do you think that?” And it was realistic enough to convince people that they were talking to a human being, while in fact they were just talking to a computer program. And so, if you had asked people who had been fooled by the system, whether they were really dealing with AI, they would have told you, “yes, this has to be AI.”

Obviously we no longer believe in that today, and we place the bar a lot higher when it comes to AI. But there is still that tendency to think that somehow intelligence cannot be reproduced, and surely if you can get some kind of computer or whatever sort of computer you might be talking about to emulate that sort of functionality and to produce that sort of functionality, then surely this cannot be intelligence, it’s got to be some kind of a trick. But obviously, if you also look over the years, we’ve gotten computers to do all sorts of tasks that we thought perhaps were going to be beyond the reach of these computers.

And so, I think we’re making progress towards emulating many of the activities that would traditionally be viewed as being part of human intelligence. And yet, as you pointed out, I think at the beginning, there is a lot more to be done. So common sense reasoning, general intelligence, those are the more elusive tasks just because of the diversity of – the diverse facility that you need to exhibit in order to truly be able to reproduce that functionality in a scalable and general manner, and that’s obviously the big challenge for research in AI over the years to come.

Are we going to get there or not? I think that eventually we will. How long it’s going to take us to get there? I wouldn’t dare to predict, but I think that at some point we will get there, at some point we will likely build – and we’ve already done that in some fields, we will likely build functionality that exceeds the capability of human beings. We’ve done that with facial recognition, we’ve done that with chess, we’ve done that actually in a number of different sectors. We might very well have done that – we’re not quite there, but we might very well at some point get that in the area of autonomous driving as well.

So you mentioned common sense, and it’s true that every Turing test capable chatbot I come across, I ask the same question which is, “What’s bigger, a nickel or the Sun?” And I’ve never had one that could answer it. Because nickel is ambiguous… That seems to a human to be a very simple question, and yet it turns out, it isn’t. Why is that?

And I think at the Allen Institute, they’re working on common sense and trying to get AI to pass like 5th grade science tests, but why is that? What is it that humans can do that we haven’t figured out how to get machines to do that enables us to have common sense and them not to?

Right. So these are, amazingly enough, when people started working in AI, they saw that the toughest tasks for computers to solve would be tasks such as doing math or playing a game of chess. And they thought that the easiest ones would be the sorts of things that kids, five-year-olds or seven-year-olds are able to do. It turned out to be the opposite, it turned out that the kinds of tasks that a five-year-old or a seven-year-old can do are still the tasks that are eluding computers today.

And a big part of that is common sense reasoning, and that’s the state of the art today. So it’s the ability to somehow – so we’re very good at building computers that are going to be ‘one-track mind’ types of computers if you want. They’re going to be very good at solving these very specialized tasks, and as long as you keep on giving them problems of the same type, they’re going to continue to do extremely well, and actually better than human beings.

But as soon as you’re falling out of that sort of well-defined space, and you’re opening up the set of context and a set of problems that you’re going to be presenting to computers, then you find that it’s a lot more challenging to build a program that’s always capable of falling back on its feet. That’s really what we’re dealing with today.

Well, you know people do transfered learning very well, we take the stuff that we…

With occasional mistakes too, we are not perfect.

No, but if I told you to picture two fish: one is swimming in the ocean, and one is the same fish in formaldehyde in a laboratory. It’s safe to say you don’t sit around thinking about that all day. And then I say, “Are they at the same temperature?” You would probably say no. “Do they smell the same?” No. “Are they the same weight?” Yeah. And you can you can answer all these questions because you have this model I guess, of how the world works.

That’s right.

And why are we not able yet to instantiate that into a machine do you think, Is it that we don’t know how, or we don’t have the computers, or we don’t have the data or we don’t know how to build an unsupervised learner, or what?

So there are multiple answers to this question. There are people who are of the view that it’s just an engineering problem, and that if in fact, you were to use the tools that we have available today, and you just use them to populate these massive knowledge bases with all the facts that are out there, you might be able to produce some of the intelligence that we are missing today in computers. There’s been an effort like that called Cyc.

I don’t know if you are familiar with Doug Lenat, and he’s been doing this for, I don’t know, how many years at this point. I’m thinking something like close to 30 plus years, and he’s built a massive knowledge base and actually with some impressive results. And at the same time, I would argue that it’s probably not enough. It’s more than just having all the facts, it’s also the ability to adapt and the ability to discover things that were not necessarily pre-programmed.

And that’s where I think these more flexible ways of reasoning that are also more approximate in nature and that are closer to the types of technologies that we’ve seen developed under the umbrella of neural networks and deep learning, that’s where I think there’s a lot of promise also. And so, ultimately I think we’re going to need to marry these two different approaches to eventually get to a point where we can start mimicking some of that common sense reasoning that we human beings tend to be pretty good at.

Listen to this episode or read the full transcript at www.VoicesinAI.com

[voices_in_ai_link_back]

Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

Source: Voices in AI – Episode 90: A Conversation with Norman Sadeh

May 07, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data interpretation  Source

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>> 3 Technology Advances that Would Dramatically Reduce the Impact of COVID-19 (and Future Pandemics) by administrator

>> Office Depot Stitches Together the Customer Journey Across Multiple Touchpoints by analyticsweek

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This course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and… more

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Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

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Q:What is statistical power?
A: * sensitivity of a binary hypothesis test
* Probability that the test correctly rejects the null hypothesis H0H0 when the alternative is true H1H1
* Ability of a test to detect an effect, if the effect actually exists
* Power=P(reject H0|H1istrue)
* As power increases, chances of Type II error (false negative) decrease
* Used in the design of experiments, to calculate the minimum sample size required so that one can reasonably detects an effect. i.e: ‘how many times do I need to flip a coin to conclude it is biased?’
* Used to compare tests. Example: between a parametric and a non-parametric test of the same hypothesis

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What we have is a data glut. – Vernon Vinge

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Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

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[ FACT OF THE WEEK]

According to execs, the influx of data is putting a strain on IT infrastructure. 55 percent of respondents reporting a slowdown of IT systems and 47 percent citing data security problems, according to a global survey from Avanade.

Sourced from: Analytics.CLUB #WEB Newsletter

Understanding #BigData #BigOpportunity in HR – @MarcRind #FutureOfData

[youtube https://www.youtube.com/watch?v=EwS7Jb9tubI]

Understanding #BigData #BigOpportunity in HR – @MarcRind #FutureOfData

Youtube: https://www.youtube.com/watch?v=EwS7Jb9tubI
iTunes: http://math.im/itunes

In This podcast Marc Rind from ADP talked about big data in HR. He shared some of the best practices and opportunities that resides in HR data. Marc also shared some tactical steps to perform to help build a better data driven teams to execute data driven strategies. This podcast is great for folks looking to explore the depth of HR data and opportunities that resides in it.

Podcast Link:
iTunes: http://math.im/itunes
GooglePlay: http://math.im/gplay

Marc’s BIO:
Marc is responsible for leading the research and development of Automatic Data Processing’s (ADP’s) Analytics and Big Data initiative. In this capacity, Marc drives the innovation and thought leadership in building ADP’s Client Analytics platform. ADP Analytics provides its clients not only the ability to read the pulse of its own human capital…but also provides the information on how they stack up within their industry, along with the best courses of action in order to achieve its goals through quantifiable insights.

Marc was also an instrumental leader behind the small business market payroll platform; RUN Powered by ADP®. Marc lead a number of the technology teams responsible for delivering its critically acclaimed product focused on its innovative user experience for small business owners

Prior to joining ADP, Marc’s innovative spirit and fascination with data was forged at Bolt Media; a dot-com start-up based in NY’s “Silicon Alley”. The company was an early predecessor to today’s social media outlets. As an early ‘Data Scientist’; Marc focused on the patterns and predictions of site usage through the harnessing of the data on its +10 million user profiles.

About #Podcast:

#FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

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Originally Posted at: Understanding #BigData #BigOpportunity in HR – @MarcRind #FutureOfData