Serious question here. What's the difference between these arguments?
1. You shouldn't ban BitTorrent. It's just a protocol. Just because some people use it to steal digital content doesn't mean BitTorrent is inherently bad.
2. You shouldn't ban guns. It's just a device. Just because some people use it to kill innocents doesn't mean guns are inherently bad.
Java, a popular framework that allows developers to write code once and seamlessly deploy it across multiple platforms, has been a topic of conversation lately among developers and users alike. Many have criticised Java-powered apps to be "too memory intensive."
Computer science graduates don't want code monkey jobs. They want the software engineer jobs that pay 2x the salary of code monkey jobs. I'm talking about the Google/Apple/Facebook/HFT-calibre jobs that are paying $300K+ total comp in Silicon Valley and elsewhere. A college education provides the foundation of algorithms, computer architecture, programming language semantics, and specialization that allow the graduates to continue working and learning effectively and efficiently, where the diploma is the de facto proof that the student is capable of doing so.
for quite some time now thinking about getting into perhaps a new programming language and technology, like NodeJS or Java/Kotlin or something
You are in for disappointment if you think that getting into a career in AI / ML is anything like a programming hobby, such as picking up NodeJS or Java over a few weekends. First, let me make clear the terminology I'm using. Artificial Intelligence is a broad field. Although the public perception of AI is of software/robots like HAL with which humans can talk or interact, the field also encompasses knowledge representation, reasoning, and learning. Machine learning is then an important subfield of AI, and it involves supervised learning, unsupervised learning, reinforcement learning, etc. Over the last several years, many people have started to conflate AI and ML, but for someone knowledgable in these fields, the distinction is clear. ML is the practical application of algorithms towards taking inputs and producing an output prediction, and it's this area that contains the vast majority of jobs in "AI". Some basic applications of ML include spam filtering, face detection and recognition, product recommendations, fraud detection, revenue forecasting, gait and step detection, voice recognition, etc. If you look at that list and think about them, you'll come to realize that you've probably been consuming ML results for the last five years or much longer. If you want to work as a "ML engineer" in this area, you'll have to be knowledgable with ML algorithms, setting up data pipelines, running experimentation, and using ML software, such as scikit-learn, R, Caffe, Tensorflow, etc.
I manage a ML team at a large company. Let me make clear: Unless you have a strong academic background in this field, no one will take you seriously. I recently applied to be a principal engineer working on an AI/ML personal assistant, and the recruiter told me straightforwardly that the hiring managers are not interviewing anyone unless they have a recent PhD related to deep learning. I'm a bit elitist about this as well: I tend to turn away candidates that don't have at least an MS or PhD in a field related to ML. Why is this so? Because you need a rigorous background to understand why and how ML works, and this involves understanding loss functions, gradients, training vs. validation error, decision boundaries, optimization, and other things. You need to understand these things because a lot of current ML involves choosing the right knob settings (hyperparameters) that make your ML work best. If your ML algorithm isn't working well, how do you fix it? That's where this rigorous background comes in handy.
Now, there are many things related to ML that you can still work on if you don't have a strong background. As opposed to a ML specialist, there are plenty of positions related to data engineering (e.g. setting up and maintaining huge data pipelines), infrastructure administration (e.g. installing and mastering all aspects of Hadoop and Spark), visualization (e.g. creating dashboards that take fresh data and display it), among many others.
Your logic is faulty. In this smartphone case, there is one manufacturer, Samsung, and one product, the Galaxy Note 7. It is clear there is a defect. In the case of your "millions of car fires", they are generally spread out randomly over all manufacturers and models. If there is ever a specific case, such as GM's faulty ignition switches in particular models from particular years, then yes, there will be a recall and people on TV will tell folks not to drive those models.
I have bought rechargeable batteries for the last 20 years. Not a single one of them has caught fire. In the case of the Galaxy Note 7, there is obviously a single, focused product that has a critical flaw.
Because the Verge is the blog when we achieved a great scientific endeavor (landing a craft on a comet), all they could do was complain about a scientist's shirt.
Take this reviewer's commentary with a grain of salt. As with all "audiophiles", he bases his opinion on just plain subjective emotion associated with product brands, individual tastes, and nationalistic biases, not any type of fact. He doesn't like the $5,500 Sony headphones? Then why did he like the Sennheiser $55,000 headphones (yes, that's right, $55K headphones) or the Focal $3,999 headphones? And I really hate to bring this up because it's ugly, but maybe his review comes down to simple nationalism? The reviewer (Vlad Savov) is based in Europe, and Sennheiser is from Germany and Focal is from France. And Sony is (duh) from Japan.
The original article says this card was first announced at an AI meet-up:
At an artificial intelligence (AI) meet-up at Stanford University this evening, NVIDIA CEO Jen-Hsun Huang first announced, and then actually gave away a few brand-new, Pascal-based NVIDIA TITAN X GPUs.
In fact, the Titan X is currently the preferred GPU for deep learning thanks to its 12GB memory. But I'm not going to argue that this card can be a great GPU for both gaming and deep learning (unlike the Quadro which is largely for CAD-like applications).
The word "doppelganger" and its concept have Germanic origins from the 1700s. You may want to start reading literature and newspapers in addition to just websites and road signs.
Samsung, like many companies, releases earnings guidance early for investors to chew on. They consistently release the guidance the first week of each quarter and then the full earnings report by the end of the first month of each quarter. They have done this every year for as long as I can remember (going back 3-4 years now). The full earnings report's numbers are usually well within 1 percent of everything that was reported in the guidance. See, for example, the April 2016 guidance and the April 2016 report.
I used to take notes with paper and pencil, but you can't search through old notes unless you scan and OCR your content.
I instead have been using E-mail clients for the last several years (whether it's company Outlook or personal Gmail). This has several advantages:
1. You can search through your notes.
2. If on corporate Outlook, there is security thanks to the IT department.
3. You can have rich markup if you need it.
4. You can immediately email out meeting notes.
The original article has a small blurb that compares sales over the first three years:
Microsoft’s follow-up console, the Xbox One, has not sold nearly as well as the 360. In 2008, less than three years after it was launched, the company said the 360 had sold over 19 million units worldwide. The Xbox One was released in 2013, and has sold about 10 million units in roughly the same amount of time as its predecessor.
not NBC Universal. The point of OP's article is the comparison of subscribers between Netflix and Comcast. People who subscribe to Comcast want the service, whether it's cable to Internet. The fact that Comcast owns NBC is not very relevant here. No one says "I want to subscribe to Comcast to get NBC."
Why are you comparing these two companies? Netflix is a content provider. Comcast is a (cable and Internet) service provider. That's like comparing Amazon with the UPS.
Surprise, surprise. Being rude to a company results in bad service from that company. Hardly news except that it was [AT&T / Comcast / insert any company] that was the victim. Maybe the entitled customer has learned his lesson, but probably not.
Can anyone comment on R vs. Python vs. something else for data science? I'm talking in terms of usefulness, maintainability, and finding enough candidates who know it (even hires straight from college)?
If you had the reading comprehension skills and common sense of a high school student, you would understand that the OP is a working engineer, not a grad student. But since you seem to be a mathematician or something of that ilk, you can be partially forgiven.
I was in the same position as OP about 5 years ago. I have a PhD in CS from many years back but in operating systems and programming languages. Around 2010 I wanted to get into machine learning and decided to enroll part-time in a university to take some classes. Currently, I am leading a small team of engineers that work on ML-related topics.
Here are some points that the OP needs to understand.
1. There are two different levels of expertise with working on machine learning: either as a library/tool user, or as a ML algorithm developer. It is EXACTLY analogous to how one approaches SQL: You can make a great living being a SQL user and knowing how to write efficient queries and build indexes, or you can go deeper and build the SQL engine itself along with its query optimizer, storage layer, etc. If you want to use ML as a library/tool user, you can have a great career as long as you know what tools and algorithms to use. If you want to be a ML algorithm developer, that means you want to work on the innards, such as using new SVM kernels or building new deep learning networks; for this role, you'll usually need a PhD-calibre background heavy in math. I personally started out as a library/tool user with Weka and Mallet, but as I used them more, I was able to understand the math behind them.
2. ML is an abstract field, and it's best to approach it from an applications point of view. Pick a problem that needs ML, such as natural language processing or image recognition. It's important to pick a problem that has an abundant amount of labelled data. There are some fields such as voice recognition where it is terribly difficult to get real labelled data. For NLP (aka computational linguistics), you can start with some basic problems such as document classification (e.g. for this document, is it about sports, business, entertainment, etc.?) or sentiment analytics (e.g. for this Twitter tweet, is it positive or negative?). There are lots of good datasets in the NLP field.
3. You can explore datasets from the Kaggle competitions and the University of California, Irvine, repository: http://archive.ics.uci.edu/ml/
4. Pick a tool and stick with it. I have used Weka, Mallet, and R. You can also use Python and Matlab.
5. When you read the literature, you will find two nearly-synonymous terms: "machine learning" and "data mining". Both are closely related. Machine learning historically comes from the AI community and generally focuses on building better ML algorithms and solving supervised ML problems. Data mining historically comes from the database community and generally focuses on using tools and solving unsupervised ML problems (e.g. finding clusters of similar customers).
6. At the end of the day, creating a better solution does not come down to the ML algorithms themselves. Rather, the better solution comes from the amount of data and what features you are able to extract. As for the many ML algorithms for supervised learning: at the end of the day, your main responsibility will come down to picking the one that best suits your application. It is just like picking which sorting algorithm to use: when do you use Quicksort, and when do you use Mergesort?
7. Here are some really good books that I have personally read:
Beginner level:
- Programming Collective Intelligence by T. Segaran.
- Introduction to Data Mining by P.-N. Tan and M. Steinbach.
Intermediate level:
- Data Mining: Practical Machine Learning Tools by I. Witten and E. Frank. (goes with the Weka tool)
Advanced level:
- Artificial intelligence: A Modern Approach by S. Russell and P. Norvig. (touches on all aspects of AI, such as tic-tac-toe algorithms with minimax and First Order Logic)
- Introduction to Machine Learning by E. Alpaydin
PROTIP: How to tell if you're reading an advanced machine learning book -- if the index contains reference to Vapnik–Chervonenkis dimension or shattering, then the book is hardcore.
Since this report is about US students, then these students already have far more opportunity than those in foreign countries. In practical terms, here are the "opportunities" and decisions that US students have:
1. Do I spend another hour watching TV after school, or do I study?
2. Do I go out on Friday to party, or do I work on my homework?
3. Do I choose to focus on getting into college, or not?
4. Do I choose to major in a STEM field, or do I major in a humanities field?
Those are the opportunities and the decisions. Those who can obtain a high-paying software job apparently made the most with what opportunities they had and made the right choices.
Slashdot Mirror
Serious question here. What's the difference between these arguments?
1. You shouldn't ban BitTorrent. It's just a protocol. Just because some people use it to steal digital content doesn't mean BitTorrent is inherently bad.
2. You shouldn't ban guns. It's just a device. Just because some people use it to kill innocents doesn't mean guns are inherently bad.
Java, a popular framework that allows developers to write code once and seamlessly deploy it across multiple platforms, has been a topic of conversation lately among developers and users alike. Many have criticised Java-powered apps to be "too memory intensive."
Computer science graduates don't want code monkey jobs. They want the software engineer jobs that pay 2x the salary of code monkey jobs. I'm talking about the Google/Apple/Facebook/HFT-calibre jobs that are paying $300K+ total comp in Silicon Valley and elsewhere. A college education provides the foundation of algorithms, computer architecture, programming language semantics, and specialization that allow the graduates to continue working and learning effectively and efficiently, where the diploma is the de facto proof that the student is capable of doing so.
for quite some time now thinking about getting into perhaps a new programming language and technology, like NodeJS or Java/Kotlin or something
You are in for disappointment if you think that getting into a career in AI / ML is anything like a programming hobby, such as picking up NodeJS or Java over a few weekends. First, let me make clear the terminology I'm using. Artificial Intelligence is a broad field. Although the public perception of AI is of software/robots like HAL with which humans can talk or interact, the field also encompasses knowledge representation, reasoning, and learning. Machine learning is then an important subfield of AI, and it involves supervised learning, unsupervised learning, reinforcement learning, etc. Over the last several years, many people have started to conflate AI and ML, but for someone knowledgable in these fields, the distinction is clear. ML is the practical application of algorithms towards taking inputs and producing an output prediction, and it's this area that contains the vast majority of jobs in "AI". Some basic applications of ML include spam filtering, face detection and recognition, product recommendations, fraud detection, revenue forecasting, gait and step detection, voice recognition, etc. If you look at that list and think about them, you'll come to realize that you've probably been consuming ML results for the last five years or much longer. If you want to work as a "ML engineer" in this area, you'll have to be knowledgable with ML algorithms, setting up data pipelines, running experimentation, and using ML software, such as scikit-learn, R, Caffe, Tensorflow, etc.
I manage a ML team at a large company. Let me make clear: Unless you have a strong academic background in this field, no one will take you seriously. I recently applied to be a principal engineer working on an AI/ML personal assistant, and the recruiter told me straightforwardly that the hiring managers are not interviewing anyone unless they have a recent PhD related to deep learning. I'm a bit elitist about this as well: I tend to turn away candidates that don't have at least an MS or PhD in a field related to ML. Why is this so? Because you need a rigorous background to understand why and how ML works, and this involves understanding loss functions, gradients, training vs. validation error, decision boundaries, optimization, and other things. You need to understand these things because a lot of current ML involves choosing the right knob settings (hyperparameters) that make your ML work best. If your ML algorithm isn't working well, how do you fix it? That's where this rigorous background comes in handy.
Now, there are many things related to ML that you can still work on if you don't have a strong background. As opposed to a ML specialist, there are plenty of positions related to data engineering (e.g. setting up and maintaining huge data pipelines), infrastructure administration (e.g. installing and mastering all aspects of Hadoop and Spark), visualization (e.g. creating dashboards that take fresh data and display it), among many others.
Your logic is faulty. In this smartphone case, there is one manufacturer, Samsung, and one product, the Galaxy Note 7. It is clear there is a defect. In the case of your "millions of car fires", they are generally spread out randomly over all manufacturers and models. If there is ever a specific case, such as GM's faulty ignition switches in particular models from particular years, then yes, there will be a recall and people on TV will tell folks not to drive those models.
I don't know where you are getting your "facts", but there have been much more than 3 phones catching fire.
As of September 15, 2016, the US CPSC reported 26 reports of burns and 55 reports of property damage, including fires in cars and a garage.
As of October 10, 2016, there have been at least 5 reports of replacement phones catching fire.
I have bought rechargeable batteries for the last 20 years. Not a single one of them has caught fire. In the case of the Galaxy Note 7, there is obviously a single, focused product that has a critical flaw.
Because the Verge is the blog when we achieved a great scientific endeavor (landing a craft on a comet), all they could do was complain about a scientist's shirt.
Take this reviewer's commentary with a grain of salt. As with all "audiophiles", he bases his opinion on just plain subjective emotion associated with product brands, individual tastes, and nationalistic biases, not any type of fact. He doesn't like the $5,500 Sony headphones? Then why did he like the Sennheiser $55,000 headphones (yes, that's right, $55K headphones) or the Focal $3,999 headphones? And I really hate to bring this up because it's ugly, but maybe his review comes down to simple nationalism? The reviewer (Vlad Savov) is based in Europe, and Sennheiser is from Germany and Focal is from France. And Sony is (duh) from Japan.
The OP says he is using RAID 1 (mirroring). Why is RAID 1 not a backup?
At an artificial intelligence (AI) meet-up at Stanford University this evening, NVIDIA CEO Jen-Hsun Huang first announced, and then actually gave away a few brand-new, Pascal-based NVIDIA TITAN X GPUs.
In fact, the Titan X is currently the preferred GPU for deep learning thanks to its 12GB memory. But I'm not going to argue that this card can be a great GPU for both gaming and deep learning (unlike the Quadro which is largely for CAD-like applications).
The word "doppelganger" and its concept have Germanic origins from the 1700s. You may want to start reading literature and newspapers in addition to just websites and road signs.
I've heard of this concept many times. Usually it's stated as "Somewhere in the word, there is someone who looks exactly like you". If you haven't seen this theory stated before, you probably don't read much, and/or your life experiences are limited.
Samsung, like many companies, releases earnings guidance early for investors to chew on. They consistently release the guidance the first week of each quarter and then the full earnings report by the end of the first month of each quarter. They have done this every year for as long as I can remember (going back 3-4 years now). The full earnings report's numbers are usually well within 1 percent of everything that was reported in the guidance. See, for example, the April 2016 guidance and the April 2016 report.
I instead have been using E-mail clients for the last several years (whether it's company Outlook or personal Gmail). This has several advantages:
1. You can search through your notes.
2. If on corporate Outlook, there is security thanks to the IT department.
3. You can have rich markup if you need it.
4. You can immediately email out meeting notes.
Microsoft’s follow-up console, the Xbox One, has not sold nearly as well as the 360. In 2008, less than three years after it was launched, the company said the 360 had sold over 19 million units worldwide. The Xbox One was released in 2013, and has sold about 10 million units in roughly the same amount of time as its predecessor.
not NBC Universal. The point of OP's article is the comparison of subscribers between Netflix and Comcast. People who subscribe to Comcast want the service, whether it's cable to Internet. The fact that Comcast owns NBC is not very relevant here. No one says "I want to subscribe to Comcast to get NBC."
Why are you comparing these two companies? Netflix is a content provider. Comcast is a (cable and Internet) service provider. That's like comparing Amazon with the UPS.
Surprise, surprise. Being rude to a company results in bad service from that company. Hardly news except that it was [AT&T / Comcast / insert any company] that was the victim. Maybe the entitled customer has learned his lesson, but probably not.
Wrong message to send to corporations.
Can anyone comment on R vs. Python vs. something else for data science? I'm talking in terms of usefulness, maintainability, and finding enough candidates who know it (even hires straight from college)?
If you had the reading comprehension skills and common sense of a high school student, you would understand that the OP is a working engineer, not a grad student. But since you seem to be a mathematician or something of that ilk, you can be partially forgiven.
Here are some points that the OP needs to understand.
1. There are two different levels of expertise with working on machine learning: either as a library/tool user, or as a ML algorithm developer. It is EXACTLY analogous to how one approaches SQL: You can make a great living being a SQL user and knowing how to write efficient queries and build indexes, or you can go deeper and build the SQL engine itself along with its query optimizer, storage layer, etc. If you want to use ML as a library/tool user, you can have a great career as long as you know what tools and algorithms to use. If you want to be a ML algorithm developer, that means you want to work on the innards, such as using new SVM kernels or building new deep learning networks; for this role, you'll usually need a PhD-calibre background heavy in math. I personally started out as a library/tool user with Weka and Mallet, but as I used them more, I was able to understand the math behind them.
2. ML is an abstract field, and it's best to approach it from an applications point of view. Pick a problem that needs ML, such as natural language processing or image recognition. It's important to pick a problem that has an abundant amount of labelled data. There are some fields such as voice recognition where it is terribly difficult to get real labelled data. For NLP (aka computational linguistics), you can start with some basic problems such as document classification (e.g. for this document, is it about sports, business, entertainment, etc.?) or sentiment analytics (e.g. for this Twitter tweet, is it positive or negative?). There are lots of good datasets in the NLP field.
3. You can explore datasets from the Kaggle competitions and the University of California, Irvine, repository: http://archive.ics.uci.edu/ml/
4. Pick a tool and stick with it. I have used Weka, Mallet, and R. You can also use Python and Matlab.
5. When you read the literature, you will find two nearly-synonymous terms: "machine learning" and "data mining". Both are closely related. Machine learning historically comes from the AI community and generally focuses on building better ML algorithms and solving supervised ML problems. Data mining historically comes from the database community and generally focuses on using tools and solving unsupervised ML problems (e.g. finding clusters of similar customers).
6. At the end of the day, creating a better solution does not come down to the ML algorithms themselves. Rather, the better solution comes from the amount of data and what features you are able to extract. As for the many ML algorithms for supervised learning: at the end of the day, your main responsibility will come down to picking the one that best suits your application. It is just like picking which sorting algorithm to use: when do you use Quicksort, and when do you use Mergesort?
7. Here are some really good books that I have personally read:
Beginner level:
- Programming Collective Intelligence by T. Segaran.
- Introduction to Data Mining by P.-N. Tan and M. Steinbach.
Intermediate level:
- Data Mining: Practical Machine Learning Tools by I. Witten and E. Frank. (goes with the Weka tool)
Advanced level:
- Artificial intelligence: A Modern Approach by S. Russell and P. Norvig. (touches on all aspects of AI, such as tic-tac-toe algorithms with minimax and First Order Logic)
- Introduction to Machine Learning by E. Alpaydin
PROTIP: How to tell if you're reading an advanced machine learning book -- if the index contains reference to Vapnik–Chervonenkis dimension or shattering, then the book is hardcore.
I'm sorry that you left AT&T and its unlimited plan. I've been with AT&T since 2008 and its unlimited plan. I pay $70/month, inclusive of everything.
wireless plan here in the USA. Additionally, AT&T recently increased the throttle cap from 5GB to 22GB. I'm paying $64/month, by the way.
1. Do I spend another hour watching TV after school, or do I study?
2. Do I go out on Friday to party, or do I work on my homework?
3. Do I choose to focus on getting into college, or not?
4. Do I choose to major in a STEM field, or do I major in a humanities field?
Those are the opportunities and the decisions. Those who can obtain a high-paying software job apparently made the most with what opportunities they had and made the right choices.