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Recently I've played around with the Seam Carving algorithm for the content-aware image resizing.
It turned out that the algorithm is pretty powerful, elegant but yet simple. One of the interesting parts was that it might be optimized with the Dynamic Progrming approach. Also, it may perform a simple object removal from the image without even modern ML algorithms.
I've tried to describe my experience here in the interactive article:
https://trekhleb.dev/blog/2021/...
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Algorithms real life implementation
On the way to your college canteen? -> A* search
Waiting in line in the canteen? -> Queue
Notice that girl standing in front? -> Linear search
Searching for her dad in the phone book? -> Binary search
Stupid! Google it! -> Trie
Search for her on Facebook! -> Depth-first search
Found her! Friend request? Accepted! Send a Hi! -> Graph
Writing her a secret love letter? -> Caesar cipher
Uploading your first date pic on fb? -> Image compression algorithms
Looking through her Whatsapp messages? -> KMP algorithm
She found out and had your first fight? -> Start over with some gifts! Backtracking
Got her list of items to buy? -> Array
Too many items! Low on cash, maybe? -> Priority queue
Making her play treasure hunt for her gifts? -> Linked list
Wait! Go back! Is that a ring? -> Stack
Girl’s family not agreeing to your proposal? -> Divide and conquer
Got married? Congrats! Going for your honeymoon? -> Travelling salesman problem
Your mom packing luggage for you? -> 0/1 Knapsack problem
She packed your favorite pickles? -> Hash table
Driving to the airport? -> Breadth-first search1 -
After 5 frustrating days, I have my laptop running again. Just in time for a data structures and algorithms exam.
TL;DR: driver issues aren't fun.
It all started on Friday, after the creators update. I was doing notes on lectures, and Windows crashed. I thought not much of it, it was just a "random" crash. I'd gotten a similar crash before, but I didn't think anything of it. This time was different, again it was my touchpad drivers that caused the issue, but this time a restart didn't work. I couldn't boot into Windows. I had to roll back to the last recovery point, effectively undoing the update.
This was fine, and fixed the issue, until Windows automatically updated my touchpad again, after me previously changing the driver. Another restart later and I couldn't boot. Time to roll back to recovery, right? Wrong. My drive had somehow, corrupt most of the Windows files.
And so, starts the journey of dismantling my laptop, changing the hard drive and putting it back together, a process that took 3 days due to not having the correct tools originally, and a late delivery.
(I could have rolled back to my backup system image, but that was before the creators update, and would have essentially postponed the issues I was having)
Finally, I managed to get Windows loaded from boot media (thankfully, they seem to tie your Windows licence to your account now) and am currently in the process of regaining all my lost files (which I have to pull from a system image, so it's a lot of digging through compressed files).
On a positive note, things are running well, and the faster hard drive (7200rpm vs 5400rpm) is a nice upgrade. And the touchpad drivers (the same one that kept crashing) haven't caused any problems since.
Now at least, I can get back to programming :D2 -
Learning to tech to speed up learning.
Using a new cooperative learning technique, AI Lab researchers cut by half the time it took a pair of robot agents to learn to maneuver to opposite sides of a virtual room.
A combination of deep learning and reinforcement learning algorithms are responsible for computers achieving dominance at challenging board games like chess and Go, a growing number of video games, including Ms. Pac-Man, and some card games, including poker. But for all the progress, computers still get stuck the closer a game resembles real life, with hidden information, multiple players, continuous play, and a mix of short and long-term rewards that make computing the optimal move hopelessly complex.
Image: Dong-ki Kim
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The hype of Artificial Intelligence and Neutral Net gets me sick by the day.
We all know that the potential power of AI’s give stock prices a bump and bolster investor confidence. But too many companies are reluctant to address its very real limits. It has evidently become a taboo to discuss AI’s shortcomings and the limitations of machine learning, neural nets, and deep learning. However, if we want to strategically deploy these technologies in enterprises, we really need to talk about its weaknesses.
AI lacks common sense. AI may be able to recognize that within a photo, there’s a man on a horse. But it probably won’t appreciate that the figures are actually a bronze sculpture of a man on a horse, not an actual man on an actual horse.
Let's consider the lesson offered by Margaret Mitchell, a research scientist at Google. Mitchell helps develop computers that can communicate about what they see and understand. As she feeds images and data to AIs, she asks them questions about what they “see.” In one case, Mitchell fed an AI lots of input about fun things and activities. When Mitchell showed the AI an image of a koala bear, it said, “Cute creature!” But when she showed the AI a picture of a house violently burning down, the AI exclaimed, “That’s awesome!”
The AI selected this response due to the orange and red colors it scanned in the photo; these fiery tones were frequently associated with positive responses in the AI’s input data set. It’s stories like these that demonstrate AI’s inevitable gaps, blind spots, and complete lack of common sense.
AI is data-hungry and brittle. Neural nets require far too much data to match human intellects. In most cases, they require thousands or millions of examples to learn from. Worse still, each time you need to recognize a new type of item, you have to start from scratch.
Algorithmic problem-solving is also severely hampered by the quality of data it’s fed. If an AI hasn’t been explicitly told how to answer a question, it can’t reason it out. It cannot respond to an unexpected change if it hasn’t been programmed to anticipate it.
Today’s business world is filled with disruptions and events—from physical to economic to political—and these disruptions require interpretation and flexibility. Algorithms alone cannot handle that.
"AI lacks intuition". Humans use intuition to navigate the physical world. When you pivot and swing to hit a tennis ball or step off a sidewalk to cross the street, you do so without a thought—things that would require a robot so much processing power that it’s almost inconceivable that we would engineer them.
Algorithms get trapped in local optima. When assigned a task, a computer program may find solutions that are close by in the search process—known as the local optimum—but fail to find the best of all possible solutions. Finding the best global solution would require understanding context and changing context, or thinking creatively about the problem and potential solutions. Humans can do that. They can connect seemingly disparate concepts and come up with out-of-the-box thinking that solves problems in novel ways. AI cannot.
"AI can’t explain itself". AI may come up with the right answers, but even researchers who train AI systems often do not understand how an algorithm reached a specific conclusion. This is very problematic when AI is used in the context of medical diagnoses, for example, or in any environment where decisions have non-trivial consequences. What the algorithm has “learned” remains a mystery to everyone. Even if the AI is right, people will not trust its analytical output.
Artificial Intelligence offers tremendous opportunities and capabilities but it can’t see the world as we humans do. All we need do is work on its weaknesses and have them sorted out rather than have it overly hyped with make-believes and ignore its limitations in plain sight.
Ref: https://thriveglobal.com/stories/...6 -
Hi guys,
can somebody recommend me a digital camera, which can be controlled programmatically by computer aka. Rasberry Pi?
I want to try some image processing algorithms and time lapse on live video. The price should be around 200-300$ and it is really important that I can program it.3 -
- Finish "Introduction to algorithms"
- Learn some genetic algorithms
- Get my hands dirty on reinforcement learning
- Learn more about data streaming application (My currently app is still using plain stupid REST to transport image). I don't know, maybe Kafka and RabbitMQ.
- Learn to implement some distributed system prototypes to get fitter at this topic. There must be more than REST for communicating between components.
- Implementing a searching module for my app with elastic search.
- Employ redis at sometime for background tasks.
- Get my handy dirty on some operating system concepts (Interprocess Communication, I am looking at you)
- Take a look at Assembly (I dont want to do much with Assembly, maybe just want to implement one or two programs to know how things work)
- Learn a bit of parallel computing with CUDA to know what the hell Tensorflow is doing with my graphic card.
- Maybe finishing my first research paper
- Pass my electrical engineering exam (I suck at EE)1 -
I have one question to everyone:
I am basically a full stack developer who works with cloud technologies for platform development. For past 5-6 months I am working on product which uses machine learning algorithms to generate metadata from video.
One of the algorithm uses tensorflow to predict locale from an image. It takes an image of size ~500 kb and takes around 15 sec to predict the 5 possible locale from a pre-trained model. Now when I send more than 100 r ki requests to the code concurrently, it stops working and tensor flow throws some error. I am using a 32 core vcpu with 120 GB ram. When I ask the decision scientists from my team they say that the processing is high. A lot of calculation is happening behind the scene. It requires GPU.
As far as I understand, GPU make sense while training but while prediction or testing I do not think we will need such heavy infra. Please help me understand if I am wrong.
PS : all the decision scientists in the team basically dumb fucks, and they always have one answer use GPU.8
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