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Dear companies..
There is a fucking difference between:
-pattern recognition
-machine learning
And
- artificial INTELLIGENCE....
Learning from experience is NOT THE SAME as being able to make conclusions out of unknown conditions and figuring out new stuff without any input.8 -
Facebook be like: "We'll never sell your data to other companies. We'll just do ourselves what other companies like Cambridge analytica did, but more aggressive and better"
Apparently they are predicting what you'll do in the future and how you'll behave based on what you did in the past. This will give them the ability to sell ads based on what your future self will do, thus leading to your future self being forged by those ads.
This is extremely dangerous.
Source:
https://theintercept.com/2018/04/...9 -
Bootcamps get you up and running in coding quickly. If you are a programmer, companies are only interested on how quickly, error free and cheaply you produce marketable output. Bootcamps enable this.
More or less you are not more than a former assembly line worker putting parts on a car platform. Your value is not very high as you may be exchanged at any time at their will.
Nevertheless, you can earn money quickly. You trade in your youth and time which might be a dead end in the long-term. Trends go to machine learning, artificial intelligence. They will not need Bootcamp people and code workers.
It is better you set up Bootcamps and sell them versus absolving this. Like selling shovels during the gold rush, but not working in the mud of Alaska by yourself.
Your choice is: Making quick money, which fades anyway; or striving for the long-term future proof career.
C/S degrees from Technical Universities of reputation give to you the right direction under a strategic consideration. Companies which pay well, or freelancing with a solid acknowledged background, will always look for top graduates. People from Bootcamps are just OK for hammering assembly line coding. Even worse with SCRUM in one noisy room under enormous team server pressure controls, counting your lines of code per minute, with pale people all around. And groups of controllers never acknowledging nor trusting your work.
To acquire a serious degree, a Bachelor is nothing. Here, in INDIA, Bachelor now is what a former high school grade was. You must carry a diploma or Masters degree combined with internships at big companies with high brand recognition. This will require 4–6 years of your lifetime. You can support this financially by working part-time freelancing as making some projects front- or back-end web, data analysis and else.
Bootcamp people will lose in the long-term. They are the modern cannon fudder of software production.
It is your choice. Personally, I would never do Bootcamps. Quality and sustainability require time, deep studies and devotion. -
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
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