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CategoryProgramming

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Software engineers working on project and programming in company

Automated Code Generation Tools Can Solve Problems

We may be seeing the rebirth of an old approach to productivity that finds a middle ground between too constrained and too risky

A programming language creates a middle space between the way humans think and the way computers think. What's the best compromise point?

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Close-up view of the Difference Engine

Lovelace: The Programmer Who Spooked Alan Turing

Ada Lovelace understood her mentor Charles Babbage’s plans for his new Analytical Engine and was better than he at explaining what it could do

Turing thought that computers could be got to think. Thus he had to address Lovelace’s objection from a century earlier, that they could not be creative.

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Shirts

Why Your Computer Will Never Talk to You

As a jokester recently demonstrated, even “shirts without stripes” is a fundamental, unsolvable problem for computers

At first, “shirts without stripes” might not seem like much of an issue but it turns out that many important and interesting problems for computers fundamentally reduce to this “halting problem.” And understanding human language is one of these problems.

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Shot of Corridor in Working Data Center Full of Rack Servers and Supercomputers with Internet connection Visualization Projection.

What’s Hard for Computers Is Easy for Humans

Some of the surprising things computers have a hard time doing and why

We often hear that what’s hard for humans is easy for computers. But it turns out that many kinds of problems are exceedingly hard for computers to solve. This class of problems, known as NP-Complete (NPC), was independently discovered by Stephen Cook and Leonid Levin.

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magician hands with magic wand showing trick

Current Artificial Intelligence Research Is Unscientific

The assumption that the human mind can be reduced to a computer program has never really been tested

Because AI research is based on a fundamental assumption that has not been scientifically tested—that the human mind can be reduced to a computer—then the research itself cannot be said to be scientific.

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Is Moore’s Law Over?

Rapid increase in computing power may become a thing of the past

If Moore’s Law fails, AI may settle in as a part of our lives like the automobile but it will not really be the Ruler of All except for those who choose that lifestyle. Even so, a belief that we will, for example, merge with computers by 2045 (the Singularity) is perhaps immune to the march of mere events. Entire arts and entertainment industries depend on the expression of such beliefs.

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Machines Never Lie but Programmers… Sometimes

A creative claim is floating around out there that bad AI results can arise from machine “deception”

We might avoid worrying that our artificial intelligence machines are trying to deceive us if we called it “Automated Intelligence rather than “Artificial Intelligence.”

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What To Ask a Programmer in a Job Interview

Does your candidate have the inner attributes needed to grow as a developer and face new challenges? Key questions can help you find out

Good computer programmers are very opinionated people. If you find a computer programmer who is not opinionated, that’s usually because the programmer hasn’t taken the time to think about the task. Those types of people tend to be order-takers, not inventors.

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Pro Tips for Hiring the Best Computer Programmers

It’s easier when we stop to think about how programming differs from other jobs

Hiring a programmer is different because you are rarely looking for a fixed set of skills. Nearly everything the programmer does is an invention. The thing you are usually hiring the programmer for is not a fixed task but the ability to adapt to whatever is coming up next. For example, twelve years ago, nobody knew the degree to which mobile phones would run our businesses. The idea of hiring mobile developers was unheard of.

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Will an AI Win a Nobel Prize for Science All by Itself One Day?

No, but Support Vector Machines (SVMs) can allow scientists to frame questions so that a comprehensible answer is more likely

AI can certainly help scientists. But to understand why AI can’t do science on its own, we should take a look at the NP-Hard Problem in computer science. The “Hard” is in the name of the problem for a reason… 

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2019 AI Hype Countdown #3: Quantum Supremacy? Less Supreme Than It Sounded

It’s possible that Google’s result can be generalized to more useful scenarios than the test case though it isn't immediately obvious how

What Google really achieved was increased stability in its quantum computing platform—keeping qubits stable has been a hard problem in quantum computing for a long time. That was certainly a step forward, but advertising it as “quantum supremacy” was certainly a classic exercise in hype.

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2019 AI Hype Countdown #5: Transhumanism never grows old

The idea that we can upload our brains to computers to avoid death shows a fundamental misunderstanding of the differences between types of thinking

Computers are very effective but they operate with a very limited set of causal abilities. Humans work from an entirely different set of causal abilities. Uploading your brain to a computer is not a question of technology. It can’t work in principle.

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Moore or Less: Why the Exponential Speed of AI Can’t Be Sustained

Faster computers only help the performance of AI algorithms that require search marginally.

Exponential growth is often the beginning of a sigmoid or s-shaped curve where growth that appears to be exponential but eventually slows and reaches a saturation point. We see this in nature, for example, in bacteria. 

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Just a light frost—or AI winter?

It’s nice to be right once in a while—check out the evidence for yourself

About a year ago, I wrote that mounting AI hype would likely give way to yet another AI winter. Now, according to the panelists at “the world’s leading academic AI conference” the temperature is already falling.

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Jonathan Bartlett recalls the Rise and Fall of PlayStation 3 Supercomputers

And how, at one point, he got kicked out of a WalMart on that account. Hey, high tech means vulnerability

When the PlayStation 3 came out, there was no other computer like it (late 2006/early 2007). The design was especially appealing for two uses: high-speed graphics and scientific calculations. So, despite its reputation, it got used for purposes other than playing games. 

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Google vs. IBM?: Quantum Supremacy Isn’t the Big Fix Anyway

If human thought is a halting oracle, then even quantum computing will not allow us to replicate human intelligence

Google’s quantum supremacy claim is certainly fascinating and controversial, but even if true, it ultimately only amounts to an incremental and even inconsequential improvement in the state of AI and ML, due to the still-unmet need for a halting oracle.

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Occam’s Razor Can Shave Away Data Snooping

The greater an object's information content, the lower its probability.

One technique to avoid data snooping is based on the intersection of information theory and probability: An object’s probability is related to its information content. The greater an object’s information content, the lower its probability. We measure a model’s information content as the logarithmic difference between the probability that the data occurred by chance and the number of bits required to store the model. The negative exponential of the difference is the model’s probability of occurring by chance. If the data cannot be compressed, then these two values are equal. Then the model has zero information and we cannot know if the data was generated by chance or not. For a dataset that is incompressible and uninformative, swirl some tea Read More ›

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Big Tech Tries to Fight Racist and Sexist Data

The trouble is, no machine can be better than its underlying training data. That’s baked in

The problem with machine learning-based AI in police work is not so much its inherent bias (none of us is bias-free) but the delegation to a machine of what should be a human decision.

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Machine Learning, Part 3: Don’t Snoop on Your Data

You risk using a feature for prediction that is common to the dataset, but not to the problem you are studying

As long as we can establish that our theories, hypotheses, and/or models are independent of the data, then we can trust that their predictive power will generalize beyond the data we have observed.

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Machine Learning, Part 2: Supervised Learning

Machine learning isn’t hard to understand; it’s just different. Let’s start with the most common type

The neat thing about machine learning is that the algorithm can extract general principles from the dataset that can then be applied to new problems. It is like the story that Newton observed an apple fall and then derived from it the general law of gravity that applies to the entire universe.

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