Complex Problems Require Complex Tools

  • Building on the basic tools, more complex living creatures have a wider range of problems they can solve: They can memorize different associations, connections, and mechanical moves and stratagems.
  • The more complex the problem, the more tools are needed in combination to solve it. The more tools there are, the more flexibility there is to solve the new challenges.
  • Even for complex problems, each living being's individual situation and circumstances are important.
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Problem Solving

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We can think of intelligence as a flexible set of skills: a toolbox. And the most basic tools in the intelligence toolbox are:

  • The ability to gather information through senses, to navigate and react to the external world in the right way.
  • The ability to save information and to use it
  • The ability to learn.

These tools enable creatures that appear to be stupid to act in surprisingly intelligent ways.

What Intelligence Is

Intelligence is a mechanism to solve problems (especially the ones related to survival). It includes the ability to gather knowledge, to learn, to be creative, to form strategies, or think critically.

It manifests itself in a huge variety of behaviors.

Learning is the process of putting together a sequence of thoughts and actions.

It is a series of repeatable behaviors that can be diversified and adapted.

  • Not all scientists agree on what counts as intelligence.
  • We think of intelligence as a trait (like height or strength) but we find it difficult to define it.

Being creative translates into building something new and valuable from apparently unrelated things. In the context of intelligence, this means making new and unusual connections: pairing input with memories and skills, to come up with a unique solution to a problem.

Another aspect of creativity is applying a new tool/resource to a task.

Gathering materials for later use is connected to an advanced dimension of problem-solving: planning.

Planning means considering activities required for the desired goal and putting them together in a plan. When unexpected conditions and new possibilities arise, they need to be evaluated according to whether they match the plan or not.

Information is much more powerful if we can keep it and save it. This is where memory enters.

Memory is the ability to save and recall information so we don't have to go back to square one every time we perceive something as important.

Because we as humans are able to work together and share knowledge across generations, we can overcome challenges beyond any single individual's ability. This allowed us to shape the planet on our liking.

We also created new problems in the process: tax forms, but also climate change and antibiotic resistance, for example. To solve these, we need to look past short-term survival and think about the distant future.

Information is the basis of action for all living things.

Without it, we are not able to control and predict our surroundings, or to react appropriately and flexibly to them.

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Big data Hadoop
  • Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT) , that's a key consideration.
  • Computing power. Hadoop's distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
  • Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
  • Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
  • Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
  • Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.

MapReduce programming is not a good match for all problems. It’s good for simple information requests and problems that can be divided into independent units, but it's not efficient for iterative and interactive analytic tasks. MapReduce is file-intensive. Because the nodes don’t intercommunicate except through sorts and shuffles, iterative algorithms require multiple map-shuffle/sort-reduce phases to complete. This creates multiple files between MapReduce phases and is inefficient for advanced analytic computing.

There’s a widely acknowledged talent gap. It can be difficult to find entry-level programmers who have sufficient Java skills to be productive with MapReduce. That's one reason distribution providers are racing to put relational (SQL) technology on top of Hadoop. It is much easier to find programmers with SQL skills than MapReduce skills. And, Hadoop administration seems part art and part science, requiring low-level knowledge of operating systems, hardware and Hadoop kernel settings.

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Artificial intelligence (AI)

Artificial intelligence (AI) has been around since the 1950s. The original pioneers dreamed of a computer that could perform tasks like humans, such as playing chess or translating languages. But the plans didn't come to fruition, and AI soon fell out of favour.

AI technology continued to improve exponentially over the next few decades. Many organisations now embrace AI as a core element of their business.

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