As technology advances and the internet gets dramatically more powerful, the need to retain information in our heads diminishes.
Google and other search engines which deploy AI, work as our 'memory partners' and provide us access to most of the human knowledge.
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Future-proofing your career to stay relevant isn't about learning how to code or going back to college.
It is about having a career plan with a long-term vision, taking into account the current job-market conditions, economic factors, emerging opportunities, personal interests, and family realities.
A life cycle of a job is shrinking rapidly, and if you're not re-inventing yourself or pivoting on time, you are rendered out of work sooner than in the past decades.
We need to check our career plan and ask ourselves what skills need to be developed to pursue future opportunities, in this shifting economy.
Neuroevolution is a form of artificial intelligence. It is a meta-algorithm, an algorithm for designing algorithms. It adopts the principles of biological evolution in order to design smarter algor...
Traditionally, evolutionary algorithms are used to solve specific problems. For instance, the ability to control a two-legged robot. Solutions that perform the best on some metrics are selected to produce offspring.
In spite of successes, these algorithms are more computationally intensive than approaches such as "deep learning."
It goes beyond traditional evolutionary approaches. It explains innovation. Instead of optimizing for a specific goal, it embraces the creative exploration of a diverse population of solutions.
The steppingstone’s potential can be seen by analogy with biological evolution: feathers likely evolved for insulation and only later became handy for flight.
The instructions tell a computer how to transform a set of facts into useful information.
The facts are data. The useful information is knowledge for peopl...
To a computer, input is the information needed to make decisions.
For example, if you get dressed, you will need information such as what clothes are available to you, then you might consider the temperature, the season, and personal preferences.
Computation is the heart of an algorithm and involves arithmetic, decision-making, and repetition.
To apply this to getting dressed, you make decisions by doing some math on input quantities. Wearing a jacket might depend on the temperature. To a computer, part of getting dressed algorithm would be "if it is below 50 degrees and raining, then pick the rain jacket and a long-sleeved shirt."
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