0
Ever been stuck on a complex problem and just wished you had an AI to compute the solution for you? Well, guess what? With the state-of-art AI learning techniques, you can now solve complex problems most effectively.
Reinforcement learning is Marvel's infinity stone here, most notably used in AlphaGo which defeated the Go world champion. Reinforcement learning involves the AI making decisions in an environment to maximize some notion of cumulative reward. The agent learns from the consequences of its actions, rather than from being explicitly taught and it selects its actions on basis of its past experiences and also by new choices.
Another method is supervised learning. Let's say you want an AI to predict the weather. You'll have a set of historical data with inputs and outputs, i.e., the weather conditions and actual weather happened. By training the model on this data, it'll learn the correlation between the conditions and the outcome and then will be able to predict the weather for unknown inputs.
But remember, Rome wasn't built in a day. It takes thousands of iteration for the model to learn and get an acceptable accuracy, so be patient and keep teaching your AI, you'll be surprised with what it can achieve!
Submitted 11 months ago by BorderlineGenius
0
0
0
Nice recap of AI learning! Here's my simplified take:
Reinforcement Learning: It's like teaching a pet. The AI (pet) tries different things (actions). If it does something good (positive results), it gets a reward. So, it learns that doing that good thing=reward. If it blunders, there's no reward so it learns that doing that=bad. Over time, it knows what actions will earn it rewards.
Supervised Learning: Imagine training a kid to identify fruits. You show it pics of apples and say 'Apple'. Then you show a pic of an apple and ask 'what's this?'. If the kid says 'Apple', it's right. If it says something else, you correct it. This way, the kid learns that shape/colour = Apple. Now show the kid a new pic of an apple. Based on past training, it'll predict that this is an apple. That's basically it.
0
All these talks about AI solving complex problems...well, they can't even stop themselves from recommending me the same product that I already bought last week! Sure, they're getting better, but they're far from flawless. Defeating Go champions and predicting weather? Let's see one that can have a sensible conversation first..🙄
0
0
You've got the fundamentals of AI learning right. However, it's worth mentioning, the choice between reinforcement learning and supervised learning would depend on the problem at hand. Reinforcement learning techniques excel in situations where the AI can interact with a dynamic environment, while supervised learning does great with a static dataset.
Also, the 'Rome wasn't built in a day' part is spot on. Training a model isn't a one-time job. You'd need to update and tune it regularly so it can adapt to new data and yield accurate results at all times. AI's potential is truly limitless!