Example Of Objectives And Goals: Fill & Download for Free

Unsupervised learning. In particular, what objective function should we use? Maximizing likelihood of the observed data, or even of future observed data, seems like the wrong thing to aim for. Consider, for example, predicting every pixel in the next N frames of video. Do we care about the exact intensity values? No, we care about predicting what the world is going to do next (will the car turn left or right? will the glass break if I drop it?). Somehow humans and animals seem to learn to predict at this higher level of abstraction, in terms of objects and relations, without ever receiving any such labeled data. Multi-task reinforcement learning will help, but learning from scalar reward alone seems too limited. Learning to predict the outcome of one's actions seems like it might help (and this can be used in goal-based planning).

In 8th grade for a science fair project, I created and implemented an algorithm for generating/checking of geometry proofs.The goal of the algorithm was to generate a proof of some statement given a set of given statements and a database of theorems. This project took me about a year to complete and was a huge learning opportunity for me, seeing as I had no formal education in algorithms and programming.The main structural construction I made was to split up mathematical objects into three main categories.Objects - these are the fundamental building blocks of the engine. (points, lines, triangles)Statements - a statement is simply an ordered collection of objects. (two angles are congruent, a triangle is isosceles)Theorems - a theorem is a transformation between collections of statements. In other words, a theorem takes a collection of given statements and transforms them into a set of resulting statements.Using these three definitions, I split up my code into four layers of increasing abstraction where the fourth layer was the engine itself. The layers of abstraction formed an elegant hierarchy of mathematical structures, and by the time I began working on the algorithm itself, everything had been neatly laid out in front of me.The actual algorithm involved three stages.Pre-Processing - this stage converts and organizes user commands into databases of objects and statements. Then the databases are fed through a geometric analyzer which would adds additional structures and further formats the databases to be used in the main stage.Processing - this is the main stage of the algorithm; a database of theorems and the enhanced problem information data generated by the geometric analyzer are inputted into a solution tree builder. This tree builder outputs a tree of possible paths which can be taken to prove the required statement.Post-Processing - this stage analyzes the solution tree and extracts the possible proofs or determines if the statement has no proof. Then it will convert the proof into a readable format and output the proof to the user.The solution tree builder went backwards, starting with the statement which was supposed to be proven and working backwards to the given statements. This worked similar to a reverse ray trace in computer graphics.The solution tree builder is recursive and each recursive iteration can be loosely described as follows:List Generation - Create a list of rules that can be used to prove the desired statement.Elimination - eliminate rules from the above list which would not be effective in proving the statement, and give each rule a ranking based on predicted usefulness in the final proof.Rule Tree Construction - recursively create nodes from rules in the list and format the rule tree to fit the new nodes, using the ranking system from the previous stage.Here is a more detailed block diagram of the tree building algorithm:After this stage, the post-processing stage would trim the tree and extract the solution if one exists.Tree Snipping - Traverse the tree and identify any nodes which do not contribute to the proof, and cut them from the tree.Branch Elimination - Traverse the tree a second time and identify the parent branch of the cut nodes and determine if entire branches can be eliminated. This classified nodes as either AND nodes or OR nodes and used logical rules to eliminate branches.Multiple Solution Determination - identify all available solutions and display them on the screen.Here are some examples of the algorithm in action:The details of the algorithm are very interesting and I will definitely be writing a more in depth description on Quora in the near future.

The problem with both is partisanship over principle.Take, for example, the election of George W. Bush where Scalia, Rehnquist, and Thomas ensured Bush’s election by using the equal protection clause of the 14th amendment acting as activist judges — the very type they ridicule and claim to abhor. This is only one example of many.In addition to partisanship, the anti-politics coming out of the right-wing establishment seems to be corrupting both.These anti-politics are false opinions and methods by which judgment, responsibility, and action are rendered meaningless. They insist that nothing exists but self-interest (“This is politics!” they say) — even the value of life itself, on a planetary scale, becomes a relative value. Meaninglessness yields Nothing and Nothing is the goal of anti-politics.The only antidote to anti-politics is the promise of the political, the promise of objectivity and ethics, the promise of justice.