Palantir posted a manifesto that reads like the ramblings of a comic book villain


Because we get asked a lot.

The Technological Republic, in brief.

1. Silicon Valley owes a moral debt to the country that made its rise possible. The engineering elite of Silicon Valley has an affirmative obligation to participate in the defense of the nation.

2. We must rebel against the tyranny of the apps. Is the iPhone our greatest creative if not crowning achievement as a civilization? The object has changed our lives, but it may also now be limiting and constraining our sense of the possible.

3. Free email is not enough. The decadence of a culture or civilization, and indeed its ruling class, will be forgiven only if that culture is capable of delivering economic growth and security for the public.

4. The limits of soft power, of soaring rhetoric alone, have been exposed. The ability of free and democratic societies to prevail requires something more than moral appeal. It requires hard power, and hard power in this century will be built on software.

5. The question is not whether A.I. weapons will be built; it is who will build them and for what purpose. Our adversaries will not pause to indulge in theatrical debates about the merits of developing technologies with critical military and national security applications. They will proceed.

6. National service should be a universal duty. We should, as a society, seriously consider moving away from an all-volunteer force and only fight the next war if everyone shares in the risk and the cost.

7. If a U.S. Marine asks for a better rifle, we should build it; and the same goes for software. We should as a country be capable of continuing a debate about the appropriateness of military action abroad while remaining unflinching in our commitment to those we have asked to step into harm’s way.

8. Public servants need not be our priests. Any business that compensated its employees in the way that the federal government compensates public servants would struggle to survive.

9. We should show far more grace towards those who have subjected themselves to public life. The eradication of any space for forgiveness—a jettisoning of any tolerance for the complexities and contradictions of the human psyche—may leave us with a cast of characters at the helm we will grow to regret.

10. The psychologization of modern politics is leading us astray. Those who look to the political arena to nourish their soul and sense of self, who rely too heavily on their internal life finding expression in people they may never meet, will be left disappointed.

11. Our society has grown too eager to hasten, and is often gleeful at, the demise of its enemies. The vanquishing of an opponent is a moment to pause, not rejoice.

12. The atomic age is ending. One age of deterrence, the atomic age, is ending, and a new era of deterrence built on A.I. is set to begin.

13. No other country in the history of the world has advanced progressive values more than this one. The United States is far from perfect. But it is easy to forget how much more opportunity exists in this country for those who are not hereditary elites than in any other nation on the planet.

14. American power has made possible an extraordinarily long peace. Too many have forgotten or perhaps take for granted that nearly a century of some version of peace has prevailed in the world without a great power military conflict. At least three generations — billions of people and their children and now grandchildren — have never known a world war.

15. The postwar neutering of Germany and Japan must be undone. The defanging of Germany was an overcorrection for which Europe is now paying a heavy price. A similar and highly theatrical commitment to Japanese pacifism will, if maintained, also threaten to shift the balance of power in Asia.

16. We should applaud those who attempt to build where the market has failed to act. The culture almost snickers at Musk’s interest in grand narrative, as if billionaires ought to simply stay in their lane of enriching themselves . . . . Any curiosity or genuine interest in the value of what he has created is essentially dismissed, or perhaps lurks from beneath a thinly veiled scorn.

17. Silicon Valley must play a role in addressing violent crime. Many politicians across the United States have essentially shrugged when it comes to violent crime, abandoning any serious efforts to address the problem or take on any risk with their constituencies or donors in coming up with solutions and experiments in what should be a desperate bid to save lives.

18. The ruthless exposure of the private lives of public figures drives far too much talent away from government service. The public arena—and the shallow and petty assaults against those who dare to do something other than enrich themselves—has become so unforgiving that the republic is left with a significant roster of ineffectual, empty vessels whose ambition one would forgive if there were any genuine belief structure lurking within.

19. The caution in public life that we unwittingly encourage is corrosive. Those who say nothing wrong often say nothing much at all.

20. The pervasive intolerance of religious belief in certain circles must be resisted. The elite’s intolerance of religious belief is perhaps one of the most telling signs that its political project constitutes a less open intellectual movement than many within it would claim.

21. Some cultures have produced vital advances; others remain dysfunctional and regressive. All cultures are now equal. Criticism and value judgments are forbidden. Yet this new dogma glosses over the fact that certain cultures and indeed subcultures . . . have produced wonders. Others have proven middling, and worse, regressive and harmful.

22. We must resist the shallow temptation of a vacant and hollow pluralism. We, in America and more broadly the West, have for the past half century resisted defining national cultures in the name of inclusivity. But inclusion into what?

Excerpts from the #1 New York Times Bestseller The Technological Republic: Hard Power, Soft Belief, and the Future of the West, by Alexander C. Karp & Nicholas W. Zamiska



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Python Serialization – Table of Content

Serialization in Python

Serialization in python is a process to serialize data in a species that is user-friendly, human-readable, and easily inspected. There are two very common python serialization libraries that serialize data objects in python. They are ‘HDF5’ and ‘Pickle’ which take dictionaries as well as Tensorflow models for storage purposes and transmission.

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Why Python Serialization?

The serialization process allows the python user to send, receive and save his data alongside maintaining the original structure also. The user finds it very useful to save a certain kind of data in the database so that he can reuse it later whenever it is needed. It can also be used to transmit data on a server network and the user can access it on any system later on.

The process of serialization is also very helpful for projects related to data science. For instance, the process of dataset preprocessing can be very time-consuming, hence preprocessing is done just once that too before saving the data on the disk. It is preferred that the user performs preprocessing each time he uses it. It also eliminates memory limitation problems for big data too which is heavy for loading in the memory as a single piece. So when the data is split into smaller chunks, the user is able to load every single chunk for preprocessing, and he can then save the outputs to the disk, removing all the data chunks from the memory.

Python Serialization: Text Based

The process of textual serialization means serializing the data in some specific format that is easy to understand, human-readable as well as easily inspected. Formats which are text-based are mainly language agnostic and they can be formed with the help of any language related to programming.

JSON is a standard format that is used to exchange data between servers and web clients. JSON is known to serialize the objects in a plain text file format and allow for easy visual identification to the user. JSON stores the objects in the form of key-value pairs, just like a dictionary in Python. JSON is a built-in library in python which makes it a breeze for the user to work with JSON. 

It is very easy to perform JSON serialization just like creating a JSON file and dumping the object. This is done with the help of the dump() method. This method has two arguments which are:  

  • The object user is serializing
  • File which will store the serialized object.

Python JSON has two main functions which it works with:

  • dump(): This function helps to convert a Python object into JSON format
  • Loads(): This function helps to convert the JSON string back into a Python object.

The table below will show the conversion of the python data type into a JSON type:

dict-object

List, tuple- array

str- String

True- true

Int, float- Number

False- false

None- null

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YAML

YAML is not a Markup Language but it is actually a parent set of JSON made in a way to be more comprehensible to the user. The most important and distinguishing feature of YAML is the capacity to create references for other objects in the same file. Another most important advantage is that it is possible to write comments in python. This feature has proved very useful to work with the configuration files also.

Python Serialization: Binary Formats

It is not possible for binary formats in serialization to be human-readable; however they are faster in general and also require much lesser space than text-based counterparts. Let us see some very popular binary formats below:

Pickle

It is a very popular format for python serialization. It is used to serialize almost all the Python object types. Pickle is considered to be an original serialization format used for Python, hence when a user plans to serialize objects in python that he expects to share and he must use with many other languages used for programming, he has to be mindful of the issues such as cross-compatibility. Similarly, pickle works in the same way for various Python versions. The user cannot unpickle a file present in the XXX version, which he picked in the python ZZZ version. So by doing such unnecessary changes, the execution of malicious code gets tough.

Let us see an example below and understand how pickling is performed in python:


import pickle

 

class example_class:

    x_number = 10

    x_string = "Welcome to the tutorial"

    x_list = [10, 20, 30]

    x_dict = {"Heya": "x", "How": 5, "you": [10, 20, 30]}

    x_tuple = (2, 3)

 

my_object = example_class()

 

my_pickled_object = pickle.dumps(my_object)  

print(f"This would be pickled object:\n{my_pickled_object}\n")

 

my_object.a_dict = None

 

my_unpickled_object = pickle.loads(my_pickled_object) 

print(

    f"The dictionary of unpickled object is:\n{my_unpickled_object.a_dict}\n")

 

 Output

This would be pickled object:

b'\x80\x04\x95!\x00\x00\x00\x00\x00\x00\x00\x8c\x08__main__\x94\x8c\rexample_class\x94\x93\x94)\x81\x94.'

 

Traceback (most recent call last):

  File "", line 19, in

AttributeError: 'example_class' object has no attribute 'a_dict'

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Module Interface for Pickling and Unpickling

The data format is always Python-specific for the pickle module. That is why it is always important to write the essentially required code when the user is performing the process of serialization or deserialization. dumps() is the Python function that is used to serialize an object hierarchy whereas loads() is the function that is used to de-serialize the same.

Pickle Protocols

Protocols in pickle act like the convention measures to deconstruct and construct the python objects. There are in total of 5 protocols that a user can use in pickling. Whenever a user uses a higher protocol version, he will need the latest version of Python to obtain the highly compatible as well as readable pickle.

Protocol version 0: This version is readable by humans. It is compatible to use with data and interfaces from the older python versions.
Protocol version 1: It is known to be an old binary format. Just like protocol version 0, it is also compatible with older python versions.
Protocol version 2: It came into effect during the release of python version 2.3. This version is well known for providing new styles in picking.
Protocol version 3: This version was discovered during the release of python version 3.0. It is famous for supporting byte objects however the major drawback with this version is it gets unpicked by python version 2.0
Protocol version 4: This version was discovered during the release of python version 3.4. This is able to support large objects and various different objects can be picked too. It is also famous for supporting data optimization.

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Numpy

It is a very popular python library used by the user to work with large and multidimensional arrays as well as matrices. It stands for numerical python. They are open source and free to use but slow to process. NumPy arrays can be stored in one continuous place in the memory; however this same is not possible for lists. Processes can therefore access as well as manipulate the arrays very efficiently.

Let us see an example below and understand how the Numpy library is used in python:


import numpy as np

arr = np.array( [[ 10, 20, 30],

[ 40, 20, 50]] )

 

print("The type of array is: ", type(arr))

 

print("The no of dimensions are: ", arr.ndim)

 

print("The shape of the array is: ", arr.shape)

 

print("The size of the array is: ", arr.size)

 

print("Array stores elements of the type: ", arr.dtype)

 

 Output

The type of array is:  <class 'numpy.ndarray'>

The no of dimensions are:  2

The shape of the array is:  (2, 3)

The size of the array is:  6

Array stores elements of the type:  int64

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Conclusion

Serialization is a process that aims at simplifying the data storage methods for a data scientist. Serialization in Python is one of the most important features that ease the data conversion interface of the data. In this article, we have talked about why we need serialization. The serialization process allows the python user to send, receive and save his data alongside maintaining the original structure also. The user finds it very useful to save a certain kind of data in the database so that he can reuse it later whenever it is needed. 

We have also discussed JSON and YAML in python. Then we talked about binary formats of python serialization which are pickle and NumPy. In this sub-topic, we will also have a glance at module instances of pickling and unpickling along with pickle protocols. Now we will be discussing some frequently asked questions by the developers and will give solutions for them.

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