This is a summary of the book titled “Chaos Kings” written by Scott Patterson, a seasoned financial reporter and author who talks about how Wall Street traders make billions in the New Age of Crisis. The book revolves around the theme of “black swan” investing, a pessimistic brand of trading, that seems to have worked very well for a few during the pandemic.  Market crashes and political chaos are growing more common. The pandemic showed that an always pessimistic portfolio offers downside risk protection.

The coronavirus pandemic has highlighted the importance of "black swan" investing strategies, which involve unexpected events that create volatility. The concept was popularized by Nassim Taleb, who believed that unknown threats lurk at all times and investors should be prepared. In early 2020, the pandemic spread, creating a black swan for Universa Investments, a Miami investment firm that operates the Black Swan Protection Protocol Fund. The fund reported a gain of 4,144% in three months, generating outsized profits in case of a crash. However, the market bust quickly reversed itself, setting the stage for future black swans. The era of instant communication and porous borders has created a breeding ground for conspiracy theories and contagions, with the pandemic-era disruptions to the supply chain illustrating the fallout from seemingly far-off risks. Climate change is leading to more dangerous extreme weather, affecting property owners and insurers. Political extremism is also on the rise, with the US electorate's mood swings indicating a rise in "polycrisis," characterized by uncertainty, instability, and dangerous feedback loops.

Mark Spitznagel, a renowned trader, learned trading discipline at a commodities exchange and worked for Everett Klipp, a star trader. Klipp taught Spitznagel the counterintuitive strategy of loving to lose money and hate to make money, which meant never holding onto a position after it took a small loss. Spitznagel became a trader on the Chicago Board of Trade (CBOT) at age 22, trading his own money and focusing on Treasury bonds. He survived the bond crash of 1994 and switched to a hedging strategy, performing well during the Asian crisis of 1997 and the 1998 blowup of Long Term Capital Management. Nassim Taleb, a Wall Street trader, also faced challenges as a trader, growing skeptical of Wall Street's theories of financial engineering. In the late 1990s, Taleb met Spitznagel and decided to go into business together. Their firm, Empirica Capital, could have hugely profited after the September 11, 2001, attacks, but clients were reluctant to gain from the crisis.

Taleb, a contrarian investor, gained media attention with his book Fooled by Randomness and met French mathematician Benoit Mandelbrot. However, Empirica was losing money in a market without volatility, leading to Taleb's departure in 2004. He later wrote The Black Swan, which became a bestseller. Despite skepticism, Universa Investments became extremely profitable during the 2008 stock market crash, making $1 billion and outperforming the market by 115%. Universa's success was due to its larger bet against the overall market, which allowed it to replicate its gains in the future.

Despite the pandemic, this firm struggled to attract investors, but in 2017, it landed a $1.5 billion investment from the California Public Employees Retirement System (CalPERS). In 2018, Volmageddon hit, and CalPERS ramped up its investment to $5 billion. However, in 2019, CalPERS closed out its investment with Universa just before the coronavirus first appeared in Wuhan, China. As the pandemic took hold in China, Universa managed to recruit new clients and snap up S&P 500 puts and VIX call options on the cheap.

The pandemic exposed the importance of a pessimistic portfolio for protection against downside risks. Universa, a hedge fund, demonstrated its hedging strategy effectively, despite the coronavirus market crash. Spitznagel's wealth soared to $250 million, and CalPERS's decision to withdraw funds before the crash led to internal disputes. Meanwhile, Taleb argued that bitcoin was worthless, as it had no intrinsic worth and needed constant attention from miners. Despite the crypto crash, stock investors were still high, with the Dow Jones Industrial Average reaching 36,000. Geopolitical minefields, such as Russia's invasion of Ukraine and Putin's war, were also a concern. Both investors had misread the situation, highlighting the need for a balanced approach to risk management.

Previous book summaries: BookSummary46.docx

Summarizing Software: SummarizerCodeSnippets.docx. 

 

Data plane access to secrets

One of the biggest consolidators of data access aside from storage is a keyvault. Many clients access keys, secrets, and certificates from, say, an Azure KeyVault and this calls for access control in both the management portal and the IaC. An Azure Keyvault is a service that provides secure storage and management of keys, secrets, and certificates. It offers two ways to control access to its data plane: role-based access control (RBAC) and access control policies (ACP)¹.

RBAC is an authorization system built on Azure Resource Manager that provides fine-grained access management of Azure resources. It allows you to assign predefined or custom roles to users, groups, service principals, and managed identities at different scopes, such as management group, subscription, resource group, or individual resource².

ACP is a legacy authorization system, native to Keyvault, that provides access to keys, secrets, and certificates. It allows you to assign individual permissions to security principals at Keyvault scope¹.

Azure Key Vaults should be provisioned with role-based access control (RBAC) for managing access to the key vault itself. This enables us to assign specific roles (such as owner, contributor, or reader) to users, groups, or applications. RBAC ensures fine-grained access control and aligns with the principle of least privilege by granting access based on specific roles assigned to users. On the other hand, access control policies are used within the key vault to manage access to specific key vault resources. These policies define which users, groups, or applications can perform specific operations (such as getting, setting, deleting keys or secrets). Access control policies provide a higher level of granularity for managing permissions within the key vault. Therefore, both RBAC and access control policies should be used for proper access management in Azure Key Vaults. RBAC should be used to manage access to the key vault itself, while access control policies should be used to manage access to specific resources within the key vault.

The key advantages of RBAC over ACP involve:

- A unified access control model for Azure resources

- Centralized access management and auditing

- Better control over the right to grant access to keys, secrets, and certificates

- Integration with Privileged Identity Management

- Support for deny assignments

The trouble with data plane role-based access control is that the directives is that it is often neglected when control plane rbac is assigned. Even when they are specified the assignments must usually be indirect in the form of Active directory groups so that memberships can be assigned or revoked without disturbing the assignment. On the other hand, ACPs can be specified directly on the resource and specific to a user or principal and not require different group memberships for different accesses.  ACPs also hardly require to be captured in the IaC as it is assumed that applications and code access are anyways authorized in IaC by necessary RBAC. In this way, when KeyVault resources are restored, ACPs can be reset without loss of functionality and authorized users interested to gain access can add themselves again

 

The role of generative AI on fleet formation and optimization:

When fleet operators leverage superimposed layers on geographical maps, they can leverage AI and predictive analytics to enhance their decision-making on routes and schedules, increase efficiency, improve reliability, and enhance sustainability. The automations involve data collection, analysis, visualizations, predictions, and optimizations. Data-driven recommendations become available to these operators.

Specifically, these include:

Data collection: from a variety of sources such as sensors, cameras, GPS or external databases.

Data analysis: to extract patterns for vehicle performance, driver behavior, customer preferences, or traffic conditions.

Data visualization: such as dashboards, charts, maps, or reports

Data prediction: with models for regression and classifications

And data optimization: such as for route planning, maintenance scheduling, vehicle allocation, or formation adjustment.

The 3D-formation and flight path between source and destination across rocky terrain, on the other hand, has very little data other than contour maps, weather conditions and other environmental factors but they can still be viewed in segments and sequences and generative AI encodes and decodes state using transformers.

Specific use cases include: 

• Designing optimal flight paths and schedules for multiple drones, taking into account factors like weather, traffic, terrain, and regulations. Generative AI can use techniques like reinforcement learning or evolutionary algorithms to find the best solutions for complex optimization problems¹².
• Generating realistic simulations and scenarios for testing and training drone operators and systems. Generative AI can use techniques like generative adversarial networks (GANs) or variational autoencoders (VAEs) to create synthetic data and environments that mimic real-world conditions³⁴.
• Enhancing the capabilities and performance of drones, such as improving their vision, navigation, communication, and autonomy. Generative AI can use techniques like convolutional neural networks (CNNs) or transformers to process and generate visual, audio, or textual data from drones’ sensors and cameras⁵⁶.

More information about generative AI and its applications is available via the following resources:

• The Generative AI Dossier: A collection of high-impact use cases of generative AI across six major industries, including transportation and mobility, by Deloitte.
• AI Drones: How Artificial Intelligence Works in Drones and Examples: An article that showcases 13 companies that are using AI to improve a new generation of intelligent drones, by Built In.
• AI-Driven Fleet Management: Predict, Optimize, Automate: An article that explains how AI-based recommendations can help skilled fleet workers excel, by Hitachi.
• The Essential Guide to Applying AI to the Drone Delivery Ecosystem: A guide that covers the key applications of AI to drone delivery, such as obstacle detection and avoidance, GPS-free navigation, contingency management and emergency landings, delivery drop, and safe landings, by CloudFactory.

 

How to draw a graph image?

A simple way to do it is to use prebuilt libraries using algorithms like the Kamada-Kawai and Fructerman-Reingold layout algorithms

Sample implementation:

From igraph import *

g = Graph()

vertex_labels=[‘A’, ‘B’, ‘C’, ‘D’, ‘E’]

attributes={}

attributes[“label”] = vertex_labels

g.add_vertices(5, **attributes).add_edges([(0, 1),

(1, 2),

(2, 3),

(3, 4),

(4, 0),

(2, 4),

(0, 3),

(4, 1)]

layout = g.layout(“kamada_kawai”)

plot(g, layout=layout)

If we control spacing ourselves to spread the edges with little or no crossing lines, we could anneal the costs of overlapping.

# The following program lays out a graph with little or no crossing lines.

from PIL import Image, ImageDraw

import math

import random

 

vertex = ['A','B','C','D','E']

links=[('A', 'B'),

('B', 'C'),

('C', 'D'),

('D', 'E'),

('E', 'A'),

('C', 'E'),

('A', 'D'),

('E', 'B')]

domain=[(10,370)]*(len(vertex)*2)

 

def randomoptimize(domain,costf):

    best=999999999

    bestr=None

    for i in range(1000):

        # Create a random solution

        r=[random.randint(domain[i][0],domain[i][1]) for i in range(len(domain))]

        # Get the cost

        cost=costf(r)

        # Compare it to the best one so far

        if cost<best:

            best=cost

            bestr=r

    return r

 

def annealingoptimize(domain,costf,T=10000.0,cool=0.95,step=1):

    # Initialize the values randomly

    vec=[float(random.randint(domain[i][0],domain[i][1]))

         for i in range(len(domain))]

 

    while T>0.1:

        # Choose one of the indices

        i=random.randint(0,len(domain)-1)

        # Choose a direction to change it

        dir=random.randint(-step,step)

        # Create a new list with one of the values changed

        vecb=vec[:]

        vecb[i]+=dir

        if vecb[i]<domain[i][0]: vecb[i]=domain[i][0]

        elif vecb[i]>domain[i][1]: vecb[i]=domain[i][1]

 

        # Calculate the current cost and the new cost

        ea=costf(vec)

        eb=costf(vecb)

        p=pow(math.e,(-eb-ea)/T)

        # Is it better, or does it make the probability

        # cutoff?

        if (eb<ea or random.random( )<p):

            vec=vecb

 

        # Decrease the temperature

        T=T*cool

    return vec

 

def crosscount(v):

    # Convert the number list into a dictionary of person:(x,y)

    loc=dict([(vertex[i],(v[i*2],v[i*2+1])) for i in range(0,len(vertex))])

    total=0

 

    # Loop through every pair of links

    for i in range(len(links)):

      for j in range(i+1,len(links)):

 

        # Get the locations

        (x1,y1),(x2,y2)=loc[links[i][0]],loc[links[i][1]]

        (x3,y3),(x4,y4)=loc[links[j][0]],loc[links[j][1]]

       

        den=(y4-y3)*(x2-x1)-(x4-x3)*(y2-y1)

       

        # den==0 if the lines are parallel

        if den==0: continue

 

        # Otherwise ua and ub are the fraction of the

        # line where they cross

        ua=((x4-x3)*(y1-y3)-(y4-y3)*(x1-x3))/den

        ub=((x2-x1)*(y1-y3)-(y2-y1)*(x1-x3))/den

       

        # If the fraction is between 0 and 1 for both lines

        # then they cross each other

        if ua>0 and ua<1 and ub>0 and ub<1:

            total+=1

 

    for i in range(len(vertex)):

        for j in range(i+1,len(vertex)):

          # Get the locations of the two nodes

          (x1,y1),(x2,y2)=loc[vertex[i]],loc[vertex[j]]

 

          # Find the distance between them

          dist=math.sqrt(math.pow(x1-x2,2)+math.pow(y1-y2,2))

          # Penalize any nodes closer than 50 pixels

          if dist<50:

            total+=(1.0-(dist/50.0))

    return total

                       

def drawnetwork(loc):

    #create the image

    img = Image.new('RGB', (400,400),(255,255,255))

    draw=ImageDraw.Draw(img)

    #create the position dict

    pos=dict([(vertex[i],(loc[i*2],loc[i*2+1])) for i in range(0, len(vertex))])

    #Draw Links

    for (a,b) in links:

        draw.line((pos[a],pos[b]), fill=(255,0,0))

    #Draw vertex

    for (n,p) in pos.items():

        draw.text(p,n,(0,0,0))

    img.save('graph.jpg', 'JPEG')

    img.show()

 

sol=randomoptimize(domain,crosscount)

crosscount(sol)

sol=annealingoptimize(domain,crosscount,step=50,cool=0.99)

crosscount(sol)

drawnetwork(sol)

#codingexercise: https://1drv.ms/w/s!Ashlm-Nw-wnWhOgxp_uQRRXpdZ8wOA?e=AClqyl

 

How to write a chatbot?

The following code explains how to write a chatbot:

import React, { useState } from 'react';

 

const Chat = () => {

    const [query, setQuery] = useState('');

    const [response, setResponse] = useState('');

    const handleQueryChange = (event: React.ChangeEvent<HTMLInputElement>) => {

        setQuery(event.target.value);

    };

 

    const handleSubmit = async (event: React.FormEvent<HTMLFormElement>) => {

        event.preventDefault();

 

        try {

            const response = await fetch('https://oai-ravinote-3.openai.azure.com/openai/deployments/key-ravinote-3/chat/completions?api-version=2023-07-01-preview', {

                method: 'POST',

                headers: {

                    'Content-Type': 'application/json',

                    'api-key': '', // specify api key here

                    'deployment-id': 'key-ravinote-3'

                },

                body: JSON.stringify({"messages":[{"role": "user", "content": query}]}),

            });

            const data = await response.json();

            setResponse(data.choices[0].message.content);

        } catch (error) {

            setResponse('Something went wrong. 429: Too many requests. Please try again later.');

            console.error('Error:', error);

        }

    };

 

    return (

        <div>

            <h1>AskRavi</h1>

            <h2> What would you like to know? This search covers all his writings.</h2>

            <form onSubmit={handleSubmit}>

                <input id="query" type="text" width="50%" value={query} onChange={handleQueryChange} />

                <button type="submit">Send</button>

            </form>

            <div>{response}</div>

        </div>

    );

};

 

export default Chat;

 

 

A conjecture about Artificial Intelligence:

ChatGPT is increasingly in the news for its ability to mimic a human conversationalist and for being versatile. It introduces a significant improvement over the sequential encoding with the GPT-3 family of parallelizable learning. This article wonders if the state considered to be in summation form so that as the text continues to be encoded, the overall state is continuously accumulated in a streaming manner. But first a general introduction to the subject.

ChatGPT is based on a type of neural network called a transformer. These are models that can translate text, write poems and op-ed and even generate code. Newer natural language processing (NLP) models like BERT, GPT-3 or T5 are all based on transformers. Transformers are incredibly impactful as compared to their predecessors that were also based on neural networks. Just to recap, neural networks are models for analyzing complicated data by discovering hidden layers that represent the latency semantics in each data and often referred to as the hidden layer between an input layer of data and an output layer of encodings. Neural networks can handle a variety of data including images, video, audio and text. There are different types of neural networks optimized for different types of data. If we are analyzing images, we would typically use a convolutional neural network so called because it often begins with embedding the original data into a common shared space before it undergoes synthesis, training and testing. The embedding space is constructed from an initial collection of representative data. For example, it could refer to a collection of 3D shapes drawn from real world images. Space organizes latent objects between images and shapes which are complete representation of objects. The use of CNN is a technique that focuses on the salient invariant embedded objects rather than the noise.

CNNs worked great for detecting objects in images but did not do as well for languages or tasks such as summarizing text or generating text. The Recurrent Neural Network was introduced to adjust this for language tasks such as translation where an RNN would take a sentence from the source language and translate it to a destination language one word at a time and then sequentially generate the translations. Sequential is important because the words order matter for the meaning of a sentence as in “The dog chased the cat” versus “The cat chased the dog”. RNNs had a few problems. They could not handle large sequences of text, like long paragraphs. They were also not fast enough to handle large data because they were sequential. The ability to train on large data is considered a competitive advantage when it comes to NLP tasks because the models become more tuned.

Transformers changed that and in fact, were developed for the purposes of translation.  Unlike RNNs, they could be parallelized. This meant that transformers could be used to train on large data sets.  GPT-3 that writes poetry and code and writes conversations was trained on almost 45 Terabytes of text data and including the entire world wide web. It scales well with a huge data set.

Transformers work very well because of three components: 1. Positional Encoding, 2. Attention and 3. Self-Attention.  Positional encoding is about enhancing the data with positional information rather than encoding it in the structure of the network. As we train the network on lots of text data, the transformers learn to interpret those positional encodings. It really helped transformers easier to train than RNN. Attention refers to a concept that originated from the paper aptly titled “Attention is all you need”. It is a structure that allows a text model to look at every single word in the original sentence when deciding to translate the word in the output. A heat map for attention helps with understanding the word and its grammar. While attention is for understanding the alignment of words, self-attention is for understanding the underlying meaning of a word to disambiguate it from other usages. This often involves an internal representation of the word also referred to as its state. When attention is directed towards the input text, there can be differences understood between say “server, can I have the check” and the “I crashed the server” to interpret the references to a human versus a machine server. The context of the surrounding words helps with this state.

BERT, an NLP model makes use of attention and can be used for a variety of purposes such as text summarization, question answering, classification and finding similar sentences. BERT also helps with Google search and Google cloud AutoML language. Google has made BERT available for download via TensorFlow library while Hugging Face company has made Transformers available in Python language.

Basis for the conjecture to use stream processing for state encoding rather than parallel batches is derived from the online processing of Big Data as discussed in:  http://1drv.ms/1OM29ee

 

This is a summary of a book titled Inclusion On Purpose written by Ruchika Tulshyan and published by MIT in 2022. She had earlier published the viral article “Stop telling women they have imposter syndrome.” Racism and prejudice against women of color persist worldwide, affecting the US economy and global economy. Racism is present daily and can be attributed to cultural white supremacy, which associates wealth and achievement with whiteness. Women of color face additional barriers due to gender- and race-based biases combined. Diversity, equity, and inclusion (DEI) efforts often ignore intersectionality, with women of color outnumbering women of all other racial identities in the workforce.

Racism costs everyone, including about $1 trillion in lost GDP each year in the US. Women of color still fill lower-paying jobs out of proportion to their population numbers. Stereotyping, bias, and discrimination hold them back, and inclusion in STEM professions remains stagnate due to biases against women of color in math and computing.

To make work safe for women of color, leaders and influencers must admit their racism and support them. They should embrace willful inclusion, encourage candid conversations about race, remove fear of reprisal, and provide direct feedback about job performance.

White people must acknowledge their privilege and develop purposeful inclusion skills by taking others' perspectives and developing empathy. Acknowledge that two people in the same workplace experience it differently, and show compassion to build trust and recruit and retain people of all backgrounds. Admit your racism and support it by challenging every instance of racism you encounter and questioning your own advancement over that of marginalized peers. Overcome complacency and build empathy by seeking different ideas and perspectives, advocating for unbiased hiring systems, and including representatives of every group in workforce decisions.

Managers and leaders must embrace willful inclusion and speak up when they witness acts of bias, racism, discrimination, or exclusion. Recognize that women of color have likely dealt with hostility and discrimination for years or decades, and actively listen to them. Adopt an inclusion mindset and stay open and curious about conversations about race, religion, nationality, and other factors. Gather anonymous workforce data to gather information on experiences, promotion rates, engagement, and performance of women, women of color, and other minority groups.

Workplaces that expect women of color to hide parts of themselves are not the problem. Establish a specific code of conduct concerning Diversity and Inclusion (DEI) and communicate consequences for breaches. Remove fear of reprisal and ensure psychological safety for women of color. Provide anonymous means for reporting bias and take action when you see bias.

Create conditions that allow people of color to bring their whole selves to work. Encourage marginalized employees to have a voice through well-funded and respected employee resource groups. Consider how your privilege and influence can help someone with a weaker voice.

Managers should recognize and give credit to women of color for their contributions and ideas. In meetings, ensure no one dominates or interrupts and everyone gets an opportunity to share their thoughts and ideas. When hiring, avoid cultural fit and seek cultural addition. Take responsibility as a leader or hiring manager and don't make HR solely responsible for diversity.

Hiring for cultural fit is a prevalent and exclusionary hiring practice. It is crucial to examine sourcing channels, including qualifications in job ads, and design structured interviews led by diverse interviewers. Women should demand fair wages and pay them fairly, as they earn less on average than men. Leaders should lead pay conversations without negotiations, and share compensation openly and ask others about it. Coach women to ask for fair wages, but don't force them. Provide direct, specific feedback about job performance, starting with positive examples. Focus on objective goals in annual performance reviews and offer supportive guidance if defensiveness is encountered.

Previous book summary: BookSummary43.docx
Summarizing Software: SummarizerCodeSnippets.docx  

 #codingexercise: CodingExercise-01-24-2024.docx

 

Friends of appropriate ages:

There are n persons on a social media website. You are given an integer array ages where ages[i] is the age of the i^th person.

A Person x will not send a friend request to a person y (x != y) if any of the following conditions is true:

• age[y] <= 0.5 * age[x] + 7
• age[y] > age[x]
• age[y] > 100 && age[x] < 100

Otherwise, x will send a friend request to y.

Note that if x sends a request to y, y will not necessarily send a request to x. Also, a person will not send a friend request to themself.

Return the total number of friend requests made.

 

Example 1:

Input: ages = [16,16]

Output: 2

Explanation: 2 people friend request each other.

Example 2:

Input: ages = [16,17,18]

Output: 2

Explanation: Friend requests are made 17 -> 16, 18 -> 17.

Example 3:

Input: ages = [20,30,100,110,120]

Output: 3

Explanation: Friend requests are made 110 -> 100, 120 -> 110, 120 -> 100.

 

Constraints:

• n == ages.length
• 1 <= n <= 2 * 10⁴
• 1 <= ages[i] <= 120

class Solution {

    public int numFriendRequests(int[] ages) {

        int[][] requests = new int[ages.length][ages.length];

        for (int i = 0; i < ages.length; i++) {

            for (int j = 0; j < ages.length; j++){

                requests[i][j] = 0;

            }

        }

        int sum = 0;

        for (int i = 0; i < ages.length; i++) {

            for (int j = 0; j < ages.length; j++){

                if (i == j) continue;

                if (ages[j] <= 0.5 * ages[i] + 7) continue;

                if (ages[j] > ages[i]) continue;

                if (ages[j] > 100 && ages[i] < 100) continue;

                requests[i][j] = 1;

                sum += 1;

            }

        }

        return sum;

    }

}