This is a summary of the book titled “Your Stone Age Brain in the Screen Age: Coping with digital distraction and sensory overload” written by Richard Cytowic and published by MIT Press in 2024. The author is a neurologist who explains how screens are grabbing your attention and how we can regain them. His book talks about the impact of continuous alerts, notifications and stimulations on the human brain and why reclaiming your attention and engaging with real-world is not only pertinent and significant but also a necessity. Excessive screen time is reducing brain development in children, resulting in reduced impulse control even leading to psychological harm. We are hardwired with sensory perceptions which makes it difficult to peel away our eyes from the flickering screen. Cellphone and tablet usage has given rise to increased body dysmorphia and virtual autism. Depriving children of sufficient human contact can inhibit the development of empathy. Screen addictions can put you on a “hedonic treadmill”. Protect your sleep and make space for silence and connection to fight against digital toxicity.

Excessive screen time is causing brain damage in children, resulting in reduced impulse control and reduced attention span. Addiction, originating from the Latin word "addictum," refers to the time spent serving a master. Many people don't see excessive screen time as a problem, but this blindness is due to tech giants exploiting human psychology to keep people glued to their screens. Social media addiction can be fatal and trigger severe psychological harm, with injuries caused by inattentive cellphone usage resulting in 76,000 emergency room visits in the past two decades. The human brain's two hemispheres support different skill sets and distinct conceptualizations of identity, making it difficult to stop giving attention to screens. The internal battle for control between the two hemispheres is a result of the way the brain's two hemispheres interact, making it difficult to stop giving attention to screens.

The brain's sensitivity to change, which helped early humans survive, has led to the rise of digital distractions and increased body dysmorphia. The brain's orienting reflex allows it to misperceive digital sound and visual interruptions as having life-or-death significance. This has led to the development of new mental health conditions, such as Snapchat dysmorphia, a new type of body dysmorphic disorder (BDD), where individuals become distressed when their real-life face doesn't look like their edited digital one. Children with virtual autism show dramatic improvements once digital screens are removed. Heavy screen time can also result in the development of autism-like behaviors in younger children, as they can't learn to make eye contact or display context-appropriate facial expressions. Child psychiatrist Victoria Dunckley identifies digital devices as the primary source of issues in children without autism spectrum disorder (ASD). Parents can limit their child's development of virtual autism by organizing in-person play dates and limiting screen time for children 12 years old and younger.

Attachment theory, based on research by Harry Harlow, suggests that depriving children of human contact can inhibit the development of empathy. Harlow's experiments showed that chimpanzees raised without warmth and comfort were unable to comfort themselves, leading to a "pit of despair" and a lack of relational understanding. The iPhone generation may face similar fate to those raised without warmth, as they may escape into digital worlds at the expense of developing empathy and healthy attention spans. Smartphone addictions can trap individuals on a "hedonic treadmill," chasing fleeting moments of happiness and lacking genuine inner contentment. The perpetual unpredictability of digital rewards makes them never less exciting, leading to a constant state of craving. The brain treats the cues for the addiction as more salient than the reward itself, trapping individuals in a constant state of craving. As children escape into digital worlds, they may do so at the expense of developing empathy and healthy attention spans, as empathy requires the ability to focus on another person long enough to understand a different perspective.

To protect your sleep from blue screen light, follow these self-care and sleep hygiene practices:

1. Establish consistent bedtime and wake-up times, block out light sources, and choose a restorative sleep posture.

2. Keep your bedroom temperature between 65°F and 68°F, using a cooling gel pillow or mattress pad.

3. Keep the bathroom lights low, using low-wattage LED nightlights and candles.

4. Consider taking a walk outdoors before bed, limit digital device use, and get natural light.

5. Rethink digital habits, making room for silence and connection.

6. Engage in niksen, the art of doing nothing and putting life on pause for a few minutes.

7. Switch to paper media, writing by hand, and avoiding streaming while eating.

By following these practices, you can improve your sleep and reduce the risk of health consequences such as rapid cellular aging.

 Given a wire grid of size N * N with N-1 horizontal edges and N-1 vertical edges along the X and Y axis respectively, and a wire burning out every instant as per the given order using three matrices A, B, C such that the wire that burns is

(A[T], B[T] + 1), if C[T] = 0 or

(A[T] + 1, B[T]), if C[T] = 1

Determine the instant after which the circuit is broken

     public static boolean checkConnections(int[] h, int[] v, int N) {

        boolean[][] visited = new boolean[N][N];

        dfs(h, v, visited,0,0);

        return visited[N-1][N-1];

    }

    public static void dfs(int[]h, int[]v, boolean[][] visited, int i, int j) {

        int N = visited.length;

        if (i < N && j < N && i>= 0 && j >= 0 && !visited[i][j]) {

            visited[i][j] = true;

            if (v[i * (N-1) + j] == 1) {

                dfs(h, v, visited, i, j+1);

            }

            if (h[i * (N-1) + j] == 1) {

                dfs(h, v, visited, i+1, j);

            }

            if (i > 0 && h[(i-1)*(N-1) + j] == 1) {

                dfs(h,v, visited, i-1, j);

            }

            if (j > 0 && h[(i * (N-1) + (j-1))] == 1) {

                dfs(h,v, visited, i, j-1);

            }

        }

    }

    public static int burnout(int N, int[] A, int[] B, int[] C) {

        int[] h = new int[N*N];

        int[] v = new int[N*N];

        for (int i = 0; i < N*N; i++) { h[i] = 1; v[i] = 1; }

        for (int i = 0; i < N; i++) {

            h[(i * (N)) + N - 1] = 0;

            v[(N-1) * (N) + i] = 0;

        }

        System.out.println(printArray(h));

        System.out.println(printArray(v));

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

            if (C[i] == 0) {

                v[A[i] * (N-1) + B[i]] = 0;

            } else {

                h[A[i] * (N-1) + B[i]] = 0;

            }

            if (!checkConnections(h,v, N)) {

                return i+1;

            }

        }

        return -1;

    }

        int[] A = new int[9];

        int[] B = new int[9];

        int[] C = new int[9];

        A[0] = 0; B [0] = 0; C[0] = 0;

        A[1] = 1; B [1] = 1; C[1] = 1;

        A[2] = 1; B [2] = 1; C[2] = 0;

        A[3] = 2; B [3] = 1; C[3] = 0;

        A[4] = 3; B [4] = 2; C[4] = 0;

        A[5] = 2; B [5] = 2; C[5] = 1;

        A[6] = 1; B [6] = 3; C[6] = 1;

        A[7] = 0; B [7] = 1; C[7] = 0;

        A[8] = 0; B [8] = 0; C[8] = 1;

        System.out.println(burnout(9, A, B, C));

1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0

8

Alternatively,

    public static boolean burnWiresAtT(int N, int[] A, int[] B, int[] C, int t) {

        int[] h = new int[N*N];

        int[] v = new int[N*N];

        for (int i = 0; i < N*N; i++) { h[i] = 1; v[i] = 1; }

        for (int i = 0; i < N; i++) {

            h[(i * (N)) + N - 1] = 0;

            v[(N-1) * (N) + i] = 0;

        }

        System.out.println(printArray(h));

        System.out.println(printArray(v));

        for (int i = 0; i < t; i++) {

            if (C[i] == 0) {

                v[A[i] * (N-1) + B[i]] = 0;

            } else {

                h[A[i] * (N-1) + B[i]] = 0;

            }

        }

        return checkConnections(h, v, N);

    }

    public static int binarySearch(int N, int[] A, int[] B, int[] C, int start, int end) {

        if (start == end) {

            if (!burnWiresAtT(N, A, B, C, end)){

                return end;

            }

            return -1;

        } else {

            int mid = (start + end)/2;

            if (burnWiresAtT(N, A, B, C, mid)) {

                return binarySearch(N, A, B, C, mid + 1, end);

            } else {

                return binarySearch(N, A, B, C, start, mid);

            }

        }

    }

1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0

8

 #codingexercise

Get the widest difference between two indexes in an array of random integers such that the integer at the second index is greater than that of the first.

import java.util.Arrays;

public class LongestIncreasingSubsequence {

    public static void main(String[] args) {

        int[] arr = {10, 22, 9, 33, 21, 50, 41, 60, 80};

        System.out.println(GetWidestDifferenceBetweenIndicesOfLIS(arr));

    }

    public static int GetWidestDifferenceBetweenIndicesofLIS(int[] arr) {

        int n = arr.length;

        int[] lis = new int[n+1];

        for (int i = 0; i < n+1; i++) {

                lis[i] = 1; // Initialize LIS value for each element

        }

        for (int i = 1; i < n; i++) {

            for (int j = 0; j < i; j++) {

                if (arr[i] > arr[j]) {

                    lis[i] = Math.max(lis[i], lis[j] + 1);

                }

            }

        }

       // lis = 1,2,1,3,2,4,4,5,6

        int max_length = Arrays.stream(lis).max().orElse(0);

        if (max_length == 0) return 0;

        if (max_length == 1) return 1;

        int last = -1;

        for (int i = n-1; i >= 0; i++) {

              if (lis[I] == max_length ) {

                    last = i;

              }

        }

        int first = 0;

        for (int i = 0; i < n; i++) {

             if (lis[i] == 2) {

                 first = i;

                 break;

             }

        }

        for(int I = 0; I < first; i++) {

               if (arr[i] < arr[first]) {

                    first = I;

                    break;

               }

        }

        Return last-first+1;

    }

}

9

#booksummary:  BookSummary233.docx

 Problem statement: Given the root of a binary tree, check whether it is a mirror of itself (i.e., symmetric around its center).

Example 1:

                 1

                2 2

             3 4 4 3

Input: root = [1,2,2,3,4,4,3]

Output: true

Example 2:

                 1

                2 2

       null 3 null 3

Input: root = [1,2,2,null,3,null,3]

Output: false

Constraints:

The number of nodes in the tree is in the range [1, 1000].

-100 <= Node.val <= 100

/**

 * Definition for a binary tree node.

 * public class TreeNode {

 * int val;

 * TreeNode left;

 * TreeNode right;

 * TreeNode() {}

 * TreeNode(int val) { this.val = val; }

 * TreeNode(int val, TreeNode left, TreeNode right) {

 * this.val = val;

 * this.left = left;

 * this.right = right;

 * }

 * }

 */

class Solution {

    public boolean isSymmetric(TreeNode root) {

        List<Integer> serialized = new ArrayList<Integer>();

        InorderTraversal(root, serialized);

        return IsPalindrome(serialized);

    }

    public boolean isPalindrome(List<Integer> serialized) {

        int i = 0;

        int j = serialized.count() - 1;

        while (i < j) {

            if (serialized.getAt(i) != serialized.getAt(j)) {

                return false;

            }

            i++;

            j--;

        }

        return true;

    }

    public void InorderTraversal(TreeNode root, List<Imteger> serialized) {

        if (root == null) {

            serialized.Add(Integer.MinValue);

            return;

        }

        InOrderTraversal(root.left);

        serialized.Add(root.val);

        InOrderTraversal(root.right);

    }

}

  Infrastructure engineering for AI projects often deal with text based inputs for analysis and predictions whether they are sourced from chatbots facing the customers, service ticket notes, or a variety of data stores and warehouses but the ability to convert text into audio format is also helpful for many scenarios, including DEI requirements and is quite easy to setup and offers the convenience to listen when screens are small, inadequate for usability and are difficult to read. Well-known audio formats can be played on any device and not just phones.

Although there are many dedicated text-to-speech software and online services available, some for free, and with programmability via web requests, there are built-in features of the public cloud service portfolio that make such capabilities more mainstream and at par with the rest of the AI/ML pipelines. This article includes one such implementation towards the end but first an introduction to this feature and capabilities as commercially available.

Most audio is characterized by amplitude as measured in decibels preferably at least 24db, the stream or bit rate which should preferably be at least 192kbps, and a limiter. Different voices can be generated with variations in amplitude, pitch, and tempo much like how singing is measured. Free text-to-speech software, whether standalone, or online gives conveniences in the input and output formats. For example, Natural Reader allows you to load documents and convert them to audio files. Balabolka can save narrations as a variety of audio file formats with customizations for pronunciations and voice settings. Panopreter Basic, also free software, add both input formats and mp3 output format. TTSMaker supports 100+ languages and 600+ AI voices for commercial purposes. Murf AI although not entirely free has a converter that supports 200+ realistic AI voices and 20+ languages. Licensing and distribution uses varies with each software maker.

Public-cloud based capabilities for text-to-speech can be instantiated with a resource initialization from the corresponding service in their service portfolio. The following explains just how to do that.

Sample implementation:

1. Get text input over a web api:

from flask import Flask, request, jsonify, send_file

import os

import azure.cognitiveservices.speech as speechsdk

app = Flask(__name__)

# Azure Speech Service configuration

SPEECH_KEY = "<your-speech-api-key>"

SERVICE_REGION = "<your-region>"

speech_config = speechsdk.SpeechConfig(subscription=SPEECH_KEY, region=SERVICE_REGION)

speech_config.set_speech_synthesis_output_format(speechsdk.SpeechSynthesisOutputFormat.Audio16Khz32KBitRateMonoMp3)

speech_config.speech_synthesis_voice_name = "en-US-GuyNeural" # Set desired voice

@app.route('/text-to-speech', methods=['POST'])

def text_to_speech():

    try:

        # Check if text is provided directly or via file

        if 'text' in request.form:

            text = request.form['text']

        elif 'file' in request.files:

            file = request.files['file']

            text = file.read().decode('utf-8')

        else:

            return jsonify({"error": "No text or file provided"}), 400

        # Generate speech from text

        audio_filename = "output.mp3"

        file_config = speechsdk.audio.AudioOutputConfig(filename=file_name)

        synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=file_config)

        result = synthesizer.speak_text_async(text).get()

        if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:

            # Save audio to file

            with open(audio_filename, "wb") as audio_file:

                audio_file.write(result.audio_data)

            return send_file(audio_filename, as_attachment=True)

        else:

            return jsonify({"error": f"Speech synthesis failed: {result.reason}"}), 500

    except Exception as e:

        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":

    app.run(host="0.0.0.0", port=5000)

2. Prerequisites to run the script:

a. pip install flask azure-cognitiveservices-speech

b. Create an Azure Speech resource in the Azure portal and retrieve the SPEECH_KEY and SERVICE_REGION from the resources’ keys and endpoint section and use them in place of `<your-speech-api-key>` and `<your-region>` above

c. Save the script and run it in any host as `python app.py`

3. Sample trial

a. With curl request as `curl -X POST -F "text=Hello, this is a test." http://127.0.0.1:5000/text-to-speech --output output.mp3`

b. Or as file attachment with `curl -X POST -F "file=@example.txt" http://127.0.0.1:5000/text-to-speech --output output.mp3`

c. The mp3 audio file generated can be played.

Sample output: https://b67.s3.us-east-1.amazonaws.com/output.mp3

Pricing: Perhaps the single most sought-after feature on text-to-speech is the use of natural sounding voice and service providers often markup the price or even eliminate programmability options for the range of the natural-voices offered. This severely limits the automation of audio books. A comparison of costs might also illustrate the differences between the service providers. Public Cloud text-to-speech services typically charge $4 and $16 per million characters for standard and neural voices respectively which is about 4-5 audio books. Custom voices require about $30 per million characters while dedicated providers such as Natural Voice with more readily available portfolio of voices charge about $60/month as a subscription fee and limits on words. This is still costly but automation of audio production for books is here to stay simply because of the time and effort saved.

 Problem: Count the number of ways to climb up the staircase and we can modify the number of steps at any time to 1 or 2

Solution: int getCount(int n)

{

    int [] dp = new int[n+2];

    dp [0] = 0;

    dp [1] = 1;

    dp [2] = 2;

    for (int k = 3; k <= n; k++) {

                 dp [k] = dp [k-1] + dp [k-2];

    }

   return dp [n];

}

Problem: Rotate a n x n matrix by 90 degrees:

Solution:

static void matrixRotate(int[][] A, int r0, int c0, int rt, int ct)

        {

            if (r0 >= rt) return;

            if (c0 >= ct) return;

            var top = new int[ct-c0+1];

            int count = 0;

            for (int j = 0; j <= ct-c0; j++){

                  top[count] = A[0][j];

                  count++;

            }

            count--;

            for (int j = ct; j >= c0; j--)

            A[c0][j] = A[ct-j][0];

            for (int i = r0; i <= rt; i++)

            A[i][c0] = A[rt][i];

            for (int j = c0; j <= ct; j++)

            A[rt][j] = A[ct-j][ct];

            for (int i = rt; i >= r0; i--) {

                   A[i][ct] = top[count];

                   count--;

            }

            matrixRotate(A, r0+1, c0+1, rt-1, ct-1);

        }

// Before:

1 2 3

4 5 6

7 8 9

// After:

7 4 1

8 5 2

9 6 3

// Before

1 2

3 4

// After

3 1

4 2

 This is a summary of the book titled “Raising an entrepreneur: 10 rules for nurturing Risk takers, Problem Solvers, and Change Makers” written by Margot Machol Bisnow and published by New Harbinger in 2016. The author is a businesswoman and a mom who cautions parents from being overprotective and instead suggests to guide their children gently in discovering their strengths and passions, give them enormous freedom, letting them fail and face adversity in a way that builds resilience, selflessness, determination and “grit”. These very traits make successful entrepreneurs. Although the right balance is hard to find and it might seem that the author contradicts herself as she leans from side to side, she advocates for raising independent successful kids and support their dreams. Arming the children with morals, values, and a commitment to help others, installing grit, encouraging perseverance and hard work, emphasizing purpose over financial security, giving them freedom to do it their way, making mistakes and learning from them, letting them figure out, and praising and recognizing them are some of her other suggestions.

Parents should support and believe in their children's potential as entrepreneurs or employees. Encourage them to explore various ideas, fields, and career choices, and not force them into "safe" fields. Encourage them to discover their passions and excel in their chosen fields, regardless of whether they lead to conventional jobs or financial security. Instill morals, values, and a sense of mission in them to push through the work world's setbacks and disappointments.

Nurturing future entrepreneurs requires parents to believe in them, support them, and stand up for them. Encourage their passions, regardless of their preferences, and support their curiosity as they explore different things. For example, Michael Chasen's passion for video games led him to earn an MBA and start Blackboard, a tech firm he sold for $1.5 billion. Parents should also set boundaries, such as no TV on weekdays, and support their children's decision to start a business instead of going to college.

Parents should trust their children's passions and encourage them to pursue their passions without intervention. Paige Mycoskie's parents believed in her love for art and encouraged her to build a clothing company with her savings. Jayne Plank, despite her heavy workload, encouraged her children's passions early on, giving them freedom to pursue their passions.

Let your child learn to win and lose without intervention, introducing them to a wide range of sports and activities. Sports help kids understand that failure doesn't define them and increases their risk tolerance. Parents should also give their children a chance to figure out their passion, even if it's not something they would have chosen.

Successful entrepreneurs can range from academic misfits to college dropouts, so parents should support their children's interests at school as they support their sports and other pursuits. Avoid pushing kids towards math and science if their passion is art, and don't listen to teachers and principals who want their child to conform.

Parents should encourage their children to pursue their passions and pursue their passions, even if it means changing schools or standing up to teachers and administrators. They should also support their children in pursuing their academic areas they love, even if that means changing schools or standing up to teachers and administrators. Mentors can be great in guiding future entrepreneurs, as they can offer harsher feedback and provide a different perspective than parents can bring themselves. Parents should trust their children, give them freedom, and offer genuine praise, avoiding presenting them with "trophies" for everything they do. They should guide them to persevere through failure and adversity, as their confidence will grow each time they overcome a challenge. Embracing adversity can build character and resilience, as seen with Sean Stephenson, who graduated from college, interned for President Bill Clinton, and now advises executives nationwide. By fostering these qualities, parents can help their children become successful entrepreneurs and contribute to the growth of their communities. To foster a positive future for your children, it is essential to nurture compassion, encourage competitiveness, and be a great family. Encourage your children to help others and challenge conventions, as it is difficult to sustain their efforts solely for personal gain. Be a great family by providing a supportive environment and allowing your kids to pursue their passions. Instill awareness of a larger purpose in your children, using spiritual alternatives or faith as a compass.

Lead by following, allowing your children to chart their own path and focus on what they want to do. Encourage them to try many things until they find what inspires them. Don't raise your kids to feel entitled, but to work hard, persevere, and fight for a better future for themselves and others. Remember, loving your children is not the same as trusting and believing in them.

#codingexercise: CodingExercise-02-22-2025.docx

Exercises

 Predict the Number

Programming challenge description:

The example sequence 011212201220200112 ... is constructed as follows:

1. The first element in the sequence is 0.

2. For each iteration, repeat the following action: take a copy of the entire current sequence, replace 0 with 1, 1 with 2, and 2 with 0, and place it at the end of the current sequence. E.g.

0 -> 01 -> 0112 -> 01121220 -> ...

Create an algorithm which determines what number is at the Nth position in the sequence (using 0-based indexing).

Input:

Your program should read lines from standard input. Each line contains an integer N such that 0 <= N <= 3000000000.

Output:

Print out the number which is at the Nth position in the sequence.

Test 1

Test Input

Download Test 1 Input

5

Expected Output

Download Test 1 Output

2

Test 2

Test Input

Download Test 2 Input

25684

Expected Output

Download Test 2 Output

0

import java.io.BufferedReader;

import java.io.IOException;

import java.io.InputStreamReader;

import java.nio.charset.StandardCharsets;

public class Main {

  private static int getDigitAt(long position) {

    StringBuilder sb = new StringBuilder("0");

    int result = -1;

    long start = 1;

    for (long i = 0; i < Long.MAX_VALUE && start-1 <= position; i++) {

            String candidate = sb.toString();

            candidate = candidate.replace("0", "X").replace("1", "Y").replace("2","Z").replace("X","1").replace("Y", "2").replace("Z", "0");

            start += candidate.length();

            sb.append(candidate);

    }

    result = Integer.parseInt(String.valueOf(sb.charAt((int)position)));

    return result;

  }

  public static void main(String[] args) throws IOException {

    InputStreamReader reader = new InputStreamReader(System.in, StandardCharsets.UTF_8);

    BufferedReader in = new BufferedReader(reader);

    String line;

    while ((line = in.readLine()) != null) {

      long position = Long.MAX_VALUE;

      try {

        position = Long.parseLong(line);

      } catch (NumberFormatException e) {

      }

      if (position != Long.MAX_VALUE) {

        int digit = getDigitAt(position);

        System.out.println(digit);

      }

    }

  }

}

Running test cases... Done

– – – – – – – – – – – – – –

Test 1

Passed Collapse

Test Input:

5

Expected Output:

2

Your Output:

2

Test 2

Passed Collapse

Test Input:

25684

Expected Output:

0

Your Output:

0

 

Problem 2:

You are painting a fence of n posts with k different colors. You must paint the posts following these rules:

• Every post must be painted exactly one color.

• There cannot be three or more consecutive posts with the same color.

Given the two integers n and k, return the number of ways you can paint the fence.

Example 1:

Input: n = 3, k = 2

Output: 6

Explanation: All the possibilities are shown.

Note that painting all the posts red or all the posts green is invalid because there cannot be three posts in a row with the same color.

Example 2:

Input: n = 1, k = 1

Output: 1

Example 3:

Input: n = 7, k = 2

Output: 42

Constraints:

• 1 <= n <= 50

• 1 <= k <= 105

• The testcases are generated such that the answer is in the range [0, 231 - 1] for the given n and k.

Solution:

class Solution {

    public int numWays(int n, int k) {

     int[] dp = new int[n+2];

     dp[0] = 0;

     dp[1] = k;

     dp[2] = k + k*(k-1);

     for (int i = 3; i <=n; i++) {

         dp[i] = dp[i-1] * (k-1) + dp[i-2]*(k-1);

     }

     return dp[n];

    }

}

Accepted

Runtime: 0 ms

Case 1

Case 2

Case 3

Input

n =

3

k =

2

Output

6

Expected

6

Input

n =

1

k =

1

Output

1

Expected

1

Input

n =

7

k =

2

Output

42

Expected

42

 

Problem #3:

Make Array Zero by Subtracting Equal Amounts

You are given a non-negative integer array nums. In one operation, you must:

• Choose a positive integer x such that x is less than or equal to the smallest non-zero element in nums.

• Subtract x from every positive element in nums.

Return the minimum number of operations to make every element in nums equal to 0.

Example 1:

Input: nums = [1,5,0,3,5]

Output: 3

Explanation:

In the first operation, choose x = 1. Now, nums = [0,4,0,2,4].

In the second operation, choose x = 2. Now, nums = [0,2,0,0,2].

In the third operation, choose x = 2. Now, nums = [0,0,0,0,0].

Example 2:

Input: nums = [0]

Output: 0

Explanation: Each element in nums is already 0 so no operations are needed.

Constraints:

• 1 <= nums.length <= 100

• 0 <= nums[i] <= 100

import java.util.*;

import java.util.stream.*;

class Solution {

    public int minimumOperations(int[] nums) {

        List<Integer> list = Arrays.stream(nums).boxed().collect(Collectors.toList());

        var nonZero = list.stream().filter(x -> x > 0).collect(Collectors.toList());

        int count = 0;

        while(nonZero.size() > 0) {

            var min = nonZero.stream().mapToInt(x -> x).min().getAsInt();

            nonZero = nonZero.stream().map(x -> x - min).filter(x -> x > 0).collect(Collectors.toList());

            count++;

        }

        return count;

    }

}

Input

nums =

[1,5,0,3,5]

Output

3

Expected

3

Input

nums =

[0]

Output

0

Expected

0

 

SQL Schema

Table: Books

+----------------+---------+

| Column Name | Type |

+----------------+---------+

| book_id | int |

| name | varchar |

| available_from | date |

+----------------+---------+

book_id is the primary key of this table.

Table: Orders

+----------------+---------+

| Column Name | Type |

+----------------+---------+

| order_id | int |

| book_id | int |

| quantity | int |

| dispatch_date | date |

+----------------+---------+

order_id is the primary key of this table.

book_id is a foreign key to the Books table.

Write an SQL query that reports the books that have sold less than 10 copies in the last year, excluding books that have been available for less than one month from today. Assume today is 2019-06-23.

Return the result table in any order.

The query result format is in the following example.

Example 1:

Input:

Books table:

+---------+--------------------+----------------+

| book_id | name | available_from |

+---------+--------------------+----------------+

| 1 | "Kalila And Demna" | 2010-01-01 |

| 2 | "28 Letters" | 2012-05-12 |

| 3 | "The Hobbit" | 2019-06-10 |

| 4 | "13 Reasons Why" | 2019-06-01 |

| 5 | "The Hunger Games" | 2008-09-21 |

+---------+--------------------+----------------+

Orders table:

+----------+---------+----------+---------------+

| order_id | book_id | quantity | dispatch_date |

+----------+---------+----------+---------------+

| 1 | 1 | 2 | 2018-07-26 |

| 2 | 1 | 1 | 2018-11-05 |

| 3 | 3 | 8 | 2019-06-11 |

| 4 | 4 | 6 | 2019-06-05 |

| 5 | 4 | 5 | 2019-06-20 |

| 6 | 5 | 9 | 2009-02-02 |

| 7 | 5 | 8 | 2010-04-13 |

+----------+---------+----------+---------------+

Output:

+-----------+--------------------+

| book_id | name |

+-----------+--------------------+

| 1 | "Kalila And Demna" |

| 2 | "28 Letters" |

| 5 | "The Hunger Games" |

+-----------+--------------------+

SELECT DISTINCT b.book_id, b.name

FROM books b

LEFT JOIN Orders o on b.book_id = o.book_id

GROUP BY b.book_id, b.name,

DATEDIFF(day, DATEADD(year, -1, '2019-06-23'), o.dispatch_date),

DATEDIFF(day, b.available_from, DATEADD(month, -1, '2019-06-23'))

HAVING SUM(o.quantity) IS NULL OR

DATEDIFF(day, DATEADD(year, -1, '2019-06-23'), o.dispatch_date) < 0 OR

(DATEDIFF(day, DATEADD(year, -1, '2019-06-23'), o.dispatch_date) > 0 AND DATEDIFF(day, b.available_from, DATEADD(month, -1, '2019-06-23')) > 0 AND SUM(o.quantity) < 10);

Case 1

Input

Books =

| book_id | name | available_from |

| ------- | ---------------- | -------------- |

| 1 | Kalila And Demna | 2010-01-01 |

| 2 | 28 Letters | 2012-05-12 |

| 3 | The Hobbit | 2019-06-10 |

| 4 | 13 Reasons Why | 2019-06-01 |

| 5 | The Hunger Games | 2008-09-21 |

Orders =

| order_id | book_id | quantity | dispatch_date |

| -------- | ------- | -------- | ------------- |

| 1 | 1 | 2 | 2018-07-26 |

| 2 | 1 | 1 | 2018-11-05 |

| 3 | 3 | 8 | 2019-06-11 |

| 4 | 4 | 6 | 2019-06-05 |

| 5 | 4 | 5 | 2019-06-20 |

| 6 | 5 | 9 | 2009-02-02 |

| 7 | 5 | 8 | 2010-04-13 |

Output

| book_id | name |

| ------- | ---------------- |

| 2 | 28 Letters |

| 1 | Kalila And Demna |

| 5 | The Hunger Games |

Expected

| book_id | name |

| ------- | ---------------- |

| 1 | Kalila And Demna |

| 2 | 28 Letters |

| 5 | The Hunger Games |

 

This is a review of the book titled “Founder Brand” written by Dave Gerhardt and published by Lioncrest Publishing in 2022. As a marketing expert, he explains why and how a company’s founder must turn out to be an effective personal brand. His framework for creating “a founder brand” is a worthwhile investment for all entrepreneurs as his consultancy has demonstrated.  A founder brand provides both an ongoing connection with a new and existing customer base and continuous public feedback for strategic growth and without spending on polling and advertising.

A founder brand is crucial for building awareness, trust, and credibility in marketing. Focusing on the founder provides a personal connection, establishing trust with customers and connecting them to a human being. Dave Gerhardt, a conversation-focused marketing company, created a brand for Drift founder David Cancel, which led to a successful podcast and increased revenue. To create a founder brand, tell the founder's story, create content, publish, and master the feedback loop. Transparent, open, and emotionally vulnerable stories make it easier for the niche to connect. Gerhardt recommends targeting a specific group of potential customers and marketing the founder brand directly to them.

Drift transformed its lead form issue into a problem-solving case history by offering articles, videos, and podcasts to solve the issue. This led to a book and a better marketing channel. To determine your niche, define the ideal customer for your product, identify a common problem it solves, offer your solution, and end with the product name. To become a social media presence, post short messages on Twitter and LinkedIn, create a podcast, and engage in public speaking. Share your reality with online followers to build credibility and trust.

Starting a niche podcast requires determining expertise, researching similar podcasts, and showcasing your knowledge and personality. Plan the format, length, and frequency, and prepare a backlog of prerecorded episodes. Utilize a hosting site like Transistor.fm to publish your podcast and promote it on social media. Maximize podcast content to generate traffic to your blog or corporate website and offer perks for subscribers. Stay with your podcast for at least 12 months, build a community, and accept speaking opportunities. A continuous loop of online followers and integrating content from outside activities into your podcast can transform you into a mini media company.

Dave Gerhardt's book offers both actionable insights and basic information about social media and podcasts, making it useful for beginners and those fluent in the web and social media. Gerhardt presents a clever and useful strategy, providing general tactics and direction.

#Codingexercise: Codingexercise-02-19-2025.docx

 Infrastructure engineering for AI projects often deal with text based inputs for analysis and predictions whether they are sourced from chatbots facing the customers, service ticket notes, or a variety of data stores and warehouses but the ability to convert text into audio format is also helpful for many scenarios, including DEI requirements and is quite easy to setup and offers the convenience to listen when screens are small, inadequate for usability and are difficult to read. Well-known audio formats can be played on any device and not just phones.

Although there are many dedicated text-to-speech software and online services available, some for free, and with programmability via web requests, there are built-in features of the public cloud service portfolio that make such capabilities more mainstream and at par with the rest of the AI/ML pipelines. This article includes one such implementation towards the end but first an introduction to this feature and capabilities as commercially available.

Most audio is characterized by amplitude as measured in decibels preferably at least 24db, the stream or bit rate which should preferably be at least 192kbps, and a limiter. Different voices can be generated with variations in amplitude, pitch, and tempo much like how singing is measured. Free text-to-speech software, whether standalone, or online gives conveniences in the input and output formats. For example, Natural Reader allows you to load documents and convert them to audio files. Balabolka can save narrations as a variety of audio file formats with customizations for pronunciations and voice settings. Panopreter Basic, also free software, add both input formats and mp3 output format. TTSMaker supports 100+ languages and 600+ AI voices for commercial purposes. Murf AI although not entirely free has a converter that supports 200+ realistic AI voices and 20+ languages. Licensing and distribution uses varies with each software maker.

Public-cloud based capabilities for text-to-speech can be instantiated with a resource initialization from the corresponding service in their service portfolio. The following explains just how to do that.

Sample implementation:

1. Get text input over a web api:

from flask import Flask, request, jsonify, send_file

import os

import azure.cognitiveservices.speech as speechsdk

app = Flask(__name__)

# Azure Speech Service configuration

SPEECH_KEY = "<your-speech-api-key>"

SERVICE_REGION = "<your-region>"

speech_config = speechsdk.SpeechConfig(subscription=SPEECH_KEY, region=SERVICE_REGION)

speech_config.set_speech_synthesis_output_format(speechsdk.SpeechSynthesisOutputFormat.Audio16Khz32KBitRateMonoMp3)

speech_config.speech_synthesis_voice_name = "en-US-GuyNeural" # Set desired voice

@app.route('/text-to-speech', methods=['POST'])

def text_to_speech():

    try:

        # Check if text is provided directly or via file

        if 'text' in request.form:

            text = request.form['text']

        elif 'file' in request.files:

            file = request.files['file']

            text = file.read().decode('utf-8')

        else:

            return jsonify({"error": "No text or file provided"}), 400

        # Generate speech from text

        audio_filename = "output.mp3"

        file_config = speechsdk.audio.AudioOutputConfig(filename=file_name)

        synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=file_config)

        result = synthesizer.speak_text_async(text).get()

        if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:

            # Save audio to file

            with open(audio_filename, "wb") as audio_file:

                audio_file.write(result.audio_data)

            return send_file(audio_filename, as_attachment=True)

        else:

            return jsonify({"error": f"Speech synthesis failed: {result.reason}"}), 500

    except Exception as e:

        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":

    app.run(host="0.0.0.0", port=5000)

2. Prerequisites to run the script:

a. pip install flask azure-cognitiveservices-speech

b. Create an Azure Speech resource in the Azure portal and retrieve the SPEECH_KEY and SERVICE_REGION from the resources’ keys and endpoint section and use them in place of `<your-speech-api-key>` and `<your-region>` above

c. Save the script and run it in any host as `python app.py`

3. Sample trial

a. With curl request as `curl -X POST -F "text=Hello, this is a test." http://127.0.0.1:5000/text-to-speech --output output.mp3`

b. Or as file attachment with `curl -X POST -F "file=@example.txt" http://127.0.0.1:5000/text-to-speech --output output.mp3`

c. The mp3 audio file generated can be played.

Sample output: https://b67.s3.us-east-1.amazonaws.com/output.mp3

Pricing: Perhaps the single most sought-after feature on text-to-speech is the use of natural sounding voice and service providers often markup the price or even eliminate programmability options for the range of the natural-voices offered. This severely limits the automation of audio books. A comparison of costs might also illustrate the differences between the service providers. Public Cloud text-to-speech services typically charge $4 and $16 per million characters for standard and neural voices respectively which is about 4-5 audio books. Custom voices require about $30 per million characters while dedicated providers such as Natural Voice with more readily available portfolio of voices charge about $60/month as a subscription fee and limits on words. This is still costly but automation of audio production for books is here to stay simply because of the time and effort saved.

 https://ezcloudiac.com/info/index.html

 Public-cloud based capabilities for text-to-speech can be instantiated with a resource initialization from the corresponding service in their service portfolio. The following explains just how to do that.

Sample implementation:

1. Get text input over a web api:

from flask import Flask, request, jsonify, send_file

import os

import azure.cognitiveservices.speech as speechsdk

app = Flask(__name__)

# Azure Speech Service configuration

SPEECH_KEY = "<your-speech-api-key>"

SERVICE_REGION = "<your-region>"

speech_config = speechsdk.SpeechConfig(subscription=SPEECH_KEY, region=SERVICE_REGION)

speech_config.set_speech_synthesis_output_format(speechsdk.SpeechSynthesisOutputFormat.Audio16Khz32KBitRateMonoMp3)

speech_config.speech_synthesis_voice_name = "en-US-GuyNeural" # Set desired voice

@app.route('/text-to-speech', methods=['POST'])

def text_to_speech():

    try:

        # Check if text is provided directly or via file

        if 'text' in request.form:

            text = request.form['text']

        elif 'file' in request.files:

            file = request.files['file']

            text = file.read().decode('utf-8')

        else:

            return jsonify({"error": "No text or file provided"}), 400

        # Generate speech from text

        audio_filename = "output.mp3"

        file_config = speechsdk.audio.AudioOutputConfig(filename=file_name)

        synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=file_config)

        result = synthesizer.speak_text_async(text).get()

        if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:

            # Save audio to file

            with open(audio_filename, "wb") as audio_file:

                audio_file.write(result.audio_data)

            return send_file(audio_filename, as_attachment=True)

        else:

            return jsonify({"error": f"Speech synthesis failed: {result.reason}"}), 500

    except Exception as e:

        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":

    app.run(host="0.0.0.0", port=5000)

2. Prerequisites to run the script:

a. pip install flask azure-cognitiveservices-speech

b. Create an Azure Speech resource in the Azure portal and retrieve the SPEECH_KEY and SERVICE_REGION from the resources’ keys and endpoint section and use them in place of `<your-speech-api-key>` and `<your-region>` above

c. Save the script and run it in any host as `python app.py`

3. Sample trial

a. With curl request as `curl -X POST -F "text=Hello, this is a test." http://127.0.0.1:5000/text-to-speech --output output.mp3`

b. Or as file attachment with `curl -X POST -F "file=@example.txt" http://127.0.0.1:5000/text-to-speech --output output.mp3`

c. The mp3 audio file generated can be played.

Sample output: https://b67.s3.us-east-1.amazonaws.com/output.mp3

Pricing: Perhaps the single most sought-after feature on text-to-speech is the use of natural sounding voice and service providers often markup the price or even eliminate programmability options for the range of the natural-voices offered. This severely limits the automation of audio books. A comparison of costs might also illustrate the differences between the service providers. Public Cloud text-to-speech services typically charge $4 and $16 per million characters for standard and neural voices respectively which is about 4-5 audio books. Custom voices require about $30 per million characters while dedicated providers such as Natural Voice with more readily available portfolio of voices charge about $60/month as a subscription fee and limits on words. This is still costly but automation of audio production for books is here to stay simply because of the time and effort saved.

#codingexercise: CodingExercise-02-16-2025.docx

 This is the summary of a book titled “Generative AI in practice” written by Bernard Marr and published by Wiley in 2024 so it’s almost hot off the press. It surveys 100+ amazing ways Generative Artificial Intelligence is changing business and society.  GenAI gives companies a competitive advantage but also poses unintended risks at societal and organizational levels. Urging for its use towards good, the author explains how it is poised to disrupt industries across sectors and calls for usage in a way that inspires innovation, openness and collaboration. It provides a leap in our understanding and efficiency and will likely result in productivity savings across  Carefully identifying and mitigating the risks when harnessing this power, is going to be a ubiquitous requirement. Higher engagement with the potential to draw customers towards retail, gaming, software and product development and a shift that goes beyond organizational hierarchies is going to be the norm. 

Gen AI, a groundbreaking subset of AI, is poised to disrupt work and life in radical ways. The concept of machines displaying human-like intelligence dates back to 1950 when Alan Turing introduced the Turing Test. Advances in predictive analytics, data mining, big data, and deep learning have set the stage for GenAI models. GenAI demonstrates significant creative power through the development of neural networks, particularly "generative models," which push the boundaries of what humans once believed machines could do. The use of automation and human-machine collaboration could save 70% of the time some processes now require. GenAI will affect nearly every industry, making many workers redundant while creating productivity savings. McKinsey anticipates that GenAI will boost the global economy by US$4.4 trillion each year. Jobs that may experience redundancies include content creators, graphic designers, stock traders, translators, paralegals, factory workers, clerical workers, and journalists. While AI will eliminate some jobs, it will also create new ones, such as data curators and cleaners, and AI prompt engineers, trainers, and ethics officers. 

GenAI has the potential to revolutionize various industries, including education, healthcare, and legal services. However, it is crucial to identify and mitigate risks, such as the spread of misinformation, dependence on AI, and potential harms in critical sectors like defense. Organizations must ensure the ethical use of GenAI by assessing and mitigating risks, implementing robust data governance, preventing data bias, and embracing transparency and accountability. 

In education, GenAI can help teachers personalize student learning experiences, providing feedback on struggling students. In healthcare, GenAI chatbots can provide individualized health advice and assist doctors in providing hyperpersonalized patient care. GenAI can also accelerate drug discovery and development by generating new molecular structures. 

 

In law, GenAI can create efficiencies for law firms by predicting case outcomes, using chatbots to expand services, and improving legal services. Additionally, GenAI and intelligent systems will revolutionize customer service and engagement, with companies like Octopus Energy seeing significant gains. 

In conclusion, while GenAI has the potential to transform various industries, organizations must ensure the ethical use of AI and seek support from AI and data experts when faced with complex issues. 

GenAI is set to revolutionize the video game industry by assisting developers in every stage of game development, including character creation, 2D model creation, and non-linear storylines. It can also automate musical scores, produce immersive soundscapes, and generate quests and missions. GenAI can also create simulated players to test gameplay experiences and use data analysis to predict potential issues. Unity's AI marketplace offers developers solutions like voice generators and asset creation mechanisms, while the Auctoria platform allows users to create game assets more easily. GenAI is expected to accelerate software and product development, speeding up delivery and boosting creative ideation. It can also help designers complete their code and detect bugs, and open up the possibility of creating digital twins. However, embracing GenAI requires a shift in organizational culture, shifting towards curiosity, humility, adaptability, and collaboration. Companies must rethink traditional approaches to work, giving employees autonomy to collaborate on cross-functional teams within a learning culture that prioritizes openness.  

 

A previous article described the formation of a UAV swarm when aligning to a point, line and plane. While the shape formations in these cases are known, the size depends on the number of units in the formation, the minimum distance between units, the presence of external infringements and constraints and the margin required to maintain from such constraints. The article also described the ability to distribute drones to spread as close to the constraints using self-organizing maps which is essentially drawing each unit to the nearest real-world element that imposes a constraint. This establishes the maximum boundaries for the space that the UAV swarm occupies with the core being provided by the point, line, or plane that the units must align to. Given the minimum-maximum combination and the various thresholds for the factors cited, the size of the shape for the UAV swarm at a point of time can be determined.

This article argues that the vectorization, clustering and model does not just apply to the UAV swarm formation in space but also applies to maintaining a balance between constraints and sizing and determining the quality of the formation, using Vector-Search-as-a-judge.  The idea is borrowed from LLM-as-a-judge which helps to constantly evaluate and monitor many AI applications of various LLMs used for specific domains including Retrieval Augmented Generation aka RAG based chatbots. By virtue of automated evaluation with over 80% agreement on human judgements and a simple 1 to 5 grading scale, the balance between constraints and sizing can be consistently evaluated and even enforced. It may not be at par with human grading and might require several auto-evaluation samples but these can be conducted virtually without any actual flights of UAV swarms. A good choice of hyperparameters is sufficient to ensure reproducibility, single-answer grading, and reasoning about the grading process. Emitting the metrics for correctness, comprehensiveness and readability is sufficient in this regard. The overall workflow for this judge is also similar to the self-organizing map in terms of data preparation, indexing relevant data, and information retrieval.

As with all AI models, it is important to ensure AI safety and security to include a diverse set of data and to leverage the proper separation of the read-write and read-only accesses needed between the model and the judge. Use of a feedback loop to emit the gradings as telemetry and its inclusion into the feedback loop for the model when deciding on the formation shape and size, albeit optional, can ensure the parameters of remaining under the constraints imposed is always met.

The shape and size of the UAV formation is deterministic at a point of time but how it changes over time depends on the selection of waypoints between source and destination as well as the duration permitted for the swarm to move collectively or stream through and regroup at the waypoint. A smooth trajectory was formed between the waypoints and each unit could adhere to the trajectory by tolerating formation variations.

Perhaps, the biggest contribution of the vectorization of all constraints in a landscape is that a selection of waypoints offering the least resistance for the UAV swarm to keep its shape and size to pass through can be determined by an inverse metric to the one that was used for self-organizing maps.

 This is a summary of the book titled “The Intelligence Revolution: Transforming your business with AI” written by Bernard Marr and published by Kogan Page in 2020. This is the prequel to the summary on “Generative AI in practice” also by the same author and holds even more pertinence and relevance than it did at the time. No one disputes the “transformative potential” of Artificial Intelligence anymore and the author details the way companies are embracing this revolution to achieve their strategic goals. Spotting AI opportunities and realizing it while using AI and big data ethically and respecting consumer privacy are his main purports. He calls this the Fourth Industrial Revolution and argues for using the power to better serve internal and external stakeholders. Data driven insights can help optimize solutions across all areas of consumers’ lives. The AI solutions are most effective when combined with a robust strategy that reflects your unique needs. There are best practices that can help navigate AI transformation and he suggests to invest in a four-layer technology stack to effectively seize AI opportunities. The Intelligence Revolution requires leaders to step up in new adaptive ways.

The Fourth Industrial Revolution is set to revolutionize everyday life by introducing AI and big data, transforming how people work and live. AI-driven technologies will make lab-grown food, autonomous driving, and smarter homes more accessible. Industries like farming and fishing are already leaning into technological developments, with the fishing industry potentially becoming unrecognizable in the coming years.

AI, or machines that can learn and take action independently and autonomously, is fueling advances in machine learning. There are two types of AI: narrow AI, which simulates human thought patterns, and generalized AI, which can learn and behave in ways similar to human thought and actions. Machines can already think like humans, reading text and understanding idioms and slang in over 20 languages.

The digitization of the world is driving advances in machine and deep learning, as machines learn with increasing accuracy as more data becomes available. In the age of big data, machines are even learning to identify human emotions through "affective computing." Companies must reflect on their strategic goals and how they might use AI tools to achieve desired outcomes.

AI can be leveraged to enhance business opportunities by creating smarter products, offering smart services, and optimizing business processes with smart tools. By incorporating AI capabilities into products, businesses can collect data on customers' habits and preferences, improving product design and delivering better solutions. AI is revolutionizing everyday home products, mobility and transportation, manufacturing and industry, and sports. Smart products can track users' sleep patterns, provide personalized recommendations, and enhance efficiency. Companies can also embed sensors in industrial machinery to improve efficiency and enhance employee capabilities. AI insights are changing the experience of exercise and sports, with smart boxing gloves and sensors on basketball nets providing insights for better shots. The health sector is also undergoing significant changes as people connect medical applications and devices to the Internet of Medical Things (IoMT), helping healthcare providers collect patient data and monitor vital signs. By leveraging AI, businesses can better serve internal and external stakeholders and optimize solutions across all areas of consumers' lives.

AI solutions are most effective when combined with a robust strategy that reflects your unique needs. Identify potential AI opportunities by reflecting on all possible use cases within your company, focusing on specific objectives, KPIs, and data types. Consider the legal and ethical implications of your projects, such as collecting data with users' consent. Determine the infrastructure and technology needed and identify any skills gaps or implementation challenges.

Successful AI starts with strategy, not the technology itself. Talk about change management and prioritize one to three potential applications that represent the biggest growth potential or solve major business challenges. Ethically navigate your AI transformation by embracing best practices, such as building trust, avoiding "the black box problem," embracing skepticism, checking for bias, and following official guidelines for responsible AI. This will help you navigate the legal and ethical aspects of AI transformation and ensure a positive company culture surrounding AI.

To effectively seize AI opportunities, invest in a four-layer technology stack. This includes data collection from various sources, data storage, data processing and analytics, and data output and reporting. Leaders must develop skills such as agility, emotional intelligence, cultural intelligence, humility, accountability, vision, courage, intuition, authenticity, and focus on company's core objectives.

The Intelligence Revolution requires leaders to step up in new, adaptive ways, treating change as an opportunity and navigating uncertainty. Strategic vision is vital when navigating uncertainty, as the collective use of AI across organizations could have devastating effects. However, if implemented with care, AI could dramatically improve everyday life, transform work, and help tackle challenges like climate change. The choices made now could determine whether AI becomes a "force for good" or something more dystopian.

Notice the many parallels between Marr’s and Gilbertson’s recommendations to businesses.

#Codingexercise: https://1drv.ms/w/c/d609fb70e39b65c8/EeNEEcMlb4BOu2622MeJOoYBplhXDQbaoibcr6JYyMggIw?e=0ZOwZ1

 Infrastructure engineering for AI projects often deal with text based inputs for analysis and predictions whether they are sourced from chatbots facing the customers, service ticket notes, or a variety of data stores and warehouses but the ability to convert text into audio format is also helpful for many scenarios, including DEI requirements and is quite easy to setup and offers the convenience to listen when screens are small, inadequate for usability and are difficult to read. Well-known audio formats can be played on any device and not just phones.

Although there are many dedicated text-to-speech software and online services available, some for free, and with programmability via web requests, there are built-in features of the public cloud service portfolio that make such capabilities more mainstream and at par with the rest of the AI/ML pipelines. This article includes one such implementation towards the end but first an introduction to this feature and capabilities as commercially available.

Most audio is characterized by amplitude as measured in decibels preferably at least 24db, the stream or bit rate which should preferably be at least 192kbps, and a limiter. Different voices can be generated with variations in amplitude, pitch, and tempo much like how singing is measured. Free text-to-speech software, whether standalone, or online gives conveniences in the input and output formats. For example, Natural Reader allows you to load documents and convert them to audio files. Balabolka can save narrations as a variety of audio file formats with customizations for pronunciations and voice settings. Panopreter Basic, also free software, add both input formats and mp3 output format. TTSMaker supports 100+ languages and 600+ AI voices for commercial purposes. Murf AI although not entirely free has a converter that supports 200+ realistic AI voices and 20+ languages. Licensing and distribution uses varies with each software maker.

Public-cloud based capabilities for text-to-speech can be instantiated with a resource initialization from the corresponding service in their service portfolio. The following explains just how to do that.

Sample implementation:

1. Get text input over a web api:

from flask import Flask, request, jsonify, send_file

import os

import azure.cognitiveservices.speech as speechsdk

app = Flask(__name__)

# Azure Speech Service configuration

SPEECH_KEY = "<your-speech-api-key>"

SERVICE_REGION = "<your-region>"

speech_config = speechsdk.SpeechConfig(subscription=SPEECH_KEY, region=SERVICE_REGION)

speech_config.speech_synthesis_voice_name = "en-US-JennyNeural" # Set desired voice

@app.route('/text-to-speech', methods=['POST'])

def text_to_speech():

    try:

        # Check if text is provided directly or via file

        if 'text' in request.form:

            text = request.form['text']

        elif 'file' in request.files:

            file = request.files['file']

            text = file.read().decode('utf-8')

        else:

            return jsonify({"error": "No text or file provided"}), 400

        # Generate speech from text

        audio_filename = "output.mp3"

        synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config)

        result = synthesizer.speak_text_async(text).get()

        if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:

            # Save audio to file

            with open(audio_filename, "wb") as audio_file:

                audio_file.write(result.audio_data)

            return send_file(audio_filename, as_attachment=True)

        else:

            return jsonify({"error": f"Speech synthesis failed: {result.reason}"}), 500

    except Exception as e:

        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":

    app.run(host="0.0.0.0", port=5000)

2. Prerequisites to run the script:

a. pip install flask azure-cognitiveservices-speech

b. Create an Azure Speech resource in the Azure portal and retrieve the SPEECH_KEY and SERVICE_REGION from the resources’ keys and endpoint section and use them in place of `<your-speech-api-key>` and `<your-region>` above

c. Save the script and run it in any host as `python app.py`

3. Sample trial

a. With curl request as `curl -X POST -F "text=Hello, this is a test." http://127.0.0.1:5000/text-to-speech --output output.mp3`

b. Or as file attachment with `curl -X POST -F "file=@example.txt" http://127.0.0.1:5000/text-to-speech --output output.mp3`

c. The mp3 audio file generated can be played.

Reference: previous articles

CodingExercise-02-12-2025

 There are N points (numbered from 0 to N−1) on a plane. Each point is colored either red ('R') or green ('G'). The K-th point is located at coordinates (X[K], Y[K]) and its color is colors[K]. No point lies on coordinates (0, 0).

We want to draw a circle centered on coordinates (0, 0), such that the number of red points and green points inside the circle is equal. What is the maximum number of points that can lie inside such a circle? Note that it is always possible to draw a circle with no points inside.

Write a function that, given two arrays of integers X, Y and a string colors, returns an integer specifying the maximum number of points inside a circle containing an equal number of red points and green points.

Examples:

1. Given X = [4, 0, 2, −2], Y = [4, 1, 2, −3] and colors = "RGRR", your function should return 2. The circle contains points (0, 1) and (2, 2), but not points (−2, −3) and (4, 4).

class Solution {

    public int solution(int[] X, int[] Y, String colors) {

        // find the maximum

        double max = Double.MIN_VALUE;

        int count = 0;

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

        {

            double dist = X[i] * X[i] + Y[i] * Y[i];

            if (dist > max)

            {

                max = dist;

            }

        }

        for (double i = Math.sqrt(max) + 1; i > 0; i -= 0.1)

        {

            int r = 0;

            int g = 0;

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

            {

                if (Math.sqrt(X[j] * X[j] + Y[j] * Y[j]) > i)

                {

                    continue;

                }

                if (colors.substring(j, j+1).equals("R")) {

                    r++;

                }

                else {

                    g++;

                }

            }

            if ( r == g && r > 0) {

                int min = r * 2;

                if (min > count)

                {

                    count = min;

                }

            }

        }

        return count;

    }

}

Compilation successful.

Example test: ([4, 0, 2, -2], [4, 1, 2, -3], 'RGRR')

OK

Example test: ([1, 1, -1, -1], [1, -1, 1, -1], 'RGRG')

OK

Example test: ([1, 0, 0], [0, 1, -1], 'GGR')

OK

Example test: ([5, -5, 5], [1, -1, -3], 'GRG')

OK

Example test: ([3000, -3000, 4100, -4100, -3000], [5000, -5000, 4100, -4100, 5000], 'RRGRG')

OK

 A previous article described the formation of a UAV swarm when aligning to a point, line and plane. While the shape formations in these cases are known, the size depends on the number of units in the formation, the minimum distance between units, the presence of external infringements and constraints and the margin required to maintain from such constraints. The article also described the ability to distribute drones to spread as close to the constraints using self-organizing maps which is essentially drawing each unit to the nearest real-world element that imposes a constraint. This establishes the maximum boundaries for the space that the UAV swarm occupies with the core being provided by the point, line, or plane that the units must align to. Given the minimum-maximum combination and the various thresholds for the factors cited, the size of the shape for the UAV swarm at a point of time can be determined.

This article argues that the vectorization, clustering and model does not just apply to the UAV swarm formation in space but also applies to maintaining a balance between constraints and sizing and determining the quality of the formation, using Vector-Search-as-a-judge. The idea is borrowed from LLM-as-a-judge which helps to constantly evaluate and monitor many AI applications of various LLMs used for specific domains including Retrieval Augmented Generation aka RAG based chatbots. By virtue of automated evaluation with over 80% agreement on human judgements and a simple 1 to 5 grading scale, the balance between constraints and sizing can be consistently evaluated and even enforced. It may not be at par with human grading and might require several auto-evaluation samples but these can be conducted virtually without any actual flights of UAV swarms. A good choice of hyperparameters is sufficient to ensure reproducibility, single-answer grading, and reasoning about the grading process. Emitting the metrics for correctness, comprehensiveness and readability is sufficient in this regard. The overall workflow for this judge is also similar to the self-organizing map in terms of data preparation, indexing relevant data, and information retrieval.

As with all AI models, it is important to ensure AI safety and security to include a diverse set of data and to leverage the proper separation of the read-write and read-only accesses needed between the model and the judge. Use of a feedback loop to emit the gradings as telemetry and its inclusion into the feedback loop for the model when deciding on the formation shape and size, albeit optional, can ensure the parameters of remaining under the constraints imposed is always met.

The shape and size of the UAV formation is deterministic at a point of time but how it changes over time depends on the selection of waypoints between source and destination as well as the duration permitted for the swarm to move collectively or stream through and regroup at the waypoint. A smooth trajectory was formed between the waypoints and each unit could adhere to the trajectory by tolerating formation variations.

Perhaps, the biggest contribution of the vectorization of all constraints in a landscape is that a selection of waypoints offering the least resistance for the UAV swarm to keep its shape and size to pass through can be determined by an inverse metric to the one that was used for self-organizing maps.