Podcasts about himanshu

Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they discuss the transformative world of Generative AI. Together, they uncover the ways in which generative AI agents are changing the way we interact with technology, automating tasks and delivering new possibilities.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead of Editorial Services.   Nikita: Hi everyone! Last week was Part 2 of our conversation on core AI concepts, where we went over the basics of data science. In Part 3 today, we'll look at generative AI and gen AI agents in detail. To help us with that, we have Himanshu Raj, Principal AI/ML Instructor. Hi Himanshu, what's the difference between traditional AI and generative AI?  01:01 Himanshu: So until now, when we talked about artificial intelligence, we usually meant models that could analyze information and make decisions based on it, like a judge who looks at evidence and gives a verdict. And that's what we call traditional AI that's focused on analysis, classification, and prediction.  But with generative AI, something remarkable happens. Generative AI does not just evaluate. It creates. It's more like a storyteller who uses knowledge from the past to imagine and build something brand new. For example, instead of just detecting if an email is spam, generative AI could write an entirely new email for you.  Another example, traditional AI might predict what a photo contains. Generative AI, on the other hand, creates a brand-new photo based on description. Generative AI refers to artificial intelligence models that can create entirely new content, such as text, images, music, code, or video that resembles human-made work.  Instead of simple analyzing or predicting, generative AI produces something original that resembles what a human might create.   02:16 Lois: How did traditional AI progress to the generative AI we know today?  Himanshu: First, we will look at small supervised learning. So in early days, AI models were trained on small labeled data sets. For example, we could train a model with a few thousand emails labeled spam or not spam. The model would learn simple decision boundaries. If email contains, "congratulations," it might be spam. This was efficient for a straightforward task, but it struggled with anything more complex.  Then, comes the large supervised learning. As the internet exploded, massive data sets became available, so millions of images, billions of text snippets, and models got better because they had much more data and stronger compute power and thanks to advances, like GPUs, and cloud computing, for example, training a model on millions of product reviews to predict customer sentiment, positive or negative, or to classify thousands of images in cars, dogs, planes, etc.  Models became more sophisticated, capturing deeper patterns rather than simple rules. And then, generative AI came into the picture, and we eventually reached a point where instead of just classifying or predicting, models could generate entirely new content.  Generative AI models like ChatGPT or GitHub Copilot are trained on enormous data sets, not to simply answer a yes or no, but to create outputs that look and feel like human made. Instead of judging the spam or sentiment, now the model can write an article, compose a song, or paint a picture, or generate new software code.  03:55 Nikita: Himanshu, what motivated this sort of progression?   Himanshu: Because of the three reasons. First one, data, we had way more of it thanks to the internet, smartphones, and social media. Second is compute. Graphics cards, GPUs, parallel computing, and cloud systems made it cheap and fast to train giant models.  And third, and most important is ambition. Humans always wanted machines not just to judge existing data, but to create new knowledge, art, and ideas.   04:25 Lois: So, what's happening behind the scenes? How is gen AI making these things happen?  Himanshu: Generative AI is about creating entirely new things across different domains. On one side, we have large language models or LLMs.  They are masters of generating text conversations, stories, emails, and even code. And on the other side, we have diffusion models. They are the creative artists of AI, turning text prompts into detailed images, paintings, or even videos.  And these two together are like two different specialists. The LLM acts like a brain that understands and talks, and the diffusion model acts like an artist that paints based on the instructions. And when we connect these spaces together, we create something called multimodal AI, systems that can take in text and produce images, audio, or other media, opening a whole new range of possibilities.  It can not only take the text, but also deal in different media options. So today when we say ChatGPT or Gemini, they can generate images, and it's not just one model doing everything. These are specialized systems working together behind the scenes.  05:38 Lois: You mentioned large language models and how they power text-based gen AI, so let's talk more about them. Himanshu, what is an LLM and how does it work?  Himanshu: So it's a probabilistic model of text, which means, it tries to predict what word is most likely to come next based on what came before.  This ability to predict one word at a time intelligently is what builds full sentences, paragraphs, and even stories.  06:06 Nikita: But what's large about this? Why's it called a large language model?   Himanshu: It simply means the model has lots and lots of parameters. And think of parameters as adjustable dials the model fine tuned during learning.  There is no strict rule, but today, large models can have billions or even trillions of these parameters. And the more the parameters, more complex patterns, the model can understand and can generate a language better, more like human.  06:37 Nikita: Ok… and image-based generative AI is powered by diffusion models, right? How do they work?  Himanshu: Diffusion models start with something that looks like pure random noise.  Imagine static on an old TV screen. No meaningful image at all. From there, the model carefully removes noise step by step to create something more meaningful and think of it like sculpting a statue. You start with a rough block of stone and slowly, carefully you chisel away to reveal a beautiful sculpture hidden inside.  And in each step of this process, the AI is making an educated guess based on everything it has learned from millions of real images. It's trying to predict.   07:24 Stay current by taking the 2025 Oracle Fusion Cloud Applications Delta Certifications. This is your chance to demonstrate your understanding of the latest features and prove your expertise by obtaining a globally recognized certification, all for free! Discover the certification paths, use the resources on MyLearn to prepare, and future-proof your skills. Get started now at mylearn.oracle.com.  07:53 Nikita: Welcome back! Himanshu, for most of us, our experience with generative AI is with text-based tools like ChatGPT. But I'm sure the uses go far beyond that, right? Can you walk us through some of them?  Himanshu: First one is text generation. So we can talk about chatbots, which are now capable of handling nuanced customer queries in banking travel and retail, saving companies hours of support time. Think of a bank chatbot helping a customer understand mortgage options or virtual HR Assistant in a large company, handling leave request. You can have embedding models which powers smart search systems.  Instead of searching by keywords, businesses can now search by meaning. For instance, a legal firm can search cases about contract violations in tech and get semantically relevant results, even if those exact words are not used in the documents.  The third one, for example, code generation, tools like GitHub Copilot help developers write boilerplate or even functional code, accelerating software development, especially in routine or repetitive tasks. Imagine writing a waveform with just a few prompts.  The second application, is image generation. So first obvious use is art. So designers and marketers can generate creative concepts instantly. Say, you need illustrations for a campaign on future cities. Generative AI can produce dozens of stylized visuals in minutes.  For design, interior designers or architects use it to visualize room layouts or design ideas even before a blueprint is finalized. And realistic images, retail companies generate images of people wearing their clothing items without needing real models or photoshoots, and this reduces the cost and increase the personalization.  Third application is multimodal systems, and these are combined systems that take one kind of input or a combination of different inputs and produce different kind of outputs, or can even combine various kinds, be it text image in both input and output.  Text to image It's being used in e-commerce, movie concept art, and educational content creation. For text to video, this is still in early days, but imagine creating a product explainer video just by typing out the script. Marketing teams love this for quick turnarounds. And the last one is text to audio.  Tools like ElevenLabs can convert text into realistic, human like voiceovers useful in training modules, audiobooks, and accessibility apps. So generative AI is no longer just a technical tool. It's becoming a creative copilot across departments, whether it's marketing, design, product support, and even operations.  10:42 Lois: That's great! So, we've established that generative AI is pretty powerful. But what kind of risks does it pose for businesses and society in general?  Himanshu: The first one is deepfakes. Generative AI can create fake but highly realistic media, video, audios or even faces that look and sound authentic.  Imagine a fake video of a political leader announcing a policy, they never approved. This could cause mass confusion or even impact elections. In case of business, deepfakes can be also used in scams where a CEO's voice is faked to approve fraudulent transactions.  Number two, bias, if AI is trained on biased historical data, it can reinforce stereotypes even when unintended. For example, a hiring AI system that favors male candidates over equally qualified women because of historical data was biased.  And this bias can expose companies to discrimination, lawsuits, brand damage and ethical concerns. Number three is hallucinations. So sometimes AI system confidently generate information that is completely wrong without realizing it.   Sometimes you ask a chatbot for a legal case summary, and it gives you a very convincing but entirely made up court ruling. In case of business impact, sectors like health care, finance, or law hallucinations can or could have serious or even dangerous consequences if not caught.  The fourth one is copyright and IP issues, generative AI creates new content, but often, based on material it was trained on. Who owns a new work? A real life example could be where an artist finds their unique style was copied by an AI that was trained on their paintings without permission.  In case of a business impact, companies using AI-generated content for marketing, branding or product designs must watch for legal gray areas around copyright and intellectual properties. So generative AI is not just a technology conversation, it's a responsibility conversation. Businesses must innovate and protect.  Creativity and caution must go together.   12:50 Nikita: Let's move on to generative AI agents. How is a generative AI agent different from just a chatbot or a basic AI tool?  Himanshu: So think of it like a smart assistant, not just answering your questions, but also taking actions on your behalf. So you don't just ask, what's the best flight to Vegas? Instead, you tell the agent, book me a flight to Vegas and a room at the Hilton. And it goes ahead, understands that, finds the options, connects to the booking tools, and gets it done.   So act on your behalf using goals, context, and tools, often with a degree of autonomy. Goals, are user defined outcomes. Example, I want to fly to Vegas and stay at Hilton. Context, this includes preferences history, constraints like economy class only or don't book for Mondays.  Tools could be APIs, databases, or services it can call, such as a travel API or a company calendar. And together, they let the agent reason, plan, and act.   14:02 Nikita: How does a gen AI agent work under the hood?  Himanshu: So usually, they go through four stages. First, one is understands and interprets your request like natural language understanding. Second, figure out what needs to be done, in this case flight booking plus hotel search.  Third, retrieves data or connects to tools APIs if needed, such as Skyscanner, Expedia, or a Calendar. And fourth is takes action. That means confirming the booking and giving you a response like your travel is booked. Keep in mind not all gen AI agents are fully independent.  14:38 Lois: Himanshu, we've seen people use the terms generative AI agents and agentic AI interchangeably. What's the difference between the two?  Himanshu: Agentic AI is a broad umbrella. It refers to any AI system that can perceive, reason, plan, and act toward a goal and may improve and adapt over time.   Most gen AI agents are reactive, not proactive. On the other hand, agentic AI can plan ahead, anticipate problems, and can even adjust strategies.  So gen AI agents are often semi-autonomous. They act in predefined ways or with human approval. Agentic systems can range from low to full autonomy. For example, auto-GPT runs loops without user prompts and autonomous car decides routes and reactions.  Most gen AI agents can only make multiple steps if explicitly designed that way, like a step-by-step logic flows in LangChain. And in case of agentic AI, it can plan across multiple steps with evolving decisions.  On the memory and goal persistence, gen AI agents are typically stateless. That means they forget their goal unless you remind them. In case of agentic AI, these systems remember, adapt, and refine based on goal progression. For example, a warehouse robot optimizing delivery based on changing layouts.  Some generative AI agents are agentic, like auto GPT. They use LLMs to reason, plan, and act, but not all. And likewise not all agentic AIs are generative. For example, an autonomous car, which may use computer vision control systems and planning, but no generative models.  So agentic AI is a design philosophy or system behavior, which could be goal-driven, autonomous, and decision making. They can overlap, but as I said, not all generative AI agents are agentic, and not all agentic AI systems are generative.  16:39 Lois: What makes a generative AI agent actually work?  Himanshu: A gen AI agent isn't just about answering the question. It's about breaking down a user's goal, figuring out how to achieve it, and then executing that plan intelligently. These agents are built from five core components and each playing a critical role.  The first one is goal. So what is this agent trying to achieve? Think of this as the mission or intent. For example, if I tell the agent, help me organized a team meeting for Friday. So the goal in that case would be schedule a meeting.  Number 2, memory. What does it remember? So this is the agent's context awareness. Storing previous chats, preferences, or ongoing tasks. For example, if last week I said I prefer meetings in the afternoon or I have already shared my team's availability, the agent can reuse that. And without the memory, the agent behaves stateless like a typical chatbot that forgets context after every prompt.  Third is tools. What can it access? Agents aren't just smart, they are also connected. They can be given access to tools like calendars, CRMs, web APIs, spreadsheets, and so on.  The fourth one is planner. So how does it break down the goal? And this is where the reasoning happens. The planner breaks big goals into a step-by-step plans, for example checking team availability, drafting meeting invite, and then sending the invite. And then probably, will confirm the booking. Agents don't just guess. They reason and organize actions into a logical path.  And the fifth and final one is executor, who gets it done. And this is where the action takes place. The executor performs what the planner lays out. For example, calling APIs, sending message, booking reservations, and if planner is the architect, executor is the builder.   18:36 Nikita: And where are generative AI agents being used?  Himanshu: Generative AI agents aren't just abstract ideas, they are being used across business functions to eliminate repetitive work, improve consistency, and enable faster decision making. For marketing, a generative AI agent can search websites and social platforms to summarize competitor activity. They can draft content for newsletters or campaign briefs in your brand tone, and they can auto-generate email variations based on audience segment or engagement history.  For finance, a generative AI agent can auto-generate financial summaries and dashboards by pulling from ERP spreadsheets and BI tools. They can also draft variance analysis and budget reports tailored for different departments. They can scan regulations or policy documents to flag potential compliance risks or changes.  For sales, a generative AI agent can auto-draft personalized sales pitches based on customer behavior or past conversations. They can also log CRM entries automatically once submitting summary is generated. They can also generate battlecards or next-step recommendations based on the deal stage.  For human resource, a generative AI agent can pre-screen resumes based on job requirements. They can send interview invites and coordinate calendars. A common theme here is that generative AI agents help you scale your teams without scaling the headcount.   20:02 Nikita: Himanshu, let's talk about the capabilities and benefits of generative AI agents.  Himanshu: So generative AI agents are transforming how entire departments function. For example, in customer service, 24/7 AI agents handle first level queries, freeing humans for complex cases.  They also enhance the decision making. Agents can quickly analyze reports, summarize lengthy documents, or spot trends across data sets. For example, a finance agent reviewing Excel data can highlight cash flow anomalies or forecast trends faster than a team of analysts.  In case of personalization, the agents can deliver unique, tailored experiences without manual effort. For example, in marketing, agents generate personalized product emails based on each user's past behavior. For operational efficiency, they can reduce repetitive, low-value tasks. For example, an HR agent can screen hundreds of resumes, shortlist candidates, and auto-schedule interviews, saving HR team hours each week.  21:06 Lois: Ok. And what are the risks of using generative AI agents?  Himanshu: The first one is job displacement. Let's be honest, automation raises concerns. Roles involving repetitive tasks such as data entry, content sorting are at risk. In case of ethics and accountability, when an AI agent makes a mistake, who is responsible? For example, if an AI makes a biased hiring decision or gives incorrect medical guidance, businesses must ensure accountability and fairness.  For data privacy, agents often access sensitive data, for example employee records or customer history. If mishandled, it could lead to compliance violations. In case of hallucinations, agents may generate confident but incorrect outputs called hallucinations. This can often mislead users, especially in critical domains like health care, finance, or legal.  So generative AI agents aren't just tools, they are a force multiplier. But they need to be deployed thoughtfully with a human lens and strong guardrails. And that's how we ensure the benefits outweigh the risks.  22:10 Lois: Thank you so much, Himanshu, for educating us. We've had such a great time with you! If you want to learn more about the topics discussed today, head over to mylearn.oracle.com and get started on the AI for You course.  Nikita: Join us next week as we chat about AI workflows and tools. Until then, this is Nikita Abraham…  Lois: And Lois Houston signing off!  22:32 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.  

In this episode, Lois Houston and Nikita Abraham continue their discussion on AI fundamentals, diving into Data Science with Principal AI/ML Instructor Himanshu Raj. They explore key concepts like data collection, cleaning, and analysis, and talk about how quality data drives impactful insights.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Hi everyone! Last week, we began our exploration of core AI concepts, specifically machine learning and deep learning. I'd really encourage you to go back and listen to the episode if you missed it.   00:52 Lois: Yeah, today we're continuing that discussion, focusing on data science, with our Principal AI/ML Instructor Himanshu Raj.  Nikita: Hi Himanshu! Thanks for joining us again. So, let's get cracking! What is data science?  01:06 Himanshu: It's about collecting, organizing, analyzing, and interpreting data to uncover valuable insights that help us make better business decisions. Think of data science as the engine that transforms raw information into strategic action.  You can think of a data scientist as a detective. They gather clues, which is our data. Connect the dots between those clues and ultimately solve mysteries, meaning they find hidden patterns that can drive value.  01:33 Nikita: Ok, and how does this happen exactly?  Himanshu: Just like a detective relies on both instincts and evidence, data science blends domain expertise and analytical techniques. First, we collect raw data. Then we prepare and clean it because messy data leads to messy conclusions. Next, we analyze to find meaningful patterns in that data. And finally, we turn those patterns into actionable insights that businesses can trust.  02:00 Lois: So what you're saying is, data science is not just about technology; it's about turning information into intelligence that organizations can act on. Can you walk us through the typical steps a data scientist follows in a real-world project?  Himanshu: So it all begins with business understanding. Identifying the real problem we are trying to solve. It's not about collecting data blindly. It's about asking the right business questions first. And once we know the problem, we move to data collection, which is gathering the relevant data from available sources, whether internal or external.  Next one is data cleaning. Probably the least glamorous but one of the most important steps. And this is where we fix missing values, remove errors, and ensure that the data is usable. Then we perform data analysis or what we call exploratory data analysis.  Here we look for patterns, prints, and initial signals hidden inside the data. After that comes the modeling and evaluation, where we apply machine learning or deep learning techniques to predict, classify, or forecast outcomes. Machine learning, deep learning are like specialized equipment in a data science detective's toolkit. Powerful but not the whole investigation.  We also check how good the models are in terms of accuracy, relevance, and business usefulness. Finally, if the model meets expectations, we move to deployment and monitoring, putting the model into real world use and continuously watching how it performs over time.  03:34 Nikita: So, it's a linear process?  Himanshu: It's not linear. That's because in real world data science projects, the process does not stop after deployment. Once the model is live, business needs may evolve, new data may become available, or unexpected patterns may emerge.  And that's why we come back to business understanding again, defining the questions, the strategy, and sometimes even the goals based on what we have learned. In a way, a good data science project behaves like living in a system which grows, adapts, and improves over time. Continuous improvement keeps it aligned with business value.   Now, think of it like adjusting your GPS while driving. The route you plan initially might change as new traffic data comes in. Similarly, in data science, new information constantly help refine our course. The quality of our data determines the quality of our results.   If the data we feed into our models is messy, inaccurate, or incomplete, the outputs, no matter how sophisticated the technology, will be also unreliable. And this concept is often called garbage in, garbage out. Bad input leads to bad output.  Now, think of it like cooking. Even the world's best Michelin star chef can't create a masterpiece with spoiled or poor-quality ingredients. In the same way, even the most advanced AI models can't perform well if the data they are trained on is flawed.  05:05 Lois: Yeah, that's why high-quality data is not just nice to have, it's absolutely essential. But Himanshu, what makes data good?   Himanshu: Good data has a few essential qualities. The first one is complete. Make sure we aren't missing any critical field. For example, every customer record must have a phone number and an email. It should be accurate. The data should reflect reality. If a customer's address has changed, it must be updated, not outdated. Third, it should be consistent. Similar data must follow the same format. Imagine if the dates are written differently, like 2024/04/28 versus April 28, 2024. We must standardize them.   Fourth one. Good data should be relevant. We collect only the data that actually helps solve our business question, not unnecessary noise. And last one, it should be timely. So data should be up to date. Using last year's purchase data for a real time recommendation engine wouldn't be helpful.  06:13 Nikita: Ok, so ideally, we should use good data. But that's a bit difficult in reality, right? Because what comes to us is often pretty messy. So, how do we convert bad data into good data? I'm sure there are processes we use to do this.  Himanshu: First one is cleaning. So this is about correcting simple mistakes, like fixing typos in city names or standardizing dates.  The second one is imputation. So if some values are missing, we fill them intelligently, for instance, using the average income for a missing salary field. Third one is filtering. In this, we remove irrelevant or noisy records, like discarding fake email signups from marketing data. The fourth one is enriching. We can even enhance our data by adding trusted external sources, like appending credit scores from a verified bureau.  And the last one is transformation. Here, we finally reshape data formats to be consistent, for example, converting all units to the same currency. So even messy data can become usable, but it takes deliberate effort, structured process, and attention to quality at every step.  07:26 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest technology. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 08:10 Nikita: Welcome back! Himanshu, we spoke about how to clean data. Now, once we get high-quality data, how do we analyze it?  Himanshu: In data science, there are four primary types of analysis we typically apply depending on the business goal we are trying to achieve.  The first one is descriptive analysis. It helps summarize and report what has happened. So often using averages, totals, or percentages. For example, retailers use descriptive analysis to understand things like what was the average customer spend last quarter? How did store foot traffic trend across months?  The second one is diagnostic analysis. Diagnostic analysis digs deeper into why something happened. For example, hospitals use this type of analysis to find out, for example, why a certain department has higher patient readmission rates. Was it due to staffing, post-treatment care, or patient demographics?  The third one is predictive analysis. Predictive analysis looks forward, trying to forecast future outcomes based on historical patterns. For example, energy companies predict future electricity demand, so they can better manage resources and avoid shortages. And the last one is prescriptive analysis. So it does not just predict. It recommends specific actions to take.  So logistics and supply chain companies use prescriptive analytics to suggest the most efficient delivery routes or warehouse stocking strategies based on traffic patterns, order volume, and delivery deadlines.   09:42 Lois: So really, we're using data science to solve everyday problems. Can you walk us through some practical examples of how it's being applied?  Himanshu: The first one is predictive maintenance. It is done in manufacturing a lot. A factory collects real time sensor data from machines. Data scientists first clean and organize this massive data stream, explore patterns of past failures, and design predictive models.  The goal is not just to predict breakdowns but to optimize maintenance schedules, reducing downtime and saving millions. The second one is a recommendation system. It's prevalent in retail and entertainment industries. Companies like Netflix or Amazon gather massive user interaction data such as views, purchases, likes.  Data scientists structure and analyze this behavioral data to find meaningful patterns of preferences and build models that suggest relevant content, eventually driving more engagement and loyalty. The third one is fraud detection. It's applied in finance and banking sector.  Banks store vast amounts of transaction record records. Data scientists clean and prepare this data, understand typical spending behaviors, and then use statistical techniques and machine learning to spot unusual patterns, catching fraud faster than manual checks could ever achieve.  The last one is customer segmentation, which is often applied in marketing. Businesses collect demographics and behavioral data about their customers. Instead of treating all the customers same, data scientists use clustering techniques to find natural groupings, and this insight helps businesses tailor their marketing efforts, offers, and communication for each of those individual groups, making them far more effective.  Across all these examples, notice that data science isn't just building a model. Again, it's understanding the business need, reviewing the data, analyzing it thoughtfully, and building the right solution while helping the business act smarter.  11:44 Lois: Thank you, Himanshu, for joining us on this episode of the Oracle University Podcast. We can't wait to have you back next week for part 3 of this conversation on core AI concepts, where we'll talk about generative AI and gen AI agents.     Nikita: And if you want to learn more about data science, visit mylearn.oracle.com and search for the AI for You course. Until next time, this is Nikita Abraham…  Lois: And Lois Houston signing off!  12:13 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

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ClimateAi co-founder and CEO Himanshu Gupta explains how his company uses machine learning to forecast extreme weather and help businesses adapt to climate volatility. Himanshu shares his journey from rural India to co-founding ClimateAi while he was an MBA student. He describes how ClimateAi's "biophysics-driven AI" combines limited weather and crop yield data to inform procurement, logistics, and planting decisions for a quarter of the top 200 food and beverage companies. He also shares examples of government partnerships focused on food security and national supply chain resilience and offers insight on the future of adaptation technologies and enterprise AI. Finally, Himanshu gives advice to those looking to work at the intersection of AI and resilience in the food and agriculture industry. This episode is a part of our series on Climate Resilience, which also features Sarah Russell, Managing Director at Google X; Jacqueline Novogratz, CEO of Acumen; and Alex Berkowitz, CEO of Coastal Protection Services. Visit climaterising.org to explore the entire series!

Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they dive deeper into the world of artificial intelligence, analyzing the types of machine learning. They also discuss deep learning, including how it works, its applications, and its advantages and challenges. From chatbot assistants to speech-to-text systems and image recognition, they explore how deep learning is powering the tools we use today.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence. If you missed it, I really recommend listening to that episode before you start this one. Today, we're going to explore some foundational AI concepts, starting with machine learning. After that, we'll discuss the two main machine learning models: supervised learning and unsupervised learning. And we'll close with deep learning. Lois: Himanshu Raj, our Principal AI/ML Instructor, joins us for today's episode. Hi Himanshu! Let's dive right in. What is machine learning?  01:12 Himanshu: Machine learning lets computers learn from examples to make decisions or predictions without being told exactly what to do. They help computers learn from past data and examples so they can spot patterns and make smart decisions just like humans do, but faster and at scale.  01:31 Nikita: Can you give us a simple analogy so we can understand this better? Himanshu: When you train a dog to sit or fetch, you don't explain the logic behind the command. Instead, you give this dog examples and reinforce correct behavior with rewards, which could be a treat, a pat, or a praise. Over time, the dog learns to associate the command with the action and reward. Machine learning learns in a similar way, but with data instead of dog treats. We feed a mathematical system called models with multiple examples of input and the desired output, and it learns the pattern. It's trial and error, learning from the experience.  Here is another example. Recognizing faces. Humans are incredibly good at this, even as babies. We don't need someone to explain every detail of the face. We just see many faces over time and learn the patterns. Machine learning models can be trained the same way. We showed them thousands or millions of face images, each labeled, and they start to detect patterns like eyes, nose, mouth, spacing, different angles. So eventually, they can recognize faces they have seen before or even match new ones that are similar. So machine learning doesn't have any rules, it's just learning from examples. This is the kind of learning behind things like face ID on your smartphone, security systems that recognizes employees, or even Facebook tagging people in your photos. 03:05 Lois: So, what you're saying is, in machine learning, instead of telling the computer exactly what to do in every situation, you feed the model with data and give it examples of inputs and the correct outputs. Over time, the model figures out patterns and relationships within the data on its own, and it can make the smart guess when it sees something new. I got it! Now let's move on to how machine learning actually works? Can you take us through the process step by step? Himanshu: Machine learning actually happens in three steps. First, we have the input, which is the training data. Think of this as showing the model a series of examples. It could be images of historical sales data or customer complaints, whatever we want the machine to learn from. Next comes the pattern finding. This is the brain of the system where the model starts spotting relationships in the data. It figures out things like customer who churn or leave usually contacts support twice in the same month. It's not given rules, it just learns patterns based on the example. And finally, we have output, which is the prediction or decision. This is the result of all this learning. Once trained, the computer or model can say this customer is likely to churn or leave. It's like having a smart assistant that makes fast, data-driven guesses without needing step by step instruction. 04:36 Nikita: What are the main elements in machine learning? Himanshu: In machine learning, we work with two main elements, features and labels. You can think of features as the clues we provide to the model, pieces of information like age, income, or product type. And the label is the solution we want the model to predict, like whether a customer will buy or not.  04:55 Nikita: Ok, I think we need an example here. Let's go with the one you mentioned earlier about customers who churn. Himanshu: Imagine we have a table with data like customer age, number of visits, whether they churned or not. And each of these rows is one example. The features are age and visit count. The label is whether the customer churned, that is yes or no. Over the time, the model might learn patterns like customer under 30 who visit only once are more likely to leave. Or frequent visitors above age 45 rarely churn. If features are the clues, then the label is the solution, and the model is the brain of the system. It's what's the machine learning builds after learning from many examples, just like we do. And again, the better the features are, the better the learning. ML is just looking for patterns in the data we give it. 05:51 Lois: Ok, we're with you so far. Let's talk about the different types of machine learning. What is supervised learning? Himanshu: Supervised learning is a type of machine learning where the model learns from the input data and the correct answers. Once trained, the model can use what it learned to predict the correct answer for new, unseen inputs. Think of it like a student learning from a teacher. The teacher shows labeled examples like an apple and says, "this is an apple." The student receives feedback whether their guess was right or wrong. Over time, the student learns to recognize new apples on their own. And that's exactly how supervised learning works. It's learning from feedback using labeled data and then make predictions. 06:38 Nikita: Ok, so supervised learning means we train the model using labeled data. We already know the right answers, and we're essentially teaching the model to connect the dots between the inputs and the expected outputs. Now, can you give us a few real-world examples of supervised learning? Himanshu: First, house price prediction. In this case, we give the model features like a square footage, location, and number of bedrooms, and the label is the actual house price. Over time, it learns how to predict prices for new homes. The second one is email: spam or not. In this case, features might include words in the subject line, sender, or links in the email. The label is whether the email is spam or not. The model learns patterns to help us filter our inbox, as you would have seen in your Gmail inboxes. The third one is cat versus dog classification. Here, the features are the pixels in an image, and the label tells us whether it's a cat or a dog. After seeing many examples, the model learns to tell the difference on its own. Let's now focus on one very common form of supervised learning, that is regression. Regression is used when we want to predict a numerical value, not a category. In simple terms, it helps answer questions like, how much will it be? Or what will be the value be? For example, predicting the price of a house based on its size, location, and number of rooms. Or estimating next quarter's revenue based on marketing spend.  08:18 Lois: Are there any other types of supervised learning? Himanshu: While regression is about predicting a number, classification is about predicting a category or type. You can think of it as the model answering is this yes or no, or which group does this belong to.  Classification is used when the goal is to predict a category or a class. Here, the model learns patterns from historical data where both the input variables, known as features, and the correct categories, called labels, are already known.  08:53 Ready to level-up your cloud skills? The 2025 Oracle Fusion Cloud Applications Certifications are here! These industry-recognized credentials validate your expertise in the latest Oracle Fusion Cloud solutions, giving you a competitive edge and helping drive real project success and customer satisfaction. Explore the certification paths, prepare with MyLearn, and position yourself for the future. Visit mylearn.oracle.com to get started today. 09:25 Nikita: Welcome back! So that was supervised machine learning. What about unsupervised machine learning, Himanshu? Himanshu: Unlike supervised learning, here, the model is not given any labels or correct answers. It just handed the raw input data and left to make sense of it on its own.  The model explores the data and discovers hidden patterns, groupings, or structures on its own, without being explicitly told what to look for. And it's more like a student learning from observations and making their own inferences. 09:55 Lois: Where is unsupervised machine learning used? Can you take us through some of the use cases? Himanshu: The first one is product recommendation. Customers are grouped based on shared behavior even without knowing their intent. This helps show what the other users like you also prefer. Second one is anomaly detection. Unusual patterns, such as fraud, network breaches, or manufacturing defects, can stand out, all without needing thousands of labeled examples. And third one is customer segmentation. Customers can be grouped by purchase history or behavior to tailor experiences, pricing, or marketing campaigns. 10:32 Lois: And finally, we come to deep learning. What is deep learning, Himanshu? Himanshu: Humans learn from experience by seeing patterns repeatedly. Brain learns to recognize an image by seeing it many times. The human brain contains billions of neurons. Each neuron is connected to others through synapses. Neurons communicate by passing signals. The brain adjusts connections based on repeated stimuli. Deep learning was inspired by how the brain works using artificial neurons and connections. Just like our brains need a lot of examples to learn, so do the deep learning models. The more the layers and connections are, the more complex patterns it can learn. The brain is not hard-coded. It learns from patterns. Deep learning follows the same idea. Metaphorically speaking, a deep learning model can have over a billion neurons, more than a cat's brain, which have around 250 million neurons. Here, the neurons are mathematical units, often called nodes, or simply as units. Layers of these units are connected, mimicking how biological neurons interact. So deep learning is a type of machine learning where the computer learns to understand complex patterns. What makes it special is that it uses neural networks with many layers, which is why we call it deep learning. 11:56 Lois: And how does deep learning work? Himanshu: Deep learning is all about finding high-level meaning from low-level data layer by layer, much like how our brains process what we see and hear. A neural network is a system of connected artificial neurons, or nodes, that work together to learn patterns and make decisions.  12:15 Nikita: I know there are different types of neural networks, with ANNs or Artificial Neural Networks being the one for general learning. How is it structured? Himanshu: There is an input layer, which is the raw data, which could be an image, sentence, numbers, a hidden layer where the patterns are detected or the features are learned, and the output layer where the final decision is made. For example, given an image, is this a dog? A neural network is like a team of virtual decision makers, called artificial neurons, or nodes, working together, which takes input data, like a photo, and passes it through layers of neurons. And each neuron makes a small judgment and passes its result to the next layer.  This process happens across multiple layers, learning more and more complex patterns as it goes, and the final layer gives the output. Imagine a factory assembly line where each station, or the layer, refines the input a bit more. By the end, you have turned raw parts into something meaningful. And this is a very simple analogy. This structure forms the foundations of many deep learning models.  More advanced architectures, like convolutional neural networks, CNNs, for images, or recurrent neural networks, RNN, for sequences built upon this basic idea. So, what I meant is that the ANN is the base structure, like LEGO bricks. CNNs and RNNs use those same bricks, but arrange them in a way that are better suited for images, videos, or sequences like text or speech.  13:52 Nikita: So, why do we call it deep learning? Himanshu: The word deep in deep learning does not refer to how profound or intelligent the model is. It actually refers to the number of layers in the neural network. It starts with an input layer, followed by hidden layers, and ends with an output layer. The layers are called hidden, in the sense that these are black boxes and their data is not visible or directly interpretable to the user. Models which has only one hidden layer is called shallow learning. As data moves, each layer builds on what the previous layer has learned. So layer one might detect a very basic feature, like edges or colors in an image. Layer two can take those edges and starts forming shapes, like curves or lines. And layer three use those shapes to identify complete objects, like a face, a car, or a person. This hierarchical learning is what makes deep learning so powerful. It allows the model to learn abstract patterns and generalize across complex data, whether it's visual, audio, or even language. And that's the essence of deep learning. It's not just about layers. It's about how each layer refines the information and one step closer to understanding. 15:12 Nikita: Himanshu, where does deep learning show up in our everyday lives? Himanshu: Deep learning is not just about futuristic robots, it's already powering the tools we use today. So think of when you interact with a virtual assistant on a website. Whether you are booking a hotel, resolving a banking issue, or asking customer support questions, behind the scenes, deep learning models understand your text, interpret your intent, and respond intelligently. There are many real-life examples, for example, ChatGPT, Google's Gemini, any airline website's chatbots, bank's virtual agent. The next one is speech-to-text systems. Example, if you have ever used voice typing on your phone, dictated a message to Siri, or used Zoom's live captions, you have seen this in action already. The system listens to your voice and instantly converts it into a text. And this saves time, enhances accessibility, and helps automate tasks, like meeting transcriptions. Again, you would have seen real-life examples, such as Siri, Google Assistant, autocaptioning on Zoom, or YouTube Live subtitles. And lastly, image recognition. For example, hospitals today use AI to detect early signs of cancer in x-rays and CT scans that might be missed by the human eye. Deep learning models can analyze visual patterns, like a suspicious spot on a lung's X-ray, and flag abnormalities faster and more consistently than humans. Self-driving cars recognize stop signs, pedestrians, and other vehicles using the same technology. So, for example, cancer detection in medical imaging, Tesla's self-driving navigation, security system synchronizes face are very prominent examples of image recognition. 17:01 Lois: Deep learning is one of the most powerful tools we have today to solve complex problems. But like any tool, I'm sure it has its own set of pros and cons. What are its advantages, Himanshu? Himanshu: It is high accuracy. When trained with enough data, deep learning models can outperform humans. For example, again, spotting early signs of cancer in X-rays with higher accuracy. Second is handling of unstructured data. Deep learning shines when working with messy real-world data, like images, text, and voice. And it's why your phone can recognize your face or transcribe your speech into text. The third one is automatic pattern learning. Unlike traditional models that need hand-coded features, deep learning models figure out important patterns by themselves, making them extremely flexible. And the fourth one is scalability. Once trained, deep learning systems can scale easily, serving millions of customers, like Netflix recommending movies personalized to each one of us. 18:03 Lois: And what about its challenges? Himanshu: The first one is data and resource intensive. So deep learning demands huge amount of labeled data and powerful computing hardware, which means high cost, especially during training. The second thing is lacks explainability. These models often act like a black box. We know the output, but it's hard to explain exactly how the model reached that decision. This becomes a problem in areas like health care and finance where transparency is critical. The third challenge is vulnerability to bias. If the data contains biases, like favoring certain groups, the model will learn and amplify those biases unless we manage them carefully. The fourth and last challenge is it's harder to debug and maintain. Unlike a traditional software program, it's tough to manually correct a deep learning model if it starts behaving unpredictably. It requires retraining with new data. So deep learning offers powerful opportunities to solve complex problems using data, but it also brings challenges that require careful strategy, resources, and responsible use. 19:13 Nikita: We're taking away a lot from this conversation. Thank you so much for your insights, Himanshu.  Lois: If you're interested to learn more, make sure you log into mylearn.oracle.com and look for the AI for You course. Join us next week for part 2 of the discussion on AI Concepts & Terminology, where we'll focus on Data Science. Until then, this is Lois Houston… Nikita: And Nikita Abraham signing off! 19:39 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

A throwback to one of Tiff's favourite episodes with Chef Himanshu Saini, who leads Tresind Studio the first and only Indian restaurant with 3 Michelin⁠ stars in the world. When Tiff sat down with him in late-2023, they talked about the evolution of their menu, how their divine broths come to life, and the emotional power of cooking. We're currently on a summer break and will be back with new episodes in September.

Ever wondered how brands go viral in today's digital age? In this episode of ELI podcast, we sit down with Himanshu Singla, the visionary CEO & Co-Founder of Idiotic Media, one of India's leading influencer and meme marketing agencies!Himanshu is at the forefront of transforming traditional marketing, focusing on meme marketing, influencer marketing, and cutting-edge content strategies that resonate with modern audiences. With over 7 years of experience steering Idiotic Media to success, he shares invaluable insights on:- The art and science behind viral content: How do memes become powerful marketing tools?- Navigating the influencer landscape: What does it take to create impactful, data-driven influencer campaigns?- Building a bootstrapped empire: Lessons learned from 6.5 years of building Idiotic Media from the ground up.- The future of digital marketing: What trends should brands and marketers be paying attention to?Whether you're an aspiring entrepreneur, a marketing professional, or simply curious about how the internet shapes consumer behavior, this conversation with Himanshu Singla is packed with actionable advice and inspiring stories.About Himanshu Singla:Himanshu Singla is the CEO & Co-Founder of Idiotic Media, a pioneering agency specializing in new-age marketing, including meme and influencer marketing. He's passionate about replacing old-age marketing tactics with innovative, engaging strategies. Prior to Idiotic Media, he co-founded Movense Entertainment, a virtual reality video sharing platform, and served as CTO at Creatify Web Services, focusing on digital technology and marketing.#HimanshuSingla #IdioticMedia #Podcast #Marketing #DigitalMarketing #InfluencerMarketing #MemeMarketing #ContentMarketing #Entrepreneurship #Startup #India #Business #ViralMarketing #NewAgeMarketing #Innovation #PodcastName #[YourPodcastName]

The Rollup TV is brought to you by:Boundless: https://beboundless.xyz/AltLayer: https://www.altlayer.io/Mantle: https://www.mantle.xyz/Vertex: https://vertexprotocol.com/Subsquid: https://www.sqd.ai/Summer: https://summer.fi/Join The Rollup Family:Website: https://therollup.co/Spotify: https://open.spotify.com/show/1P6ZeYd..Podcast: https://therollup.co/category/podcastFollow us on X: https://www.x.com/therollupcoFollow Rob on X: https://www.x.com/robbie_rollupFollow Andy on X: https://www.x.com/ayyyeandyJoin our TG group: https://t.me/+8ARkR_YZixE5YjBhThe Rollup Disclosures: https://therollup.co/the-rollup-disclTimestamps00:00 Introduction to The Roll Up TV and Guests01:13 Recent Market Developments and Token Collapses03:39 Bybit's Hybrid DEX Announcement06:05 ETF Filings and Staking Language09:00 Trump and Tron Going Public11:50 Introduction to Himanshu and Sentient12:35 The Vision Behind Sentient and Decentralized AI18:20 The Importance of Verification in AI24:29 Challenges in AI and Crypto Integration28:20 Innovative Projects within the Sentient Ecosystem33:05 DC Builder and Zero Knowledge Machine Learning40:56 Proof of Personhood and the Future of Identity44:33 Decentralized Identity and ZK Technologies50:02 AI in Crypto: Current Landscape and Innovations54:42 The Future of Identity Platforms and User Privacy01:00:08 Blockchain Bottlenecks: Execution vs. Consensus01:05:45 Institutional Interest Beyond Ethereum01:08:32 The Dynamics of On-Chain Speculation01:12:14 The Role of Wallets in User Retention01:18:52 The Future of Stablecoins and AI Integration01:28:51 The Future of Payments: Stablecoins vs. Real-Time Payment Networks01:35:33 The Role of FinTech in Crypto Payments01:36:30 Understanding the Value of Tokens in Crypto01:49:03 JP Morgan's Entry into Stablecoins01:52:20 Building a Brand: Azuki and the Anime Industry

In this conversation, John Harcar interviews Himanshu Jain, who shares his inspiring journey from arriving in the U.S. with limited resources to becoming a successful real estate investor. Himanshu discusses the challenges he faced, his transition from a buy-and-hold strategy to syndication, and his current projects. He emphasizes the importance of networking, having the right team, and the need for adaptability in the ever-changing real estate market. Himanshu also offers valuable advice for new investors, highlighting that every day is a good day to start in real estate. Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true ‘white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a “mini-mastermind” with Mike and his private clients on an upcoming “Retreat”, either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas “Big H Ranch”? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

Liberale venner!Klaus har vært på Gründer og investorcruise med SMB Norge nylig.Der tok han en kort liten prat med tidligere FpU-formann Himanshu Gulati, nåværende stortingsrepresentant fra Akershus FrP.De snakker litt om å stille opp for omgivelsene, med utgangspunkt i "En gang FpUer, alltid FpUer".Hva skjedde egentlig med kriseloven som holdt på å bli vedtatt?Hva tenker Himanshu om den illiberale utviklingen vi ser rundt om i verden?Handelskrigen har bare tapere.Næringspolitikken dagens regjering står bak har vært katastrofal, og det er særdeles viktig å ha et godt samarbeid på borgerlig side fremover.Vinner borgerlig side valget skal man kunne samarbeide godt mener Himanshu Gulati.Husk å skrive en liten omtale av oss i Apple Podcast, samt gi oss 5 stjerner i Spotify og Apple Podcast!Vennligst abonner på podcasten i din egen app, så blir du varslet når nye episoder kommer ut.Følg/kontakt oss her: liberalaften@gmail.comhttps://www.facebook.com/liberalerenpodcast/https://www.instagram.com/liberalerenpodcast/https://twitter.com/LiberalerenPRate oss gjerne også i de apper som tilbyr dette!Skriv også positive kommentarer i de podcast apper hvor det er mulig.Kontakt oss / send inn spørsmål:www.podpage.com/liberaleren-podcastLes dine daglige nyheter på Liberaleren:https://www.liberaleren.no/Støtt Liberaleren gjennom diverse bidrag her:https://www.liberaleren.no/donasjoner/Finn mer:https://www.podpage.com/liberaleren-podcastVIPPS valgfrie kroner til Liberaleren: 579172Liberaleren TV:https://www.youtube.com/channel/UCHChWhwyiNrhDlfmvgJRbrALiberaleren Podcast på YouTube:https://www.youtube.com/channel/UCb_4G55--BGOb0vCAf2AFmgLiberal hilsning fra Klaus og Himanshu! Hosted on Acast. See acast.com/privacy for more information.

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“The theme that I found with IPSO [International Psychoanalytical Studies Organization] was that there was a common theme [in psychoanalytic training].  There was an initial phase full of terror and excitement, and then a middle phase of maybe some lethargy or apathy or disillusionment. In that middle phase, many candidates found IPSO, or IPSO found them, where they found refuge. They found solace. They found community, not just at their local institutes, but at this kind of world market. Many of the candidates talk about what a timely and wonderful experience it was to be seen, to be validated by fellow candidates in a way that only fellow candidates can do. At least a couple of the authors have written about how they were delighted to see that more than anything else we are similar as human beings, no matter where we're from.”  Episode Description:  We begin with recognizing the deep attachment that many analytic candidates have about their training experiences, which includes affections and resentments. Himanshu outlines the process of reaching out to candidates globally, inviting them to share their reflections on their journeys. We read from a sampling of their essays that eloquently describe their idealizations and de-idealizations, their delights and their burdens, their profound regard for the mysteries of the mind and the appreciation of the power of psychoanalysis to engage with it. We discuss the importance of IPSO, the difficulties associated with Covid and the relevance of our field's traumatic origins. Himanshu closes with sharing his story of encountering an insightful analytic supervisor during his residency and declaring "I want to be like him." Linked Episode:Episode 89: Wisdom and Enthusiasm for Today's Candidates with Fred Busch, PhD   Our Guest: Himanshu Agrawal, MD is an adult and child psychiatrist and recently completed psychoanalytic training through the Minnesota Psychoanalytic Institute. He is an associate professor of psychiatry and behavioral medicine at the Medical College of Wisconsin in Milwaukee where he sees patients, conducts research, and teaches. He recently completed his term as the president of the candidates' council of the American Psychoanalytic Association Recommended Readings: Busch F (Ed), Dear candidate. Routledge, 2020   Agrawal H,  Trials and Tribulations of being a candidate. The American Psychoanalyst, winter 2022   Kernberg O, Thirty methods to destroy the creativity of psychoanalytic candidates. International Journal of psychoanalysis, 77, 1031- 1040

This is the Catchup on 3 Things by The Indian Express and I'm Flora Swain.Today is the 23rd of December and here are the headlines.Prime Minister Narendra Modi hailed the central government's efforts to provide ‘lakhs of government jobs in the last 1.5 years'. Addressing a Rozgar Mela virtually today, PM Modi said that his government set a “record” by giving permanent government jobs to almost 10 lakh people in the course of the last 18 months. PM Modi stated, quote, “There is a campaign going on to provide government jobs in various ministries, departments and institutions of the country. Today also, more than 71,000 youths have been given appointment letters,” unquote.Meanwhile, Three members of the Khalistan Zindabad Force, who were allegedly involved in grenade attacks at police establishments in border areas of Punjab, were killed in an encounter in Uttar Pradesh's Pilibhit district today. The encounter was jointly conducted by the police forces from Punjab and UP. While the Punjab Police said in the morning that the men had been arrested, police in UP confirmed later that the men had died a little before 10 am. The deceased have been identified as Gurvinder Singh, Virendra Singh alias Ravi, and Jasan Preet Singh alias Pratap Singh, all residents of Gurdaspur.Chief Minister Himanta Biswa Sarma said today that six Bangladeshis have been apprehended by the Assam Police for entering the Indian territory illegally and handed over to the authorities of the neighbouring country, He, however, did not mention the sector of the India-Bangladesh border, where they were held. The chief minister said on X, quote ‘No place for illegal infiltration in Assam, carrying out their strict monitoring against infiltration attempts, Assam police apprehended 6 Bangladeshi nationals and pushed them across the border,” unquote.Meanwhile, the police in Uttar Pradesh's Bijnor said today they arrested the main accused in the abduction of comedian Sunil Pal and actor Mushtaq Mohammed Khan after an encounter late Sunday. While the police arrested the main accused Lavi Pal, his accomplice Himanshu managed to escape during the cross-firing. Lavi Pal was carrying a reward of Rs 25,000 on his head and had been absconding since being booked by the Meerut and Bijnor police for abduction or ransom of Mushtaq Khan on 20th of November and Sunil Pal on 2nd of December.On the global front, US President-elect Donald Trump announced the appointment of Sriram Krishnan, an aide of billionaire Elon Musk and Microsoft's ex-employee, as the Senior Policy Advisor for Artificial Intelligence at the White House Office of Science and Technology Policy. Trump, in a post on his social media platform Truth Social said, quote “Sriram Krishnan will focus on ensuring continued American leadership in AI and help shape and coordinate AI policy across Government, including working with the President's Council of Advisors on Science and Technology.” unquote.This was the Catch Up on 3 Things by The Indian Express.

Himanshu Sahay is the Co-Founder and CTO of Arch. In this episode recorded live at Emergence in Prague, Sahay and The Block's Frank Chaparro discuss the evolution of crypto lending after the 2022 market turmoil and Arch's approach to expansion and collateral. OUTLINE 00:00 Introduction 01:16 Intro to Arch  03:15 Shifting collateral standards  07:33 The state of crypto credit 12:48 New administration, new regulation 14:35 Debanking and Barron Trump 17:41 Arch's global expansion 19:29 Encounters with Voyager and Celsius 20:47 Conclusion GUEST LINKS Himanshu Sahay - https://www.linkedin.com/in/himanshusahay/ Himanshu Sahay on X - https://x.com/hhsahay Arch - https://archlending.com/ Arch on X - https://x.com/ArchLending This episode is brought to you by our sponsor: Polkadot Polkadot is the blockspace ecosystem for boundless innovation. To discover more, head to polkadot.network

Have you ever wondered if men and women can truly have platonic friendships—free from romantic tension? Or felt the pressure to abandon opposite-sex friendships in the name of a relationship? In this sizzling, soul-stirring episode of The Brave Table, I sit down with my two favorite brother besties—Himanshu Jocker, founder of Epic Businesses in Jaipur, India, and Erwin B. Valencia, a former mental health coach for the NBA and founder of the Gratitude Gang Foundation in the Philippines. Together, we dive into how opposite-sex friendships can enrich your life, your romantic relationships, and your personal growth. We discuss setting boundaries, navigating jealousy, and even the dynamics of “friend-zoning.” This is a must-listen if you've ever struggled to maintain balance between friendship and romance or wondered how to nurture healthy, meaningful relationships across the gender spectrum.

Featuring Himanshu Warden, Founder and CEO of Thevasa, an Indian fashion company. (Recorded 10/3/24)

Critical Insights on Colonial Modes of Seeing Cattle in India: Tracing the Pre-history of Green and White Revolutions (Springer 2024) traces the contours of the symbiotic relationship between crop cultivation and cattle rearing in India by reading against the grain of several official accounts from the late colonial period to the 1980s. It also skillfully unpacks the multiple cultural expressions that revolve around cattle in India and the wider subcontinent to show how this domestic animal has greatly impacted political discourses in South Asia from colonial times, into the postcolonial period. The author begins by demonstrating the dependence between the nomadic cattle breeder and the settled cultivator, at the nexus of land-livestock-agriculture, as indicated in the writings of Sir Albert Howard, who espoused some of the most sophisticated ideas on integration, holism, and mixed farming in an era when agricultural research was marked by increasing specialisation and compartmentalization.  The book springboards with the views of colonial experts who worked at imperial science institutions but passionately voiced dissenting opinions due to their emotional investment in the lives of Indian peasants, of whom Howard was a leading light. The book presents Howard and his contemporaries' writings to then engage contemporary debates surrounding organic agriculture and climate change, tracing the path out of the treadmill of industrial agriculture and factory farming. In doing so, the book shows how, historically, animal rearing has been critically linked to livelihood strategies in the Indian subcontinent. At once a dispassionate reflection on the role played by cattle and water buffaloes in not just supporting farm operations in the agro-pastoral landscape, but also in contributing to millions of livelihoods in sustainable ways while fulfilling the animal protein in the Indian diet, the book presents contemporary lessons on development perspectives relating to sustainable and holistic agriculture. A rich and sweeping treatment of this aspect of environmental history in India that tackles the transformations prompted by the arrival of veterinary medicine, veterinary education and notions of scientific livestock management, the book is a rare read for historians, environmentalists, agriculturalists, development practitioners, and animal studies scholars with a particular interest in South Asia. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Critical Insights on Colonial Modes of Seeing Cattle in India: Tracing the Pre-history of Green and White Revolutions (Springer 2024) traces the contours of the symbiotic relationship between crop cultivation and cattle rearing in India by reading against the grain of several official accounts from the late colonial period to the 1980s. It also skillfully unpacks the multiple cultural expressions that revolve around cattle in India and the wider subcontinent to show how this domestic animal has greatly impacted political discourses in South Asia from colonial times, into the postcolonial period. The author begins by demonstrating the dependence between the nomadic cattle breeder and the settled cultivator, at the nexus of land-livestock-agriculture, as indicated in the writings of Sir Albert Howard, who espoused some of the most sophisticated ideas on integration, holism, and mixed farming in an era when agricultural research was marked by increasing specialisation and compartmentalization.  The book springboards with the views of colonial experts who worked at imperial science institutions but passionately voiced dissenting opinions due to their emotional investment in the lives of Indian peasants, of whom Howard was a leading light. The book presents Howard and his contemporaries' writings to then engage contemporary debates surrounding organic agriculture and climate change, tracing the path out of the treadmill of industrial agriculture and factory farming. In doing so, the book shows how, historically, animal rearing has been critically linked to livelihood strategies in the Indian subcontinent. At once a dispassionate reflection on the role played by cattle and water buffaloes in not just supporting farm operations in the agro-pastoral landscape, but also in contributing to millions of livelihoods in sustainable ways while fulfilling the animal protein in the Indian diet, the book presents contemporary lessons on development perspectives relating to sustainable and holistic agriculture. A rich and sweeping treatment of this aspect of environmental history in India that tackles the transformations prompted by the arrival of veterinary medicine, veterinary education and notions of scientific livestock management, the book is a rare read for historians, environmentalists, agriculturalists, development practitioners, and animal studies scholars with a particular interest in South Asia. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/history

Critical Insights on Colonial Modes of Seeing Cattle in India: Tracing the Pre-history of Green and White Revolutions (Springer 2024) traces the contours of the symbiotic relationship between crop cultivation and cattle rearing in India by reading against the grain of several official accounts from the late colonial period to the 1980s. It also skillfully unpacks the multiple cultural expressions that revolve around cattle in India and the wider subcontinent to show how this domestic animal has greatly impacted political discourses in South Asia from colonial times, into the postcolonial period. The author begins by demonstrating the dependence between the nomadic cattle breeder and the settled cultivator, at the nexus of land-livestock-agriculture, as indicated in the writings of Sir Albert Howard, who espoused some of the most sophisticated ideas on integration, holism, and mixed farming in an era when agricultural research was marked by increasing specialisation and compartmentalization.  The book springboards with the views of colonial experts who worked at imperial science institutions but passionately voiced dissenting opinions due to their emotional investment in the lives of Indian peasants, of whom Howard was a leading light. The book presents Howard and his contemporaries' writings to then engage contemporary debates surrounding organic agriculture and climate change, tracing the path out of the treadmill of industrial agriculture and factory farming. In doing so, the book shows how, historically, animal rearing has been critically linked to livelihood strategies in the Indian subcontinent. At once a dispassionate reflection on the role played by cattle and water buffaloes in not just supporting farm operations in the agro-pastoral landscape, but also in contributing to millions of livelihoods in sustainable ways while fulfilling the animal protein in the Indian diet, the book presents contemporary lessons on development perspectives relating to sustainable and holistic agriculture. A rich and sweeping treatment of this aspect of environmental history in India that tackles the transformations prompted by the arrival of veterinary medicine, veterinary education and notions of scientific livestock management, the book is a rare read for historians, environmentalists, agriculturalists, development practitioners, and animal studies scholars with a particular interest in South Asia. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/intellectual-history

Critical Insights on Colonial Modes of Seeing Cattle in India: Tracing the Pre-history of Green and White Revolutions (Springer 2024) traces the contours of the symbiotic relationship between crop cultivation and cattle rearing in India by reading against the grain of several official accounts from the late colonial period to the 1980s. It also skillfully unpacks the multiple cultural expressions that revolve around cattle in India and the wider subcontinent to show how this domestic animal has greatly impacted political discourses in South Asia from colonial times, into the postcolonial period. The author begins by demonstrating the dependence between the nomadic cattle breeder and the settled cultivator, at the nexus of land-livestock-agriculture, as indicated in the writings of Sir Albert Howard, who espoused some of the most sophisticated ideas on integration, holism, and mixed farming in an era when agricultural research was marked by increasing specialisation and compartmentalization.  The book springboards with the views of colonial experts who worked at imperial science institutions but passionately voiced dissenting opinions due to their emotional investment in the lives of Indian peasants, of whom Howard was a leading light. The book presents Howard and his contemporaries' writings to then engage contemporary debates surrounding organic agriculture and climate change, tracing the path out of the treadmill of industrial agriculture and factory farming. In doing so, the book shows how, historically, animal rearing has been critically linked to livelihood strategies in the Indian subcontinent. At once a dispassionate reflection on the role played by cattle and water buffaloes in not just supporting farm operations in the agro-pastoral landscape, but also in contributing to millions of livelihoods in sustainable ways while fulfilling the animal protein in the Indian diet, the book presents contemporary lessons on development perspectives relating to sustainable and holistic agriculture. A rich and sweeping treatment of this aspect of environmental history in India that tackles the transformations prompted by the arrival of veterinary medicine, veterinary education and notions of scientific livestock management, the book is a rare read for historians, environmentalists, agriculturalists, development practitioners, and animal studies scholars with a particular interest in South Asia. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sociology

Critical Insights on Colonial Modes of Seeing Cattle in India: Tracing the Pre-history of Green and White Revolutions (Springer 2024) traces the contours of the symbiotic relationship between crop cultivation and cattle rearing in India by reading against the grain of several official accounts from the late colonial period to the 1980s. It also skillfully unpacks the multiple cultural expressions that revolve around cattle in India and the wider subcontinent to show how this domestic animal has greatly impacted political discourses in South Asia from colonial times, into the postcolonial period. The author begins by demonstrating the dependence between the nomadic cattle breeder and the settled cultivator, at the nexus of land-livestock-agriculture, as indicated in the writings of Sir Albert Howard, who espoused some of the most sophisticated ideas on integration, holism, and mixed farming in an era when agricultural research was marked by increasing specialisation and compartmentalization.  The book springboards with the views of colonial experts who worked at imperial science institutions but passionately voiced dissenting opinions due to their emotional investment in the lives of Indian peasants, of whom Howard was a leading light. The book presents Howard and his contemporaries' writings to then engage contemporary debates surrounding organic agriculture and climate change, tracing the path out of the treadmill of industrial agriculture and factory farming. In doing so, the book shows how, historically, animal rearing has been critically linked to livelihood strategies in the Indian subcontinent. At once a dispassionate reflection on the role played by cattle and water buffaloes in not just supporting farm operations in the agro-pastoral landscape, but also in contributing to millions of livelihoods in sustainable ways while fulfilling the animal protein in the Indian diet, the book presents contemporary lessons on development perspectives relating to sustainable and holistic agriculture. A rich and sweeping treatment of this aspect of environmental history in India that tackles the transformations prompted by the arrival of veterinary medicine, veterinary education and notions of scientific livestock management, the book is a rare read for historians, environmentalists, agriculturalists, development practitioners, and animal studies scholars with a particular interest in South Asia. Learn more about your ad choices. Visit megaphone.fm/adchoices

Critical Insights on Colonial Modes of Seeing Cattle in India: Tracing the Pre-history of Green and White Revolutions (Springer 2024) traces the contours of the symbiotic relationship between crop cultivation and cattle rearing in India by reading against the grain of several official accounts from the late colonial period to the 1980s. It also skillfully unpacks the multiple cultural expressions that revolve around cattle in India and the wider subcontinent to show how this domestic animal has greatly impacted political discourses in South Asia from colonial times, into the postcolonial period. The author begins by demonstrating the dependence between the nomadic cattle breeder and the settled cultivator, at the nexus of land-livestock-agriculture, as indicated in the writings of Sir Albert Howard, who espoused some of the most sophisticated ideas on integration, holism, and mixed farming in an era when agricultural research was marked by increasing specialisation and compartmentalization.  The book springboards with the views of colonial experts who worked at imperial science institutions but passionately voiced dissenting opinions due to their emotional investment in the lives of Indian peasants, of whom Howard was a leading light. The book presents Howard and his contemporaries' writings to then engage contemporary debates surrounding organic agriculture and climate change, tracing the path out of the treadmill of industrial agriculture and factory farming. In doing so, the book shows how, historically, animal rearing has been critically linked to livelihood strategies in the Indian subcontinent. At once a dispassionate reflection on the role played by cattle and water buffaloes in not just supporting farm operations in the agro-pastoral landscape, but also in contributing to millions of livelihoods in sustainable ways while fulfilling the animal protein in the Indian diet, the book presents contemporary lessons on development perspectives relating to sustainable and holistic agriculture. A rich and sweeping treatment of this aspect of environmental history in India that tackles the transformations prompted by the arrival of veterinary medicine, veterinary education and notions of scientific livestock management, the book is a rare read for historians, environmentalists, agriculturalists, development practitioners, and animal studies scholars with a particular interest in South Asia. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/british-studies

In this episode, Himanshu and Prem discuss the intricacies of budget optimization in PPC advertising, delving into the importance of strategic budget allocation, the significance of ad type level budgeting, and the utility of budget rules.Common mistakes in budgeting practices are also addressed, providing with valuable insights into optimizing the advertising strategies for better ROI.RESOURCESRead our News Feed.Book an Amazon Advertising audit.Follow me on Twitter.Amazon design examples.Follow our team.$85 to $117k in 45 days. 2-minute breakdown of what we did.Message George.

Himanshu Verma, VP of Engineering and Country Leader at Eventbrite - the world's largest and most trusted events marketplace. At Eventbrite, Himanshu oversee's the development team that builds cutting edge cloud, mobile and marketplace technology. Himanshu's career spans more than two decades of engineering and product development leadership at some of India's and the world's largest and best known tech companies, including Oracle, Yahoo!, Flipkart, and most recently, Amazon. In this episode I chat with Himanshu about where he's seeing the most value from implementing AI, his experience working in both big tech and startups, and his upbringing in a small town in Northern India. --------------- The Asian Tech Leaders podcast is proudly supported by Vultr, an advanced cloud platform that is revolutionizing how developers build and deploy applications. Their cloud infrastructure, featuring globally available cloud compute, offers unparalleled performance without the vendor lock-in or outrageous egress charges. See what all the buzz is about when you visit GetVultr.com/ATL and use code ATL250 for $250 in cloud credit.

Himanshu, a core contributor to Sentient, discusses the vision and mission of the project in this conversation. Sentient aims to create a decentralized alternative to centralized AI, where contributors are rewarded for their contributions and the AI economy is more participatory. The project recently raised $85 million in funding led by Peter Thiel's fund. Himanshu explains that while $85 million may seem like a lot in the crypto world, it is not enough considering the expensive resources required for AI, such as compute and talent. He discusses his background in academia and his journey into building different systems related to blockchain and AI. He also explains how the idea for Sentient came about and the decision to focus on building a counterpart to centralized AI. He emphasizes the importance of participation in the AI economy and the need for a more inclusive and decentralized approach. He addresses the market forces that favor crypto AI, such as the availability of compute and the potential for a more powerful economic flywheel. He also discusses the challenges of attracting AI talent to the crypto space and explains how Sentient aims to build models and create an open economy where anyone can contribute and earn rewards. Sentient aims to solve the monetization problem of open source AI models through their Open Monetizable Loyal (OML) Models and Other Artifacts. OML models are open source, can be monetized, and are loyal to the builder's preferred alignment and safety rules. The OML protocol uses backdoor attacks as a basic primitive to tie ownership and monetization to the actual model. Sentient plans to attract and incentivize AI developers by offering distribution and revenue opportunities for their models. The platform will be released in a demo version at DevCon, with hackathons and limited circles experiencing it before that. Himanshu's Twitter: https://x.com/hstyagi Sentient's Twitter: https://x.com/sentient_agi Chapters 00:00 Introduction and Funding in Crypto AI 03:29 The Vision: Building a Decentralized Alternative to Centralized AI 09:07 Creating an Open and Participatory AI Economy 14:51 Sentient as an AI Company: Building Models and Providing AI 19:38 The Potential of Crypto AI and Access to Capital 30:38 Attracting AI Talent and the Role of the Younger Generation 34:32 The Future of Crypto AI: A More Inclusive and Decentralized AI Economy 35:01 Solving the Monetization Problem of Open Source AI Models 44:41 Introducing OML Models: Open, Monetizable, and Loyal 48:31 Using Backdoor Attacks to Tie Ownership and Monetization 51:25 Attracting and Incentivizing AI Developers with Sentient 01:00:33 Upcoming Release and Hackathons at DevCon Disclosures This podcast is strictly informational and educational and is not investment advice or a solicitation to buy or sell any tokens or securities or to make any financial decisions. Do not trade or invest in any project, tokens, or securities based upon this podcast episode. The host and members at Delphi Ventures may personally own tokens or art that are mentioned on the podcast. Our current show features paid sponsorships which may be featured at the start, middle, and/or the end of the episode. These sponsorships are for informational purposes only and are not a solicitation to use any product, service or token.

In this episode, Mariah Muhammad speaks with Himanshu Tiwari, Director of Quality and Risk Management at Sun Life Health Medical and Dental, about the challenges and opportunities in integrating medical and dental care, the need for robust quality metrics in dental health, and strategies for effective leadership in the evolving healthcare landscape.

Dubai Michelin-star Chef Himanshu Saini talks about the importance of representing your culture when starting in the culinary industry, what it means to be a Michelin-star Chef, and more.See omnystudio.com/listener for privacy information.

Continuing our special 10-part series on the Virtually Speaking Podcast: "Exploring VMware Cloud Foundation" in Episode 4,titled “VCF Compute”, Himanshu Singh, Director of vSphere Product Marketing, navigates us through the spectrum of vSphere editions, highlighting their adaptability for diverse customer needs. He then showcases the enhanced value proposition of vSphere within VMware Cloud Foundation, harnessing the synergy with NSX and Aria Automation to elevate private cloud infrastructures. Drawing from the essence of VMware vSphere, Himanshu emphasizes its role as the enterprise workload engine, integrating cutting-edge cloud infrastructure technology with DPU and GPU-based acceleration to amplify workload performance. vSphere optimizes IT environments, bolstering availability, simplifying lifecycle management, and streamlining maintenance for heightened operational efficiency. Moreover, it establishes an intrinsically secure infrastructure engine, fortified out-of-the-box and complemented by straightforward hardening guidance for compliance adherence. Links Mentioned: VCF Landing Page Announcing General Availability of VMware Cloud Foundation 5.1.1 VCF Webinars VCF YouTube Page Virtually Speaking YouTube Page Virtually Speaking Podcast Watch the Entire Series Ep 01: Inside the Private Cloud Ep 02: What's Inside Ep 03: The Cloud Admin Journey Ep 04: VCF Compute Ep 05: VCF Storage Ep 06: VCF Networking Ep 07: A Cloud Management Experience Ep 08: VMware Private AI Ep 09: Data Services Manager  Ep 10: VMware vDefend  The Virtually Speaking Podcast The Virtually Speaking Podcast is a technical podcast dedicated to discussing VMware topics related to private and hybrid cloud. Each week Pete Flecha and John Nicholson bring in various subject matter experts from VMware and from within the industry to discuss their respective areas of expertise. If you're new to the Virtually Speaking Podcast check out all episodes on vspeakingpodcast.com and follow on TwitterX @VirtSpeaking

In this episode, we speak with Himanshu Kohli, Co-Founder of Client Associates. Client Associates is a leading private wealth management firm dedicated to providing personalized financial solutions to high-net-worth individuals and families. With a strong presence in the Indian financial landscape, Client Associates has built a reputation for trust and excellence, guiding their clients through complex wealth management processes. Himanshu Kohli's journey is one of vision, innovation, and a deep understanding of wealth dynamics. Since its inception, Client Associates has evolved to address the growing needs of India's affluent, adapting to changing market conditions and client expectations. Himanshu's commitment to building a firm that not only manages wealth but also plans for succession and legacy is truly inspiring. His insights into building capabilities, fostering trust, and understanding the mindset of the wealthy in India offer valuable lessons for anyone interested in wealth management. In our conversation, we delve into the origins of Client Associates, the challenges and successes faced along the way, and the future of private wealth management in India. Himanshu shares his personal experiences, life learnings, and advice for the younger generation aspiring to enter this field. This episode provides a comprehensive look at the intricacies of managing wealth and the importance of adapting to a rapidly changing environment. We're also very delighted to share that we are now a part of the Zerodha Collective Network! Grateful to be a part of and have the support. Here's what we talked about: 0:00 - Preview 0:48 - Introduction 1:38 - Emerging Indian Wealth 4:22 - Starting Private Wealth Management 11:56 - Entering the Market 19:26 - Evolution of Client Associates 22:36 - Planning Succession Wealth 28:32 - Building Capabilities 33:04 - Building Trust 36:48 - How the Rich in India Think 41:56 - Adapting to Change 49:43 - Managing Risk 52:59 - Learning from Clients 53:42 - Himanshu's Life Lessons 56:11 - Leading Client Associates 1:03:48 - Life Beyond Client Associates 1:08:48 - His Biggest Superpower 1:09:48 - Advice for the Younger Generation 1:12:45 - Outro Tune in to gain insights from one of the leading minds in private wealth management and learn how to navigate the complexities of wealth creation and preservation Instagram of Jivraj - https://www.instagram.com/jivrajsinghsachar/ #indiansiliconvalley #isv #indiansiliconvalleypodcast #isvpodcast #jivrajsinghsachar

It's the Brownload on my side - Nice to meet you! This podcast comes three by one! And if none of that makes sense, you need to hit play! I've got tales of human encounters from my travels, Kej meets Himanshu-kaka and Sach is getting stressed out while having a massage!

Himanshu Palsule is CEO of Cornerstone, which provides a workforce agility platform to identify skill gaps and development opportunities within organizations. Himanshu joined as CEO in Jan 2022, bringing more than 35 years of diverse experience leading global organizations. Prior to joining Cornerstone, he was President of Epicor where he was responsible for managing vertical businesses and overseeing product operations. Palsule was previously CTO and Head of Strategy at Sage Software. Key highlights: Learning to live within your own skin and recalibrating yourself Concentric circles of evaluating ideas - Control, Influence, Interest Key trait when you have a seat at the table: Humility What is important when you have to say “I don't know” Going beyond the cliche and making customer obsession real What does it really mean to have a flat organization What does a future-ready Product Manager look like Connect with Himanshu Palsule, CEO of Cornerstone: https://www.linkedin.com/in/himanshu-palsule/ Connect with Rahul Abhyankar, Host of Product Leader's Journey: https://www.linkedin.com/in/rahulabhyankar/

 1000+ Prisoners Pardoned For Eid!Residents will enjoy 4 days of free parking this eid break!A resident received an adorable message from the driver after tipping him!Joined by Bhupender nath the founder of Trèsind Studio and Chef Himanshu Saini 

In this episode of the Life Science Success Podcast, Don interviews Himanshu Gadgil  from Enzine during Bio International. Himanshu explains Enzine's dual focus on developing biosimilars and their role as a CDMO with a game-changing platform, Enzine X. They discuss how Enzine's continuous manufacturing technology significantly cuts costs and speeds up market access for clients. Himanshu also talks about Enzine's global presence, clientele, and their future plans for expansion in the U.S. and India. Tune in to learn about the innovative approaches Enzine is bringing to the biotech industry.   00:00 Welcome and Introduction 00:07 Overview of Enzine Biotech 00:26 Enzine's Unique Manufacturing Platform 01:08 Clientele and Market Reach 01:41 Timelines and Efficiency 02:08 Future of Continuous Manufacturing 04:22 Geographical Expansion and Workforce 05:02 Enzine's Differentiators 06:10 Conclusion and Final Thoughts  

Join Himanshu on his emotional journey as he shares his experience of moving back to India after 18 years abroad. From Singapore to Canada and now back to his homeland, Himanshu opens up about his background, life in different countries, the decision-making process behind the moves, and why India holds a special place in his heart. He discusses the challenges and joys of relocating, the planning involved, settling into a new life in India, and offers valuable advice for those considering a similar move. Don't miss this insightful and heartfelt story of rediscovering roots and embracing new beginnings.

Oracle has been actively focusing on bringing AI to the enterprise at every layer of its tech stack, be it SaaS apps, AI services, infrastructure, or data.   In this episode, hosts Lois Houston and Nikita Abraham, along with senior instructors Hemant Gahankari and Himanshu Raj, discuss OCI AI and Machine Learning services. They also go over some key OCI Data Science concepts and responsible AI principles.   Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:   00:00 The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let's go! 00:33 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:46 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hey everyone! In our last episode, we dove into Generative AI and Language Learning Models.  Lois: Yeah, that was an interesting one. But today, we're going to discuss the AI and machine learning services offered by Oracle Cloud Infrastructure, and we'll look at the OCI AI infrastructure. Nikita: I'm also going to try and squeeze in a couple of questions on a topic I'm really keen about, which is responsible AI. To take us through all of this, we have two of our colleagues, Hemant Gahankari and Himanshu Raj. Hemant is a Senior Principal OCI Instructor and Himanshu is a Senior Instructor on AI/ML. So, let's get started! 01:36 Lois: Hi Hemant! We're so excited to have you here! We know that Oracle has really been focusing on bringing AI to the enterprise at every layer of our stack.  Hemant: It all begins with data and infrastructure layers. OCI AI services consume data, and AI services, in turn, are consumed by applications.  This approach involves extensive investment from infrastructure to SaaS applications. Generative AI and massive scale models are the more recent steps. Oracle AI is the portfolio of cloud services for helping organizations use the data they may have for the business-specific uses.  Business applications consume AI and ML services. The foundation of AI services and ML services is data. AI services contain pre-built models for specific uses. Some of the AI services are pre-trained, and some can be additionally trained by the customer with their own data.  AI services can be consumed by calling the API for the service, passing in the data to be processed, and the service returns a result. There is no infrastructure to be managed for using AI services.  02:58 Nikita: How do I access OCI AI services? Hemant: OCI AI services provide multiple methods for access. The most common method is the OCI Console. The OCI Console provides an easy to use, browser-based interface that enables access to notebook sessions and all the features of all the data science, as well as AI services.  The REST API provides access to service functionality but requires programming expertise. And API reference is provided in the product documentation. OCI also provides programming language SDKs for Java, Python, TypeScript, JavaScript, .Net, Go, and Ruby. The command line interface provides both quick access and full functionality without the need for scripting.  03:52 Lois: Hemant, what are the types of OCI AI services that are available?  Hemant: OCI AI services is a collection of services with pre-built machine learning models that make it easier for developers to build a variety of business applications. The models can also be custom trained for more accurate business results. The different services provided are digital assistant, language, vision, speech, document understanding, anomaly detection.  04:24 Lois: I know we're going to talk about them in more detail in the next episode, but can you introduce us to OCI Language, Vision, and Speech? Hemant: OCI Language allows you to perform sophisticated text analysis at scale. Using the pre-trained and custom models, you can process unstructured text to extract insights without data science expertise. Pre-trained models include language detection, sentiment analysis, key phrase extraction, text classification, named entity recognition, and personal identifiable information detection.  Custom models can be trained for named entity recognition and text classification with domain-specific data sets. In text translation, natural machine translation is used to translate text across numerous languages.  Using OCI Vision, you can upload images to detect and classify objects in them. Pre-trained models and custom models are supported. In image analysis, pre-trained models perform object detection, image classification, and optical character recognition. In image analysis, custom models can perform custom object detection by detecting the location of custom objects in an image and providing a bounding box.  The OCI Speech service is used to convert media files to readable texts that's stored in JSON and SRT format. Speech enables you to easily convert media files containing human speech into highly exact text transcriptions.  06:12 Nikita: That's great. And what about document understanding and anomaly detection? Hemant: Using OCI document understanding, you can upload documents to detect and classify text and objects in them. You can process individual files or batches of documents. In OCR, document understanding can detect and recognize text in a document. In text extraction, document understanding provides the word level and line level text, and the bounding box, coordinates of where the text is found.  In key value extraction, document understanding extracts a predefined list of key value pairs of information from receipts, invoices, passports, and driver IDs. In table extraction, document understanding extracts content in tabular format, maintaining the row and column relationship of cells. In document classification, the document understanding classifies documents into different types.  The OCI Anomaly Detection service is a service that analyzes large volume of multivariate or univariate time series data. The Anomaly Detection service increases the reliability of businesses by monitoring their critical assets and detecting anomalies early with high precision. Anomaly Detection is the identification of rare items, events, or observations in data that differ significantly from the expectation.  07:55 Nikita: Where is Anomaly Detection most useful? Hemant: The Anomaly Detection service is designed to help with analyzing large amounts of data and identifying the anomalies at the earliest possible time with maximum accuracy. Different sectors, such as utility, oil and gas, transportation, manufacturing, telecommunications, banking, and insurance use Anomaly Detection service for their day-to-day activities.  08:23 Lois: Ok…and the first OCI AI service you mentioned was digital assistant… Hemant: Oracle Digital Assistant is a platform that allows you to create and deploy digital assistants, which are AI driven interfaces that help users accomplish a variety of tasks with natural language conversations. When a user engages with the Digital Assistant, the Digital Assistant evaluates the user input and routes the conversation to and from the appropriate skills.  Digital Assistant greets the user upon access. Upon user requests, list what it can do and provide entry points into the given skills. It routes explicit user requests to the appropriate skills. And it also handles interruptions to flows and disambiguation. It also handles requests to exit the bot.  09:21 Nikita: Excellent! Let's bring Himanshu in to tell us about machine learning services. Hi Himanshu! Let's talk about OCI Data Science. Can you tell us a bit about it? Himanshu: OCI Data Science is the cloud service focused on serving the data scientist throughout the full machine learning life cycle with support for Python and open source.  The service has many features, such as model catalog, projects, JupyterLab notebook, model deployment, model training, management, model explanation, open source libraries, and AutoML.  09:56 Lois: Himanshu, what are the core principles of OCI Data Science?  Himanshu: There are three core principles of OCI Data Science. The first one, accelerated. The first principle is about accelerating the work of the individual data scientist. OCI Data Science provides data scientists with open source libraries along with easy access to a range of compute power without having to manage any infrastructure. It also includes Oracle's own library to help streamline many aspects of their work.  The second principle is collaborative. It goes beyond an individual data scientist's productivity to enable data science teams to work together. This is done through the sharing of assets, reducing duplicative work, and putting reproducibility and auditability of models for collaboration and risk management.  Third is enterprise grade. That means it's integrated with all the OCI Security and access protocols. The underlying infrastructure is fully managed. The customer does not have to think about provisioning compute and storage. And the service handles all the maintenance, patching, and upgrades so user can focus on solving business problems with data science.  11:11 Nikita: Let's drill down into the specifics of OCI Data Science. So far, we know it's cloud service to rapidly build, train, deploy, and manage machine learning models. But who can use it? Where is it? And how is it used? Himanshu: It serves data scientists and data science teams throughout the full machine learning life cycle.  Users work in a familiar JupyterLab notebook interface, where they write Python code. And how it is used? So users preserve their models in the model catalog and deploy their models to a managed infrastructure.  11:46 Lois: Walk us through some of the key terminology that's used. Himanshu: Some of the important product terminology of OCI Data Science are projects. The projects are containers that enable data science teams to organize their work. They represent collaborative work spaces for organizing and documenting data science assets, such as notebook sessions and models.  Note that tenancy can have as many projects as needed without limits. Now, this notebook session is where the data scientists work. Notebook sessions provide a JupyterLab environment with pre-installed open source libraries and the ability to add others. Notebook sessions are interactive coding environment for building and training models.  Notebook sessions run in a managed infrastructure and the user can select CPU or GPU, the compute shape, and amount of storage without having to do any manual provisioning. The other important feature is Conda environment. It's an open source environment and package management system and was created for Python programs.  12:53 Nikita: What is a Conda environment used for? Himanshu: It is used in the service to quickly install, run, and update packages and their dependencies. Conda easily creates, saves, loads, and switches between environments in your notebooks sessions. 13:07 Nikita: Earlier, you spoke about the support for Python in OCI Data Science. Is there a dedicated library? Himanshu: Oracle's Accelerated Data Science ADS SDK is a Python library that is included as part of OCI Data Science.  ADS has many functions and objects that automate or simplify the steps in the data science workflow, including connecting to data, exploring, and visualizing data. Training a model with AutoML, evaluating models, and explaining models. In addition, ADS provides a simple interface to access the data science service mode model catalog and other OCI services, including object storage.  13:45 Lois: I also hear a lot about models. What are models? Himanshu: Models define a mathematical representation of your data and business process. You create models in notebooks, sessions, inside projects.  13:57 Lois: What are some other important terminologies related to models? Himanshu: The next terminology is model catalog. The model catalog is a place to store, track, share, and manage models.  The model catalog is a centralized and managed repository of model artifacts. A stored model includes metadata about the provenance of the model, including Git-related information and the script. Our notebook used to push the model to the catalog. Models stored in the model catalog can be shared across members of a team, and they can be loaded back into a notebook session.  The next one is model deployments. Model deployments allow you to deploy models stored in the model catalog as HTTP endpoints on managed infrastructure.  14:45 Lois: So, how do you operationalize these models? Himanshu: Deploying machine learning models as web applications, HTTP API endpoints, serving predictions in real time is the most common way to operationalize models. HTTP endpoints or the API endpoints are flexible and can serve requests for the model predictions. Data science jobs enable you to define and run a repeatable machine learning tasks on fully managed infrastructure.  Nikita: Thanks for that, Himanshu.  15:18 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You'll find training on everything from cloud computing, database, and security, artificial intelligence, and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification. Visit mylearn.oracle.com to get started.  15:46 Nikita: Welcome back! The Oracle AI Stack consists of AI services and machine learning services, and these services are built using AI infrastructure. So, let's move on to that. Hemant, what are the components of OCI AI Infrastructure? Hemant: OCI AI Infrastructure is mainly composed of GPU-based instances. Instances can be virtual machines or bare metal machines. High performance cluster networking that allows instances to communicate to each other. Super clusters are a massive network of GPU instances with multiple petabytes per second of bandwidth. And a variety of fully managed storage options from a single byte to exabytes without upfront provisioning are also available.  16:35 Lois: Can we explore each of these components a little more? First, tell us, why do we need GPUs? Hemant: ML and AI needs lots of repetitive computations to be made on huge amounts of data. Parallel computing on GPUs is designed for many processes at the same time. A GPU is a piece of hardware that is incredibly good in performing computations.  GPU has thousands of lightweight cores, all working on their share of data in parallel. This gives them the ability to crunch through extremely large data set at tremendous speed.  17:14 Nikita: And what are the GPU instances offered by OCI? Hemant: GPU instances are ideally suited for model training and inference. Bare metal and virtual machine compute instances powered by NVIDIA GPUs H100, A100, A10, and V100 are made available by OCI.  17:35 Nikita: So how do we choose what to train from these different GPU options?  Hemant: For large scale AI training, data analytics, and high performance computing, bare metal instances BM 8 X NVIDIA H100 and BM 8 X NVIDIA A100 can be used.  These provide up to nine times faster AI training and 30 times higher acceleration for AI inferencing. The other bare metal and virtual machines are used for small AI training, inference, streaming, gaming, and virtual desktop infrastructure.  18:14 Lois: And why would someone choose the OCI AI stack over its counterparts? Hemant: Oracle offers all the features and is the most cost effective option when compared to its counterparts.  For example, BM GPU 4.8 version 2 instance costs just $4 per hour and is used by many customers.  Superclusters are a massive network with multiple petabytes per second of bandwidth. It can scale up to 4,096 OCI bare metal instances with 32,768 GPUs.  We also have a choice of bare metal A100 or H100 GPU instances, and we can select a variety of storage options, like object store, or block store, or even file system. For networking speeds, we can reach 1,600 GB per second with A100 GPUs and 3,200 GB per second with H100 GPUs.  With OCI storage, we can select local SSD up to four NVMe drives, block storage up to 32 terabytes per volume, object storage up to 10 terabytes per object, file systems up to eight exabyte per file system. OCI File system employs five replicated storage located in different fault domains to provide redundancy for resilient data protection.  HPC file systems, such as BeeGFS and many others are also offered. OCI HPC file systems are available on Oracle Cloud Marketplace and make it easy to deploy a variety of high performance file servers.  20:11 Lois: I think a discussion on AI would be incomplete if we don't talk about responsible AI. We're using AI more and more every day, but can we actually trust it? Hemant: For us to trust AI, it must be driven by ethics that guide us as well. Nikita: And do we have some principles that guide the use of AI? Hemant: AI should be lawful, complying with all applicable laws and regulations. AI should be ethical, that is it should ensure adherence to ethical principles and values that we uphold as humans. And AI should be robust, both from a technical and social perspective. Because even with the good intentions, AI systems can cause unintentional harm. AI systems do not operate in a lawless world. A number of legally binding rules at national and international level apply or are relevant to the development, deployment, and use of AI systems today. The law not only prohibits certain actions but also enables others, like protecting rights of minorities or protecting environment. Besides horizontally applicable rules, various domain-specific rules exist that apply to particular AI applications. For instance, the medical device regulation in the health care sector.  In AI context, equality entails that the systems' operations cannot generate unfairly biased outputs. And while we adopt AI, citizens right should also be protected.  21:50 Lois: Ok, but how do we derive AI ethics from these? Hemant: There are three main principles.  AI should be used to help humans and allow for oversight. It should never cause physical or social harm. Decisions taken by AI should be transparent and fair, and also should be explainable. AI that follows the AI ethical principles is responsible AI.  So if we map the AI ethical principles to responsible AI requirements, these will be like, AI systems should follow human-centric design principles and leave meaningful opportunity for human choice. This means securing human oversight. AI systems and environments in which they operate must be safe and secure, they must be technically robust, and should not be open to malicious use.  The development, and deployment, and use of AI systems must be fair, ensuring equal and just distribution of both benefits and costs. AI should be free from unfair bias and discrimination. Decisions taken by AI to the extent possible should be explainable to those directly and indirectly affected.  23:21 Nikita: This is all great, but what does a typical responsible AI implementation process look like?  Hemant: First, a governance needs to be put in place. Second, develop a set of policies and procedures to be followed. And once implemented, ensure compliance by regular monitoring and evaluation.  Lois: And this is all managed by developers? Hemant: Typical roles that are involved in the implementation cycles are developers, deployers, and end users of the AI.  23:56 Nikita: Can we talk about AI specifically in health care? How do we ensure that there is fairness and no bias? Hemant: AI systems are only as good as the data that they are trained on. If that data is predominantly from one gender or racial group, the AI systems might not perform as well on data from other groups.  24:21 Lois: Yeah, and there's also the issue of ensuring transparency, right? Hemant: AI systems often make decisions based on complex algorithms that are difficult for humans to understand. As a result, patients and health care providers can have difficulty trusting the decisions made by the AI. AI systems must be regularly evaluated to ensure that they are performing as intended and not causing harm to patients.  24:49 Nikita: Thank you, Hemant and Himanshu, for this really insightful session. If you're interested in learning more about the topics we discussed today, head on over to mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course.  Lois: That's right, Niki. You'll find demos that you watch as well as skill checks that you can attempt to better your understanding. In our next episode, we'll get into the OCI AI Services we discussed today and talk about them in more detail. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 25:25 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

In this episode of the IoT For All Podcast, Himanshu Mehrotra, Vice President of Product Management at FourKites, joins Ryan Chacon to discuss IoT in the freight industry. The conversation covers how IoT and AI enable revolutionary shipping visibility solutions, such as real-time transport visibility platforms, the importance of IoT in providing real-time data and predictive analytics to ensure timely production and delivery, automating gate operations and utilizing smart labels for granular tracking, data standardization and intermittent connectivity challenges, computer vision and cameras as sensors, and using generative AI to offer recommendations for optimizing the supply chain. Himanshu Mehrotra is the Vice President of Product Management at FourKites. He oversees the core shipment visibility solutions and the data platform that enables FourKites to connect to external ecosystems of data signals, either from direct integrations, signals from IoT devices, or onboarded devices on assets. Himanshu has over two decades of experience in supply chain technology. Prior to FourKites, he shaped the solution strategy and go-to-market approach for control tower and logistics solutions at Blue Yonder, where he also leveraged machine learning-based predictive analytics. Himanshu has been at the forefront of the supply chain industry's most transformational changes, witnessing firsthand the profound impact of events such as the pandemic on retail and manufacturing and the revolutionary influence of innovators like Amazon on customer behavior. FourKites extends real-time visibility beyond transportation into yards, warehouses, stores, and more, tracking over 3.2 million shipments daily across 200+ countries and territories. FourKites combines real-time data and powerful AI to help companies make their supply chain more efficient. Over 1,500 of the world's most recognized brands - including 9 of the top 10 CPG and 18 of the top 20 food and beverage companies - trust FourKites to reduce costs and increase customer satisfaction. Discover more about supply chain and IoT at https://www.iotforall.com More about FourKites: https://www.fourkites.com Connect with Himanshu: https://www.linkedin.com/in/himanshu-mehrotra-0401772/ (00:00) Intro (00:23) Himanshu Mehrotra and FourKites (03:14) IoT in the freight industry and supply chain (08:04) Advancements in IoT devices and connectivity (10:32) What does real-time IoT data enable? (12:42) Data standardization (14:07) Intermittent connectivity challenges (15:47) Turning data into value with AI (19:39) Using generative AI in the supply chain (21:03) What is the future of IoT in the freight industry (23:46) Cameras as sensors and computer vision (26:30) Learn more and follow up Subscribe on YouTube: https://bit.ly/2NlcEwm​ Join Our Newsletter: https://www.iotforall.com/iot-newsletter Follow Us on Social: https://linktr.ee/iot4all Check out the IoT For All Media Network: https://www.iotforall.com/podcast-overview

Explore the latest developments in advertising on our podcast episode. Join Prem and Himanshu from Georges Blog as they discuss Amazon's new 'Sponsored TV' ad type. In this episode, we share insights from a recent three-week case study where we tested this new ad format. Tune in to gain valuable insights into the effectiveness of Sponsored TV ads and their potential impact on your advertising strategy.Learn more about the case studies here.RESOURCESRead our News Feed.Book an Amazon Advertising audit.Follow me on Twitter.Amazon design examples.Follow our team.$85 to $117k in 45 days. 2-minute breakdown of what we did.Message George.RESOURCESRead our News Feed.Book an Amazon Advertising audit.Follow me on Twitter.Amazon design examples.Follow our team.$85 to $117k in 45 days. 2-minute breakdown of what we did.Message George.

               In S5 E5 I am delighted to welcome  Professor Himanshu Tambe to the podcast. Himanshu's passion is to empower individuals and organisations to thrive through continuous education. He is  currently Visiting Faculty at the Singapore Management University (SMU) and the Indian School of Business (ISB) teaching Design of Business, Organisation Design, Leadership and Workforce Analytics. He also operates an early-stage software product company focused on optimising operations.  Prior to this,  he held several senior roles with Accenture Strategy & Consulting, the last one being the Managing Director for the Talent & Organisation Consulting business in Southeast Asia and India. Before that he worked for Arthur D Little, the world's oldest consulting firm; established and operated a niche Strategy and Organisation Design company; and worked as an automobile manufacturing engineer at the very start of his career.       Over a 30-year career in consulting and industry,  he has proudly served more than 100 organisations across Public Sector, Metals & Mining and Banking in India, Singapore, Malaysia, Indonesia, Korea, Australia, and Europe. His  work has been focused on designing and implementing Business Models, Organisation Design, Process Models, and Large-Scale Behaviour Change to deliver measurable improvements in the performance of people and organization. Over this period, Himanshu has acquired deep experience facilitating senior executive teams to execute change through vision and values alignment.  Beyond the workplace he is, like me, an avid yoga practitioner and meditator and is learning jazz dance.     In this conversation Himanshu shares his insights from the global business environment on the key trends shaping the future of work and workforce.  We discuss modern work and role redesign, humans versus machine, data-driven change, the quest to reconnect with meaning and purpose and investing in "hinge" leadership and  unfreezing the frozen middle or core work-unit leaders. Many themes will be familiar to regular listeners and ultimately we are left with more questions and a call to action to reimagine the work environment. Thank you Himanshu. Episode links:LinkedIn: https://www.linkedin.com/in/himanshutambe/ Himanshu Tambe on The ISB Leadercast Podcast https://podcasts.apple.com/au/podcast/leadercast/id1691914486?i=1000626210529Digital Health Festival Melbourne May 7/8 2024 https://digitalhealthfest.com.au/Calling all Clinician Innovators :Applications have opened for the CICA Lab Incubator program. More details here: https://www.cicalab.co/cicalab-incubator The Mind Full Medic Podcast is proudly sponsored by the MBA NSW-ACT Find out more about their service or donate today at www.mbansw.org.auDisclaimer: The content in this podcast is not intended to constitute or be a substitute for professional medical advice, diagnosis or treatment. Always seek the advice of your doctor or other qualified health care professional. Moreover views expressed here are our own and do not necessarily reflect those of our employers or other official organisations.

In the fifth episode of the Kiln Rendez-Vous podcast, Edgar Roth engages with Himanshu Tyagi, Co-founder, and CTO of Witness Chain. The conversation delves into Tyagi's background and the factors that led him to explore optimistic rollups. Witness Chain specializes in providing watchtowers for rollups, DePIN, and AI Coprocessors. These programmable watchtowers enhance transaction validation on rollups by monitoring and addressing faulty transactions. The episode discusses key aspects such as an overview of Witness Chain, the significance of watchtowers in rollups, pricing and incentives for their watchtower service, insights into the future of Witness Chain and DePIN chains, and the potential for cross-chain applications.

Culture is a big component of software engineering success. A commitment to excellence and employee satisfaction is crucial. Success begins at the top with strong, passionate leadership.

Carers are people who provide unpaid care and support to someone who needs help with their day-to-day living, who has a disability, mental health condition, or any health condition requiring care and assistance. Carer Gateway is an Australian Government program providing support to carers. The program's digital photographic exhibition 'Real Carers Real Stories - In Their Own Words' features ten carers. Aakriti Chhetri is one of the unpaid carers. She provides support to her friends Himanshu and Neha who live with muscular dystrophy. Chhetri spoke to SBS Nepali about her experience of working as a carer in a new country, the rewards, and the learnings from her caregiving journey. - तपाईँ अस्ट्रेलियामा बस्नु हुन्छ भने तपाईँले 'केयरर' भन्ने शब्द सुन्नु भएको नै होला। केयरर अर्थात् स्याहारकर्ता भनेर ती व्यक्तिहरूलाई चिनिन्छ जसले अशक्तता, मानसिक समस्या वा कुनै किसिमको स्वास्थ्य समस्या भएका व्यक्तिहरूको स्याहार गर्दछन्। यस्तै केयररहरूको अनुभव सँगालेर अस्ट्रेलिया सरकार अन्तर्गत रहेको 'केयरर गेटवे'ले 'रियल केयरर्स रियल स्टोरिज - इन देयर ओन वर्ड्स' नामक एक डिजिटल फोटो प्रदर्शनी गरेको छ। त्यसमा एडिलेडकी आकृति क्षेत्रीलाई पनि देख्न सकिन्छ। छ वर्ष अघि विद्यार्थीको रूपमा अस्ट्रेलिया आएकी क्षेत्री, हाल एडिलेडको एक एज्ड केयरमा कम्युनिटी प्रोजेक्ट अफिसरको रूपमा कार्यरत छिन्। उनी सिड्नीमा रहेका आफ्ना दुई जना साथीहरूको केयरर हुन्। केयरर हुँदाको आफ्नो अनुभव र यसरी कसैलाई स्याहार गरिरहेका मानिसहरूका लागि केही सल्लाह सुझाव सहित क्षेत्रीले एसबीएस नेपालीसँग गरेको कुराकानी सुन्नुहोस्।

In this episode of Molecule to Market, you'll go inside the outsourcing space of the global drug development sector with Himanshu Gadgil, CEO at Enzene Biosciences. Your host, Raman Sehgal, discusses the pharmaceutical and biotechnology supply chain with Himanshu, covering: How a personal tragedy led him from the US to India on a mission to make an impact Shifting from a technical to commercial focus to launch several biosimilars in India and beyond Being at the inception of a big pharma biotech spin-out focused on building a platform of innovation that contributes to society Taking its cost-effective, continuous manufacturing platform from India to the US via a CDMO vertical focused on novel biologics Dr. Himanshu Gadgil serves as the CEO at Enzene Biosciences Ltd. Under his services, Enzene has grown from a start-up biotech to a multi-vertical, multi-site product development and manufacturing service-based biopharmaceutical company. Prior to Enzene, he worked as the Sr. Vice President at Intas Pharmaceutical Ltd. where he was instrumental in turning around the commercial product pipeline by launching several biosimilar products in multiple geographies. During his stint in the US, he led different facets of process and product development at Amgen, spearheading IND, BLA, and Market authorizations of various blockbuster biotech products. At the inception of his career, he joined Waters Corporation, where he pioneered the development of QBD, enabling multi-attribute methodologies for biopharmaceutical characterization. Himanshu holds a Ph.D. in Biochemistry from the University of Tennessee and is a passionate scientific leader and innovator with over 50 publications and patents. Please subscribe, tell your industry colleagues and join us in celebrating and promoting the value and importance of the global life science outsourcing space. We'd also appreciate a positive rating! Molecule to Market is sponsored and funded by ramarketing, an international marketing, design, digital and content agency helping companies differentiate, get noticed and grow in life sciences.

Chef Himanshu Saini is one of Dubai's most acclaimed chefs. His mission is to change the perception of Indian cuisine and elevate how we experience Indian food today. His restaurant, Trèsind Studio, is his ode to the culinary legacy of his roots and his perspective on Indian cuisine. We will hear about Himanshu Saini's childhood in an agricultural family in India, with plenty of fresh produce, herbs, and flowers used in the kitchen. At the end of the podcast he will reveal his favourite restaurant recommendations in Dubai, India and in the rest of the world. All of the recommendations mentioned in this podcast and thousands more are available for free in the World of Mouth app: https://www.worldofmouth.app/ Hosted on Acast. See acast.com/privacy for more information.

Join me as I sit down with the founders of Arch Lending, delving into the intricate world of crypto lending and blockchain financial services. From understanding the importance of qualified custodians to the tax implications of crypto-backed loans, this interview is a treasure trove of insights for both newcomers and seasoned crypto enthusiasts. Plus, get a sneak peek into Arch Lending's upcoming features and their vision for the future.

Himanshu kept seeing paisley everywhere: from his home temple to his couch cushions to his mom's pants. Some of these products were Indian but a lot weren't. How did this pattern get embraced by the world? See articlesofinterest.substack.com for images, links and more.

Today, Dee and Anand discuss the latest business and financial news, including Microsoft's decision to lay off 10,000 employees, the reported plummet in Shein's valuation as they seek $3 billion, and the Oxfam report stating that the richest 1% of people amassed almost two-thirds of new wealth created in the last two years. They also discuss recent developments in the world of pop culture, national news, politics, and world news, including Dr. Dre selling music assets to Universal Music and Shamrock, police surveillance in Beverly Hills, and Donald Trump's potential return to Twitter and Facebook. Plus, they dive into the world of fintech and crypto, discussing Arch's new crypto lending product and the gathering of prostitutes in Davos for the annual meeting of the global elite. Lastly. the gentlemen drop their weekly Winners, Losers, and Content. - written by ChatGPT Timeline of What Was Discussed: Microsoft's strange decision. (3:12)  Netflix's subscriptions are popping! (6:30)  The big challenge for Shein. (10:46)  How the richest 1% of people amassed almost two-thirds of new wealth created in the last two years. (18:12)  Peter Thiel is doing WELL. (23:02)  Dr. Dre is cashing out! (25:53)  The Beverly Hills police are watching you. (32:13)  The Don is back! (36:33)  The strange concept of Davos. (38:11)  A Group Chat exclusive interview with Dhruv and Himanshu from Arch. (41:12)  Winners, Losers, and Content. (58:12)  Group Chat Shout Outs. (1:05:34)  Related Links/Products Mentioned  Chatty Kathy Club  Microsoft is laying off 10,000 employees  Reed Hastings steps down as Netflix CEO amid subscriber gains  Shein valuation reportedly plummets by a third as it seeks $3B  The richest 1% of people amassed almost two-thirds of new wealth created in the last two years, Oxfam has said. - unusual_whales on Twitter  Trump backer Peter Thiel reportedly made $1.8 billion cashing out an 8-year bet on crypto – when he was still touting a massive bitcoin price surge  Dr. Dre Selling Music Assets to Universal Music and Shamrock  In (and Above) Beverly Hills, Police Are Watching  BREAKING: Donald Trump is preparing to come back to Twitter and Facebook, per NBC News  Prostitutes gather in Davos for annual meeting of global elite  Fintech Firm Arch Starts Crypto Lending Product, Raises $2.75M  Arch - The Easiest Way to Borrow Against Crypto and NFTs  Musk's Twitter Saw Revenue Drop 35% in Q4, Sharply Below Projections  What to know about extraordinary measures as debt ceiling hits  Zerofux Clothing Co  Connect with Group Chat! Watch The Pod #1 Newsletter In The World For The Gram Tweet With Us Exclusive Facebook Content We're @groupchatpod on Snapchat

The Afters with Himanshu “Heems” Suri On our new weekly lightning round mini ep with Himanshu “Heems” Suri, we're fucking around with the Grammys, Stoffa, reliving your golden years, erasing Das Racist from history, Nav's barber, Danny Brown's orthodontist, being a known racist, only eating Pizza Hut and Taco Bell, collabing with Rammstein vs. Insane Clown Posse, white people, Ezra Koening, Indian food and much more. For more Throwing Fits, check us out on Patreon: www.patreon.com/throwingfits.

The best rapper on this pod, first coolest. This week, the boys are welcoming the man himself, Himanshu “Heems” Suri, to the stu. Heems came through to spit bars on why exactly Das Racist broke up, fumbling the bag, tour life pre-social media, the curse of being ahead of your time, the enduring legacy of “Combination Pizza Hut and Taco Bell”, getting roofied in Japan, the joke rap label, learning how to take care of your mental health, memories of Anthony Bourdain, the best Indian food in Queens, leaving rap for the corporate world of a 9 to 5, ambitions of a retired rapper, whether or not Nav stole his swag, the culture shock of attending Wesleyan, going to high school with James, gangs of Stuyvesant High, supporting Arsenal, blowing bags at Stoffa, a very special TF freestyle and much more on this deep and dynamic episode of The Only Podcast That Matters™. For more Throwing Fits, check us out on Patreon: www.patreon.com/throwingfits. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app