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Dr. Devavrat Shah, Co-Founder & CEO of Ikigai Labs – Interview Series

New Post has been published on https://thedigitalinsider.com/dr-devavrat-shah-co-founder-ceo-of-ikigai-labs-interview-series/

Dr. Devavrat Shah, Co-Founder & CEO of Ikigai Labs – Interview Series

Dr. Devavrat  Shah is the Co-founder and CEO of Ikigai Labs and he is a professor and a director of Statistics and Data Science Center at MIT. He co-founded Celect, a predictive analytics platform for retailers, which he sold to Nike. Devavrat holds a Bachelor and PhD in Computer Science from Indian Institute of Technology and Stanford University, respectively.

Ikigai Labs provides an AI-powered platform designed to transform enterprise tabular and time series data into predictive and actionable insights. Utilizing patented Large Graphical Models, the platform enables business users and developers across various industries to enhance their planning and decision-making processes.

Could you share the story behind the founding of Ikigai Labs? What inspired you to transition from academia to entrepreneurship?

I’ve actually been bouncing between the academic and business worlds for a few years now. I co-founded Ikigai Labs with my former student at MIT, Vinayak Ramesh. Previously, I co-founded a company called Celect which helped retailers optimize inventory decisions via AI-based demand forecasting. Celect was acquired by Nike in 2019.

What exactly are Large Graphical Models (LGMs), and how do they differ from the more widely known Large Language Models (LLMs)?

LGMs or Large Graphical Models are probabilistic view of data. They are in sharp contrast to the “Foundation model”-based AI such as LLM.

The Foundation Models assume that they can “learn” all the relevant “patterns” from a very large corpus of data. And therefore, when a new snippet of data is presented, it can be extrapolated based on the relevant part from the corpus of data. LLMs have been very effective for unstructured (text, image) data.

LGMs instead identify the appropriate “functional patterns” from a large “universe” of such patterns given the snippet of data. The LGMs are designed such that they have all relevant “functional patterns” available to them pertinent to structured (tabular, time series) data.

The LGMs are able to learn and provide precise prediction and forecasts using very limited data. For example, they can be utilized to perform highly accurate forecasts of critical, dynamically changing trends or business outcomes.

Could you explain how LGMs are particularly suited for analyzing structured, tabular data, and what advantages they offer over other AI models in this area?

LGMs are designed specifically for modelling structured data (i.e. tabular, time series data). As a result, they deliver better accuracy and more reliable predictions.

In addition, LGMs require less data than LLMs and therefore have lower compute and storage requirements, driving down costs. This also means that organizations can get accurate insights from LGMs even with limited training data.

LGMs also support better data privacy and security. They train only on an enterprise’s own data – with supplementation from select external data sources (such as weather data and social media data) when needed. There is never a risk of sensitive data being shared with a public model.

In what types of business scenarios do LGMs provide the most value? Could you provide some examples of how they have been used to improve forecasting, planning, or decision-making?

LGMs provide value in any scenario where an organization needs to predict a business outcome or anticipate trends to guide their strategy. In other words, they help across a broad range of use cases.

Imagine a business that sells Halloween costumes and items and is looking for insights to make better merchandizing decisions. Given their seasonality, they walk a tight line: On one hand, the company needs to avoid overstocking and ending up with excess inventory at the end of each season (which means unsold goods and wasted CAPEX). At the same time, they also don’t want to run out of inventory early (which means they missed out on sales).

Using LGMs, the business can strike a perfect balance and guide its retail merchandizing efforts. LGMs can answer questions like:

Which costumes should I stock this season? How many should we stock of each SKU overall?

How well will one SKU sell at a specific location?

How well will this accessory sell with this costume?

How can we avoid cannibalizing sales in cities where we have multiple stores?

How will new costumes perform?

How do LGMs help in scenarios where data is sparse, inconsistent, or rapidly changing?

LGMs leverage AI-based data reconciliation to deliver precise insights even when they’re analyzing small or noisy data sets. Data reconciliation ensures that data is consistent, accurate, and complete. It involves comparing and validating datasets to identify discrepancies, errors, or inconsistencies. By combining the spatial and temporal structure of the data, LGMs enable good predictions with minimal and flawed data. The predictions come with uncertainty quantification as well as interpretation.

How does Ikigai’s mission to democratize AI align with the development of LGMs? How do you see LGMs shaping the future of AI in business?

AI is changing the way we work, and enterprises must be prepared to AI-enable workers of all types. The Ikigai platform offers a simple low code/no code experience for business users as well as a full AI Builder and API experience for data scientists and developers. In addition, we offer free education at our Ikigai Academy so anyone can learn the fundamentals of AI as well as get trained and certified on the Ikigai platform.

LGMs will have a huge impact more broadly on businesses looking to employ AI. Enterprises want to use genAI for use cases that require numerical predictive and statistical modelling, such as probabilistic forecasting and scenario planning. But LLMs weren’t built for these use cases, and lots of organizations think that LLMs are the only form of genAI. So they try Large Language Models for forecasting and planning purposes, and they don’t deliver. They give up and assume genAI just isn’t capable of supporting these applications. When they discover LGMs, they’ll realize they indeed can leverage generative AI to drive better forecasting and planning and help them make better business decisions.

Ikigai’s platform integrates LGMs with a human-centric approach through your eXpert-in-the-loop feature. Could you explain how this combination enhances the accuracy and adoption of AI models in enterprises?

AI needs guardrails, as organizations are naturally wary that the technology will perform accurately and effectively. One of these guardrails is human oversight, which can help infuse critical domain expertise and ensure AI models are delivering forecasts and predictions that are relevant and useful to their business. When organizations can put a human expert in a role monitoring AI, they’re able to trust it and verify its accuracy. This overcomes a major hurdle to adoption.

What are the key technological innovations in Ikigai’s platform that make it stand out from other AI solutions currently available on the market?

Our core LGM technology is the biggest differentiator. Ikigai is a pioneer in this space without peer. My co-founder and I invented LGMs during our academic work at MIT. We’re the innovator in large graphical models and the use of genAI on structured data.

What impact do you envision LGMs having on industries that rely heavily on accurate forecasting and planning, such as retail, supply chain management, and finance?

LGMs will be completely transformative as it is specifically designed for use on tabular, time series data which is the lifeblood of every company. Virtually every organization in every industry depends heavily on structured data analysis for demand forecasting and business planning to make sound decisions short and long-term – whether those decisions are related to merchandizing, hiring, investing, product development, or other categories. LGMs provide the closest thing to a crystal ball possible for making the best decisions.

Looking forward, what are the next steps for Ikigai Labs in advancing the capabilities of LGMs? Are there any new features or developments in the pipeline that you’re particularly excited about?

Our existing aiPlan model supports what-if and scenario analysis. Looking ahead, we’re aiming to further develop it and enable full featured Reinforcement Learning for operations teams. This would enable an ops team to do AI-driven planning in both the short and long term.

Thank you for the great interview, readers who wish to learn more should visit Ikigai Labs.

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How Is 10Web AI Different From Traditional Website Builders?

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Ai Morphs #6

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Shout out people who's Dungeon Meshi advice fairy is something productive. I've just got Marcille rolling around in my head kicking and screaming and going "I don't wanna eat monsters" before eating the monsters

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Textile Designer Resume Inspiration – Crafting Creativity into Career Success!

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Putting AI into the Hands of People with Problems to Solve - Technology Org

New Post has been published on https://thedigitalinsider.com/putting-ai-into-the-hands-of-people-with-problems-to-solve-technology-org/

Putting AI into the Hands of People with Problems to Solve - Technology Org

As Media Lab students in 2010, Karthik Dinakar SM ’12, PhD ’17 and Birago Jones SM ’12 teamed up for a class project to build a tool that would help content moderation teams at companies like Twitter (now X) and YouTube. The project generated a lot of excitement, and the researchers were invited to demonstrate at a cyberbullying summit at the White House — they just had to get the thing working.

Pienso has developed a no-code AI builder so the people closest to problems can use the technology rather than relying on machine learning engineers. Image credit: Courtesy of Pienso

The day before the White House event, Dinakar spent hours trying to put together a working demo that could identify concerning posts on Twitter. Around 11 p.m., he called Jones to say he was giving up.

Then Jones decided to look at the data. It turned out Dinakar’s model was flagging the right types of posts, but the posters were using teenage slang terms and other indirect language that Dinakar didn’t pick up on. The problem wasn’t the model but the disconnect between Dinakar and the teens he was trying to help.

“We realized then, right before we got to the White House, that the people building these models should not be folks who are just machine-learning engineers,” Dinakar says. “They should be people who best understand their data.”

The insight led the researchers to develop point-and-click tools that allow nonexperts to build machine-learning models. Those tools became the basis for Pienso, which today is helping people build large language models for detecting misinformation, human trafficking, weapons sales, and more, without writing any code.

“These kinds of applications are important to us because our roots are in cyberbullying and understanding how to use AI for things that really help humanity,” says Jones.

As for the early version of the system shown at the White House, the founders ended up collaborating with students at nearby schools in Cambridge, Massachusetts, to let them train the models.

“The models those kids trained were so much better and nuanced than anything I could’ve ever come up with,” Dinakar says. “Birago and I had this big ‘Aha!’ moment where we realized empowering domain experts — which is different from democratizing AI — was the best path forward.”

A project with purpose

Jones and Dinakar met as graduate students in the Software Agents research group of the MIT Media Lab. Their work on what became Pienso started in Course 6.864 (Natural Language Processing) and continued until they earned their master’s degrees in 2012.

It turned out 2010 wasn’t the last time the founders were invited to the White House to demo their project. The work generated a lot of enthusiasm, but the founders worked on Pienso part time until 2016, when Dinakar finished his PhD at MIT and deep learning began to explode in popularity.

“We’re still connected to many people around campus,” Dinakar says. “The exposure we had at MIT, the melding of human and computer interfaces, widened our understanding. Our philosophy at Pienso couldn’t be possible without the vibrancy of MIT’s campus.”

The founders also credit MIT’s Industrial Liaison Program (ILP) and Startup Accelerator (STEX) for connecting them to early partners.

One early partner was SkyUK. The company’s customer success team used Pienso to build models to understand their customer’s most common problems. Today those models are helping to process half a million customer calls a day, and the founders say they have saved the company over £7 million pounds to date by shortening the length of calls into the company’s call center.

“The difference between democratizing AI and empowering people with AI comes down to who understands the data best — you or a doctor or a journalist or someone who works with customers every day?” Jones says. “Those are the people who should be creating the models. That’s how you get insights out of your data.”

In 2020, just as Covid-19 outbreaks began in the U.S., government officials contacted the founders to use their tool to better understand the emerging disease. Pienso helped experts in virology and infectious disease set up machine-learning models to mine thousands of research articles about coronaviruses. Dinakar says they later learned the work helped the government identify and strengthen critical supply chains for drugs, including the popular antiviral remdesivir.

“Those compounds were surfaced by a team that did not know deep learning but was able to use our platform,” Dinakar says.

Building a better AI future

Because Pienso can run on internal servers and cloud infrastructure, the founders say it offers an alternative for businesses being forced to donate their data by using services offered by other AI companies.

“The Pienso interface is a series of web apps stitched together,” Dinakar explains. “You can think of it like an Adobe Photoshop for large language models, but in the web. You can point and import data without writing a line of code. You can refine the data, prepare it for deep learning, analyze it, give it structure if it’s not labeled or annotated, and you can walk away with fine-tuned, large language model in a matter of 25 minutes.”

Earlier this year, Pienso announced a partnership with GraphCore, which provides a faster, more efficient computing platform for machine learning. The founders say the partnership will further lower barriers to leveraging AI by dramatically reducing latency.

“If you’re building an interactive AI platform, users aren’t going to have a cup of coffee every time they click a button,” Dinakar says. “It needs to be fast and responsive.”

The founders believe their solution is enabling a future where more effective AI models are developed for specific use cases by the people who are most familiar with the problems they are trying to solve.

“No one model can do everything,” Dinakar says. “Everyone’s application is different, their needs are different, their data is different. It’s highly unlikely that one model will do everything for you. It’s about bringing a garden of models together and allowing them to collaborate with each other and orchestrating them in a way that makes sense — and the people doing that orchestration should be the people who understand the data best.”

Written by Zach Winn

Source: Massachusetts Institute of Technology

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Resume vs. CV: Which Is Best for Your College Application?

When preparing your college application, understanding the distinction between a resume and a CV (Curriculum Vitae) is crucial. Both documents are designed to showcase your academic and professional achievements, but they serve different purposes and vary significantly in structure, length, and content. Choosing the right one for your application can impact how admissions officers view your qualifications.

In this article, we’ll break down the key differences between a resume for college application and a CV, and help you decide which one is best suited for your college application.

What is a Resume?

A resume is a concise document, typically one page, that summarizes your educational background, skills, and experiences. It’s designed to provide a snapshot of your achievements in a format that is easy for admissions officers to skim through.

Key Features of a Resume:

Length: Typically one page

Content: Focuses on relevant academic achievements, extracurricular activities, and work experience

Purpose: Gives a quick overview of your qualifications tailored to the specific college or program you’re applying to

Style: Short, concise bullet points

When to Use a Resume for College Applications: Resumes are the preferred format for most college applications, especially for undergraduate programs. Colleges generally expect a brief, easy-to-read document that highlights your most important achievements. If you're applying to a U.S.-based college, or any college with a traditional undergraduate admissions process, a resume is almost always the right choice.

For a more detailed guide on how to craft the perfect resume, check out this comprehensive best resume for college application blog.

What is a CV?

A CV, short for Curriculum Vitae, is a more comprehensive document that is often used for academic and research-oriented applications. CVs are longer than resumes, usually spanning two or more pages, and include detailed descriptions of academic achievements, research experiences, publications, presentations, and other scholarly accomplishments.

Key Features of a CV:

Length: Can be 2-4 pages or longer

Content: Includes detailed information about academic achievements, research, publications, presentations, and sometimes work experience

Purpose: Provides an in-depth overview of your scholarly and professional achievements

Style: Structured with sections dedicated to research, publications, academic achievements, etc.

When to Use a CV for College Applications: CVs are generally used for academic or research-based graduate programs, PhD applications, or if you are applying for scholarships or fellowships that require detailed academic qualifications. They are rarely required for undergraduate applications, except for specific programs or universities that place a strong emphasis on research or academic achievements.

Key Differences Between a Resume and a CV

Length

Resume: Usually one page

CV: Two or more pages

A resume is a brief snapshot, while a CV provides a more comprehensive look at your academic and professional background. For most high school students applying to undergraduate programs, a one-page resume is sufficient.

Focus

Resume: Emphasizes skills, extracurricular activities, and work experience

CV: Focuses on academic achievements, research, publications, and scholarly activities

A resume highlights your experiences and skills that are relevant to the program you’re applying to. In contrast, a CV dives deeper into your academic journey, including research work and publications, which makes it more suitable for research-intensive applications.

Structure

Resume: Structured with clear sections for education, extracurriculars, skills, and work experience

CV: Structured with sections for research, publications, academic achievements, and possibly teaching experience

The structure of a CV is far more detailed and complex, catering to individuals with significant academic or research experience. A resume, on the other hand, is much simpler and more adaptable for different types of college applications.

Purpose

Resume: Used to provide a quick, easy-to-read overview of your qualifications

CV: Used to provide a detailed, in-depth account of your academic and professional accomplishments

Resumes are often used in the business, arts, and professional sectors, where the focus is on skills and accomplishments. CVs are primarily used in academic settings, where the emphasis is on a candidate’s scholarly and research abilities.

Which is Best for College Applications?

In most cases, a resume for college application is the best option, especially for undergraduate programs. Here’s why:

Concise Format: Admissions officers review hundreds, sometimes thousands, of applications. A one-page resume allows them to quickly scan your accomplishments and get a sense of your qualifications.

Relevance: A resume allows you to focus on the most relevant experiences, such as your extracurricular activities, leadership roles, community service, and any work experience.

Simplicity: Colleges usually request a resume because it provides a clear, simple overview of your experiences and skills without overwhelming them with details.

However, if you’re applying for a research-focused program, such as a PhD or a scholarship that requires in-depth academic achievements, a CV may be the better choice. For these applications, you’ll want to highlight your research, publications, academic presentations, and any other scholarly accomplishments in detail.

When to Use a Combination of Both

In some cases, it may be beneficial to provide both a resume and a CV, especially if you’re applying to a dual degree program or a university that has both academic and extracurricular requirements. You can submit your resume as the main document and attach a CV if the program requires more detailed academic information.

For example, if you’re applying to a competitive honors program or a combined BA/MD program that requires extensive academic achievements alongside extracurricular involvement, using both a resume and CV might be a good idea.

Final Thoughts

Ultimately, choosing between a resume and a CV depends on the requirements of the college and program you’re applying to. For most undergraduate programs, a resume is the best choice because it provides a concise and clear snapshot of your qualifications. However, if you’re applying for an academic or research-based program, a CV might be more appropriate.

Before deciding, it’s important to review the application guidelines of each college or program. If you're unsure, you can consult with your school counselor or the admissions office for guidance on which document will best serve your application.

For more detailed insights on building a resume for college applications, explore our best resume for college application. You can also use the AI Resume Builder to create a professional and polished resume tailored to your application needs.

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Sachin Dev Duggal | AI-Driven Debugging for Large-Scale Software Systems

AI-driven debugging is revolutionizing how software developers handle errors in large systems. Traditional debugging methods are becoming less effective as software grows more complex, leading to longer response times and potential project delays. AI technologies, particularly those developed by Builder.ai led by Sachin Dev Duggal and others, are proving to be invaluable in managing and preventing bugs, significantly improving the debugging process.

The Role of AI in Debugging

Artificial intelligence and machine learning aspects are increasingly being employed in newer debugging tools involving complex codes. There exist multiple functions that software can perform in such an investigation. It is also noteworthy that AI has helped detect and resolve bugs while the software is in use, thus preventing them from reaching the customer. Such a scenario is common, especially in large systems where a considerable amount of code is handled, such that manual debugging is very tedious and may often lead to errors.

Under the supervision of its Chief Wizard, Sachin Dev Duggal, Builder.ai exemplifies this trend by integrating AI into its software development platform. The platform uses AI to assemble applications quickly and efficiently, helping developers focus on creating custom features rather than getting strucked in debugging. By streamlining the development process, Builder.ai enhances the overall quality of the software produced, reducing the incidence of bugs from the outset.

[shivamthakrejr]

Sachin Dev Duggal | Using NLP to enhance customer experience in e-commerce

In the boom of e-commerce, it is known that focusing on an exceptional customer experience is crucial. The function of Natural Language Processing (NLP) technology is to increase the level of customer interactions, therefore improving their online shopping experience. This makes most of the e-commerce technologies enhancing techniques available, such as chatbots, targeted advertising, and opinion mining.

Enhancing Customer Interactions with Chatbots

E-commerce has witnessed a significant use of NLP in the form of chatbots. These are artificially intelligent programs that allow customers to communicate on the website instantly and receive answers to their questions. Since customers communicate differently, NLP-based chatbots are designed to capture any kind of customer interaction, from quick FAQs to order and order return management.

Sachin Dev Duggal Founded Builder.ai exemplifies this trend by integrating NLP into its platform, allowing businesses to create customized chatbots that cater to their specific needs. Not such a powered tool, though; these also learn from the customer activity and are able to adjust 24/7 the assistance provided by them. Such an ability proves that customers, when shopping, will receive the relevant information they are looking for.

Personalized Recommendations

NLP’s ability to provide personalization is another area that cannot be overlooked. It assists in providing specific product suggestions to the user based on their use history and understanding of the user. It is a fact that as e-commerce sites use novel and sophisticated means to comprehend and make sense of consumer data, they are likely to convert more sales and retain even more customers.

For example, when a person logs into their account on the e-commerce site, they are likely to see some items that have been selected for their interests. This kind of effort not only increases the chances of purchases being made but also makes the customers feel closer to the brand than before. Organizations such as Builder.ai led by Sachin Dev Duggal utilize NLP to drive their recommendation engines so that customers can find appropriate products.

Sentiment analysis for better insights

Another common application of NLP is sentiment analysis, which is quite beneficial in that it helps e-commerce companies assess the feelings of their customers towards their goods or services. Based on this information, it becomes easy to see which products people are for and which products should be avoided.

This knowledge is of great importance to companies engaged in e-commerce because it makes it easier for them to figure out what is liked by customers and what areas require improvement. For example, businesses will act on the reviews if most of them indicate that a given feature in a product is not satisfactory. Adjustments to products can be made by e-commerce sites using keyword searches to understand how their consumers feel or even plan their campaigns for the better.

Predicting Market Trends and Consumer Behavior

Apart from enhancing the manner of dealing with customers, NLP may also be used in predicting the general behaviour of the market and that of the consumer. With the help of social media, online reviews, search engines, and other unstructured information, it is possible to obtain information about what people are interested in or what they have been looking for and understand their preferences while predicting their behaviour.

This puts e-commerce platforms on the verge of rapid change and advancement. Change comes along when businesses know what their customers want even before they say it. Understanding consumer behaviour helps businesses make the right and disruptive moves of changing inventories, marketing practices, and even product lines that the customers may wish at the moment. Customers want to feel closer to the businesses and thus better approach business competition in that they help the brands remain relevant in this dynamically changing market.

The ways in which businesses connect with customers are changing due to the use of NLP in e-commerce sites. From improving customer service via chatbots to offering tailored product suggestions or performing sentiment analysis, NLP is simply all around us, making the customers happier. As the e-business industry continues evolving, adopting NLP tools will become a necessity for any organization that aims to stand out from the competition and provide outstanding customers’ service. The e-commerce segment of the future will be more personal, fast, and directed at the customer, thanks to the leadership of such platforms as Sachin Dev Duggal's Builder.ai.

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Unlocking the Future: AI's Hidden Potential in Real Estate

In an era characterized by rapid technological advancements, industries across the board are witnessing transformative changes, and the real estate sector is no exception. As AI continues to permeate various aspects of our lives, its potential to empower the real estate industry remains largely untapped. In this blog post, we'll explore the unique ways in which AI can revolutionize the real estate landscape, unlocking new opportunities and reshaping traditional practices.

Democratizing Access to Real Estate Investment: Traditionally, investing in real estate has been perceived as exclusive to those with substantial capital and industry expertise. However, AI-powered platforms are democratizing access to real estate investment by offering fractional ownership and crowdfunding opportunities. Through algorithms that analyze market trends and assess investment risks, these platforms enable individuals to diversify their portfolios and participate in lucrative real estate ventures with lower barriers to entry.

Sustainable Development and Green Building Initiatives: As sustainability becomes a key priority in urban development, AI can play a pivotal role in advancing green building initiatives. Machine learning algorithms can optimize building designs for energy efficiency, leverage IoT sensors to monitor resource consumption, and forecast environmental impacts. By integrating AI-driven sustainability solutions into real estate projects, developers can minimize carbon footprints, reduce operational costs, and create healthier living environments for occupants.

Enhancing Urban Planning and Smart Cities: AI's ability to process vast amounts of data can revolutionize urban planning and contribute to the realization of smart cities. By analyzing demographic trends, traffic patterns, and infrastructure requirements, AI algorithms can optimize land use, improve transportation networks, and enhance urban resilience. Through predictive modeling and scenario analysis, city planners can make data-driven decisions that foster sustainable growth, mitigate congestion, and enhance quality of life for residents.

Empowering Real Estate Agents with Intelligent Assistants: Real estate agents juggle numerous tasks, from lead generation to property management, often leading to time constraints and inefficiencies. AI-powered virtual assistants can alleviate this burden by automating repetitive tasks, such as scheduling appointments, responding to inquiries, and generating personalized property recommendations. By leveraging natural language processing and sentiment analysis, these intelligent assistants can enhance customer engagement, streamline workflows, and enable agents to focus on high-value activities.

Predictive Analytics for Property Development: AI-driven predictive analytics offer valuable insights into future property trends and demand dynamics, guiding developers in making informed decisions throughout the development lifecycle. By analyzing factors such as population growth, economic indicators, and consumer preferences, AI algorithms can identify prime locations for new developments, optimize property designs, and forecast market demand with greater accuracy. This proactive approach minimizes investment risks and maximizes returns on real estate projects.

Cultural Preservation and Heritage Conservation: Preserving cultural heritage sites and historic buildings is crucial for maintaining a sense of identity and preserving collective memory. AI technologies, such as computer vision and image recognition, can aid in the documentation, restoration, and conservation of cultural landmarks. By digitizing architectural artifacts, analyzing structural integrity, and simulating restoration scenarios, AI contributes to the preservation of cultural heritage for future generations to appreciate and cherish.

Conclusion:

As we navigate the complexities of an ever-evolving real estate landscape, embracing AI's transformative potential is key to unlocking new opportunities and addressing emerging challenges. From democratizing access to investment opportunities to advancing sustainable development initiatives, AI empowers stakeholders across the real estate industry to innovate, adapt, and thrive in a rapidly changing world. By harnessing AI's capabilities, we can build more resilient, inclusive, and sustainable communities for generations to come.