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WASHINGTON (AP) — A machinists strike. Another safety problem involving its troubled top-selling airliner. A plunging stock price. 2024 was already a dispiriting year for Boeing, the American aviation giant. But when one of the company's jets crash-landed in South Korea on Sunday, killing all but two of the 181 people on board, it brought to a close an especially unfortunate year for Boeing. The cause of the crash remains under investigation, and aviation experts were quick to distinguish Sunday's incident from the company’s earlier safety problems. Alan Price, a former chief pilot at Delta Air Lines who is now a consultant, said it would be inappropriate to link the incident Sunday to two fatal crashes involving Boeing’s troubled 737 Max jetliner in 2018 and 2019. In January this year, a door plug blew off a 737 Max while it was in flight, raising more questions about the plane. The Boeing 737-800 that crash-landed in Korea, Price noted, is “a very proven airplane. "It’s different from the Max ...It’s a very safe airplane.’’ For decades, Boeing has maintained a role as one of the giants of American manufacturing. But the the past year's repeated troubles have been damaging. The company's stock price is down more than 30% in 2024. The company's reputation for safety was especially tarnished by the 737 Max crashes, which occurred off the coast of Indonesia and in Ethiopia less than five months apart in 2018 and 2019 and left a combined 346 people dead. In the five years since then, Boeing has lost more than $23 billion. And it has fallen behind its European rival, Airbus, in selling and delivering new planes. Last fall, 33,000 Boeing machinists went on strike, crippling the production of the 737 Max, the company's bestseller, the 777 airliner and 767 cargo plane. The walkout lasted seven weeks, until members of the International Association of Machinists and Aerospace Workers agreed to an offer that included 38% pay raises over four years. In January, a door plug blew off a 737 Max during an Alaska Airlines flight. Federal regulators responded by imposing limits on Boeing aircraft production that they said would remain in place until they felt confident about manufacturing safety at the company. In July, Boeing agreed to plead guilty to conspiracy to commit fraud for deceiving the Federal Aviation Administration regulators who approved the 737 Max. Acting on Boeing’s incomplete disclosures, the FAA approved minimal, computer-based training instead of more intensive training in flight simulators. Simulator training would have increased the cost for airlines to operate the Max and might have pushed some to buy planes from Airbus instead. (Prosecutors said they lacked evidence to argue that Boeing’s deception had played a role in the crashes.) But the plea deal was rejected this month by a federal judge in Texas, Reed O’Connor , who decided that diversity, inclusion and equity or DEI policies in the government and at Boeing could result in race being a factor in choosing an official to oversee Boeing’s compliance with the agreement. Boeing has sought to change its culture. Under intense pressure over safety issues, David Calhoun departed as CEO in August. Since January, 70,000 Boeing employees have participated in meetings to discuss ways to improve safety.Larson Financial Group LLC Sells 85 Shares of Dover Co. (NYSE:DOV)
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Share Tweet Share Share Email Himanshu Sinha is a globally recognized thought leader in AI and data science, currently serving as the Director of Advanced Data Science at Marriott International. An AI innovator with deep-learning based patent to his name and a published author of the best-selling book “Cognitive Horizons: Navigating the Landscape of Artificial Intelligence” His book is widely adopted by educational institutions to guide data science capabilities and inspire the next generation of AI practitioners. As a foundational contributor to machine learning and AI research, Himanshu has published extensively in internationally acclaimed journals , pushing the boundaries of innovation. With over 18 years of experience, Himanshu has led data science capabilities at organizations like CVS, Precisely, and Wipro, delivering transformative results across industries. Currently, he is driving AI-powered innovation in the hospitality sector, redefining customer experiences at Marriott International. Himanshu’s respected record of using data, analytics, and AI to deliver significant business impact is complemented by his role as an advisor and coach to executives, boards, and data leaders. His visionary leadership continues to transform organizations and inspire the global AI community. Himanshu Sinha Please tell us your name and a little more about yourself. My name is Himanshu Sinha, and I am a data scientist with over 15 years of experience in leveraging machine learning and AI to drive transformative change across industries. From fintech to healthcare and hospitality, I have had the privilege of developing and deploying innovative AI solutions that blend technical excellence with business strategy. My expertise spans a wide range of areas, including generative AI, predictive analytics, and data observability. Beyond my technical work, I am a passionate mentor, a published co-author of books on AI and ML, and a patented innovator. I thrive on building solutions that not only solve complex problems but also inspire others to push the boundaries of what technology can achieve. You’ve had an extraordinary career in AI and machine learning, spanning various industries like healthcare, hospitality, and fintech. Can you walk us through your journey and how it has shaped your current role as Director of Advanced Data Science at Marriott International? I began my professional journey in consumer-focused research for a CPG brand, here I developed a fascination for consumer behaviour change indicated through and their business implications . This passion deepened as I worked on leveraging state-of-the-art machine learning techniques to analyze behavioral changes and their impact on business outcomes. My focus has always been on helping organizations use patterns in data to inform strategy and drive results. Working in large scale consulting such as Wipro brought me close to enterprises’s pain areas and help create machine learning cored solutions for them. In 2012, I established a Center of Excellence for analytics at Agilis, a lesser-known company at the time, helping telecom clients predict fraud and mitigate risks like churn. Over the years, Agilis went through multiple acquisitions, becoming Infogix and later Precisely Inc. During this period, I led the development of a bespoke software product with an embedded ML-based ‘Anomaly Detection’ system. This solution predicts ‘data drift and shift’, ensuring data quality and integrity. Today, it’s a multimillion-dollar offering used by Fortune 100 companies to maintain operational excellence of data dependent functions. Currently, as the Director of Advanced Data Science at Marriott International, I lead transformative AI initiatives, including personalization models, generative AI applications, and global acquisition strategies. My journey has been about building scalable AI solutions that align technical innovation with business impact, and I continue to be inspired by the limitless potential of machine learning and AI to transform industries. In your article, “The Ultimate Blueprint for Enterprise Chatbots: Simplify, Scale, Succeed”, you discuss the challenges of fragmented chatbot strategies. What inspired you to advocate for a unified chatbot framework, and how do you see it transforming industries? The inspiration came from witnessing the inefficiencies and frustrations caused by fragmented chatbot strategies in enterprises—siloed implementations, inconsistent user experiences, and high maintenance costs. I realized the need for a unified framework that could streamline development, ensure scalability, and provide a seamless experience across touchpoints. A unified chatbot framework transforms industries by enabling businesses to deliver consistent, personalized interactions while reducing operational overhead. It empowers enterprises to integrate. Enterprises often face the dilemma of balancing ROI with the complexity of AI solutions. How do you approach this challenge in your projects, and can you share a specific example where you had to make such decisions? Balancing ROI with AI complexity is a common challenge, but I approach it by focusing on simplification and scalability without compromising value. In my article, The Ultimate Blueprint for Enterprise Chatbots: Simplify, Scale, Succeed , I discussed how fragmented chatbot strategies often create inefficiencies and diminish user satisfaction. To address this, I advocate for a unified framework that prioritizes intent-driven design and reusable components. A specific example is a chatbot project for a travel enterprise. Initially, individual bots were handling separate use cases like booking management, trip planning, and customer support, leading to redundancy and confusion. By implementing a unified triage bot as the primary entry point, we streamlined user interactions and routed intents to specialized APIs. This reduced operational complexity and boosted ROI by consolidating efforts into a cohesive system. The result? Enhanced user satisfaction and scalable deployment across multiple business units—all while maintaining a strong ROI through reduced maintenance costs and improved efficiency. Simplifying complexity is not just about technology—it’s about creating value for both users and businesses. Your work highlights generative AI’s role in creating personalized user experiences, such as credit card recommendations. How do you envision the future of generative AI in personalization across industries? Generative AI is revolutionizing personalization by creating tailored, intuitive experiences that resonate with individual users. In the context of chatbots, like those for credit card recommendations, generative AI plays a pivotal role in breaking decision paralysis. It simplifies complex choices by presenting relevant options in a conversational and engaging way, helping customers make faster and more confident decisions. Looking ahead, I envision generative AI expanding its influence across industries—from retail to healthcare—where it will not only personalize user interactions but also anticipate needs, provide proactive solutions, and streamline decision-making. The future of personalization lies in AI’s ability to combine deep contextual understanding with dynamic adaptability, ultimately creating experiences that are both efficient and deeply human-centered. You’ve authored books, published scholarly articles, and filed patents in advanced AI domains. How do you balance your role as a technical leader and a thought leader in the ever-evolving field of AI? Balancing my roles as a technical and thought leader in AI comes down to a shared foundation: curiosity , giving back to learners community and a commitment to impact. As a technical leader, I focus on driving practical innovations—building systems like scalable enterprise chatbots or anomaly detection frameworks that solve real business problems. This hands-on work keeps me connected to the evolving challenges and opportunities in AI. As a thought leader, I view my role as amplifying these learnings for a broader audience. Writing articles on social blogs like “The Ultimate Blueprint for Enterprise Chatbots” allows me to distill complex ideas into actionable strategies, fostering industry-wide collaboration and growth. It’s a synergy—my technical work feeds my thought leadership, while my engagement with the AI community sharpens my ability to lead in dynamic environments. Ultimately, the key is staying grounded in purpose: using AI not just to innovate, but to inspire and drive meaningful change. Many of your projects aim to enhance customer experience through AI, like anomaly detection and marketing optimization. How do you align technical innovation with customer-centric outcomes? I believe the key to aligning technical innovation with customer-centric outcomes is to start with the end in mind—understanding what the customer truly values. Whether it’s anomaly detection, marketing optimization, or enterprise chatbots, my approach combines cutting-edge AI techniques with a deep focus on creating seamless and meaningful experiences for users. For instance, in my article “ The Ultimate Blueprint for Enterprise Chatbots: Simplify, Scale, Succeed ,” I emphasized building unified frameworks that deliver consistent, personalized customer interactions. Similarly, my work on marketing optimization involves leveraging predictive models to understand behavioral patterns, enabling hyper-targeted engagement that feels intuitive to the customer. At the core, it’s about using AI as a bridge—not just to solve technical problems but to anticipate and enhance the customer journey. By embedding intelligence into every interaction, we not only drive business impact but also create solutions that resonate with people in real and tangible ways. With increasing emphasis on data privacy regulations like GDPR and CCPA, how do you ensure that your AI models remain compliant while delivering actionable insights? Ensuring compliance with data privacy regulations like GDPR and CCPA starts with embedding guardrails into the design and deployment of AI models. Guardrails ensure that privacy, security, and transparency are foundational, not an afterthought. In my work, I advocate for privacy-by-design principles—data minimization, anonymization, and secure pipelines are non-negotiables. For instance, when building models for personalized customer targeting or chatbots, we ensure that data used is aggregated and anonymized, aligning with both regulations and user trust. Balancing compliance with actionable insights requires robust governance frameworks. These frameworks monitor data usage and provide explainability, ensuring that while insights drive business value, they do so ethically and legally. In the age of AI, it’s not just about what we can do with data, but what we should do, and that’s where compliance meets innovation. As an internationally recognized expert, what advice would you give aspiring data scientists looking to make a significant impact in the field of AI and machine learning? My advice to aspiring data scientists is simple: focus on solving practical problems and always prioritize the user experience. AI and machine learning aren’t just about building sophisticated models—they’re about creating solutions that make a tangible difference. Start by immersing yourself in real-world challenges. Learn how businesses operate, identify pain points, and think about how data and AI can address them. For instance, in my own journey, I’ve worked on everything from predictive analytics to creating a unified chatbot framework. These experiences taught me that the best solutions are the ones that simplify complexity and put the user first. Stay curious, keep experimenting, and don’t shy away from asking questions like: “How does this impact the end user?” and “Is this scalable for enterprise needs?” A data scientist’s true power lies not just in technical expertise but in the ability to translate it into meaningful impact. Keep that as your north star, and you’ll create solutions that truly transform industries. Related Items: AI , AI Visionary , Generative AI , Himanshu Sinha , Industry Leader , machine learning Share Tweet Share Share Email Recommended for you AdTech Innovation Boosting Publisher Revenue: Interview with Mykyta Plastomak on Pubcircle’s AI-Driven AdTech Innovations and Global Growth Strategy. Nikhil Purwaha on the Future of AI-Powered Fraud Prevention and Scam Reduction Broker Complaint Alert Introduces AI-Driven Solutions to Revolutionize Crypto Recovery and Enhance Investor Protection CommentsA Legacy of Peace: Remembering former President Jimmy CarterHistory has been kind to Jimmy Carter in a way the present never was during his one term as US president. His four years were dogged by economic "stagflation", which began during the term of his predecessor Richard Nixon, and America's stumbles on foreign policy. The surprise 1980 landslide loss to Ronald Reagan was deemed a referendum on Carter's leadership. Voters had daily reminders that their commander in chief was unable to free the dozens of Americans held captive in an embassy in the Iranian capital, Tehran. The enduring myth that Carter failed to act was strengthened by the fact the release of the hostages came after his departure from the White House. But when the crisis began 444 days earlier, no-one could have anticipated how long it would last, and how it would shape American politics. The fall of the Shah of Iran The seeds of the hostage crisis were planted in the chaos of Iran's Islamic Revolution. Iran and the United States had been on friendly terms while Mohammad Reza Pahlavi was the Shah of Iran — the country's royal ruler. The Shah came to the throne in Tehran during World War II and his power in the oil-rich country was shored up in the 1950s after the US and UK backed a coup to depose the country's democratically elected prime minister. Carter hosted a state dinner for the Shah and his wife at the White House in November 1977 and, in turn, the Shah entertained Carter in Tehran on New Year's Day in 1978. But over the ensuing year, the Shah faced violent unrest at home as religious leader Ayatollah Khomeini returned from exile to overthrow the autocrat. The Shah fled to Egypt on January 16, 1979, and a month later the government collapsed. In October, the Shah arrived in New York to undergo surgery, angering Khomeini and his supporters, who demanded he be returned to stand trial. Khomeini called for a "purge" of "American-loving rotten brains", and encouraged activist students to "expand their attacks" against the US and Israel, America's major ally in the Middle East. The 52 hostages On November 4, 1979, hundreds of Iranian students breached the gates of the US embassy in Tehran. They quickly occupied the compound. Some had intended a peaceful sit-in, but the situation deteriorated rapidly. The armed mob took 66 Americans hostage. Consular employee Robert C Ode, who at 65 was the oldest person taken captive, recalled in his diary that the students tied his hands behind his back so tightly with nylon cord that it cut off the circulation. I strongly protested the violation of my diplomatic immunity, but these protests were ignored. Some students attempted to talk with us, stating how they didn't hate Americans — only our US government, President Carter, etc. We were not permitted to talk to our fellow hostages and from then on our hands were tied day and night and only removed while we were eating or had to go to the bathroom. Six American diplomats were able to avoid capture and spent three months hiding in the Canadian and Swedish embassies — their rescue would later be the plot of the 2012 movie Argo . About the same time as the US embassy was occupied, the British embassy was also stormed by Iranian students, but they left after several hours. Khomeini condoned the occupation of the embassies, threatening to do "whatever is necessary" to bring the Shah back for trial and force Britain to hand over exiled prime minister Shapour Bakhtiar. Two weeks later, on Khomeini's orders, the demonstrators freed five women and eight black men. Non-American hostages were also freed. Another American hostage was released on July 11, 1980 due to illness. The remaining 52 were moved around the compound constantly, handcuffed, beaten, tortured and forced to undergo mock executions at gunpoint. Operation Eagle Claw ends in disaster Carter took significant steps to sanction Iran in the first few months of the hostage crisis. He froze Iranian assets, stopped importing oil from Iran and expelled 183 Iranian diplomats from the US. Fifty thousand Iranian students in America were also told to report to the nearest immigration office and warned they would be deported if they were found to be in violation of the terms of their visas. But the militants didn't relent, and threatened to burn the embassy and kill the hostages if the US attempted any military action against Iran. Carter's actions worked in Khomeini's favour as he sought to free Iran from America's control and use his supreme powers to roll out Islamic doctrine. At 1am on April 25, 1980, the White House revealed it had attempted a military operation to rescue the hostages, known as Operation Eagle Claw. But the operation had failed: eight US servicemen were dead and several others injured. The rescuers got nowhere near the embassy — the mission was aborted when three of the eight helicopters suffered various equipment failures. As they withdrew from the rendezvous point in the desert, one of the helicopters collided with a transport plane, killing crew on both aircraft. Their bodies were taken to the embassy in Tehran, where they were put on display during a press conference. Iran arranged for them to be returned to the US the following month. A post-White House legacy Carter took full responsibility for the failed rescue attempt. With the hostages' lives at stake, he couldn't risk another military operation in Iran, and had to walk the slow diplomatic path to secure their freedom. Stephen Loosley from the United States Studies Centre at Sydney University says news coverage of the crisis was extensive for a time when media didn't run 24/7. "Both [US news anchors] Ted Koppel and Walter Cronkite would keep a laser-like focus on the hostage crisis," Mr Loosley says. "They'd keep the number of hostages up on the screen every night, and the number of days that the hostages had been incarcerated. "Americans never really lost sight of the fact that their people were imprisoned in a very hostile environment in Tehran." On July 27, 1980, the Shah died in a Cairo military hospital. The return of his wealth to Iran became a key part of the agreement to free the hostages, known as the Algiers Accords. The accords were signed on January 19, 1981, the day before Carter was due to leave the White House. He'd lost the November 1980 election to Republican candidate Ronald Reagan, a former Hollywood actor and governor of California. The hostages were meant to be released while Carter was still president, but a delay meant they were freed in the first few hours of Reagan's administration on January 20. "The Iranians refused to give Carter the satisfaction of saying the hostages were released on his watch," Mr Loosley says. "Ronald Reagan is viewed as the president who secured the release of the hostages, because of the timing." In the next decade, Reagan would be credited with playing a major role in ending the Cold War, while Carter faded into relative obscurity Carter described the Iran hostage crisis as "the most difficult period of my life". The hostages themselves were traumatised by the ordeal and spent more than 30 years fighting for compensation, which was granted in 2015. Mr Loosley says Americans look more favourably on what Carter did after his time in the White House. The Carter Center, a not-for-profit set up by Carter and his wife Rosalynn in 1982, worked to improve human rights and health worldwide. One of its greatest achievements was the near-eradication of Guinea worm disease, caused by a water-borne parasite. The Carters also built homes with social housing organisation charity Habitat for Humanity. "He's looked upon with a fair amount of affection and respect," Mr Loosley said. "Unfortunately his presidency is seen as somewhat of a low point in in the post-war period because of the Iran hostage crisis." ABC