Watch CEO Sylvain Forté discuss SESAMm’s solutions for corporations focused on supply chain and client reputational risk monitoring during an interview with FF News at FinovateEurope last March.
Watch the full recording:
Watch CEO Sylvain Forté discuss SESAMm’s solutions for corporations focused on supply chain and client reputational risk monitoring during an interview with FF News at FinovateEurope last March.
Watch the full recording:
Join SESAMm for our annual SESAMm Day event at the Palais Brongniart in Paris on October 6th, from 8:30 to 10:30 AM.
This year, we're organizing SESAMm Day as a breakfast: hear a panel of clients exchange best practices and put your own questions to them, get a look at what's new on the platform and what's coming next, and connect over coffee with the SESAMm team and other ESG, risk, and compliance professionals.
Full agenda and speaker lineup coming soon.
The aerospace and defense industry is essential to global technology and transportation, playing a crucial role in maintaining international security and connectivity. However, this sector faces intense scrutiny due to its significant impact on environmental, social, and governance (ESG) factors. Amidst challenges like safety lapses and whistleblower revelations, stakeholders are increasingly relying on advanced AI technologies to gain insights into potential controversies. Such technologies have enabled a deeper understanding of the complex ESG issues that permeate the industry, revealing not only the specific challenges faced by companies like Boeing but also providing a broader view of the sector's commitment to corporate responsibility and sustainability.
This article explores the aerospace industry and its ESG challenges, backed up by a case study of industry giant Boeing. It also explains how we used SESAMm’s AI-powered tools to detect these controversies beforehand.
This article is a preview of the webinar entitled "The Boeing Scandal: Can AI Predict Controversies Before Traditional Tools?" based on SESAMm's proprietary research. Sylvain Forté, CEO and Cofounder, and Emna Abid, Research and Analytics Team Lead at SESAMm, will lead the webinar and will share SESAMm's findings in detail on the Boeing case and the use of AI to detect these types of controversies ahead of time.
The top market players in the aerospace and defense industry command 8.3% of the overall market's online mentions. This sector is increasingly scrutinized for its ESG practices amidst technological advancements and global policy shifts.
Our study processed our large data lake to identify key aerospace players: Northrop Grumman, Lockheed Martin, General Dynamics, Airbus, and Boeing, from 2015 onwards. It found a surge in online mentions, especially after Boeing's plane crashes post-2018. Both Airbus and Boeing saw increased attention, highlighting the competitive and evolving aerospace industry, where online presence correlates with market position shifts and significant events.
Polarity, indicating a company's mix of positive and negative opinions, ranges from -1 to 1. A zero score shows equal positive and negative sentiment. Brands with high e-reputation often score above 0.5.
The aerospace market experiences significant highs and lows. Lockheed Martin has seen a positive impact from new contracts and technological advancements, particularly between 2016 and 2018, boosting its reputation and value. In contrast, Boeing faces significant challenges due to safety lapses, including 737 MAX crashes, legal issues, and whistleblower claims, negatively affecting its perception and highlighting the industry's vulnerability to reputational risks.
The aerospace industry has faced increasing scrutiny over its ESG practices. Among the key players, the American aerospace company Boeing has been prominently featured in media discussions, not only due to its market distinction but also because of its ESG challenges that have sparked significant controversy.
The word cloud displays key topics about Boeing, particularly the 737 Max controversies, including safety issues and FAA oversight. "737 Max," "Boeing," "safety," "death," and "FAA" are the main terms that show their prominence in discussions. The visualization also touches on "lawsuits," "Senate hearings," and "missed inspections," indicating the wide range of concerns surrounding Boeing's regulatory, safety, and ethical challenges.
These incidents underscore the aerospace industry's urgent need for reforms to prioritize safety and ethics over profit. SESAMm's TextReveal® platform plays a key role in detecting such ESG controversies early by analyzing vast amounts of data and helping stakeholders understand and address the intricacies of corporate accountability and regulatory compliance.
TextReveal’s web data analysis of over five million public and private companies is essential for keeping tabs on ESG investment risks. To learn more about how you can analyze web data or to request a demo, reach out to one of our representatives.
The AI field is growing, and whether good or bad, people are doing more than talking about it; they’re using it more than ever. However, despite this increased use, I’ve noticed that, for some, their perception tends to alternate between false and too-high expectations of AI.
One case, in particular, was in 2021, Gartner placed natural language processing (NLP) at the top of its list of loaded expectations in terms of the Gartner hype cycle. As a result, many expected a potential “winter of AI,” so to speak. Yet, in 2022, we discovered the potential that we haven’t even touched on the true value AI could deliver.
Will there be a “winter of AI,” and are expectations bloated?
No, I don’t think so. As the past year has shown us, AI still has more to offer, a pocket of value that we have yet to see. I believe that while many people now accept that AI will be a transformative force—thanks to the fast democratization of large language models—our society hasn’t yet fully considered the actual changes it will make by lowering the barrier to access intelligence globally.
Progress in image generation, analysis, and computer vision—think autonomous driving—has leaped and bounded in the past year, and so has the progress in NLP, particularly in the natural language understanding (NLU) and natural language generation (NLG) aspects. We’re at a tipping point that will likely transform our world in the same way that the internet has.
Today, we’re seeing the development of natural language processing through large language models, such as with the emergence of ChatGPT based on OpenAI’s large language model version GPT-3.
Astounding fact: ChatGPT’s growth in user adoption skyrocketed past one million users within a week of launching. In comparison, no other tech company has reached this feat in this short of a time frame. But the adoption rate is only part of it.
This advance has profoundly affected creative jobs because this might be the first time an AI generative system can create high-quality content. In public mode, users have tapped ChatGPT to do everything, from generating basic reports and ideas to writing lectures and producing code.
With a high adoption rate comes great opportunity. Any startup seeing this level of success could become the most funded project ever. And more, there’s revenue. OpenAI, as the example, could make one billion dollars by 2024, according to a report via Reuters.
On the other side of the same coin, however, there are greater risks due to AI generative system advancement. For example, with AI assistance, human hackers can develop more sophisticated phishing campaigns—hacking mechanisms based on social engineering.
Despite the risks, we still haven’t seen what’s yet to come with generative AI. GPT-4, for instance, is rumored to launch in 2023. I believe it will be a massive improvement over GPT-3, which is already mind-blowing.
And on the point of NLG and these large language models, there’s a lot that’s feasible in process automation. For context, creative content gets the most attention; it’s the area that makes more headlines. But I would also watch advancements in technical content and automated code generation, for example.
Because of today’s AI advancements, it’s now possible for tools like ChatGPT to generate near-ready-to-use source code. That means instead of only being fun to play around with, these are becoming enterprise tools, making it possible for developers to automate technical tasks at scale.
NLP—specifically natural language understanding, which SESAMm works on—is not untouched by these applications. Many of these large language models can perform zero-short learning, which means NLU can be performed without pre-training, a huge advance in this industry. However, zero-short learning is insufficient for many advanced sentiment and ESG analysis tasks. We still need additional data sets to fine-tune the data for a specific purpose.
What does this mean for the natural language generation sector? Many startups—especially anything around chatbots—have folded, some just in Q4 of 2022. ChatGPT’s success means it’s solved and replaced the need for many of them, and basically, anything content creation on the B2C side has and will struggle.
Otherwise, things are looking good in our sector. For example, at SESAMm, we’re focused on what I call “last-mile AI.” In our specific business application, you can’t bypass the need for a data set because we’re trying to attain a precise result for specific, often risk-related applications. Open-source large language models like GPT-3 and BERT can get you mostly there, and that’s fine for general purposes. But for “last-mile AI” applications, there’s a lot you can’t do without additional work.
And here lies what I think is one of SESAMm’s defensive edges: the “last-mile AI.”
Instead of finding ways to protect its algorithms, the AI business community would do better to defend its use cases because the algorithm’s value will decrease progressively. In contrast, the value of a use case’s purpose and the data set used to achieve the use case will grow.
Computing power and the resources it takes to train large language models remain challenging to applications like OpenAI. It takes electricity, heat, and money to train these models, and AI has an environmental impact. So far, we’ve justified this cost in the name of optimization—meaning that we put in this extra cost upfront so that the likely efficiency will offset or reduce that cost later—but it’s still a cost to incur.
AI companies, especially those in the NLG space, will do well to find their competitive edges, areas optimized for a specific purpose like “last-mile AI.” Companies like OpenAI will likely continue to optimize their models for quicker responses but don’t necessarily have the problem of solving for a specific use case.
At SESAMm, for instance, a big challenge and expertise we developed in-house is inference time—or how quickly we can apply the model to an article or an individual sentence. Because we’re processing so much live content, the more time it takes to process—milliseconds multiplied by a billion—the more costly it is.
Our data lake currently holds over 20 billion articles, messages, etc., from over 14 years, and we add 10 million more daily. That’s a lot of content to analyze. But we make it so our clients can access the data within seconds.
The need to optimize models for fast inference and adapt to deep industry-specific use cases will remain one of the key reasons companies will have to continue re-training their own models. That doesn’t mean large language models don’t add value here. Their open-source versions simply become an impressive building block for any NLP application and accelerate the rate of innovation and productivity in the whole field.
When Google launched BERT in November 2018, we quipped that Google had open-sourced this system as a joke because no one could put it into production because BERT was so big. Many companies didn’t have the computing capabilities to do anything with it at the time. Now we do.
This year, Google did it again; they released a model that’s even bigger than GPT-3. Of course, almost no one besides Google can put that model into production now. But my point is that there will always be computing, resources, and other challenges to making AI advancements. That’s why I think AI companies must focus on defensive and competitive edges.
Regardless of the challenges, I see good things happening in the NLU space being massively improved by large language models. I see improvements as we incorporate these models today compared to deep-learning models trained from scratch a few years ago. I also see a significant decrease in the amount of data we need to fine-tune results, reaching and focusing on the final client use case more quickly.
From a natural language generation perspective, I believe large language models will transform the world. And I’m really excited about this era because this transformation supports my deepest purpose, leveraging AI to accelerate innovative decision-making. We do this by giving decision-makers access to technology that analyzes research content, news, and discussions. And if we increase the rate of innovation or the quality of decision-making by 10% globally, the impact could be huge for all industries: healthcare, finance, fashion, you name it. Industry leaders can make better ESG and SDG choices that will affect our world on a grander scale.
2023 will be an exciting time for AI, specifically for NLG and NLU. Of course, we’ll continue to see AI innovations. But more importantly, leaders will have better insights to make better decisions, creators will create more—and more complex—content, and overall, the applications will become more specific to solving the needs of particular use cases.
Here’s to the new era of AI in 2023. Cheers!
SESAMm is a leading NLP technology company serving global investment firms, corporations, and investors, such as private equity firms, hedge funds, and other asset management firms. SESAMm provides datasets and NLP capabilities through TextReveal® to generate alternative data for use cases, such as ESG and SDG, sentiment, private equity due diligence, corporate studies, and more. With access to SESAMm’s massive data lake, comprised of 20 billion articles and messages and growing, its clients can make better investment decisions.
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