Jeanne Le Garrec, International Operations Manager of Basic Technology Interview conducted by Amel Rigneau, Founder of DigitalMind Media
Jeanne, great to have you. Can you tell us more about your field of work: Natural Language Processing in AI? What is it exactly?
Natural Language Processing (NLP) is the analysis of natural language. It is one of the capabilities contained within the broad area of AI and one of the core elements used in enterprise search and information discovery. In order for the “machine” to give you the results you want, it needs to understand what you’re asking for and which document is most likely to answer your query.
Linguistics knowledge, machine learning and statistical models combined enables the software to understand language “produced” by a human being. Natural Language Processing also includes speech recognition, text analytics, text generation, machine translation (like the Google Translate tool you might be using).
The issues we’re facing now are how to sort and prioritize these results, how to reduce false positives and have access only to the relevant information. When making a query, you could get hundreds, thousands of results. But, how many are actually helpful and spot on?
I understand we need “better search” but why text analytics? Isn’t Google enough?
We produce more text data than it is possible for someone to read. Just take yourself for example: think about how many emails you send, how many social media posts you share, how many documents you work on, how many articles you read a day, and maybe you have your own blog. Think about the amount of text data you consume per day, and times it by 365, times the number of people producing and accessing digital content.
So how are people going to navigate through this information or their company internal data? It always comes to the same struggle: what is relevant? Where is the information I need? And also, how do you search when you can’t use a keyword search? What if the information you seek is available but in a different language?
The famous search engines that we all know (Google, Yahoo, Bing, etc) are easy to use and great to navigate around. The issue is that they don’t help search on your internal data, they don’t analyze your data; plus it’s keyword based, which is limiting. This is why, for quite some time now, companies have been looking to implement “smart, Google-like” search engines to navigate through all the data they have, but they also need the analysis tools for accuracy. This is called Enterprise Search.
Companies have to use data analytics to use insight-driven decisions to be more competitive. How do you make the “right” decisions if you do not have all the information in hand? What I find really exciting about Enterprise Search, NLP and AI is that their applications are only limited by our imagination. Your business, your reputation, your security depends not just on how much information you have available, but the value you can get out of it on the spot.
Keyword search is only one way to go, limiting the exploration of available information. On the contrary, text analytics can sort documents to be filtered by topic, concept, language, category, feelings, emotions, and entity type. As we become more data driven, automate steps, operate more critical tasks, evaluate risk using AI, we need to go beyond the conventional keyword search to uncover the accurate and relevant information. The goal is to go one step further in accelerating decision making and maybe even try to predict the future.
You are working mainly with banks and government agencies around the world. What are their main concerns? What will the trend be?
They all want the same thing, getting quicker and more reliable answers to their questions. Tools used before by the intelligence community to detect threats and stop terrorist attacks are now being implemented in the financial world to fight fraud and money laundering. Law firms or private companies also want faster data search to capture key information.
Whether we talk to American, French, Australian or Singaporean banks: automation of processes is their focus, more precisely “digitization and automation” is what we’re hearing over and over again. Financial institutions are looking for the right tools to accelerate screening and onboarding. They need to comply with all the newest and even stricter AML/KYC international regulations without slowing down their business. They turn to AI and technology to reduce risk and be more efficient.
Some teams can spend 12-16 hours checking a complex transaction or a company’s background. Operations want to reduce that time to just a couple of hours but without threatening the quality of the work. They can’t “afford” missing one hit, missing one person of interest, and missing one threat.
When it comes to working with government agencies, extracting leads and intelligence from social media has become essential for several reasons:
– Fake news management. This has been a real international buzzword! How to identify fake news quickly and contain them? Singapore is actually the first country to take a “legal” action against fake news.
– Threat and crime detection. Governments want to implement tools to help them crawl all the webs (clear, dark, and deep web) and get the most out of social media for predictive analytics applications. The Embers project, now called Presage, is a platform that has been able to predict major events in Venezuela and Brazil by analyzing blogs, social media posts, images. The initial goal of this research was to predict civil disorder to help police forces anticipate and prepare for such events. This research showed that they could, on average, predict an event, 7 days in advance with a result of 80 to 90 percent of accurate forecasts! Scary and exciting at the same time! Now, they’re working on the same model to predict disease outbreaks.
– Assessing public opinion. Governments’ interest in social media can also be more “commercial.” Just like a company would exploit it for brand management and satisfaction assessment, governments will use it to learn what people think about the president, the minister, a new policy, a new law, a public event.
The next step is to continue to leverage NLP for more automation and deeper analysis. For instance, customers ask for more granularity in emotion analysis in surveys, for their chatbot, in comments, and in posts. There is a continuous need to go deeper and for more customization.
Another request we hear more and more is document summary based on key elements and sentences. This leads us to machine generated text. We’re not in production yet for this feature, but getting close! Once this is mastered, a whole new world of automation will open its doors. I can imagine people not just wanting a summary about one document, but a summary of a set of documents.
Do you remember when I said earlier that companies try to predict the future and that imagination was the limit? The main question is: what is your ultimate goal? What do you need to do tomorrow that you cannot do today?
In a nutshell, what questions should corporates ask themselves to start their text data analytics journey?
Whether it’s a non profit organization, a bank, a startup, an agency, they should ask themselves the following questions:
Have I got all the information I need to make the right decisions?
If the answer is no, then: – What am I missing to be more competitive/more productive? – What information am I missing that I wish I had? – What type of content do I have available? – How much do I really know about what’s in my text data?
Once you know what you have, think about how you use it: – Am I using manual processes to perform critical business analytics? – Do I increase risk by not using all of my data? – Am I missing things I know I have?
Finally, think about what you want: – What do I want to know from my data? – How do I want to visualize that information? – Who should have access to which level of information?
This should help you drive your evaluation and transformation to get more from your text data. One thing to keep in mind though is that data is so vast and diverse. Exploiting it correctly takes many steps, and you should not be fooled. No provider can be an expert in everything: data cleaning, analysis of text, voice, images, videos, figures, data aggregation, visualization, etc. To survive, a company needs to evolve with its market and continuously integrate new technologies. It’s via the combination of human skills and technologies that a company will stay competitive and attractive for customers.
What do you do to bring more diversity to Singapore’s tech hub?
Well first, I have diverse experiences myself. I’m happy to speak English with my somewhat British accent while proudly wearing my Boston Red Sox cap and eating homemade quesadillas and frijoles (leftover from 6 months in Monterrey, Mexico). And on top of all that, I’ll be bragging how great french wines are!
More seriously, Singapore is a perfect example of diversity. You never know who the person you’re talking to is from and that’s the beauty of it. Team work here is by default international. Each person has his/her own culture, own reasoning, background, business habits, vision, own ambition.
I also volunteer for a local non-profit organization Hello Tomorrow Singapore, which is part of a global non-profit. Its raison d’etre is to bring together researchers, entrepreneurs, academics, and governments to raise awareness and for investment in Deep Tech. This brings a very diverse and exciting ecosystem together.
The team I work with here is amazing: knowledgeable and passionate about this initiative. The startups and researchers we meet are all working on breakthrough innovations in health, agriculture, well being, aeronautics. This group also facilitates networking with players outside of Singapore such as Cambodia, Indonesia, Hong-Kong, Thailand. An interesting melting pot of thinkers and innovators.
Diversity in culture, backgrounds, age, sex, and experience is necessary to come up with breakthrough discoveries and reasoning.
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