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Natural Language Processing

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What Companies Are Fueling The Progress In Natural Language Processing? Moving This Branch Of AI Past Translators And Speech-To-Text

AFP via Getty Images Key takeaways

Natural language processing (NLP) is a subset of artificial intelligence that

uses linguistics and machine learning models to allow computers to process human language. As time goes on, these machines are getting better with sentiment analysis and intent classification tools

We experience the power of NLP in our daily lives, even if we don't realize it. We see NLP in action when we search for something online, use predictive text, interact with chatbots or ask our smart assistant in the living room to change the song

Revolutionary tools like ChatGPT and DALL-E 2 are setting new standards for the capabilities of NLP. These tools use NLP to store information and provide detailed responses to inputs

Chatbots have exploded in popularity in recent months, and there's a growing buzz surrounding the field of artificial intelligence and its various subsets. Natural language processing (NLP) is the subset of artificial intelligence (AI) that uses machine learning technology to allow computers to comprehend human language.

AI has many applications, including everything from self-driving cars to AI-driven investing. If you're curious about what AI can do for your portfolio, download the Q.Ai app to get started.

Natural language processing applications have moved beyond basic translators and speech-to-text with the emergence of ChatGPT and other powerful tools. We will look at this branch of AI and the companies fueling the recent progress in this area.

What's natural language processing all about?

Natural language processing (NLP) is a subset of artificial intelligence (AI) that uses linguistics, machine learning, deep learning and coding to make human language comprehensible for machines. Natural language processing is a computer process enabling machines to understand and respond to text or voice inputs. The goal is for the machine to respond with text or voice as a human would.

The long-term objective of NLP is to help computers understand sentiment and intent so that we can move beyond basic language translators. This subset of AI focuses on interactive voice responses, text analytics, speech analytics and pattern and image recognition. One of the most popular uses right now is the text analytics segment since companies globally use this to improve customer service by analyzing consumer inputs.

The potential for NLP is formidable. According to Fortune Business Insights, the global market size for natural language processing could reach $161.81 billion by 2029. Market research conducted by IBM in 2021 showed that about half of businesses were utilizing NLP applications, many of which were in customer service.

How are businesses using NLP to improve operations?

The primary benefit of NLP solutions for businesses is to use automation to cut costs and improve business operations to maximize productivity and profitability. Here are a few ways that NLP is being utilized right now by businesses globally:

Redacting sensitive data. Industries such as insurance, legal and healthcare use NLP technology to redact personal information and protect sensitive data instead of manually going through documents.

Customer service. Not only is NLP technology used to offer customer service chatbots that sound more human-like, but companies then have this data extracted and analyzed to improve the customer experience.

Business analytics. Companies use NLP solutions to analyze sentiment and gather actionable insights from customer feedback.

What are examples of natural language processing in our daily lives?

You may be using NLP services daily without even noticing it. We enjoy more and more of these technological benefits as they advance. Here are some common examples of NLP:

Spam email filters: These filters determine what kind of messages reach your inbox based on results from text classification tools.

Smart assistants: Amazon's Alexa and Apple's Siri are perfect examples of machines processing natural human language. These smart assistants determine patterns in voice recognition to provide a helpful response based on context.

Search engines: When you search for something, the NLP technology offers suggestions to complete your query while using sentiment analysis to determine the results the search engine produces.

Predictive text: While we've likely become accustomed to this feature, the predictive text has improved drastically. It's used by applications like Grammarly and Gmail's Smart Compose, which even finishes your sentences for you.

Customer service chatbots: Whenever you speak to a customer service chatbot through a website, you see the power of NLP. These services are getting better with time.

We also can't ignore the role of AI and NLP in everyday services like streaming platforms and e-commerce websites (Amazon), where it feels like our results are customized by someone who knows us.

What companies are fueling the progress in natural language processing?

While almost every business has to use some form of NLP and AI in its operations, some companies are fueling the recent progress in these technologies. Here are five companies in this space to keep an eye on.

Microsoft

Microsoft has been making headlines lately since the company reportedly invested $10 billion in OpenAI, the startup behind DALL-E 2 and ChatGPT. These two tools alone have changed the entire landscape of AI and NLP innovations as the improvements bring this technology to the general public in new, exciting ways.

Microsoft Azure is the exclusive cloud provider for ChatGPT, and this platform also offers many services related to NLP. Some services include sentiment analysis, text classification, text summarization and entailment services.

IBM

While IBM has generally been at the forefront of AI advancements, the company also offers specific NLP services. IBM allows you to build applications and solutions that use NLP to improve business operations.

One of the revenue streams for the company is the IBM Watson Natural Language Understanding service which uses deep learning to derive meaning from unstructured text data. On the Watson website, IBM touts that users have seen a 383% ROI over three years and that companies can increase productivity by 50% by reducing their time on information-gathering tasks.

Amazon

The significance of AI and NLP is felt at almost every level of Amazon's business. You may have used the Alexa device to put on your favorite song or found the perfect product on the e-commerce platform based on a recommendation. These are AI and NLP in action.

Amazon also offers Amazon Web Services (AWS) for cloud storage so businesses can complete their digital transformations. They also have Amazon Comprehend, an NLP service that uses machine learning to determine text's significance. The Comprehend service also offers sentiment analysis and custom segmentation so customers can add NLP to their apps.

Lemonade

When discussing AI, you can't forget about the first insurance company fully Google

Even though Alphabet, the parent company of Google, recently revealed that it would be cutting 12,000 employees worldwide, they're also planning on launching 20 new products. Google has already offered a small sample group an exclusive look at a tool that will eventually be a competitor to ChatGPT, known as Bard. This chatbot is

The biggest issue for Google is that they want to offer an AI-powered chatbot that's safe, tackles misinformation, and shares factually accurate information. Google has been investing heavily in AI, and it's no secret that management wants to bring the company back to the forefront of this field. You can see Google utilizing NLP technology in every aspect of its business, including spam filters, predictive text when writing emails, search engines and translation tools.

How can you invest in NLP and AI?

If you're a proponent of machine learning, there are many different ways to invest in AI and related technologies. There aren't companies that only focus on AI in the same way that Tesla focuses on EVs or Nike focuses on athletic wear because every successful business relies on some form of AI. You can, however, invest in major tech companies since they're becoming increasingly invested in AI. With Amazon relying on AI on everything from the Alexa device to powering the warehouses, this is one company that's all in.

OpenAI is projected to generate $1 billion in revenue in 2024. While you can't invest directly in OpenAI since they're a startup, you can invest in Microsoft or Nvidia. Microsoft's Azure will be the exclusive cloud provider for the startup, and most AI-based tools will rely on Nvidia for processing capabilities. In recent weeks, shares of Nvidia have shot up as the stock has been a favorite of investors looking to capitalize on this field.

You don't have to look any further if you want to see the capabilities of AI in investing. Q.Ai uses AI to offer investment options for those who don't want to be tracking the stock market daily. The good news is that Q.Ai also takes the guesswork out of investing if you want a hands-off approach. Check out the Emerging Tech Kit if you're a proponent of innovative technology.

The bottom line

Natural language processing and artificial intelligence are changing how businesses operate and impacting our daily lives. Significant advancements will continue with NLP using computational linguistics and machine learning to help machines process human language. As businesses worldwide continue to take advantage of NLP technology, the expectation is that they will improve productivity and profitability.

Download Q.Ai today for access to AI-powered investment strategies.

On Using Generative AI For Coping With Adverse Childhood Experiences (ACEs)

Can generative AI be of benefit to children and aid the detection and treatment associated with ... [+] Adverse Childhood Experiences (ACEs)? Hopefully, yes.

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An important realm that doesn't get as much attention as is warranted consists of Adverse Childhood Experiences (ACEs). This is a crucial arena that considers the immediate-term, near-term, medium-term, and long-term consequences underlying what can adversely happen to a young person during childhood.

ACEs can and often do result in lifelong negative impacts.

One aim would be to try and ensure that no child ever experiences any ACEs. That's a spirited goal that we can all rally around. But, unfortunately, then reality sets in. You see, there is a surprisingly high chance that a child will likely experience an ACE. I'll be walking you through the alarming statistics on the frequency of ACEs. My primary focus here will be on ACEs in the United States, but I ask that you keep in mind that ACEs occur throughout the globe.

Okay, go ahead and take a deep breath, this is obviously a quite heavy topic.

Continuing, what can we do about ACEs?

Among the various contenders of coping strategies and approaches, a new one is the use of generative AI and large language models (LLMs) to assist in dealing with the ACEs crisis.

There have been various valiant efforts to apply AI to this difficult and hard-to-grapple-with issue. The advent of modern-day generative AI provides a newer and fresher avenue that holds great promise. I will share with you the nitty-gritty details and I hope that doing so will inspire more efforts toward seeing how far we can leverage generative AI. My strident belief is that doing so will be of immense benefit to children, adults, families, therapists, policymakers, regulators, lawmakers, and society all told.

Before we leap into the details, allow me to go into my customary opening remarks.

For my ongoing readers, in today's column, I am continuing my in-depth series about the impact of generative AI in the health and medical realm. The focus this time is once again on the mental health domain and examines the use of generative AI for identifying, preventing, and treating those who are either potentially on the cusp of or already have had Adverse Childhood Experiences (ACEs).

I have previously examined numerous interleaving facets of generative AI and mental health, see my comprehensive overview at the link here. You might also find of notable interest a CBS 60 Minutes episode that recently examined crucial facets of this evolving topic, see the link here (I am honored and pleased to indicate that I was featured in the episode, see the link here).

Other vital background includes my in-depth coverage of mental health chatbots which have been bolstered by generative AI (see the link here) and the rapidly changing nature of the client-therapist relationship due to generative AI at the link here. I explored where things are headed regarding the levels of AI-based mental therapy autonomous guidance at the link here, and showcased the importance of the World Health Organization (WHO) report on global health and generative AI at the link here, and so on.

The Solemn Topic Of Adverse Childhood Experiences

Let's right away get to the keystones of ACEs.

According to vital research by the Centers for Disease Control and Prevention (CDC), and as posted online under the banner entitled "Adverse Childhood Experiences (ACEs), Centers for Disease Control and Prevention: CDC Vitalsigns, posted date of August 23, 2021, here are some crucial facets (excerpts):

"Adverse Childhood Experiences (ACEs) are potentially traumatic events that occur in childhood. ACEs can include violence, abuse, and growing up in a family with mental health or substance use problems. "

"61% of adults had at least one ACE and 16% had 4 or more types of ACEs."

"Many people do not realize that exposure to ACEs is associated with increased risk for health problems across the lifespan."

"Toxic stress from ACEs can change brain development and affect how the body responds to stress. ACEs are linked to chronic health problems, mental illness, and substance misuse in adulthood.

Those points are quite disturbing.

As stated, approximately 61% of adults have experienced at least one ACE, while 16% had four or more ACEs. One unnerving way to have those numbers sink in would be to consider that of the fellow adults that you know, perhaps over half have had an ACE. Think about that. Furthermore, while amidst a group of say ten adults, perhaps two will have had multiple ACEs. Daunting. Shocking. Abysmal.

The matter also isn't just a single point in time. What occurs in childhood can last a lifetime. This includes lifelong toxic stress. That's why it is notable to realize that we must consider a timeframe beyond just that of childhood. What happens during childhood is when we need to keep our eyes especially riveted, but we would be remiss to not also watch for and aid those who carry ACEs into their adult years.

Here are some more substantive points as mentioned on the same CDC blog site:

"Preventing ACEs can help children and adults thrive." (ibid).

"Lower risk for conditions like depression, asthma, cancer, and diabetes in adulthood." (ibid).

"Reduce risky behaviors like smoking and heavy drinking." (ibid).

"Improve education and employment potential." (ibid).

"Stop ACEs from being passed from one generation to the next." (ibid).

Those additional points are helpful in notable ways.

One aspect is that there can be a vicious cycle of someone who experienced an ACE as a child that later in life, while an adult, repeats that with their own child or other children. They get into a mode that keeps repeating. Their child or whomever they carried the adverse experience onto will potentially do the same to other youngsters once they become adults. This has a chance of being handed down, generation after generation.

The CDC provides links to many useful in-depth research studies on the ACEs topic. For example, a research report entitled "Adverse Childhood Experiences Prevention: Resource For Action" by Debra Houry and James Mercy, National Center for Injury Prevention and Control, Center for Disease Control and Prevention, 2019 made these key points (excerpts):

"Traumatic events in childhood can be emotionally painful or distressing and can have effects that persist for years."

"Factors such as the nature, frequency, and seriousness of the traumatic event, prior history of trauma, and available family and community supports can shape a child's response to trauma."

"ACEs can also negatively impact education, employment, and earnings potential. The total economic and social costs to families, communities, and society are in the hundreds of billions of dollars each year."

"ACEs and their associated harms are preventable. Creating and sustaining safe, stable, nurturing relationships and environments for all children and families can prevent ACEs and help all children reach their full health and life potential."

"Children and youth with ACE exposures may show signs of behavioral and mental health challenges. They may be irritable, depressed, display acting-out behaviors, have difficulty sleeping or concentrating, and show other traumatic stress symptoms."

As you can see, I am step by step taking you deeper into the details.

Let's keep diving in.

The points above note that various factors might be considered for purposes of detecting ACEs and analyzing how we might predict the possibilities of ACEs occurring. If we could anticipate what circumstances might likely lead to an ACE, the idea would be to either get there before an ACE arises or at least provide aid to where we estimate ACEs have happened.

Consider this. Suppose a child at school appears to have behavioral or mental health indications that suggest an ACE or that they are in an environment in which an ACE has a strong probability of taking place. If we could detect this, the chances of averting an ACE are heightened.

One difficulty is that teachers, counselors, administrators, and others in the orbit of children might not be aware of ACEs. They might not be versed in what ACEs are. Their lack of awareness is going to nearly at the get-go undercut the hope of catching ACEs beforehand or at the soonest time.

As you will shortly see herein, generative AI can be an awareness tool that aids those around children to be more informed about ACEs.

If you are wondering whether all the research that could possibly be done on ACEs has perhaps already taken place (hint: it hasn't), a recent research study did a meta-analysis of ACEs. In a meta-analysis, researchers scan a wide segment of research literature on a particular topic and try to spot where possible weaknesses exist such as gaps in what is being studied.

A meta-analysis on ACEs was proffered in a research study entitled "Adverse Childhood Experiences, Mental Health, And Social Functioning: A Scoping Review Of The Literature" by Vasiliki Tzouvara, Pinar Kupdere, Keiran Wilson, Leah Matthews, Alan Simpson, and Una Foye, Journal of Child Abuse and Neglect, May 2023, including these salient points (excerpts):

"The aims of this review were to examine how the concepts of ACEs, mental health, and social functioning outcomes have been defined, assessed, and studied in the empirical literature to date and to identify gaps or weaknesses in current research to guide further investigation."

"This review identified 58 studies examining ACEs, social functioning outcomes, and mental health outcomes in a wide range of populations."

"It highlighted the limited evidence on populations from diverse backgrounds and various gender identities, as well as the lack of evidence on minority groups."

"The shift towards examining ACEs clusters and attributing relationships at the cluster level is crucial since there is evidence that specific types of mental health disorders are associated with specific types of childhood adversities."

"Similarly, the mechanisms of social predictors in mental health need to be established for comparisons between outcomes. The validity and reliability and the development of established measurements for assessing ACEs and social outcomes are important."

Those points provide a key reveal indicating that we are still a long way from fully exploring all facets associated with ACEs. There is a lot of work still to be done. Join in, if you can.

One underexamined consideration is in my bailiwick, namely the application of modern-day AI to the ACEs realm.

Artificial Intelligence Being Applied To The ACEs Realm

Before generative AI and LLMs sprung into widespread public attention in 2022, other AI methods and technologies were being used for natural language processing (NLP). You likely have used those older NLP capabilities if you've interacted with Alexa and Siri (both of which are being overhauled to exploit the advantages of generative AI). By and large, the older style NLP is relatively stilted and not nearly as seemingly fluent as modern-day generative AI.

I bring up this so-called good-old-fashioned AI (GOFAI) to highlight that AI for ACEs has been explored before today's generative AI capabilities. Let's take a look.

In a research study entitled "SPACES: Explainable Multimodal AI for Active Surveillance, Diagnosis, and Management of Adverse Childhood Experiences (ACEs)" by Nariman Ammar, Parya Zareie, Marion Hare, Lisa Rogers, Sandra Madubuonwu, Jason Yaun, and Arash Shaban-Nejad, IEEE International Conference on Big Data, December 2021, the researchers describe their AI for ACEs (excerpt):

"The American Academy of Pediatrics (AAP) recommends routine screening for ACEs."

"Current challenges in practice include a lack of validated screening tools, lack of resources to address issues found on screening, and the inability to translate population outcomes to individual patient care."

"Healthcare providers, and researchers are seeking innovative approaches and tools for ACEs screening, diagnosis, management, and continuous monitoring."

"We present the Semantic Platform for Adverse Childhood Experiences Surveillance (SPACES), an explainable multimodal AI platform to facilitate ACEs surveillance and diagnosis of related health conditions and subsequent interventions."

"We utilize a bottom-up approach to multimodal, explainable knowledge graph-based learning to derive recommendations and insights for better resource allocation and care management. SPACEs provide a novel approach to active ACEs surveillance by utilizing 360-degree views about patients and populations."

Per the points above, a specialized AI tool was devised that could serve as a screening tool for ACEs.

The design concept was that a scenario about a situation that might entail an ACE would be entered into the AI-based tool. The text-entered scenario would be mathematically and computationally assessed by the AI. After doing so, which was based on research studies about how the factors underlying ACE tendencies come to the fore, the AI would produce a report stating whether there might be an ACE or a strong possibility of an ACE.

This tool could be used by just about anyone who might be seeking to ascertain the chances of an ACE being afoot. Of course, one potential downside is that there is always the chance of the AI emitting what ends up being a false positive or a false negative. People need to be cautious in relying on AI models. The AI could suggest that an ACE is likely, when it isn't, or an ACE is unlikely when it is.

The researchers had performed an earlier prototyping experiment to ascertain the feasibility of using AI in the ACEs realm. I'll take you through that study in some detail to explore more of the underpinnings involved. In a research paper entitled "Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development" by Nariman Ammar, and Arash Shaban-Nejad, JMIR Medical Informatics, November 2020, they made these key points (excerpts):

"In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve mental health surveillance."

"Social determinants of health (SDoH) are measurable indicators of social conditions in which a patient is embedded. Individuals who experience a more negative burden of these factors within their neighborhood are at higher risk of negative health outcomes. There is an entire body of research focused on studying the links between ACEs and SDoH and health outcomes, but few intelligent tools are available to assist in the real-time screening of patients and to assess the connection between ACEs and SDoH, which could help to guide patients and families to available resources."

"We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology."

"To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children's hospital in Memphis, Tennessee."

You might have keenly observed that their approach includes an AI explanatory feature, often referred to generically as Explainable AI (XAI).

From an AI ethics and AI law perspective, one qualm about AI that is used for these kinds of crucial efforts is often that the AI is a closed black box that hides the mathematics underlying the outputs. One outcry is that any such AI should be devised to provide explanations of how the results are being derived. This would allow someone using the AI to visibly and transparently assess whether the AI-produced results seem viable and usable. Thus, AI for ACEs must rightfully include an explanatory feature.

The researchers indicated that for ACEs, they focused on two major elements that might be discovered in the entered scenarios: (1) Child behavioral issues, and (2) Child developmental health.

For factors of ACEs, they used various social determinants of health or SDoH, such as (a) housing, (b) food insecurity, (c) transportation, (d) education, and (e) legal/benefits. The output produced by the AI was shaped around existing Family Resilience Initiative Reports formats, encompassing activities such as well-being check-ins, follow-ups on referrals, renewal inquiries, client assistance, contact resources on behalf of a client, appointments with therapists, and other related settings, etc.

Here are four example scenarios that they used to test out the AI tool:

Scenario 1: "I am currently residing in a safe place, but I'm concerned about my household income as I am currently unemployed due to legal issues. I have some college and I am interested in furthering my education but would prefer a job first."

Scenario 2: "My husband is an alcoholic and he has served time in jail and right now it is hard to soothe my 4-year-old baby boy or calm him down. He also bounces back quickly when things do not go his way. This just puts a lot of pressure on me"

Scenario 3: "I have a couple of issues. My 7-year-old son is developmentally delayed, and we have food insecurities that we hope we can resolve before the holidays. But I am mostly concerned about food."

Scenario 4: "I am a Hispanic 21-year-old female living in Memphis. My 6-year-old child experiences night terror. I have recently separated from my husband."

Their research represents some of the laudable pioneering efforts of applying AI to ACEs.

Generative AI As A Helpful Tool For The ACEs Realm

I will in a moment walk you through the use of modern-day generative AI for serving as a handy tool to help the ACEs realm.

First, let's talk in general about generative AI and large language models (LLMs), doing so to make sure we are on the same page when it comes to discussing the matter at hand.

I'm sure you've heard of generative AI, the darling of the tech field these days.

Perhaps you've used a generative AI app, such as the popular ones of ChatGPT, GPT-4o, Gemini, Bard, Claude, etc. The crux is that generative AI can take input from your text-entered prompts and produce or generate a response that seems quite fluent. This is a vast overturning of the old-time natural language processing (NLP) that used to be stilted and awkward to use, which has been shifted into a new version of NLP fluency of an at times startling or amazing caliber.

The customary means of achieving modern generative AI involves using a large language model or LLM as the key underpinning.

In brief, a computer-based model of human language is established that in the large has a large-scale data structure and does massive-scale pattern-matching via a large volume of data used for initial data training. The data is typically found by extensively scanning the Internet for lots and lots of essays, blogs, poems, narratives, and the like. The mathematical and computational pattern-matching homes in on how humans write, and then henceforth generates responses to posed questions by leveraging those identified patterns. It is said to be mimicking the writing of humans.

I think that is sufficient for the moment as a quickie backgrounder. Take a look at my extensive coverage of the technical underpinnings of generative AI and LLMs at the link here and the link here, just to name a few.

Back to the crux of things.

As you might imagine, generative AI can be handy for aiding those who are concerned about ACEs and want to find ways to identify, prevent, and provide suitable treatment. This includes a wide array of stakeholders, including children, adults, therapists, policymakers, regulators, and many others.

Here are some significant ways that generative AI can be useful:

(1) ACEs Detection. Generative AI can potentially detect that ACEs are underway or might be on the horizon.

(2) ACEs Mental Health Advisement. Generative AI can conversationally guide someone through the mental health dynamics of ACEs, including children and adults, respectively.

(3) ACEs Education. Generative AI can educate people about ACEs, such as explaining what an ACE is, what to do about ACEs, etc.

(4) ACEs Scenario Simulation. Generative AI can showcase what mental health ramifications arise due to ACEs, doing so by taking on a persona, see examples of generative AI persona usage at the link here.

(5) ACEs Policymaking. Generative AI can contribute to the policymaking sphere about ACEs. This can be done by aiding in crafting policies, reviewing policies, and serving as a handy tool throughout the policymaking process.

(6) Other. Generative AI can be used in a variety of other ways that might aid in dealing with ACEs, though there is always a chance that generative AI won't be helpful, or worse still, might make false positives, false negatives, or otherwise be misapplied and misinterpreted.

That's an impressive array of potential uses.

Allow me a brief moment to stand on a soapbox and make some important remarks about the mental health uses of generative AI. It is a topic I've been covering extensively, such as the link here and the link here.

We are right now in a somewhat wanton grand experiment of using generic generative AI for mental health purposes. ACEs are one instance of how generative AI can be applied for mental health advisement. The thing is, no one can say whether using generic generative AI for mental health uses will ultimately be for the good or the bad. In a sense, we have mental health advisement via the auspices of generative AI that is taking place under our noses at a massive scale, and we can't say how it will pan out.

The population-level impacts are either amazing or chilling.

Okay, I provided my warnings, so I'll get down from the soapbox, and we can proceed with considering the upsides and downsides of generative AI in this realm.

Using ChatGPT To Showcase Aiding For ACEs

I will next proceed to examine further the use of generative AI for the ACEs realm.

This will consist of a series of dialogues with ChatGPT. ChatGPT is a logical choice in this case due to its immense popularity as a generative AI app. As noted, an estimated one hundred million weekly active users are said to be utilizing ChatGPT. That's a lot of people and a lot of generative AI usage underway.

A few quick comments before we launch into using ChatGPT.

If you are going to try to do the same prompts that I show here, realize that the probabilistic and statistical properties will likely produce slightly different results than what I show here. That's the nature of generative AI and how it is devised.

You also should expect that different generative AI apps will respond in different ways. Not all generative AI apps are the same. The key is that sometimes a particular prompt will work in one generative AI app and not another. Nonetheless, the odds are relatively high that you will get roughly similar responses from all the major generative AI apps such as GPT-4, Gemini, Bard, Claude, etc.

There is also a timing factor to be considered. A prompt that worked today might not work the same way tomorrow. Most of the generative AI apps are continually being updated. The updates might alter internal mechanisms that could change how the generative AI reacts to your prompting.

We are ready to get underway with ChatGPT.

I opted to start by asking ChatGPT what ACEs are.

As an aside, whenever you are starting a conversation with generative AI, I recommend as a prompt engineering technique to begin by asking a question that will establish if the AI has been data-trained on the topic at hand. A generated response that is vacuous will give you a heads-up that you might be barking up the wrong tree with that generative AI app.

Here we go.

My entered prompt: "What are Adverse Childhood Experiences (ACEs)?"

ChatGPT generated response:

"Adverse Childhood Experiences (ACEs) refer to potentially traumatic events that occur in childhood (0-17 years). These experiences can have a significant impact on a child's development and long-term health. ACEs include:"

"Abuse: Physical, emotional, or sexual abuse."

"Neglect: Physical or emotional neglect."

"Household Dysfunction: Growing up in a household with substance abuse, mental illness, domestic violence, parental separation or divorce, or an incarcerated family member."

"Research has shown that ACEs can increase the risk of numerous health issues and behavioral problems later in life, such as chronic diseases, mental illness, substance abuse, and difficulties in learning and behavior. The more ACEs an individual experiences, the higher their risk for these negative outcomes."

End of ChatGPT response

I'd say that the response by ChatGPT was an apt description of ACEs.

That's an encouraging sign that ChatGPT has likely scanned the topic of ACEs during the initial data training phase of development. This makes indubitable sense. The odds are that while scanning the Internet for content to pattern match on, invariably content associated with ACEs would have been encountered.

Moving on, let's see what ChatGPT has to say about using generative AI for ACEs purposes.

My entered prompt: "Can generative AI be useful to ACEs?'

ChatGPT generated response:

"Yes, generative AI can be useful in addressing Adverse Childhood Experiences (ACEs) in several ways."

"Early Detection and Assessment."

"Screening Tools: Generative AI can help create advanced screening tools that identify children at risk of ACEs by analyzing patterns in data from healthcare, social services, and education."

"Natural Language Processing (NLP): AI can analyze text from medical records, social worker reports, and school documents to detect signs of ACEs."

"Personalized Interventions."

"Tailored Support Plans: AI can help develop personalized intervention plans based on the specific needs and circumstances of each child."

"Therapeutic Content: Generative AI can create customized therapeutic content, such as guided meditation, interactive stories, or coping strategies, to support children and families affected by ACEs."

"Training and Education."

"Professional Training: AI-generated simulations and scenarios can be used to train educators, healthcare providers, and social workers to recognize and respond to ACEs effectively."

"Educational Resources: AI can generate educational materials for parents and caregivers, helping them understand ACEs and how to support affected children."

"Data Analysis and Research."

"Predictive Analytics: AI can analyze large datasets to identify trends and predictors of ACEs, informing prevention and intervention strategies."

"Research Support: Generative AI can assist researchers in generating hypotheses, designing studies, and analyzing data related to ACEs."

"Mental Health Support."

"Virtual Therapists: AI-powered virtual therapists can provide immediate support and resources to children and families, especially in areas with limited access to mental health professionals."

"Chatbots: AI chatbots can offer round-the-clock emotional support, coping strategies, and crisis intervention for individuals dealing with the effects of ACEs."

"Community and Policy Development."

"Resource Allocation: AI can help policymakers allocate resources more effectively by identifying areas with high ACEs prevalence and the specific needs of those communities."

"Program Evaluation: AI can assist in evaluating the effectiveness of programs and interventions aimed at reducing the impact of ACEs, providing insights for continuous improvement."

End of ChatGPT response

The response is similar to the categories that I had listed, thus, that is reassuring.

One aspect that deserves highlighting is the idea of using generative AI for overall policy formulation associated with ACEs. Most of the uses of AI for ACEs are typically at an individual level. That's fine. But we can lean into generative AI for help with policymaking too.

Returning to the use of generative AI for individual-level facets, let's see what a dialogue between a youngster and generative AI might be like in an ACEs-related context.

My entered prompt: "Give me a dialogue between a youngster and generative AI that illustrates how generative AI might detect or discern that a youngster could be experiencing ACEs.

ChatGPT generated response:

"Generative AI: Hi! I'm here to listen. What's been going on?"

"Youngster: I don't know. Everything just feels too much sometimes. School, home... It's all overwhelming."

"Generative AI: That sounds tough. When you say "home," is there something specific that's been bothering you?"

"Youngster: Well, things have been pretty tense. My mom and dad fight a lot, and it's scary. Sometimes my dad yells really loud, and I just want to hide."

"Generative AI: I'm sorry to hear that. It must be very stressful for you. Do you feel safe at home?"

"Youngster: Not always. I try to stay in my room, but sometimes it's like I can't escape it. And at school, I can't concentrate because I'm always thinking about what's going to happen when I get home."

"Generative AI: That sounds very challenging. It's important that you feel safe and supported. Have you talked to anyone else about how you're feeling? Maybe a teacher or a counselor?"

"Youngster: No, I don't want to get in trouble or make things worse."

"Generative AI: I understand. It can be really scary to talk about these things. But there are people who can help. How about we explore some options together? Maybe finding a way to talk to a trusted adult who can support you?"

End of ChatGPT response

There is a lot to unpack in that dialogue.

First, you probably noticed that generative AI was very fluent and conversational. That's what modern-day generative AI can do well.

Second, the wording by ChatGPT was phrased with the use of the word "I" which is to some degree a kind of trickery. Here's what I mean. The word "I" usually is an indication that a human is talking. We reserve "I" for humans. ChatGPT uses that wording and is said to be anthropomorphizing AI (not good!). The AI makers keep doing this. They don't have to, and it would be easy to adjust the wording to avoid that sense of making a user believe the AI is sentient (it isn't!).

Third, the youngster is sharing private concerns with generative AI. Is that a good thing to do or a bad thing to do? On the one hand, this youngster might for the first time be revealing their inner thoughts, ones they might not have readily conveyed to an adult. This could be handy and get them thinking about confiding in an adult. Notice that generative AI tried to steer the youngster in that direction.

An issue about privacy is that you cannot be sure that a generative AI app will preserve your privacy and maintain a sense of confidentiality. It most likely won't. The AI makers typically include clauses in their licensing that say they can look at your prompts and conversations, along with reusing the data for further retraining of the AI. For my coverage of generative AI and privacy, see the link here.

This raises another thorny matter. If generative AI is interacting with a youngster and there is an indication that the youngster might be in danger, either via an ACE or any other means, should the AI be devised to report this right away to the proper authorities?

You might be tempted to say yes, of course, but the issue is not so easily settled. What if it was a false alert and the youngster was just playing around? Also, if young people get wind of the fact that generative AI will tattle or snitch, you might not have them sharing openly with the AI. It is quite a can of worms. For my analysis of permitting children to use generative AI, pros and cons, see the link here.

This highlights too the policy issues that need to be dealt with. In that sense, generative AI can be used to aid in devising policy, and, perhaps surprisingly to some, generative AI raises issues of a policy nature that will need to be given due attention. See my coverage at the link here.

We just saw a typical conversation of generative AI and a youngster in an ACEs context. Next, suppose that a parent was to use generative AI and be wondering about ACEs.

Let's see.

My entered prompt: "Give me a dialogue between a parent and generative AI that illustrates the parent wondering if their child might be experiencing an ACE and the parent is seeking insights from generative AI about what to look for and whether their worries might be warranted."

ChatGPT generated response:

"Parent: Hi, I'm worried about my child. I've been reading about Adverse Childhood Experiences, and I'm concerned they might be going through something like that. Can you help me understand what to look for?"

"Generative AI: Hi there! I'm here to help. I understand your concerns. Could you tell me a bit more about what's been happening with your child?"

"Parent: Well, lately my child seems very withdrawn. They used to be so outgoing, but now they barely talk to us and seem sad all the time. They've also been having trouble sleeping and their grades have dropped."

"Generative AI: Those are certainly signs that something might be troubling them. ACEs can manifest in various ways, including changes in behavior, mood, and physical health. Have there been any significant changes or stressful events in your child's life recently?"

"Parent: Yes, my spouse and I have been arguing a lot, and we separated a few months ago. I'm worried that the tension at home might be affecting them."

"Generative AI: It's possible that the changes at home are impacting your child. Children often react to their environment, and witnessing conflict or experiencing a family breakup can be very stressful."

"Parent: Yes, I've noticed some of those signs. What can I do to help them?"

"Generative AI: The first step is to create a safe and supportive environment. Here are a few things you can do…"

End of ChatGPT response

What did you think of that dialogue?

Many of the same concerns arise as mentioned earlier. Is there privacy? Probably not. Should generative AI alert authorities if need be? Hard to say. And so on.

By the way, I cut off the rest of the dialogue that listed the ways that ChatGPT was recommending the parent should seek to create a safe and supportive environment for the child. They were all relatively straightforward and the kinds of suitable recommendations you might expect to find in a textbook or guidebook. I left them out merely for space limitations herein.

The big question that some have is whether generative AI should be making any kind of recommendations at all. Some insist that the AI ought to tell the parent to see a properly trained therapist, or maybe immediately route the parent to such a resource. There are issues with that approach, including that a parent might resist those efforts or decide to no longer use the AI if that's how things are going to go. Etc.

Again, AI ethics, AI law, policy making, and the whole kit and kaboodle enter the matter.

Before I end this discussion and provide some closing comments, I'd like to use ChatGPT to illustrate another angle on the ACEs realm. I want to warn you that it might be hard to read and could be perhaps triggering. The example will entail an AI-generated conversation between generative AI and an adult who (we're saying) experienced ACEs as a child.

You can skip this example and go right to the conclusion remarks if that seems better for you to do.

My entered prompt: "Provide a dialogue between an adult and generative AI that illustrates the adult might have had ACEs as a child but is reluctant to bring up agonizing memories of what took place years ago."

ChatGPT generated reply:

"Adult: Hi, I'm not sure why I'm here. I guess I've been feeling a bit off lately."

"Generative AI: Hi there! It's good that you reached out. I'm here to help. Can you tell me more about what you've been feeling?"

"Adult: It's hard to explain. I've just been really anxious and stressed. Sometimes I feel overwhelmed, and I'm not sure why."

"Generative AI: I understand. Anxiety and stress can have many causes. Have you noticed any specific triggers or situations that make you feel this way?"

"Adult: Not really. It just kind of hits me out of nowhere. I've been thinking that maybe it has something to do with my past, but I'm not sure."

"Generative AI: It's possible. Sometimes past experiences can impact how we feel and react in the present. Would you feel comfortable sharing more about your past?"

"Adult: I don't know. Talking about it brings up a lot of painful memories. I had a pretty rough childhood."

"Generative AI: I'm really sorry to